Haptic identification of objects using a modular soft robotic gripper. This work presents a soft hand capable of robustly grasping and identifying objects based on internal state measurements. A highly compliant hand allows for intrinsic robustness to grasping uncertainty, but the specific configuration of the hand and object is not known, leaving undetermined if a grasp was successful in picking up the right object. A soft finger was adapted and combined to form a three finger gripper that can easily be attached to existing robots, for example, to the wrist of the Baxter robot. Resistive bend sensors were added within each finger to provide a configuration estimate sufficient for distinguishing between a set of objects. With one data point from each finger, the object grasped by the gripper can be identified. A clustering algorithm to find the correspondence for each grasped object is presented for both enveloping grasps and pinch grasps. This hand is a first step towards robust proprioceptive soft grasping.

Question: How to identify objects using a soft robotic gripper?


Droop control in LV-grids. Remote electrification with island supply systems, the increasing acceptance of the microgrids concept and the penetration of the interconnected grid with DER and RES require the application of inverters and the development of new control algorithms. One promising approach is the implementation of conventional f/U-droops into the respective inverters, thus down scaling the conventional grid control concept to the low voltage grid. Despite contradicting line parameters, the applicability of this proceeding is outlined and the boundary conditions are derived

Question: What is the difference between a microgrid and a conventional grid?


Locally Adaptive Color Correction for Underwater Image Dehazing and Matching. Underwater images are known to be strongly deteriorated by a combination of wavelength-dependent light attenuation and scattering. This results in complex color casts that depend both on the scene depth map and on the light spectrum. Color transfer, which is a technique of choice to counterbalance color casts, assumes stationary casts, defined by global parameters, and is therefore not directly applicable to the locally variable color casts encountered in underwater scenarios. To fill this gap, this paper introduces an original fusion-based strategy to exploit color transfer while tuning the color correction locally, as a function of the light attenuation level estimated from the red channel. The Dark Channel Prior (DCP) is then used to restore the color compensated image, by inverting the simplified Koschmieder light transmission model, as for outdoor dehazing. Our technique enhances image contrast in a quite effective manner and also supports accurate transmission map estimation. Our extensive experiments also show that our color correction strongly improves the effectiveness of local keypoints matching.

Question: How to restore color corrected images?


Aggregating User Input in Ecology Citizen Science Projects. Camera traps (remote, automatic cameras) are revolutionizing large-scale studies in ecology. The Serengeti Lion Project has used camera traps to produce over 1.5 million pictures of animals in the Serengeti. To analyze these pictures, the Project created Snapshot Serengeti, a citizen science website where volunteers can help classify animals. To increase accuracy, each photo is shown to multiple users and a critical step is aggregating individual classifications. In this paper, we present a new aggregation algorithm which achieves an accuracy of 98.6%, better than many human experts. Our algorithm also requires fewer users per photo than existing methods. The algorithm is intuitive and designed so that nonexperts can understand the end results. Ecology seeks to understand the interrelationships of species with one another and with their environment. Monitoring many species of animals simultaneously has traditionally been very difficult. Camera traps (remote, automatic cameras) are revolutionizing ecological research by providing a non-invasive, cost-effective approach for large-scale monitoring. Ecologists are currently using these traps in the Serengeti National Park, one of the world's last large intact natural areas, to understand the dynamics of its dozens of large mammals (Swanson et al. 2014). As of November 2013, the ecologists have spent 3 years using more than 200 cameras spread over 1,125 square kilometers to take more than 1.5 million photos. In order to process so many images, the ecologists, along with Zooniverse (a citizen science platform), created Snapshot Serengeti, a web site where over 35,000 volunteers helped classify the species in the photos (Zooniverse 2014a). Since volunteers can make mistakes, each photo is shown to multiple users. A critical step is to combine these classifications into one aggregate classification: e.g., if 4 out of 5 users classify a photo as containing a zebra,

Question: What is the accuracy of the aggregation algorithm?


Digital signature based on PlayGamal algorithm. To keep the security and integrity of digital messages, can be applied cryptography method. Digital Signature is one of the vital digital data, so the secrecy must be maintained at the time of distribution and storage of data. This study aims to combine modern cryptographic methods with ElGamal traditional algorithm method is called PlayGamal Cipher Algorithm. Moreover. the method will be implement as application for digital signature. The proposed method will be compare with the existing El-Gamal to know the performance during encryption and decryption process.

Question: What is PlayGamal Cipher Algorithm?


Attacking Deterministic Signature Schemes Using Fault Attacks. Many digital signature schemes rely on random numbers that are unique and non-predictable per signature. Failures of random number generators may have catastrophic effects such as compromising private signature keys. In recent years, many widely-used cryptographic technologies adopted deterministic signature schemes because they are presumed to be safer to implement. In this paper, we analyze the security of deterministic ECDSA and EdDSA signature schemes and show that the elimination of random number generators in these schemes enables new kinds of fault attacks. We formalize these attacks and introduce practical attack scenarios against EdDSA using the Rowhammer fault attack. EdDSA is used in many widely used protocols such as TLS, SSH, and IPSec, and we show that these protocols are not vulnerable to our attack. We formalize the necessary requirements of protocols using these deterministic signature schemes to be vulnerable, and discuss mitigation strategies and their effect on fault attacks against deterministic signature schemes.

Question: What is the difference between deterministic and random signature schemes?


Ant colony system: a cooperative learning approach to the traveling salesman problem. This paper introduces ant colony system (ACS), a distributed algorithm that is applied to the traveling salesman problem (TSP). In ACS, a set of cooperating agents called ants cooperate to find good solutions to TSPs. Ants cooperate using an indirect form of communication mediated by pheromone they deposit on the edges of the TSP graph while building solutions. We study ACS by running experiments to understand its operation. The results show that ACS outperforms other nature-inspired algorithms such as simulated annealing and evolutionary computation, and we conclude comparing ACS-3-opt, a version of ACS augmented with a local search procedure, to some of the best performing algorithms for symmetric and asymmetric TSPs. Accepted for publication in the IEEE Transactions on Evolutionary Computation, Vol.1, No.1, 1997. In press.

Question: What is the traveling salesman problem?


Principles and practice in reporting structural equation analyses.. Principles for reporting analyses using structural equation modeling are reviewed, with the goal of supplying readers with complete and accurate information. It is recommended that every report give a detailed justification of the model used, along with plausible alternatives and an account of identifiability. Nonnormality and missing data problems should also be addressed. A complete set of parameters and their standard errors is desirable, and it will often be convenient to supply the correlation matrix and discrepancies, as well as goodness-of-fit indices, so that readers can exercise independent critical judgment. A survey of fairly representative studies compares recent practice with the principles of reporting recommended here.

Question: What is a structural equation model?


Deep Representation Learning for Human Motion Prediction and Classification. Generative models of 3D human motion are often restricted to a small number of activities and can therefore not generalize well to novel movements or applications. In this work we propose a deep learning framework for human motion capture data that learns a generic representation from a large corpus of motion capture data and generalizes well to new, unseen, motions. Using an encoding-decoding network that learns to predict future 3D poses from the most recent past, we extract a feature representation of human motion. Most work on deep learning for sequence prediction focuses on video and speech. Since skeletal data has a different structure, we present and evaluate different network architectures that make different assumptions about time dependencies and limb correlations. To quantify the learned features, we use the output of different layers for action classification and visualize the receptive fields of the network units. Our method outperforms the recent state of the art in skeletal motion prediction even though these use action specific training data. Our results show that deep feedforward networks, trained from a generic mocap database, can successfully be used for feature extraction from human motion data and that this representation can be used as a foundation for classification and prediction.

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


A Framework for Discovering Internal Financial Fraud Using Analytics. In today's knowledge based society, financial fraud has become a common phenomenon. Moreover, the growth in knowledge discovery in databases and fraud audit has made the detection of internal financial fraud a major area of research. On the other hand, auditors find it difficult to apply a majority of techniques in the fraud auditing process and to integrate their domain knowledge in this process. In this Paper a framework called "Knowledge-driven Internal Fraud Detection (KDIFD)" is proposed for detecting internal financial frauds. The framework suggests a process-based approach that considers both forensic auditor's tacit knowledge base and computer-based data analysis and mining techniques. The proposed framework can help auditor in discovering internal financial fraud more efficiently.

Question: What is a knowledge-driven internal fraud detection framework?


Poisson surface reconstruction. We show that surface reconstruction from oriented points can be cast as a spatial Poisson problem. This Poisson formulation considers all the points at once, without resorting to heuristic spatial partitioning or blending, and is therefore highly resilient to data noise. Unlike radial basis function schemes, our Poisson approach allows a hierarchy of locally supported basis functions, and therefore the solution reduces to a well conditioned sparse linear system. We describe a spatially adaptive multiscale algorithm whose time and space complexities are proportional to the size of the reconstructed model. Experimenting with publicly available scan data, we demonstrate reconstruction of surfaces with greater detail than previously achievable.

Question: How to reconstruct a surface from a set of points?


Analysis and design of proximity fuze based on waveguide discontinuity. In order to satisfy the special requirement of antenna directivity base on proximity Fuze, the concept of the waveguide discontinuity was introduced which could be a method of designing antenna. The beam of radiation should deviate from axis direction of bomb in a certain angle The frequency was between 24 GHz and 25 GHz.Circular waveguide was connected to coaxial waveguide. This form could inspire high model in discontinuity place. Electric field of coupling model could lead to a oblique radiation pattern. In the meanwhile, two symmetrical metal column was loaded in waveguide in purpose of changing impedance of antenna. The test results demonstrated the veracity of the analysis.

Question: What is the purpose of the metal column?


Standardization of the in-car gesture interaction space. Driven by technological advancements, gesture interfaces have recently found their way into vehicular prototypes of various kind. Unfortunately, their application is less than perfect and detailed information about preferred gesture execution regions, spatial extent, and time behavior are not available yet. Providing car (interior) manufacturer with gesture characteristics would allow them to design future in-vehicle concepts in a way to not interfere with gestural interaction. To tackle the problem, this research aims as preliminary work for a later standardization of the diverse properties of gestures and gesture classes similarly to what is already standardized in norms such as ISO 3958/4040 for placement and reachability of traditional controls and indicators. We have set up a real driving experiment recording trajectories and time behavior of gestures related to car and media control tasks. Data evaluation reveals that most of the subjects perform gestures in the same region (bounded by a "triangle" steering wheel, rear mirror, and gearshift) and with similar spatial extent (on average below 2 sec.). The generated density plots can be further used for an initial discussion about gesture execution in the passenger compartment. The final aim is to propose a new standard on permitted gesture properties (time, space) in the car.

Question: What is the spatial extent of gestures in a car?


New directions in cryptography. Two kinds of contemporary developments in crypcommunications over an insecure channel order to use cryptogtography are examined. Widening applications of teleprocessraphy to insure privacy, however, it currently necessary for the ing have given rise to a need for new types of cryptographic communicating parties to share a key which is known to no systems, which minimize the need for secure key distribution one else. This is done by sending the key in advance over some channels and supply the equivalent of a written signature. This secure channel such a private courier or registered mail. A paper suggests ways to solve these currently open problems. private conversation between two people with no prior acquainIt also discusses how the theories of communication and computance is a common occurrence in business, however, and it is tation are beginning to provide the tools to solve cryptographic unrealistic to expect initial business contacts to be postponed problems of long standing. long enough for keys to be transmitted by some physical means. The cost and delay imposed by this key distribution problem is a major barrier to the transfer of business communications

Question: How does cryptography work?


Partitioning graphs to speedup Dijkstra's algorithm. We study an acceleration method for point-to-point shortest-path computations in large and sparse directed graphs with given nonnegative arc weights. The acceleration method is called the arc-flag approach and is based on Dijkstra's algorithm. In the arc-flag approach, we allow a preprocessing of the network data to generate additional information, which is then used to speedup shortest-path queries. In the preprocessing phase, the graph is divided into regions and information is gathered on whether an arc is on a shortest path into a given region. The arc-flag method combined with an appropriate partitioning and a bidirected search achieves an average speedup factor of more than 500 compared to the standard algorithm of Dijkstra on large networks (1 million nodes, 2.5 million arcs). This combination narrows down the search space of Dijkstra's algorithm to almost the size of the corresponding shortest path for long-distance shortest-path queries. We conduct an experimental study that evaluates which partitionings are best suited for the arc-flag method. In particular, we examine partitioning algorithms from computational geometry and a multiway arc separator partitioning. The evaluation was done on German road networks. The impact of different partitions on the speedup of the shortest path algorithm are compared. Furthermore, we present an extension of the speedup technique to multiple levels of partitions. With this multilevel variant, the same speedup factors can be achieved with smaller space requirements. It can, therefore, be seen as a compression of the precomputed data that preserves the correctness of the computed shortest paths.

Question: What is the average speedup factor of the arc-flag method?


Computational Thinking in Practice : How STEM Professionals Use CT in Their Work. The goal of this study is to bring current computational thinking in STEM educational efforts in line with the increasingly computational nature of STEM research practices. We conducted interviews with STEM practitioners in various fields to understand the nature of CT as it happens in authentic research settings and to revisit a first iteration of our definition of CT in form of a taxonomy. This exploration gives us insight into how scientists use computers in their work and help us identify what practices are important to include in high school STEM learning contexts. Our findings will inform the design of classroom activities to better prepare today's students for the modern STEM landscape that awaits them.

Question: What is computational thinking?


Developing a frame of reference for ex-ante IT/IS investment evaluation. Investment appraisal techniques are an integral part of many traditional capital budgeting processes. However, the adoption of Information Systems (IS) and the development of resulting infrastructures are being increasingly viewed on the basis of consumption. Consequently, decision-makers are now moving away from the confines of rigid capital budgeting processes, which have traditionally compared IS with non-ISrelated investments. With this in mind, the authors seek to dissect investment appraisal from the broader capital budgeting process to allow a deeper understanding of the mechanics involved with IS justification. This analysis presents conflicting perspectives surrounding the scope and sensitivity of traditional appraisal methods. In contributing to this debate, the authors present taxonomies of IS benefit types and associated natures, and discuss the resulting implications of using traditional appraisal techniques during the IS planning and decision-making process. A frame of reference that can be used to navigate through the variety of appraisal methods available to decision-makers is presented and discussed. Taxonomies of appraisal techniques that are classified by their respective characteristics are also presented. Perspectives surrounding the degree of involvement that financial appraisal should play during decision making and the limitations surrounding investment appraisal techniques are identified. European Journal of Information Systems (2002) 11, 74–82. DOI: 10.1057/palgrave/ejis/3000411

Question: What is the difference between traditional and consumption-based investment appraisal?


How Amazon web services uses formal methods. Engineers use TLA+ to prevent serious but subtle bugs from reaching production.

Question: How does Amazon web services use formal methods?


Demand Side Management in Smart Grid Using Heuristic Optimization. Demand side management (DSM) is one of the important functions in a smart grid that allows customers to make informed decisions regarding their energy consumption, and helps the energy providers reduce the peak load demand and reshape the load profile. This results in increased sustainability of the smart grid, as well as reduced overall operational cost and carbon emission levels. Most of the existing demand side management strategies used in traditional energy management systems employ system specific techniques and algorithms. In addition, the existing strategies handle only a limited number of controllable loads of limited types. This paper presents a demand side management strategy based on load shifting technique for demand side management of future smart grids with a large number of devices of several types. The day-ahead load shifting technique proposed in this paper is mathematically formulated as a minimization problem. A heuristic-based Evolutionary Algorithm (EA) that easily adapts heuristics in the problem was developed for solving this minimization problem. Simulations were carried out on a smart grid which contains a variety of loads in three service areas, one with residential customers, another with commercial customers, and the third one with industrial customers. The simulation results show that the proposed demand side management strategy achieves substantial savings, while reducing the peak load demand of the smart grid.

Question: What is the difference between a smart grid and a traditional grid?


Transcriptome-wide Identification of RNA-Binding Protein and MicroRNA Target Sites by PAR-CLIP. RNA transcripts are subject to posttranscriptional gene regulation involving hundreds of RNA-binding proteins (RBPs) and microRNA-containing ribonucleoprotein complexes (miRNPs) expressed in a cell-type dependent fashion. We developed a cell-based crosslinking approach to determine at high resolution and transcriptome-wide the binding sites of cellular RBPs and miRNPs. The crosslinked sites are revealed by thymidine to cytidine transitions in the cDNAs prepared from immunopurified RNPs of 4-thiouridine-treated cells. We determined the binding sites and regulatory consequences for several intensely studied RBPs and miRNPs, including PUM2, QKI, IGF2BP1-3, AGO/EIF2C1-4 and TNRC6A-C. Our study revealed that these factors bind thousands of sites containing defined sequence motifs and have distinct preferences for exonic versus intronic or coding versus untranslated transcript regions. The precise mapping of binding sites across the transcriptome will be critical to the interpretation of the rapidly emerging data on genetic variation between individuals and how these variations contribute to complex genetic diseases.

Question: What is the difference between a transcriptome and a transcriptome-wide?


Oral squamous cell carcinoma overview.. Most cancer in the head and neck is squamous cell carcinoma (HNSCC) and the majority is oral squamous cell carcinoma (OSCC). This is an overview of oral squamous cell carcinoma (OSCC) highlighting essential points from the contributors to this issue, to set the scene. It emphasises the crucial importance of prevention and the necessarily multidisciplinary approach to the diagnosis and management of patients with OSCC.

Question: What is the difference between oral and head and neck cancer?


SimSensei Demonstration: A Perceptive Virtual Human Interviewer for Healthcare Applications. We present the SimSensei system, a fully automatic virtual agent that conducts interviews to assess indicators of psychological distress. We emphasize on the perception part of the system, a multimodal framework which captures and analyzes user state for both behavioral understanding and interactional purposes.

Question: How does the system work?


Moving ERP Systems to the Cloud-Data Security Issues. This paper brings to light data security issues and concerns for organizations by moving their Enterprise Resource Planning (ERP) systems to the cloud. Cloud computing has become the new trend of how organizations conduct business and has enabled them to innovate and compete in a dynamic environment through new and innovative business models. The growing popularity and success of the cloud has led to the emergence of cloud-based Software-as-a-Service (SaaS) ERP systems, a new alternative approach to traditional on-premise ERP systems. Cloud-based ERP has a myriad of benefits for organizations. However, infrastructure engineers need to address data security issues before moving their enterprise applications to the cloud. Cloud-based ERP raises specific concerns about the confidentiality and integrity of the data stored in the cloud. Such concerns that affect the adoption of cloud-based ERP are based on the size of the organization. Small to medium enterprises (SMEs) gain the maximum benefits from cloud-based ERP as many of the concerns around data security are not relevant to them. On the contrary, larger organizations are more cautious in moving their mission critical enterprise applications to the cloud. A hybrid solution where organizations can choose to keep their sensitive applications on-premise while leveraging the benefits of the cloud is proposed in this paper as an effective solution that is gaining momentum and popularity for large organizations.

Question: What are the security concerns of moving ERP systems to the cloud?


The role of physical embodiment in human-robot interaction. Autonomous robots are agents with physical bodies that share our environment. In this work, we test the hypothesis that physical embodiment has a measurable effect on performance and perception of social interactions. Support of this hypothesis would suggest fundamental differences between virtual agents and robots from a social standpoint and have significant implications for human-robot interaction. We measure task performance and perception of a robot's social abilities in a structured but open-ended task based on the Towers of Hanoi puzzle. Our experiment compares aspects of embodiment by evaluating: (1) the difference between a physical robot and a simulated one; (2) the effect of physical presence through a co-located robot versus a remote tele-present robot. We present data from a pilot study with 12 subjects showing interesting differences in perception of remote physical robot's and simulated agent's attention to the task, and task enjoyment

Question: What is the difference between a physical robot and a simulated one?


Performance Management : A model and research agenda. © International Association for Applied Psychology, 2004. Published by Blackwell Publishing, 9600 Garsington Road, Oxford OX4 2DQ, UK and 350 Main Street, Malden, MA 02148, USA. Blackwell Publishing, Ltd. Oxford, UK APPS pplied Psychology: an International Review 0269-994X © Blackwell Publishing 2004 ct ber 2004 53 4 riginal Arti le PERFORMANCE MANAGEMENT D N HARTOG ET AL. Performance Management: A Model and Research Agenda

Question: What is the difference between a model and a research agenda?


Coping with autism: a journey toward adaptation.. As the number of individuals with autism grows, it is critical for nurses in all settings to understand how autism influences the family unit, as they will likely interact with these children, the adults, and their families. The intent of this descriptive narrative study was to explore the experiences of families of individuals with autism as perceived by the mother. Through personal interviews, 16 mothers' perceptions of the impact of autism on the family unit during different stages of the life cycle were revealed through a constructivist lens. Pediatric nurses employed in acute care settings, community, and schools are poised to assess and support these families following diagnosis and throughout the child's life.

Question: How does autism affect the family unit?


Diced cartilage grafts in rhinoplasty surgery: current techniques and applications.. BACKGROUND The author has used diced cartilage grafts in nasal surgery for more than 30 years. However, the number of cases and the variety of techniques have increased dramatically over the past 6 years.   METHODS The author uses three methods of diced cartilage in rhinoplasty surgery: diced cartilage, diced cartilage wrapped in fascia (with fascia then sewn closed), and diced cartilage covered with fascia (with the recipient area covered with fascia after graft placement). The constructs are highly varied to fit the specific defect. Pieces of diced cartilage without any fascia can be placed in pockets in the peripyriform area, radix, or alongside structural rib grafts.   RESULTS Over a 2-year period, the author treated 546 rhinoplasty cases in which 79 patients (14 percent) had a total of 91 diced cartilage grafts. There were 34 primary and 45 secondary operations involving the radix (n = 11), half-length grafts (n = 14), full-length dorsum (n = 43), peripyriform (n = 16), infralobule (n = 4), and lateral wall (n = 3). All charts were reviewed for the 256 rhinoplasties performed in 2006 of which 30 patients had 35 diced cartilage grafts. With a median follow-up of 19 months (range, 13 to 25 months), two patients had had revisions unrelated to their diced cartilage grafts. The three most common technical problems were overcorrection, visibility, and junctional step-offs.   CONCLUSIONS Diced cartilage grafts are a valuable addition to rhinoplasty surgery. They are highly flexible and useful throughout the nose. Their use simplifies one of the greatest challenges in all of rhinoplasty--dorsal augmentation. Complications have been relatively minor and their correction relatively simple.

Question: What is the author's technique for diced cartilage grafts?


Maximum Performance Computing with Dataflow Engines. Multidisciplinary dataflow computing is a powerful approach to scientific computing that has led to orders-of-magnitude performance improvements for a wide range of applications.

Question: What is a dataflow engine?


Clickstream clustering using weighted longest common subsequences. Categorizing visitors based on their interactions with a we bsite is a key problem in web usage mining. The clickstreams generated by various users often follow disti nct patterns, the knowledge of which may help in providing customized content. In this paper, we propose a novel and eff ective algorithm for clustering webusers based on a function of the longest common subsequence of their clickst reams that takes into account both the trajectory taken through a website and the time spent at each page. Results are presented on weblogs of www.sulekha.com to illustrate the techniques. keywords : web usage mining, clickstream, subsequence, clustering.

Question: How to cluster web users based on their clickstreams?


Localized Multiple Kernel Learning With Dynamical Clustering and Matrix Regularization. Localized multiple kernel learning (LMKL) is an attractive strategy for combining multiple heterogeneous features with regard to their discriminative power for each individual sample. However, the learning of numerous local solutions may not scale well even for a moderately sized training set, and the independently learned local models may suffer from overfitting. Hence, in existing local methods, the distributed samples are typically assumed to share the same weights, and various unsupervised clustering methods are applied as preprocessing. In this paper, to enable the learner to discover and benefit from the underlying local coherence and diversity of the samples, we incorporate the clustering procedure into the canonical support vector machine-based LMKL framework. Then, to explore the relatedness among different samples, which has been ignored in a vector  $\ell _{p}$ -norm analysis, we organize the cluster-specific kernel weights into a matrix and introduce a matrix-based extension of the  $\ell _{p}$ -norm for constraint enforcement. By casting the joint optimization problem as a problem of alternating optimization, we show how the cluster structure is gradually revealed and how the matrix-regularized kernel weights are obtained. A theoretical analysis of such a regularizer is performed using a Rademacher complexity bound, and complementary empirical experiments on real-world data sets demonstrate the effectiveness of our technique.

Question: What is the difference between the  $\ell _{p}$ -norm and the matrix-regularized kernel weights?


The Challenge of Big Data in Public Health: An Opportunity for Visual Analytics. Public health (PH) data can generally be characterized as big data. The efficient and effective use of this data determines the extent to which PH stakeholders can sufficiently address societal health concerns as they engage in a variety of work activities. As stakeholders interact with data, they engage in various cognitive activities such as analytical reasoning, decision-making, interpreting, and problem solving. Performing these activities with big data is a challenge for the unaided mind as stakeholders encounter obstacles relating to the data's volume, variety, velocity, and veracity. Such being the case, computer-based information tools are needed to support PH stakeholders. Unfortunately, while existing computational tools are beneficial in addressing certain work activities, they fall short in supporting cognitive activities that involve working with large, heterogeneous, and complex bodies of data. This paper presents visual analytics (VA) tools, a nascent category of computational tools that integrate data analytics with interactive visualizations, to facilitate the performance of cognitive activities involving big data. Historically, PH has lagged behind other sectors in embracing new computational technology. In this paper, we discuss the role that VA tools can play in addressing the challenges presented by big data. In doing so, we demonstrate the potential benefit of incorporating VA tools into PH practice, in addition to highlighting the need for further systematic and focused research.

Question: What is the role of visual analytics in public health?


Office Procedure as Practical Action: Models of Work and System Design. The design of office technology relies upon underlying conceptions of human organization and action. The goal of building office information systems requires a representation of office work and its relevant objects. The concern of this paper is that although system designers recognize the centrality of procedural tasks in the office, they tend to ignore the actual work involved in accomplishing those tasks. A perspicuous instance of work in an accounting office is used to recommend a new line of research into the practical problems of office work, and to suggest preliminary implications of that research for office systems design.

Question: What is the goal of building office information systems?


A Taxonomy of Domain-Specific Aspect Languages. Domain-Specific Aspect Languages (DSALs) are Domain-Specific Languages (DSLs) designed to express crosscutting concerns. Compared to DSLs, their aspectual nature greatly amplifies the language design space. We structure this space in order to shed light on and compare the different domain-specific approaches to deal with crosscutting concerns. We report on a corpus of 36 DSALs covering the space, discuss a set of design considerations, and provide a taxonomy of DSAL implementation approaches. This work serves as a frame of reference to DSAL and DSL researchers, enabling further advances in the field, and to developers as a guide for DSAL implementations.

Question: What is a taxonomy of domain-specific aspect languages?


Fast Change Point Detection on Dynamic Social Networks. A number of real world problems in many domains (e.g. sociology, biology, political science and communication networks) can be modeled as dynamic networks with nodes representing entities of interest and edges representing interactions among the entities at different points in time. A common representation for such models is the snapshot model where a network is defined at logical time-stamps. An important problem under this model is change point detection. In this work we devise an effective and efficient three-step-approach for detecting change points in dynamic networks under the snapshot model. Our algorithm achieves up to 9X speedup over the state-of-the-art while improving quality on both synthetic and real world networks.

Question: What is a snapshot model?


Mixing Coins of Different Quality: A Game-Theoretic Approach. Cryptocoins based on public distributed ledgers can differ in their quality due to different subjective values users assign to coins depending on the unique transaction history of each coin. We apply game theory to study how qualitative differentiation between coins will affect the behavior of users interested in improving their anonymity through mixing services. We present two stylized models of mixing with perfect and imperfect information and analyze them for three distinct quality propagation policies: poison, haircut, and seniority. In the game of perfect information, mixing coins of high quality remains feasible under certain conditions, while imperfect information eventually leads to a mixing market where only coins of the lowest quality are mixed.

Question: How can we mix coins of different quality?


The status of HTS ship propulsion motor developments. The development of ship propulsion synchronous motors with high temperature superconductor (HTS) field windings for Naval electric ship applications has progressed to the point where a full scale motor is now under construction. A 5 MW, 230-rpm prototype ship propulsion motor was built and tested by the Center for Advanced Power Systems (CAPS) on behalf of U.S. Office of Naval Research (ONR). It met or exceeded all its design goals. Currently, a 36.5 MW, 120-rpm ship propulsion motor is being built for delivery to ONR at the end of 2006. This paper presents test results of the 5 MW motor and the status of the 36.5 MW motor

Question: What is the status of the HTS ship propulsion motor developments?


An evolutionary game-theoretic modeling for heterogeneous information diffusion. In this paper, we model and analyze the information diffusion in heterogeneous social networks from an evolutionary game perspective. Users interact with each other according to their individual fitness, which are heterogeneous among different user types. We first study a model where in each social interaction the payoff of a user is independent of the type of the interacted user. In such a case, we derive the information diffusion dynamics of each type of users as well as that of the overall network. The evolutionarily stable states (ESSs) of the dynamics are determined accordingly. Afterwards, we investigate a more general model where in each interaction the payoff of a user depends on the type of the interacted user. We show that the local influence dynamics change much more quickly than the global strategy population dynamics and the former keeps track of the latter throughout the information diffusion process. Based on this observation, the global strategy population dynamics are derived. Finally, simulations are conducted to verify the theoretical results.

Question: What is the difference between the heterogeneous and homogeneous information diffusion?


Cross-Lingual Propagation for Deep Sentiment Analysis. Across the globe, people are voicing their opinion in social media and various other online fora. Given such data, modern deep learning-based sentiment analysis methods excel at determining the sentiment polarity of what is being said about companies, products, etc. Unfortunately, such deep methods require significant training data, while for many languages, resources and training data are scarce. In this work, we present a cross-lingual propagation algorithm that yields sentiment embedding vectors for numerous languages. We then rely on a dual-channel convolutional neural architecture to incorporate them into the network. This allows us to achieve gains in deep sentiment analysis across a range of languages and domains.

Question: How to train a deep learning model for sentiment analysis?


Deep Learning Based Forecasting of Critical Infrastructure Data. Intelligent monitoring and control of critical infrastructure such as electric power grids, public water utilities and transportation systems produces massive volumes of time series data from heterogeneous sensor networks. Time Series Forecasting (TSF) is essential for system safety and security, and also for improving the efficiency and quality of service delivery. Being highly dependent on various external factors, the observed system behavior is usually stochastic, which makes the next value prediction a tricky and challenging task that usually needs customized methods. In this paper we propose a novel deep learning based framework for time series analysis and prediction by ensembling parametric and nonparametric methods. Our approach takes advantage of extracting features at different time scales, which improves accuracy without compromising reliability in comparison with the state-of-the-art methods. Our experimental evaluation using real-world SCADA data from a municipal water management system shows that our proposed method outperforms the baseline methods evaluated here.

Question: What is a SCADA?


Big Data Mobile Services for New York City Taxi Riders and Drivers. In recent years, taxis in New York City have been equipped with GPS sensors to record the start and end locations of every trip, in conjunction with fare collection equipment recording trip time, fare, and distance. This paper analyzes this vast dataset with big data technologies. We use MapReduce and Hive in order to understand traffic and travel patterns, discover relationships among the data, and make predictions on the taxi network. We also propose a service model combining the results of our data analysis with smartphone applications to offer users various taxi-related services based on results of our big data analysis.

Question: What is the big data mobile services for new york city taxi riders and drivers?


DroidTrace: A ptrace based Android dynamic analysis system with forward execution capability. Android, being an open source smartphone operating system, enjoys a large community of developers who create new mobile services and applications. However, it also attracts malware writers to exploit Android devices in order to distribute malicious apps in the wild. In fact, Android malware are becoming more sophisticated and they use advanced "dynamic loading" techniques like Java reflection or native code execution to bypass security detection. To detect dynamic loading, one has to use dynamic analysis. Currently, there are only a handful of Android dynamic analysis tools available, and they all have shortcomings in detecting dynamic loading. The aim of this paper is to design and implement a dynamic analysis system which allows analysts to perform systematic analysis of dynamic payloads with malicious behaviors. We propose "DroidTrace", a ptrace based dynamic analysis system with forward execution capability. Our system uses ptrace to monitor selected system calls of the target process which is running the dynamic payloads, and classifies the payloads behaviors through the system call sequence, e.g., behaviors such as file access, network connection, inter-process communication and even privilege escalation. Also, DroidTrace performs "physical modification" to trigger different dynamic loading behaviors within an app. Using DroidTrace, we carry out a large scale analysis on 36,170 dynamic payloads in 50,000 apps and 294 malware in 10 families (four of them are zero-day) with various dynamic loading behaviors.

Question: What is a ptrace based dynamic analysis system?


Wearable impedance analyzer based on AD5933. In this paper a system for measuring impedance, based on AD5933 circuit is presented. The impedance measuring range is between 9 Ω and 18 MΩ for a 1 kHz ÷ 100 kHz frequency range. Possibilities of expanding this range of measurement are also presented in the paper. The system calibration is automatic and the relative error of the impedance modulus measurement is in the range ±2%. Measured impedance main parameters are shown locally on an OLED display but can also be stored in an SD memory card for further processing. The system is portable, modular and adaptable to a large number of applications.

Question: What is an impedance analyzer?


Structural Insights into the Niemann-Pick C1 (NPC1)-Mediated Cholesterol Transfer and Ebola Infection. Niemann-Pick disease type C (NPC) is associated with mutations in NPC1 and NPC2, whose gene products are key players in the endosomal/lysosomal egress of low-density lipoprotein-derived cholesterol. NPC1 is also the intracellular receptor for Ebola virus (EBOV). Here, we present a 4.4 Å structure of full-length human NPC1 and a low-resolution reconstruction of NPC1 in complex with the cleaved glycoprotein (GPcl) of EBOV, both determined by single-particle electron cryomicroscopy. NPC1 contains 13 transmembrane segments (TMs) and three distinct lumenal domains A (also designated NTD), C, and I. TMs 2-13 exhibit a typical resistance-nodulation-cell division fold, among which TMs 3-7 constitute the sterol-sensing domain conserved in several proteins involved in cholesterol metabolism and signaling. A trimeric EBOV-GPcl binds to one NPC1 monomer through the domain C. Our structural and biochemical characterizations provide an important framework for mechanistic understanding of NPC1-mediated intracellular cholesterol trafficking and Ebola virus infection.

Question: What is the structure of the NPC1-EBOV GPcl complex?


Automatic generation of high-performance modular multipliers for arbitrary mersenne primes on FPGAs. Modular multiplication is a fundamental and performance determining operation in various public-key cryptosystems. High-performance modular multipliers on FPGAs are commonly realized by several small-sized multipliers, an adder tree for summing up the digit-products, and a reduction circuit. While small-sized multipliers are available in pre-fabricated high-speed DSP slices, the adder tree and the reduction circuit are implemented in standard logic. The latter operations represent the performance bottleneck to high-performance implementations. Previous works attempted to minimize the critical path of the adder tree by rearranging digit-products on digit-level. We report improved performance by regrouping digit-products on bit-level, while incorporating the reduction for Mersenne primes. Our approach leads to very fast modular multipliers, whose latency and throughput characteristics outperform all previous results. We formalize our approach and provide algorithms to automatically generate high-performance modular multipliers for arbitrary Mersenne primes from any small-sized multipliers.

Question: How to generate a high-performance modular multiplier for arbitrary mersenne primes?


Instantaneous ego-motion estimation using multiple Doppler radars. The estimation of the ego-vehicle's motion is a key capability for advanced driving assistant systems and mobile robot localization. The following paper presents a robust algorithm using radar sensors to instantly determine the complete 2D motion state of the ego-vehicle (longitudinal, lateral velocity and yaw rate). It evaluates the relative motion between at least two Doppler radar sensors and their received stationary reflections (targets). Based on the distribution of their radial velocities across the azimuth angle, non-stationary targets and clutter are excluded. The ego-motion and its corresponding covariance matrix are estimated. The algorithm does not require any preprocessing steps such as clustering or clutter suppression and does not contain any model assumptions. The sensors can be mounted at any position on the vehicle. A common field of view is not required, avoiding target association in space. As an additional benefit, all targets are instantly labeled as stationary or non-stationary.

Question: How to estimate ego-motion using multiple Doppler radars?


Why did TD-Gammon Work?. Although TD-Gammon is one of the major successes in machine learning, it has not led to similar impressive breakthroughs in temporal difference learning for other applications or even other games. We were able to replicate some of the success of TD-Gammon, developing a competitive evaluation function on a 4000 parameter feed-forward neural network, without using back-propagation, reinforcement or temporal difference learning methods. Instead we apply simple hill-climbing in a relative fitness environment. These results and further analysis suggest that the surprising success of Tesauro's program had more to do with the co-evolutionary structure of the learning task and the dynamics of the backgammon game itself.

Question: Why did TD-Gammon work?


Learning by Association — A Versatile Semi-Supervised Training Method for Neural Networks. In many real-world scenarios, labeled data for a specific machine learning task is costly to obtain. Semi-supervised training methods make use of abundantly available unlabeled data and a smaller number of labeled examples. We propose a new framework for semi-supervised training of deep neural networks inspired by learning in humans. Associations are made from embeddings of labeled samples to those of unlabeled ones and back. The optimization schedule encourages correct association cycles that end up at the same class from which the association was started and penalizes wrong associations ending at a different class. The implementation is easy to use and can be added to any existing end-to-end training setup. We demonstrate the capabilities of learning by association on several data sets and show that it can improve performance on classification tasks tremendously by making use of additionally available unlabeled data. In particular, for cases with few labeled data, our training scheme outperforms the current state of the art on SVHN.

Question: What is the name of the framework?


A New Gradient Based Character Segmentation Method for Video Text Recognition. The current OCR cannot segment words and characters from video images due to complex background as well as low resolution of video images. To have better accuracy, this paper presents a new gradient based method for words and character segmentation from text line of any orientation in video frames for recognition. We propose a Max-Min clustering concept to obtain text cluster from the normalized absolute gradient feature matrix of the video text line image. Union of the text cluster with the output of Canny operation of the input video text line is proposed to restore missing text candidates. Then a run length algorithm is applied on the text candidate image for identifying word gaps. We propose a new idea for segmenting characters from the restored word image based on the fact that the text height difference at the character boundary column is smaller than that of the other columns of the word image. We have conducted experiments on a large dataset at two levels (word and character level) in terms of recall, precision and f-measure. Our experimental setup involves 3527 characters of English and Chinese, and this dataset is selected from TRECVID database of 2005 and 2006.

Question: How to segment characters from video images?


Vehicle Localization along a Previously Driven Route Using Image Database. In most autonomous driving applications, such as parking and commuting, a vehicle follows a previously taken route, or almost the same route. In this paper, we propose a method to localize a vehicle along a previously driven route using images. The proposed method consists of two stages: offline creation of a database, and online localization. In the offline stage, a database is created from images that are captured when the vehicle drives a route for the first time. The database consists of images, 3D positions of feature points estimated by structure-from-motion, and a topological graph. In the online stage, the method first identifies the database image that is most similar to the current image by topometric localization, which considers topological information on a metric scale. The vehicle poses are then estimated from the 3D-2D correspondences of matching feature points between the current image and the identified database image. In an experiment, we estimated vehicle poses using images captured in an indoor parking lot.

Question: How to localize a vehicle along a previously driven route using images?


Information Systems and Healthcare XXXII: Understanding the Multidimensionality of Information Systems Use: A Study of Nurses' Use of a Mandated Electronic Medical Record System.

Question: What is the multidimensionality of information systems use?


1 . RELATED WORK 1 . 1 3 D Reconstruction. Creating and animating realistic 3D human faces is an important element of virtual reality, video games, and other areas that involve interactive 3D graphics. In this paper, we propose a system to generate photorealistic 3D blendshape-based face models automatically using only a single consumer RGB-D sensor. The capture and processing requires no artistic expertise to operate, takes 15 seconds to capture and generate a single facial expression, and approximately 1 minute of processing time per expression to transform it into a blendshape model. Our main contributions include a complete end-to-end pipeline for capturing and generating photorealistic blendshape models automatically and a registration method that solves dense correspondences between two face scans by utilizing facial landmarks detection and optical flows. We demonstrate the effectiveness of the proposed method by capturing different human subjects with a variety of sensors and puppeteering their 3D faces with real-time facial performance retargeting. The rapid nature of our method allows for just-in-time construction of a digital face. To that end, we also integrated our pipeline with a virtual reality facial performance capture system that allows dynamic embodiment of the generated faces despite partial occlusion of the user's real face by the head-mounted display.

Question: How to create a photorealistic 3D face model?


Deep keyphrase generation with a convolutional sequence to sequence model. Keyphrases can provide highly condensed and valuable information that allows users to quickly acquire the main ideas. Most previous studies realize the automatic keyphrase extraction through dividing the source text into multiple chunks and then rank and select the most suitable ones. These approaches ignore the deep semantics behind the text and could not predict the keyphrases not appearing in the source text. A sequence to sequence model to generate keyphrases from vocabulary could solve the issues above. However, traditional sequence to sequence model based on recurrent neural network(RNN) suffers from low efficiency problem. We propose an architecture based entirely on convolutional neural networks. Compared to recurrent models, computations can be completely parallelized over all elements so as to better exploit the GPU hardware. Our use of gated linear units alleviates gradient propagation and we equip each decoder layer with a separate attention model. Moreover, we incorporate a copying mechanism to handle out-of-vocabulary phrases. In experiments, we evaluate our model on six datasets, and our proposed model is demonstrated to outperform state-of-the-art baseline models consistently and significantly, both on extracting the keyphrases existing in the source text and generating the absent keyphrases based on the sematic meaning of the text.

Question: How to generate keyphrases from vocabulary?


On the Kinematic Analysis of Robotic Mechanisms. The kinematic analyses, of manipulators and other robotic devices composed of mechanical links, usually depend on the solution of sets of nonlinear equations. There are a variety of both numerical and algebraic techniques available to solve such systems of equations and to give bounds on the number of solutions. These solution methods have also led to an understanding of how special choices of the various structural parameters of a mechanism influence the number of solutions inherent to the kinematic geometry of a given structure. In this paper, results from studying the kinematic geometry of such systems are reviewed, and the three most useful solution techniques are summarized. The solution techniques are polynomial continuation, Gröbner bases, and elimination. We then discuss the results that have been obtained with these techniques in the solution of two basic problems, namely, the inverse kinematics for serial-chain manipulators, and the direct kinematics of in-parallel platform devices. KEY WORDS—inverse kinematics, direct kinematics, manipulators, in-parallel manipulators, series manipulators, polynomial continuation, characteristic polynomial

Question: What is the difference between inverse kinematics and direct kinematics?


Optical Fiber Sensors for Aircraft Structural Health Monitoring. Aircraft structures require periodic and scheduled inspection and maintenance operations due to their special operating conditions and the principles of design employed to develop them. Therefore, structural health monitoring has a great potential to reduce the costs related to these operations. Optical fiber sensors applied to the monitoring of aircraft structures provide some advantages over traditional sensors. Several practical applications for structures and engines we have been working on are reported in this article. Fiber Bragg gratings have been analyzed in detail, because they have proved to constitute the most promising technology in this field, and two different alternatives for strain measurements are also described. With regard to engine condition evaluation, we present some results obtained with a reflected intensity-modulated optical fiber sensor for tip clearance and tip timing measurements in a turbine assembled in a wind tunnel.

Question: What is the potential of optical fiber sensors for aircraft structural health monitoring?


A Survey of In-Band Full-Duplex Transmission: From the Perspective of PHY and MAC Layers. In-band full-duplex (IBFD) transmission represents an attractive option for increasing the throughput of wireless communication systems. A key challenge for IBFD transmission is reducing self-interference. Fortunately, the power associated with residual self-interference can be effectively canceled for feasible IBFD transmission with combinations of various advanced passive, analog, and digital self-interference cancellation schemes. In this survey paper, we first review the basic concepts of IBFD transmission with shared and separated antennas and advanced self-interference cancellation schemes. Furthermore, we also discuss the effects of IBFD transmission on system performance in various networks such as bidirectional, relay, and cellular topology networks. This survey covers a wide array of technologies that have been proposed in the literature as feasible for IBFD transmission and evaluates the performance of the IBFD systems compared to conventional half-duplex transmission in connection with theoretical aspects such as the achievable sum rate, network capacity, system reliability, and so on. We also discuss the research challenges and opportunities associated with the design and analysis of IBFD systems in a variety of network topologies. This work also explores the development of MAC protocols for an IBFD system in both infrastructure-based and ad hoc networks. Finally, we conclude our survey by reviewing the advantages of IBFD transmission when applied for different purposes, such as spectrum sensing, network secrecy, and wireless power transfer.

Question: What is the difference between a survey and a review?


The Price of Anarchy in Supply Chains: Quantifying the Efficiency of Price-Only Contracts. I this paper, we quantify the efficiency of decentralized supply chains that use price-only contracts. With a price-only contract, a buyer and a seller agree only on a constant transaction price, without specifying the amount that will be transferred. It is well known that these contracts do not provide incentives to the parties to coordinate their inventory/capacity decisions. We measure efficiency with the price of anarchy (PoA), defined as the largest ratio of profits between the integrated supply chain (that is, fully coordinated) and the decentralized supply chain. We characterize the efficiency of various supply chain configurations: push or pull inventory positioning, two or more stages, serial or assembly systems, single or multiple competing suppliers, and single or multiple competing retailers.

Question: What is the price of anarchy?


Secure Coding Practices in Java: Challenges and Vulnerabilities. The Java platform and its third-party libraries provide useful features to facilitate secure coding. However, misusing them can cost developers time and effort, as well as introduce security vulnerabilities in software. We conducted an empirical study on StackOverflow posts, aiming to understand developers' concerns on Java secure coding, their programming obstacles, and insecure coding practices.  We observed a wide adoption of the authentication and authorization features provided by Spring Security---a third-party framework designed to secure enterprise applications. We found that programming challenges are usually related to APIs or libraries, including the complicated cross-language data handling of cryptography APIs, and the complex Java-based or XML-based approaches to configure Spring Security. In addition, we reported multiple security vulnerabilities in the suggested code of accepted answers on the StackOverfow forum. The vulnerabilities included disabling the default protection against Cross-Site Request Forgery (CSRF) attacks, breaking SSL/TLS security through bypassing certificate validation, and using insecure cryptographic hash functions. Our findings reveal the insufficiency of secure coding assistance and documentation, as well as the huge gap between security theory and coding practices.

Question: What is the difference between a security vulnerability and a security flaw?


Integrality and Separability of Input Devices. Current input device taxonomies and other frameworks typically emphasize the mechanical structure of input devices. We suggest that selecting an appropriate input device for an interactive task requires looking beyond the physical structure of devices to the deeper perceptual structure of the task, the device, and the interrelationship between the perceptual structure of the task and the control properties of the device. We affirm that perception is key to understanding performance of multidimensional input devices on multidimensional tasks. We have therefore extended the theory of processing of percetual structure to graphical interactive tasks and to the control structure of input devices. This allows us to predict task and device combinations that lead to better performance and hypothesize that performance is improved when the perceptual structure of the task matches the control structure of the device. We conducted an experiment in which subjects performed two tasks with different perceptual structures, using two input devices with correspondingly different control structures, a three-dimensional tracker and a mouse. We analyzed both speed and accuracy, as well as the trajectories generated by subjects as they used the unconstrained three-dimensional tracker to perform each task. The result support our hypothesis and confirm the importance of matching the perceptual structure of the task and the control structure of the input device.

Question: What is the perceptual structure of a task?


A New Discrete Particle Swarm Optimization Algorithm. Particle Swarm Optimization (PSO) has been shown to perform very well on a wide range of optimization problems. One of the drawbacks to PSO is that the base algorithm assumes continuous variables. In this paper, we present a version of PSO that is able to optimize over discrete variables. This new PSO algorithm, which we call Integer and Categorical PSO (ICPSO), incorporates ideas from Estimation of Distribution Algorithms (EDAs) in that particles represent probability distributions rather than solution values, and the PSO update modifies the probability distributions. In this paper, we describe our new algorithm and compare its performance against other discrete PSO algorithms. In our experiments, we demonstrate that our algorithm outperforms comparable methods on both discrete benchmark functions and NK landscapes, a mathematical framework that generates tunable fitness landscapes for evaluating EAs.

Question: What is a discrete optimization algorithm?


Robust Nighttime Vehicle Detection by Tracking and Grouping Headlights. Nighttime traffic surveillance is difficult due to insufficient and unstable appearance information and strong background interference. We present in this paper a robust nighttime vehicle detection system by detecting, tracking, and grouping headlights. First, we train AdaBoost classifiers for headlights detection to reduce false alarms caused by reflections. Second, to take full advantage of the complementary nature of grouping and tracking, we alternately optimize grouping and tracking. For grouping, motion features produced by tracking are used by headlights pairing. We use a maximal independent set framework for effective pairing, which is more robust than traditional pairing-by-rules methods. For tracking, context information provided by pairing is employed by multiple object tracking. The experiments on challenging datasets and quantitative evaluation show promising performance of our method.

Question: Robust nighttime vehicle detection by tracking and grouping headlights


From Hunt the Wumpus to EverQuest: Introduction to Quest Theory. The paper will explore how the landscape types and the quest types are used in various games, how they structure the gameplay, how they act as bones for the game-content (graphics, dialogue, sound) and how they sometimes form the base on which a story is imposed and related to the player. The question then becomes, how does the quest structure influence the story structure? How do the limitations of the quest combinations limit the kinds of story that are possible? How rich can the imposed story be, without breaking the gameplay? Are landscape and quest-structure the dominant factors in quest game design, to which the story-ambitions must defer? The main thesis of the paper is that if we understand the powerful but simple structure the grammar of quests (how they work, how they are used) we can understand both the limits and the potential of these kinds of games.

Question: How does the quest structure influence the story structure?


The meaning of compassion fatigue to student nurses: an interpretive phenomenological study. Background: Compassion fatigue is a form of occupational stress which occurs when individuals are exposed to suffering and trauma on an ongoing basis. The purpose of this study was to explore the experiences of compassion fatigue among student nurses following their first clinical placement in a UK health care setting during 2015. Methods: The aim of this study was to explore students' thoughts and feelings about compassion fatigue using reflective poems as a source of data. An interpretive phenomenological approach was taken using a purposeful sampling strategy which aimed to explore in depth meaning of the concept as experienced by the students. Results: From this study it is clear that students experience compassion fatigue and this has a psychological effect on their wellbeing and ability to learn in the clinical practice setting. Reflective poetry writing enabled articulation of feelings which were at times negative and linked to the student's status as a novice nurse. Conclusions: Students experience compassion fatigue and educators need to find ways to provide support in both clinical and university settings. Positive practices such as shared reflection and the use of creative teaching methods might be beneficial, to support exploration of feelings, build resilience and effective ways of coping.

Question: What is compassion fatigue?


A survey on unmanned aerial vehicle collision avoidance systems. Collision avoidance is a key factor in enabling the integration of unmanned aerial vehicle into real life use, whether it is in military or civil application. For a long time there have been a large number of works to address this problem; therefore a comparative summary of them would be desirable. This paper presents a survey on the major collision avoidance systems developed in up to date publications. Each collision avoidance system contains two main parts: sensing and detection, and collision avoidance. Based on their characteristics each part is divided into different categories; and those categories are explained, compared and discussed about advantages and disadvantages in this paper.

Question: What is the difference between a collision avoidance system and a collision avoidance system?


Wireless Body Area Network (WBAN): A Survey on Reliability, Fault Tolerance, and Technologies Coexistence. Wireless Body Area Network (WBAN) has been a key element in e-health to monitor bodies. This technology enables new applications under the umbrella of different domains, including the medical field, the entertainment and ambient intelligence areas. This survey paper places substantial emphasis on the concept and key features of the WBAN technology. First, the WBAN concept is introduced and a review of key applications facilitated by this networking technology is provided. The study then explores a wide variety of communication standards and methods deployed in this technology. Due to the sensitivity and criticality of the data carried and handled by WBAN, fault tolerance is a critical issue and widely discussed in this paper. Hence, this survey investigates thoroughly the reliability and fault tolerance paradigms suggested for WBANs. Open research and challenging issues pertaining to fault tolerance, coexistence and interference management and power consumption are also discussed along with some suggested trends in these aspects.

Question: What is the canadian military?


NestedNet: Learning Nested Sparse Structures in Deep Neural Networks. Recently, there have been increasing demands to construct compact deep architectures to remove unnecessary redundancy and to improve the inference speed. While many recent works focus on reducing the redundancy by eliminating unneeded weight parameters, it is not possible to apply a single deep network for multiple devices with different resources. When a new device or circumstantial condition requires a new deep architecture, it is necessary to construct and train a new network from scratch. In this work, we propose a novel deep learning framework, called a nested sparse network, which exploits an n-in-1-type nested structure in a neural network. A nested sparse network consists of multiple levels of networks with a different sparsity ratio associated with each level, and higher level networks share parameters with lower level networks to enable stable nested learning. The proposed framework realizes a resource-aware versatile architecture as the same network can meet diverse resource requirements, i.e., anytime property. Moreover, the proposed nested network can learn different forms of knowledge in its internal networks at different levels, enabling multiple tasks using a single network, such as coarse-to-fine hierarchical classification. In order to train the proposed nested network, we propose efficient weight connection learning and channel and layer scheduling strategies. We evaluate our network in multiple tasks, including adaptive deep compression, knowledge distillation, and learning class hierarchy, and demonstrate that nested sparse networks perform competitively, but more efficiently, compared to existing methods.

Question: What is a nested sparse network?


Measuring emotional intelligence with the MSCEIT V2.0.. Does a recently introduced ability scale adequately measure emotional intelligence (EI) skills? Using the Mayer-Salovey-Caruso Emotional Intelligence Test (MSCEIT; J. D. Mayer, P. Salovey, & D. R. Caruso, 2002b), the authors examined (a) whether members of a general standardization sample and emotions experts identified the same test answers as correct, (b) the test's reliability, and (c) the possible factor structures of EI. Twenty-one emotions experts endorsed many of the same answers, as did 2,112 members of the standardization sample, and exhibited superior agreement, particularly when research provides clearer answers to test questions (e.g., emotional perception in faces). The MSCEIT achieved reasonable reliability, and confirmatory factor analysis supported theoretical models of EI. These findings help clarify issues raised in earlier articles published in Emotion.

Question: What is the MSCEIT?


Recommendations for the Assessment of Blend and Content Uniformity: Modifications to Withdrawn FDA Draft Stratified Sampling Guidance. The following paper describes the International Society for Pharmaceutical Engineering (ISPE)-sponsored Blend Uniformity and Content Uniformity Group's proposed modifications to the withdrawn FDA draft guidance document for industry "Powder Blends and Finished Dosage Units—Stratified In-Process Dosage Unit Sampling and Assessment." The modifications targeted FDA's primary concerns that led to the withdrawal of the draft guidance document, which were insufficient blend uniformity testing and that a one-time passing of the criteria stated in USP General Chapter <905> Uniformity of Dosage Units testing lacks confidence to ensure the content uniformity of a batch. The Group's approach discusses when triplicate blend samples should be analyzed and the importance of performing variance component analysis on the data to identify root causes of non-uniformity. The Group recommends the use of statistically based approaches, acceptance criteria, and sampling plans for assessing content uniformity for batch release that provide increased confidence that future samples drawn from the batch will comply with USP <905>. Alternative statistical approaches, sampling plans, and acceptance criteria, including modern analytical method (e.g., process analytical technology (PAT)) sampling plans, may be substituted for those mentioned in this paper, with justification. This approach also links blend and content uniformity testing to the three stages of the life cycle process validation approach. A framework for the assessment of blend and content uniformity that provides greater assurance of passing USP <905> is presented.

Question: How to assess content uniformity of a batch?


HYDRA: hybrid design for remote attestation (using a formally verified microkernel). Remote Attestation (RA) allows a trusted entity (verifier) to securely measure internal state of a remote untrusted hardware platform (prover). RA can be used to establish a static or dynamic root of trust in embedded and cyber-physical systems. It can also be used as a building block for other security services and primitives, such as software updates and patches, verifiable deletion and memory resetting. There are three major types of RA designs: hardware-based, software-based, and hybrid, each with its own set of benefits and drawbacks.  This paper presents the first hybrid RA design - called HYDRA - that builds upon formally verified software components that ensure memory isolation and protection, as well as enforce access control to memory and other resources. HYDRA obtains these properties by using the formally verified seL4 microkernel. (Until now, this was only attainable with purely hardware-based designs.) Using seL4 imposes fewer hardware requirements on the underlying microprocessor. Also, building upon a formally verified software component increases confidence in security of the overall design of HYDRA and its implementation. We instantiate HYDRA on two commodity hardware platforms and assess the performance and overhead of performing RA on such platforms via experimentation; we show that HYDRA can attest 10MB of memory in less than 250msec when using a Speck-based cryptographic checksum.

Question: What is the hybrid design for remote attestation?


Denial of Service in Sensor Networks. S ensor networks hold the promise of facilitating large-scale, real-time data processing in complex environments. Their foreseeable applications will help protect and monitor critical military, environmental, safety-critical , or domestic infrastructures and resources. In these and other vital or security-sensitive deployments, keeping the network available for its intended use is essential. The stakes are high: Denial of service attacks against such networks may permit real-world damage to the health and safety of people. Without proper security mechanisms, networks will be confined to limited, controlled environments , negating much of the promise they hold. The limited ability of individual sensor nodes to thwart failure or attack makes ensuring network availability more difficult. To identify denial of service vulnerabilities, we analyze two effective sensor network protocols that did not initially consider security. These examples demonstrate that consideration of security at design time is the best way to ensure successful network deployment. Advances in miniaturization combined with an insatiable appetite for previously unrealizable information gathering have lead to the development of new kinds of networks. In many areas, static infrastruc-tures are giving way to dynamic ad-hoc networks. One manifestation of these trends is the development of highly application-dependent sensor networks. Developers build sensor networks to collect and analyze low-level data from an environment of interest. Accomplishing the network's goal often depends on local cooperation, aggregation, or data processing because individual nodes have limited capabilities. Physically small, nodes have tiny or irreplaceable power reserves, communicate wirelessly, and may not possess unique identifiers. Further, they must form ad hoc relationships in a dense network with little or no preexisting infrastructure. Protocols and algorithms operating in the net

Question: What is a sensor network?


A General Greedy Approximation Algorithm with Applications. Greedy approximation algorithms have been frequently used to obtain sparse solutions to learning problems. In this paper, we present a general greedy algorithm for solving a class of convex optimization problems. We derive a bound on the rate of approximation for this algorithm, and show that our algorithm includes a number of earlier studies as special cases.

Question: What is a greedy approximation algorithm?


Reading the mind in cartoons and stories: an fMRI study of 'theory of mind' in verbal and nonverbal tasks. Previous functional imaging studies have explored the brain regions activated by tasks requiring 'theory of mind'--the attribution of mental states. Tasks used have been primarily verbal, and it has been unclear to what extent different results have reflected different tasks, scanning techniques, or genuinely distinct regions of activation. Here we report results from a functional magnetic resonance imaging study (fMRI) involving two rather different tasks both designed to tap theory of mind. Brain activation during the theory of mind condition of a story task and a cartoon task showed considerable overlap, specifically in the medial prefrontal cortex (paracingulate cortex). These results are discussed in relation to the cognitive mechanisms underpinning our everyday ability to 'mind-read'.

Question: What is the paracingulate cortex?


PAMS: A new position-aware multi-sensor dataset for human activity recognition using smartphones. Nowadays smartphones are ubiquitous in various aspects of our lives. The processing power, communication bandwidth, and the memory capacity of these devices have surged considerably in recent years. Besides, the variety of sensor types, such as accelerometer, gyroscope, humidity sensor, and bio-sensors, which are embedded in these devices, opens a new horizon in self-monitoring of physical daily activities. One of the primary steps for any research in the area of detecting daily life activities is to test a detection method on benchmark datasets. Most of the early datasets limited their work to collecting only a single type of sensor data such as accelerometer data. While some others do not consider age, weight, and gender of the subjects who have participated in collecting their activity data. Finally, part of the previous works collected data without considering the smartphone's position. In this paper, we introduce a new dataset, called Position-Aware Multi-Sensor (PAMS). The dataset contains both accelerometer and gyroscope data. The gyroscope data boosts the accuracy of activity recognition methods as well as enabling them to detect a wider range of activities. We also take the user information into account. Based on the biometric attributes of the participants, a separate learned model is generated to analyze their activities. We concentrate on several major activities, including sitting, standing, walking, running, ascending/descending stairs, and cycling. To evaluate the dataset, we use various classifiers, and the outputs are compared to the WISDM. The results show that using aforementioned classifiers, the average precision for all activities is above 88.5%. Besides, we measure the CPU, memory, and bandwidth usage of the application collecting data on the smartphone.

Question: What is the average precision for all activities?


On Software Implementation of High Performance GHASH Algorithms. There have been several modes of operations available for symmetric key block ciphers, among which Galois Counter Mode (GCM) of operation is a standard. GCM mode of operation provides con dentiality with the help of symmetric key block cipher operating in counter mode. The authentication component of GCM comprises of Galois hash (GHASH) computation which is a keyed hash function. The most important component of GHASH computation is carry-less multiplication of 128bit operands which is followed by a modulo reduction. There have been a number of schemes proposed for e cient software implementation of carry-less multiplication to improve performance of GHASH by increasing the speed of multiplications. This thesis focuses on providing an e cient way of software implementation of high performance GHASH function as being proposed by Meloni et al., and also on the implementation of GHASH using a carry-less multiplication instruction provided by Intel on their Westmere architecture. The thesis work includes implementation of the high performance GHASH and its comparison to the older or standard implementation of GHASH function. It also includes comparison of the two implementations using Intel's carry-less multiplication instruction. This is the rst time that this kind of comparison is being done on software implementations of these algorithms. Our software implementations suggest that the new GHASH algorithm, which was originally proposed for the hardware implementations due to the required parallelization, can't take advantage of the Intel carry-less multiplication instruction PCLMULQDQ. On the other hand, when implementations are done without using the PCLMULQDQ instruction the new algorithm performs better, even if its inherent parallelization is not utilized. This suggest that the new algorithm will perform better on embedded systems that do not support PCLMULQDQ.

Question: What is the difference between the old and new GHASH algorithms?


A Revised Discrete Particle Swarm Optimization for Cloud Workflow Scheduling. A cloud workflow system is a type of platform service which facilitates the automation of distributed applications based on the novel cloud infrastructure. Compared with grid environment, data transfer is a big overhead for cloud workflows due to the market-oriented business model in the cloud environments. In this paper, a Revised Discrete Particle Swarm Optimization (RDPSO) is proposed to schedule applications among cloud services that takes both data transmission cost and computation cost into account. Experiment is conducted with a set of workflow applications by varying their data communication costs and computation costs according to a cloud price model. Comparison is made on make span and cost optimization ratio and the cost savings with RDPSO, the standard PSO and BRS (Best Resource Selection) algorithm. Experimental results show that the proposed RDPSO algorithm can achieve much more cost savings and better performance on make span and cost optimization.

Question: What is a cloud price model?


Large-Scale Analysis of Soccer Matches Using Spatiotemporal Tracking Data. Although the collection of player and ball tracking data is fast becoming the norm in professional sports, large-scale mining of such spatiotemporal data has yet to surface. In this paper, given an entire season's worth of player and ball tracking data from a professional soccer league (≈400,000,000 data points), we present a method which can conduct both individual player and team analysis. Due to the dynamic, continuous and multi-player nature of team sports like soccer, a major issue is aligning player positions over time. We present a "role-based" representation that dynamically updates each player's relative role at each frame and demonstrate how this captures the short-term context to enable both individual player and team analysis. We discover role directly from data by utilizing a minimum entropy data partitioning method and show how this can be used to accurately detect and visualize formations, as well as analyze individual player behavior.

Question: What is a role-based representation?


Anomaly detection and monitoring in Internet of Things communication. The Internet of Things (IoT) presents unique challenges in detecting anomaly and monitoring all connected devices in a network. Moreover, one of the objectives of anonymity in communication is to protect the data traffic of devices. The summary status visualization is indispensable to depict all devices/sensors that are most indicative of a pending failure and a predictive power/energy. Thus, this paper proposes a multi-platform monitoring and anomaly detection system that supports heterogeneous devices. The proposed system addresses the problems of: (i) how to monitor the network to prevent device failures and (ii) how to design a comprehensive feature for the early anomaly detection of IoT communication.

Question: How to monitor the network to prevent device failures?


A Survey on NoSQL Stores. Recent demands for storing and querying big data have revealed various shortcomings of traditional relational database systems. This, in turn, has led to the emergence of a new kind of complementary nonrelational data store, named as NoSQL. This survey mainly aims at elucidating the design decisions of NoSQL stores with regard to the four nonorthogonal design principles of distributed database systems: data model, consistency model, data partitioning, and the CAP theorem. For each principle, its available strategies and corresponding features, strengths, and drawbacks are explained. Furthermore, various implementations of each strategy are exemplified and crystallized through a collection of representative academic and industrial NoSQL technologies. Finally, we disclose some existing challenges in developing effective NoSQL stores, which need attention of the research community, application designers, and architects.

Question: What is the difference between a relational database and a NoSQL database?


A fuzzy classification system for prediction of the results of the basketball games. Prediction of the sports game results is an interesting topic that has gained attention lately. Mostly there are used stochastical methods of uncertainty description. In this work it is presented a preliminary approach to build a fuzzy model to basketball game results prediction. Ten fuzzy rule learning algorithms are selected, conducted and compared against standard linear regression with use of the KEEL system. Feature selection algorithms are applied and a majority voting is used in order to select the most representative features.

Question: What is a fuzzy model?


Analyzing Behavior Specialized Acceleration. Hardware specialization has become a promising paradigm for overcoming the inefficiencies of general purpose microprocessors. Of significant interest are Behavioral Specialized Accelerators (BSAs), which are designed to efficiently execute code with only certain properties, but remain largely configurable or programmable. The most important strength of BSAs -- their ability to target a wide variety of codes -- also makes their interactions and analysis complex, raising the following questions: can multiple BSAs be composed synergistically, what are their interactions with the general purpose core, and what combinations favor which workloads? From a methodological standpoint, BSAs are also challenging, as they each require ISA development, compiler and assembler extensions, and either simulator or RTL models.  To study the potential of BSAs, we propose a novel modeling technique called the Transformable Dependence Graph (TDG) - a higher level alternative to the time-consuming traditional compiler+simulator approach, while still enabling detailed microarchitectural models for both general cores and accelerators. We then propose a multi-BSA organization, called ExoCore, which we model and study using the TDG. A design space exploration reveals that an ExoCore organization can push designs beyond the established energy-performance frontiers for general purpose cores. For example, a 2-wide OOO processor with three BSAs matches the performance of a conventional 6-wide OOO core, has 40% lower area, and is 2.6x more energy efficient.

Question: What is the difference between a general purpose core and a specialized accelerator?


Pulsed radiofrequency treatment in interventional pain management: mechanisms and potential indications—a review. The objective of this review is to evaluate the efficacy of Pulsed Radiofrequency (PRF) treatment in chronic pain management in randomized clinical trials (RCTs) and well-designed observational studies. The physics, mechanisms of action, and biological effects are discussed to provide the scientific basis for this promising modality. We systematically searched for clinical studies on PRF. We searched the MEDLINE (PubMed) and EMBASE database, using the free text terms: pulsed radiofrequency, radio frequency, radiation, isothermal radiofrequency, and combination of these. We classified the information in two tables, one focusing only on RCTs, and another, containing prospective studies. Date of last electronic search was 30 May 2010. The methodological quality of the presented reports was scored using the original criteria proposed by Jadad et al. We found six RCTs that evaluated the efficacy of PRF, one against corticosteroid injection, one against sham intervention, and the rest against conventional RF thermocoagulation. Two trials were conducted in patients with lower back pain due to lumbar zygapophyseal joint pain, one in cervical radicular pain, one in lumbosacral radicular pain, one in trigeminal neuralgia, and another in chronic shoulder pain. From the available evidence, the use of PRF to the dorsal root ganglion in cervical radicular pain is compelling. With regards to its lumbosacral counterpart, the use of PRF cannot be similarly advocated in view of the methodological quality of the included study. PRF application to the supracapular nerve was found to be as efficacious as intra-articular corticosteroid in patients with chronic shoulder pain. The use of PRF in lumbar facet arthropathy and trigeminal neuralgia was found to be less effective than conventional RF thermocoagulation techniques.

Question: What is the mechanism of action of PRF?


Computational intelligence in sports: Challenges and opportunities within a new research domain. Computational intelligence is a branch of artificial intelligence that comprises algorithms inspired by nature. The common characteristics of all these algorithms is their collective intelligence and adaptability to a changing environment. Due to their efficiency and simplicity, these algorithms have been employed for problem solving across social and natural sciences. The aim of this paper is to demonstrate that nature-inspired algorithms are also useful within the domain of sport, in particular for obtaining safe and effective training plans targeting various aspects of performance. We outline the benefits and opportunities of applying computational intelligence in sports, and we also comment on the pitfalls and challenges for the future development of this emerging research domain. © 2015 Elsevier Inc. All rights reserved.

Question: What is the difference between computational intelligence and artificial intelligence?


Neural Program Meta-Induction. Most recently proposed methods for Neural Program Induction work under the assumption of having a large set of input/output (I/O) examples for learning any underlying input-output mapping. This paper aims to address the problem of data and computation efficiency of program induction by leveraging information from related tasks. Specifically, we propose two approaches for cross-task knowledge transfer to improve program induction in limited-data scenarios. In our first proposal, portfolio adaptation, a set of induction models is pretrained on a set of related tasks, and the best model is adapted towards the new task using transfer learning. In our second approach, meta program induction, a k-shot learning approach is used to make a model generalize to new tasks without additional training. To test the efficacy of our methods, we constructed a new benchmark of programs written in the Karel programming language [17]. Using an extensive experimental evaluation on the Karel benchmark, we demonstrate that our proposals dramatically outperform the baseline induction method that does not use knowledge transfer. We also analyze the relative performance of the two approaches and study conditions in which they perform best. In particular, meta induction outperforms all existing approaches under extreme data sparsity (when a very small number of examples are available), i.e., fewer than ten. As the number of available I/O examples increase (i.e. a thousand or more), portfolio adapted program induction becomes the best approach. For intermediate data sizes, we demonstrate that the combined method of adapted meta program induction has the strongest performance.

Question: What is neural program induction?


Severity Analyses of Single-Vehicle Crashes Based on Rough Set Theory. A single-vehicle crash is a typical pattern of traffic accidents and tends to cause heavy loss. The purpose of this study is to identify the factors significantly influencing single-vehicle crash injury severity, using a data selected from Beijing city for a 4-year period. Rough set theory was applied to complete the injury severity analysis, and followed by applying cross-validation method to estimate the prediction accuracy of extraction rules. Results show that it is effective for analyzing the severity of Single-vehicle crashes with rough set theory.

Question: What is the purpose of this study?


Action Search: Learning to Search for Human Activities in Untrimmed Videos. Traditional approaches for action detection use trimmed data to learn sophisticated action detector models. Although these methods have achieved great success at detecting human actions, we argue that huge information is discarded when ignoring the process, through which this trimmed data is obtained. In this paper, we propose Action Search, a novel approach that mimics the way people annotate activities in video sequences. Using a Recurrent Neural Network, Action Search can efficiently explore a video and determine the time boundaries during which an action occurs. Experiments on the THUMOS14 dataset reveal that our model is not only able to explore the video efficiently but also accurately find human activities, outperforming stateof-the-art methods.

Question: How to search for human activities in untrimmed videos?


Attitudes towards following meat, vegetarian and vegan diets: an examination of the role of ambivalence. Vegetarianism within the U.K. is growing in popularity, with the current estimate of 7% of the population eating a vegetarian diet. This study examined differences between the attitudes and beliefs of four dietary groups (meat eaters, meat avoiders, vegetarians and vegans) and the extent to which attitudes influenced intentions to follow each diet. In addition, the role of attitudinal ambivalence as a moderator variable was examined. Completed questionnaires were obtained from 111 respondents (25 meat eaters, 26 meat avoiders, 34 vegetarians, 26 vegans). In general, predictions were supported, in that respondents displayed most positive attitudes and beliefs towards their own diets, and most negative attitudes and beliefs towards the diet most different form their own. Regression analyses showed that, as predicted by the Theory of Planned Behaviour, attitudes, subjective norm and perceived behavioural control were significant predictors of intention to follow each diet (apart from the vegetarian diet, where subjective norm was non-significant). In each case, attitudinal ambivalence was found to moderate the attitude-intention relationship, such that attitudes were found to be stronger predictors at lower levels of ambivalence. The results not only highlight the extent to which such alternative diets are an interesting focus for psychological research, but also lend further support to the argument that ambivalence in an important influence on attitude strength.

Question: What is the role of attitudinal ambivalence in the attitude-intention relationship?