From e-commerce to social commerce: A close look at design features. E-commerce is undergoing an evolution through the adoption of Web 2.0 capabilities to enhance customer participation and achieve greater economic value. This new phenomenon is commonly referred to as social commerce, however it has not yet been fully understood. In addition to the lack of a stable and agreed-upon definition, there is little research on social commerce and no significant research dedicated to the design of social commerce platforms. This study offers literature review to explain the concept of social commerce, tracks its nascent state-of-the-art, and discusses relevant design features as they relate to e-commerce and Web 2.0. We propose a new model and a set of principles for guiding social commerce design. We also apply the model and guidelines to two leading social commerce platforms, Amazon and Starbucks on Facebook. The findings indicate that, for any social commerce website, it is critical to achieve a minimum set of social commerce design features. These design features must cover all the layers of the proposed model, including the individual, conversation, community and commerce levels. 2012 Elsevier B.V. All rights reserved.

Question: What is the difference between e-commerce and social commerce?


Identifying Personality Traits , and Especially Traits Resulting in Violent Behavior through Automatic Handwriting Analysis. Handwriting analysis is a process that has been carried out for centuries. But its effectiveness when analyzing the behavior and personality of an individual is still a debate. Is it possible to detect potential deviant behavior and personality traits of an individual by carrying out an analysis of his/her handwriting? There are two methods of handwriting analysis: Graphology is the method of psychological analysis, while forensic document examination or handwriting identification which is the examination of documents and writing samples by a known source, or person. In this paper we have carried out research of the various state of the art technologies available in analyzing an individual's behavior based on their handwriting and the effectiveness of predicting the character and personality of that individual. We also wanted to determine if we can uncover handedness, authorship and gender through analysis. Apart from working on Lewinson-Zubin method of analyzing handwriting, various online tools are also available for handwriting analysis, such as: NEURO SCRIPT, WANDA, CEDAR-FOX, and Gaussian Mixture Model.

Question: What is the effectiveness of handwriting analysis in predicting the character and personality of an individual?


HOPE: Enabling Efficient Service Orchestration in Software-Defined Data Centers. The functional scope of today's software-defined data centers (SDDC) has expanded to such an extent that servers face a growing amount of critical background operational tasks like load monitoring, logging, migration, and duplication, etc. These ancillary operations, which we refer to as management operations, often nibble the stringent data center power envelope and exert a tremendous amount of pressure on front-end user tasks. However, existing power capping, peak shaving, and time shifting mechanisms mainly focus on managing data center power demand at the "macro level" -- they do not distinguish ancillary background services from user tasks, and therefore often incur significant performance degradation and energy overhead.  In this study we explore "micro-level" power management in SDDC: tuning a specific set of critical loads for the sake of overall system efficiency and performance. Specifically, we look at management operations that can often lead to resource contention and energy overhead in an IaaS SDDC. We assess the feasibility of this new power management paradigm by characterizing the resource and power impact of various management operations. We propose HOPE, a new system optimization framework for eliminating the potential efficiency bottleneck caused by the management operations in the SDDC. HOPE is implemented on a customized OpenStack cloud environment with heavily instrumented power infrastructure. We thoroughly validate HOPE models and optimization efficacy under various user workload scenarios. Our deployment experiences show that the proposed technique allows SDDC to reduce energy consumption by 19%, reduce management operation execution time by 25.4%, and in the meantime improve workload performance by 30%.

Question: How much energy does it take to run a data center?


GPU-based cone beam computed tomography. The use of cone beam computed tomography (CBCT) is growing in the clinical arena due to its ability to provide 3D information during interventions, its high diagnostic quality (sub-millimeter resolution), and its short scanning times (60 s). In many situations, the short scanning time of CBCT is followed by a time-consuming 3D reconstruction. The standard reconstruction algorithm for CBCT data is the filtered backprojection, which for a volume of size 256(3) takes up to 25 min on a standard system. Recent developments in the area of Graphic Processing Units (GPUs) make it possible to have access to high-performance computing solutions at a low cost, allowing their use in many scientific problems. We have implemented an algorithm for 3D reconstruction of CBCT data using the Compute Unified Device Architecture (CUDA) provided by NVIDIA (NVIDIA Corporation, Santa Clara, California), which was executed on a NVIDIA GeForce GTX 280. Our implementation results in improved reconstruction times from minutes, and perhaps hours, to a matter of seconds, while also giving the clinician the ability to view 3D volumetric data at higher resolutions. We evaluated our implementation on ten clinical data sets and one phantom data set to observe if differences occur between CPU and GPU-based reconstructions. By using our approach, the computation time for 256(3) is reduced from 25 min on the CPU to 3.2 s on the GPU. The GPU reconstruction time for 512(3) volumes is 8.5 s.

Question: How does a GPU work?


A –$21.2$ -dBm Dual-Channel UHF Passive CMOS RFID Tag Design. Previous research results showed that UHF passive CMOS RFID tags had difficulty to achieve sensitivity less than -20 dBm. This paper presents a dual-channel 15-bit UHF passive CMOS RFID tag prototype that can work at sensitivity lower than -20 dBm. The proposed tag chip harvests energy and backscatters uplink data at 866.4-MHz (for ETSI) or 925-MHz (for FCC) channel and receives downlink data at 433-MHz channel. Consequently, the downlink data transmission does not interrupt our tag from harvesting RF energy. To use the harvested energy efficiently, we design a tag chip that includes neither a regulator nor a VCO such that the harvested energy is completely used in receiving, processing, and backscattering data. Without a regulator, our tag uses as few active analog circuits as possible in the receiver front-end. Instead, our tag uses a novel digital circuit to decode the received data. Without a VCO, the design of our tag can extract the required clock signal from the downlink data. Measurement result shows that the sensitivity of the proposed passive tag chip can reach down to -21.2 dBm. Such result corresponds to a 19.6-m reader-to-tag distance under 36-dBm EIRP and 0.4-dBi tag antenna gain. The chip was fabricated in TSMC 0.18- μm CMOS process. The die area is 0.958 mm ×0.931mm.

Question: How to design a passive UHF RFID tag?


Low-profile dual-band circularly polarized microstrip antenna for GNSS applications. This paper presents a design of a micro-strip circularly polarized antenna intended for the Global Navigation Satellite Systems (GNSS). The presented device is composed of a micro-strip slotted patch antenna printed on a Rogers RO3006 substrate, a foam layer of 2 mm thick and a wideband commercial 3-dB SMT coupler. The combined fullwave antenna results with the measured S-Parameters of the coupler shows very good performances in terms of antenna matching and axial ratio on larger bandwidths.

Question: What is a microstrip antenna?


Action understanding and active inference. We have suggested that the mirror-neuron system might be usefully understood as implementing Bayes-optimal perception of actions emitted by oneself or others. To substantiate this claim, we present neuronal simulations that show the same representations can prescribe motor behavior and encode motor intentions during action–observation. These simulations are based on the free-energy formulation of active inference, which is formally related to predictive coding. In this scheme, (generalised) states of the world are represented as trajectories. When these states include motor trajectories they implicitly entail intentions (future motor states). Optimizing the representation of these intentions enables predictive coding in a prospective sense. Crucially, the same generative models used to make predictions can be deployed to predict the actions of self or others by simply changing the bias or precision (i.e. attention) afforded to proprioceptive signals. We illustrate these points using simulations of handwriting to illustrate neuronally plausible generation and recognition of itinerant (wandering) motor trajectories. We then use the same simulations to produce synthetic electrophysiological responses to violations of intentional expectations. Our results affirm that a Bayes-optimal approach provides a principled framework, which accommodates current thinking about the mirror-neuron system. Furthermore, it endorses the general formulation of action as active inference.

Question: What is the mirror-neuron system?


Accelerometer based wireless air mouse using Arduino micro-controller board. With the day to day advancements in technology, the interaction between the human and the digital world is diminishing. Lot of improvement has taken place in the mobile technology from button keypads to touch screens. However the current PCs still need a pad to operate its mouse and are wired most of the time. The idea is to develop a wireless mouse which works on the hand gestures of the user without the need of a pad making an effortless interaction between the human and the computer. The implementation is done using a sensor named accelerometer to sense the hand gestures. Accelerometer is a motion sensor which sense changes in motion in any of the three axes. In here the accelerometer will be at the users side, attached to the hand to sense the movement and gives the output to a micro-controller to process it. Necessary modifications are done by the microcontroller to these values and are transmitted through a RF module to the PC. At the receiving end a mouse control program which contains functions to control the mouse reads these values and performs the necessary action.

Question: How to make a wireless mouse?


Predicting the severity of a reported bug. The severity of a reported bug is a critical factor in deciding how soon it needs to be fixed. Unfortunately, while clear guidelines exist on how to assign the severity of a bug, it remains an inherent manual process left to the person reporting the bug. In this paper we investigate whether we can accurately predict the severity of a reported bug by analyzing its textual description using text mining algorithms. Based on three cases drawn from the open-source community (Mozilla, Eclipse and GNOME), we conclude that given a training set of sufficient size (approximately 500 reports per severity), it is possible to predict the severity with a reasonable accuracy (both precision and recall vary between 0.65–0.75 with Mozilla and Eclipse; 0.70–0.85 in the case of GNOME).

Question: How can we predict the severity of a bug?


Adolescence-limited and life-course-persistent antisocial behavior: a developmental taxonomy.. A dual taxonomy is presented to reconcile 2 incongruous facts about antisocial behavior: (a) It shows impressive continuity over age, but (b) its prevalence changes dramatically over age, increasing almost 10-fold temporarily during adolescence. This article suggests that delinquency conceals 2 distinct categories of individuals, each with a unique natural history and etiology: A small group engages in antisocial behavior of 1 sort or another at every life stage, whereas a larger group is antisocial only during adolescence. According to the theory of life-course-persistent antisocial behavior, children's neuropsychological problems interact cumulatively with their criminogenic environments across development, culminating in a pathological personality. According to the theory of adolescence-limited antisocial behavior, a contemporary maturity gap encourages teens to mimic antisocial behavior in ways that are normative and adjustive.

Question: What is the difference between the 2 types of antisocial behavior?


Modeling of electric vehicle loads for power flow analysis based on PSAT. This paper proposes the modeling of electric vehicle(EV) loads for power flow analysis based on User Define Models (UDMs) in power system analysis toolbox (PSAT) programming, According to the development of EV technology especially motor controller and battery, the EV has totally designed and made for using in city area as small city cars. To study the performance of EV, this aims to present the model of EV loads for using to solve power flow analysis in the real power system. EV load is modeled as the function box equivalent on PSAT that can connect to the single line diagram of power system model. The IEEE 14 bus system is selected for simulating the performance of EV and charging station which compared between PQ load base case and EV load. The simulation result showed that the EV load was installed at bus No.14. The rated power of EV load was increased from 5.0 p.u. to 79 p.u. This proposed EV load model can be used to solve the power flow analysis with continuation power flow (CPF) method. The EV load model is directly effected to voltage stability margin when the EV load increased. This study can be verified that proposed EV load can use to study the EV load in the future works.

Question: How to model the EV load in PSAT?


Photovoltaic Power Conditioning System With Line Connection. A photovoltaic (PV) power conditioning system (PCS) with line connection is proposed. Using the power slope versus voltage of the PV array, the maximum power point tracking (MPPT) controller that produces a smooth transition to the maximum power point is proposed. The dc current of the PV array is estimated without using a dc current sensor. A current controller is suggested to provide power to the line with an almost-unity power factor that is derived using the feedback linearization concept. The disturbance of the line voltage is detected using a fast sensing technique. All control functions are implemented in software with a single-chip microcontroller. Experimental results obtained on a 2-kW prototype show high performance such as an almost-unity power factor, a power efficiency of 94%, and a total harmonic distortion (THD) of 3.6%

Question: What is a photovoltaic power conditioning system?


Deep Learning without Poor Local Minima. In this paper, we prove a conjecture published in 1989 and also partially address an open problem announced at the Conference on Learning Theory (COLT) 2015. For an expected loss function of a deep nonlinear neural network, we prove the following statements under the independence assumption adopted from recent work: 1) the function is non-convex and non-concave, 2) every local minimum is a global minimum, 3) every critical point that is not a global minimum is a saddle point, and 4) the property of saddle points differs for shallow networks (with three layers) and deeper networks (with more than three layers). Moreover, we prove that the same four statements hold for deep linear neural networks with any depth, any widths and no unrealistic assumptions. As a result, we present an instance, for which we can answer to the following question: how difficult to directly train a deep model in theory? It is more difficult than the classical machine learning models (because of the non-convexity), but not too difficult (because of the nonexistence of poor local minima and the property of the saddle points). We note that even though we have advanced the theoretical foundations of deep learning, there is still a gap between theory and practice.

Question: How difficult to directly train a deep model in theory?


A soft hand model for physically-based manipulation of virtual objects. We developed a new hand model for increasing the robustness of finger-based manipulations of virtual objects. Each phalanx of our hand model consists of a number of deformable soft bodies, which dynamically adapt to the shape of grasped objects based on the applied forces. Stronger forces directly result in larger contact areas, which increase the friction between hand and object as would occur in reality. For a robust collision-based soft body simulation, we extended the lattice-shape matching algorithm to work with adaptive stiffness values, which are dynamically derived from force and velocity thresholds. Our implementation demonstrates that this approach allows very precise and robust grasping, manipulation and releasing of virtual objects and performs in real-time for a variety of complex scenarios. Additionally, laborious tuning of object and friction parameters is not necessary for the wide range of objects that we typically grasp with our hands.

Question: What is a soft hand model?


Textual resource acquisition and engineering. and engineering J. Chu-Carroll J. Fan N. Schlaefer W. Zadrozny A key requirement for high-performing question-answering (QA) systems is access to high-quality reference corpora from which answers to questions can be hypothesized and evaluated. However, the topic of source acquisition and engineering has received very little attention so far. This is because most existing systems were developed under organized evaluation efforts that included reference corpora as part of the task specification. The task of answering Jeopardy!i questions, on the other hand, does not come with such a well-circumscribed set of relevant resources. Therefore, it became part of the IBM Watsoni effort to develop a set of well-defined procedures to acquire high-quality resources that can effectively support a high-performing QA system. To this end, we developed three procedures, i.e., source acquisition, source transformation, and source expansion. Source acquisition is an iterative development process of acquiring new collections to cover salient topics deemed to be gaps in existing resources based on principled error analysis. Source transformation refers to the process in which information is extracted from existing sources, either as a whole or in part, and is represented in a form that the system can most easily use. Finally, source expansion attempts to increase the coverage in the content of each known topic by adding new information as well as lexical and syntactic variations of existing information extracted from external large collections. In this paper, we discuss the methodology that we developed for IBM Watson for performing acquisition, transformation, and expansion of textual resources. We demonstrate the effectiveness of each technique through its impact on candidate recall and on end-to-end QA performance.

Question: What is the methodology that we developed for IBM Watson for performing acquisition, transformation, and expansion of textual resources?


Learning to detect unseen object classes by between-class attribute transfer. We study the problem of object classification when training and test classes are disjoint, i.e. no training examples of the target classes are available. This setup has hardly been studied in computer vision research, but it is the rule rather than the exception, because the world contains tens of thousands of different object classes and for only a very few of them image, collections have been formed and annotated with suitable class labels. In this paper, we tackle the problem by introducing attribute-based classification. It performs object detection based on a human-specified high-level description of the target objects instead of training images. The description consists of arbitrary semantic attributes, like shape, color or even geographic information. Because such properties transcend the specific learning task at hand, they can be pre-learned, e.g. from image datasets unrelated to the current task. Afterwards, new classes can be detected based on their attribute representation, without the need for a new training phase. In order to evaluate our method and to facilitate research in this area, we have assembled a new large-scale dataset, "Animals with Attributes", of over 30,000 animal images that match the 50 classes in Osherson's classic table of how strongly humans associate 85 semantic attributes with animal classes. Our experiments show that by using an attribute layer it is indeed possible to build a learning object detection system that does not require any training images of the target classes.

Question: What is the difference between a learning object detection system and a learning object classification system?


Lightweight Management of Resource-Constrained Sensor Devices in Internet of Things. It is predicted that billions of intelligent devices and networks, such as wireless sensor networks (WSNs), will not be isolated but connected and integrated with computer networks in future Internet of Things (IoT). In order to well maintain those sensor devices, it is often necessary to evolve devices to function correctly by allowing device management (DM) entities to remotely monitor and control devices without consuming significant resources. In this paper, we propose a lightweight RESTful Web service (WS) approach to enable device management of wireless sensor devices. Specifically, motivated by the recent development of IPv6-based open standards for accessing wireless resource-constrained networks, we consider to implement IPv6 over low-power wireless personal area network (6LoWPAN)/routing protocol for low power and lossy network (RPL)/constrained application protocol (CoAP) protocols on sensor devices and propose a CoAP-based DM solution to allow easy access and management of IPv6 sensor devices. By developing a prototype cloud system, we successfully demonstrate the proposed solution in efficient and effective management of wireless sensor devices.

Question: What is the difference between a wireless sensor network and a wireless personal area network?


Indoor Navigation Validation Framework for Visually Impaired Users. In this paper, we introduce the first validation framework of an indoor navigation system for blind and visually impaired (BVI) users, which is a significant step toward the development of cost effective indoor way-finding solutions for BVI users. The BVI users require detailed landmark-based navigation instructions that will help them arrive at the chosen destination independently. These users will interact with the navigation instructions on a smartphone using an accessible user interface. The validation framework includes the following three main components: 1) virtual reality-based simulation that simulates a BVI user traversing and interacting with the physical environment, developed using Unity game engine; 2) generation of action codes that emulate the avatar movement in the virtual environment, developed using a natural language processing parser of the navigation instructions; and 3) accessible user interface, which enables the user to access the navigation instructions, developed using Sikuli script library. We introduce a case study that illustrates the use of the validation tool using PERCEPT system. It is our strong belief that the validation framework we provide in this paper will encourage other developers to invent indoor navigation systems for BVI users. In addition, we would like to mention that this tool is the first step of validating an indoor navigation system and should be followed by trials with human subjects.

Question: What is the first large-scale Canadian peacekeeping mission?


Clinical Applications of Brain-Computer Interfaces: Current State and Future Prospects. Braincomputer interfaces (BCIs) allow their users to communicate or control external devices using brain signals rather than the brain's normal output pathways of peripheral nerves and muscles. Motivated by the hope of restoring independence to severely disabled individuals and by interest in further extending human control of external systems, researchers from many fields are engaged in this challenging new work. BCI research and development has grown explosively over the past two decades. Efforts have begun recently to provide laboratory-validated BCI systems to severely disabled individuals for real-world applications. In this paper, we discuss the current status and future prospects of BCI technology and its clinical applications. We will define BCI, review the BCI-relevant signals from the human brain, and describe the functional components of BCIs. We will also review current clinical applications of BCI technology and identify potential users and potential applications. Lastly, we will discuss current limitations of BCI technology, impediments to its widespread clinical use, and expectations for the future.

Question: What is the current status of brain computer interfaces?


Histogram-based image hashing scheme robust against geometric deformations. In this paper, we propose a robust image hash algorithm by using the invariance of the image histogram shape to geometric deformations. Robustness and uniqueness of the proposed hash function are investigated in detail by representing the histogram shape as the relative relations in the number of pixels among groups of two different bins. It is found from extensive testing that the histogram-based hash function has a satisfactory performance to various geometric deformations, and is also robust to most common signal processing operations thanks to the use of Gaussian kernel low-pass filter in the preprocessing phase.

Question: What is a histogram-based image hashing scheme?


Fast Inference in Sparse Coding Algorithms with Applications to Object Recognition. Adaptive sparse coding methods learn a possibly overcomplete set of basis functions, such that natural image patches can be reconstructed by linearly combining a small subset of these bases. The applicability of these methods to visual object recognition tasks has been limited because of the prohibitive cost of the optimization algorithms required to compute the sparse representation. In this work we propose a simple and efficient algorithm to learn basis functions. After training, this model also provides a fast and smooth approximator to the optimal representation, achieving even better accuracy than exact sparse coding algorithms on visual object recognition tasks.

Question: What is the cost of the optimization algorithm?


Data Center Energy Consumption Modeling: A Survey. Data centers are critical, energy-hungry infrastructures that run large-scale Internet-based services. Energy consumption models are pivotal in designing and optimizing energy-efficient operations to curb excessive energy consumption in data centers. In this paper, we survey the state-of-the-art techniques used for energy consumption modeling and prediction for data centers and their components. We conduct an in-depth study of the existing literature on data center power modeling, covering more than 200 models. We organize these models in a hierarchical structure with two main branches focusing on hardware-centric and software-centric power models. Under hardware-centric approaches we start from the digital circuit level and move on to describe higher-level energy consumption models at the hardware component level, server level, data center level, and finally systems of systems level. Under the software-centric approaches we investigate power models developed for operating systems, virtual machines and software applications. This systematic approach allows us to identify multiple issues prevalent in power modeling of different levels of data center systems, including: i) few modeling efforts targeted at power consumption of the entire data center ii) many state-of-the-art power models are based on a few CPU or server metrics, and iii) the effectiveness and accuracy of these power models remain open questions. Based on these observations, we conclude the survey by describing key challenges for future research on constructing effective and accurate data center power models.

Question: What is the energy consumption of a data center?


Learning model-based strategies in simple environments with hierarchical q-networks. Recent advances in deep learning have allowed artificial agents to rival human-level performance on a wide range of complex tasks; however, the ability of these networks to learn generalizable strategies remains a pressing challenge. This critical limitation is due in part to two factors: the opaque information representation in deep neural networks and the complexity of the task environments in which they are typically deployed. Here we propose a novel Hierarchical Q-Network (HQN), motivated by theories of the hierarchical organization of the human prefrontal cortex, that attempts to identify lower dimensional patterns in the value landscape that can be exploited to construct an internal model of rules in simple environments. We draw on combinatorial games, where there exists a single optimal strategy for winning that generalizes across other features of the game, to probe the strategy generalization of the HQN and other reinforcement learning (RL) agents using variations of Wythoff's game. Traditional RL approaches failed to reach satisfactory performance on variants of Wythoff's Game; however, the HQN learned heuristic-like strategies that generalized across changes in board configuration. More importantly, the HQN allowed for transparent inspection of the agent's internal model of the game following training. Our results show how a biologically inspired hierarchical learner can facilitate learning abstract rules to promote robust and flexible action policies in simplified training environments with clearly delineated optimal strategies. 1 ar X iv :1 80 1. 06 68 9v 1 [ cs .A I] 2 0 Ja n 20 18

Question: What is the difference between a reinforcement learning agent and a hierarchical reinforcement learning agent?


KEGGgraph: a graph approach to KEGG PATHWAY in R and bioconductor. MOTIVATION KEGG PATHWAY is a service of Kyoto Encyclopedia of Genes and Genomes (KEGG), constructing manually curated pathway maps that represent current knowledge on biological networks in graph models. While valuable graph tools have been implemented in R/Bioconductor, to our knowledge there is currently no software package to parse and analyze KEGG pathways with graph theory.   RESULTS We introduce the software package KEGGgraph in R and Bioconductor, an interface between KEGG pathways and graph models as well as a collection of tools for these graphs. Superior to existing approaches, KEGGgraph captures the pathway topology and allows further analysis or dissection of pathway graphs. We demonstrate the use of the package by the case study of analyzing human pancreatic cancer pathway.   AVAILABILITY KEGGgraph is freely available at the Bioconductor web site (http://www.bioconductor.org). KGML files can be downloaded from KEGG FTP site (ftp://ftp.genome.jp/pub/kegg/xml).

Question: How to parse KEGG pathways in R?


Integrating low-rank and group-sparse structures for robust multi-task learning. Multi-task learning (MTL) aims at improving the generalization performance by utilizing the intrinsic relationships among multiple related tasks. A key assumption in most MTL algorithms is that all tasks are related, which, however, may not be the case in many real-world applications. In this paper, we propose a robust multi-task learning (RMTL) algorithm which learns multiple tasks simultaneously as well as identifies the irrelevant (outlier) tasks. Specifically, the proposed RMTL algorithm captures the task relationships using a low-rank structure, and simultaneously identifies the outlier tasks using a group-sparse structure. The proposed RMTL algorithm is formulated as a non-smooth convex (unconstrained) optimization problem. We propose to adopt the accelerated proximal method (APM) for solving such an optimization problem. The key component in APM is the computation of the proximal operator, which can be shown to admit an analytic solution. We also theoretically analyze the effectiveness of the RMTL algorithm. In particular, we derive a key property of the optimal solution to RMTL; moreover, based on this key property, we establish a theoretical bound for characterizing the learning performance of RMTL. Our experimental results on benchmark data sets demonstrate the effectiveness and efficiency of the proposed algorithm.

Question: What is the difference between a low-rank and a group-sparse structure?


Formal analysis of privacy requirements specifications for multi-tier applications. Companies require data from multiple sources to develop new information systems, such as social networking, e-commerce and location-based services. Systems rely on complex, multi-stakeholder data supply-chains to deliver value. These data supply-chains have complex privacy requirements: privacy policies affecting multiple stakeholders (e.g. user, developer, company, government) regulate the collection, use and sharing of data over multiple jurisdictions (e.g. California, United States, Europe). Increasingly, regulators expect companies to ensure consistency between company privacy policies and company data practices. To address this problem, we propose a methodology to map policy requirements in natural language to a formal representation in Description Logic. Using the formal representation, we reason about conflicting requirements within a single policy and among multiple policies in a data supply chain. Further, we enable tracing data flows within the supply-chain. We derive our methodology from an exploratory case study of Facebook platform policy. We demonstrate the feasibility of our approach in an evaluation involving Facebook, Zynga and AOL-Advertising policies. Our results identify three conflicts that exist between Facebook and Zynga policies, and one conflict within the AOL Advertising policy.

Question: How to map privacy requirements specifications to a formal representation in Description Logic?


Deep analysis for development of RDF, RDFS and OWL ontologies with protege. In SW (SW), Ontology is an important layer architecture that is develop with the help of web ontology language (OWL) and makes the web semantic. OWL is designs and creates metadata that required for developing SW applications. It is an essential part of SW. In this paper we design an education and non-education domain ontology framework which is developed by using protégé an ontology editor tool developed by Stanford University. We also show the output for both ontologies by using pellet reasoner based on the proposed ontology frameworks. We analyze from our result that OWL improves the inference compare to Resource Description Framework (RDF) and Resource Description Framework Schema (RDFS). OWL also provides more relevant information compare to RDF and RDFS and OWL precision and recall is better compare to RDF and RDFS. In our paper we used Pellet reasoner that retrieve relevant information from the education and non education ontology and protégé 4.3 alpha tool for the construction of education and non education domain ontology.

Question: What is the difference between RDF and RDFS?


XWand: UI for intelligent spaces. The XWand is a novel wireless sensor package that enables styles of natural interaction with intelligent environments. For example, a user may point the wand at a device and control it using simple gestures. The XWand system leverages the intelligence of the environment to best determine the user's intention. We detail the hardware device, signal processing algorithms to recover position and orientation, gesture recognition techniques, a multimodal (wand and speech) computational architecture and a preliminary user study examining pointing performance under conditions of tracking availability and audio feedback.

Question: What is the XWand?


Disruptive Innovation : Removing the Innovators ' Dilemma. The objectives of this research are to co-create understanding and knowledge on the phenomenon of disruptive innovation in order to provide pragmatic clarity on the term's meaning, impact and implications. This will address the academic audience's gap in knowledge and provide help to practitioners wanting to understand how disruptive innovation can be fostered as part of a major competitive strategy. This paper reports on the first eighteen months of a three year academic and industrial investigation. It presents a new pragmatic definition drawn from the literature and an overview of the conceptual framework for disruptive innovation that was co-created via the collaborative efforts of academia and industry. The barriers to disruptive innovation are presented and a best practice case study of how one company is overcoming these barriers is described. The remainder of the research, which is supported by a European Commission co-sponsored project called Disrupt-it, will focus on developing and validating tools to help overcome these barriers. Thomond, P., Herzberg, T. and Lettice, F. (2003). "Disruptive Innovation: Removing the Innovators' Dilemma". Knowledge into Practice British Academy of Management Annual Conference, Harrogate, UK, September 2003. 2 1.0. Introduction and Background. In his ground breaking book "The Innovator's Dilemma: When New Technologies Cause Great Firms to Fail", Clayton Christensen first coined the phrase 'disruptive technologies'. He showed that time and again almost all the organisations that have 'died' or been displaced from their industries because of a new paradigm of customer offering could see the disruption coming but did nothing until it was too late (Christensen, 1997). They assess the new approaches or technologies and frame them as either deficient or as an unlikely threat much to the managers' regret and the organisation's demise (Christensen 2002). In the early 1990s,

Question: What is disruptive innovation?


Factorized adaptation for deep neural network. In this paper, we propose a novel method to adapt context-dependent deep neural network hidden Markov model (CD-DNN-HMM) with only limited number of parameters by taking into account the underlying factors that contribute to the distorted speech signal. We derive this factorized adaptation method from the perspectives of joint factor analysis and vector Taylor series expansion, respectively. Evaluated on Aurora 4, the proposed method can get 19.0% and 10.6% relative word error rate reduction on test set B and D with only 20 adaptation utterances, and can have decent improvement with as few as two adaptation utterances. We also show that the proposed method is better than feature discriminative linear regression (fDLR), an existing DNN adaptation method. Its small number of parameters and short training time offer an attractive solution to low-footprint speech applications.

Question: How to adapt deep neural network?


(MP)2T: Multiple People Multiple Parts Tracker. We present a method for multi-target tracking that exploits the persistence in detection of object parts. While the implicit representation and detection of body parts have recently been leveraged for improved human detection, ours is the first method that attempts to temporally constrain the location of human body parts with the express purpose of improving pedestrian tracking. We pose the problem of simultaneous tracking of multiple targets and their parts in a network flow optimization framework and show that parts of this network need to be optimized separately and iteratively, due to inter-dependencies of node and edge costs. Given potential detections of humans and their parts separately, an initial set of pedestrian tracklets is first obtained, followed by explicit tracking of human parts as constrained by initial human tracking. A merging step is then performed whereby we attempt to include part-only detections for which the entire human is not observable. This step employs a selective appearance model, which allows us to skip occluded parts in description of positive training samples. The result is high confidence, robust trajectories of pedestrians as well as their parts, which essentially constrain each other's locations and associations, thus improving human tracking and parts detection. We test our algorithm on multiple real datasets and show that the proposed algorithm is an improvement over the state-of-the-art.

Question: How to track multiple people?


360ProbDASH: Improving QoE of 360 Video Streaming Using Tile-based HTTP Adaptive Streaming. Recently, there has been a significant interest towards 360-degree panorama video. However, such videos usually require extremely high bitrate which hinders their widely spread over the Internet. Tile-based viewport adaptive streaming is a promising way to deliver 360-degree video due to its on-request portion downloading. But it is not trivial for it to achieve good Quality of Experience (QoE) because Internet request-reply delay is usually much higher than motion-to-photon latency. In this paper, we leverage a probabilistic approach to pre-fetch tiles countering viewport prediction error, and design a QoE-driven viewport adaptation system, 360ProbDASH. It treats user's head movement as probability events, and constructs a probabilistic model to depict the distribution of viewport prediction error. A QoE-driven optimization framework is proposed to minimize total expected distortion of pre-fetched tiles. Besides, to smooth border effects of mixed-rate tiles, the spatial quality variance is also minimized. With the requirement of short-term viewport prediction under a small buffer, it applies a target-buffer-based rate adaptation algorithm to ensure continuous playback. We implement 360ProbDASH prototype and carry out extensive experiments on a simulation test-bed and real-world Internet with real user's head movement traces. The experimental results demonstrate that 360ProbDASH achieves at almost 39% gains on viewport PSNR, and 46% reduction on spatial quality variance against the existed viewport adaptation methods.

Question: How to improve the quality of 360 video streaming?


Challenges and implications of using ultrasonic communications in intra-body area networks. Body area networks (BANs) promise to enable revolutionary biomedical applications by wirelessly interconnecting devices implanted or worn by humans. However, BAN wireless communications based on radio-frequency (RF) electromagnetic waves suffer from poor propagation of signals in body tissues, which leads to high levels of attenuation. In addition, in-body transmissions are constrained to be low-power to prevent overheating of tissues and consequent death of cells. To address the limitations of RF propagation in the human body, we propose a paradigm shift by exploring the use of ultrasonic waves as the physical medium to wirelessly interconnect in-body implanted devices. Acoustic waves are the transmission technology of choice for underwater communications, since they are known to propagate better than their RF counterpart in media composed mainly of water. Similarly, we envision that ultrasound (e.g., acoustic waves at non-audible frequencies) will provide support for communications in the human body, which is composed for 65% of water. In this paper, we first assess the feasibility of using ultrasonic communications in intra-body BANs, i.e., in-body networks where the devices are biomedical sensors that communicate with an actuator/gateway device located inside the body. We discuss the fundamentals of ultrasonic propagation in tissues, and explore important tradeoffs, including the choice of a transmission frequency, transmission power, bandwidth, and transducer size. Then, we discuss future research challenges for ultrasonic networking of intra-body devices at the physical, medium access and network layers of the protocol stack.

Question: What is the difference between RF and ultrasound?


Computer analysis of computed tomography scans of the lung: a survey. Current computed tomography (CT) technology allows for near isotropic, submillimeter resolution acquisition of the complete chest in a single breath hold. These thin-slice chest scans have become indispensable in thoracic radiology, but have also substantially increased the data load for radiologists. Automating the analysis of such data is, therefore, a necessity and this has created a rapidly developing research area in medical imaging. This paper presents a review of the literature on computer analysis of the lungs in CT scans and addresses segmentation of various pulmonary structures, registration of chest scans, and applications aimed at detection, classification and quantification of chest abnormalities. In addition, research trends and challenges are identified and directions for future research are discussed.

Question: What is the size of the canadian military?


Explainable , Normative , and Justified Agency. In this paper, we pose a new challenge for AI researchers – to develop intelligent systems that support justified agency. We illustrate this ability with examples and relate it to two more basic topics that are receiving increased attention – agents that explain their decisions and ones that follow societal norms. In each case, we describe the target abilities, consider design alternatives, note some open questions, and review prior research. After this, we return to justified agency, offering a hypothesis about its relation to explanatory and normative behavior. We conclude by proposing testbeds and experiments to evaluate this empirical claim and encouraging other researchers to contribute to this crucial area. 1 Background and Motivation Autonomous artifacts, from self-driving cars to drones to household robots, are becoming more widely adopted, and this trend seems likely to accelerate in the future. The increasing reliance on these devices has raised concerns about our ability to understand their behavior and our capacity to ensure their safety. Before intelligent agents can gain widespred acceptance in society, they must be able to communicate their decision making to humans in ways that convince us they actually share our aims. This challenge involves two distinct but complementary issues. The first is the need for agents to explain the reasons they carried out particular courses of action in terms that we can understand. Langley et al. (2017) have referred to this as explainable agency. The second is the need for assurance that, when agents pursue explicit goals, they will also follow the many implicit rules of society. We will refer to this ability as normative agency. Both of these functions are necessary underpinnings of trustable autonomous systems, and two capabilities are closely intertwined. Consider a scenario in which a person drives a friend with a ruptured appendix to the hospital. The driver exceeds

Question: What is the difference between explainable and normative agency?


The quality of online social relationships. Online relationships are less valuable than offline ones. Indeed, their net benefit depends on whether they supplement or substitute for offline social relationships.

Question: What is the net benefit of online social relationships?


Cross-modal plasticity: where and how?. Animal studies have shown that sensory deprivation in one modality can have striking effects on the development of the remaining modalities. Although recent studies of deaf and blind humans have also provided convincing behavioural, electrophysiological and neuroimaging evidence of increased capabilities and altered organization of spared modalities, there is still much debate about the identity of the brain systems that are changed and the mechanisms that mediate these changes. Plastic changes across brain systems and related behaviours vary as a function of the timing and the nature of changes in experience. This specificity must be understood in the context of differences in the maturation rates and timing of the associated critical periods, differences in patterns of transiently existing connections, and differences in molecular factors across brain systems.

Question: What is cross-modal plasticity?


OFDM : Basic Concepts , Scope & its Applications. Orthogonal frequency division multiplexing (OFDM) is a special case of multicarrier transmission where a single DataStream is transmitted over a number of lower rate subcarriers. In July 1998, the IEEE standardization group decided to select OFDM as the basis for their new 5-GHz standard aiming a range of data stream from 6 up to 54 Mbps. This new standard is the first one to use OFDM in packetbased communications. In wireless communication, concept of parallel transmission of symbols is used to achieve high throughput and better transmission quality. Orthogonal Frequency Division Multiplexing (OFDM) is one of the techniques for parallel transmission. The idea of OFDM is to split the total transmission bandwidth into a number of orthogonal subcarriers in order to transmit the symbols using these subcarriers in parallel. In this paper we will discuss the basics of OFDM techniques, role of OFDM in this era, its benefits and losses and also some of its application. Keywords— Orthogonal frequency division multiplexing(OFDM); BER; ISI; PAPR; DVB; DAB.

Question: What is the role of OFDM in this era?


The Prediction of Earnings Using Financial Statement Information: Empirical Evidence With Logit Models and Artificial Neural Networks. In the past three decades, earnings have been one of the most researched variables in accounting. Empirical research provided substantial evidence on its usefulness in the capital markets but evidence in predicting earnings has been limited, yielding inconclusive results. The purpose of this study is to validate and extend prior research in predicting earnings by examining aggregate and industry-specific data. A sample of 10,509 firm-year observations included in the Compustat database for the period 1982-91 is used in the study. The stepwise logistic regression results of the present study indicated that nine earnings and non-earnings variables can be used to predict earnings. These predictor variables are not identical to those reported in prior studies. These results are also extended to the manufacturing industry. Two new variables are identified to be significant in this industry. Moreover, an Artificial Neural Network (ANN) approach is employed to complement the logistic regression results. The ANN model's performance is at least as high as the logistic regression model's predictive ability.

Question: What is the prediction of earnings using financial statement information?


WarpGAN: Automatic Caricature Generation. We propose, WarpGAN, a fully automatic network that can generate caricatures given an input face photo. Besides transferring rich texture styles, WarpGAN learns to automatically predict a set of control points that can warp the photo into a caricature, while preserving identity. We introduce an identity-preserving adversarial loss that aids the discriminator to distinguish between different subjects. Moreover, WarpGAN allows customization of the generated caricatures by controlling the exaggeration extent and the visual styles. Experimental results on a public domain dataset, WebCaricature, show that WarpGAN is capable of generating a diverse set of caricatures while preserving the identities. Five caricature experts suggest that caricatures generated by WarpGAN are visually similar to hand-drawn ones and only prominent facial features are exaggerated. ∗ indicates equal contribution

Question: How large is the canadian military?


A Measure of Polarization on Social Media Networks Based on Community Boundaries. Polarization in social media networks is a fact in several scenarios such as political debates and other contexts such as same-sex marriage, abortion and gun control. Understanding and quantifying polarization is a longterm challenge to researchers from several areas, also being a key information for tasks such as opinion analysis. In this paper, we perform a systematic comparison between social networks that arise from both polarized and non-polarized contexts. This comparison shows that the traditional polarization metric – modularity – is not a direct measure of antagonism between groups, since non-polarized networks may be also divided into fairly modular communities. To bridge this conceptual gap, we propose a novel polarization metric based on the analysis of the boundary of a pair of (potentially polarized) communities, which better captures the notions of antagonism and polarization. We then characterize polarized and non-polarized social networks according to the concentration of high-degree nodes in the boundary of communities, and found that polarized networks tend to exhibit low concentration of popular nodes along the boundary. To demonstrate the usefulness of our polarization measures, we analyze opinions expressed on Twitter on the gun control issue in the United States, and conclude that our novel metrics help making sense of opinions expressed on online media.

Question: What is the concentration of popular nodes in the boundary of communities?


Transforming Cooling Optimization for Green Data Center via Deep Reinforcement Learning. Cooling system plays a critical role in a modern data center (DC). Developing an optimal control policy for DC cooling system is a challenging task. The prevailing approaches often rely on approximating system models that are built upon the knowledge of mechanical cooling, electrical and thermal management, which is difficult to design and may lead to suboptimal or unstable performances. In this paper, we propose utilizing the large amount of monitoring data in DC to optimize the control policy. To do so, we cast the cooling control policy design into an energy cost minimization problem with temperature constraints, and tap it into the emerging deep reinforcement learning (DRL) framework. Specifically, we propose an end-toend cooling control algorithm (CCA) that is based on the actorcritic framework and an off-policy offline version of the deep deterministic policy gradient (DDPG) algorithm. In the proposed CCA, an evaluation network is trained to predict an energy cost counter penalized by the cooling status of the DC room, and a policy network is trained to predict optimized control settings when gave the current load and weather information. The proposed algorithm is evaluated on the EnergyPlus simulation platform and on a real data trace collected from the National Super Computing Centre (NSCC) of Singapore. Our results show that the proposed CCA can achieve about 11% cooling cost saving on the simulation platform compared with a manually configured baseline control algorithm. In the trace-based study, we propose a de-underestimation validation mechanism as we cannot directly test the algorithm on a real DC. Even though with DUE the results are conservative, we can still achieve about 15% cooling energy saving on the NSCC data trace if we set the inlet temperature threshold at 26.6 degree Celsius.

Question: What is the cooling cost saving of the proposed algorithm?


Fast and extensible building modeling from airborne LiDAR data. This paper presents an automatic algorithm which reconstructs building models from airborne LiDAR (light detection and ranging) data of urban areas. While our algorithm inherits the typical building reconstruction pipeline, several major distinct features are developed to enhance efficiency and robustness: 1) we design a novel vegetation detection algorithm based on differential geometry properties and unbalanced SVM; 2) after roof patch segmentation, a fast boundary extraction method is introduced to produce topology-correct water tight boundaries; 3) instead of making assumptions on the angles between roof boundary lines, we propose a data-driven algorithm which automatically learns the principal directions of roof boundaries and uses them in footprint production. Furthermore, we show the extendability of our algorithm by supporting non-flat object patterns with the help of only a few user interactions. We demonstrate the efficiency and accuracy of our algorithm by showing experiment results on urban area data of several different data sets.

Question: What is the difference between a building and a building model?


Object Detection Using the Statistics of Parts. In this paper we describe a trainable object detector and its instantiations for detecting faces and cars at any size, location, and pose. To cope with variation in object orientation, the detector uses multiple classifiers, each spanning a different range of orientation. Each of these classifiers determines whether the object is present at a specified size within a fixed-size image window. To find the object at any location and size, these classifiers scan the image exhaustively. Each classifier is based on the statistics of localized parts. Each part is a transform from a subset of wavelet coefficients to a discrete set of values. Such parts are designed to capture various combinations of locality in space, frequency, and orientation. In building each classifier, we gathered the class-conditional statistics of these part values from representative samples of object and non-object images. We trained each classifier to minimize classification error on the training set by using Adaboost with Confidence-Weighted Predictions (Shapire and Singer, 1999). In detection, each classifier computes the part values within the image window and looks up their associated class-conditional probabilities. The classifier then makes a decision by applying a likelihood ratio test. For efficiency, the classifier evaluates this likelihood ratio in stages. At each stage, the classifier compares the partial likelihood ratio to a threshold and makes a decision about whether to cease evaluation—labeling the input as non-object—or to continue further evaluation. The detector orders these stages of evaluation from a low-resolution to a high-resolution search of the image. Our trainable object detector achieves reliable and efficient detection of human faces and passenger cars with out-of-plane rotation.

Question: What is the difference between a face detector and a car detector?


Active Object Categorization on a Humanoid Robot. We present a Bag of Words-based active object categorization technique implemented and tested on a humanoid robot. The robot is trained to categorize objects that are handed to it by a human operator. The robot uses hand and head motions to actively acquire a number of different views. A view planning scheme using entropy minimization reduces the number of views needed to achieve a valid decision. Categorization results are significantly improved by active elimination of background features using robot arm motion. Our experiments cover both, categorization when the object is handed to the robot in a fixed pose at training and testing, and object pose independent categorization. Results on a 4-class object database demonstrate the classification efficiency, a significant gain from multi-view compared to single-view classification, and the advantage of view planning. We conclude that humanoid robotic systems can be successfully applied to actively categorize objects a task with many potential applications ranging from edutainment to active surveillance.

Question: What is the difference between a robot and a human?


Can Twitter be used to predict county excessive alcohol consumption rates?. OBJECTIVES The current study analyzes a large set of Twitter data from 1,384 US counties to determine whether excessive alcohol consumption rates can be predicted by the words being posted from each county.   METHODS Data from over 138 million county-level tweets were analyzed using predictive modeling, differential language analysis, and mediating language analysis.   RESULTS Twitter language data captures cross-sectional patterns of excessive alcohol consumption beyond that of sociodemographic factors (e.g. age, gender, race, income, education), and can be used to accurately predict rates of excessive alcohol consumption. Additionally, mediation analysis found that Twitter topics (e.g. 'ready gettin leave') can explain much of the variance associated between socioeconomics and excessive alcohol consumption.   CONCLUSIONS Twitter data can be used to predict public health concerns such as excessive drinking. Using mediation analysis in conjunction with predictive modeling allows for a high portion of the variance associated with socioeconomic status to be explained.

Question: Can Twitter be used to predict county excessive alcohol consumption rates?


Visual pattern discovery for architecture image classification and product image search. Many objects have repetitive elements, and finding repetitive patterns facilitates object recognition and numerous applications. We devise a representation to describe configurations of repetitive elements. By modeling spatial configurations, visual patterns are more discriminative than local features, and are able to tackle with object scaling, rotation, and deformation. We transfer the pattern discovery problem into finding frequent subgraphs from a graph, and exploit a graph mining algorithm to solve this problem. Visual patterns are then exploited in architecture image classification and product image retrieval, based on the idea that visual pattern can describe elements conveying architecture styles and emblematic motifs of brands. Experimental results show that our pattern discovery approach has promising performance and is superior to the conventional bag-of-words approach.

Question: What is the name of the algorithm?


OpenHSM: An Open Key Life Cycle Protocol for Public Key Infrastructure's Hardware Security Modules. The private keys used in a PKI are its most important asset. Protect these keys from unauthorised use or disclosure is essential to secure a PKI. Relying parties need assurances that the private key used to sign their certificates is controlled and managed following pre-defined statement policy. Hardware Security Modules (HSM) offer physical and logical protection and should be considered for any PKI deployment. The software that manages keys inside an HSM should control all life cycle of a private key. Normally this kind of equipment implements a embedded key management protocol and this protocols are not available to public scrutiny due to industrial interests. Other important issue is that HSMs are targeted in their development to the Bank industry and not to PKI, making some important PKI issues, like, strict key usage control and a secure auditing trail, play a secondary role. This paper presents an open protocol to securely manage private keys inside HSMs. The protocol is described, analysed and discussed.

Question: What is an HSM?


Ship Classification in TerraSAR-X Images With Feature Space Based Sparse Representation. Ship classification is the key step in maritime surveillance using synthetic aperture radar (SAR) imagery. In this letter, we develop a new ship classification method in TerraSAR-X images based on sparse representation in feature space, in which the sparse representation classification (SRC) method is exploited. In particular, to describe the ship more accurately and to reduce the dimension of the dictionary in SRC, we propose to employ a representative feature vector to construct the dictionary instead of utilizing the image pixels directly. By testing on a ship data set collected from TerraSAR-X images, we show that the proposed method is superior to traditional methods such as the template matching (TM), K-nearest neighbor (K-NN), Bayes and Support Vector Machines (SVM).

Question: What is the ship classification method in TerraSAR-X images?


A continuous-time sigma-delta modulator with 88-dB dynamic range and 1.1-MHz signal bandwidth. This paper presents the design and experimental results of a continuous-time /spl Sigma//spl Delta/ modulator for ADSL applications. Multibit nonreturn-to-zero (NRZ) DAC pulse shaping is used to reduce clock jitter sensitivity. The nonzero excess loop delay problem in conventional continuous-time /spl Sigma//spl Delta/ modulators is solved by our proposed architecture. A prototype third-order continuous-time /spl Sigma//spl Delta/ modulator with 5-bit internal quantization was realized in a 0.5-/spl mu/m double-poly triple-metal CMOS technology, with a chip area of 2.4 /spl times/ 2.4 mm/sup 2/. Experimental results show that the modulator achieves 88-dB dynamic range, 84-dB SNR, and 83-dB SNDR over a 1.1-MHz signal bandwidth with an oversampling ratio of 16, while dissipating 62 mW from a 3.3-V supply.

Question: What is the dynamic range of a sigma delta modulator?


A modified antipodal Vivaldi antenna (AVA) with elliptical slotting edge (ESE) for ultra-wideband (UWB) applications. As the demand of UWB system increases nowadays, several types of hybrid technique on antenna design are studied, modified, and proposed for UWB applications. In this work, an enhanced performance of modified antipodal Vivaldi antenna (AVA) for targeting ultra-wideband (UWB) frequency range between 2.17 GHz and 10.6 GHz has been proposed. This proposed UWB antenna is using FR-4 substrate with dielectric constant, εr = 4.4 and the electrical conductivity tangent loss, tanΔ = 0.019. This antenna design is based on the modified from the dual exponential tapered slot antenna (DETSA) with varying of the exponential flares design for obtain the best return loss of the antenna within the frequency bandwidth of the UWB. It has two exponential flares that follow the characteristic of exponential curve. Next, an elliptical slotting edge (ESE) with fixed horizontal radius and varied vertical radius is implemented to the antenna which effectively increases the performance of the antenna at lower frequencies. These modifications are done without changing the antenna size, which is 70 mm × 90 mm in dimension, which is approximately 0.5λ × 0.6λ, where λ is the wavelength of 2 GHz. The effects of the parameters of the antenna characteristic are also studied. In this case, the parametric study on varying distance between elliptical slots, Ks. It shows that, the higher value of Ks will effect to shift to the resonant frequency and increase the return loss of the antenna. At 9.104 GHz of resonant frequencies, it shows of the best return loss of 48.809 dB. Rather than that, the side lobe levels of the radiation pattern of the antenna are reduced in the lower frequencies.

Question: What is the return loss of the antenna?


HIGH DIMENSIONAL SPACES. In this paper, we analyze deep learning from a mathematical point of view and derive several novel results. The results are based on intriguing mathematical properties of high dimensional spaces. We first look at perturbation based adversarial examples and show how they can be understood using topological arguments in high dimensions. We point out fallacy in an argument presented in a published paper in 2015 by Goodfellow et al., see reference [10], and we present a more rigorous, general and correct mathematical result to explain adversarial examples in terms of topology of image manifolds. Second, we look at optimization landscapes of deep neural networks and examine the number of saddle points relative to that of local minima. Third, we show how multiresolution nature of images explains perturbation based adversarial examples in form of a stronger result. Our results state that expectation of L2-norm of adversarial perturbations shrinks to 0 as image resolution becomes arbitrarily large. Finally, by incorporating the parts-whole manifold learning hypothesis for natural images, we investigate the working of deep neural networks and root causes of adversarial examples and discuss how future improvements can be made and how adversarial examples can be eliminated.

Question: What is the difference between a local minimum and a saddle point?


Experiments With Some Programs That Search Game Trees. Many problems in artificial intelligence involve the searching of large trees of alternative possibilities—for example, game-playing and theorem-proving. The problem of efficiently searching large trees is discussed. A new method called "dynamic ordering" is described, and the older minimax and Alpha-Beta procedures are described for comparison purposes. Performance figures are given for six variations of the game of kalah. A quantity called "depth ratio" is derived which is a measure of the efficiency of a search procedure. A theoretical limit of efficiency is calculated and it is shown experimentally that the dynamic ordering procedure approaches that limit.

Question: What is the depth ratio?


BRIDE - A toolchain for framework-independent development of industrial service robot applications. Software integration is still a challenging and time consuming task and therefore a major part of the development of industrial and domestic service robot applications. The presented toolchain BRIDE is able to streamline this process by the separation of user roles and the separation of developer concerns of software components to ensure a frame-work independent implementation. The impact of the BRIDE toolchain in the development process is demonstrated in a case study on the SyncMM mobile manipulation control framework.

Question: What is a framework-independent development?


Millimeter-Wave TE20-Mode SIW Dual-Slot-Fed Patch Antenna Array With a Compact Differential Feeding Network. A millimeter-wave series–parallel patch antenna array is presented, in which the dual-slot feeding structure is handily implemented using the intrinsic field distribution of TE20 mode in substrate-integrated waveguide (SIW). One 28 GHz patch antenna element fed by the TE20-mode SIW is first designed, achieving a 10 dB impedance bandwidth of 10.2% and a simulated peak gain of 6.48 dBi. Based on the antenna element, a <inline-formula> <tex-math notation="LaTeX">$4 \times 4$ </tex-math></inline-formula> array with a compact series–parallel differential feeding network is developed accordingly. Due to the novel compact SIW-based series–parallel feeding network, the antenna array can achieve superior radiation performances, which is the highlight of this communication. The simulation and measurement results of the proposed antenna array are in good agreement, demonstrating a performance of 8.5% impedance bandwidth, 19.1 dBi peak gain, symmetrical radiation patterns, and low cross-polarization levels (−30 dB in E-plane and −25 dB in H-plane) in the operating frequency band of 26.65–29.14 GHz.

Question: What is the size of the antenna array?


Compressed Representations for Web and Social Graphs 1. Compressed representations have become effective to store and access large Web and social graphs, in order to support various graph querying and mining tasks. The existing representations exploit various typical patterns in those networks and provide basic navigation support. In this paper we obtain unprecedented results by finding "dense subgraph" patterns and combining them with techniques such as node orderings and compact data structures. On those representations we support out-neighbor and out/in-neighbor queries, as well as mining queries based on dense subgraphs. First, we propose a compression scheme for Web graphs that reduces edges by representing dense subgraphs with "virtual nodes"; over this scheme we apply node orderings and other compression techniques. With this approach we match the best current compression ratios that support out-neighbor queries (i.e., nodes pointed from a given node), using 1.0–1.8 bits per edge (bpe) on large Web graphs, and retrieving each neighbor of a node in 0.6–1.0 microseconds (μsec). When supporting both outand in-neighbor queries, instead, our technique generally offers the best time when using little space. If the reduced graph, instead, is represented with a compact data structure that supports bidirectional navigation, we obtain the most compact Web graph representations (0.9–1.5 bpe) that support out/in-neighbor navigation, yet the time per neighbor extracted raises to around 5–20 μsec. We also propose a compact data structure that represents dense subgraphs without using virtual nodes. It allows us to recover out/in-neighbors and answer other more complex queries on the dense subgraphs identified. This structure is not competitive on Web graphs, but on social networks it achieves 4–13 bpe and 8–12 μsec per out/in-neighbor retrieved, which improves upon all existing representations.

Question: What is the largest social network?


Multi-label classification by exploiting label correlations. a Department of Computer Science and Technology, Tongji University, Shanghai 201804, PR China b Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 2G7, Canada c Key Laboratory of Embedded System and Service Computing, Ministry of Education, Tongji University, Shanghai 201804, PR China d System Research Institute, Polish Academy of Sciences, Warsaw, Poland e School of Software, Jiangxi Agricultural University, Nanchang 330013, PR China

Question: What is the difference between the first and the second author?


JINS MEME algorithm for estimation and tracking of concentration of users. Activity tracking using a wearable device is an emerging research field. Large-scale studies on activity tracking performed with eyewear-type wearable devices remains a challenging area owing to the negative effect such devices have on users' looks. To cope with this challenge, JINS Inc., an eyewear retailer in Japan, has developed a state-of-the-art smart eyewear called JINS MEME. The design of JINS MEME is almost the same as that of Wellington-type eyeglasses so that people can use it in their daily lives. JINS MEME is equipped with sensors to detect a user's eye movement and head motion. In addition to these functions of JINS MEME, JINS developed an application to measure concentration levels of users. In this demonstration, users will experience wearing the JINS MEME glasses and their concentration will be measured while they perform a certain task at our booth.

Question: What is the name of the eyewear company?


Multi-robot long-term persistent coverage with fuel constrained robots. In this paper, we present an algorithm to solve the Multi-Robot Persistent Coverage Problem (MRPCP). Here, we seek to compute a schedule that will allow a fleet of agents to visit all targets of a given set while maximizing the frequency of visitation and maintaining a sufficient fuel capacity by refueling at depots. We also present a heuristic method to allow us to compute bounded suboptimal results in real time. The results produced by our algorithm will allow a team of robots to efficiently cover a given set of targets or tasks persistently over long periods of time, even when the cost to transition between tasks is dynamic.

Question: What is the MRPCP?


Morphological, molecular and statistical tools to identify Castanea species and their hybrids. The aim of this study was to investigate, in relation to reference samples, the genetic structure of several natural Castanea populations of unknown origin and of hybrids from the clone collection of the Lourizán Forest Research Center. A total of 115 individuals sampled from four Castanea sativa stands located in the northwest of Spain, 61 Castanea crenata individuals and 27 Castanea mollissima individuals were classified on the basis of four morphological traits, and genotyped with 11 microsatellite loci to define a set of reference samples. The data analyzed with the program STRUCTURE detected four clusters: the two Asiatic species and two clusters in C. sativa. From these reference samples, pure individuals and hybrids of known genealogy were simulated to determine the efficiency with which STRUCTURE and NEWHYBRIDS assigned them to pure or hybrid groups and to a specific genealogical class, respectively. As expected, the discrimination and assignment of the simulated individuals improved with increasing F st value. The two clusters identified within C. sativa may correspond to gene pools with different adaptive characteristics previously identified in provenance tests; pure and admixed populations of both C. sativa gene pools were identified.

Question: What is the aim of this study?


Psychometric Properties of Physical Activity and Leisure Motivation Scale in Farsi: an International Collaborative Project on Motivation for Physical Activity and Leisure.. BACKGROUND Given the importance of regular physical activity, it is crucial to evaluate the factors favoring participation in physical activity. We aimed to report the psychometric analysis of the Farsi version of the Physical Activity and Leisure Motivation Scale (PALMS).   METHODS The Farsi version of PALMS was completed by 406 healthy adult individuals to test its factor structure and concurrent validity and reliability.   RESULTS Conducting the exploratory factor analysis revealed nine factors that accounted for 64.6% of the variances. The PALMS reliability was supported with a high internal consistency of 0.91 and a high test-retest reliability of 0.97 (95% CI: 0.97-0.98). The association between the PALMS and its previous version Recreational Exercise Motivation Measure scores was strongly significant (r= 0.86, P < 0.001).   CONCLUSION We have shown that the Farsi version of the PALMS appears to be a valuable instrument to measure motivation for physical activity and leisure.

Question: What is the psychometric properties of the Farsi version of the PALMS?


The Stanford Typed Dependencies Representation. This paper examines the Stanford typed dependencies representation, which was designed to provide a straightforward description of grammatical relations for any user who could benefit from automatic text understanding. For such purposes, we argue that dependency schemes must follow a simple design and provide semantically contentful information, as well as offer an automatic procedure to extract the relations. We consider the underlying design principles of the Stanford scheme from this perspective, and compare it to the GR and PARC representations. Finally, we address the question of the suitability of the Stanford scheme for parser evaluation.

Question: What is the Stanford Typed Dependencies Representation?


Single-Fed Broadband Circularly Polarized Stacked Patch Antenna With Horizontally Meandered Strip for Universal UHF RFID Applications. In this paper, a horizontally meandered strip (HMS) feed technique is proposed to achieve good impedance matching and symmetrical broadside radiation patterns for a single-fed broadband circularly polarized stacked patch antenna, which is suitable for universal ultrahigh frequency (UHF) RF identification (RFID) applications. The antenna is composed of two corner truncated patches and an HMS, all of which are printed on the upper side of the FR4 substrates. One end of the HMS is connected to the main patch by a probe, while the other end is connected to an SMA connector. Simulation results are compared with the measurements, and a good agreement is obtained. The measurements show that the antenna has an impedance bandwidth (VSWR <; 1.5) of about 25.8% (758-983 MHz), a 3-dB axial ratio (AR) bandwidth of about 13.5% (838-959 MHz), and a gain level of about 8.6 dBic or larger within the 3-dB AR bandwidth. Therefore, the proposed antenna can be a good candidate for universal UHF RFID readers operating at the UHF band of 840-955 MHz. In addition, a parametric study and a design guideline of the proposed antenna are presented to provide the engineers with information for designing, modifying, and optimizing such an antenna. At last, the proposed antenna is validated in RFID system applications.

Question: What is the purpose of the antenna?


An improved power quality induction heater using zeta converter. This paper presents an induction heater (IH) with power factor correction (PFC) for domestic induction heating applications. A half bridge voltage fed series resonant inverter (VFSRI) based IH is fed from the front end PFC corrected zeta converter. The PFC-zeta converter operating in continuous inductor conduction mode (CICM) is used to regulate the DC link voltage. The switch current stress of PFC-zeta converter is controlled by operating zeta converter in CICM mode using a well-established internal current loop and outer voltage loop approach. The PFC-zeta converter based voltage fed IH converts intermediate DC bus voltage to high frequency (30 kHz) voltage suitable for domestic induction heating. A 2 kW PFC-zeta converter based IH is designed and its performance is simulated in MATLAB with power quality indices within an IEC 61000-3-2 standard.

Question: What is the power quality index of a zeta converter?


The humble Bayesian: model checking from a fully Bayesian perspective.. Gelman and Shalizi (2012) criticize what they call the 'usual story' in Bayesian statistics: that the distribution over hypotheses or models is the sole means of statistical inference, thus excluding model checking and revision, and that inference is inductivist rather than deductivist. They present an alternative hypothetico-deductive approach to remedy both shortcomings. We agree with Gelman and Shalizi's criticism of the usual story, but disagree on whether Bayesian confirmation theory should be abandoned. We advocate a humble Bayesian approach, in which Bayesian confirmation theory is the central inferential method. A humble Bayesian checks her models and critically assesses whether the Bayesian statistical inferences can reasonably be called upon to support real-world inferences.

Question: What is the Bayesian approach to model checking?


An Improved Variable On-Time Control Strategy for a CRM Flyback PFC Converter. The traditional critical conduction mode (CRM) flyback PFC converter with constant on-time control strategy usually suffers low power factor (PF) and high total harmonic distortion (THD) due to the nonsinusoidal input current waveform. In order to solve this problem, an improved variable on-time control strategy for the CRM flyback PFC converter is proposed in this letter. A simple analog divider circuit consisting of an operational amplifier, two signal switches, and an RC filter is proposed to modulate the turn-on time of the primary switch, and the PF and THD of the CRM flyback PFC converter can be evidently improved. The theoretical analysis is presented and the experiment results verify the advantages of the proposed control scheme.

Question: How to improve the power factor of a CRM flyback PFC converter?


Decision making, the P3, and the locus coeruleus-norepinephrine system.. Psychologists and neuroscientists have had a long-standing interest in the P3, a prominent component of the event-related brain potential. This review aims to integrate knowledge regarding the neural basis of the P3 and to elucidate its functional role in information processing. The authors review evidence suggesting that the P3 reflects phasic activity of the neuromodulatory locus coeruleus-norepinephrine (LC-NE) system. They discuss the P3 literature in the light of empirical findings and a recent theory regarding the information-processing function of the LC-NE phasic response. The theoretical framework emerging from this research synthesis suggests that the P3 reflects the response of the LC-NE system to the outcome of internal decision-making processes and the consequent effects of noradrenergic potentiation of information processing.

Question: What is the locus coeruleus-norepinephrine system?


High Switching Performance of 1700-V, 50-A SiC Power MOSFET Over Si IGBT/BiMOSFET for Advanced Power Conversion Applications. Due to wider band gap of silicon carbide (SiC) compared to silicon (Si), MOSFET made in SiC has considerably lower drift region resistance, which is a significant resistive component in high-voltage power devices. With low on-state resistance and its inherently low switching loss, SiC MOSFETs can offer much improved efficiency and compact size for the converter compared to those using Si devices. In this paper, we report switching performance of a new 1700-V, 50-A SiC MOSFET designed and developed by Cree, Inc. Hard-switching losses of the SiC MOSFETs with different circuit parameters and operating conditions are measured and compared with the 1700-V Si BiMOSFET and 1700-V Si IGBT, using same test set-up. Based on switching and conduction losses, the operating boundary of output power and switching frequency of these devices are found out in a dc-dc boost converter and compared. The switching dv/dts and di/dts of SiC MOSFET are captured and discussed in the perspective of converter design. To validate the continuous operation, three dc-dc boost converters using these devices, are designed and tested at 10 kW of power with 1 kV of output voltage and 10 kHz of switching frequency. 1700V SiC Schottky diode is used as the blocking diode in each case. Corresponding converter efficiencies are evaluated and the junction temperature of each device is estimated. To demonstrate high switching frequency operation, the SiC MOSFET is switched upto 150 kHz within permissible junction temperature rise. A switch combination of the 1700-V SiC MOSFET and 1700-V SiC Schottky diode connected in series is also evaluated for zero voltage switching turn-ON behavior and compared with those of bipolar Si devices. Results show substantial power loss saving with the use of SiC MOSFET.

Question: What is the switching frequency of a SiC MOSFET?


Advances on Interactive Machine Learning. Interactive Machine Learning (IML) is an iterative learning process that tightly couples a human with a machine learner, which is widely used by researchers and practitioners to effectively solve a wide variety of real-world application problems. Although recent years have witnessed the proliferation of IML in the field of visual analytics, most recent surveys either focus on a specific area of IML or aim to summarize a visualization field that is too generic for IML. In this paper, we systematically review the recent literature on IML and classify them into a task-oriented taxonomy built by us. We conclude the survey with a discussion of open challenges and research opportunities that we believe are inspiring for future work in IML.

Question: What is the task-oriented taxonomy of IML?


The theory of multidimensional persistence. Persistent homology captures the topology of a filtration - a one-parameter family of increasing spaces - in terms of a complete discrete invariant. This invariant is a multiset of intervals that denote the lifetimes of the topological entities within the filtration. In many applications of topology, we need to study a multifiltration: a family of spaces parameterized along multiple geometric dimensions. In this paper, we show that no similar complete discrete invariant exists for multidimensional persistence. Instead, we propose the rank invariant, a discrete invariant for the robust estimation of Betti numbers in a multifiltration, and prove its completeness in one dimension.

Question: What is the theory of multidimensional persistence?


Comparing Business and Household Sector Innovation in Consumer Products : Findings from a Representative Study in the United Kingdom. In a first survey of its type, we measure development and modification of consumer products by product users in a representative sample of 1,173 UK consumers aged 18 and over. We estimate this previously unmeasured type of innovation to be quite large: 6.1% of UK consumers – nearly 2.9 million individuals have engaged in consumer product innovation during the prior three years. In aggregate, consumers' annual product development expenditures are more than 1.4 times larger than the annual consumer product R&D expenditures of all firms in the UK combined. Consumers engage in many small projects which seem complementary to the innovation efforts of incumbent producers. Consumer innovators very seldom protect their innovations via intellectual property, and 17% diffuse to others. These results imply that official statistics partly miss relevant innovation activity, and that existing companies should reconfigure their product development systems to find and build upon prototypes developed by consumers. 1 MIT Sloan School of Management, Cambridge MA, USA. 2 RSM Erasmus University, Rotterdam, The Netherlands. 3 EIM Business and Policy Research, Zoetermeer, The Netherlands. 4 Centre for Research in Innovation Management, University of Brighton, United Kingdom. * Corresponding author. E-mail: evhippel@mit.edu. Phone: +1 617-253-7155

Question: How large is the UK's business sector?


IoT smart parking system for reducing green house gas emission. Rapid climate change results natural calamity and severe economic impact and threats to the life. Burning fossil fuels by the medium of transportation contributes 1/3 of portion in increasing greenhouse emission and leading to raise surface temperate. Commuters in and around the developed cities faces difficulties in finding parking lot due to lack of notification process and autonomous parking systems. This causes commuters to take multiple rounds trips to get the parking slot which causes burning additional fuel and ultimately producing excessive CO2 emission. This paper describes solution to smart parking system using Internet of Things (IoT) to override parking hazards and explains how does it helps to minimize emitting greenhouse gases. IoT enables smart parking system using the system of interconnected Raspberry Pi, Distance Sensor, Pi Camera devices together. This hardware reacts to one another collects data and transmits to cloud storage.

Question: How does IoT smart parking system help to reduce greenhouse gas emission?


Electronic Data Interchange and Small Organizations: Adoption and Impact of Technology. Many EDI researchers and practitioners have recognized the importance of high penetration levels for the success of EDI. Unfortunately, such penetration is partly impeded by the resistance of small companies to become EDI capable. To investigate this issue, three major factors are identified that influence the EDI adoption practices of small firms. These factors are: organizational readiness (because of the low levels of IT sophistication and resource availability of small firms), external pressures to adopt (because of the weak market positions of small firms and the network nature of the technology), and perceived benefits (because of the limited impact that IT has on small firms due to under-utilization and lack of integration). By combining the anticipated effects of these factom, we developed a framework of EDI adoption by small businesses. The applicability of this framework is empirically demonstrated using the results of seven case studies. Finally, recommendations are made for the development of successful EDI partner expansion plans. These include the development of a long-term EDI partner expansion plan from the very beginning, the individual assessment of each partner's EDI preparedness level, and the selection of appropriate influence tactics to expedite adoption by small partners. Specifically, it is suggested that EDI initiators pursue promotional efforts to improve partners' perceptions of EDI benefits, provide financial and technological assistance to partners with low organizational readiness, and carefully select and enact influence strategies to reduce resistance.

Question: What is the impact of EDI on small organizations?


A Symbolic Approach for Explaining Errors in Image Classification Tasks. Machine learning algorithms, despite their increasing success in handling object recognition tasks, still seldom perform without error. Often the process of understanding why the algorithm has failed is the task of the human who, using domain knowledge and contextual information, can discover systematic shortcomings in either the data or the algorithm. This paper presents an approach where the process of reasoning about errors emerging from a machine learning framework is automated using symbolic techniques. By utilizing spatial and geometrical reasoning between objects in a scene, the system is able to describe misclassified regions in relation to its context. The system is demonstrated in the remote sensing domain where objects and entities are detected in satellite images.

Question: What is a symbolic approach for explaining errors in image classification tasks?


A Taxonomy of Workflow Management Systems for Grid Computing. With the advent of Grid and application technologies, scientists and engineers are building more and more complex applications to manage and process large data sets, and execute scientific experiments on distributed resources. Such application scenarios require means for composing and executing complex workflows. Therefore, many efforts have been made towards the development of workflow management systems for Grid computing. In this paper, we propose a taxonomy that characterizes and classifies various approaches for building and executing workflows on Grids. We also survey several representative Grid workflow systems developed by various projects world-wide to demonstrate the comprehensiveness of the taxonomy. The taxonomy not only highlights the design and engineering similarities and differences of state-of-the-art in Grid workflow systems, but also identifies the areas that need further research.

Question: What is a workflow management system?


Free-form shape design using triangulated surfaces. We present an approach to modeling with truly mutable yet completely controllable free-form surfaces of arbitrary topology. Surfaces may be pinned down at points and along curves, cut up and smoothly welded back together, and faired and reshaped in the large. This style of control is formulated as a constrained shape optimization, with minimization of squared principal curvatures yielding graceful shapes that are free of the parameterization worries accompanying many patch-based approaches. Triangulated point sets are used to approximate these smooth variational surfaces, bridging the gap between patch-based and particle-based representations. Automatic refinement, mesh smoothing, and re-triangulation maintain a good computational mesh as the surface shape evolves, and give sample points and surface features much of the freedom to slide around in the surface that oriented particles enjoy. The resulting surface triangulations are constructed and maintained in real time.

Question: What is a triangulated surface?


BDO-SD: An efficient scheme for big data outsourcing with secure deduplication. In the big data era, duplicated copies of data may be generated by various smart devices. Although convergent encryption has been extensively adopted for secure deduplication, it is still very challenging to efficiently and reliably manage the convergent keys and support keyword search over encrypted data. This paper addresses this problem by presenting an efficient scheme for big data outsourcing with secure deduplication, named BDO-SD. Specifically, with the convergent encryption technique, data owners can outsource their data to the cloud server with data deduplication; with the idea of public key encryption and keywords search, a data user can query encrypted data while preserving its privacy. Security analysis demonstrates that our proposed BDO-SD scheme can achieve the data confidentiality, query privacy and key security. Simulation results show that compared with other schemes, our scheme can achieve better efficiency than the existing data deduplication schemes.

Question: What is the efficiency of the proposed scheme?


The Nature of Statistical Learning Theory.

Question: What is the nature of statistical learning theory?


Can Invisible Watermarks Resolve Rightful Ownerships?. Digital watermarks have been proposed in recent literature as the means for copyright protection of multimedia data. In this paper we address the capability of invisible watermarking schemes to resolve copyright ownerships. We will show that rightful ownerships cannot be resolved by current watermarking schemes alone. In addition, in the absence of standardization of watermarking procedures, anyone can claim ownership of any watermarked image. Specifically, we provide counterfeit watermarking schemes that can be performed on a watermarked image to allow multiple claims of rightful ownerships. We also propose non-invertible watermarking schemes in this paper and discuss in general the usefulness of digital watermarks in identifying the rightful copyright owners. The results, coupled with the recent attacks on some image watermarks, further imply that we have to carefully re-think our approaches to invisible watermarking of images, and re-evaluate the promises, applications and limitations of such digital means of copyright protection.

Question: How can invisible watermarks resolve rightful ownerships?


The web impact factor: a critical review. Purpose – We analyse the link-based web site impact measure known as the Web Impact Factor (WIF). It is a quantitative tool for evaluating and ranking web sites, top-level domains and sub-domains. We also discuss the WIF's advantages and disadvantages, data collection problems, and validity and reliability of WIF results. Design/methodology/approach – A key to webometric studies has been the use of largescale search engines, such as Yahoo and AltaVista that allow measurements to be made of the total number of pages in a web site and the total number of backlinks to the web site. These search engines provide similar possibilities for the investigation of links between web sites/pages to those provided by the academic journals citation databases from the Institute of Scientific Information (ISI). But the content of the Web is not of the same nature and quality as the databases maintained by the ISI. Findings – This paper reviews how the WIF has been developed and applied. It has been suggested that Web Impact Factors can be calculated as a way of comparing the attractiveness of web sites or domains on the Web. It is concluded that, while the WIF is arguably useful for quantitative intra-country comparison, application beyond this (i.e., to inter-country assessment) has little value. Originality/value – The paper attempts to make a critical review over literature on the WIF and associated indicators.

Question: What is the web impact factor?