Segmentation of edges into lines and arcs. .4 long standing problem in computer vision is the extraction of meaningfulfeatures from images. This paper describes a method of segmenting curves in images into a combination of circular arcs and straight lines. This uses a recursive algorithm that first analyses lists of connected edge points and finds a polygonal description, and then analyses this description fitting arcs to groups of connected lines. The result is a description of image edges consisting of circular arcs and lines. The algorithm uses no thresholding. Instead the best option is chosen at each decision stage.

Question: How to segment edges into lines and arcs


Walking to public transit: steps to help meet physical activity recommendations.. BACKGROUND Nearly half of Americans do not meet the Surgeon General's recommendation of > or =30 minutes of physical activity daily. Some transit users may achieve 30 minutes of physical activity daily solely by walking to and from transit. This study estimates the total daily time spent walking to and from transit and the predictors of achieving 30 minutes of physical activity daily by doing so.   METHODS Transit-associated walking times for 3312 transit users were examined among the 105,942 adult respondents to the 2001 National Household Travel Survey, a telephone-based survey sponsored by the U.S. Department of Transportation to assess American travel behavior.   RESULTS Americans who use transit spend a median of 19 minutes daily walking to and from transit; 29% achieve > or =30 minutes of physical activity a day solely by walking to and from transit. In multivariate analysis, rail users, minorities, people in households earning <$15,000 a year, and people in high-density urban areas were more likely to spend > or =30 minutes walking to and from transit daily.   CONCLUSIONS Walking to and from public transportation can help physically inactive populations, especially low-income and minority groups, attain the recommended level of daily physical activity. Increased access to public transit may help promote and maintain active lifestyles. Results from this study may contribute to health impact assessment studies (HIA) that evaluate the impact of proposed public transit systems on physical activity levels, and thereby may influence choices made by transportation planners.

Question: How much time do people spend walking to and from public transit?


EMG analysis of the scapular muscles during a shoulder rehabilitation program.. The purpose of this study was to determine which exercises most effectively use the scapular muscles. Eight muscles in 9 healthy subjects were studied with indwelling electromyographic electrodes and cinematography while performing 16 exercises. The 8 muscles studied were the upper, middle, and lower trapezius; levator scapula; rhomboids; pectoralis minor; and the middle and lower serratus anterior. Each exercise was divided into arcs of motion and the electromyographic activity was quantified as a percentage of the maximal manual muscle test. The optimal exercises for each muscle were identified based on intensity (greater than 50% maximal manual muscle test) and duration (over at least 3 consecutive arcs of motion) of the muscle activity. Twelve of the exercises qualified as top exercises for all of the muscles. On further analysis, a group of 4 exercises was shown to make up the core of a scapular muscle strengthening program. Those 4 exercises include scaption (scapular plane elevation), rowing, push-up with a plus, and press-up.

Question: Which exercises are the best for strengthening the scapular muscles?


FILA: Fine-grained indoor localization. Indoor positioning systems have received increasing attention for supporting location-based services in indoor environments. WiFi-based indoor localization has been attractive due to its open access and low cost properties. However, the distance estimation based on received signal strength indicator (RSSI) is easily affected by the temporal and spatial variance due to the multipath effect, which contributes to most of the estimation errors in current systems. How to eliminate such effect so as to enhance the indoor localization performance is a big challenge. In this work, we analyze this effect across the physical layer and account for the undesirable RSSI readings being reported. We explore the frequency diversity of the subcarriers in OFDM systems and propose a novel approach called FILA, which leverages the channel state information (CSI) to alleviate multipath effect at the receiver. We implement the FILA system on commercial 802.11 NICs, and then evaluate its performance in different typical indoor scenarios. The experimental results show that the accuracy and latency of distance calculation can be significantly enhanced by using CSI. Moreover, FILA can significantly improve the localization accuracy compared with the corresponding RSSI approach.

Question: How to improve the localization accuracy?


Wideband Tapered-Slot Antenna with Corrugated Edges for GPR Applications. A novel Wideband tapered-slot antenna for GPR applications is proposed. A conventional approach to reduce unwanted interactions between the antenna and the ground surface requires extensive microwave absorbers treatment. For the same purpose we propose to use corrugations along the antenna edges. The corrugations improve directivity and wideband matching of the antenna with free space retaining also good radiation efficiency. Further improvement of the antenna performance has been achieved by patching the corrugated areas of the antenna with the resistance card strips. The elimination of the foam-type absorbers makes the antenna more robust and easier in fabrication.

Question: What is a wideband tapered slot antenna?


Complimentary Memory Technologies to Reform the Homogeneity , Energy Landscape , and Volatility of Future Memory Hierarchies. With the limits of transistor size rapidly approaching and the memory wall approaching us at unfathomable speeds, a need to revisit our existing memory technologies has arisen. Fortunately, there are new non-volatile memory (NVM) technologies on the horizon showing great promise. In this paper we will examine some of the new NVM technologies being developed. We will present the basic characteristics of these new technologies as well as the properties that make them unique and the possibilities for the future they offer. Keywords—non-volatile; volatile; memory; memristor; RRAM; MRAM; SRAM; STT-RAM; PCM; PRAM

Question: What are the properties of the new non-volatile memory technologies?


Research on non-invasive glucose concentration measurement by NIR transmission. Diabetes is a widely spreading disease which is known as one of the life threatening disease in the world. It occurs not only among adults and elderly, but also among infants and children. Blood glucose measurements are indispensable to diabetes patients to determine their insulin dose intake. Invasive blood glucose measurement ways which are high in accuracy are common but they are uncomfortable and have higher risk of infections especially for elders, pregnant and children. As a change, non-invasive blood glucose measurement techniques are introduced to provide a reliable and pain free method for monitoring glucose level without puncturing the skin. In this paper, a non-invasive glucose monitoring setup was developed using near infrared by detecting the transmission laser power. The detecting system included the semiconductor laser diode as light source, the S302C light power probe which detected the incident light and, the PM100USB transmit data to the computer. The specific infrared spectrum (1310 nm) was used as the incident beam. A proportional relationship between the laser power and the glucose concentration was proved by comparing the resulting laser power for a few of glucose aqueous solution samples with glucose concentration estimated value at the same circumstances.

Question: What is the relationship between the laser power and the glucose concentration?


Question answering passage retrieval using dependency relations. State-of-the-art question answering (QA) systems employ term-density ranking to retrieve answer passages. Such methods often retrieve incorrect passages as relationships among question terms are not considered. Previous studies attempted to address this problem by matching dependency relations between questions and answers. They used strict matching, which fails when semantically equivalent relationships are phrased differently. We propose fuzzy relation matching based on statistical models. We present two methods for learning relation mapping scores from past QA pairs: one based on mutual information and the other on expectation maximization. Experimental results show that our method significantly outperforms state-of-the-art density-based passage retrieval methods by up to 78% in mean reciprocal rank. Relation matching also brings about a 50% improvement in a system enhanced by query expansion.

Question: What is the difference between a question and an answer?


Keyword extraction based on tf/idf for Chinese news document. Keyword extraction is an important research topic of information retrieval. This paper gave the specification of keywords in Chinese news documents based on analyzing linguistic characteristics of news documents and then proposed a new keyword extraction method based on tf/idf with multi-strategies. The approach selected candidate keywords of uni-, bi-and tri- grams, and then defines the features according to their morphological characters and context information. Moreover, the paper proposed several strategies to amend the incomplete words gotten from the word segmentation and found unknown potential keywords in news documents. Experimental results show that our proposed method can significantly outperform the baseline method. We also applied it to retrospective event detection. Experimental results show that the accuracy and efficiency of news retrospective event detection can be significantly improved.

Question: What is the specification of keywords in Chinese news documents?


Colour image segmentation using the self-organizing map and adaptive resonance theory. We propose a new competitive-learning neural network model for colour image segmentation. The model, which is based on the adaptive resonance theory (ART) of Carpenter and Grossberg and on the self-organizing map (SOM) of Kohonen, overcomes the limitations of (i) the stability–plasticity trade-offs in neural architectures that employ ART; and (ii) the lack of on-line learning property in the SOM. In order to explore the generation of a growing feature map using ART and to motivate the main contribution, we first present a preliminary experimental model, SOMART, based on Fuzzy ART. Then we propose the new model, SmART, that utilizes a novel lateral control of plasticity to resolve the stability–plasticity problem. SmART has been experimentally found to perform well in RGB colour space, and is believed to be more coherent than Fuzzy ART. q 2005 Elsevier Ltd. All rights reserved.

Question: What is the difference between the self-organizing map and the adaptive resonance theory?


A meta-analysis of working memory impairments in children with attention-deficit/hyperactivity disorder.. OBJECTIVE To determine the empirical evidence for deficits in working memory (WM) processes in children and adolescents with attention-deficit/hyperactivity disorder (ADHD).   METHOD Exploratory meta-analytic procedures were used to investigate whether children with ADHD exhibit WM impairments. Twenty-six empirical research studies published from 1997 to December, 2003 (subsequent to a previous review) met our inclusion criteria. WM measures were categorized according to both modality (verbal, spatial) and type of processing required (storage versus storage/manipulation).   RESULTS Children with ADHD exhibited deficits in multiple components of WM that were independent of comorbidity with language learning disorders and weaknesses in general intellectual ability. Overall effect sizes for spatial storage (effect size = 0.85, CI = 0.62 - 1.08) and spatial central executive WM (effect size = 1.06, confidence interval = 0.72-1.39) were greater than those obtained for verbal storage (effect size = 0.47, confidence interval = 0.36-0.59) and verbal central executive WM (effect size = 0.43, confidence interval = 0.24-0.62).   CONCLUSION Evidence of WM impairments in children with ADHD supports recent theoretical models implicating WM processes in ADHD. Future research is needed to more clearly delineate the nature, severity, and specificity of the impairments to ADHD.

Question: What is the difference between working memory and short term memory?


Resource management in a changing and uncertain climate. © The Ecological Society of America www.frontiersinecology.org C change has already had important effects on ecological systems (Parmesan 2006; Root and Schneider 2006; IPCC 2007a; Rosenzweig et al. 2008). Projected changes in climate for the coming century are all greater than the climatic changes the Earth has experienced in the past 100 years (IPCC 2007b). Consequently, future changes in climate are likely to result in even more dramatic ecological responses, including declines in particularly sensitive species (eg corals), continued shifts in species distributions, and substantial changes in ecosystem processes (IPCC 2007a). Changes in hydrologic and fire regimes will fundamentally alter ecological systems. Sea-level rise, in particular, will have dramatic effects on coastal systems (Watson et al. 1996). Changes in phenology will affect the delicate relationships between pollinators and plants, parasites and hosts, foragers and forage, and predators and prey (eg Memmott et al. 2007). Despite the pervasiveness of climate change, most land, water, and resource managers are still following management plans that were developed before there was a scientific consensus that climate-change impacts were both real and substantial (Pyke et al. 2008). Climate change poses difficult challenges for many already overstretched natural resource managers, who must deal with day-to-day crises and who have little access to climate experts. Much of the widely available information about climate change focuses on global or regional scales that are often too broad to fully inform the management of specific nature reserves or regional forests. Moreover, much of the information that is available has a high level of uncertainty, and thus can be difficult to interpret. Finally, although climate change is having impacts today, the largest impacts are still decades in the future. Envisioning these impacts and acting well in advance of

Question: What is the largest impact of climate change?


Application of Artificial Neural Networks to Multiple Criteria Inventory Classification. Original scientific paper Inventory classification is a very important part of inventory control which represents the technique of operational research discipline. A systematic approach to the inventory control and classification may have a significant influence on company competitiveness. The paper describes the results obtained by investigating the application of neural networks in multiple criteria inventory classification. Various structures of a back-propagation neural network have been analysed and the optimal one with the minimum Root Mean Square error selected. The predicted results are compared to those obtained by the multiple criteria classification using the analytical hierarchy process.

Question: What is the difference between neural networks and analytical hierarchy process?


Fake News in Social Networks. We model the spread of news as a social learning game on a network. Agents can either endorse or oppose a claim made in a piece of news, which itself may be either true or false. Agents base their decision on a private signal and their neighbors' past actions. Given these inputs, agents follow strategies derived via multi-agent deep reinforcement learning and receive utility from acting in accordance with the veracity of claims. Our framework yields strategies with agent utility close to a theoretical, Bayes optimal benchmark, while remaining flexible to model re-specification. Optimized strategies allow agents to correctly identify most false claims, when all agents receive unbiased private signals. However, an adversary's attempt to spread fake news by targeting a subset of agents with a biased private signal can be successful. Even more so when the adversary has information about agents' network position or private signal. When agents are aware of the presence of an adversary they re-optimize their strategies in the training stage and the adversary's attack is less effective. Hence, exposing agents to the possibility of fake news can be an effective way to curtail the spread of fake news in social networks. Our results also highlight that information about the users' private beliefs and their social network structure can be extremely valuable to adversaries and should be well protected.

Question: How can fake news be prevented?


Modeling trend progression through an extension of the Polya Urn Process. Knowing how and when trends are formed is a frequently visited research goal. In our work, we focus on the progression of trends through (social) networks. We use a random graph (RG) model to mimic the progression of a trend through the network. The context of the trend is not included in our model. We show that every state of the RG model maps to a state of the Polya process. We find that the limit of the component size distribution of the RG model shows power-law behaviour. These results are also supported by simulations.

Question: How do you model the progression of a trend through a network?


ACQR: A Novel Framework to Identify and Predict Influential Users in Micro-Blogging. As key roles of online social networks, influential users in micro-blogging have the ability to influence the attitudes or behaviour of others. When it comes to marketing, the users' influence should be associated with a certain topic or field on which people have different levels of preference and expertise. In order to identify and predict influential users in a specific topic more effectively, users' actual influential capability on a certain topic and potential influence unlimited by topics is combined into a novel comprehensive framework named "ACQR" in this research. ACQR framework depicts the attributes of the influentials from four aspects, including activeness (A), centrality (C), quality of post (Q) and reputation (R). Based on this framework, a data mining method is developed for discovering and forecasting the top influentials. Empirical results reveal that our ACQR framework and the data mining method by TOPSIS and SVMs (with polynomial and RBF kernels) can perform very well in identifying and predicting influential users in a certain topic (such as iPhone 5). Furthermore, the dynamic change processes of users' influence from longitudinal perspective are analysed and suggestions to the sales managers are provided.

Question: What is the ACQR framework?


Design and Fabrication of Integrated Magnetic MEMS Energy Harvester for Low Frequency Applications. An integrated vibration-to-electrical MEMS electromagnetic energy harvester with low resonant frequency is designed, simulated, fabricated and tested. Special structures and magnet arrays of the device are designed and simulated for favorable low frequency performance. Both FEM simulations and system-level simulations are conducted to investigate the induced voltage of the device. Two types of harvester are fabricated with different magnet arrays and the magnetic field of the two harvesters is investigated. With the combination of the microfabricated metal structures and the electroplated CoNiMnP permanent micro magnets, the designed harvesters are of small size (5 mm ×5 mm × 0.53 mm) without manual assembly of conventional bulk magnets. The output power density is 0.03 μW/cm3 with optimized resistance load at 64 Hz. This magnetic harvester is suitable for batch fabrication through MEMS process and can be utilized to drive microelectronic devices, such as micro implantable and portable devices.

Question: What is the output power density of the harvester?


Virtual InfiniBand clusters for HPC clouds. High Performance Computing (HPC) employs fast interconnect technologies to provide low communication and synchronization latencies for tightly coupled parallel compute jobs. Contemporary HPC clusters have a fixed capacity and static runtime environments; they cannot elastically adapt to dynamic workloads, and provide a limited selection of applications, libraries, and system software. In contrast, a cloud model for HPC clusters promises more flexibility, as it provides elastic virtual clusters to be available on-demand. This is not possible with physically owned clusters.  In this paper, we present an approach that makes it possible to use InfiniBand clusters for HPC cloud computing. We propose a performance-driven design of an HPC IaaS layer for InfiniBand, which provides throughput and latency-aware virtualization of nodes, networks, and network topologies, as well as an approach to an HPC-aware, multi-tenant cloud management system for elastic virtualized HPC compute clusters.

Question: What is the difference between a cloud and a cluster?


Efficient Road Detection and Tracking for Unmanned Aerial Vehicle. An unmanned aerial vehicle (UAV) has many applications in a variety of fields. Detection and tracking of a specific road in UAV videos play an important role in automatic UAV navigation, traffic monitoring, and ground-vehicle tracking, and also is very helpful for constructing road networks for modeling and simulation. In this paper, an efficient road detection and tracking framework in UAV videos is proposed. In particular, a graph-cut-based detection approach is given to accurately extract a specified road region during the initialization stage and in the middle of tracking process, and a fast homography-based road-tracking scheme is developed to automatically track road areas. The high efficiency of our framework is attributed to two aspects: the road detection is performed only when it is necessary and most work in locating the road is rapidly done via very fast homography-based tracking. Experiments are conducted on UAV videos of real road scenes we captured and downloaded from the Internet. The promising results indicate the effectiveness of our proposed framework, with the precision of 98.4% and processing 34 frames per second for 1046 × 595 videos on average.

Question: How to detect a road in a UAV video?


RTL Hardware IP Protection Using Key-Based Control and Data Flow Obfuscation. Recent trends of hardware intellectual property (IP) piracy and reverse engineering pose major business and security concerns to an IP-based system-on-chip (SoC) design flow. In this paper, we propose a Register Transfer Level (RTL) hardware IP protection technique based on low-overhead key-based obfuscation of control and data flow. The basic idea is to transform the RTL core into control and data flow graph (CDFG) and then integrate a well-obfuscated finite state machine (FSM) of special structure, referred as "Mode-Control FSM", into the CDFG in a manner that normal functional behavior is enabled only after application of a specific input sequence. We provide formal analysis of the effectiveness of the proposed approach and present a simple metric to quantify the level of obfuscation. We also present an integrated design flow that implements the proposed obfuscation at low computational overhead. Simulation results for two open-source IP cores show that high levels of security is achievable at nominal area and power overheads under delay constraint.

Question: How to protect IP from reverse engineering?


Biodynamic Excisional Skin Tension Lines for Cutaneous Surgery.

Question: What is biodynamic excisional skin tension lines for cutaneous surgery?


Evaluation in Design-Oriented Research. Design has been recognized for a long time both as art and as science. In the sixties of the previous century design-oriented research began to draw the attention of scientific researchers and methodologists, not only in technical engineering but also in the social sciences. However, a rather limited methodology for design-oriented research has been developed, especially as to the social sciences. In this article we introduce evaluation methodology and research methodology as a systematic input in the process of designing. A designing cycle is formulated with six stages, and for each of these stages operations, guidelines and criteria for evaluation are defined. All this may be used for a considerable improvement of the process and product of designing.

Question: What is design-oriented research?


A Game-like Application for Dance Learning Using a Natural Human Computer Interface. Game-based learning and gamification techniques are recently becoming a popular trend in the field of Technology Enhanced Learning. In this paper, we mainly focus on the use of game design elements for the transmission of Intangible Cultural Heritage (ICH) knowledge and, especially, for the learning of traditional dances. More specifically, we present a 3D game environment that employs an enjoyable natural human computer interface, which is based on the fusion of multiple depth sensors data in order to capture the body movements of the user/learner. In addition, the system automatically assesses the learner's performance by utilizing a combination of Dynamic Time Warping (DTW) with Fuzzy Inference System (FIS) approach and provides feedback in a form of a score as well as instructions from a virtual tutor in order to promote self-learning. As a pilot use case, a Greek traditional dance, namely Tsamiko, has been selected. Preliminary small-scaled experiments with students of the Department of Physical Education and Sports Science at Aristotle University of Thessaloniki have shown the great potential of the proposed application.

Question: What is a game-like application for dance learning using a natural human computer interface?


The Columbia-GWU System at the 2016 TAC KBP BeSt Evaluation. We present the components of the ColumbiaGWU contribution to the 2016 TAC KBP BeSt Evaluation.

Question: What is the Columbia-GWU System?


Towards Multi-Stakeholder Utility Evaluation of Recommender Systems. A core value in recommender systems is personalization, the idea that the recommendations produced are those that match the user's preferences. However, in many real-world recommendation contexts, the concerns of additional stakeholders may come into play, such as the producers of items or those of the system owner. Some researchers have examined special cases of such concerns, for example, in reciprocal recommendation. However, there has not been a comprehensive treatment of the integration of multiple stakeholders into recommendation calculations. The paper suggests a utility-based framework for representing stakeholder values in recommendation actions and calculating a multidimensional utility. We demonstrate how a standard algorithm performs in a simulation of a multi-stakeholder recommendation task requiring on-line optimization, and show that a simple greedy approach can lead to enhanced overall utility with minimal loss of accuracy for users.

Question: What is a utility-based framework for representing stakeholder values in recommendation actions?


Elastic textures for additive fabrication. We introduce elastic textures: a set of parametric, tileable, printable, cubic patterns achieving a broad range of isotropic elastic material properties: the softest pattern is over a thousand times softer than the stiffest, and the Poisson's ratios range from below zero to nearly 0.5. Using a combinatorial search over topologies followed by shape optimization, we explore a wide space of truss-like, symmetric 3D patterns to obtain a small family. This pattern family can be printed without internal support structure on a single-material 3D printer and can be used to fabricate objects with prescribed mechanical behavior. The family can be extended easily to create anisotropic patterns with target orthotropic properties. We demonstrate that our elastic textures are able to achieve a user-supplied varying material property distribution. We also present a material optimization algorithm to choose material properties at each point within an object to best fit a target deformation under a prescribed scenario. We show that, by fabricating these spatially varying materials with elastic textures, the desired behavior is achieved.

Question: What is additive fabrication?


CCVis : Visual Analytics of Student Online Learning Behaviors Using Course Clickstream Data. As more and more college classrooms utilize online platforms to facilitate teaching and learning activities, analyzing student online behaviors becomes increasingly important for instructors to effectively monitor and manage student progress and performance. In this paper, we present CCVis, a visual analytics tool for analyzing the course clickstream data and exploring student online learning behaviors. Targeting a large college introductory course with over two thousand student enrollments, our goal is to investigate student behavior patterns and discover the possible relationships between student clickstream behaviors and their course performance. We employ higher-order network and structural identity classification to enable visual analytics of behavior patterns from the massive clickstream data. CCVis includes four coordinated views (the behavior pattern, behavior breakdown, clickstream comparative, and grade distribution views) for user interaction and exploration. We demonstrate the effectiveness of CCVis through case studies along with an ad-hoc expert evaluation. Finally, we discuss the limitation and extension of this work.

Question: What is a case study?


TCA: An Efficient Two-Mode Meta-Heuristic Algorithm for Combinatorial Test Generation (T). Covering arrays (CAs) are often used as test suites for combinatorial interaction testing to discover interaction faults of real-world systems. Most real-world systems involve constraints, so improving algorithms for covering array generation (CAG) with constraints is beneficial. Two popular methods for constrained CAG are greedy construction and meta-heuristic search. Recently, a meta-heuristic framework called two-mode local search has shown great success in solving classic NPhard problems. We are interested whether this method is also powerful in solving the constrained CAG problem. This work proposes a two-mode meta-heuristic framework for constrained CAG efficiently and presents a new meta-heuristic algorithm called TCA. Experiments show that TCA significantly outperforms state-of-the-art solvers on 3-way constrained CAG. Further experiments demonstrate that TCA also performs much better than its competitors on 2-way constrained CAG.

Question: What is a covering array?


The Relationships among Acceleration , Agility , Sprinting Ability , Speed Dribbling Ability and Vertical Jump Ability in 14-Year-Old Soccer Players. The aim of this study was to evaluate the relationships among acceleration, agility, sprinting ability, speed dribbling ability and vertical jump ability in 14-year-old soccer players. Twenty-five young soccer players (average age 13.52 ± 0.51 years; height 158.81 ± 5.76 cm; weight 48.92 ± 6.48 kg; training age 3.72 ± 0.64 years) performed a series of physical tests: Yo-Yo Intermittent Recovery Test Level 1 (YYIRT); zigzag agility with the ball (ZAWB); without the ball (ZAWOB); sprinting ability (10-m, 20-m and 30-m); speed dribbling ability (SDA), acceleration ability (FLST) and jumping ability (counter-movement jump (CMJ), squat jump (SJ) and drop jump (DJ). The results showed that 10-m sprint was correlated with 20-m (r = 0.682), 30-m sprint (r = 0.634) and also SDA, FLST and ZAWOB (r = 0.540, r = 0.421; r = 0.533 respectively). Similarly, 20-m sprint was correlated with 30-m sprint (r = 0.491) and ZAWOB (r = 0.631). 30-m sprint was negatively correlated with CMJ (r = -0.435), while strong and moderate correlated with FLST (r = 0.742), ZAWOB (r = 0.657). In addition, CMJ strong correlated with SJ and DJ (r = 0.779, r = 0.824 respectively). CMJ, SJ and DJ were negatively correlated with ZAWOB. Furthermore, SDA strong and moderate correlated with FLST (r = 0.875), ZAWB (r = 0.718) and ZAWOB (r = 0.645). Similarly, a correlation was also found among FLST, ZAWB and ZAWOB (r = 0.421, r = 0.614 respectively). Finally, ZAWOB was strong and moderate correlated with the performance of soccer players in different field tests. In conclusion, the findings of the present study indicated that agility without the ball associated with the all physical fitness components. In addition, agility performance affects acceleration, sprint and jumping performances in young soccer players. Therefore, soccer players should focus on agility e

Question: What is the relationship between agility and acceleration?


Inpainting of Continuous Frames of Old Movies Based on Deep Neural Network. With the changes of eras, traditional movies may suffer continuous frame damage after digits due to improper preservation. In order to solve this problem, we proposed a new inpainting technique for continuous video sequences based on deep neural networks. We introduced the latest image restoration techniques for inpainting key frames of new scenes. Then we use the deep neural network interpolation algorithm to interpolate the intermediate frames, so that the video can achieve a coherent effect in time. In order to preserve the original information as much as possible, we only replace the damaged areas and preserve most of undamaged areas, and finally perform image blending. We test different types of videos and compare them with other methods. Our method has higher quality video inpainting than existing methods.

Question: How to inpaint a continuous frame of old movies?


CareLog: a selective archiving tool for behavior management in schools. Identifying the function of problem behavior can lead to the development of more effective interventions. One way to identify the function is through functional behavior assessment (FBA). Teachers conduct FBA in schools. However, the task load of recording the data manually is high, and the challenge of accurately identifying antecedents and consequences is significant while interacting with students. These issues often result in imperfect information capture. CareLog allows teachers more easily to conduct FBAs and enhances the capture of relevant information. In this paper, we describe the design process that led to five design principles that governed the development of CareLog. We present results from a five-month, quasi-controlled study aimed at validating those design principles. We reflect on how various constraints imposed by special education settings impact the design and evaluation process for HCI practitioners and researchers.

Question: What is the function of problem behavior?


Speech recognition with primarily temporal cues.. Nearly perfect speech recognition was observed under conditions of greatly reduced spectral information. Temporal envelopes of speech were extracted from broad frequency bands and were used to modulate noises of the same bandwidths. This manipulation preserved temporal envelope cues in each band but restricted the listener to severely degraded information on the distribution of spectral energy. The identification of consonants, vowels, and words in simple sentences improved markedly as the number of bands increased; high speech recognition performance was obtained with only three bands of modulated noise. Thus, the presentation of a dynamic temporal pattern in only a few broad spectral regions is sufficient for the recognition of speech.

Question: How does speech recognition work?


Development of a deep silicon phase Fresnel lens using Gray-scale lithography and deep reactive ion etching. We report the first fabrication and development of a deep phase Fresnel lens (PFL) in silicon through the use of gray-scale lithography and deep-reactive ion etching (DRIE). A Gaussian tail approximation is introduced as a method of predicting the height of photoresist gray levels given the relative amount of transmitted light through a gray-scale optical mask. Device mask design is accomplished through command-line scripting in a CAD tool to precisely define the millions of pixels required to generate the appropriate profile in photoresist. Etch selectivity during DRIE pattern transfer is accurately controlled to produce the desired scaling factor between the photoresist and silicon profiles. As a demonstration of this technology, a 1.6-mm diameter PFL is etched 43 /spl mu/m into silicon with each grating profile designed to focus 8.4 keV photons a distance of 118 m.

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


Recurrent Neural Aligner: An Encoder-Decoder Neural Network Model for Sequence to Sequence Mapping. We introduce an encoder-decoder recurrent neural network model called Recurrent Neural Aligner (RNA) that can be used for sequence to sequence mapping tasks. Like connectionist temporal classification (CTC) models, RNA defines a probability distribution over target label sequences including blank labels corresponding to each time step in input. The probability of a label sequence is calculated by marginalizing over all possible blank label positions. Unlike CTC, RNA does not make a conditional independence assumption for label predictions; it uses the predicted label at time t−1 as an additional input to the recurrent model when predicting the label at time t. We apply this model to end-to-end speech recognition. RNA is capable of streaming recognition since the decoder does not employ attention mechanism. The model is trained on transcribed acoustic data to predict graphemes and no external language and pronunciation models are used for decoding. We employ an approximate dynamic programming method to optimize negative log likelihood, and a sampling-based sequence discriminative training technique to fine-tune the model to minimize expected word error rate. We show that the model achieves competitive accuracy without using an external language model nor doing beam search decoding.

Question: What is the difference between recurrent neural aligner and connectionist temporal classification?


The balanced scorecard: a foundation for the strategic management of information systems. Ž . The balanced scorecard BSC has emerged as a decision support tool at the strategic management level. Many business leaders now evaluate corporate performance by supplementing financial accounting data with goal-related measures from the following perspectives: customer, internal business process, and learning and growth. It is argued that the BSC concept can be adapted to assist those managing business functions, organizational units and individual projects. This article develops a Ž . balanced scorecard for information systems IS that measures and evaluates IS activities from the following perspectives: business value, user orientation, internal process, and future readiness. Case study evidence suggests that a balanced IS scorecard can be the foundation for a strategic IS management system provided that certain development guidelines are followed, appropriate metrics are identified, and key implementation obstacles are overcome. q 1999 Elsevier Science B.V. All rights reserved.

Question: What is the balanced scorecard?


CLOS: Integrating Object-Oriented and Functional Programming. Lisp has a long history as a functional language,* where action is invoked by calling a procedure, and where procedural abstraction and encapsulation provide convenient modularity boundaries. A number of attempts have been made to graft object-oriented programming into this framework without losing the essential character of Lisp—to include the benefits of data abstraction, extensible type classification, incremental operator definition, and code reuse through an inheritance hierarchy. The Common Lisp Object System (CLOS) [3], a result of the ANSI standardization process for Common Lisp, represents a marriage of these two traditions. This article explores the landscape in which the major object-oriented facilities exist, showing how the CLOS solution is effective within the two contexts.

Question: What is the history of lisp?


Gender differences in narcissism: a meta-analytic review.. Despite the widely held belief that men are more narcissistic than women, there has been no systematic review to establish the magnitude, variability across measures and settings, and stability over time of this gender difference. Drawing on the biosocial approach to social role theory, a meta-analysis performed for Study 1 found that men tended to be more narcissistic than women (d = .26; k = 355 studies; N = 470,846). This gender difference remained stable in U.S. college student cohorts over time (from 1990 to 2013) and across different age groups. Study 1 also investigated gender differences in three facets of the Narcissistic Personality Inventory (NPI) to reveal that the narcissism gender difference is driven by the Exploitative/Entitlement facet (d = .29; k = 44 studies; N = 44,108) and Leadership/Authority facet (d = .20; k = 40 studies; N = 44,739); whereas the gender difference in Grandiose/Exhibitionism (d = .04; k = 39 studies; N = 42,460) was much smaller. We further investigated a less-studied form of narcissism called vulnerable narcissism-which is marked by low self-esteem, neuroticism, and introversion-to find that (in contrast to the more commonly studied form of narcissism found in the DSM and the NPI) men and women did not differ on vulnerable narcissism (d = -.04; k = 42 studies; N = 46,735). Study 2 used item response theory to rule out the possibility that measurement bias accounts for observed gender differences in the three facets of the NPI (N = 19,001). Results revealed that observed gender differences were not explained by measurement bias and thus can be interpreted as true sex differences. Discussion focuses on the implications for the biosocial construction model of gender differences, for the etiology of narcissism, for clinical applications, and for the role of narcissism in helping to explain gender differences in leadership and aggressive behavior. Readers are warned agains

Question: What is the difference between vulnerable narcissism and the more commonly studied form of narcissism?


Maturity Models for Systems Thinking. Recent decades have seen a rapid increase in the complexity of goods, products, and services that society has come to demand. This has necessitated a corresponding growth in the requirements demanded of organizational systems and the people who work in them. The competence a person requires to be effective in working in such systems has become an area of increased interest to scholars and practitioners in many disciplines. How can we assess the degree to which a person is executing the competencies required to do good systems work? Several industries now utilize maturity models in the attempt to evaluate and cultivate people's ability to effectively execute complex tasks. This paper will examine current thought regarding the value and pitfalls of maturity models. It will identify principles and exemplars that could guide the development of a Maturity Model of Systems Thinking Competence (MMSTC) for the varied roles people inhabit in systems contexts.

Question: What is a maturity model?


The pathogenesis of Campylobacter jejuni-mediated enteritis.. Campylobacter jejuni, a gram-negative spiral shaped bacterium, is a frequent cause of gastrointestinal food-borne illness in humans throughout the world. Illness with C. jejuni ranges from mild to severe diarrheal disease. This article focuses on Campylobacter virulence determinants and their potential role in the development of C. jejuni-mediated enteritis. A model is presented that diagrams the interactions of C. jejuni with the intestinal epithelium. Additional work to identify and characterize C. jejuni virulence determinants is certain to provide novel insights into the diversity of strategies employed by bacterial pathogens to cause disease.

Question: What is the role of Campylobacter jejuni in the development of Campylobacter enteritis?


Distributed-system Features and Challenges Shiviz Is a New Distributed System Debugging Visualization Tool Debugging Distributed Systems. D istributed systems pose unique challenges for software developers. Reasoning about concurrent activities of system nodes and even understanding the system's communication topology can be difficult. A standard approach to gaining insight into system activity is to analyze system logs. Unfortunately, this can be a tedious and complex process. This article looks at several key features and debugging challenges that differentiate distributed systems from other kinds of software. The article presents several promising tools and ongoing research to help resolve these challenges.

Question: What is a distributed system?


A quasi-orthogonal space-time block code. It has been shown that a complex orthogonal design that provides full diversity and full transmission rate for a space–time block code is not possible for more than two antennas. Previous attempts have been concentrated in generalizing orthogonal designs which provide space–time block codes with full diversity and a high transmission rate. In this work, we design rate one codes which are quasi-orthogonal and provide partial diversity. The decoder of the proposed codes works with pairs of transmitted symbols instead of single symbols.

Question: What is a quasi-orthogonal space-time block code?


Linux kernel integrity measurement using contextual inspection. This paper introduces the Linux Kernel Integrity Monitor (LKIM) as an improvement over conventional methods of software integrity measurement. LKIM employs contextual inspection as a means to more completely characterize the operational integrity of a running kernel. In addition to cryptographically hashing static code and data in the kernel, dynamic data structures are examined to provide improved integrity measurement. The base approach examines structures that control the execution flow of the kernel through the use of function pointers as well as other data that affect the operation of the kernel. Such structures provide an efficient means of extending the kernel operations, but they are also a means of inserting malicious code without modifying the static parts. The LKIM implementation is discussed and initial performance data is presented to show that contextual inspection is practical

Question: What is the linux kernel integrity measurement using contextual inspection?


Spinal and supraspinal factors in human muscle fatigue.. Muscle fatigue is an exercise-induced reduction in maximal voluntary muscle force. It may arise not only because of peripheral changes at the level of the muscle, but also because the central nervous system fails to drive the motoneurons adequately. Evidence for "central" fatigue and the neural mechanisms underlying it are reviewed, together with its terminology and the methods used to reveal it. Much data suggest that voluntary activation of human motoneurons and muscle fibers is suboptimal and thus maximal voluntary force is commonly less than true maximal force. Hence, maximal voluntary strength can often be below true maximal muscle force. The technique of twitch interpolation has helped to reveal the changes in drive to motoneurons during fatigue. Voluntary activation usually diminishes during maximal voluntary isometric tasks, that is central fatigue develops, and motor unit firing rates decline. Transcranial magnetic stimulation over the motor cortex during fatiguing exercise has revealed focal changes in cortical excitability and inhibitability based on electromyographic (EMG) recordings, and a decline in supraspinal "drive" based on force recordings. Some of the changes in motor cortical behavior can be dissociated from the development of this "supraspinal" fatigue. Central changes also occur at a spinal level due to the altered input from muscle spindle, tendon organ, and group III and IV muscle afferents innervating the fatiguing muscle. Some intrinsic adaptive properties of the motoneurons help to minimize fatigue. A number of other central changes occur during fatigue and affect, for example, proprioception, tremor, and postural control. Human muscle fatigue does not simply reside in the muscle.

Question: What is the difference between central and peripheral fatigue?


Performance Enhancement of Boost Converter Based on PID Controller Plus Linear-to-Nonlinear Translator. Here, A PID controller plus a novel linear-to-nonlinear translator is proposed and applied to the boost converter to improve the operating performance of the boost converter under large transient variations in load all over the quiescent dc input voltage range. The proposed translator is inserted between the PID controller and the main power stage such that the PID controller considers the behavior of the boost converter to be quasi-linear, thereby suppressing the effect of nonlinear behavior of the boost converter on the PID controller. Besides, variations in dc gain of the control-to-output transfer function with the translator are smaller than those without the translator, thus making system control easy to attain. Also, one-comparator counter-based sampling without any A-D converter is presented herein to realize the proposed control strategy. As mentioned above, the proposed control strategy can make the boost converter stably operated under large load transient responses all over the quiescent dc input voltage range.

Question: How to improve the performance of boost converter?


Sexual Orientation and Gender Identity Data Collection in Clinical Settings and in Electronic Health Records: A Key to Ending LGBT Health Disparities.. The Institute of Medicine's (IOM's) 2011 report on the health of LGBT people pointed out that there are limited health data on these populations and that we need more research. It also described what we do know about LGBT health disparities, including lower rates of cervical cancer screening among lesbians, and mental health issues related to minority stress. Patient disclosure of LGBT identity enables provider-patient conversations about risk factors and can help us reduce and better understand disparities. It is essential to the success of Healthy People 2020's goal of eliminating LGBT health disparities. This is why the IOM's report recommended data collection in clinical settings and on electronic health records (EHRs). The Center for Medicare and Medicaid Services and the Office of the National Coordinator of Health Information Technology rejected including sexual orientation and gender identity (SOGI) questions in meaningful use guidelines for EHRs in 2012 but are considering this issue again in 2013. There is overwhelming community support for the routine collection of SOGI data in clinical settings, as evidenced by comments jointly submitted by 145 leading LGBT and HIV/AIDS organizations in January 2013. Gathering SOGI data in EHRs is supported by the 2011 IOM's report on LGBT health, Healthy People 2020, the Affordable Care Act, and the Joint Commission. Data collection has long been central to the quality assurance process. Preventive health care from providers knowledgeable of their patients' SOGI can lead to improved access, quality of care, and outcomes. Medical and nursing schools should expand their attention to LGBT health issues so that all clinicians can appropriately care for LGBT patients.

Question: What is the difference between sexual orientation and gender identity?


Approaches , methods , metrics , measures , and subjectivity in ontology evaluation : A survey. Ontology evaluation is concerned with ascertain two important aspects of ontologies: quality and correctness. The distinction between the two is attempted in this survey as a way to better approach ontology evaluation. The role that ontologies play on the semantic web at large has been has been argued to have catalyzed the proliferation of ontologies in existence. This has also presented the challenge of deciding one the suitability of an given ontology to one's purposes as compared with another ontology in a similar domain. This survey intends to analyze the state of the art in ontology evaluate spanning such topics as the approaches to ontology evaluation, the metrics and measures used. Particular interest is given to Data-driven ontology evaluation with special emphasis on the notion of bias and it's relevance to evaluation results. Chief among the outputs of this survey is the gap analysis on the topic of ontology evaluation.

Question: What is the difference between quality and correctness?


Building a Large Annotated Corpus of English: The Penn Treebank. In this paper, we review our experience with constructing one such large annotated corpus--the Penn Treebank, a corpus consisting of over 4.5 million words of American English. During the first three-year phase of the Penn Treebank Project (1989-1992), this corpus has been annotated for part-of-speech (POS) information. In addition, over half of it has been annotated for skeletal syntactic structure. Comments University of Pennsylvania Department of Computer and Information Science Technical Report No. MSCIS-93-87. This technical report is available at ScholarlyCommons: http://repository.upenn.edu/cis_reports/237 Building A Large Annotated Corpus of English: The Penn Treebank MS-CIS-93-87 LINC LAB 260 Mitchell P. Marcus Beatrice Santorini Mary Ann Marcinkiewicz University of Pennsylvania School of Engineering and Applied Science Computer and Information Science Department Philadelphia, PA 19104-6389

Question: What is the size of the penn treebank?


A measurement study of google play. Although millions of users download and use third-party Android applications from the Google Play store, little information is known on an aggregated level about these applications. We have built PlayDrone, the first scalable Google Play store crawler, and used it to index and analyze over 1,100,000 applications in the Google Play store on a daily basis, the largest such index of Android applications. PlayDrone leverages various hacking techniques to circumvent Google's roadblocks for indexing Google Play store content, and makes proprietary application sources available, including source code for over 880,000 free applications. We demonstrate the usefulness of PlayDrone in decompiling and analyzing application content by exploring four previously unaddressed issues: the characterization of Google Play application content at large scale and its evolution over time, library usage in applications and its impact on application portability, duplicative application content in Google Play, and the ineffectiveness of OAuth and related service authentication mechanisms resulting in malicious users being able to easily gain unauthorized access to user data and resources on Amazon Web Services and Facebook.

Question: What is the largest scale crawler of android applications?


Edge Computing – EDGE 2018. Edge computing systems (Cloudlet, Fog Computing, Multi-access Edge Computing) provide numerous benefits to information technology: reduced latency, improved bandwidth, battery lifetime, etc. Despite all the benefits, edge computing systems have several issues that could significantly reduce the performance of certain applications. Indeed, current edge computing technologies do not assure ultra-low latency for real-time applications and they encounter overloading issues for data processing. To solve the aforementioned issues, we propose Home Edge Computing (HEC): a new three-tier edge computing architecture that provides data storage and processing in close proximity to the users. The term "Home" in Home Edge Computing does not restrain our work to the homes of the users, we take into account other places where the users could connect to the Internet such as: companies, shopping malls, hospitals, etc. Our three-tier architecture is composed of a Home Server, an Edge Server and a Central Cloud which we also find in traditional edge computing architectures. The Home Server is located within the vicinities of the users which allow the achievement of ultra-low latency for applications that could be processed by the said server; this also help reduce the amount of data that could be treated in the Edge Server and the Central Cloud. We demonstrate the validity of our architecture by leveraging the EdgeCloudSim simulation platform. The results of the simulation show that our proposal can, in fact, help achieve ultra-low latency and reduce overloading issues.

Question: What is the difference between edge computing and home computing?


Mixed-effects modeling with crossed random effects for subjects and items. This paper provides a non-technical introduction to mixed-effects models for the analysis of repeated measurement data with subjects and items as crossed random effects. A worked-out example of how to use recent software for mixed-effects modeling is provided. Simulation studies illustrate the advantages offered by mixedeffects analyses compared to traditional analyses based on quasi-F tests, by-subjects analyses, combined by-subjects and by-items analyses, and random regression. Applications and possibilities across a range of domains of inquiry are discussed. 1 General Introduction Psycholinguists and other cognitive psychologists use convenience samples for their experiments, often based on participants within the local university community. When analyzing the data from these experiments, participants are treated as random variables, because the interest of most studies is not about ∗ Corresponding author. Address: Max Planck Institute for Psycholinguistics, P.O. Box 310, 6500 AH, Nijmegen, The Netherlands Email addresses: baayen@mpi.nl (R.H. Baayen), Doug.Davidson@fcdonders.ru.nl (D.J. Davidson), bates@stat.wisc.edu (D.M. Bates). Preprint submitted to Elsevier Februari 3, 2006 experimental effects present only in the individuals who participated in the experiment, but rather in effects present in speakers everywhere, either within the language studied, or human language users in general. The differences between individuals due to genetic, developmental, environmental, social, political, or chance factors are modeled jointly by means of a participant random effect. A similar logic applies to linguistic materials. Psycholinguists construct materials for the tasks that they employ by a variety of means, but most importantly, most materials in a single experiment do not exhaust all possible syllables, words, or sentences that could be found in a given language, and most choices of language to

Question: What is a mixed-effects model?


Variational Bayesian Inference for Big Data Marketing Models 1. Hierarchical Bayesian approaches play a central role in empirical marketing as they yield individual-level parameter estimates that can be used for targeting decisions. MCMC methods have been the methods of choice for estimating hierarchical Bayesian models as they are capable of providing accurate individual-level estimates. However, MCMC methods are computationally prohibitive and do not scale well when applied to massive data sets that have become common in the current era of "Big Data". We introduce to the marketing literature a new class of Bayesian estimation techniques known as variational Bayesian (VB) inference. These methods tackle the scalability challenge via a deterministic optimization approach to approximate the posterior distribution and yield accurate estimates at a fraction of the computational cost associated with simulation-based MCMC methods. We exploit and extend recent developments in variational Bayesian inference and highlight how two VB estimation approaches – Mean-field VB (that is analogous to Gibbs sampling) for conjugate models and Fixed-form VB (which is analogous to Metropolis-Hasting) for nonconjugate models – can be effectively combined for estimating complex marketing models. We also show how recent advances in parallel computing and in stochastic optimization can be used to further enhance the speed of these VB methods. Using simulated as well as real data sets, we apply the VB approaches to several commonly used marketing models (e.g. mixed linear, logit, selection, and hierarchical ordinal logit models), and demonstrate how the VB inference is widely applicable for marketing problems.

Question: What is the difference between Gibbs sampling and Metropolis-Hasting?


FA*IR: A Fair Top-k Ranking Algorithm. In this work, we define and solve the Fair Top-k Ranking problem, in which we want to determine a subset of k candidates from a large pool of n » k candidates, maximizing utility (i.e., select the "best" candidates) subject to group fairness criteria.  Our ranked group fairness definition extends group fairness using the standard notion of protected groups and is based on ensuring that the proportion of protected candidates in every prefix of the top-k ranking remains statistically above or indistinguishable from a given minimum. Utility is operationalized in two ways: (i) every candidate included in the top-k should be more qualified than every candidate not included; and (ii) for every pair of candidates in the top-k, the more qualified candidate should be ranked above.  An efficient algorithm is presented for producing the Fair Top-k Ranking, and tested experimentally on existing datasets as well as new datasets released with this paper, showing that our approach yields small distortions with respect to rankings that maximize utility without considering fairness criteria. To the best of our knowledge, this is the first algorithm grounded in statistical tests that can mitigate biases in the representation of an under-represented group along a ranked list.

Question: What is the FA*IR algorithm?


The Q-CHAT (Quantitative CHecklist for Autism in Toddlers): a normally distributed quantitative measure of autistic traits at 18-24 months of age: preliminary report.. We report a major revision of the CHecklist for Autism in Toddlers (CHAT). This quantitative CHAT (Q-CHAT) contains 25 items, scored on a 5 point scale (0-4). The Q-CHAT was completed by parents of n = 779 unselected toddlers (mean age 21 months) and n = 160 toddlers and preschoolers (mean age 44 months) with an Autism Spectrum Condition (ASC). The ASC group (mean (SD) = 51.8 (14.3)) scored higher on the Q-CHAT than controls (26.7 (7.8)). Boys in the control group (27.5 (7.8)) scored higher than girls (25.8 (7.7)). The intraclass correlation for test-retest reliability was 0.82 (n = 330). The distribution in the control group was close to normal. Full examination of the clinical validity of the Q-CHAT and test properties is underway.

Question: What is the Q-CHAT?


Design and performance analysis of a high-speed air-cored axial-flux permanent-magnet generator with circular magnets and coils. Air-cored, axial flux permanent-magnet (AFPM) machines have magnetic and mechanical characteristics considered to be ideal for compact high speed electric power generation applications. To date, research on high speed AFPM machines is primarily from a mechanical perspective, with emphasis on higher power level applications such as in hybrid vehicles. Recently, there has been an increasing interest in man-portable mobile power sources with a power envelope of around 1kW, for mission critical applications. In this paper, a high speed air-cored surface mounted AFPM machine with circular magnets and coils is proposed. Comprehensive theoretical analysis and three-dimensional (3-D) electromagnetic finite element analysis (FEA) are developed to evaluate the performance of the machine. In addition, the rotor's mechanical stresses developed by high rotational speed are evaluated to ensure mechanical integrity. Finally, a prototype machine is developed for validation. Both the experimental and predicted results demonstrate that the proposed generator possesses distinct advantages over other systems, such as high efficiency, high power factor, and a simple and robust structure that offers a high degree of technology readiness.

Question: What is the power envelope of a high speed air-cored axial-flux permanent-magnet generator with circular magnets and coils?


Quantum Reinforcement Learning. The key approaches for machine learning, particularly learning in unknown probabilistic environments, are new representations and computation mechanisms. In this paper, a novel quantum reinforcement learning (QRL) method is proposed by combining quantum theory and reinforcement learning (RL). Inspired by the state superposition principle and quantum parallelism, a framework of a value-updating algorithm is introduced. The state (action) in traditional RL is identified as the eigen state (eigen action) in QRL. The state (action) set can be represented with a quantum superposition state, and the eigen state (eigen action) can be obtained by randomly observing the simulated quantum state according to the collapse postulate of quantum measurement. The probability of the eigen action is determined by the probability amplitude, which is updated in parallel according to rewards. Some related characteristics of QRL such as convergence, optimality, and balancing between exploration and exploitation are also analyzed, which shows that this approach makes a good tradeoff between exploration and exploitation using the probability amplitude and can speedup learning through the quantum parallelism. To evaluate the performance and practicability of QRL, several simulated experiments are given, and the results demonstrate the effectiveness and superiority of the QRL algorithm for some complex problems. This paper is also an effective exploration on the application of quantum computation to artificial intelligence.

Question: What is quantum reinforcement learning?


ServerSwitch: A Programmable and High Performance Platform for Data Center Networks. As one of the fundamental infrastructures for cloud computing, data center networks (DCN) have recently been studied extensively. We currently use pure software-based systems, FPGA based platforms, e.g., NetFPGA, or OpenFlow switches, to implement and evaluate various DCN designs including topology design, control plane and routing, and congestion control. However, software-based approaches suffer from high CPU overhead and processing latency; FPGA based platforms are difficult to program and incur high cost; and OpenFlow focuses on control plane functions at present. In this paper, we design a ServerSwitch to address the above problems. ServerSwitch is motivated by the observation that commodity Ethernet switching chips are becoming programmable and that the PCI-E interface provides high throughput and low latency between the server CPU and I/O subsystem. ServerSwitch uses a commodity switching chip for various customized packet forwarding, and leverages the server CPU for control and data plane packet processing, due to the low latency and high throughput between the switching chip and server CPU. We have built our ServerSwitch at low cost. Our experiments demonstrate that ServerSwitch is fully programmable and achieves high performance. Specifically, we have implemented various forwarding schemes including source routing in hardware. Our in-network caching experiment showed high throughput and flexible data processing. Our QCN (Quantized Congestion Notification) implementation further demonstrated that ServerSwitch can react to network congestions in 23us. ∗Correspondent author. †This work was performed when Zhiqiang Zhou was a visiting student at Microsoft Research Asia.

Question: What is the difference between a programmable and high performance platform for data center networks and a commodity Ethernet switching chip?


An indicator of research front activity: Measuring intellectual organization as uncertainty reduction in document sets. When using scientific literature to model scholarly discourse, a research specialty can be operationalized as an evolving set of related documents. Each publication can be expected to contribute to the further development of the specialty at the research front. The specific combinations of title words and cited references in a paper can then be considered as a signature of the knowledge claim in the paper: new words and combinations of words can be expected to represent variation, while each paper is at the same time selectively positioned into the intellectual organization of a field using context-relevant references. Can the mutual information among these three dimensions— title words, cited references, and sequence numbers—be used as an indicator of the extent to which intellectual organization structures the uncertainty prevailing at a research front? The effect of the discovery of nanotubes (1991) on the previously existing field of fullerenes is used as a test case. Thereafter, this method is applied to science studies with a focus on scientometrics using various sample delineations. An emerging research front about citation analysis can be indicated.

Question: How can we measure the uncertainty of a research front?


Pulsed Out of Awareness: EEG Alpha Oscillations Represent a Pulsed-Inhibition of Ongoing Cortical Processing. Alpha oscillations are ubiquitous in the brain, but their role in cortical processing remains a matter of debate. Recently, evidence has begun to accumulate in support of a role for alpha oscillations in attention selection and control. Here we first review evidence that 8-12 Hz oscillations in the brain have a general inhibitory role in cognitive processing, with an emphasis on their role in visual processing. Then, we summarize the evidence in support of our recent proposal that alpha represents a pulsed-inhibition of ongoing neural activity. The phase of the ongoing electroencephalography can influence evoked activity and subsequent processing, and we propose that alpha exerts its inhibitory role through alternating microstates of inhibition and excitation. Finally, we discuss evidence that this pulsed-inhibition can be entrained to rhythmic stimuli in the environment, such that preferential processing occurs for stimuli at predictable moments. The entrainment of preferential phase may provide a mechanism for temporal attention in the brain. This pulsed inhibitory account of alpha has important implications for many common cognitive phenomena, such as the attentional blink, and seems to indicate that our visual experience may at least some times be coming through in waves.

Question: What is the role of alpha oscillations in the brain?


Cross-Entropy Clustering. We build a general and highly applicable clustering theory, which we call cross-entropy clustering (shortly CEC) which joins advantages of classical kmeans (easy implementation and speed) with those of EM (affine invariance and ability to adapt to clusters of desired shapes). Moreover, contrary to k-means and EM, CEC finds the optimal number of clusters by automatically removing groups which carry no information. Although CEC, similarly like EM, can be build on an arbitrary family of densities, in the most important case of Gaussian CEC the division into clusters is affine invariant, while the numerical complexity is comparable to that of k-means.

Question: What is cross-entropy clustering?


Deep Joint Rain Detection and Removal from a Single Image. In this paper, we address a rain removal problem from a single image, even in the presence of heavy rain and rain streak accumulation. Our core ideas lie in our new rain image model and new deep learning architecture. We add a binary map that provides rain streak locations to an existing model, which comprises a rain streak layer and a background layer. We create a model consisting of a component representing rain streak accumulation (where individual streaks cannot be seen, and thus visually similar to mist or fog), and another component representing various shapes and directions of overlapping rain streaks, which usually happen in heavy rain. Based on the model, we develop a multi-task deep learning architecture that learns the binary rain streak map, the appearance of rain streaks, and the clean background, which is our ultimate output. The additional binary map is critically beneficial, since its loss function can provide additional strong information to the network. To handle rain streak accumulation (again, a phenomenon visually similar to mist or fog) and various shapes and directions of overlapping rain streaks, we propose a recurrent rain detection and removal network that removes rain streaks and clears up the rain accumulation iteratively and progressively. In each recurrence of our method, a new contextualized dilated network is developed to exploit regional contextual information and to produce better representations for rain detection. The evaluation on real images, particularly on heavy rain, shows the effectiveness of our models and architecture.

Question: How to detect rain streaks from a single image?


Characterizing Horn Antenna Signals for Breast Cancer Detection. An in-depth analysis of signals from horn antennas used in breast cancer detection research has been carried out. It was found that following the excitation signal, both the double-ridged and the quad-ridged ultra-wideband horn antennas radiate additional unwanted signals that persistently oscillate. Undesirable signal oscillations sources were identified as the horn antenna cavity resonance and inherent antenna LC resonance. These signals interfere with the tumor's signal response and need to be eliminated for successful detection of the cancerous growth. This paper proposes solutions to remove or minimize these signals without affecting antenna parameters such as bandwidth, gain, ports isolation, and polarization isolation. Modification of the antenna cavity successfully suppressed the unwanted cavity oscillation. Modification of the antenna waveguide reduced inductance and consequently mitigated LC oscillation. The resulting time and frequency domain horn antenna signals demonstrate the effectiveness of the proposed methods. Finally, a breast phantom with a tumor is simulated using signals from the original and a modified horn antenna. The delay and sum method is used to create images. The breast images demonstrate enhanced image quality through the reduction of clutter using the proposed techniques. The signal-to-clutter ratios are 0.448 and 1.6823 dB for the images produced by using the original and modified antennas, respectively.

Question: How to detect breast cancer?


Why do we trust new technology? A study of initial trust formation with organizational information systems. Recent trust research in the information systems (IS) field has described trust as a primary predictor of technology usage and a fundamental construct for understanding user perceptions of technology. Initial trust formation is particularly relevant in an IS context, as users must overcome perceptions of risk and uncertainty before using a novel technology. With initial trust in a more complex, organizational information system, there are a number of external determinants, trusting bases, that may explain trust formation and provide organizations with the needed levers to form or change individuals' initial trust in technology. In this study, a research model of initial trust formation is developed and includes trusting bases, trusting beliefs, trusting attitude and subjective norm, and trusting intentions. Eight trusting base factors are assessed including personality, cognitive, calculative, and both technology and organizational factors of the institutional base. The model is empirically tested with 443 subjects in the context of initial trust in a national identity system (NID). The proposed model was supported and the results indicate that subjective norm and the cognitive–reputation, calculative, and organizational situational normality base factors significantly influence initial trusting beliefs and other downstream trust constructs. Factors from some of the more commonly investigated bases, personality and technology institutional, did not significantly affect trusting beliefs. The findings have strategic implications for agencies implementing e-government systems and organizational information systems in general. Published by Elsevier B.V.

Question: What is the difference between a national identity system and an organizational information system?


Transductive Adversarial Networks (TAN). Transductive Adversarial Networks (TAN) is a novel domain-adaptation machine learning framework that is designed for learning a conditional probability distribution on unlabelled input data in a target domain, while also only having access to: (1) easily obtained labelled data from a related source domain, which may have a different conditional probability distribution than the target domain, and (2) a marginalised prior distribution on the labels for the target domain. TAN leverages a fully adversarial training procedure and a unique generator/encoder architecture which approximates the transductive combination of the available sourceand target-domain data. A benefit of TAN is that it allows the distance between the sourceand target-domain label-vector marginal probability distributions to be greater than 0 (i.e. different tasks across the source and target domains) whereas other domain-adaptation algorithms require this distance to equal 0 (i.e. a single task across the source and target domains). TAN can, however, still handle the latter case and is a more generalised approach to this case. Another benefit of TAN is that due to being a fully adversarial algorithm, it has the potential to accurately approximate highly complex distributions. Theoretical analysis demonstrates the viability of the TAN framework.

Question: What is a transductive adversarial network?


A Universal Part-of-Speech Tagset. To facilitate future research in unsupervised induction of syntactic structure and to standardize best-practices, we propose a tagset that consists of twelve universal part-of-speech categories. In addition to the tagset, we develop a mapping from 25 different treebank tagsets to this universal set. As a result, when combined with the original treebank data, this universal tagset and mapping produce a dataset consisting of common parts-of-speech for 22 different languages. We highlight the use of this resource via three experiments, that (1) compare tagging accuracies across languages, (2) present an unsupervised grammar induction approach that does not use gold standard part-of-speech tags, and (3) use the universal tags to transfer dependency parsers between languages, achieving state-of-the-art results.

Question: What is a universal part-of-speech tagset?


Frequency-based anomaly detection for the automotive CAN bus. The modern automobile is controlled by networked computers. The security of these networks was historically of little concern, but researchers have in recent years demonstrated their many vulnerabilities to attack. As part of a defence against these attacks, we evaluate an anomaly detector for the automotive controller area network (CAN) bus. The majority of attacks are based on inserting extra packets onto the network. But most normal packets arrive at a strict frequency. This motivates an anomaly detector that compares current and historical packet timing. We present an algorithm that measures inter-packet timing over a sliding window. The average times are compared to historical averages to yield an anomaly signal. We evaluate this approach over a range of insertion frequencies and demonstrate the limits of its effectiveness. We also show how a similar measure of the data contents of packets is not effective for identifying anomalies. Finally we show how a one-class support vector machine can use the same information to detect anomalies with high confidence.

Question: What is the CAN bus?


Complexity in Phonological Treatment: Clinical Factors.. The construct of complexity has been advanced recently as a potentially contributing variable in the efficacy of treatment for children with functional phonological disorders. Thus far, complexity has been defined in terms of linguistic and psycholinguistic structure, articulatory phonetic variables, and conventional clinical factors. The focus of this paper is on clinical complexity as it influences the selection of target sounds for treatment, with three clinical factors reviewed: consistency of the error, normative age of acquisition, and number of errors to be treated. The collective findings suggest that treatment of seemingly more complex targets results in greater phonological gains. These results are integrated with converging evidence from other populations and language and learning domains.

Question: What is the difference between a phonological disorder and a phonological processing disorder?


LSDA: Large Scale Detection Through Adaptation. A major challenge in scaling object detection is the difficulty of obtaining labeled images for large numbers of categories. Recently, deep convolutional neural networks (CNNs) have emerged as clear winners on object classification benchmarks, in part due to training with 1.2M+ labeled classification images. Unfortunately, only a small fraction of those labels are available for the detection task. It is much cheaper and easier to collect large quantities of image-level labels from search engines than it is to collect detection data and label it with precise bounding boxes. In this paper, we propose Large Scale Detection through Adaptation (LSDA), an algorithm which learns the difference between the two tasks and transfers this knowledge to classifiers for categories without bounding box annotated data, turning them into detectors. Our method has the potential to enable detection for the tens of thousands of categories that lack bounding box annotations, yet have plenty of classification data. Evaluation on the ImageNet LSVRC-2013 detection challenge demonstrates the efficacy of our approach. This algorithm enables us to produce a >7.6K detector by using available classification data from leaf nodes in the ImageNet tree. We additionally demonstrate how to modify our architecture to produce a fast detector (running at 2fps for the 7.6K detector). Models and software are available at lsda.berkeleyvision.org.

Question: What is the difference between classification and detection?


An improved traffic signs recognition and tracking method for driver assistance system. We introduce a new computer vision based system for robust traffic sign recognition and tracking. Such a system presents a vital support for driver assistance in an intelligent automotive. Firstly, a color based segmentation method is applied to generate traffic sign candidate regions. Secondly, the HoG features are extracted to encode the detected traffic signs and then generating the feature vector. This vector is used as an input to an SVM classifier to identify the traffic sign class. Finally, a tracking method based on optical flow is performed to ensure a continuous capture of the recognized traffic sign while accelerating the execution time. Our method affords high precision rates under different challenging conditions.

Question: What is a HoG?


A Microservice Architecture for the Intranet of Things and Energy in Smart Buildings: Research Paper. This paper presents challenges and issues in smart buildings and the Internet of Things (IoT), which we identified in years of research in real buildings. To tackle these challenges, a decentralized service-oriented architecture based on a message-oriented middleware has been implemented for the domain of smart buildings. It uses a network-transparent IoT message bus and provides the means for composing applications from auxiliary services, which facilitate device abstraction, protocol adaption, modularity, and maintainability. We demonstrate the flexibility of our architecture by describing how three distinct applications---privacy-enhancing energy data visualization, automated building energy management, and a generic user interface---can be integrated and operated simultaneously in our real smart building laboratory. We compare the advantages of our architecture to conventional ones and provide a best-practice solution for the Intranet of Things and Energy in smart buildings.

Question: What is the difference between a microservice architecture and a service-oriented architecture?


Location Privacy Violation via GPS-Agnostic Smart Phone Car Tracking. Smart phones nowadays are equipped with global positioning systems (GPS) chips to enable navigation and location-based services. A malicious app with the access to GPS data can easily track the person who carries the smart phone. People may disable the GPS module and turn it on only when necessary to protect their location privacy. However, in this paper, we demonstrate that an attacker is still able to track a person by using the embedded magnetometer sensor in victim's smart phone, even when the GPS module is disabled all the time. Moreover, this attack neither requests user permissions related to locations for installation, nor does its operation rely on wireless signals like WiFi positioning or suffer from signal propagation loss. Only the angles of a car's turning measured by the magnetometer sensor of a driver's smart phone are utilized. Without loss of generality, we focus on car tracking, since cars are popular transportation tools in developed countries, where smart phones are commonly used. Inspired by the intuition that a car may exhibit different turning angles at different road intersections, we find that an attacker can match car turning angles to a map to infer the actual path that the driver takes. We address technical challenges about car turn angle extraction, map database construction, and path matching algorithm design to make this attack practical and efficient. We also perform an evaluation using real-world driving paths to verify the relationship between the numbers of turns and the time cost of the matching algorithm. The results show that it is possible for attacker to precisely pinpoint the actual path when the driving path includes 11 turns or more. More simulations are performed to demonstrate the attack with lager selected local areas.

Question: How to track a car?


Self-monitoring: appraisal and reappraisal.. Theory and research on self-monitoring have accumulated into a sizable literature on the impact of variation in the extent to which people cultivate public appearances in diverse domains of social functioning. Yet self-monitoring and its measure, the Self-Monitoring Scale, are surrounded by controversy generated by conflicting answers to the critical question, Is self-monitoring a unitary phenomenon? A primary source of answers to this question has been largely neglected--the Self-Monitoring Scale's relations with external criteria. We propose a quantitative method to examine the self-monitoring literature and thereby address major issues of the controversy. Application of this method reveals that, with important exceptions, a wide range of external criteria tap a dimension directly measured by the Self-Monitoring Scale. We discuss what this appraisal reveals about with self-monitoring is and is not.

Question: What is the self-monitoring scale?


Stochastic Gradient Descent Tricks. Chapter 1 strongly advocates the stochastic back-propagation method to train neural networks. This is in fact an instance of a more general technique called stochastic gradient descent (SGD). This chapter provides background material, explains why SGD is a good learning algorithm when the training set is large, and provides useful recommendations.

Question: What is a stochastic gradient descent?


A lightweight gravity-balanced exoskeleton for home rehabilitation of upper limbs. Stroke recovery requires intensive and continuous rehabilitation over a long period of time. Access to existing rehabilitation devices is limited to hospitals due to the considerable cost and maintenance. This paper proposes a lightweight, low-cost gravity-balanced exoskeleton for home rehabilitation of upper limbs. Gravity balancing is based on a two-bar mechanism that can fit the periphery of a human arm. A type of planar flexural spring is proposed to achieve the required spring potential energy. This type of spring has a thin cross-section and can be designed and fabricated conveniently. A model is constructed to optimize the geometry and dimension of the exoskeleton. Performance is evaluated through a static analysis. The results show that the proposed exoskeleton is both simpler and lighter than existing passive devices. It can be easily custom-made for different arm masses and sizes. We expect the exoskeleton to be used in both clinical and homecare environments for the arm rehabilitation of stroke patients.

Question: What is a gravity-balanced exoskeleton?


SwiPIN: Fast and Secure PIN-Entry on Smartphones. In this paper, we present SwiPIN, a novel authentication system that allows input of traditional PINs using simple touch gestures like up or down and makes it secure against human observers. We present two user studies which evaluated different designs of SwiPIN and compared it against traditional PIN. The results show that SwiPIN performs adequately fast (3.7 s) to serve as an alternative input method for risky situations. Furthermore, SwiPIN is easy to use, significantly more secure against shoulder surfing attacks and switching between PIN and SwiPIN feels natural.

Question: How fast is the swipin?


On the Consistency of Top-k Surrogate Losses. The top-k error is often employed to evaluate performance for challenging classification tasks in computer vision as it is designed to compensate for ambiguity in ground truth labels. This practical success motivates our theoretical analysis of consistent top-k classification. To this end, we define top-k calibration as a necessary and sufficient condition for consistency, for bounded below loss functions. Unlike prior work, our analysis of top-k calibration handles non-uniqueness of the predictor scores, and extends calibration to consistency – providing a theoretically sound basis for analysis of this topic. Based on the topk calibration analysis, we propose a rich class of top-k calibrated Bregman divergence surrogates. Our analysis continues by showing previously proposed hinge-like top-k surrogate losses are not top-k calibrated and thus inconsistent. On the other hand, we propose two new hinge-like losses, one which is similarly inconsistent, and one which is consistent. Our empirical results highlight theoretical claims, confirming our analysis of the consistency of these losses.

Question: What is the consistency of top-k surrogate losses?


Online Learning with Predictable Sequences. We present methods for online linear optimization that take advantage of benign (as opposed to worst-case) sequences. Specifically if the sequence encountered by the learner is described well by a known "predictable process", the algorithms presented enjoy tighter bounds as compared to the typical worst case bounds. Additionally, the methods achieve the usual worst-case regret bounds if the sequence is not benign. Our approach can be seen as a way of adding prior knowledge about the sequence within the paradigm of online learning. The setting is shown to encompass partial and side information. Variance and path-length bounds [11, 9] can be seen as particular examples of online learning with simple predictable sequences. We further extend our methods and results to include competing with a set of possible predictable processes (models), that is "learning" the predictable process itself concurrently with using it to obtain better regret guarantees. We show that such model selection is possible under various assumptions on the available feedback. Our results suggest a promising direction of further research with potential applications to stock market and time series prediction.

Question: What is the difference between benign and worst-case sequences?


A Hybrid EP and SQP for Dynamic Economic Dispatch with Nonsmooth Fuel Cost Function. Dynamic economic dispatch (DED) is one of the main functions of power generation operation and control. It determines the optimal settings of generator units with predicted load demand over a certain period of time. The objective is to operate an electric power system most economically while the system is operating within its security limits. This paper proposes a new hybrid methodology for solving DED. The proposed method is developed in such a way that a simple evolutionary programming (EP) is applied as a based level search, which can give a good direction to the optimal global region, and a local search sequential quadratic programming (SQP) is used as a fine tuning to determine the optimal solution at the final. Ten units test system with nonsmooth fuel cost function is used to illustrate the effectiveness of the proposed method compared with those obtained from EP and SQP alone.

Question: What is a hybrid method?


A Practical Method for Modeling PCB Transmission Lines with Conductor Surface Roughness and Wideband Dielectric Properties. This paper discusses the modeling techniques to account for transmission line high frequency effects, and proposes a method to integrate these techniques into a practical and design-worthy model generation flow. The frequency tabulated transmission line models are capable of predicting wideband dielectric characteristics and high frequency conductor losses due to skin effect and surface roughness. They can be used to accurately model high volume, low cost printed circuit board traces which often have roughened trace surfaces and pronounced frequency variation in dielectric properties. The model accuracy is verified up to 20 GHz in frequency domain, thus suitable for multi-gigabit signaling analysis

Question: What is the frequency of the model?


Information-Driven Dynamic Sensor Collaboration for Tracking Applications. This article overviews the information-driven approach to sensor collaboration in ad hoc sensor networks. The main idea is for a network to determine participants in a " sensor collaboration " by dynamically optimizing the information utility of data for a given cost of communication and computation. A definition of information utility is introduced, and several approximate measures of the information utility are developed for reasons of computational tractability. We illustrate the use of this approach using examples drawn from tracking applications. I. INTRODUCTION The technology of wirelessly networked micro-sensors promises to revolutionize the way we live, work, and interact with the physical environment. For example, tiny, inexpensive sensors can be " sprayed " onto roads, walls, or machines to monitor and detect a variety of interesting events such as highway traffic, wildlife habitat condition, forest fire, manufacturing job flow, and military battlefield situation.

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


A Memory Bandwidth-Efficient Hybrid Radix Sort on GPUs. Sorting is at the core of many database operations, such as index creation, sort-merge joins, and user-requested output sorting. As GPUs are emerging as a promising platform to accelerate various operations, sorting on GPUs becomes a viable endeavour. Over the past few years, several improvements have been proposed for sorting on GPUs, leading to the first radix sort implementations that achieve a sorting rate of over one billion 32-bit keys per second. Yet, state-of-the-art approaches are heavily memory bandwidth-bound, as they require substantially more memory transfers than their CPU-based counterparts. Our work proposes a novel approach that almost halves the amount of memory transfers and, therefore, considerably lifts the memory bandwidth limitation. Being able to sort two gigabytes of eight-byte records in as little as 50 milliseconds, our approach achieves a 2.32-fold improvement over the state-of-the-art GPU-based radix sort for uniform distributions, sustaining a minimum speed-up of no less than a factor of 1.66 for skewed distributions. To address inputs that either do not reside on the GPU or exceed the available device memory, we build on our efficient GPU sorting approach with a pipelined heterogeneous sorting algorithm that mitigates the overhead associated with PCIe data transfers. Comparing the end-to-end sorting performance to the state-of-the-art CPU-based radix sort running 16 threads, our heterogeneous approach achieves a 2.06-fold and a 1.53-fold improvement for sorting 64 GB key-value pairs with a skewed and a uniform distribution, respectively.

Question: What is the memory bandwidth of a GPU?


Surface reconstruction from unorganized points. Computer Graphics, 26, 2, July 1992 Surface Reconstruction from Unorganized Points Hugues Hoppe* Tony DeRose* Tom Duchampt John McDonald$ Werner Stuetzle~ University of Washington Seattle, WA 98195 We describe and demonstrate an algorithm that takes as input an unorganized set of points {xl, . . . . x.} c IR3 on or near an unknown manifold M, and produces as output a simplicial surface that approximates M. Neither the topology, the presence of boundaries, nor the geometry of M are assumed to be known in advance — all are inferred automatically from the data. This problem natu rally arises in a variety of practical situations such as range scanning an object from multiple view points, recovery of biological shapes from two-dimensional slices, and interactive surface sketching. CR

Question: How to reconstruct a surface from unorganized points?


Semantic Similarity in a Taxonomy: An Information-Based Measure and its Application to Problems of Ambiguity in Natural Language. This article presents a measure of semantic similarity in an is-a taxonomy based on the notion of shared information content. Experimental evaluation against a benchmark set of human similarity judgments demonstrates that the measure performs better than the traditional edge-counting approach. The article presents algorithms that take advantage of taxonomic similarity in resolving syntactic and semantic ambiguity, along with experimental results demonstrating their e ectiveness.

Question: What is the taxonomy of the canadian military?