A survey of trust computation models for service management in internet of things systems. In this paper we survey trust computation models for Internet of things (IoT) systems for the purpose of service management, i.e., whether or not to select an IoT device as a service provider. Future IoT systems will connect the physical world into cyberspace everywhere and everything via billions of smart objects, and are expected to have a high economic impact. To date there is little work on trust computation in IoT environments for service management, especially for dealing with misbehaving owners of IoT devices that provide services to other IoT devices in the system. Our approach is to classify existing trust computation models for service management in IoT systems based on five essential design dimensions for a trust computation model: trust composition, trust propagation, trust aggregation, trust update, and trust formation. We summarize pros and cons of each dimension's options, and highlight the effectiveness of defense mechanisms against malicious attacks. We also summarize the most, least, and little visited trust computation techniques in the literature and provide insight on the effectiveness of trust computation techniques as applying to IoT systems. Finally, we identify gaps in IoT trust computation research and suggest future research directions.

Question: What is the difference between a trust computation model and a trust model?


Word sense disambiguation as a traveling salesman problem. Word sense disambiguation (WSD) is a difficult problem in Computational Linguistics, mostly because of the use of a fixed sense inventory and the deep level of granularity. This paper formulates WSD as a variant of the traveling salesman problem (TSP) to maximize the overall semantic relatedness of the context to be disambiguated. Ant colony optimization, a robust nature-inspired algorithm, was used in a reinforcement learning manner to solve the formulated TSP. We propose a novel measure based on the Lesk algorithm and Vector Space Model to calculate semantic relatedness. Our approach to WSD is comparable to state-of-the-art knowledge-based and unsupervised methods for benchmark datasets. In addition, we show that the combination of knowledge-based methods is superior to the most frequent sense heuristic and significantly reduces the difference between knowledge-based and supervised methods. The proposed approach could be customized for other lexical disambiguation tasks, such as Lexical Substitution or Word Domain Disambiguation.

Question: What is the traveling salesman problem?


Design of a Compact Dual-Band Annular-Ring Slot Antenna. This letter aims at miniaturizing an annular-ring slot antenna that is suitable for the 2.4/5-GHz dual-band operations. The miniaturization purpose was achieved by embedding in the center patch of the antenna a pair of slots to excite three resonant modes. The resonant band of the first excited resonant mode was lowered from that of the unperturbed annular-ring slot antenna, whereas those of the second and third excited resonant modes were combined to form a wide upper operating band by appropriately adjusting the dimensions of the embedded slots. The proposed antenna proves to have very similar copolarization radiation patterns in its two operating bands and have enough antenna gains for practical applications.

Question: How to design a compact dual-band annular-ring slot antenna?


CortexNet: a Generic Network Family for Robust Visual Temporal Representations. In the past five years we have observed the rise of incredibly well performing feed-forward neural networks trained supervisedly for vision related tasks. These models have achieved super-human performance on object recognition, localisation, and detection in still images. However, there is a need to identify the best strategy to employ these networks with temporal visual inputs and obtain a robust and stable representation of video data. Inspired by the human visual system, we propose a deep neural network family, CortexNet, which features not only bottom-up feedforward connections, but also it models the abundant top-down feedback and lateral connections, which are present in our visual cortex. We introduce two training schemes — the unsupervised MatchNet and weakly supervised TempoNet modes — where a network learns how to correctly anticipate a subsequent frame in a video clip or the identity of its predominant subject, by learning egomotion clues and how to automatically track several objects in the current scene. Find the project website at tinyurl.com/CortexNet.

Question: What is the project website?


Dual- and Multiband U-Slot Patch Antennas. A wide band patch antenna fed by an L-probe can be designed for dual- and multi-band application by cutting U-slots on the patch. Simulation and measurement results are presented to illustrate this design.

Question: How can I design a patch antenna for dual- and multi-band application?


Strategies Critical Paths to Improving Employee Commitment. This article addresses how key leader communication strategies can increase worker loyalty. Leader communication has long been shown to be a critical factor in superior worker motivation and performance (Levering, 1988; Robbins, 2001), and has great potential to aid organizations in their quest for committed employees. Organizations' need for committed employees is now acute due to such recent shifts in the business environment as the current economic slowdown (which places a premium on increasing worker productivity), skilled worker shortages, increasing ethnic and cultural diversity, rapid technological innovations, and growing organizational reliance on "knowledge workers" to achieve competitive advantage. Research clearly shows that leader communication practices play an integral role in developing and sustaining the employee commitment demanded by the preceding scenarios (Goleman, 1998; Goleman, 2000; Reina & Reina, 1999). However, leaders are often faced with a plethora of options and communication techniques that are not directly linked with strategic goals. To make sense of these various communication tactics, leaders need a systemic method which links practice to results. This article presents such a model, which can also be used to diagnostically assess training needs. In essence, the model is a set of strategy-based "best practices" that guide leaders to more effectively transmit and foster organizational trust; which, in turn, will significantly enhance worker loyalty and organizational outcomes. To best explain this model and its relevance, this study first examines the link between leader communication and worker loyalty. Next, selected, well-documented leader communication skills and practices are discussed, followed by a section that presents the Motivating Language Model and a resultant "best practices" checklist for managers. Finally, our study concludes with suggested future directions fo

Question: How can I improve my communication skills?


Interaction and outeraction: instant messaging in action. We discuss findings from an ethnographic study of instant messaging (IM) in the workplace and its implications for media theory. We describe how instant messaging supports a variety of informal communication tasks. We document the affordances of IM that support flexible, expressive communication. We describe some unexpected uses of IM that highlight aspects of communication which are not part of current media theorizing. They pertain to communicative processes people use to connect with each other and to manage communication, rather than to information exchange. We call these processes "outeraction". We discuss how outeractional aspects of communication affect media choice and patterns of media use.

Question: What is the difference between media and communication?


RGB-D Based Action Recognition with Light-weight 3D Convolutional Networks. Different from RGB videos, depth data in RGB-D videos provide key complementary information for tristimulus visual data which potentially could achieve accuracy improvement for action recognition. However, most of the existing action recognition models solely using RGB videos limit the performance capacity. Additionally, the state-of-the-art action recognition models, namely 3D convolutional neural networks (3D-CNNs) contain tremendous parameters suffering from computational inefficiency. In this paper, we propose a series of 3D lightweight architectures for action recognition based on RGB-D data. Compared with conventional 3D-CNN models, the proposed lightweight 3D-CNNs have considerably less parameters involving lower computation cost, while it results in favorable recognition performance. Experimental results on two public benchmark datasets show that our models can approximate or outperform the state-of-the-art approaches. Specifically, on the RGB+DNTU (NTU) dataset, we achieve 93.2% and 97.6% for crosssubject and cross-view measurement, and on the NorthwesternUCLA Multiview Action 3D (N-UCLA) dataset, we achieve 95.5% accuracy of cross-view.

Question: What is the difference between RGB and RGB-D?


Local Probabilistic Models for Link Prediction. One of the core tasks in social network analysis is to predict the formation of links (i.e. various types of relationships) over time. Previous research has generally represented the social network in the form of a graph and has leveraged topological and semantic measures of similarity between two nodes to evaluate the probability of link formation. Here we introduce a novel local probabilistic graphical model method that can scale to large graphs to estimate the joint co-occurrence probability of two nodes. Such a probability measure captures information that is not captured by either topological measures or measures of semantic similarity, which are the dominant measures used for link prediction. We demonstrate the effectiveness of the co-occurrence probability feature by using it both in isolation and in combination with other topological and semantic features for predicting co-authorship collaborations on real datasets.

Question: What is the co-occurrence probability of two nodes?


A service-oriented mobile augmented reality architecture for personalized museum environments. This paper presents a mobile augmented reality platform that exploits service-orientation for the visualisation of cultural media objects in personalised interactive museum environments. The service orientated architecture is composed of a mobile client, web service framework and service providers utilized to perform augmented reality tasks as well as support for media contents acquisition and consumption via a mobile or wireless network. The mobile client performs augmented reality tasks including multiple object tracking of physical 3D objects concurrently and then visualizes associated media contents (e.g. 3D models, videos, images, text, social media data, etc.) that are associated with the tracked physical (or reference) objects and then overlaid in the correct perspective in the real scene in a personalised museum visualisation scenario. A unique feature includes media content acquisition from open service providers through a web service framework. Typical services include, a photogrammetry service that allows users to obtain virtual 3D models of preferred cultural objects through image-based reconstruction techniques. These acquired 3D reconstruction contents can then be integrated with existing associated contents in a personalized augmented reality museum environment that can be stored and saved for later viewing, such as in the home after visiting a particular augmented reality based museum interactive. This paper focuses on describing the service orientation architectural components that supports personalized mobile augmented reality museum environments.

Question: What is a service-oriented mobile augmented reality architecture for personalized museum environments?


A GPU-based implementation of motion detection from a moving platform. We describe a GPU-based implementation of motion detection from a moving platform. Motion detection from a moving platform is inherently difficult as the moving camera induces 2D motion field in the entire image. A step compensating for camera motion is required prior to estimating of the background model. Due to inevitable registration errors, the background model is estimated according to a sliding window of frames to avoid the case where erroneous registration influences the quality of the detection for the whole sequence. However, this approach involves several characteristics that put a heavy burden on real-time CPU implementation. We exploit GPU to achieve significant acceleration over standard CPU implementations. Our GPU-based implementation can build the background model and detect motion regions at around 18 fps on 320times240 videos that are captured for a moving camera.

Question: How to detect motion from a moving camera?


Dual-Band Transmitarrays With Dual-Linear Polarization at Ka-Band. Dual-band transmitarray antennas are demonstrated at Ka-band with the capability of forming independent linearly polarized beams with a given polarization in each frequency band, while sharing the same radiating aperture. The proposed three-layer unit-cell is based on identical narrow microstrip patches printed on both receiving and transmitting layers and connected by a metallized via hole. The metal layers are printed on two identical substrates bonded with a thin film, and the designed unit-cell exhibits a 180° phase resolution (i.e., 1-b phase quantization). The dual-band dual-polarized property of the transmitarray is achieved by interleaving unit-cells operating in the down-link and up-link frequency bands. Four different prototypes are characterized to demonstrate the relevance of the proposed concepts. A good agreement is obtained between the radiation patterns, gain curves, and cross-polarization levels measured and computed in both frequency bands and polarizations.

Question: How does the dual-band transmitarray work?


A two-stage training deep neural network for small pedestrian detection. In the present paper, we propose a deep network architecture in order to improve the accuracy of pedestrian detection. The proposed method contains a proposal network and a classification network that are trained separately. We use a single shot multibox detector (SSD) as a proposal network to generate the set of pedestrian proposals. The proposal network is fine-tuned from a pre-trained network by several pedestrian data sets of large input size (512 × 512 pixels) in order to improve detection accuracy of small pedestrians. Then, we use a classification network to classify pedestrian proposals. We then combine the scores from the proposal network and the classification network to obtain better final detection scores. Experiments were evaluated using the Caltech test set, and, compared to other state-of-the-art methods of pedestrian detection task, the proposed method obtains better results for small pedestrians (30 to 50 pixels in height) with an average miss rate of 42%.

Question: What is a deep neural network?


Features for Measuring Credibility on Facebook Information. Abstract—Nowadays social media information, such as news, links, images, or VDOs, is shared extensively. However, the effectiveness of disseminating information through social media lacks in quality: less fact checking, more biases, and several rumors. Many researchers have investigated about credibility on Twitter, but there is no the research report about credibility information on Facebook. This paper proposes features for measuring credibility on Facebook information. We developed the system for credibility on Facebook. First, we have developed FB credibility evaluator for measuring credibility of each post by manual human's labelling. We then collected the training data for creating a model using Support Vector Machine (SVM). Secondly, we developed a chrome extension of FB credibility for Facebook users to evaluate the credibility of each post. Based on the usage analysis of our FB credibility chrome extension, about 81% of users' responses agree with suggested credibility automatically computed by the proposed system.

Question: How to measure credibility on Facebook?


Beyond One-hot Encoding: lower dimensional target embedding. Target encoding plays a central role when learning Convolutional Neural Networks. In this realm, One-hot encoding is the most prevalent strategy due to its simplicity. However, this so widespread encoding schema assumes a flat label space, thus ignoring rich relationships existing among labels that can be exploited during training. In large-scale datasets, data does not span the full label space, but instead lies in a low-dimensional output manifold. Following this observation, we embed the targets into a lowdimensional space, drastically improving convergence speed while preserving accuracy. Our contribution is two fold: (i) We show that random projections of the label space are a valid tool to find such lower dimensional embeddings, boosting dramatically convergence rates at zero computational cost; and (ii) we propose a normalized eigenrepresentation of the class manifold that encodes the targets with minimal information loss, improving the accuracy of random projections encoding while enjoying the same convergence rates. Experiments on CIFAR-100, CUB200-2011, Imagenet, and MIT Places demonstrate that the proposed approach drastically improves convergence speed while reaching very competitive accuracy rates.

Question: What is the difference between one-hot encoding and random projections?


A Mining and Visualizing System for Large-Scale Chinese Technical Standards. A technical standard is an established norm or requirement in regard to technical systems. It is usually a formal document that establishes uniform engineering or technical criteria, methods, processes and practices. Every year, more than 10K standards are released by standardization organizations, such as International Standardization Organization (ISO), American National Standards Institute (ANSI), and China National Institute of Standardization (CNIS). Thereinto, China publishes more than 4K national and industrial standards per year. Confronted with the large amount of standards, how to manage and make full use of these standards efficiently and effectively has become a major issue to be addressed. In this paper, we introduce a mining and visualizing system for large-scale Chinese technical standards from spatial and temporal perspectives. Specifically, we propose an s-index metric to measure the contribution of a drafting unit. Moreover, we develop a multiple dimensional mining and visualizing analysis system for standardization experts and users to explore regional differences and temporal trends of standards in China. Our system has run on 95K Chinese standards spanning from 2001 to 2016 and provided online service. The extensive experiments and case study demonstrate the effectiveness of our proposed system.

Question: What is a mining and visualizing system for large-scale Chinese technical standards?


Ultra-High-Voltage Charge Pump Circuit in Low-Voltage Bulk CMOS Processes With Polysilicon Diodes. An on-chip ultra-high-voltage charge pump circuit realized with the polysilicon diodes in the low-voltage bulk CMOS process is proposed in this work. Because the polysilicon diodes are fully isolated from the silicon substrate, the output voltage of the charge pump circuit is not limited by the junction breakdown voltage of MOSFETs. The polysilicon diodes can be implemented in the standard CMOS processes without extra process steps. The proposed ultra-high-voltage charge pump circuit has been fabricated in a 0.25-mum 2.5-V standard CMOS process. The output voltage of the four-stage charge pump circuit with 2.5-V power-supply voltage (VDD=2.5 V) can be pumped up to 28.08 V, which is much higher than the n-well/p-substrate breakdown voltage (~18.9 V) in a 0.25-mum 2.5-V bulk CMOS process

Question: How to make a ultra-high-voltage charge pump circuit?


The Levels of Conceptual Interoperability Model. Interoperability of systems is not a cookie-cutter-function. There are various levels of interoperability between two systems ranging from no interoperability to full interoperability. In the technical domain, various models for levels of interoperability already exist and are used successfully to determine the degree of interoperability between information technology systems. However, such models are not yet established in the domain of conceptual modeling. This paper introduces a general model dealing with various levels of conceptual interoperability that goes beyond the technical reference models for interoperable solutions. The model is intended to become a bridge between the conceptual design and the technical design for implementation, integration, or federation. It should also contribute to the standardization of V&V procedures as well as to the documentation of systems that are designed to be federated. It is furthermore a framework to determine in the early stages of the federation development process whether meaningful interoperability between systems is possible. To this end, the scope of the model goes beyond the implementation level of actual standards, which focus on the exchange of data using standardized formats and interfaces. Another practical application of the model is, that it enhances the only recently published DoD Net-Centric Data Strategy for the Global Information Grid (GIG) and is directly applicable to derive necessary metadata to reach the DoD Data Goal to "enable Data to be understandable."

Question: What is the difference between interoperability and conceptual interoperability?


Computing the shortest path: A search meets graph theory. We propose shortest path algorithms that use A* search in combination with a new graph-theoretic lower-bounding technique based on landmarks and the triangle inequality. Our algorithms compute optimal shortest paths and work on any directed graph. We give experimental results showing that the most efficient of our new algorithms outperforms previous algorithms, in particular A* search with Euclidean bounds, by a wide margin on road networks and on some synthetic problem families.

Question: What is the shortest path between two points?


Texture analysis of medical images.. The analysis of texture parameters is a useful way of increasing the information obtainable from medical images. It is an ongoing field of research, with applications ranging from the segmentation of specific anatomical structures and the detection of lesions, to differentiation between pathological and healthy tissue in different organs. Texture analysis uses radiological images obtained in routine diagnostic practice, but involves an ensemble of mathematical computations performed with the data contained within the images. In this article we clarify the principles of texture analysis and give examples of its applications, reviewing studies of the technique.

Question: What is texture analysis?


Fast Pose Estimation with Parameter-Sensitive Hashing. Example-basedmethodsareeffectivefor parameterestimationproblemswhentheunderlyingsystemis simpleor thedimensionalityof the input is low. For complex andhigh-dimensional problemssuch asposeestimation,thenumberof required examplesand the computationalcomplexity rapidly becmeprohibitivelyhigh. We introducea new algorithm that learnsa setof hashingfunctionsthat efficiently index examplesrelevant to a particular estimationtask. Our algorithm extendsa recentlydevelopedmethodfor locality-sensitivehashing, which findsapproximateneighborsin timesublinearin thenumber of examples.Thismethoddependscritically on thechoiceof hashfunctions;weshowhowto find thesetof hashfunctions thatare optimallyrelevantto a particular estimationproblem.Experimentsdemonstr atethat theresultingalgorithm,which wecall Parameter -SensitiveHashing, canrapidlyandaccuratelyestimatethearticulatedposeof humanfiguresfroma large databaseof exampleimages. 0Part of thiswork wasdonewhenG.S.andP.V. werewith MitsubishiElectricResearchLabs,Cambridge,MA.

Question: How does the algorithm work?


An Exploratory Review of Design Principles in Constructivist Gaming Learning Environments.. Creating a design theory for Constructivist Gaming Learning Environment necessitates, among other things, the establishment of design principles. These principles have the potential to help designers produce games, where users achieve higher levels of learning. This paper focuses on twelve design principles: Probing, Distributed, Multiple Routes, Practice, Psychosocial Moratorium, Regime of Competence, Self-Knowledge, Collective Knowledge, Engaging, User Interface Ease of Use, On Demand and Just-in-Time Tutorial, and Achievement. We report on two pilot studies of a qualitative nature in which we test our design principles. Game play testing and observations were carried out on five Massively Multiplayer Online Games (MMOGs): RuneScape, GuildWars, Ragnarok, World of WarCraft, and Final Fantasy XI. Two educational games, Carabella Goes to College and Outbreak at WatersEdge were also observed. Our findings indicate that not all of the popular MMOGs and educational games support all of these principles.

Question: What are the design principles of constructivist gaming learning environments?


Ant System. Ant System, the first Ant Colony Optimization algorithm, showed to be a viable method for attacking hard combinatorial optimization problems. Yet, its performance, when compared to more fine-tuned algorithms, was rather poor for large instances of traditional benchmark problems like the Traveling Salesman Problem. To show that Ant Colony Optimization algorithms could be good alternatives to existing algorithms for hard combinatorial optimization problems, recent research in this ares has mainly focused on the development of algorithmic variants which achieve better performance than AS. In this article, we present – Ant System, an Ant Colony Optimization algorithm derived from Ant System. – Ant System differs from Ant System in several important aspects, whose usefulness we demonstrate by means of an experimental study. Additionally, we relate one of the characteristics specific to AS — that of using a greedier search than Ant System — to results from the search space analysis of the combinatorial optimization problems attacked in this paper. Our computational results on the Traveling Salesman Problem and the Quadratic Assignment Problem show that – Ant System is currently among the best performing algorithms for these problems.

Question: What is the difference between ant system and ant colony optimization?


Teachable robots: Understanding human teaching behavior to build more effective robot learners. While Reinforcement Learning (RL) is not traditionally designed for interactive supervisory input from a human teacher, several works in both robot and software agents have adapted it for human input by letting a human trainer control the reward signal. In this work, we experimentally examine the assumption underlying these works, namely that the human-given reward is compatible with the traditional RL reward signal. We describe an experimental platform with a simulated RL robot and present an analysis of real-time human teaching behavior found in a study in which untrained subjects taught the robot to perform a new task. We report three main observations on how people administer feedback when teaching a robot a task through Reinforcement Learning: (a) they use the reward channel not only for feedback, but also for future-directed guidance; (b) they have a positive bias to their feedback — possibly using the signal as a motivational channel; and (c) they change their behavior as they develop a mental model of the robotic learner. Given this, we made specific modifications to the simulated RL robot, and analyzed and evaluated its learning behavior in four additional experiments with human trainers. We report significant improvements on several learning measures. This work demonstrates the importance of understanding the human-teacher/robot-learner partnership in order to design algorithms that support how people want to teach while simultaneously improving the robot's learning behavior.

Question: What is the difference between reinforcement learning and supervised learning?


Generating UML Diagrams from Natural Language Specifications. The process of generating UML Diagrams from natural language specification is a highly challenging task. This paper proposes a method and tool to facilitate the requirements analysis process and extract UML diagrams from textual requirements using natural language processing (NLP) and Domain Ontology techniques. Requirements engineers analyze requirements manually to understand the scope of the system. The time spent on the analysis and the low quality of human analysis justifies the need of a tool for better understanding of the system. "Requirement analysis to Provide Instant Diagrams (RAPID)" is a desktop tool to assist requirements analysts and Software Engineering students to analyze textual requirements, finding core concepts and its relationships, and extraction UML diagrams. The evaluation of RAPID system is in the process and will be conducted through two forms of evaluation, experimental and expert evaluation.

Question: How to generate UML diagrams from natural language specifications?


Massively parallel Monte Carlo for many-particle simulations on GPUs. Current trends in parallel processors call for the design of efficient massively parallel algorithms for scientific computi ng. Parallel algorithms for Monte Carlo simulations of thermodynamic en s mbles of particles have received little attention becaus e of the inherent serial nature of the statistical sampling. In this paper, we present a massively parallel method that obeys det ailed balance and implement it for a system of hard disks on the GPU. We repro duce results of serial high-precision Monte Carlo runs to ve rify the method. This is a good test case because the hard disk equa tion of state over the range where the liquid transforms into the solid is particularly sensitive to small deviations away from the balance conditions. On a Tesla K20, our GPU implementation e xecutes over one billion trial moves per second, which is 148 times fa ster than on a single Intel Xeon E5540 CPU core, enables 27 tim es better performance per dollar, and cuts energy usage by a fac tor of 13. With this improved performance we are able to calcu l te the equation of state for systems of up to one million hard dis ks. These large system sizes are required in order to probe th e nature of the melting transition, which has been debated for the las t forty years. In this paper we present the details of our comp utational method, and discuss the thermodynamics of hard disks separa t ly in a companion paper.

Question: What is the equation of state of a hard disk?


Growth Promotion of indigenous Scenedesmus dimorphus strain under different conditions using stirred tank. The effects of culture conditions at different temperature (15-25-35°C) and nitrate concentration (0.1-0.25-0.4g/L) on biomass production and growth rate of scenedesmus dimorphus were determined. The results shown that the dry biomass weight (0.153g/L) at 15°C was the lowest. No significant difference was observed between the dry biomasses at 25°C (0.289g/L) and 35°C (0.240g/L). While the lowest growth rate was at 15°C(0.157d), the highestone was at 25°C (0.229d) and 35°C (0.195d) with insignificant deference, and The nitrate concentration had no effect on both of biomass production and growth rate.

Question: What is the lowest temperature for growth of scenedesmus dimorphus?


Wait-Free Synchronization. A wait-free implementation of a concurrent data object is one that guarantees that any process can complete any operation in a finite number of steps, regardless of the execution speeds of the other processes. The problem of constructing a wait-free implementation of one data object from another lies at the heart of much recent work in concurrent algorithms, concurrent data structures, and multiprocessor architectures. First, we introduce a simple and general technique, based on reduction to a concensus protocol, for proving statements of the form, "there is no wait-free implementation of X by Y." We derive a hierarchy of objects such that no object at one level has a wait-free implementation in terms of objects at lower levels. In particular, we show that atomic read/write registers, which have been the focus of much recent attention, are at the bottom of the hierarchy: thay cannot be used to construct wait-free implementations of many simple and familiar data types. Moreover, classical synchronization primitives such astest&set and fetch&add, while more powerful than read and write, are also computationally weak, as are the standard message-passing primitives. Second, nevertheless, we show that there do exist simple universal objects from which one can construct a wait-free implementation of any sequential object.

Question: What is the difference between atomic read/write registers and classical synchronization primitives?


Abstraction-Based Livelock/Deadlock Checking for Hardware Verification. ion-Based Livelock/Deadlock Checking for Hardware Verification In-Ho Moon and Kevin Harer Synopsys Inc. {mooni, kevinh}@synopsys.com

Question: How to check for hardware verification?


CaffePresso: An optimized library for Deep Learning on embedded accelerator-based platforms. Off-the-shelf accelerator-based embedded platforms offer a competitive energy-efficient solution for lightweight deep learning computations over CPU-based systems. Low-complexity classifiers used in power-constrained and performance-limited scenarios are characterized by operations on small image maps with 2-3 deep layers and few class labels. For these use cases, we consider a range of embedded systems with 5--20 W power budgets such as the Xilinx ZC706 board (with MXP soft vector processor), NVIDIA Jetson TX1 (GPU), TI Keystone II (DSP) as well as the Adapteva Parallella board (custom multi-core with NoC). Deep Learning computations push the capabilities of these platforms to the limit through compute-intensive evaluations of multiple 2D convolution filters per layer, and high communication requirements arising from the movement of intermediate maps across layers. We present CaffePresso, a Caffe-compatible framework for generating optimized mappings of user-supplied ConvNet specifications to target various accelerators such as FPGAs, DSPs, GPUs, RISC-multicores. We use an automated code generation and auto-tuning approach based on knowledge of the ConvNet requirements, as well as platform-specific constraints such as on-chip memory capacity, bandwidth and ALU potential. While one may expect the Jetson TX1 + cuDNN to deliver high performance for ConvNet configurations, (1) we observe a flipped result with slower GPU processing compared to most other systems for smaller embedded-friendly datasets such as MNIST and CIFAR10, and (2) faster and more energy efficient implementation on the older 28nm TI Keystone II DSP over the newer 20nm NVIDIA TX1 SoC in all cases.

Question: What is the difference between the jetson tx1 and the jetson tx2?


Towards a unified architecture for in-RDBMS analytics. The increasing use of statistical data analysis in enterprise applications has created an arms race among database vendors to offer ever more sophisticated in-database analytics. One challenge in this race is that each new statistical technique must be implemented from scratch in the RDBMS, which leads to a lengthy and complex development process. We argue that the root cause for this overhead is the lack of a unified architecture for in-database analytics. Our main contribution in this work is to take a step towards such a unified architecture. A key benefit of our unified architecture is that performance optimizations for analytics techniques can be studied generically instead of an ad hoc, per-technique fashion. In particular, our technical contributions are theoretical and empirical studies of two key factors that we found impact performance: the order data is stored, and parallelization of computations on a single-node multicore RDBMS. We demonstrate the feasibility of our architecture by integrating several popular analytics techniques into two commercial and one open-source RDBMS. Our architecture requires changes to only a few dozen lines of code to integrate a new statistical technique. We then compare our approach with the native analytics tools offered by the commercial RDBMSes on various analytics tasks, and validate that our approach achieves competitive or higher performance, while still achieving the same quality.

Question: How to integrate a new statistical technique into an RDBMS?


Regenerative braking control method and optimal scheme for electric motorcycle. This paper describes an optimal regenerative braking control scheme for a permanent magnet brushless DC motor of an electric motorcycle to achieve dual goals of the electric brake and the maximal power harvest in two cases of the road, downhill and flat, without any additional changes on the hardware. Based on regenerative braking of brushless DC motor in half bridge modulation mode, the relationship between average regenerative current on the DC bus and PWM duty cycle under different speeds was studied intensively from the three aspects of theoretical deduction, simulation based on Matlab/Simulink and platform experiments, and then a simple but effective scheme considering the actual application was proposed tentatively to achieve the dual goals. Since the braking kinetic energy is harvested in the form of the electric energy stored up in battery, the scheme could improve the driving range, safeness, noise preventability and cost effective of electric motorcycles.

Question: What is the optimal regenerative braking control scheme for electric motorcycle?


A Comparison Between Single-Phase Quasi-  $Z$-Source and Quasi-Switched Boost Inverters. The properties of a single-phase quasi Z-source inverter (qZSI) and a single-phase quasi-switched boost inverter (qSBI), both of which are single-stage buck-boost inverters, are investigated and compared. For the same operating conditions, qSBI has the following advantages over qZSI: 1) Three capacitors are saved; 2) the current rating on both of its switches and diodes is lower; 3) its boost factor is higher with an equivalent parasitic effect; and 4) its efficiency is higher. However, qSBI has one more active switch and one more diode than Z-source/ qZSIs. In addition, the capacitor voltage stress of qSBI is higher than that of qZSI. The dc and ac component circuit analysis, impedance design with low-frequency and high-frequency ripples, component stresses, and power loss calculation are presented. A prototype based on a TMS320F28335 DSP is built in order to compare the operating principle of qSBI and qZSI.

Question: What is the difference between a quasi-switched boost inverter and a quasi-Z-source inverter?


Boosting Optical Character Recognition: A Super-Resolution Approach. Text image super-resolution is a challenging yet open research problem in the computer vision community. In particular, low-resolution images hamper the performance of typical optical character recognition (OCR) systems. In this article, we summarize our entry to the ICDAR2015 Competition on Text Image Super-Resolution. Experiments are based on the provided ICDAR2015 TextSR dataset [3] and the released Tesseract-OCR 3.02 system [1]. We report that our winning entry of text image super-resolution framework has largely improved the OCR performance with low-resolution images used as input, reaching an OCR accuracy score of 77.19%, which is comparable with that of using the original high-resolution images (78.80%).

Question: How to boost OCR?


A Low-Power 32-Channel Digitally Programmable Neural Recording Integrated Circuit. We report the design of an ultra-low-power 32-channel neural-recording integrated circuit (chip) in a 0.18 μ m CMOS technology. The chip consists of eight neural recording modules where each module contains four neural amplifiers, an analog multiplexer, an A/D converter, and a serial programming interface. Each amplifier can be programmed to record either spikes or LFPs with a programmable gain from 49-66 dB. To minimize the total power consumption, an adaptive-biasing scheme is utilized to adjust each amplifier's input-referred noise to suit the background noise at the recording site. The amplifier's input-referred noise can be adjusted from 11.2 μVrms (total power of 5.4 μW) down to 5.4 μVrms (total power of 20 μW) in the spike-recording setting. The ADC in each recording module digitizes the a.c. signal input to each amplifier at 8-bit precision with a sampling rate of 31.25 kS/s per channel, with an average power consumption of 483 nW per channel, and, because of a.c. coupling, allows d.c. operation over a wide dynamic range. It achieves an ENOB of 7.65, resulting in a net efficiency of 77 fJ/State, making it one of the most energy-efficient designs for neural recording applications. The presented chip was successfully tested in an in vivo wireless recording experiment from a behaving primate with an average power dissipation per channel of 10.1 μ W. The neural amplifier and the ADC occupy areas of 0.03 mm2 and 0.02 mm2 respectively, making our design simultaneously area efficient and power efficient, thus enabling scaling to high channel-count systems.

Question: What is the area of a neural amplifier?


Imaging without lenses: achievements and remaining challenges of wide-field on-chip microscopy. We discuss unique features of lens-free computational imaging tools and report some of their emerging results for wide-field on-chip microscopy, such as the achievement of a numerical aperture (NA) of ∼0.8–0.9 across a field of view (FOV) of more than 20 mm2 or an NA of ∼0.1 across a FOV of ∼18 cm2, which corresponds to an image with more than 1.5 gigapixels. We also discuss the current challenges that these computational on-chip microscopes face, shedding light on their future directions and applications.

Question: What is the largest image that can be captured by a lens-free computational imaging tool?


A Performance Evaluation of Local Descriptors.

Question: What is a performance evaluation of local descriptors?


Opposition-based Magnetic Optimization Algorithm with parameter adaptation strategy. Magnetic Optimization Algorithm (MOA) has emerged as a promising optimization algorithm that is inspired by the principles of magnetic field theory. In this paper we improve the performance of the algorithm in two aspects. First an Opposition-Based Learning (OBL) approach is proposed for the algorithm which is applied to the movement operator of the algorithm. Second, by learning from the algorithm's past experience, an adaptive parameter control strategy which dynamically sets the parameters of the algorithm during the optimization is proposed. To show the significance of the proposed parameter adaptation strategy, we compare the algorithm with two well-known parameter setting techniques on a number of benchmark problems. The results indicate that although the proposed algorithm with the adaptation strategy does not require to set the parameters of the algorithm prior to the optimization process, it outperforms MOA with other parameter setting strategies in most large-scale optimization problems. We also study the algorithm while employing the OBL by comparing it with the original version of MOA. Furthermore, the proposed algorithm is tested and compared with seven traditional population-based algorithms and eight state-of-theart optimization algorithms. The comparisons demonstrate that the proposed algorithm outperforms the traditional algorithms in most benchmark problems, and its results is comparative to those obtained by the state-of-the-art algorithms.

Question: What is the difference between the original version of MOA and the proposed algorithm?


Deep Learning and Data Labeling for Medical Applications. This study addresses the recognition problem of the HEp-2 cell using indirect immunofluorescent (IIF) image analysis, which can facilitate the diagnosis of many autoimmune diseases by finding antibodies in the patient serum. Recently, a lot of automatic HEp-2 cell classification strategies including both shallow and deep methods have been developed, wherein the deep Convolutional Neural Networks (CNNs) have been proven to achieve impressive performance. However, the deep CNNs in general requires a fixed size of image as the input. In order to conquer the limitation of the fixed size problem, a spatial pyramid pooling (SPP) strategy has been proposed in general object recognition and detection. The SPP-net usually exploit max pooling strategies for aggregating all activated status of a specific neuron in a predefined spatial region by only taking the maximum activation, which achieved superior performance compared with mean pooling strategy in the traditional state-of-the-art coding methods such as sparse coding, linear locality-constrained coding and so on. However, the max pooling strategy in SPP-net only retains the strongest activated pattern, and would completely ignore the frequency: an important signature for identifying different types of images, of the activated patterns. Therefore, this study explores a generalized spatial pooling strategy, called K-support spatial pooling, in deep CNNs by integrating not only the maximum activated magnitude but also the response magnitude of the relatively activated patterns of a specific neuron together. This proposed K-support spatial pooling strategy in deep CNNs combines the popularly applied mean and max pooling methods, and then avoid awfully emphasizing of the maximum activation but preferring a group of activations in a supported region. The deep CNNs with the proposed K-support spatial pooling is applied for HEp-2 cell classification, and achieve promising

Question: What is the difference between mean and max pooling?


IMPLICIT SHAPE MODELS FOR OBJECT DETECTION IN 3D POINT CLOUDS. We present a method for automatic object localization and recognition in 3D point clouds representing outdoor urban scenes. The method is based on the implicit shape models (ISM) framework, which recognizes objects by voting for their center locations. It requires only few training examples per class, which is an important property for practical use. We also introduce and evaluate an improved version of the spin image descriptor, more robust to point density variation and uncertainty in normal direction estimation. Our experiments reveal a significant impact of these modifications on the recognition performance. We compare our results against the state-of-the-art method and get significant improvement in both precision and recall on the Ohio dataset, consisting of combined aerial and terrestrial LiDAR scans of 150,000 m of urban area in total.

Question: How to detect objects in 3D point clouds?


Introduction to the Special Issue: The Literature Review in Information Systems. There has been a flowering of scholarly interest in the literature review as a research method in the information systems discipline. We feel privileged to contribute to this conversation and introduce the work of the authors represented in this special issue. Some of the highlights include three new methods for conducting literature analysis and guidelines, tutorials, and approaches for coping with some of the challenges involved in carrying out a literature review. Of the three "new method" papers, one (ontological meta-analysis and synthesis) is entirely new, and two (stylized facts and critical discourse analysis) are novel in the information systems context. The other four paper address more general issues: the challenges of effective search strategies when confronted with the burgeoning volume of research available, a detailed tool-supported approach for conducting a rigorous review, a detailed tutorial for conducting a qualitative literature review, and a discussion of quality issues. Collectively, the papers place emphasis beyond the traditional "narrative synthesis" on the importance of selecting the appropriate approach for the research context and the importance of attention to quality and transparency at all stages of the process, regardless of which approach is adopted.

Question: What is the difference between a literature review and a literature analysis?


Topology Control of Multihop Wireless Networks Using Transmit Power Adjustment. We consider the problem of adjusting the transmit powers of nodes in a multihop wireless network (also called an ad hoc network) to create a desired topology. We formulate it as a constrained optimization problem with two constraints connectivity and biconnectivity, and one optimization objective maximum power used. We present two centralized algorithms for use in static networks, and prove their optimality. For mobile networks, we present two distributed heuristics that adaptively adjust node transmit powers in response to topological changes and attempt to maintain a connected topology using minimum power. We analyze the throughput, delay, and power consumption of our algorithms using a prototype software implementation, an emulation of a power-controllable radio, and a detailed channel model. Our results show that the performance of multihop wireless networks in practice can be substantially increased with topology control.

Question: How to control the topology of a wireless network?


Centrality in valued graphs : A measure of betweenness based on network flow. A new measure of centrality, C,, is introduced. It is based on the concept of network flows. While conceptually similar to Freeman's original measure, Ca, the new measure differs from the original in two important ways. First, C, is defined for both valued and non-valued graphs. This makes C, applicable to a wider variety of network datasets. Second, the computation of C, is not based on geodesic paths as is C, but on all the independent paths between all pairs of points in the network.

Question: What is the difference between the original measure and the new measure?


Finding Groups in Data: An Introduction to Cluster Analysis. Description: The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists. "Cluster analysis is the increasingly important and practical subject of finding groupings in data. The authors set out to write a book for the user who does not necessarily have an extensive background in mathematics. They succeed very well." —Mathematical Reviews "Finding Groups in Data [is] a clear, readable, and interesting presentation of a small number of clustering methods. In addition, the book introduced some interesting innovations of applied value to clustering literature." —Journal of Classification "This is a very good, easy-to-read, and practical book. It has many nice features and is highly recommended for students and practitioners in various fields of study." —Technometrics An introduction to the practical application of cluster analysis, this text presents a selection of methods that together can deal with most applications. These methods are chosen for their robustness, consistency, and general applicability. This book discusses various types of data, including interval-scaled and binary variables as well as similarity data, and explains how these can be transformed prior to clustering.

Question: What is the difference between cluster analysis and classification?


Anomaly network traffic detection algorithm based on information entropy measurement under the cloud computing environment. In recent years, there are more and more abnormal activities in the network, which greatly threaten network security. Hence, it is of great importance to collect the data which indicate the running statement of the network, and distinguish the anomaly phenomena of the network in time. In this paper, we propose a novel anomaly network traffic detection algorithm under the cloud computing environment. Firstly, the framework of the anomaly network traffic detection system is illustrated, and six type of network traffic features are consider in this work, that is, (1) number of source IP address, (2) number of source port number, (3) number of destination IP address, (4) number of destination port number, (5) Number of packet type, and (6) number of network packets. Secondly, we propose a novel hybrid information entropy and SVM model to tackle the proposed problem by normalizing values of network features and exploiting SVM detect anomaly network behaviors. Finally, experimental results demonstrate that the proposed algorithm can detect anomaly network traffic with high accuracy and it can also be used in the large scale dataset.

Question: What is anomaly network traffic detection algorithm based on information entropy measurement under the cloud computing environment?


Breaking NLI Systems with Sentences that Require Simple Lexical Inferences. We create a new NLI test set that shows the deficiency of state-of-the-art models in inferences that require lexical and world knowledge. The new examples are simpler than the SNLI test set, containing sentences that differ by at most one word from sentences in the training set. Yet, the performance on the new test set is substantially worse across systems trained on SNLI, demonstrating that these systems are limited in their generalization ability, failing to capture many simple inferences.

Question: How do you break NLI systems?


Anisotropic diffusion-based detail-preserving smoothing for image restoration. It is important in image restoration to remove noise while preserving meaningful details such as edges and fine features. The existing edge-preserving smoothing methods may inevitably take fine detail as noise or vice versa. In this paper, we propose a new edge-preserving smoothing technique based on a modified anisotropic diffusion. The proposed method can simultaneously preserve edges and fine details while filtering out noise in the diffusion process. Since the fine detail in the neighborhood of a small image window generally have a gray-level variance larger than that of the noisy background, the proposed diffusion model incorporates both local gradient and gray-level variance to preserve edges and fine details while effectively removing noise. Experimental results have shown that the proposed anisotropic diffusion scheme can effectively smooth noisy background, yet well preserve edge and fine details in the restored image. The proposed method has the best restoration result compared with other edge-preserving methods.

Question: What is the difference between edge-preserving smoothing and detail-preserving smoothing?


Multilayer feedforward networks are universal approximators.

Question: What is a universal approximator?


A generalized taxonomy of explanations styles for traditional and social recommender systems. Recommender systems usually provide explanations of their recommendations to better help users to choose products, activities or even friends. Up until now, the type of an explanation style was considered in accordance to the recommender system that employed it. This relation was one-to-one, meaning that for each different recommender systems category, there was a different explanation style category. However, this kind of one-to-one correspondence can be considered as over-simplistic and non generalizable. In contrast, we consider three fundamental resources that can be used in an explanation: users, items and features and any combination of them. In this survey, we define (i) the Human style of explanation, which provides explanations based on similar users, (ii) the Item style of explanation, which is based on choices made by a user on similar items and (iii) the Feature style of explanation, which explains the recommendation based on item features rated by the user beforehand. By using any combination of the aforementioned styles we can also define the Hybrid style of explanation. We demonstrate how these styles are put into practice, by presenting recommender systems that employ them. Moreover, since there is inadequate research in the impact of social web in contemporary recommender systems and their explanation styles, we study new emerged social recommender systems i.e. Facebook Connect explanations (HuffPo, Netflix, etc.) and geo-social explanations that combine geographical with social data (Gowalla, Facebook Places, etc.). Finally, we summarize the results of three different user studies, to support that Hybrid is the most effective explanation style, since it incorporates all other styles.

Question: What is the difference between the human, item and feature style of explanation?


HMM-based Address Parsing with Massive Synthetic Training Data Generation. Record linkage is the task of identifying which records in one or more data collections refer to the same entity, and address is one of the most commonly used fields in databases. Hence, segmentation of the raw addresses into a set of semantic fields is the primary step in this task. In this paper, we present a probabilistic address parsing system based on the Hidden Markov Model. We also introduce several novel approaches of synthetic training data generation to build robust models for noisy real-world addresses, obtaining 95.6% F-measure. Furthermore, we demonstrate the viability and efficiency of this system for large-scale data by scaling it up to parse billions of addresses.

Question: What is the address parsing system?


3D Sampling Textures for Creative Design and Manufacturing. 1 Texture synthesized from 3D scans of snake-skin and mushroom laminae. ABSTRACT 3D sampling is a robust new strategy for exploring and creating designs. 3D textures are sampled and mixed almost like music to create new multifunctional surfaces and material microstructures (Figure 1). This paper presents several design cases performed using 3D sampling techniques, demonstrating how they can be used to explore and enhance product ideas, variations, and functions in subtle and responsive ways. In each case, design variations are generated and their mechanical behavior is evaluated against performance criteria using computational simulation or empirical testing. This work aims to promote creativity and discovery by introducing irregular geometric structures within trial-and-error feedback loops. Sayjel Vijay Patel SUTD DManD Centre

Question: What is the difference between a 3D texture and a 3D model?


Weakly supervised user intent detection for multi-domain dialogues. Users interact with mobile apps with certain intents such as finding a restaurant. Some intents and their corresponding activities are complex and may involve multiple apps; for example, a restaurant app, a messenger app and a calendar app may be needed to plan a dinner with friends. However, activities may be quite personal and third-party developers would not be building apps to specifically handle complex intents (e.g., a DinnerPlanner). Instead we want our intelligent agent to actively learn to understand these intents and provide assistance when needed. This paper proposes a framework to enable the agent to learn an inventory of intents from a small set of task-oriented user utterances. The experiments show that on previously unseen user activities, the agent is able to reliably recognize user intents using graph-based semi-supervised learning methods. The dataset, models, and the system outputs are available to research community.

Question: What is a graph-based semi-supervised learning method?


Multi-stage beamforming codebook for 60GHz WPAN. Beamforming(BF) based on codebook is regarded as an attractive solution to resolve the poor link budget of millimeter-wave 60GHz wireless communication. Because the number of the elements of antenna array in 60GHz increases, the beam patterns generated are more than common BF, and it causes long set-up time during beam patterns searching. In order to reduce the set-up time, three stages protocol, namely the device (DEV) to DEV linking, sector-level searching and beam-level searching has been adopted by the IEEE 802.15.3c as an optional functionality to realize Gbps communication systems. However, it is still a challenge to create codebook of different patterns to support three stages protocol from common codebook of beam pattern. In this paper, we proposes a multi-stage codebook design and the realization architecture to support three stages BF. The multi-stage codebook can create different granularity of beam patterns and realize progressive searching. Simulation results for eight elements uniform linear array (ULA) show that this design can divide the beam searching to three stages searching without increasing the system complexity.

Question: How to create a codebook for 60GHz WPAN?


Risks and risk mitigation in global software development: A tertiary study. Context There is extensive interest in global software development (GSD) which has led to a large number of papers reporting on GSD. A number of systematic literature reviews (SLRs) have attempted to aggregate information from individual studies. Objective: We wish to investigate GSD SLR research with a focus on discovering what research has been conducted in the area and to determine if the SLRs furnish appropriate risk and risk mitigation advice to provide guidance to organizations involved with GSD. Method: We performed a broad automated search to identify GSD SLRs. Data extracted from each study included: (1) authors, their affiliation and publishing venue, (2) SLR quality, (3) research focus, (4) GSD risks, (5) risk mitigation strategies and, (6) for each SLR the number of primary studies reporting each risk and risk mitigation strategy. Results: We found a total of 37 papers reporting 24 unique GSD SLR studies. Major GSD topics covered include: (1) organizational environment, (2) project execution, (3) project planning and control and (4) project scope and requirements. We extracted 85 risks and 77 risk mitigation advice items and categorized them under four major headings: outsourcing rationale, software development, human resources, and project management. The largest group of risks was related to project management. GSD outsourcing rationale risks ranked highest in terms of primary study support but in many cases these risks were only identified by a single SLR. Conclusions: The focus of the GSD SLRs we identified is mapping the research rather than providing evidence-based guidance to industry. Empirical support for the majority of risks identified is moderate to low, both in terms of the number of SLRs identifying the risk, and in the number of primary studies providing empirical support. Risk mitigation advice is also limited, and empirical support for these items is low. 2013 El

Question: What is the largest software development company in the world?


Deep Learning for Wind Speed Forecasting in Northeastern Region of Brazil. Deep Learning is one of the latest approaches in the field of artificial neural networks. Since they were first proposed in mid-2006, Deep Learning models have obtained state-of-art results in some problems with classification and pattern recognition. However, such models have been little used in time series forecasting. This work aims to investigate the use of some of these architectures in this kind of problem, specifically in predicting the hourly average speed of winds in the Northeastern region of Brazil. The results showed that Deep Learning offers a good alternative for performing this task, overcoming some results of previous works.

Question: What is the Northeastern region of Brazil?


A single-phase grid-connected PV converter with minimal DC-link capacitor and low-frequency ripple-free maximum power point tracking. This paper presents a novel single-phase grid-connected photovoltaic (PV) converter based on current-fed dual active bridge (CF-DAB) dc-dc converter. The proposed converter allows a minimal DC-link capacitor with large voltage variation without low-frequency effect on maximum power point tracking (MPPT). An advanced control system is developed based on the converter operation principles. Minimized transformer peak current is capable to be achieved through a symmetrical dc voltage control. Simulation results are presented to validate the performance of the proposed PV converter. A 5kW laboratorial prototype is also implemented and the results will be presented later.

Question: What is a single-phase grid-connected PV converter?


Content-based image retrieval by integrating color and texture features. Content-based image retrieval (CBIR) has been an active research topic in the last decade. Feature extraction and representation is one of the most important issues in the CBIR. In this paper, we propose a content-based image retrieval method based on an efficient integration of color and texture features. As its color features, pseudo-Zernike chromaticity distribution moments in opponent chromaticity space are used. As its texture features, rotation-invariant and scale-invariant image descriptor in steerable pyramid domain are adopted, which offers an efficient and flexible approximation of early processing in the human visual system. The integration of color and texture information provides a robust feature set for color image retrieval. Experimental results show that the proposed method yields higher retrieval accuracy than some conventional methods even though its feature vector dimension is not higher than those of the latter for different test DBs.

Question: How to integrate color and texture features?


DeepCas: An End-to-end Predictor of Information Cascades. Information cascades, effectively facilitated by most social network platforms, are recognized as a major factor in almost every social success and disaster in these networks. Can cascades be predicted? While many believe that they are inherently unpredictable, recent work has shown that some key properties of information cascades, such as size, growth, and shape, can be predicted by a machine learning algorithm that combines many features. These predictors all depend on a bag of hand-crafting features to represent the cascade network and the global network structures. Such features, always carefully and sometimes mysteriously designed, are not easy to extend or to generalize to a different platform or domain. Inspired by the recent successes of deep learning in multiple data mining tasks, we investigate whether an end-to-end deep learning approach could effectively predict the future size of cascades. Such a method automatically learns the representation of individual cascade graphs in the context of the global network structure, without hand-crafted features or heuristics. We find that node embeddings fall short of predictive power, and it is critical to learn the representation of a cascade graph as a whole. We present algorithms that learn the representation of cascade graphs in an end-to-end manner, which significantly improve the performance of cascade prediction over strong baselines including feature based methods, node embedding methods, and graph kernel methods. Our results also provide interesting implications for cascade prediction in general.

Question: What is the canadian military?


Efficiently exploring architectural design spaces via predictive modeling. Architects use cycle-by-cycle simulation to evaluate design choices and understand tradeoffs and interactions among design parameters. Efficiently exploring exponential-size design spaces with many interacting parameters remains an open problem: the sheer number of experiments renders detailed simulation intractable. We attack this problem via an automated approach that builds accurate, confident predictive design-space models. We simulate sampled points, using the results to teach our models the function describing relationships among design parameters. The models produce highly accurate performance estimates for other points in the space, can be queried to predict performance impacts of architectural changes, and are very fast compared to simulation, enabling efficient discovery of tradeoffs among parameters in different regions. We validate our approach via sensitivity studies on memory hierarchy and CPU design spaces: our models generally predict IPC with only 1-2% error and reduce required simulation by two orders of magnitude. We also show the efficacy of our technique for exploring chip multiprocessor (CMP) design spaces: when trained on a 1% sample drawn from a CMP design space with 250K points and up to 55x performance swings among different system configurations, our models predict performance with only 4-5% error on average. Our approach combines with techniques to reduce time per simulation, achieving net time savings of three-four orders of magnitude.

Question: What is the time complexity of the algorithm?


3D Simulation for Robot Arm Control with Deep Q-Learning. Intelligent control of robotic arms has huge potential over the coming years, but as of now will often fail to adapt when presented with new and unfamiliar environments. Recent trends to solve this problem have seen a shift to endto-end solutions using deep reinforcement learning to learn policies from visual input, rather than relying on a handcrafted, modular pipeline. Building upon the recent success of deep Q-networks, we present an approach which uses threedimensional simulations to train a 7-DOF robotic arm in a robot arm control task without any prior knowledge. Policies accept images of the environment as input and output motor actions. However, the high-dimensionality of the policies as well as the large state space makes policy search difficult. This is overcome by ensuring interesting states are explored via intermediate rewards that guide the policy towards higher reward states. Our results demonstrate that deep Q-networks can be used to learn policies for a task that involves locating a cube, grasping, and then finally lifting. The agent is able to learn to deal with a range of starting joint configurations and starting cube positions when tested in simulation. Moreover, we show that policies trained via simulation have the potential to be directly applied to real-world equivalents without any further training. We believe that robot simulations can decrease the dependency on physical robots and ultimately improve productivity of training robot control tasks.

Question: What is a robot arm control task?


Distributed Detection of Single-Stage Multipoint Cyber Attacks in a Water Treatment Plant. A distributed detection method is proposed to detect single stage multi-point (SSMP) attacks on a Cyber Physical System (CPS). Such attacks aim at compromising two or more sensors or actuators at any one stage of a CPS and could totally compromise a controller and prevent it from detecting the attack. However, as demonstrated in this work, using the flow properties of water from one stage to the other, a neighboring controller was found effective in detecting such attacks. The method is based on physical invariants derived for each stage of the CPS from its design. The attack detection effectiveness of the method was evaluated experimentally against an operational water treatment testbed containing 42 sensors and actuators. Results from the experiments point to high effectiveness of the method in detecting a variety of SSMP attacks but also point to its limitations. Distributing the attack detection code among various controllers adds to the scalability of the proposed method.

Question: How to detect cyber attacks in a water treatment plant?


Efficient Keyword-Aware Representative Travel Route Recommendation. With the popularity of social media (e.g., Facebook and Flicker), users can easily share their check-in records and photos during their trips. In view of the huge number of user historical mobility records in social media, we aim to discover travel experiences to facilitate trip planning. When planning a trip, users always have specific preferences regarding their trips. Instead of restricting users to limited query options such as locations, activities, or time periods, we consider arbitrary text descriptions as keywords about personalized requirements. Moreover, a diverse and representative set of recommended travel routes is needed. Prior works have elaborated on mining and ranking existing routes from check-in data. To meet the need for automatic trip organization, we claim that more features of Places of Interest (POIs) should be extracted. Therefore, in this paper, we propose an efficient Keyword-aware Representative Travel Route framework that uses knowledge extraction from users' historical mobility records and social interactions. Explicitly, we have designed a keyword extraction module to classify the POI-related tags, for effective matching with query keywords. We have further designed a route reconstruction algorithm to construct route candidates that fulfill the requirements. To provide befitting query results, we explore Representative Skyline concepts, that is, the Skyline routes which best describe the trade-offs among different POI features. To evaluate the effectiveness and efficiency of the proposed algorithms, we have conducted extensive experiments on real location-based social network datasets, and the experiment results show that our methods do indeed demonstrate good performance compared to state-of-the-art works.

Question: What is the best way to organize a trip?


Asynchronous MPC with a strict honest majority using non-equivocation. Multiparty computation (MPC) among n parties can tolerate up to tsynchronous communication setting; however, in an asynchronous communication setting, the resiliency bound decreases to only t < n/3 active corruptions. We improve the resiliency bound for asynchronous MPC (AMPC) to match synchronous MPC using non-equivocation.  Non-equivocation is a message authentication mechanism to restrict a corrupted sender from making conflicting statements to different (honest) parties. It can be implemented using an increment-only counter and a digital signature oracle, realizable with trusted hardware modules readily available in commodity computers and smartphone devices. A non-equivocation mechanism can also be transferable and allows a receiver to verifiably transfer the authenticated statement to other parties. In this work, using transferable non-equivocation, we present an AMPC protocol tolerating t < n/2 faults. From a practical point of view, our AMPC protocol requires fewer setup assumptions than the previous AMPC protocol with t < n/2 by Beerliova-Trubiniova, Hirt and Nielsen [PODC 2010]: unlike their AMPC protocol, it does not require any synchronous broadcast round at the beginning of the protocol and avoids the threshold homomorphic encryption setup assumption. Moreover, our AMPC protocol is also efficient and provides a gain of Θ(n) in the communication complexity per multiplication gate, over the AMPC protocol of Beerliova-Trubiniova et al. In the process, using non-equivocation, we also define the first asynchronous verifiable secret sharing (AVSS) scheme with t < n/2, which is of independent interest to threshold cryptography.

Question: What is the resiliency bound for asynchronous MPC?


Printed $\lambda/8$ -PIFA for Penta-Band WWAN Operation in the Mobile Phone. A small-size printed planar inverted-F antenna (PIFA) operated at its one-eighth wavelength (lambda/8) mode as the fundamental resonant mode for achieving WWAN (wireless wide area network) operation in the mobile phone is presented. The proposed PIFA has a simple structure of comprising two radiating strips of length about lambda/8 at 900 MHz and is fed using a coupling feed. Compared to the traditional PIFA using a direct feed, the coupling feed greatly decreases the very large input impedance seen at the lambda/8 mode for the traditional PIFA and results in successful excitation of the lambda/8 mode for the proposed PIFA. Two lambda/8 modes are generated by the two radiating strips and occur at close frequencies at about 900 MHz to form a wide lower band to cover GSM850/900 operation. The two radiating strips also generate two higher-order modes or lambda/4 modes at about 1900 MHz to form a wide upper band for GSM1800/1900/UMTS operation. Penta-band WWAN operation is hence achieved, yet the proposed PIFA only occupies a small printed area of 15times31 mm2 or 465 mm2 on the system circuit board of the mobile phone, which is about the smallest for the internal uniplanar printed antenna capable of penta-band operation that have been reported. Details of the proposed PIFA are presented. The specific absorption rate (SAR) and hearing aid compatibility (HAC) results for the proposed PIFA are also analyzed.

Question: What is the smallest printed antenna capable of penta-band operation?


Cyber Defense Capability Model: A Foundation Taxonomy. Cyber attacks have significantly increased over the last few years, where the attackers are highly skilled, more organized and supported by other powerful actors to devise attacks towards specific targets. To aid the development of a strategic plan to defend against emerging attacks, we present a high-level taxonomy along with a cyber defense model to address the interaction and relationships between taxonomy elements. A cyber-kinetic reference model which is used widely by U.S Air Force is adopted as a baseline for the model and taxonomy development. Asset, Cyber Capability, and Preparation Process are the three high-level elements that are presented for the cyber defense capability model. The Cyber Capability, as the focal point of the study, uses three classifiers to characterize the strategic cyber defense mechanisms, which are classified by active, passive and collaborative defense. To achieve a proper cyber defense strategy, the key actors, assets and associated preparation procedure are identified. Finally, the proposed taxonomy is extensible so that additional dimensions or classifications can be added to future needs.

Question: What is a cyber defense capability model?


Depth of field rendering via adaptive recursive filtering. We present a new post-processing method for rendering high-quality depth-of-field effects in real time. Our method is based on a recursive filtering process, which adaptively smooths the image frame with local depth and circle of confusion information. Unlike previous post-filtering approaches that rely on various convolution kernels, the behavior of our filter is controlled by a weighting function defined between two neighboring pixels. By properly designing this weighting function, our method produces spatially-varying smoothed results, correctly handles the boundaries between in-focus and out-of-focus objects, and avoids rendering artifacts such as intensity leakage and blurring discontinuity. Additionally, our method works on the full frame without resorting to image pyramids. Our algorithms runs efficiently on graphics hardware. We demonstrate the effectiveness of the proposed method with several complex scenes.

Question: What is depth of field rendering?


A qualitative exploration of individuals' motivators for seeking substance user treatment.. Substance abuse continues to greatly impact the US population. The purpose of this study is to improve understanding of the reasons why individuals seek substance user treatment. Qualitative interviews were conducted with 50 men (n = 25) and women (n = 25) across eight substance user treatment programs in Durham, North Carolina, in 2010. We identify six motivating themes regarding reasons for seeking treatment and explore how these themes vary by the participants' mode of entry into treatment. Findings provide a holistic understanding of how and why individuals come into treatment that can enhance the quality and impact of substance user services.

Question: What are the motivating factors for seeking substance user treatment?


A practical model for subsurface light transport. This paper introduces a simple model for subsurface light transport in translucent materials. The model enables efficient simulation of effects that BRDF models cannot capture, such as color bleeding within materials and diffusion of light across shadow boundaries. The technique is efficient even for anisotropic, highly scattering media that are expensive to simulate using existing methods. The model combines an exact solution for single scattering with a dipole point source diffusion approximation for multiple scattering. We also have designed a new, rapid image-based measurement technique for determining the optical properties of translucent materials. We validate the model by comparing predicted and measured values and show how the technique can be used to recover the optical properties of a variety of materials, including milk, marble, and skin. Finally, we describe sampling techniques that allow the model to be used within a conventional ray tracer.

Question: What is a practical model for subsurface light transport?


Experiences of stigma and access to HAART in children and adolescents living with HIV/AIDS in Brazil.. This study describes and conceptualizes the experiences of stigma in a group of children living with HIV in São Paulo, Brazil, and evaluates the impact of access to highly active antiretroviral therapy (HAART) over the social course of AIDS and over the children's experiences of stigma. Through ethnographic research in São Paulo from 1999 to 2001, the life trajectories of 50 children ages 1-15 living with or affected by HIV were studied. Data were collected via participant observation and semi-structured informal interviews and analyzed using social theories on illness experience and social inequality. Our results demonstrate that AIDS-related stigma occurs within complex discrimination processes that change as children reach adolescence. We found that structural violence in the forms of poverty, racism, and inequalities in social status, gender, and age fuels children's experiences of stigma. We also describe how access to HAART changes the lived experience of children, reduces stigma, and brings new challenges in AIDS care such as adolescents' sexuality and treatment adherence. Based on these results, we propose structural violence as the framework to study stigma and argue that interventions to reduce stigma that solely target the perception and attitudes toward people living with HIV are limited. In contrast universal access to HAART in Brazil is a powerful intervention that reduces stigma, in that it transforms AIDS from a debilitating and fatal disease to a chronic and manageable one, belongs to a broader mechanism to assure citizens' rights, and reduces social inequalities in access to health care.

Question: What is the impact of HAART on stigma?


Vision-Controlled Micro Flying Robots: From System Design to Autonomous Navigation and Mapping in GPS-Denied Environments. Autonomous microhelicopters will soon play a major role in tasks like search and rescue, environment monitoring, security surveillance, and inspection. If they are further realized in small scale, they can also be used in narrow outdoor and indoor environments and represent only a limited risk for people. However, for such operations, navigating based only on global positioning system (GPS) information is not sufficient. Fully autonomous operation in cities or other dense environments requires microhelicopters to fly at low altitudes, where GPS signals are often shadowed, or indoors and to actively explore unknown environments while avoiding collisions and creating maps. This involves a number of challenges on all levels of helicopter design, perception, actuation, control, and navigation, which still have to be solved. The Swarm of Micro Flying Robots (SFLY) project was a European Union-funded project with the goal of creating a swarm of vision-controlled microaerial vehicles (MAVs) capable of autonomous navigation, three-dimensional (3-D) mapping, and optimal surveillance coverage in GPS-denied environments. The SFLY MAVs do not rely on remote control, radio beacons, or motion-capture systems but can fly all by themselves using only a single onboard camera and an inertial measurement unit (IMU). This article describes the technical challenges that have been faced and the results achieved from hardware design and embedded programming to vision-based navigation and mapping, with an overview of how all the modules work and how they have been integrated into the final system. Code, data sets, and videos are publicly available to the robotics community. Experimental results demonstrating three MAVs navigating autonomously in an unknown GPS-denied environment and performing 3-D mapping and optimal surveillance coverage are presented.

Question: How do you navigate in GPS-denied environments?


A Differential Equation for Modeling Nesterov's Accelerated Gradient Method: Theory and Insights. We derive a second-order ordinary differential equation (ODE), which is the limit of Nesterov's accelerated gradient method. This ODE exhibits approximate equivalence to Nesterov's scheme and thus can serve as a tool for analysis. We show that the continuous time ODE allows for a better understanding of Nesterov's scheme. As a byproduct, we obtain a family of schemes with similar convergence rates. The ODE interpretation also suggests restarting Nesterov's scheme leading to an algorithm, which can be rigorously proven to converge at a linear rate whenever the objective is strongly convex.

Question: What is the difference between Nesterov's accelerated gradient method and the ODE?


An Efficient Method of Number Plate Extraction from Indian Vehicles Image. Automatic Number Plate Recognition (ANPR) is an image-processing technology that identifies vehicles by their number plates without direct human intervention. It is an application of computer vision and important area of research due to its many applications. The main process of ANPR is divided into four stages. This paper presents a simple and efficient method for the extraction of number plate from the vehicle image based on morphological operations, thresholding and sobel edge detection, and the connected component analysis.

Question: What is the main process of ANPR?


Localization & Segmentation of Indian Car Number Plate system : A Projection based Multistage Approach. Enormous incorporation of information technologies into all aspects of recent life caused demand for processing vehicles as conceptual resources in information systems. This can be achieved by a human agent, or by special intelligent equipment which is be able to recognize vehicles by their number plates in a real environment and reflect it into conceptual resources. Because of this, various recognition techniques have been developed and number plate recognition systems are today used in various traffic and security applications, such as parking, access and border control, or tracking of stolen cars. This work deals with problematic from field of artificial intelligence, machine vision and neural networks in construction of an automatic car number plate Recognition (ACNPR). This techniques deals with enhanced shadow removal algorithm, along with horizontal and vertical projection method for number plate localization & segmentation..Work comparatively deals with methods achieving invariance of systems towards image illumination, translations and various light conditions during the capture.

Question: What is the difference between a neural network and a neural network?


Scaling Up the Accuracy of Naive-Bayes Classifiers: A Decision-Tree Hybrid. Naive Bayes induction algorithms were previously shown to be surprisingly accurate on many classi cation tasks even when the conditional independence assumption on which they are based is violated How ever most studies were done on small databases We show that in some larger databases the accuracy of Naive Bayes does not scale up as well as decision trees We then propose a new algorithm NBTree which in duces a hybrid of decision tree classi ers and Naive Bayes classi ers the decision tree nodes contain uni variate splits as regular decision trees but the leaves contain Naive Bayesian classi ers The approach re tains the interpretability of Naive Bayes and decision trees while resulting in classi ers that frequently out perform both constituents especially in the larger databases tested

Question: How does the accuracy of Naive Bayes classifiers scale up?


Introduction to Neurolinguistics. attitude is lacking in patients with anomia (inability to name), since they cannot categorize, and in patients with agrammatism (difficulties using grammatical morphemes and function words), since they cannot use elements that "have no meaning in isolation." In his description of abstract attitude, Goldstein mixes psychological factors, such as the ability to separate figure and ground, with physiological factors, like spreading and activation (i.e., the fact that stimuli can activate an abnormally large area in the cortex if thresholds of activation for neurons are changed by brain damage, which makes the patient unable to detect, for example, exactly where somebody is touching his skin). An abstract attitude is required for the fully efficient use of language and therefore the linguistic disorder arises secondarily. Words lose their function to organize the external world. Goldstein assumed different positions with respect to localization, but he was mainly an anti-localist. He claimed that the cortex could be separated into a "peripheral part" with structural localization and a "central part" which is equipotential and constructs "dynamic structures" against a "dynamic background." Lesions in the central part cause a loss of abstract attitude. It was Karl Lashley (1933, 1950), a physiologist, who presented the theory of cerebral equipotentiality and mass effect. Equipotentiality means that all parts of the cortex have the same "ability" and mass effect means that functions are dependent on how much of the cortex is used. Lashley claimed that studies of humans were too unreliable, due to humans' complex behavior, and therefore concentrated on experiments with rats in mazes. He accepted specified functions only for motor and sensory projection areas and objected to attempts to localize mental functions. He emphasized that psychological unity does not guarantee neurological unity; for example, many structures participate in intentio

Question: What is the difference between a physiologist and a psychologist?


Distributed Neural Networks for Internet of Things: The Big-Little Approach. Nowadays deep neural networks are widely used to accurately classify input data. An interesting application area is the Internet of Things (IoT), where a massive amount of sensor data has to be classified. The processing power of the cloud is attractive, however the variable latency imposes a major drawback for neural networks. In order to exploit the apparent trade-off between utilizing the available though limited embedded computing power of the IoT devices at high speed/stable latency and the seemingly unlimited computing power of Cloud computing at the cost of higher and variable latency, we propose a Big-Little architecture for deep neural networks. A small neural network trained to a subset of prioritized output classes can be used to calculate an output on the embedded devices, while a more specific classification can be calculated in the Cloud only when required. We show the applicability of this concept in the IoT domain by evaluating our approach for state of the art neural network classification problems on popular embedded devices such as the Raspberry Pi and Intel Edison.

Question: What is the big-little approach?


A Novel BiQuad Antenna for 2 . 4 GHz Wireless Link Application : A Proposed Design. An ISM Band (2.4GHz) Design for a Biquad Antenna with Reflector base is presented for satisfying the ISM Band point-to-point link Applications in this paper. This proposed design can be used for Reception as well as Transmission in Wi-Fi's, WLAN, Bluetooth and even Zigbee links. The proposed antenna consists of two squares of the same size of 1⁄4 wavelength as a radiating element and a metallic plate or grid as reflector. This antenna has a beam width of about 70 degrees and a gain in the order of 10-12 dBi. A Prototype of this Antenna is designed and constructed. Parametric study is performed to understand the characterstics of the proposed antenna. Almost good antenna performances such as radiation patterns and antenna gains over the operating bands have been observed and simulated peak gain of the antenna is 10.7 dBi at 2439MHz. The simulated return loss is-35dB, whereas simulated SWR is 1.036 over the operating bands. The Biquad antenna is simple to build and offers good directivity and gain for Point-to-Point communications [1]. It consists of two squares of the same size of 1⁄4 wavelength as a radiating element and a metallic plate or grid as reflector. This antenna has a beam width of about 70 degrees and a gain in the order of 10-12 dBi. It can be used as stand-alone antenna or as feeder for a Parabolic Dish [2]. The polarization is such that looking at the antenna from the front, if the squares are placed side by side the polarization is vertical. The element is made from a length of 2mm thick copper wire, bent into the appropriate shape. Note that the length of each " side " should be close to 31 mm as possible, when measured from center to center of the wire [4-5]. The Details of the proposed antenna design are described in the paper, and simulated results are presented and discussed in the following sections.

Question: What is the difference between a biquad and a quad?


A case for dynamic frequency tuning in on-chip networks. Performance and power are the first order design metrics for Network-on-Chips (NoCs) that have become the de-facto standard in providing scalable communication backbones for multicores/CMPs. However, NoCs can be plagued by higher power consumption and degraded throughput if the network and router are not designed properly. Towards this end, this paper proposes a novel router architecture, where we tune the frequency of a router in response to network load to manage both performance and power. We propose three dynamic frequency tuning techniques, FreqBoost, FreqThrtl and FreqTune, targeted at congestion and power management in NoCs. As enablers for these techniques, we exploit Dynamic Voltage and Frequency Scaling (DVFS) and the imbalance in a generic router pipeline through time stealing. Experiments using synthetic workloads on a 8x8 wormhole-switched mesh interconnect show that FreqBoost is a better choice for reducing average latency (maximum 40%) while, FreqThrtl provides the maximum benefits in terms of power saving and energy delay product (EDP). The FreqTune scheme is a better candidate for optimizing both performance and power, achieving on an average 36% reduction in latency, 13% savings in power (up to 24% at high load), and 40% savings (up to 70% at high load) in EDP. With application benchmarks, we observe IPC improvement up to 23% using our design. The performance and power benefits also scale for larger NoCs.

Question: What is the difference between a wormhole and a mesh interconnect?


Model-based evaluation: from dependability to security. The development of techniques for quantitative, model-based evaluation of computer system dependability has a long and rich history. A wide array of model-based evaluation techniques is now available, ranging from combinatorial methods, which are useful for quick, rough-cut analyses, to state-based methods, such as Markov reward models, and detailed, discrete-event simulation. The use of quantitative techniques for security evaluation is much less common, and has typically taken the form of formal analysis of small parts of an overall design, or experimental red team-based approaches. Alone, neither of these approaches is fully satisfactory, and we argue that there is much to be gained through the development of a sound model-based methodology for quantifying the security one can expect from a particular design. In this work, we survey existing model-based techniques for evaluating system dependability, and summarize how they are now being extended to evaluate system security. We find that many techniques from dependability evaluation can be applied in the security domain, but that significant challenges remain, largely due to fundamental differences between the accidental nature of the faults commonly assumed in dependability evaluation, and the intentional, human nature of cyber attacks.

Question: What is model-based evaluation?


Pattern Recognition and Computer Vision. Chinese painting is distinct from other art in that the painting elements are exhibited by complex water-and-ink diffusion and shows gray, white and black visual effect. Rendering such a water-and-ink painting with polychrome style is a challenging problem. In this paper, we propose a novel style transfer method for Chinese painting. We firstly decompose the Chinese painting with adaptive patches based on its structure, and locally colorize the painting. Then, the colorized image is used for guiding the process of texture transfer that is modeled in Markov Random Field (MRF). More precisely, we improve the classic texture transfer algorithm by modifying the compatibility functions for searching the optimal matching, with the chromatism information. The experiment results show that proposed adaptive patches can well preserve the original content while match the example style. Moreover, we present the transfer results with our method and recent style transfer algorithms, in order to make a comparison.

Question: How to colorize a Chinese painting?