Mediating intimacy: designing technologies to support strong-tie relationships. Intimacy is a crucial element of domestic life, and many interactive technologies designed for other purposes have been appropriated for use within intimate relationships. However, there is a deficit in current understandings of how technologies are used within intimate relationships, and how to design technologies to support intimate acts. In this paper we report on work that has addressed these deficits. We used cultural probes and contextual interviews and other ethnographically informed techniques to investigate how interactive technologies are used within intimate relationships. From this empirical work we generated a thematic understanding of intimacy and the use of interactional technologies to support intimate acts. We used this understanding to inform the design of intimate technologies. A selection of our design concepts is also presented.

Question: How do we design technologies to support intimate acts?


The Role of Context for Object Detection and Semantic Segmentation in the Wild. In this paper we study the role of context in existing state-of-the-art detection and segmentation approaches. Towards this goal, we label every pixel of PASCAL VOC 2010 detection challenge with a semantic category. We believe this data will provide plenty of challenges to the community, as it contains 520 additional classes for semantic segmentation and object detection. Our analysis shows that nearest neighbor based approaches perform poorly on semantic segmentation of contextual classes, showing the variability of PASCAL imagery. Furthermore, improvements of existing contextual models for detection is rather modest. In order to push forward the performance in this difficult scenario, we propose a novel deformable part-based model, which exploits both local context around each candidate detection as well as global context at the level of the scene. We show that this contextual reasoning significantly helps in detecting objects at all scales.

Question: What is the role of context in object detection?


New folded configuration of rectangular waveguide filters with asymmetrical transmission zeros. This paper presents a novel filter topology for the implementation of asymmetric responses with transmission zeros (TZs) in rectangular waveguide technology. It is based on a compact folded E-plane arrangement where adjacent resonators are capacitively coupled through rectangular slots, and non-adjacent resonators are coupled through simple inductive windows. The new folded configuration allows the introduction of transmission zeros above the passband, which can be easily controlled. High order filters can be designed by cascading an arbitrary number of resonators in a folded layout. Components based on this novel configuration are amenable to simple manufacturing processes and can be used in high-power environments. A triplexer containing filters implemented with the proposed topology has been designed. Measurements of a manufactured prototype are included to validate the use of this topology in practical applications.

Question: What is a triplexer?


HOW MUCH DOES INDUSTRY MATTER , REALLY ?. In this paper, we examine the importance of year, industry, corporate-parent, and businessspecific effects on the profitability of U.S. public corporations within specific 4-digit SIC categories. Our results indicate that year, industry, corporate-parent, and business-specific effects account for 2 percent, 19 percent, 4 percent, and 32 percent, respectively, of the aggregate variance in profitability. We also find that the importance of the effects differs substantially across broad economic sectors. Industry effects account for a smaller portion of profit variance in manufacturing but a larger portion in lodging/entertainment, services, wholesale/retail trade, and transportation. Across all sectors we find a negative covariance between corporate-parent and industry effects. A detailed analysis suggests that industry, corporate-parent, and business-specific effects are related in complex ways.  1997 by John Wiley & Sons, Ltd.

Question: How much does industry matter?


Characterization of RF Noise in UTBB FD-SOI MOSFET. In this paper, we report the noise measurements in the RF frequency range for ultrathin body and thin buried oxide fully depleted silicon on insulator (FD-SOI) transistors. We analyze the impact of back and front gate biases on the various noise parameters; along with discussions on the secondary effects in FD-SOI transistors which contribute to the thermal noise. Using calibrated TCAD simulations, we show that the noise figure changes with the substrate doping and buried oxide thickness.

Question: What is the noise figure of a transistor?


Social Turing Tests: Crowdsourcing Sybil Detection. As popular tools for spreading spam and malware, Sybils (or fake accounts) pose a serious threat to online communities such as Online Social Networks (OSNs). Today, sophisticated attackers are creating realistic Sybils that effec tively befriend legitimate users, rendering most automated Sybil detection techniques ineffective. In this paper, we explor e the feasibility of acrowdsourcedSybil detection system for OSNs. We conduct a large user study on the ability of humans to detect today's Sybil accounts, using a large corpus of ground-truth Sybil accounts from the Facebook and Renren networks. We analyze detection accuracy by both "experts" and "turkers" under a variety of conditions, and find that while turkers vary significantly in their effective ness, experts consistently produce near-optimal results. We use these results to drive the design of a multi-tier crowdsourcing Sybil detection system. Using our user study data, we show that this system is scalable, and can be highly effective either as a standalone system or as a complementary technique to current tools.

Question: How can we detect Sybils?


Query modeling for entity search based on terms, categories, and examples. Users often search for entities instead of documents, and in this setting, are willing to provide extra input, in addition to a series of query terms, such as category information and example entities. We propose a general probabilistic framework for entity search to evaluate and provide insights in the many ways of using these types of input for query modeling. We focus on the use of category information and show the advantage of a category-based representation over a term-based representation, and also demonstrate the effectiveness of category-based expansion using example entities. Our best performing model shows very competitive performance on the INEX-XER entity ranking and list completion tasks.

Question: Query modeling for entity search based on terms, categories, and examples. Users often search for entities instead of documents, and in this setting, are willing to provide extra input, in addition to a series of query terms, such as category information and example entities. We propose a general probabilistic framework for entity search to


A framework for mesencephalic dopamine systems based on predictive Hebbian learning.. We develop a theoretical framework that shows how mesencephalic dopamine systems could distribute to their targets a signal that represents information about future expectations. In particular, we show how activity in the cerebral cortex can make predictions about future receipt of reward and how fluctuations in the activity levels of neurons in diffuse dopamine systems above and below baseline levels would represent errors in these predictions that are delivered to cortical and subcortical targets. We present a model for how such errors could be constructed in a real brain that is consistent with physiological results for a subset of dopaminergic neurons located in the ventral tegmental area and surrounding dopaminergic neurons. The theory also makes testable predictions about human choice behavior on a simple decision-making task. Furthermore, we show that, through a simple influence on synaptic plasticity, fluctuations in dopamine release can act to change the predictions in an appropriate manner.

Question: What is a mesencephalic dopamine system?


Color nonuniformity in projection-based displays: analysis and solutions. Large-area displays made up of several projectors show significant variation in color. Here, we identify different projector parameters that cause the color variation and study their effects on the luminance and chrominance characteristics of the display. This work leads to the realization that luminance varies significantly within and across projectors, while chrominance variation is relatively small, especially across projectors of same model. To address this situation, we present a method to achieve luminance matching across all pixels of a multiprojector display that results in photometrically uniform displays. We use a camera as a measurement device for this purpose. Our method comprises a one-time calibration step that generates a per channel per projector luminance attenuation map (LAM), which is then used to correct any image projected on the display at interactive rates on commodity graphics hardware. To the best of our knowledge, this is the first effort to match luminance across all the pixels of a multiprojector display.

Question: How to make a display uniform in color?


Deep Face Recognition: A Survey. Face recognition made tremendous leaps in the last five years with a myriad of systems proposing novel techniques substantially backed by deep convolutional neural networks (DCNN). Although face recognition performance sky-rocketed using deep-learning in classic datasets like LFW, leading to the belief that this technique reached human performance, it still remains an open problem in unconstrained environments as demonstrated by the newly released IJB datasets. This survey aims to summarize the main advances in deep face recognition and, more in general, in learning face representations for verification and identification. The survey provides a clear, structured presentation of the principal, state-of-the-art (SOTA) face recognition techniques appearing within the past five years in top computer vision venues. The survey is broken down into multiple parts that follow a standard face recognition pipeline: (a) how SOTA systems are trained and which public data sets have they used; (b) face preprocessing part (detection, alignment, etc.); (c) architecture and loss functions used for transfer learning (d) face recognition for verification and identification. The survey concludes with an overview of the SOTA results at a glance along with some open issues currently overlooked by the community.

Question: What is the difference between face recognition and face verification?


Toward Automatic Activity Classification and Movement Assessment During a Sports Training Session. Motion analysis technologies have been widely used to monitor the potential for injury and enhance athlete performance. However, most of these technologies are expensive, can only be used in laboratory environments, and examine only a few trials of each movement action. In this paper, we present a novel ambulatory motion analysis framework using wearable inertial sensors to accurately assess all of an athlete's activities in real training environment. We first present a system that automatically classifies a large range of training activities using the discrete wavelet transform (DWT) in conjunction with a random forest classifier. The classifier is capable of successfully classifying various activities with up to 98% accuracy. Second, a computationally efficient gradient descent algorithm is used to estimate the relative orientations of the wearable inertial sensors mounted on the shank, thigh, and pelvis of a subject, from which the flexion-extension knee and hip angles are calculated. These angles, along with sacrum impact accelerations, are automatically extracted for each stride during jogging. Finally, normative data are generated and used to determine if a subject's movement technique differed to the normative data in order to identify potential injury-related factors. For the joint angle data, this is achieved using a curve-shift registration technique. It is envisaged that the proposed framework could be utilized for accurate and automatic sports activity classification and reliable movement technique evaluation in various unconstrained environments for both injury management and performance enhancement.

Question: How accurate is the system?


Design of novel capacitive type torque sensor for robotic applications. This paper introduces a novel capacitive type torque sensor. The developed torque sensor measures single axis torque value by using two capacitance transducing cells. To make a linear relation between the capacitance and the deformation, the shape of flexure hinge is considered to be deformed constantly. Also, the nonlinear curve fitting method is used to fit the capacitance linearly. Finally, the performance of the sensor is verified in reference to a commercial torque sensor.

Question: What is the capacitance of a capacitor?


Developing a retrieval based diagnostic aid for automated melanoma recognition of dermoscopic images. Skin cancer is one of the most frequent cancers among human beings. Whereas, malignant melanoma is the most aggressive and deadly type of skin cancer, and its incidence has been quickly increasing over the last years. The detection of the malignant melanoma in its early stages with dermoscopic images reduces the mortality considerably, hence this a crucial issue for the dermatologists. However, their interpretation is time consuming and subjective, even for trained dermatologists. The current computer-aided diagnosis (CAD) systems are mainly noninteractive in nature and their prediction represents just a cue for the dermatologist, as the final decision regarding the likelihood of the presence of a melanoma is left exclusively to him/her. Recently, developing CAD schemes that use image retrieval approach to search for the clinically relevant and visually similar lesions has been attracting research interest. Although preliminary studies have suggested that using retrieval might improve dermatologists' performance and/or increase their confidence in the decision making, this technology is still in the early development stage with lack of benchmark evaluation in ground-truth datasets to compare retrieval effectiveness. A CAD system based on both classification and retrieval would be more effective and robust. This work is focusing on by addressing the various issues related to the development of such an integrated and interactive CAD system by performing automatic lesion segmentation with an adaptive thresholding and region growing approach, extracting invariant features from lesions and classifying and retrieving those using Extreme Learning Machines (ELM) and a similarity fusion approach. Finally, various methods are evaluated with promising results in a benchmark dataset of International Skin Imaging Collaboration (ISIC).

Question: What is the purpose of this work?


Approximation Algorithms for Degree-Constrained Minimum-Cost Network-Design Problems. We study network-design problems with two different design objectives: the total cost of the edges and nodes in the network and the maximum degree of any node in the network. A prototypical example is the degree-constrained node-weighted Steiner tree problem: We are given an undirected graph G(V,E) , with a nonnegative integral function d that specifies an upper bound d(v) on the degree of each vertex v ∈ V in the Steiner tree to be constructed, nonnegative costs on the nodes, and a subset of k nodes called terminals. The goal is to construct a Steiner tree T containing all the terminals such that the degree of any node v in T is at most the specified upper bound d(v) and the total cost of the nodes in T is minimum. Our main result is a bicriteria approximation algorithm whose output is approximate in terms of both the degree and cost criteria—the degree of any node v ∈ V in the output Steiner tree is O(d(v) log  k) and the cost of the tree is O(log  k) times that of a minimum-cost Steiner tree that obeys the degree bound d(v) for each node v . Our result extends to the more general problem of constructing one-connected networks such as generalized Steiner forests. We also consider the special case in which the edge costs obey the triangle inequality and present simple approximation algorithms with better performance guarantees.

Question: What is the approximation ratio of the algorithm?


The Effects of User Features on Twitter Hate Speech Detection. The paper investigates the potential effects user features have on hate speech classification. A quantitative analysis of Twitter data was conducted to better understand user characteristics, but no correlations were found between hateful text and the characteristics of the users who had posted it. However, experiments with a hate speech classifier based on datasets from three different languages showed that combining certain user features with textual features gave slight improvements of classification performance. While the incorporation of user features resulted in varying impact on performance for the different datasets used, user network-related features provided the most consistent improvements.

Question: What is the impact of user features on hate speech detection?


Vehicle Detection and Tracking in Car Video Based on Motion Model. This paper aims at real-time in-car video analysis to detect and track vehicles ahead for safety, autodriving, and target tracing. This paper describes a comprehensive approach to localizing target vehicles in video under various environmental conditions. The extracted geometry features from the video are continuously projected onto a 1-D profile and are constantly tracked. We rely on temporal information of features and their motion behaviors for vehicle identification, which compensates for the complexity in recognizing vehicle shapes, colors, and types. We probabilistically model the motion in the field of view according to the scene characteristic and the vehicle motion model. The hidden Markov model (HMM) is used to separate target vehicles from the background and track them probabilistically. We have investigated videos of day and night on different types of roads, showing that our approach is robust and effective in dealing with changes in environment and illumination and that real-time processing becomes possible for vehicle-borne cameras.

Question: What is the difference between a vehicle and a vehicle?


An analysis on the significance of ticket analytics and defect analysis from software quality perspective. Software even though intangible should undergo evolution to fit into the ever changing real world scenarios. Each issue faced by the development and service team directly reflects in the quality of the software product. According to the related work, very few research is going on in the field of ticket and its related incident; a part of corrective maintenance. In depth research on incident tickets should be viewed as critical since, it provides information related to the kind of maintenance activities that is performed in any timestamp. Therefore classifying and analyzing tickets becomes a critical task in managing the operations of the service since each incident will be having a service level agreement associated with it. Further, incident analysis is essential to identify the patterns associated. Due to the existence of huge population of software in each organization and millions of incidents get reported per software product every year, it is practically impossible to manually analyze all the tickets. This paper focuses on projecting the importance of ticket to maintain the quality of software products and also distinguish it from the defect associated with a software system. This paper projects the importance of identifying defects in software as well as handling the incident related tickets and resolving it when viewed from the perspective of quality. It also gives an overview of the scope defect analysis and ticket analytics provide to the researchers.

Question: What is the significance of ticket analytics and defect analysis from software quality perspective?


Efficient Energy-Optimal Routing for Electric Vehicles. Traditionally routing has focused on finding shortest paths in networks with positive, static edge costs representing the distance between two nodes. Energy-optimal routing for electric vehicles creates novel algorithmic challenges, as simply understanding edge costs as energy values and applying standard algorithms does not work. First, edge costs can be negative due to recuperation, excluding Dijkstra-like algorithms. Second, edge costs may depend on parameters such as vehicle weight only known at query time, ruling out existing preprocessing techniques. Third, considering battery capacity limitations implies that the cost of a path is no longer just the sum of its edge costs. This paper shows how these challenges can be met within the framework of A* search. We show how the specific domain gives rise to a consistent heuristic function yielding an O(n) routing algorithm. Moreover, we show how battery constraints can be treated by dynamically adapting edge costs and hence can be handled in the same way as parameters given at query time, without increasing run-time complexity. Experimental results with real road networks and vehicle data demonstrate the advantages of our solution.

Question: What is the difference between preprocessing and query time?


Fuzzy Logic-Based Natural Language Processing and Its Application to Speech Recognition. In this paper we describe a fuzzy logic-based language processing method, which is applied to speech recognition. Our purpose is to create a system that can learn from a linguistic corpus the fuzzy semantic relations between the concepts represented by words and use such relations to process the word sequences generated by speech recognition systems. In particular, the system will be able to predict the words failed to be recognized by a speech recognition system. This will help to increase the accuracy of a speech recognition system. This will also serve as the first stage of deep semantic processing of speech recognition results by providing "semantic relatedness" between the recognized words. We report the fuzzy inference rule learning system, which we have developed and also report the experimental results based on the system. Key-words: Fuzzy logic, Natural Language Analysis, Speech Recognition, Corpus Linguistics.

Question: What is fuzzy logic?


Applying an optimized switching strategy to a high gain boost converter for input current ripple cancellation. This paper discusses applying various switching strategies such as conventional complementary and proportional methods on a specific converter. This converter was recently presented and it can provide high voltage gain and has current ripple cancelation ability at a preselected duty-cycle. The input current ripple is zero when the converter works at a special duty-cycle. But load disturbance leads to output voltage changes that maybe cause the duty-cycle to deviate from its preselected value. In this situation, the input current ripple cannot be completely canceled. The proposed proportional strategy is an optimized method for minimizing input current ripple at other operating duty-cycles and also it provides a voltage gain lower than conventional complementary strategy. Here, the converter's performance is analyzed under two switching strategies and the effect of various strategies has been investigated on converter parameters including output voltage and input current ripple. These considerations are verified by implementing a 100-W prototype of the proposed converter in laboratory.

Question: What is the difference between the conventional complementary and proportional strategies?


Quantitative Evaluation of DC Microgrids Availability: Effects of System Architecture and Converter Topology Design Choices. This paper presents a quantitative method to evaluate dc microgrids availability by identifying and calculating minimum cut sets occurrence probability for different microgrid architectures and converter topologies. Hence, it provides planners with an essential tool to evaluate downtime costs and decide technology deployments based on quantitative risk assessments by allowing to compare the effect that converter topologies and microgrid architecture choices have on availability. Conventional architectures with single-input converters and alternative configurations with multiple-input converters (MICs) are considered. Calculations yield that all microgrid configurations except those utilizing center converters achieve similar availability of 6-nines. Three converter topologies are used as representatives of many other circuits. These three benchmark circuits are the boost, the isolated SEPIC (ISEPIC), and the current-source half-bridge. Marginal availability differences are observed for different circuit topology choices, although architectures with MICs are more sensitive to this choice. MICs and, in particular, the ISEPIC, are identified as good compromise options for dc microgrids source interfaces. The analysis also models availability influence of local energy storage, both in batteries and generators' fuel. These models provide a quantitative way of comparing dc microgrids with conventional backup energy systems. Calculations based on widely accepted data in industry supports the analysis.

Question: What is the minimum cut set?


Injury Risk Quantification for Industrial Robots in Collaborative Operation with Humans. While the topic of human-safe robots has an appreciable academic history, only today are various solutions to this challenge becoming economically relevant. We are witnessing a maturing of technology and market expectations, paired with initiatives in the area of standardization. One of the remaining hurdles to routine deployment of robots for collaborative operation with humans is obtaining a well-founded understanding of low-level injuries of the type that such robots can potentially inflict. For the assessment of collaborative robot designs and systems, the low-level injury potential must be unambiguously quantified. A classification scheme for holding such information is presented and an outlook on its use is given.

Question: What is the low-level injury potential of industrial robots?


Stock-out Prediction in Multi-echelon Networks. In multi-echelon inventory systems the performance of a given node is affected by events that occur at many other nodes and in many other time periods. For example, a supply disruption upstream will have an effect on downstream, customer-facing nodes several periods later as the disruption "cascades" through the system. There is very little research on stock-out prediction in single-echelon systems and (to the best of our knowledge) none on multi-echelon systems. However, in real the world, it is clear that there is significant interest in techniques for this sort of stock-out prediction. Therefore, our research aims to fill this gap by using deep neural networks (DNN) to predict stock-outs in multi-echelon supply chains.

Question: What is stock-out prediction in multi-echelon networks?


Aging changes of the midfacial fat compartments: a computed tomographic study.. BACKGROUND The restoration of a natural volume distribution is a major goal in facial rejuvenation. The aims of this study were to establish a radiographic method enabling effective measurements of the midfacial fat compartments and to compare the anatomy between human cadavers of younger versus older age.   METHODS Data from computed tomographic scans of 12 nonfixed cadaver heads, divided into two age groups (group 1, 54 to 75 years, n = 6; and group 2, 75 to 104 years, n = 6), were analyzed. For evaluation of the volume distribution within a specific compartment, the sagittal diameter of the upper, middle, and lower thirds of each compartment was determined. For evaluation of a "sagging" of the compartments, the distance between the cephalad border and the infraorbital rim was determined.   RESULTS Computed tomography enables a reproducible depiction of the facial fat compartments and reveals aging changes. The distance between the fat compartments and the infraorbital rim was higher in group 2 compared with group 1. The sagittal diameter of the lower third of the compartments was higher, and the sagittal diameter of the upper third was smaller in group 2 compared with group 1. The buccal extension of the buccal fat pad was shown to be an independent, separate compartment.   CONCLUSIONS This study demonstrates an inferior migration of the midfacial fat compartments and an inferior volume shift within the compartments during aging. Additional distinct compartment-specific changes (e.g., volume loss of the deep medial cheek fat and buccal extension of the buccal fat pad) contribute to the appearance of the aged face.

Question: How does aging affect the midfacial fat compartments?


Directed Multi-Objective Optimization. While evolutionary computing inspired approaches to multi-objective optimization have many advantages over conventional approaches; they generally do not explicitly exploit directional/gradient information. This can be inefficient if the underlying objectives are reasonably smooth, and this may limit the application of such approaches to real-world problems. This paper develops a local framework for such problems by geometrically analyzing the multiobjective concepts of descent, diversity and convergence/optimality. It is shown that locally optimal, multi-objective descent direction can be calculated that maximally reduce all the objectives and a local sub-space also exists that is a basis for diversity updates. Local convergence of a point towards the optimal Pareto set is therefore assured. The concept of a population of points is also considered and it is shown that it can be used to locally increase the diversity of the population while still ensuring convergence and a method to extract the local directional information from the population is also described. The paper describes and introduces the basic theoretical concepts as well as demonstrating how they are used on simple test problems.

Question: What is a multi-objective optimization problem?


An Efficient Algorithm for Calculating the Exact Hausdorff Distance. The Hausdorff distance (HD) between two point sets is a commonly used dissimilarity measure for comparing point sets and image segmentations. Especially when very large point sets are compared using the HD, for example when evaluating magnetic resonance volume segmentations, or when the underlying applications are based on time critical tasks, like motion detection, then the computational complexity of HD algorithms becomes an important issue. In this paper we propose a novel efficient algorithm for computing the exact Hausdorff distance. In a runtime analysis, the proposed algorithm is demonstrated to have nearly-linear complexity. Furthermore, it has efficient performance for large point set sizes as well as for large grid size; performs equally for sparse and dense point sets; and finally it is general without restrictions on the characteristics of the point set. The proposed algorithm is tested against the HD algorithm of the widely used national library of medicine insight segmentation and registration toolkit (ITK) using magnetic resonance volumes with extremely large size. The proposed algorithm outperforms the ITK HD algorithm both in speed and memory required. In an experiment using trajectories from a road network, the proposed algorithm significantly outperforms an HD algorithm based on R-Trees.

Question: What is the Hausdorff distance?


Sentiment-enhanced multidimensional analysis of online social networks: Perception of the mediterranean refugees crisis. We propose an analytical framework able to investigate discussions about polarized topics in online social networks from many different angles. The framework supports the analysis of social networks along several dimensions: time, space and sentiment. We show that the proposed analytical framework and the methodology can be used to mine knowledge about the perception of complex social phenomena. We selected the refugee crisis discussions over Twitter as a case study. This difficult and controversial topic is an increasingly important issue for the EU. The raw stream of tweets is enriched with space information (user and mentioned locations), and sentiment (positive vs. negative) w.r.t. refugees. Our study shows differences in positive and negative sentiment in EU countries, in particular in UK, and by matching events, locations and perception, it underlines opinion dynamics and common prejudices regarding the refugees.

Question: How to mine knowledge about the perception of complex social phenomena?


Comparative Analysis of 6 Bit Thermometer-to-Binary Decoders for Flash Analog-to-Digital Converter. In the design of high speed Flash ADC selection of Thermometer to Binary decoder plays an important role. This paper describes different decoder topologies suitable for Flash ADCs. Comparative analysis between them is presented in terms of hardware required, propagation delay & power consumption. Result shows that fat tree & Mux based topologies are suitable for high speed conversions, but Mux based topology is effective in terms of power consumption. Presence of bubble error reduces output correction capability. In this paper advance scheme is proposed for correction of bubble error in Mux based decoder.

Question: What is a flash analog to digital converter?


Unsupervised feature learning for audio classification using convolutional deep belief networks. In recent years, deep learning approaches have gained significant interest as a way of building hierarchical representations from unlabeled data. However, to our knowledge, these deep learning approaches have not been extensively studied for auditory data. In this paper, we apply convolutional deep belief networks to audio data and empirically evaluate them on various audio classification tasks. In the case of speech data, we show that the learned features correspond to phones/phonemes. In addition, our feature representations learned from unlabeled audio data show very good performance for multiple audio classification tasks. We hope that this paper will inspire more research on deep learning approaches applied to a wide range of audio recognition tasks.

Question: What is the canadian military?


Radar Communication for Combating Mutual Interference of FMCW Radars. Commercial automotive radars used today are based on frequency modulated continuous wave signals due to the simple and robust detection method and good accuracy. However, the increase in both the number of radars deployed per vehicle and the number of such vehicles leads to mutual interference, cutting short future plans for autonomous driving and active safety functionality. We propose and analyze a radar communication (RadCom) approach to reduce this mutual interference while simultaneously offering communication functionality. We achieve this by frequency division multiplexing radar and communication, where communication is built on a decentralized carrier sense multiple access protocol and is used to adjust the timing of radar transmissions. Our simulation results indicate that radar interference can be significantly reduced, at no cost in radar accuracy.

Question: How does radar communication work?


Umbrella wheel — A stair-climbing and obstacle-handling wheel design concept. This paper proposes a new design for stair-climbing using a wheel that can split into segments and walk up stairs or surmount other obstacles often found where humans traverse, while still being able to retain a perfectly round shape for traveling on smooth ground. Using this change of configuration, staircases with a wide range of dimensions can be covered efficiently and safely. The design, named Umbrella Wheel, can consist of as many wheel segments as desired, and as few as two. A smaller or higher number of wheel segments has advantages and disadvantages depending on the specific situation. Modeling the trajectory of the wheel when as it ascends or descends stairs is given and the results are analyzed.

Question: What is the name of this wheel?


A layered naming architecture for the internet. Currently the Internet has only one level of name resolution, DNS, which converts user-level domain names into IP addresses. In this paper we borrow liberally from the literature to argue that there should be three levels of name resolution: from user-level descriptors to service identifiers; from service identifiers to endpoint identifiers; and from endpoint identifiers to IP addresses. These additional levels of naming and resolution (1) allow services and data to be first class Internet objects (in that they can be directly and persistently named), (2) seamlessly accommodate mobility and multi-homing and (3) integrate middleboxes (such as NATs and firewalls) into the Internet architecture. We further argue that flat names are a natural choice for the service and endpoint identifiers. Hence, this architecture requires scalable resolution of flat names, a capability that distributed hash tables (DHTs) can provide.

Question: What is a distributed hash table?


Proximal Tibiofibular Joint Instability and Treatment Approaches: A Systematic Review of the Literature.. PURPOSE To evaluate the treatment options, outcomes, and complications associated with proximal tibiofibular joint (PTFJ) instability, which will aim to improve surgical treatment of PTFJ instability and aid surgeons in their decision making and treatment selection.   METHODS A systematic review was performed according to Preferred Reporting Items for Systematic Review and Meta-Analysis guidelines. Inclusion criteria were as follows: PTFJ instability treatment techniques, PTFJ surgical outcomes, English language, and human studies. Exclusion criteria were cadaveric studies, animal studies, basic science articles, editorial articles, review articles, and surveys. Furthermore, we excluded studies that did not report patient follow-up time and studies without any patient-reported, clinical or radiographic outcomes at the final follow-up.   RESULTS The systematic review identified 44 studies (96 patients) after inclusion and exclusion criteria application. For the treatment of PTFJ instability, there were 18 studies (35 patients) describing nonoperative management, 3 studies (4 patients) reported on open reduction, 11 studies (25 patients) reported on fixation, 4 studies (10 patients) that described proximal fibula resection, 3 studies (11 patients) reported on adjustable cortical button repair, 2 studies (3 patients) reported on ligament reconstructions, and 5 (8 patients) studies reported on biceps femoris tendon rerouting. The most (77% to 90%) PTFJ dislocations and instability were anterolateral/unspecified anterior dislocation or instability. Improved outcomes after all forms of PTFJ instability treatment were reported; however, high complication rates were associated with both PTFJ fixation (28%) and fibular head resection (20%).   CONCLUSIONS Improved outcomes can be expected after surgical treatment of PTFJ instability. Proximal tibiofibular ligament reconst

Question: What is the most common type of ankle injury?


Mechanical recycling of waste electric and electronic equipment: a review.. The production of electric and electronic equipment (EEE) is one of the fastest growing areas. This development has resulted in an increase of waste electric and electronic equipment (WEEE). In view of the environmental problems involved in the management of WEEE, many counties and organizations have drafted national legislation to improve the reuse, recycling and other forms of recovery of such wastes so as to reduce disposal. Recycling of WEEE is an important subject not only from the point of waste treatment but also from the recovery of valuable materials.WEEE is diverse and complex, in terms of materials and components makeup as well as the original equipment's manufacturing processes. Characterization of this waste stream is of paramount importance for developing a cost-effective and environmentally friendly recycling system. In this paper, the physical and particle properties of WEEE are presented. Selective disassembly, targeting on singling out hazardous and/or valuable components, is an indispensable process in the practice of recycling of WEEE. Disassembly process planning and innovation of disassembly facilities are most active research areas. Mechanical/physical processing, based on the characterization of WEEE, provides an alternative means of recovering valuable materials. Mechanical processes, such as screening, shape separation, magnetic separation, Eddy current separation, electrostatic separation, and jigging have been widely utilized in recycling industry. However, recycling of WEEE is only beginning. For maximum separation of materials, WEEE should be shredded to small, even fine particles, generally below 5 or 10mm. Therefore, a discussion of mechanical separation processes for fine particles is highlighted in this paper. Consumer electronic equipment (brown goods), such as television sets, video recorders, are most common. It is very costly to perform manual dismantlin

Question: What is the difference between a shredder and a grinder?


Influence of PEEK Coating on Hip Implant Stress Shielding: A Finite Element Analysis. Stress shielding is a well-known failure factor in hip implants. This work proposes a design concept for hip implants, using a combination of metallic stem with a polymer coating (polyether ether ketone (PEEK)). The proposed design concept is simulated using titanium alloy stems and PEEK coatings with thicknesses varying from 100 to 400 μm. The Finite Element analysis of the cancellous bone surrounding the implant shows promising results. The effective von Mises stress increases between 81 and 92% for the complete volume of cancellous bone. When focusing on the proximal zone of the implant, the increased stress transmission to the cancellous bone reaches between 47 and 60%. This increment in load transferred to the bone can influence mineral bone loss due to stress shielding, minimizing such effect, and thus prolonging implant lifespan.

Question: What is the influence of PEEK coating on hip implant stress shielding?


Compact Broadband Microstrip Crossover With Isolation Improvement and Phase Compensation. In this letter, we present a broadband microstrip crossover structure in two different configurations. Both structures have compact size, i.e., less than 10 mm × 10 mm. Across the measured band from dc to 10 GHz, the structure 1 has less than 1 dB insertion loss, more than 20 dB return loss and at least 23.3 dB isolation. Within the same band, the structure 2 has less than 1 dB insertion loss, more than 22 dB return loss and at least 18.9 dB isolation. Phase difference between two paths in both structures is kept in less than 5 °.

Question: What is a broadband microstrip crossover?


Investigating learner attitudes toward e-books as learning tools: based on the activity theory approach. Taylor & Francis makes every effort to ensure the accuracy of all the information (the "Content") contained in the publications on our platform. However, Taylor & Francis, our agents, and our licensors make no representations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of the Content. Any opinions and views expressed in this publication are the opinions and views of the authors, and are not the views of or endorsed by Taylor & Francis. The accuracy of the Content should not be relied upon and should be independently verified with primary sources of information. Taylor and Francis shall not be liable for any losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoever or howsoever caused arising directly or indirectly in connection with, in relation to or arising out of the use of the Content.

Question: What is the difference between a book and an ebook?


Exploring the benefits of the combination of a software architecture analysis and a usability evaluation of a mobile application. Designing easy to use mobile applications is a difficult task. In order to optimize the development of a usable mobile application, it is necessary to consider the mobile usage context for the design and the evaluation of the user–system interaction of a mobile application. In our research we designed a method that aligns the inspection method "Software ArchitecTure analysis of Usability Requirements realizatioN" SATURN and a mobile usability evaluation in the form of a user test. We propose to use mobile context factors and thus requirements as a common basis for both inspection and user test. After conducting both analysis and user test, the results described as usability problems are mapped and discussed. The mobile context factors identified define and describe the usage context of a mobile application. We oftware architecture analysis

Question: How to design a mobile application?


Hybrid Particle Swarm Optimiser with breeding and subpopulations. In this paper we present two hybrid Particle Swarm Optimisers combining the idea of the particle swarm with concepts from Evolutionary Algorithms. The hybrid PSOs combine the traditional velocity and position update rules with the ideas of breeding and subpopulations. Both hybrid models were tested and compared with the standard PSO and standard GA models. This is done to illustrate that PSOs with breeding strategies have the potential to achieve faster convergence and the potential to find a better solution. The objective of this paper is to describe how to make the hybrids benefit from genetic methods and to test their potential and competetiveness on function optimisation.

Question: What is the potential of hybrid particle swarm optimisers?


Duplicate Record Detection: A Survey. Often, in the real world, entities have two or more representations in databases. Duplicate records do not share a common key and/or they contain errors that make duplicate matching a difficult task. Errors are introduced as the result of transcription errors, incomplete information, lack of standard formats, or any combination of these factors. In this paper, we present a thorough analysis of the literature on duplicate record detection. We cover similarity metrics that are commonly used to detect similar field entries, and we present an extensive set of duplicate detection algorithms that can detect approximately duplicate records in a database. We also cover multiple techniques for improving the efficiency and scalability of approximate duplicate detection algorithms. We conclude with coverage of existing tools and with a brief discussion of the big open problems in the area

Question: What is the size of the canadian military?


Fan-Beam Millimeter-Wave Antenna Design Based on the Cylindrical Luneberg Lens. A new design of two-dimensional cylindrical Luneberg lens is introduced based on TE10 mode propagation between parallel plates, with special focus on ease of manufacturing. The parallel plates are partially filled with low cost polymer material (Rexolite epsivr = 2.54) to match Luneberg's law. A planar linear tapered slot antenna (LTSA) is inserted into the air region between the parallel plates at the edge of the Luneberg lens as a feed antenna, with fine positioning to the focal point of the Luneberg lens to optimize the antenna system performance. A combined ray-optics/diffraction method is used to obtain the radiation pattern of the system and results are compared with predictions of a time domain numerical solver. Measurements done on a 10-cm Luneberg lens designed for operation at 30 GHz agree very well with predictions. For this prototype, 3-dB E- and if-plane beamwidths of 6.6deg and 54deg respectively were obtained, and the sidelobe level in the E-plane was -17.7-dB. Although the parallel plate configuration should lead to a narrow band design due to the dispersion characteristics of the TE10 mode, the measurement results demonstrate broadband characteristics with radiation efficiencies varying between 43% and 72% over the tested frequency band of 26.5-37 GHz. The designed cylindrical Luneberg lens can be used to launch multiple beams by implementing an arc array of planar LTSA elements at the periphery of the lens, and can be easily extended to higher mm-wave frequencies.

Question: What is the difference between a cylindrical lens and a parabolic lens?


Metamaterial advances for radar and communications. Metamaterial antennas have progressed considerably in the last few years. Kymeta expects to have its Ku-band metamaterial antenna commercially available in 2017. It is intended for use on vessels anywhere in the world for high speed internet using satellites. Echodyne and PARC, a Xerox Co., have developed a metamaterial array for radar and cell phone usage. The Army Research Laboratory in Adelphi MD has funded the development of a metamaterial 250–505 MHZ antenna with a λ/20 thickness. Complementing this a conventional tightly coupled dipole antenna (TCDA) has been developed which provides a 20∶1 bandwidth with a λ/40 thickness. Target cloaking has been demonstrated at microwaves using metamaterials. With cloaking the electromagnetic wave signal transmitted by a radar goes around the target making it invisible. It has also been demonstrated at L-band with a 50% bandwidth using fractals. Stealthing by absorption using a thin flexible and stretchable metamaterials sheet has been demonstrated to give 6 dB absorption over the band from 8–10 GHz, larger absorption over a narrower band. Using fractals material < 1 mm thick simulation gave a 10 dB reduction in backscatter from 2 to 20 GHz, ∼20 dB reduction from 9 to 15 GHz. Good results were obtained for large incidence angles and both polarizations. Using metamaterial one can focus 6× beyond diffraction limit at 0.38 µm (Moore's Law marches on); 40× diffraction limit, λ/80, at 375 MHz. Has been used in cell phones to provide antennas 5× smaller (1/10th λ) having 700 MHz–2.7 GHz bandwidth. Provided isolation between antennas having 2.5 cm separation equivalent to lm separation. Used for phased array wide angle impedance matching (WAIM). It has been found that n-doped graphene has a negative index of refraction.

Question: What is the difference between a metamaterial and a metamaterial antenna?


LOCOMO satcom terminal: A switchable RHCP/LHCP Array Antenna for on-the-move applications in Ka-band. This paper describes the main features of a multilayer antenna panel for a low cost Ka-band Array Antenna for on-the-move applications. The LOCOMO satcom terminal is based on a dual-polarized low-profile antenna with trasmit/receive and RHCP/LHCP switching capability.

Question: What is a low-cost Ka-band Array Antenna for on-the-move applications?


A Theoretical Framework for Serious Game Design: Exploring Pedagogy, Play and Fidelity and their Implications for the Design Process. It is widely acknowledged that digital games can provide an engaging, motivating and "fun" experience for students. However an entertaining game does not necessarily constitute a meaningful, valuable learning experience. For this reason, experts espouse the importance of underpinning serious games with a sound theoretical framework which integrates and balances theories from two fields of practice: pedagogy and game design (Kiili, 2005; Seeney & Routledge, 2009). Additionally, with the advent of sophisticated, immersive technologies, and increasing interest in the opportunities for constructivist learning offered by these technologies, concepts of fidelity and its impact on student learning and engagement, have emerged (Aldrich, 2005; Harteveld et al., 2007, 2010). This paper will explore a triadic theoretical framework for serious game design comprising play, pedagogy and fidelity. It will outline underpinning theories, review key literatures and identify challenges and issues involved in balancing these elements in the process of serious game design. DOI: 10.4018/ijgbl.2012100103 42 International Journal of Game-Based Learning, 2(4), 41-60, October-December 2012 Copyright © 2012, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. game design (Kiili, 2005; Seeney & Routledge, 2009). While a sound pedagogical framework is considered essential to their effectiveness as learning tools, equally important is the integration of game play elements which harness and sustain player engagement. Additionally, with the advent of sophisticated and immersive technologies, as exemplified in the virtual worlds of contemporary games, and increasing interest in the opportunities for constructivist learning offered by these worlds, concepts of fidelity, and its impact on student lear

Question: What is a theoretical framework for serious game design?


Detecting Text in the Wild with Deep Character Embedding Network. Most text detection methods hypothesize texts are horizontal or multi-oriented and thus define quadrangles as the basic detection unit. However, text in the wild is usually perspectively distorted or curved, which can not be easily tackled by existing approaches. In this paper, we propose a deep character embedding network (CENet) which simultaneously predicts the bounding boxes of characters and their embedding vectors, thus making text detection a simple clustering task in the character embedding space. The proposed method does not require strong assumptions of forming a straight line on general text detection, which provides flexibility on arbitrarily curved or perspectively distorted text. For character detection task, a dense prediction subnetwork is designed to obtain the confidence score and bounding boxes of characters. For character embedding task, a subnet is trained with contrastive loss to project detected characters into embedding space. The two tasks share a backbone CNN from which the multi-scale feature maps are extracted. The final text regions can be easily achieved by a thresholding process on character confidence and embedding distance of character pairs. We evaluated our method on ICDAR13, ICDAR15, MSRA-TD500, and Total Text. The proposed method achieves state-of-the-art or comparable performance on all of the datasets, and shows a substantial improvement in the irregular-text datasets, i.e. Total-Text.

Question: How does the proposed method work?


Data-driven segmentation and labeling of freehand sketches. We present a data-driven approach to derive part-level segmentation and labeling of free-hand sketches, which depict single objects with multiple parts. Our method performs segmentation and labeling simultaneously, by inferring a structure that best fits the input sketch, through selecting and connecting 3D components in the database. The problem is formulated using Mixed Integer Programming, which optimizes over both the local fitness of the selected components and the global plausibility of the connected structure. Evaluations show that our algorithm is significantly better than the straightforward approaches based on direct retrieval or part assembly, and can effectively handle challenging variations in the sketch.

Question: How to segment freehand sketches?


An efficient centroid type-reduction strategy for general type-2 fuzzy logic system.

Question: What is a type-2 fuzzy logic system?


Latent Fingerprint Matching: Fusion of Manually Marked and Derived Minutiae. Matching unknown latent fingerprints lifted from crime scenes to full (rolled or plain) fingerprints in law enforcement databases is of critical importance for combating crime and fighting terrorism. Compared to good quality full fingerprints acquired using live-scan or inking methods during enrollment, latent fingerprints are often smudgy and blurred, capture only a small finger area, and have large nonlinear distortion. For this reason, features (minutiae and singular points) in latents are typically manually marked by trained latent examiners. However, this introduces an undesired interoperability problem between latent examiners and automatic fingerprint identification systems (AFIS); the features marked by examiners are not always compatible with those automatically extracted by AFIS, resulting in reduced matching accuracy. While the use of automatically extracted minutiae from latents can avoid interoperability problem, such minutiae tend to be very unreliable, because of the poor quality of latents. In this paper, we improve latent to full fingerprint matching accuracy by combining manually marked (ground truth) minutiae with automatically extracted minutiae. Experimental results on a public domain database, NIST SD27, demonstrate the effectiveness of the proposed algorithm.

Question: How to improve the matching accuracy of latent fingerprints?


Advice weaving in AspectJ. This paper describes the implementation of advice weaving in AspectJ. The AspectJ language picks out dynamic join points in a program's execution with pointcuts and uses advice to change the behavior at those join points. The core task of AspectJ's advice weaver is to statically transform a program so that at runtime it will behave according to the AspeetJ language semantics. This paper describes the 1.1 implementation which is based on transforming bytecode. We describe how AspectJ's join points are mapped to regions of bytecode, how these regions are efficiently matched by AspectJ's pointcuts, and how pieces of advice are efficiently implemented at these regions. We also discuss both run-time and compile-time performance of this implementation.

Question: What is advice weaving?


ICTs for Improving Patients Rehabilitation Research Techniques. The world population is rapidly aging and becoming a burden to health systems around the world. In this work we present a conceptual framework to encourage the research community to develop more comprehensive and adaptive ICT solutions for prevention and rehabilitation of chronic conditions in the daily life of the aging population and beyond health facilities. We first present an overview of current international standards in human functioning and disability, and how chronic conditions are interconnected in older age. We then describe innovative mobile and sensor technologies, predictive data analysis in healthcare, and game-based prevention and rehabilitation techniques. We then set forth a multidisciplinary approach for the personalized prevention and rehabilitation of chronic conditions using unobtrusive and pervasive sensors, interactive activities, and predictive analytics, which also eases the tasks of health-related researchers, caregivers and providers. Our proposal represents a conceptual basis for future research, in which much remains to be done in terms of standardization of technologies and health terminology, as well as data protection and privacy legislation.

Question: What is the world population?


A Low-Cost Gate Driver Design Using Bootstrap Capacitors for Multilevel MOSFET Inverters. Multilevel inverters require a large number of power semiconductors. The gate driver for each power semiconductor requires its own floating or isolated DC voltage source. Traditional methods on meeting the requirement are expensive and bulky. This paper presents a gate driver design for floating voltage source type multilevel inverters. Bootstrap capacitors are used to form the floating voltage sources in the design, which allows a single DC power supply to be used by all the gate drivers. Specially configured diode plus the proper charging cycles maintain adequate capacitor voltage levels. Such a simple and low cost solution allows user to utilize the easy accessible single-channel gate drivers for multilevel inverter applications without the extra cost on the bulky isolated DC power supplies for each gate driver. A prototype 3-cell 8-level floating voltage source inverter using the method illustrates the technique

Question: How to design a low cost gate driver for a multilevel inverter?


MPNET: An End-to-End Deep Neural Network for Object Detection in Surveillance Video. Object detection is one of the most important topics in computer vision task and has obtained impressive performance thanks to the use of deep convolutional neural network. For object detection, especially in still image, it has achieved excellent performance during past two years, such as the series of R-CNN which plays a vital role in improving performance. However, with the number of surveillance videos increasing, the current methods may not meet the growing demand. In this paper, we propose a new framework named moving-object proposals generation and prediction framework (MPGP) to reduce the searching space and generate some accurate proposals which can reduce computational cost. In addition, we explore the relation of moving regions in feature map of different layers and predict candidates according to the results of previous frames. Last but not least, we utilize spatial-temporal information to strengthen the detection score and further adjust the location of the bounding boxes. Our MPGP framework can be applied to different region-based networks. Experiments on CUHK data set, XJTU data set, and AVSS data set, show that our approach outperforms the state-of-the-art approaches.

Question: What is the relation of moving regions in feature map of different layers?


An unusual case of predation: dog pack or cougar attack?. Injuries produced by animals are capable of leaving severe patterns and in some cases may result in the death of the attacked individual. Law enforcement authorities may come to erroneous conclusions about the source of the bites based on their awareness of animals present and similarities of the injuries to the untrained eye, with dreadful consequences. Expertise of a carnivore biologist and an odontologist that indentifies the particularities of bite marks may be useful for identifying the attacking species. We present the investigation of a fatal dog pack attack involving a 43-year-old man in Bell Ville (Argentina) where the evidence provided by a forensic dentist and a biologist was categorical for establishing the animal species involved. Because of the unusual characteristics of the wounds and the initial hypothesis made by local authorities of a cougar attack, habits and specific patterns of both dog pack and cougar predation on humans are discussed.

Question: What is the difference between a dog pack and a cougar attack?


Robust Cross-lingual Hypernymy Detection using Dependency Context. Cross-lingual Hypernymy Detection involves determining if a word in one language ("fruit") is a hypernym of a word in another language ("pomme" i.e. apple in French). The ability to detect hypernymy cross-lingually can aid in solving cross-lingual versions of tasks such as textual entailment and event coreference. We propose BISPARSE-DEP, a family of unsupervised approaches for cross-lingual hypernymy detection, which learns sparse, bilingual word embeddings based on dependency contexts. We show that BISPARSE-DEP can significantly improve performance on this task, compared to approaches based only on lexical context. Our approach is also robust, showing promise for low-resource settings: our dependency-based embeddings can be learned using a parser trained on related languages, with negligible loss in performance. We also crowd-source a challenging dataset for this task on four languages – Russian, French, Arabic, and Chinese. Our embeddings and datasets are publicly available.1

Question: What is the name of the dataset?


Internet Scale User-Generated Live Video Streaming: The Twitch Case. Twitch is a live video streaming platform used for broadcasting video gameplay, ranging from amateur players to eSports tournaments. This platform has gathered a substantial world wide community, reaching more than 1.7 million broadcasters and 100 million visitors every month. Twitch is fundamentally different from "static" content distribution platforms such as YouTube and Netflix, as streams are generated and consumed in real time. In this paper, we explore the Twitch infrastructure to understand how it manages live streaming delivery to an Internet-wide audience. We found Twitch manages a geo-distributed infrastructure, with presence in four continents. Our findings show that Twitch dynamically allocates servers to channels depending on their popularity. Additionally, we explore the redirection strategy of clients to servers depending on their region and the specific channel.

Question: How does twitch manage live streaming delivery to an internet-wide audience?


Learning Inference Models for Computer Vision. Computer vision can be understood as the ability to perform inference on image data. Breakthroughs in computer vision technology are often marked by advances in inference techniques, as even the model design is often dictated by the complexity of inference in them. This thesis proposes learning based inference schemes and demonstrates applications in computer vision. We propose techniques for inference in both generative and discriminative computer vision models. Despite their intuitive appeal, the use of generative models in vision is hampered by the difficulty of posterior inference, which is often too complex or too slow to be practical. We propose techniques for improving inference in two widely used techniques: Markov Chain Monte Carlo (MCMC) sampling and message-passing inference. Our inference strategy is to learn separate discriminative models that assist Bayesian inference in a generative model. Experiments on a range of generative vision models show that the proposed techniques accelerate the inference process and/or converge to better solutions. A main complication in the design of discriminative models is the inclusion of prior knowledge in a principled way. For better inference in discriminative models, we propose techniques that modify the original model itself, as inference is simple evaluation of the model. We concentrate on convolutional neural network (CNN) models and propose a generalization of standard spatial convolutions, which are the basic building blocks of CNN architectures, to bilateral convolutions. First, we generalize the existing use of bilateral filters and then propose new neural network architectures with learnable bilateral filters, which we call 'Bilateral Neural Networks'. We show how the bilateral filtering modules can be used for modifying existing CNN architectures for better image segmentation and propose a neural network approach for temporal information propagation in videos. E

Question: What is the difference between generative and discriminative models?


Iron loss analysis of interior permanent-magnet synchronous motors-variation of main loss factors due to driving condition. In this paper, the authors investigate the iron loss of interior permanent magnet motors driven by pulsewidth modulation (PWM) inverters from both results of the experiments and the finite-element analysis. In the analysis, the iron loss of the motor is decomposed into several components due to their origins, for instance, the fundamental field, carrier of the PWM inverter, slot ripples, and harmonic magnetomotive forces of the permanent magnet in order to clarify the main loss factors. The Fourier transformation and the finite-element method considering the carrier harmonics are applied to this calculation. The calculated iron loss is compared with the measurement at each driving condition. The measured and the calculated results agree well. It is clarified that the iron loss caused by the carrier of the PWM inverter is the largest component at low-speed condition under the maximum torque control, whereas the loss caused by the harmonic magnetomotive forces of the permanent magnet remarkably increase at high-speed condition under the flux-weakening control

Question: What is the iron loss of the motor?


Forward-Private Dynamic Searchable Symmetric Encryption with Efficient Search. Dynamic Searchable Symmetric Encryption (DSSE) allows to delegate keyword search and file update over an encrypted database via encrypted indexes, and therefore provides opportunities to mitigate the data privacy and utilization dilemma in cloud storage platforms. Despite its merits, recent works have shown that efficient DSSE schemes are vulnerable to statistical attacks due to the lack of forward-privacy, whereas forward-private DSSE schemes suffers from practicality concerns as a result of their extreme computation overhead. Due to significant practical impacts of statistical attacks, there is a critical need for new DSSE schemes that can achieve the forward-privacy in a more practical and efficient manner. We propose a new DSSE scheme that we refer to as Forward-private Sublinear DSSE (FS-DSSE). FS-DSSE harnesses special secure update strategies and a novel caching strategy to reduce the computation cost of repeated queries. Therefore, it achieves forward-privacy, sublinear search complexity, low end-to-end delay, and parallelization capability simultaneously. We fully implemented our proposed method and evaluated its performance on a real cloud platform. Our experimental evaluation results showed that the proposed scheme is highly secure and highly efficient compared with state-of-the-art DSSE techniques. Specifically, FS-DSSE is up to three magnitude of times faster than forward-secure DSSE counterparts, depending on the frequency of the searched keyword in the database.

Question: What is the difference between a symmetric encryption and a public key encryption?


Recent Advances in Augmented Reality. The field of Augmented Reality (AR) has existed for just over one decade, but the growth and progress in the past few years has been remarkable. In 1997, the first author published a survey [3] (based on a 1995 SIGGRAPH course lecture) that defined the field, described many problems, and summarized the developments up to that point. Since then, the field has grown rapidly. In the late 1990's, several conferences specializing in this area were started, including the International Workshop and Symposium on Augmented Reality [29], the International Symposium on Mixed Reality [30], and the Designing Augmented Reality Environments workshop. Some wellfunded interdisciplinary consortia were formed that focused on AR, notably the Mixed Reality Systems Laboratory [50] in Japan and Project ARVIKA [61] in Germany. A freely-available software toolkit (the ARToolkit) for rapidly building AR applications is now available [2]. Because of this wealth of new developments, an updated survey is needed to guide and encourage further research in this exciting area.

Question: What is the difference between augmented reality and virtual reality?


Object modelling by registration of multiple range images. We study the problem of creating a complete model of a physical object. Although this may be possible using intensity images, we use here range images which directly provide access t o three dimensional information. T h e first problem that we need t o solve is t o find the transformation between the different views. Previous approaches have either assumed this transformation t o be known (which is extremely difficult for a complete model), or computed it with feature matching (which is not accurate enough for integration). In this paper, we propose a new approach which works on range d a t a directly, and registers successive views with enough overlapping area t o get an accurate transformation between views. This is performed by minimizing afunctional which does not require point t o point matches. We give the details of the registration method and modeling procedure, and illustrate them on real range images of complex objects. 1 Introduction Creating models of physical objects is a necessary component machine of biological vision modules. Such models can then be used in object recognition, pose estimation or inspection tasks. If the object of interest has been precisely designed, then such a model exists in the form of a CAD model. In many applications, however, it is either not possible or not practical to have access to such CAD models, and we need to build models from the physical object. Some researchers bypass the problem by using a model which consist,s of multiple views ([4], [a]), but t,liis is not, always enough. If one needs a complete model of an object, the following steps are necessary: 1. data acquisition, 2. registration between views, 3. integration of views. By view we mean the 3D surface information of the object from specific point of view. While the integration process is very dependent on the representation scheme used, the precondition for performing integration consists of knowing t

Question: How to build a complete model of a physical object?


Holonomic Control of a robot with an omni-directional drive .. This paper shows how to control a robot with omnidirectional wheels, using as example robots with four motors, and generalizing to n motors. More than three wheels provide redundancy: many combinations of motors speeds can provide the same Euclidean movement. Since the system is over-determined, we show how to compute a set of consistent and optimal motor forces and speeds using the pseudoinverse of coupling matrices. This approach allows us also to perform a consistency check to determine whether a wheel is slipping on the floor or not. We show that it is possible to avoid wheel slippage by driving the robot with a motor torque under a certain threshold or handle it and make high accelerations possible.

Question: How to control a robot with omnidirectional wheels?


Information & Environment: IoT-Powered Recommender Systems. Internet of Things (IoT) infrastructure within the physical library environment is the basis for an integrative, hybrid approach to digital resource recommenders. The IoT infrastructure provides mobile, dynamic wayfinding support for items in the collection, which includes features for location-based recommendations. The evaluation and analysis herein clarified the nature of users' requests for recommendations based on their location, and describes subject areas of the library for which users request recommendations. The results indicated that users of IoTbased recommendation are interested in a broad distribution of subjects, with a short-head distribution from this collection in American and English Literature. A long-tail finding showed a diversity of topics that are recommended to users in the library book stacks with IoT-powered recommendations.

Question: What is the nature of users' requests for recommendations based on their location?


Who are the crowdworkers?: shifting demographics in mechanical turk. Amazon Mechanical Turk (MTurk) is a crowdsourcing system in which tasks are distributed to a population of thousands of anonymous workers for completion. This system is increasingly popular with researchers and developers. Here we extend previous studies of the demographics and usage behaviors of MTurk workers. We describe how the worker population has changed over time, shifting from a primarily moderate-income, U.S.-based workforce towards an increasingly international group with a significant population of young, well-educated Indian workers. This change in population points to how workers may treat Turking as a full-time job, which they rely on to make ends meet.

Question: Who are the crowdworkers?


Feature-Enhanced Probabilistic Models for Diffusion Network Inference. Cascading processes, such as disease contagion, viral marketing, and information diffusion, are a pervasive phenomenon in many types of networks. The problem of devising intervention strategies to facilitate or inhibit such processes has recently received considerable attention. However, a major challenge is that the underlying network is often unknown. In this paper, we revisit the problem of inferring latent network structure given observations from a diffusion process, such as the spread of trending topics in social media. We define a family of novel probabilistic models that can explain recurrent cascading behavior, and take into account not only the time differences between events but also a richer set of additional features. We show that MAP inference is tractable and can therefore scale to very large real-world networks. Further, we demonstrate the effectiveness of our approach by inferring the underlying network structure of a subset of the popular Twitter following network by analyzing the topics of a large number of messages posted by users over a 10-month period. Experimental results show that our models accurately recover the links of the Twitter network, and significantly improve the performance over previous models based entirely on time.

Question: What is the difference between a probabilistic model and a generative model?


A round- and computation-efficient three-party authenticated key exchange protocol. In three-party authenticated key exchange protocols, each client shares a secret only with a trusted server with assists in generating a session key used for securely sending messages between two communication clients. Compared with two-party authenticated key exchange protocols where each pair of parties must share a secret with each other, a three-party protocol does not cause any key management problem for the parties. In the literature, mainly there exist three issues in three-party authenticated key exchange protocols are discussed that need to be further improved: (1) to reduce latency, communication steps in the protocol should be as parallel as possible; (2) as the existence of a security-sensitive table on the server side may cause the server to become compromised, the table should be removed; (3) resources required for computation should be as few as possible to avoid the protocol to become an efficiency bottleneck. In various applications over networks, a quick response is required especially by light-weight clients in the mobile e-commerce. In this paper, a roundand computation-efficient three-party authenticated key exchange protocol is proposed which fulfils all of the above mentioned requirements. 2007 Elsevier Inc. All rights reserved.

Question: What is the round- and computation-efficient three-party authenticated key exchange protocol?


Modeling Label Ambiguity for Neural List-Wise Learning to Rank. List-wise learning to rank methods are considered to be the stateof-the-art. One of the major problems with these methods is that the ambiguous nature of relevance labels in learning to rank data is ignored. Ambiguity of relevance labels refers to the phenomenon that multiple documents may be assigned the same relevance label for a given query, so that no preference order should be learned for those documents. In this paper we propose a novel sampling technique for computing a list-wise loss that can take into account this ambiguity. We show the e ectiveness of the proposed method by training a 3-layer deep neural network. We compare our new loss function to two strong baselines: ListNet and ListMLE. We show that our method generalizes better and signi cantly outperforms other methods on the validation and test sets. CCS CONCEPTS •Computingmethodologies→Neural networks; •Information systems →Learning to rank;

Question: What is the difference between neural networks and neural networks?


Tachyon : Memory Throughput I / O for Cluster Computing Frameworks. As ever more big data computations start to be in-memory, I/O throughput dominates the running times of many workloads. For distributed storage, the read throughput can be improved using caching, however, the write throughput is limited by both disk and network bandwidth due to data replication for fault-tolerance. This paper proposes a new file system architecture to enable frameworks to both read and write reliably at memory speed, by avoiding synchronous data replication on writes.

Question: What is a file system architecture?


Image Difference Threshold Strategies and Shadow Detection. The paper considers two problems associated with the detection and classification of motion in image sequences obtained from a static camera. Motion is detected by differencing a reference and the "current" image frame, and therefore requires a suitable reference image and the selection of an appropriate detection threshold. Several threshold selection methods are investigated, and an algorithm based on hysteresis thresholding is shown to give acceptably good results over a number of test image sets. The second part of the paper examines the problem of detecting shadow regions within the image which are associated with the object motion. This is based on the notion of a shadow as a semi-transparent region in the image which retains a (reduced contrast) representation of the underlying surface pattern, texture or grey value. The method uses a region growing algorithm which uses a growing criterion based on a fixed attenuation of the photometric gain over the shadow region, in comparison to the reference image.

Question: How to detect shadows in images?


Modeling IoT-Based Solutions Using Human-Centric Wireless Sensor Networks. The Internet of Things (IoT) has inspired solutions that are already available for addressing problems in various application scenarios, such as healthcare, security, emergency support and tourism. However, there is no clear approach to modeling these systems and envisioning their capabilities at the design time. Therefore, the process of designing these systems is ad hoc and its real impact is evaluated once the solution is already implemented, which is risky and expensive. This paper proposes a modeling approach that uses human-centric wireless sensor networks to specify and evaluate models of IoT-based systems at the time of design, avoiding the need to spend time and effort on early implementations of immature designs. It allows designers to focus on the system design, leaving the implementation decisions for a next phase. The article illustrates the usefulness of this proposal through a running example, showing the design of an IoT-based solution to support the first responses during medium-sized or large urban incidents. The case study used in the proposal evaluation is based on a real train crash. The proposed modeling approach can be used to design IoT-based systems for other application scenarios, e.g., to support security operatives or monitor chronic patients in their homes.

Question: How to model IoT-based solutions using human-centric wireless sensor networks?


Crossbar RRAM Arrays: Selector Device Requirements During Write Operation. A comprehensive analysis of write operations (SET and RESET) in a resistance-change memory (resistive random access memory) crossbar array is carried out. Three types of resistive switching memory cells-nonlinear, rectifying-SET, and rectifying-RESET-are compared with each other in terms of voltage delivery, current delivery, and power consumption. Two different write schemes, V/2 and V/3, were considered, and the V/2 write scheme is preferred due to much lower power consumption. A simple numerical method was developed that simulates entire current flows and node voltages within a crossbar array and provides a quantitative tool for the accurate analysis of crossbar arrays and guidelines for developing reliable write operation.

Question: What is a crossbar array?


STANDARDISATION AND CLASSIFICATION OF ALERTS GENERATED BY INTRUSION DETECTION SYSTEMS. Intrusion detection systems are most popular de-fence mechanisms used to provide security to IT infrastructures. Organisation need best performance, so it uses multiple IDSs from different vendors. Different vendors are using different formats and protocols. Difficulty imposed by this is the generation of several false alarms. Major part of this work concentrates on the collection of alerts from different intrusion detection systems to represent them in IDMEF(Intrusion Detection Message Exchange Format) format. Alerts were collected from intrusion detection systems like snort, ossec, suricata etc. Later classification is attempted using machine learning technique, which helps to mitigate generation of false positives.

Question: What is IDMEF?


A trust-semantic fusion-based recommendation approach for e-business applications. a r t i c l e i n f o Collaborative Filtering (CF) is the most popular recommendation technique but still suffers from data sparsi-ty, user and item cold-start problems, resulting in poor recommendation accuracy and reduced coverage. This study incorporates additional information from the users' social trust network and the items' semantic domain knowledge to alleviate these problems. It proposes an innovative Trust–Semantic Fusion (TSF)-based recommendation approach within the CF framework. Experiments demonstrate that the TSF approach significantly outperforms existing recommendation algorithms in terms of recommendation accuracy and coverage when dealing with the above problems. A business-to-business recommender system case study validates the applicability of the TSF approach. Recommender systems are considered the most popular forms of web personalization and have become a promising and important research topic in information sciences and decision support systems Recommender systems are used to either predict whether a particular user will like a particular item or to identify a set of k items that will be of interest to a certain user, and have been used in different web-based applications including e-business, e-learning and e-tourism [8,22,31]. Currently, Collaborative Filtering (CF) is probably the most known and commonly used recommendation approach in recommender systems. CF works by collecting user ratings for items in a given domain and computing similarities between users or between items in order to produce recommendations [1,31]. CF can be further divided into user-based and item-based CF approaches. In user-based CF approach, a user will receive recommendations of items that similar users liked. In item-based CF approach, a user will receive recommendations of items that are similar to the ones that the user liked in the past [1]. Despite their popularity and success

Question: What is the difference between user-based and item-based CF?


Amharic-English Speech Translation in Tourism Domain. This paper describes speech translation from Amharic-to-English, particularly Automatic Speech Recognition (ASR) with post-editing feature and AmharicEnglish Statistical Machine Translation (SMT). ASR experiment is conducted using morpheme language model (LM) and phoneme acoustic model (AM). Likewise, SMT conducted using word and morpheme as unit. Morpheme based translation shows a 6.29 BLEU score at a 76.4% of recognition accuracy while word based translation shows a 12.83 BLEU score using 77.4% word recognition accuracy. Further, after post-edit on Amharic ASR using corpus based n-gram, the word recognition accuracy increased by 1.42%. Since post-edit approach reduces error propagation, the word based translation accuracy improved by 0.25 (1.95%) BLEU score. We are now working towards further improving propagated errors through different algorithms at each unit of speech translation cascading component.

Question: How to translate amharic to english?


The Human Brain in Numbers: A Linearly Scaled-up Primate Brain. The human brain has often been viewed as outstanding among mammalian brains: the most cognitively able, the largest-than-expected from body size, endowed with an overdeveloped cerebral cortex that represents over 80% of brain mass, and purportedly containing 100 billion neurons and 10x more glial cells. Such uniqueness was seemingly necessary to justify the superior cognitive abilities of humans over larger-brained mammals such as elephants and whales. However, our recent studies using a novel method to determine the cellular composition of the brain of humans and other primates as well as of rodents and insectivores show that, since different cellular scaling rules apply to the brains within these orders, brain size can no longer be considered a proxy for the number of neurons in the brain. These studies also showed that the human brain is not exceptional in its cellular composition, as it was found to contain as many neuronal and non-neuronal cells as would be expected of a primate brain of its size. Additionally, the so-called overdeveloped human cerebral cortex holds only 19% of all brain neurons, a fraction that is similar to that found in other mammals. In what regards absolute numbers of neurons, however, the human brain does have two advantages compared to other mammalian brains: compared to rodents, and probably to whales and elephants as well, it is built according to the very economical, space-saving scaling rules that apply to other primates; and, among economically built primate brains, it is the largest, hence containing the most neurons. These findings argue in favor of a view of cognitive abilities that is centered on absolute numbers of neurons, rather than on body size or encephalization, and call for a re-examination of several concepts related to the exceptionality of the human brain.

Question: What is the human brain?


Understanding Judgment of Information Quality and Cognitive Authority in the WWW. In the WWW, people are engaging in interaction with more, and more diverse information than ever before, so there is an increasing need for information "filtering." But because of the diversity of information resources, and the lack of traditional quality control on the WWW, the criteria of authority and quality of information that people have used for this purpose in past contexts may no longer be relevant. This paper reports on a study of people's decision making with respect to quality and authority in the WWW. Seven facets of judgment of information quality were identified: source, content, format, presentation, currency, accuracy, and speed of loading. People mentioned source credibility with two levels: institutional level and individual level. Authority was identified as a underlying theme in source credibility. Institutional authority involved: institutional domain identified by URL; institution type; and institution reputation recognized by names. Individual authority involved: identification of creator/author; creator/author affiliation; and creator/author's name. People were more or less concerned with evaluating information quality depending upon: the consequence of use of information; act or commitment based on information; and the focus of inquiry. It was also found that people believed that the web, as an institution, was less authoritative and less credible than other types of information systems.

Question: What is the difference between institutional and individual authority?


Integrated virtual commissioning an essential activity in the automation engineering process: From virtual commissioning to simulation supported engineering. Production plants within manufacturing and process industries are becoming more and more complex systems. For a safe and demand sufficient production the automation system of a production plant is mission critical. Therefore the correct functioning and engineering of the automation system is essential. Nowadays the use of a virtual commissioning step before deploying the automation software on the real production plant is used more often. Within the virtual commissioning the automation software is tested against a virtual plant model, sufficient to check out the correct functioning of the automation system. Usually virtual commissioning is used as a step at the end of the automation engineering, as proposed by VDI 4499. Within this paper an integrated virtual commissioning is proposed, where the automation software is continuously tested against a virtual plant model during the overall engineering phase. The virtual plant is growing together with the automation software and thus enables simulation supported automation engineering. Benefits of the proposed workflow are that errors can be detected immediately after the implementation and the use of the virtual plant model could be extended to various stages of the engineering workflow.

Question: What is the difference between virtual commissioning and simulation supported engineering?


Graphene plasmonics for terahertz to mid-infrared applications.. In recent years, we have seen a rapid progress in the field of graphene plasmonics, motivated by graphene's unique electrical and optical properties, tunability, long-lived collective excitation and its extreme light confinement. Here, we review the basic properties of graphene plasmons: their energy dispersion, localization and propagation, plasmon-phonon hybridization, lifetimes and damping pathways. The application space of graphene plasmonics lies in the technologically significant, but relatively unexploited terahertz to mid-infrared regime. We discuss emerging and potential applications, such as modulators, notch filters, polarizers, mid-infrared photodetectors, and mid-infrared vibrational spectroscopy, among many others.

Question: What is the application space of graphene plasmonics?


Secure Multiparty Computation and Secret Sharing. In a data-driven society, individuals and companies encounter numerous situations where private information is an important resource. How can parties handle confidential data if they do not trust everyone involved? This text is the first to present a comprehensive treatment of unconditionally secure techniques for multiparty computation (MPC) and secret sharing. In a secure MPC, each party possesses some private data, whereas secret sharing provides a way for one party to spread information on a secret such that all parties together hold full information, yet no single party has all the information. The authors present basic feasibility results from the last thirty years, generalizations to arbitrary access structures using linear secret sharing, some recent techniques for efficiency improvements, and a general treatment of the theory of secret sharing, focusing on asymptotic results with interesting applications related to MPC.

Question: What is the difference between secret sharing and multiparty computation?


Haraka - Efficient Short-Input Hashing for Post-Quantum Applications. Recently, many efficient cryptographic hash function design strategies have been explored, not least because of the SHA-3 competition. These designs are, almost exclusively, geared towards high performance on long inputs. However, various applications exist where the performance on short (fixed length) inputs matters more. Such hash functions are the bottleneck in hash-based signature schemes like SPHINCS or XMSS, which is currently under standardization. Secure functions specifically designed for such applications are scarce. We attend to this gap by proposing two short-input hash functions (or rather simply compression functions). By utilizing AES instructions on modern CPUs, our proposals are the fastest on such platforms, reaching throughputs below one cycle per hashed byte even for short inputs, while still having a very low latency of less than 60 cycles. Under the hood, this results comes with several innovations. First, we study whether the number of rounds for our hash functions can be reduced, if only second-preimage resistance (and not collision resistance) is required. The conclusion is: only a little. Second, since their inception, AES-like designs allow for supportive security arguments by means of counting and bounding the number of active S-boxes. However, this ignores powerful attack vectors using truncated differentials, including the powerful rebound attacks. We develop a general tool-based method to include arguments against attack vectors using truncated differentials.

Question: What is the fastest hash function for short inputs?


Assessment of Motor Functioning in the Preschool Period. The assessment of motor functioning in young children has become increasingly important in recent years with the acknowledgement that motor impairment is linked with cognitive, language, social and emotional difficulties. However, there is no one gold standard assessment tool to investigate motor ability in children. The aim of the current paper was to discuss the issues related to the assessment of motor ability in young pre-school children and to provide guidelines on the best approach for motor assessment. The paper discusses the maturational changes in brain development at the preschool level in relation to motor ability. Other issues include sex differences in motor ability at this young age, and evidence for this in relation to sociological versus biological influences. From the previous literature it is unclear what needs to be assessed in relation to motor functioning. Should the focus be underlying motor processes or movement skill assessment? Several key assessment tools are discussed that produce a general measure of motor performance followed by a description of tools that assess specific skills, such as fine and gross motor, ball and graphomotor skills. The paper concludes with recommendations on the best approach in assessing motor function in pre-school children.

Question: What is the best approach to assess motor ability in pre-school children?


ING FOR TEXT CLASSIFICATION. Zero-shot Learners are models capable of predicting unseen classes. In this work, we propose a Zero-shot Learning approach for text categorization. Our method involves training model on a large corpus of sentences to learn the relationship between a sentence and embedding of sentence's tags. Learning such relationship makes the model generalize to unseen sentences, tags, and even new datasets provided they can be put into same embedding space. The model learns to predict whether a given sentence is related to a tag or not; unlike other classifiers that learn to classify the sentence as one of the possible classes. We propose three different neural networks for the task and report their accuracy on the test set of the dataset used for training them as well as two other standard datasets for which no retraining was done. We show that our models generalize well across new unseen classes in both cases. Although the models do not achieve the accuracy level of the state of the art supervised models, yet it evidently is a step forward towards general intelligence in natural language processing.

Question: What is zero-shot learning?