Autonomous UAV Surveillance in Complex Urban Environments. We address the problem of multi-UAV-based surveillance in complex urban environments with occlusions. The problem consists of controlling the flight of UAVs with on-board cameras so that the coverage and recency of the information about the designated area is maximized. In contrast to the existing work, sensing constraints due to occlusions and UAV motion constraints are modeled realistically and taken into account. We propose a novel \emph{occlusion-aware} surveillance algorithm based on a decomposition of the surveillance problem into a variant of the 3D art gallery problem and an instance of traveling salesman problem for Dubins vehicles. The algorithm is evaluated on the high-fidelity \textsc{AgentFly} UAV simulation testbed which accurately models all constraints and effects involved. The results confirm the importance of occlusion-aware flight path planning, in particular in the case of narrow-street areas and low UAV flight altitudes.

Question: How to solve the problem of multi-UAV-based surveillance in complex urban environments with occlusions?


Empirical comparison of algorithms for network community detection. Detecting clusters or communities in large real-world graphs such as large social or information networks is a problem of considerable interest. In practice, one typically chooses an objective function that captures the intuition of a network cluster as set of nodes with better internal connectivity than external connectivity, and then one applies approximation algorithms or heuristics to extract sets of nodes that are related to the objective function and that "look like" good communities for the application of interest.  In this paper, we explore a range of network community detection methods in order to compare them and to understand their relative performance and the systematic biases in the clusters they identify. We evaluate several common objective functions that are used to formalize the notion of a network community, and we examine several different classes of approximation algorithms that aim to optimize such objective functions. In addition, rather than simply fixing an objective and asking for an approximation to the best cluster of any size, we consider a size-resolved version of the optimization problem. Considering community quality as a function of its size provides a much finer lens with which to examine community detection algorithms, since objective functions and approximation algorithms often have non-obvious size-dependent behavior.

Question: What is a community detection algorithm?


BiCoS: A Bi-level co-segmentation method for image classification. The objective of this paper is the unsupervised segmentation of image training sets into foreground and background in order to improve image classification performance. To this end we introduce a new scalable, alternation-based algorithm for co-segmentation, BiCoS, which is simpler than many of its predecessors, and yet has superior performance on standard benchmark image datasets.

Question: What is the objective of this paper?


CEFAM: Comprehensive Evaluation Framework for Agile Methodologies. Agile software development is regarded as an effective and efficient approach, mainly due to its ability to accommodate rapidly changing requirements, and to cope with modern software development challenges. There is therefore a strong tendency to use agile software development methodologies where applicable; however, the sheer number of existing agile methodologies and their variants hinders the selection of an appropriate agile methodology or method chunk. Methodology evaluation tools address this problem through providing detailed evaluations, yet no comprehensive evaluation framework is available for agile methodologies. We introduce the comprehensive evaluation framework for agile methodologies (CEFAM) as an evaluation tool for project managers and method engineers. The hierarchical (and mostly quantitative) evaluation criterion set introduced in this evaluation framework enhances the usability of the framework and provides results that are precise enough to be useful for the selection, adaptation and construction of agile methodologies.

Question: What is the comprehensive evaluation framework for agile methodologies?


Convolutional Neural Networks and Language Embeddings for End-to-End Dialect Recognition. • Fusion between end-to-end system and language embeddings shows better efficiency than between end-to-end system such as MFCC and FBANK • Spectrograms achieve slightly better results than MFCCs Motivation • One of the challenges of processing real-world spoken content, such as media broadcasts, is the potential presence of different dialects of a language in the material. • Dialect identification (DID) can be a useful capability to identify which dialect is being spoken during a recording. • The Arabic Multi-Genre Broadcast (MGB) Challenge tasks have provided a valuable resource for researchers interested in processing multi-dialectal Arabic speech. • Investigation of end-to-end DID approach with dataset augmentation for acoustic feature and language embeddings for linguistic feature

Question: What is the arabic multi-genre broadcast challenge?


Belief space planning assuming maximum likelihood observations. We cast the partially observable control problem as a fully observable underactuated stochastic control problem in belief space and apply standard planning and control techniques. One of the difficulties of belief space planning is modeling the stochastic dynamics resulting from unknown future observations . The core of our proposal is to define deterministic beliefsystem dynamics based on an assumption that the maximum likelihood observation (calculated just prior to the observation) is always obtained. The stochastic effects of future observation s are modeled as Gaussian noise. Given this model of the dynamics, two planning and control methods are applied. In the first, linear quadratic regulation (LQR) is applied to generate policies in the belief space. This approach is shown to be optimal for linearGaussian systems. In the second, a planner is used to find locally optimal plans in the belief space. We propose a replanning approach that is shown to converge to the belief space goal in a finite number of replanning steps. These approaches are characterized in the context of a simple nonlinear manipulation problem where a planar robot simultaneously locates and grasps an object.

Question: What is belief space planning?


The Costs and Benefits of Pair Programming. Pair or collaborative programming is where two programmers develop software side by side at one computer. Using interviews and controlled experiments, the authors investigated the costs and benefits of pair programming. They found that for a development-time cost of about 15%, pair programming improves design quality, reduces defects, reduces staffing risk, enhances technical skills, improves team communications and is considered more enjoyable at statistically significant levels.

Question: What is the cost of pair programming?


Comparison Analysis: Granger Causality and New Causality and Their Applications to Motor Imagery. In this paper we first point out a fatal drawback that the widely used Granger causality (GC) needs to estimate the autoregressive model, which is equivalent to taking a series of backward recursive operations which are infeasible in many irreversible chemical reaction models. Thus, new causality (NC) proposed by Hu et al. (2011) is theoretically shown to be more sensitive to reveal true causality than GC. We then apply GC and NC to motor imagery (MI) which is an important mental process in cognitive neuroscience and psychology and has received growing attention for a long time. We study causality flow during MI using scalp electroencephalograms from nine subjects in Brain-computer interface competition IV held in 2008. We are interested in three regions: Cz (central area of the cerebral cortex), C3 (left area of the cerebral cortex), and C4 (right area of the cerebral cortex) which are considered to be optimal locations for recognizing MI states in the literature. Our results show that: 1) there is strong directional connectivity from Cz to C3/C4 during left- and right-hand MIs based on GC and NC; 2) during left-hand MI, there is directional connectivity from C4 to C3 based on GC and NC; 3) during right-hand MI, there is strong directional connectivity from C3 to C4 which is much clearly revealed by NC than by GC, i.e., NC largely improves the classification rate; and 4) NC is demonstrated to be much more sensitive to reveal causal influence between different brain regions than GC.

Question: How to classify motor imagery?


Wearable Glove-Type Driver Stress Detection Using a Motion Sensor. Increased driver stress is generally recognized as one of the major factors leading to road accidents and loss of life. Even though physiological signals are reported as the most reliable means to measure driver stresses, they often require the use of unique and expensive sensors, which produce dynamic and varying readings within individuals. This paper presents a novel means to predict a driver's stress level by evaluating the movement pattern of the steering wheel. This is accomplished by using an inertial motion unit sensor, which is placed on a glove worn by the driver. The motion sensor selected for this paper was chosen because for its low cost and the fact that it is least affected by environmental factors as compared with a physiological signal. Experiments were conducted in three different environmental scenarios. The scenarios were classified as "urban," "highway," and "rural," and they were chosen to simulate contrasting stress conditions experienced by the driver. In this paper, skin conductance and driver self-reports served as a reference stress to predict the driver's stress level. Galvanic skin response, a well-known stress indicator, was captured along the driver's palm and the readings were transmitted to a mobile device via low energy Bluetooth for further processing. The results revealed that indirect measurement of steering wheel movement with an inertial motion sensor could obtain accuracies up to an average rate of 94.78%. This demonstrates the opportunity for inclusion of motion sensors in wireless driver assistance systems for ambulatory monitoring of stress levels.

Question: How can we predict driver stress using a motion sensor?


Data Mining of Causal Relations from Text: Analysing Maritime Accident Investigation Reports. Text mining is a process of extracting information of interest from text. Such a method includes techniques from various areas such as Information Retrieval (IR), Natural Language Processing (NLP), and Information Extraction (IE). In this study, text mining methods are applied to extract causal relations from maritime accident investigation reports collected from the Marine Accident Investigation Branch (MAIB). These causal relations provide information on various mechanisms behind accidents, including human and organizational factors relating to the accident. The objective of this study is to facilitate the analysis of the maritime accident investigation reports, by means of extracting contributory causes with more feasibility. A careful investigation of contributory causes from the reports provide opportunity to improve safety in future. Two methods have been employed in this study to extract the causal relations. They are 1) Pattern classification method and 2) Connectives method. The earlier one uses naı̈ve Bayes and Support Vector Machines (SVM) as classifiers. The latter simply searches for the words connecting cause and effect in sentences. The causal patterns extracted using these two methods are compared to the manual (human expert) extraction. The pattern classification method showed a fair and sensible performance with F-measure(average) = 65% when compared to connectives method with F-measure(average) = 58%. This study is an evidence, that text mining methods could be employed in extracting causal relations from marine accident investigation reports.

Question: What is a marine accident investigation report?


Battery Management System: An Overview of Its Application in the Smart Grid and Electric Vehicles. With the rapidly evolving technology of the smart grid and electric vehicles (EVs), the battery has emerged as the most prominent energy storage device, attracting a significant amount of attention. The very recent discussions about the performance of lithium-ion (Li-ion) batteries in the Boeing 787 have confirmed so far that, while battery technology is growing very quickly, developing cells with higher power and energy densities, it is equally important to improve the performance of the battery management system (BMS) to make the battery a safe, reliable, and cost-efficient solution. The specific characteristics and needs of the smart grid and EVs, such as deep charge/discharge protection and accurate state-of-charge (SOC) and state-of-health (SOH) estimation, intensify the need for a more efficient BMS. The BMS should contain accurate algorithms to measure and estimate the functional status of the battery and, at the same time, be equipped with state-of-the-art mechanisms to protect the battery from hazardous and inefficient operating conditions.

Question: What is the difference between a battery management system and a battery?


Deep Clustering with Convolutional Autoencoders. Deep clustering utilizes deep neural networks to learn feature representation that is suitable for clustering tasks. Though demonstrating promising performance in various applications, we observe that existing deep clustering algorithms either do not well take advantage of convolutional neural networks or do not considerably preserve the local structure of data generating distribution in the learned feature space. To address this issue, we propose a deep convolutional embedded clustering algorithm in this paper. Specifically, we develop a convolutional autoencoders structure to learn embedded features in an end-to-end way. Then, a clustering oriented loss is directly built on embedded features to jointly perform feature refinement and cluster assignment. To avoid feature space being distorted by the clustering loss, we keep the decoder remained which can preserve local structure of data in feature space. In sum, we simultaneously minimize the reconstruction loss of convolutional autoencoders and the clustering loss. The resultant optimization problem can be effectively solved by mini-batch stochastic gradient descent and back-propagation. Experiments on benchmark datasets empirically validate the power of convolutional autoencoders for feature learning and the effectiveness of local structure preservation.

Question: What is the difference between convolutional autoencoders and deep clustering?


Rubinstein Taybi Syndrome in an Indian Child due to EP300 Gene Mutation.. To the Editor : A 10-y-old girl was diagnosed with Rubinstein Taybi Syndrome (RTS) based on distinctive facial features (low hanging columella, high palate, grimacing smile, talon cusps), broad, angulated thumbs and great toes, short stature and moderate intellectual disability (Fig. 1). Her younger sister, who died at 11 mo of age due to acute myeloid leukemia, did not have clinical features of RTS. Complete sequencing of CREBBP and EP300 genes was done for the proband as previously described [1, 2]. No pathogenic mutation was noted in CREBBP gene. She was found to be heterozygous for c.1282C > T or p.P428S mutation in exon five of EP300 gene (Fig. 1). The mutation is predicted to affect the donor splicing [3]. The patient's mother was heterozygous for the same mutation. Prenatal diagnosis was offered to the couple and the fetus did not carry this mutation, and was confirmed to be unaffected postnatally. This mutation was not identified in 100 normal unrelated individuals. Deletion/duplication analysis of CREBBP and EP300 gene was not available. Our patient had horizontal palpebral fissures as described with EP300 related RTS, compared with CREBBP related RTS, who have anti-mongoloid slant [4]. Although patients with EP300 mutation have broad thumbs or toes, radial deviation has not been reported [2] (Fig. 1). This mutation was inherited from her mother who had mild hypertelorism, broad nasal bridge, normal intelligence and hands/feet. Negri et al. reported a patient with mutation in EP300 gene (p.N1511T) that was inherited from a healthy mother. Low penetrance in EP300 related RTS may present a challenge during genetic counseling and prenatal diagnosis [2]. EP300 gene somatic mutations are reported in acute leukemia and RTS patients have an increased incidence of cancer [2, 5]. Thus, we report an Indian child with Rubinstein Taybi Syndrome with familial EP300 gene mutation.

Question: What is the difference between Rubinstein Taybi Syndrome and EP300 related RTS?


Identity Mappings in Deep Residual Networks. Deep residual networks [1] have emerged as a family of extremely deep architectures showing compelling accuracy and nice convergence behaviors. In this paper, we analyze the propagation formulations behind the residual building blocks, which suggest that the forward and backward signals can be directly propagated from one block to any other block, when using identity mappings as the skip connections and after-addition activation. A series of ablation experiments support the importance of these identity mappings. This motivates us to propose a new residual unit, which makes training easier and improves generalization. We report improved results using a 1001-layer ResNet on CIFAR-10 (4.62% error) and CIFAR-100, and a 200-layer ResNet on ImageNet. Code is available at: https://github.com/KaimingHe/ resnet-1k-layers.

Question: What is the identity mapping?


Benchmarking short text semantic similarity. Short Text Semantic Similarity measurement is a ne w and rapidly growing field of research. "Short texts" are typically sentence leng th but are not required to be grammatically correct . There is great potential for applying these measure s in fields such as Information Retrieval, Dialogue Management and Question Answering. A datas et of 65 sentence pairs, with similarity ratings, produced in 2006 has become adopted as a de facto Gold Standard benchmark. This paper discusses the adoption of the 2006 dataset, lays do wn a number of criteria that can be used to determine whether a dataset should be awarded a "Go ld Standard" accolade and illustrates its use as a benchmark. Procedures for the generation of furth er Gold Standard datasets in this field are recommended.

Question: What is a gold standard dataset?


GenomeGraphs: integrated genomic data visualization with R. Biological studies involve a growing number of distinct high-throughput experiments to characterize samples of interest. There is a lack of methods to visualize these different genomic datasets in a versatile manner. In addition, genomic data analysis requires integrated visualization of experimental data along with constantly changing genomic annotation and statistical analyses. We developed GenomeGraphs, as an add-on software package for the statistical programming environment R, to facilitate integrated visualization of genomic datasets. GenomeGraphs uses the biomaRt package to perform on-line annotation queries to Ensembl and translates these to gene/transcript structures in viewports of the grid graphics package. This allows genomic annotation to be plotted together with experimental data. GenomeGraphs can also be used to plot custom annotation tracks in combination with different experimental data types together in one plot using the same genomic coordinate system. GenomeGraphs is a flexible and extensible software package which can be used to visualize a multitude of genomic datasets within the statistical programming environment R.

Question: What is the genome of the canadian military?


Multi-class AdaBoost ∗. Boosting has been a very successful technique for solving the two-class classification problem. In going from two-class to multi-class classification, most algorithms have been restricted to reducing the multi-class classification problem to multiple two-class problems. In this paper, we develop a new algorithm that directly extends the AdaBoost algorithm to the multi-class case without reducing it to multiple two-class problems. We show that the proposed multi-class AdaBoost algorithm is equivalent to a forward stagewise additive modeling algorithm that minimizes a novel exponential loss for multi-class classification. Furthermore, we show that the exponential loss is a member of a class of Fisher-consistent loss functions for multi-class classification. As shown in the paper, the new algorithm is extremely easy to implement and is highly competitive in terms of misclassification error rate.

Question: What is AdaBoost?


with Emphasis to the Computer and Internet. This article discusses the Roles of ICT in education. Information communication technologies (ICT) at present are influencing every aspect of human life. They are playing salient roles in work places, business, education, and entertainment. Moreover, many people recognize ICTs as catalysts for change; change in working conditions, handling and exchanging information, teaching methods, learning approaches, scientific research, and in accessing information. Therefore, this review article discusses the roles of ICTs, the promises, limitations and key challenges of integration to education systems. The review attempts in answering the following questions: (1) What are the benefits of ICTs in education? (2) What are the existing promises of ICT use in education systems of some developing countries? (3) What are the limitations and key challenges of ICTs integration to education systems? The review concludes that regardless of all the limitations characterizing it, ICT benefits education systems to provide quality education in alignment with constructivism, which is a contemporary paradigm of learning.

Question: What are the benefits of ICTs in education?


A Novel Deployment Scheme for Green Internet of Things. The Internet of Things (IoT) has been realized as one of the most promising networking paradigms that bridge the gap between the cyber and physical world. Developing green deployment schemes for IoT is a challenging issue since IoT achieves a larger scale and becomes more complex so that most of the current schemes for deploying wireless sensor networks (WSNs) cannot be transplanted directly in IoT. This paper addresses this challenging issue by proposing a deployment scheme to achieve green networked IoT. The contributions made in this paper include: 1) a hierarchical system framework for a general IoT deployment, 2) an optimization model on the basis of proposed system framework to realize green IoT, and 3) a minimal energy consumption algorithm for solving the presented optimization model. The numerical results on minimal energy consumption and network lifetime of the system indicate that the deployment scheme proposed in this paper is more flexible and energy efficient compared to typical WSN deployment scheme; thus is applicable to the green IoT deployment.

Question: How to deploy IoT in green?


Big Data Reduction Methods: A Survey. Research on big data analytics is entering in the new phase called fast data where multiple gigabytes of data arrive in the big data systems every second. Modern big data systems collect inherently complex data streams due to the volume, velocity, value, variety, variability, and veracity in the acquired data and consequently give rise to the 6Vs of big data. The reduced and relevant data streams are perceived to be more useful than collecting raw, redundant, inconsistent, and noisy data. Another perspective for big data reduction is that the million variables big datasets cause the curse of dimensionality which requires unbounded computational resources to uncover actionable knowledge patterns. This article presents a review of methods that are used for big data reduction. It also presents a detailed taxonomic discussion of big data reduction methods including the network theory, big data compression, dimension reduction, redundancy elimination, data mining, and machine learning methods. In addition, the open research issues pertinent to the big data reduction are also highlighted.

Question: What is the curse of dimensionality?


Dual-wideband log-periodic antennas. Two log-periodic (LP) antenna configurations able to suppress a specific frequency band are introduced in this paper. LP antennas belong to the class of frequency independent antennas and are able to achieve multi-decade wide bandwidths. We start with a parametric study of a traditional planar curved tooth LP antenna aimed in determining the means for achieving specific bandwidth. Two techniques for accomplishing consistent near/far-field dual-band performance are introduced next. The first is a LP antenna integrated with a dual-wideband feed line, and the second one is a LP with removed resonant teeth. Antennas and feeds are designed on a microstrip substrate and method of moments modeling within FEKO is used to demonstrate their performance. Required dual-band operation is 1.8-5 GHz and 7-11 GHz.

Question: What is a dual-wideband log-periodic antenna?


An analysis and critique of Research through Design: towards a formalization of a research approach. The field of HCI is experiencing a growing interest in Research through Design (RtD), a research approach that employs methods and processes from design practice as a legitimate method of inquiry. We are interested in expanding and formalizing this research approach, and understanding how knowledge, or theory, is generated from this type of design research. We conducted interviews with 12 leading HCI design researchers, asking them about design research, design theory, and RtD specifically. They were easily able to identify different types of design research and design theory from contemporary and historical design research efforts, and believed that RtD might be one of the most important contributions of design researchers to the larger research community. We further examined three historical RtD projects that were repeatedly mentioned in the interviews, and performed a critique of current RtD practices within the HCI research and interaction design communities. While our critique summarizes the problems, it also shows possible directions for further developments and refinements of the approach.

Question: What is design research?


Comparing a computer agent with a humanoid robot. HRI researchers interested in social robots have made large investments in humanoid robots. There is still sparse evidence that peoples' responses to robots differ from their responses to computer agents, suggesting that agent studies might serve to test HRI hypotheses. To help us understand the difference between people's social interactions with an agent and a robot, we experimentally compared people's responses in a health interview with (a) a computer agent projected either on a computer monitor or life-size on a screen, (b) a remote robot projected life-size on a screen, or (c) a collocated robot in the same room. We found a few behavioral and large attitude differences across these conditions. Participants forgot more and disclosed least with the collocated robot, next with the projected remote robot, and then with the agent. They spent more time with the collocated robot and their attitudes were most positive toward that robot. We discuss tradeoffs for HRI research of using collocated robots, remote robots, and computer agents as proxies of robots.

Question: What is the difference between a computer agent and a humanoid robot?


MIR_EVAL: A Transparent Implementation of Common MIR Metrics. Central to the field of MIR research is the evaluation of algorithms used to extract information from music data. We present mir_eval, an open source software library which provides a transparent and easy-to-use implementation of the most common metrics used to measure the performance of MIR algorithms. In this paper, we enumerate the metrics implemented by mir_eval and quantitatively compare each to existing implementations. When the scores reported by mir_eval differ substantially from the reference, we detail the differences in implementation. We also provide a brief overview of mir_eval's architecture, design, and intended use. 1. EVALUATING MIR ALGORITHMS Much of the research in Music Information Retrieval (MIR) involves the development of systems that process raw music data to produce semantic information. The goal of these systems is frequently defined as attempting to duplicate the performance of a human listener given the same task [5]. A natural way to determine a system's effectiveness might be for a human to study the output produced by the system and judge its correctness. However, this would yield only subjective ratings, and would also be extremely timeconsuming when evaluating a system's output over a large corpus of music. Instead, objective metrics are developed to provide a well-defined way of computing a score which indicates each system's output's correctness. These metrics typically involve a heuristically-motivated comparison of the system's output to a reference which is known to be correct. Over time, certain metrics have become standard for each ∗Please direct correspondence to craffel@gmail.com c © Colin Raffel, Brian McFee, Eric J. Humphrey, Justin Salamon, Oriol Nieto, Dawen Liang, Daniel P. W. Ellis. Licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). Attribution: Colin Raffel, Brian McFee, Eric J. Humphrey, Justin Salamon, Oriol Nieto, Dawen

Question: What is the difference between a metric and a score?


Telemanipulation with force-based display of proximity fields. In this paper we show and evaluate the design of a novel telemanipulation system that maps proximity values, acquired inside of a gripper, to forces a user can feel through a haptic input device. The command console is complemented by input-devices that give the user an intuitive control over parameters relevant to the system. Furthermore, proximity sensors enable the autonomous alignment/centering of the gripper to objects in user-selected DoFs with the potential of aiding the user and lowering the workload. We evaluate our approach in a user study that shows that the telemanipulation system benefits from the supplementary proximity information and that the workload can indeed be reduced when the system operates with partial autonomy.

Question: How to design a telemanipulation system?


Scene text extraction using stroke width transform for tourist translator on android platform. Scene text extraction is recent research area in the field of Computer Vision. Text component in an image is of particular interest, as it is easy to understand by both humans as well as machines and can be used in variety of applications like content-based image retrieval, aids for visually impaired people, translator for tourists. Although there exists a lot of research activities in this field, it is still remained as a challenging problem, mainly due to two issues: different variety of text patterns like fonts, colors, sizes, orientations; and presence of background outliers similar to text characters, such as windows, bricks. In this paper, an android application named as TravelMate is developed, which is a user-friendly application to assist the tourist navigation, while they are roaming in foreign countries. Proposed application is able to extract text from an image, which is captured by an android mobile camera. Extraction is performed using stroke width transform (SWT) approach and connected component analysis. Extracted texts are recognized using Google's open source optical character recognition (OCR) engine 'Tesseract' and translated to a target language using Google's translation. The entire result is displayed back onto the screen of the mobile Phone.

Question: How to extract text from an image?


Ant colony optimization for routing and load-balancing: survey and new directions. Although an ant is a simple creature, collectively a colony of ants performs useful tasks such as finding the shortest path to a food source and sharing this information with other ants by depositing pheromone. In the field ofant colony optimization (ACO), models ofcollective intelligenceof ants are transformed into useful optimization techniques that find applications in computer networking. In this survey, the problem-solving paradigm of ACO is explicated and compared to traditional routing algorithms along the issues of routing information, routing overhead and adaptivity. The contributions of this survey include 1) providing a comparison and critique of the state-of-the-art approaches for mitigatingstagnation (a major problem in many ACO algorithms), 2) surveying and comparing three major research in applying ACO in routing and load-balancing, and 3) discussing new directions and identifying open problems. The approaches for mitigating stagnation discussed include:evaporation, aging, pheromone smoothingand limiting, privileged pheromone layingandpheromone-heuristic control . The survey on ACO in routing/load-balancing includes comparison and critique of ant-based control and its ramifications, AntNetand its extensions, as well as ASGAand SynthECA. Discussions on new directions include an ongoing work of the authors in applyingmultiple ant colony optimizationin load-balancing.

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


Communicating with Cost-based Implicature : a Game-Theoretic Approach to Ambiguity. A game-theoretic approach to linguistic communication predicts that speakers can meaningfully use ambiguous forms in a discourse context in which only one of several available referents has a costly unambiguous form and in which rational interlocutors share knowledge of production costs. If a speaker produces a low-cost ambiguous form to avoid using the high-cost unambiguous form, a rational listener will infer that the high-cost entity was the intended entity, or else the speaker would not have risked ambiguity. We report data from two studies in which pairs of speakers show alignment of their use of ambiguous forms based on this kind of shared knowledge. These results extend the analysis of cost-based pragmatic inferencing beyond that previously associated only with fixed lexical hosts.

Question: What is the cost of using an ambiguous form?


Power control in two-tier femtocell networks. In a two tier cellular network - comprised of a central macrocell underlaid with shorter range femtocell hotspots - cross-tier interference limits overall capacity with universal frequency reuse. To quantify near-far effects with universal frequency reuse, this paper derives a fundamental relation providing the largest feasible cellular Signal-to-Interference-Plus-Noise Ratio (SINR), given any set of feasible femtocell SINRs. We provide a link budget analysis which enables simple and accurate performance insights in a two-tier network. A distributed utility- based SINR adaptation at femtocells is proposed in order to alleviate cross-tier interference at the macrocell from cochannel femtocells. The Foschini-Miljanic (FM) algorithm is a special case of the adaptation. Each femtocell maximizes their individual utility consisting of a SINR based reward less an incurred cost (interference to the macrocell). Numerical results show greater than 30% improvement in mean femtocell SINRs relative to FM. In the event that cross-tier interference prevents a cellular user from obtaining its SINR target, an algorithm is proposed that reduces transmission powers of the strongest femtocell interferers. The algorithm ensures that a cellular user achieves its SINR target even with 100 femtocells/cell-site (with typical cellular parameters) and requires a worst case SINR reduction of only 16% at femtocells. These results motivate design of power control schemes requiring minimal network overhead in two-tier networks with shared spectrum.

Question: How to control femtocell interference in a two-tier network?


Teachers' Performance Evaluation in Higher Educational Institution using Data Mining Technique. Educational Data Mining (EDM) is an evolving field exploring pedagogical data by applying different machine learning techniques/tools. It can be considered as interdisciplinary research field which provides intrinsic knowledge of teaching and learning process for effective education. The main objective of any educational institution is to provide quality education to its students. One way to achieve highest level of quality in higher education system is by discovering knowledge that predicts teachers' performance. This study presents an efficient system model for evaluation and prediction of teachers' performance in higher institutions of learning using data mining technologies. To achieve the objectives of this work, a two-layered classifier system was designed; it consists of an Artificial Neural Network (ANN) and Decision Tree. The classifier system was tested successfully using case study data from a Nigerian University in the South West of Nigeria. The data consists of academic qualifications for teachers as well as their experiences and grades of students in courses they taught among others. The attribute selected were evaluated using two feature selection methods in order to get a subset of the attributes that would make for a compact and accurate predictive model. The WEKA machine learning tool was used for the mining. The results show that, among the six attributes used, Working Experience, and Rank are rated the best two attributes that contributed mostly to the performance of teachers in this study. Also, considering the time taken to build the models and performance accuracy level, C4.5 decision tree outperformed the other two algorithms (ID3 and MLP) with good performance of 83.5% accuracy level and acceptable kappa statistics of 0.743. It does mean that C4.5 decision tree is best algorithm suitable for predicting teachers' performance in relation to the othe

Question: What is the best algorithm for predicting teachers' performance in higher educational institutions?


Toward Adversarial Online Learning and the Science of Deceptive Machines. Intelligent systems rely on pattern recognition and signaturebased approaches for a wide range of sensors enhancing situational awareness. For example, autonomous systems depend on environmental sensors to perform their tasks and secure systems depend on anomaly detection methods. The availability of large amount of data requires the processing of data in a "streaming" fashion with online algorithms. Yet, just as online learning can enhance adaptability to a non-stationary environment, it introduces vulnerabilities that can be manipulated by adversaries to achieve their goals while evading detection. Although human intelligence might have evolved from social interactions, machine intelligence has evolved as a human intelligence artifact and been kept isolated to avoid ethical dilemmas. As our adversaries become sophisticated, it might be time to revisit this question and examine how we can combine online learning and reasoning leading to the science of deceptive and counter-deceptive machines.

Question: What is the science of deceptive machines?


Evaluation of supply chain structures through modularization and postponement. This paper introduces a conceptual framework for evaluating di€erent supply chain structures in the context of modularization and postponement. In our analysis, modularization is linked to inbound logistics as the combination of di€erent components (or modules) allow for the assembly of the ®nal product. Postponement, however, corresponds with the outbound logistics since it is through the distribution function that customers' speci®c demand is satis®ed. Given this perspective, we introduce a taxonomy and develop a corresponding framework for the characterization of four supply chain structures, de®ned according to the combined levels of modularization and postponement: Rigid, Postponed, Modularized and Flexible. We also illustrate the applicability of the resulting framework through quantifying the total cost di€erential for utilizing a particular supply chain structure. By quantifying the total cost for employing a particular supply chain structure we can numerically illustrate structural results that allow for an objective comparison among them. As such, our ®ndings substantiate the empirical evidence that vertical integration along the supply chain is not always desirable. A point of emphasis in this paper is the notion that ®rms may be better o€ making combined modularization (outsourcing) and postponement decisions as opposed to separate ones (as they currently do). This also suggests that modularization and postponement decisions need not be independent and that their joint consideration extends operational advantages worthy of contemplation. Ó 2000 Elsevier Science B.V. All rights reserved.

Question: What is the difference between modularization and postponement?


Computational Creativity. Creative thinking is one of the hallmarks of human-level competence. Although it is still a poorly understood subject speculative ideas about brain processes involved in creative thinking may be implemented in computational models. A review of different approaches to creativity, insight and intuition is presented. Two factors are essential for creativity: imagination and selection or filtering. Imagination should be constrained by experience, while filtering in the case of creative use of words may be based on semantic and phonological associations. Analysis of brain processes involved in invention of new words leads to practical algorithms that create many interesting and novel names associated with a set of keywords.

Question: What is computational creativity?


Characterization of liquid crystal polymer (LCP) material and transmission lines on LCP substrates from 30 to 110 GHz. Liquid crystal polymer (LCP) is a material that has gained attention as a potential high-performance microwave substrate and packaging material. This investigation uses several methods to determine the electrical properties of LCP for millimeter-wave frequencies. Microstrip ring resonators and cavity resonators are measured in order to characterize the dielectric constant (/spl epsi//sub r/) and loss tangent (tan/spl delta/) of LCP above 30 GHz. The measured dielectric constant is shown to be steady near 3.16, and the loss tangent stays below 0.0049. In addition, various transmission lines are fabricated on different LCP substrate thicknesses and the loss characteristics are given in decibels per centimeter from 2 to 110 GHz. Peak transmission-line losses at 110 GHz vary between 0.88-2.55 dB/cm, depending on the line type and geometry. These results show, for the first time, that LCP has excellent dielectric properties for applications extending through millimeter-wave frequencies.

Question: What is the dielectric constant of LCP?


The Love of Large Numbers: A Popularity Bias in Consumer Choice.. Social learning-the ability to learn from observing the decisions of other people and the outcomes of those decisions-is fundamental to human evolutionary and cultural success. The Internet now provides social evidence on an unprecedented scale. However, properly utilizing this evidence requires a capacity for statistical inference. We examined how people's interpretation of online review scores is influenced by the numbers of reviews-a potential indicator both of an item's popularity and of the precision of the average review score. Our task was designed to pit statistical information against social information. We modeled the behavior of an "intuitive statistician" using empirical prior information from millions of reviews posted on Amazon.com and then compared the model's predictions with the behavior of experimental participants. Under certain conditions, people preferred a product with more reviews to one with fewer reviews even though the statistical model indicated that the latter was likely to be of higher quality than the former. Overall, participants' judgments suggested that they failed to make meaningful statistical inferences.

Question: How many reviews does a product need to have to be considered popular?


On mining cross-graph quasi-cliques. Joint mining of multiple data sets can often discover interesting, novel, and reliable patterns which cannot be obtained solely from any single source. For example, in cross-market customer segmentation, a group of customers who behave similarly in multiple markets should be considered as a more coherent and more reliable cluster than clusters found in a single market. As another example, in bioinformatics, by joint mining of gene expression data and protein interaction data, we can find clusters of genes which show coherent expression patterns and also produce interacting proteins. Such clusters may be potential pathways.In this paper, we investigate a novel data mining problem, mining cross-graph quasi-cliques, which is generalized from several interesting applications such as cross-market customer segmentation and joint mining of gene expression data and protein interaction data. We build a general model for mining cross-graph quasi-cliques, show why the complete set of cross-graph quasi-cliques cannot be found by previous data mining methods, and study the complexity of the problem. While the problem is difficult, we develop an efficient algorithm, Crochet, which exploits several interesting and effective techniques and heuristics to efficaciously mine cross-graph quasi-cliques. A systematic performance study is reported on both synthetic and real data sets. We demonstrate some interesting and meaningful cross-graph quasi-cliques in bioinformatics. The experimental results also show that algorithm Crochet is efficient and scalable.

Question: What is a cross-graph quasi-clique?


A new cell search scheme in 3GPP long term evolution downlink, OFDMA systems. This paper presents a cell search procedure in 3rd Generation Partnership Project (3GPP) Long Term Evolution (LTE) downlink systems. The proposed cell search procedure contains three steps. In the beginning, conventional mode detection, symbol timing detection and fractional carrier frequency offset (CFO) estimation methods are adopted so that frequency-domain signal processing can be performed subsequently. Secondly, a new joint detection method for integer carrier frequency offset and sector cell index information is proposed, which is shown to be capable of resisting symbol timing error. Finally, we also proposed a detection method for frame timing and cell identity (ID) group information without explicit channel estimates. Simulation results demonstrate that the proposed scheme with lower detection error probability is effective and robust compared with some conventional approaches.

Question: What is the cell search procedure in 3GPP long term evolution downlink, OFDMA systems?


An Analysis of the Manufacturing Messaging Specification Protocol. The Manufacturing Messaging Specification (MMS) protocol is widely used in industrial process control applications, but it is poorly documented. In this paper we present an analysis of the MMS protocol in order to improve understanding of MMS in the context of information security. Our findings show that MMS has insufficient security mechanisms, and the meagre security mechanisms that are available are not implemented in commercially available industrial devices.

Question: What is the MMS protocol?


Implementation and verification of a generic universal memory controller based on UVM. This paper presents a coverage driven constraint random based functional verification method based on the Universal Verification Methodology (UVM) using System Verilog for generic universal memory controller architecture. This universal memory controller is looking forward to improving the performance of the existing memory controllers through a complete integration of the existing memory controllers features in addition of providing novel features. It also reduces the consumed power through providing high power consumption control due to its proposed different power levels supported to fit all power scenarios. While implementing a worthy architecture like the proposed generic universal memory controller, UVM is the best choice to build well-constructed, high controlled and reusable verification environment to efficiently verify it. More than 200 coverage points have been covered to verify the validation of the integrated features which makes the proposed universal memory controller replaces the existing controllers on the scene as it provides all of their powerful features in addition of novel features to control two of the most dominated types of memory; FLASH and DRAM through one memory controller.

Question: What is the difference between a generic universal memory controller and a universal memory controller?


Learning state representation for deep actor-critic control. Deep Neural Networks (DNNs) can be used as function approximators in Reinforcement Learning (RL). One advantage of DNNs is that they can cope with large input dimensions. Instead of relying on feature engineering to lower the input dimension, DNNs can extract the features from raw observations. The drawback of this end-to-end learning is that it usually requires a large amount of data, which for real-world control applications is not always available. In this paper, a new algorithm, Model Learning Deep Deterministic Policy Gradient (ML-DDPG), is proposed that combines RL with state representation learning, i.e., learning a mapping from an input vector to a state before solving the RL task. The ML-DDPG algorithm uses a concept we call predictive priors to learn a model network which is subsequently used to pre-train the first layer of the actor and critic networks. Simulation results show that the ML-DDPG can learn reasonable continuous control policies from high-dimensional observations that contain also task-irrelevant information. Furthermore, in some cases, this approach significantly improves the final performance in comparison to end-to-end learning.

Question: What is the difference between end-to-end learning and model learning?


A Multiphase Switched-Capacitor DC–DC Converter Ring With Fast Transient Response and Small Ripple. A fully integrated step-down switched-capacitor dc–dc converter ring with 123 phases has been designed that could achieve fast dynamic voltage scaling for the microprocessor of wearable devices. The symmetrical multiphase converter ring surrounds its load in the square and supplies power to the on-chip power grid that is easily accessible at any point of the chip edges. The frequency of the <inline-formula> <tex-math notation="LaTeX">$V_{\mathrm {DD}}$ </tex-math></inline-formula>-controlled oscillator is adjusted through its supply voltage <inline-formula> <tex-math notation="LaTeX">$V_{\mathrm {DD}}$ </tex-math></inline-formula>, which allows the unity-gain frequency to be designed higher than the switching frequency. The converter ring has been fabricated in a low-leakage 65-nm CMOS process. This converter achieves a response time of 3 ns, a reference tracking speed of 2.5 V/<inline-formula> <tex-math notation="LaTeX">$\mu \text{s}$ </tex-math></inline-formula>, and a minimum output ripple of 2.2 mV. The peak efficiency is 80% at the power density of 66.6 mW/mm2, and the maximum power density is 180 mW/mm2.

Question: What is the minimum ripple of a dc–dc converter?


Online variance minimization. We consider the following type of online variance minimization problem: In every trial t our algorithms get a covariance matrix C t and try to select a parameter vector w t−1 such that the total variance over a sequence of trials $\sum_{t=1}^{T} (\boldsymbol {w}^{t-1})^{\top} \boldsymbol {C}^{t}\boldsymbol {w}^{t-1}$ is not much larger than the total variance of the best parameter vector u chosen in hindsight. Two parameter spaces in ℝ n are considered—the probability simplex and the unit sphere. The first space is associated with the problem of minimizing risk in stock portfolios and the second space leads to an online calculation of the eigenvector with minimum eigenvalue of the total covariance matrix $\sum_{t=1}^{T} \boldsymbol {C}^{t}$ . For the first parameter space we apply the Exponentiated Gradient algorithm which is motivated with a relative entropy regularization. In the second case, the algorithm has to maintain uncertainty information over all unit directions u. For this purpose, directions are represented as dyads uu ⊤ and the uncertainty over all directions as a mixture of dyads which is a density matrix. The motivating divergence for density matrices is the quantum version of the relative entropy and the resulting algorithm is a special case of the Matrix Exponentiated Gradient algorithm. In each of the two cases we prove bounds on the additional total variance incurred by the online algorithm over the best offline parameter.

Question: What is the difference between the probability simplex and the unit sphere?


A computational cognition model of perception, memory, and judgment. The mechanism of human cognition and its computability provide an important theoretical foundation to intelligent computation of visual media. This paper focuses on the intelligent processing of massive data of visual media and its corresponding processes of perception, memory, and judgment in cognition. In particular, both the human cognitive mechanism and cognitive computability of visual media are investigated in this paper at the following three levels: neurophysiology, cognitive psychology, and computational modeling. A computational cognition model of Perception, Memory, and Judgment (PMJ model for short) is proposed, which consists of three stages and three pathways by integrating the cognitive mechanism and computability aspects in a unified framework. Finally, this paper illustrates the applications of the proposed PMJ model in five visual media research areas. As demonstrated by these applications, the PMJ model sheds some light on the intelligent processing of visual media, and it would be innovative for researchers to apply human cognitive mechanism to computer science.

Question: What is the computational cognition model of perception, memory, and judgment?


Geometry-Driven Diffusion in Computer Vision. Inevitably, reading is one of the requirements to be undergone. To improve the performance and quality, someone needs to have something new every day. It will suggest you to have more inspirations, then. However, the needs of inspirations will make you searching for some sources. Even from the other people experience, internet, and many books. Books and internet are the recommended media to help you improving your quality and performance.

Question: What is the best way to improve my reading?


Clustering Validity Based on the Improved Hubert \Gamma Statistic and the Separation of Clusters. The validity of clustering is one important research field in clustering analysis, and many clustering validity functions have been proposed, especially those based on the geometrical structure of data set, such as Dunn's index and Xie-Beni index. In this way, the compactness and the separation of clusters are usually taken into account. Xie-Beni index decreases with the number of partitions increasing. It is difficult to choose the optimal number of clusters when there are lots of clusters in data. In this paper, a novel clustering validity function is proposed, which is based on the improved Huber Gamma statistic combined with the separation of clusters. Unlike other clustering validity, the function has the only maximum with the clustering number increasing. The experiments indicate that the function can be used as the optimal index for the choice of the clustering numbers

Question: How to choose the optimal number of clusters?


Bacterium-inspired robots for environmental monitoring. Locating gradient sources and tracking them over time has important applications to environmental monitoring and studies of the ecosystem. We present an approach, inspired by bacterial chemotaxis, for robots to navigate to sources using gradient measurements and a simple actuation strategy (biasing a random walk). Extensive simulations show the efficacy of the approach in varied conditions including multiple sources, dissipative sources, and noisy sensors and actuators. We also show how such an approach could be used for boundary finding. We validate our approach by testing it on a small robot (the robomote) in a phototaxis experiment. A comparison of our approach with gradient descent shows that while gradient descent is faster, our approach is better suited for boundary coverage, and performs better in the presence of multiple and dissipative sources.

Question: How to find the gradient of a function?


A New Word Language Model Evaluation Metric for Character Based Languages. Perplexity is a widely used measure to evaluate word prediction power of a word-based language model. It can be computed independently and has shown good correlation with word error rate (WER) in speech recognition. However, for character based languages, character error rate (CER) is commonly used instead of WER as the measure for speech recognition, although language model is still word based. Due to the fact that different word segmentation strategies may result in different word vocabulary for the same text corpus, in many cases, wordbased perplexity is incompetent to evaluate the combined effect of word segmentation and language model training to predict final CER. In this paper, a new word-based language model evaluation measure is proposed to account for the effect of word segmentation and the goal of predicting CER. Experiments were conducted on Chinese speech recognition. Compared to the traditional word-based perplexity, the new measure is more robust to word segmentation and shows much more consistent correlation with CER in a large vocabulary continuous Chinese speech recognition task.

Question: What is a new word-based language model evaluation measure?


Energy-Efficient Data Collection in UAV Enabled Wireless Sensor Network. In wireless sensor networks, utilizing the unmanned aerial vehicle (UAV) as a mobile data collector for the sensor nodes (SNs) is an energy-efficient technique to prolong the network lifetime. In this letter, considering a general fading channel model for the SN-UAV links, we jointly optimize the SNs' wake-up schedule and UAV's trajectory to minimize the maximum energy consumption of all SNs, while ensuring that the required amount of data is collected reliably from each SN. We formulate our design as a mixed-integer non-convex optimization problem. By applying the successive convex optimization technique, an efficient iterative algorithm is proposed to find a sub-optimal solution. Numerical results show that the proposed scheme achieves significant network energy saving as compared to benchmark schemes.

Question: How to design a UAV to collect data from a sensor network?


Antibacterial drug discovery in the resistance era. The looming antibiotic-resistance crisis has penetrated the consciousness of clinicians, researchers, policymakers, politicians and the public at large. The evolution and widespread distribution of antibiotic-resistance elements in bacterial pathogens has made diseases that were once easily treatable deadly again. Unfortunately, accompanying the rise in global resistance is a failure in antibacterial drug discovery. Lessons from the history of antibiotic discovery and fresh understanding of antibiotic action and the cell biology of microorganisms have the potential to deliver twenty-first century medicines that are able to control infection in the resistance era.

Question: What is the history of antibiotic discovery?


A fully integrated multi-CPU, GPU and memory controller 32nm processor. This paper describes the 32nm Sandy Bridge processor that integrates up to 4 high performance Intel Architecture (IA) cores, a power/performance optimized graphic processing unit (GPU) and memory and PCIe controllers in the same die. The Sandy Bridge architecture block diagram is shown in Fig. 15.1.1 and the floorplan of a four IA-core version is shown in Fig. 15.1.2. The Sandy Bridge IA core implements an improved branch prediction algorithm, a micro-operation (Uop) cache, a floating point Advanced Vector Extension (AVX), a second load port in the L1 cache and bigger register files in the out-of-order part of the machine; all these architecture improvements boost the IA core performance without increasing the thermal power dissipation envelope or the average power consumption (to preserve battery life in mobile systems). The CPUs and GPU share the same 8MB level-3 cache memory. The data flow is optimized by a high performance on die interconnect fabric (called "ring") that connects between the CPUs, the GPU, the L3 cache and the system agent (SA) unit that houses a 1600MT/s, dual channel DDR3 memory controller, a 20-lane PCIe gen2 controller, a two parallel pipe display engine, the power management control unit and the testability logic. An on die EPROM is used for configurability and yield optimization.

Question: What is the size of the cache?


Gang involvement: psychological and behavioral characteristics of gang members, peripheral youth, and nongang youth.. Research has noted the existence of a loose and dynamic gang structure. However, the psychological processes that underpin gang membership have only begun to be addressed. This study examined gang members, peripheral youth, and nongang youth across measures of criminal activity, the importance they attach to status, their levels of moral disengagement, their perceptions of out-group threat, and their attitudes toward authority. Of the 798 high school students who participated in this study, 59 were identified as gang members, 75 as peripheral youth, and 664 as nongang youth. Gang members and peripheral youth were more delinquent than nongang youth overall; however, gang members committed more minor offenses than nongang youth and peripheral youth committed more violent offenses than nongang youth. Gang members were more anti-authority than nongang youth, and both gang and peripheral youth valued social status more than nongang youth. Gang members were also more likely to blame their victims for their actions and use euphemisms to sanitize their behavior than nongang youth, whereas peripheral youth were more likely than nongang youth to displace responsibility onto their superiors. These findings are discussed as they highlight the importance of examining individual differences in the cognitive processes that relate to gang involvement.

Question: What are the psychological processes that underpin gang membership?


2-hop Blockchain : Combining Proof-of-Work and Proof-of-Stake Securely. Cryptocurrencies like Bitcoin have proven to be a phenomenal success. Bitcoin-like systems use proofof-work mechanism which is therefore considered as 1-hop blockchain, and their security holds if the majority of the computing power is under the control of honest players. However, this assumption has been seriously challenged recently and Bitcoin-like systems will fail when this assumption is broken. We propose the €rst provably secure 2-hop blockchain by combining proof-of-work (€rst hop) and proof-of-stake (second hop) mechanisms. On top of Bitcoin's brilliant ideas of utilizing the power of the honest miners, via their computing resources, to secure the blockchain, we further leverage the power of the honest users/stakeholders, via their coins/stake, to achieve this goal. Œe security of our blockchain holds if the honest players control majority of the collective resources (which consists of both computing power and stake). Œat said, even if the adversary controls more than 50% computing power, the honest players still have the chance to defend the blockchain via honest stake. ∗An early version with title "Securing Bitcoin-like Blockchains against a Malicious Majority of Computing Power" appeared in ePrint Archive in July 2016. Œe current version shares the same motivation. But the construction idea and modeling approach have been completely revised. †Virginia Commonwealth University. Email: duong'3@vcu.edu. ‡Shanghai Jiao Tong University. Most work done while visiting Cryptography Lab at Virginia Commonwealth University. Email: fanlei@sjtu.edu.cn. §Virginia Commonwealth University. Email: hszhou@vcu.edu.

Question: What is the difference between proof-of-work and proof-of-stake?


Evaluating Sketchiness as a Visual Variable for the Depiction of Qualitative Uncertainty. We report on results of a series of user studies on the perception of four visual variables that are commonly used in the literature to depict uncertainty. To the best of our knowledge, we provide the first formal evaluation of the use of these variables to facilitate an easier reading of uncertainty in visualizations that rely on line graphical primitives. In addition to blur, dashing and grayscale, we investigate the use of `sketchiness' as a visual variable because it conveys visual impreciseness that may be associated with data quality. Inspired by work in non-photorealistic rendering and by the features of hand-drawn lines, we generate line trajectories that resemble hand-drawn strokes of various levels of proficiency-ranging from child to adult strokes-where the amount of perturbations in the line corresponds to the level of uncertainty in the data. Our results show that sketchiness is a viable alternative for the visualization of uncertainty in lines and is as intuitive as blur; although people subjectively prefer dashing style over blur, grayscale and sketchiness. We discuss advantages and limitations of each technique and conclude with design considerations on how to deploy these visual variables to effectively depict various levels of uncertainty for line marks.

Question: How to visualize uncertainty in lines?


SquishedNets: Squishing SqueezeNet further for edge device scenarios via deep evolutionary synthesis. While deep neural networks have been shown in recent years to outperform other machine learning methods in a wide range of applications, one of the biggest challenges with enabling deep neural networks for widespread deployment on edge devices such as mobile and other consumer devices is high computational and memory requirements. Recently, there has been greater exploration into small deep neural network architectures that are more suitable for edge devices, with one of the most popular architectures being SqueezeNet, with an incredibly small model size of 4.8MB. Taking further advantage of the notion that many applications of machine learning on edge devices are often characterized by a low number of target classes, this study explores the utility of combining architectural modifications and an evolutionary synthesis strategy for synthesizing even smaller deep neural architectures based on the more recent SqueezeNet v1.1 macroarchitecture (considered state-of-the-art in efficient architectures) for applications with fewer target classes. In particular, architectural modifications are first made to SqueezeNet v1.1 to accommodate for a 10-class ImageNet-10 dataset, and then an evolutionary synthesis strategy is leveraged to synthesize more efficient deep neural networks based on this modified macroarchitecture. The resulting SquishedNets possess model sizes ranging from 2.4MB to 0.95MB (∼5.17X smaller than SqueezeNet v1.1, or 253X smaller than AlexNet). Furthermore, the SquishedNets are still able to achieve accuracies ranging from 81.2% to 77%, and able to process at speeds of 156 images/sec to as much as 256 images/sec on a Nvidia Jetson TX1 embedded chip. These preliminary results show that a combination of architectural modifications and an evolutionary synthesis strategy can be a useful tool for producing very small deep neural network architectures that are we

Question: What is the smallest deep neural network?


Beyond the right hemisphere: brain mechanisms mediating vocal emotional processing. Vocal perception is particularly important for understanding a speaker's emotional state and intentions because, unlike facial perception, it is relatively independent of speaker distance and viewing conditions. The idea, derived from brain lesion studies, that vocal emotional comprehension is a special domain of the right hemisphere has failed to receive consistent support from neuroimaging. This conflict can be reconciled if vocal emotional comprehension is viewed as a multi-step process with individual neural representations. This view reveals a processing chain that proceeds from the ventral auditory pathway to brain structures implicated in cognition and emotion. Thus, vocal emotional comprehension appears to be mediated by bilateral mechanisms anchored within sensory, cognitive and emotional processing systems.

Question: What is the right hemisphere?


Content-Based Image Retrieval Using Multiple Features. Algorithms of Content-Based Image Retrieval (CBIR) have been well developed along with the explosion of information. These algorithms are mainly distinguished based on feature used to describe the image content. In this paper, the algorithms that are based on color feature and texture feature for image retrieval will be presented. Color Coherence Vector based image retrieval algorithm is also attempted during the implementation process, but the best result is generated from the algorithms that weights color and texture. 80% satisfying rate is achieved.

Question: What is the best algorithm for image retrieval?


Permittivity and Loss Tangent Characterization for Garment Antennas Based on a New Matrix-Pencil Two-Line Method. The emergence of wearable antennas to be integrated into garments has revealed the need for a careful electromagnetic characterization of textile materials. Therefore, we propose in this paper a new matrix-pencil two-line method that removes perturbations in the calculated effective permittivity and loss tangent which are caused by imperfect deembedding and inhomogeneities of the textile microstrip line structure. The approach has been rigorously validated for high-frequency laminates by comparing measured and simulated data for the resonance frequency of antennas designed using the calculated parameters. The method has been successfully applied to characterize the permittivity and loss tangent of a variety of textile materials and up to a frequency of 10 GHz. Furthermore it is shown that the use of electrotextiles in antenna design influences the effective substrate permittivity.

Question: How to calculate the permittivity and loss tangent of a textile material?


A Hard-Core Predicate for all One-Way Functions. A central tool in constructing pseudorandom generators, secure encryption functions, and in other areas are "hard-core" predicates <italic>b</italic> of functions (permutations) ƒ, discovered in [Blum Micali 82]. Such <italic>b</italic>(<italic>x</italic>) cannot be efficiently guessed (substantially better than 50-50) given only ƒ(<italic>x</italic>). Both <italic>b</italic>, ƒ are computable in polynomial time. [Yao 82] transforms any one-way function ƒ into a more complicated one, ƒ<supscrpt>*</supscrpt>, which has a hard-core predicate. The construction applies the original ƒ to many small pieces of the input to ƒ<supscrpt>*</supscrpt> just to get one "hard-core" bit. The security of this bit may be smaller than any constant positive power of the security of ƒ. In fact, for inputs (to ƒ<supscrpt>*</supscrpt>) of practical size, the pieces effected by ƒ are so small that ƒ can be inverted (and the "hard-core" bit computed) by exhaustive search. In this paper we show that every one-way function, padded to the form ƒ(<italic>p</italic>, <italic>x</italic>) = (<italic>p</italic>, <italic>g</italic>(<italic>x</italic>)), ‖‖<italic>p</italic>‖‖ = ‖<italic>x</italic>‖, has by itself a hard-core predicate of the same (within a polynomial) security. Namely, we prove a conjecture of [Levin 87, sec. 5.6.2] that the scalar product of Boolean vectors <italic>p</italic>, <italic>x</italic> is a hard-core of every one-way function ƒ(<italic>p</italic>, <italic>x</italic>) = (<italic>p</italic>, <italic>g</italic>(<italic>x</italic>)). The result extends to multiple (up to the logarithm of security) such bits and to any distribution on the <italic>x</italic>'s for which ƒ is hard to invert.

Question: What is the security of the hard-core predicate?


A new word sense similarity measure in wordnet. Recognizing similarities between words is a basic element of computational linguistics and artificial intelligence applications. This paper presents a new approach for measuring semantic similarity between words via concepts. Our proposed measure is a hybrid system based on using a new Information content metric and edge counting-based tuning function. In proposed system, hierarchical structure is used to present information content instead of text corpus and our result will be improved by edge counting-based tuning function. The result of the system is evaluated against human similarity ratings demonstration and shows significant improvement in compare with traditional similarity measures.

Question: What is a new word sense similarity measure in wordnet?


Sleep Staging by Modeling Sleep Stage Transitions using Deep CRF. Sleep plays a vital role in human health, both mental and physical. Sleep disorders like sleep apnea are increasing in prevalence, with the rapid increase in factors like obesity. Sleep apnea is most commonly treated with Continuous Positive Air Pressure (CPAP) therapy, which maintains the appropriate pressure to ensure continuous airflow. It is widely accepted that in addition to preventing air passage collapse, increase in deep and REM sleep stages would be good metrics for how well the CPAP therapy is working in improving sleep health. Presently, however, there is no mechanism to easily detect a patient's sleep stages from CPAP flow data alone. We propose, for the first time, an automated sleep staging model based only on the flow signal. Recently deep neural networks have shown high accuracy on sleep staging by eliminating handcrafted features. However, these methods focus exclusively on extracting informative features from the input signal, without paying much attention to the dynamics of sleep stages in the output sequence. We propose an end-to-end framework that uses a combination of deep convolution and recurrent neural networks to extract high-level features from raw flow signal and then uses a structured output layer based on a conditional random field to model the temporal transition structure of the sleep stages. We improve upon the previous methods by 10% using our model, that can be augmented to the previous sleep staging deep learning methods. We also show that our method can be used to accurately track sleep metrics like sleep efficiency calculated from sleep stages that can be deployed for monitoring the response of CPAP therapy on sleep apnea patients. Apart from the technical contributions, we expect this study to motivate new research questions in sleep science, especially towards the understanding of sleep architecture trajectory among patients under CPAP therapy.

Question: How can we model sleep stages using flow data?


Wikipedia Chemical Structure Explorer: substructure and similarity searching of molecules from Wikipedia. BACKGROUND Wikipedia, the world's largest and most popular encyclopedia is an indispensable source of chemistry information. It contains among others also entries for over 15,000 chemicals including metabolites, drugs, agrochemicals and industrial chemicals. To provide an easy access to this wealth of information we decided to develop a substructure and similarity search tool for chemical structures referenced in Wikipedia.   RESULTS We extracted chemical structures from entries in Wikipedia and implemented a web system allowing structure and similarity searching on these data. The whole search as well as visualization system is written in JavaScript and therefore can run locally within a web page and does not require a central server. The Wikipedia Chemical Structure Explorer is accessible on-line at www.cheminfo.org/wikipedia and is available also as an open source project from GitHub for local installation.   CONCLUSIONS The web-based Wikipedia Chemical Structure Explorer provides a useful resource for research as well as for chemical education enabling both researchers and students easy and user friendly chemistry searching and identification of relevant information in Wikipedia. The tool can also help to improve quality of chemical entries in Wikipedia by providing potential contributors regularly updated list of entries with problematic structures. And last but not least this search system is a nice example of how the modern web technology can be applied in the field of cheminformatics. Graphical abstractWikipedia Chemical Structure Explorer allows substructure and similarity searches on molecules referenced in Wikipedia.

Question: What is the chemical structure of caffeine?


Exploiting Eigenposteriors for Semi-Supervised Training of DNN Acoustic Models with Sequence Discrimination. Deep neural network (DNN) acoustic models yield posterior probabilities of senone classes. Recent studies support the existence of low-dimensional subspaces underlying senone posteriors. Principal component analysis (PCA) is applied to identify eigenposteriors and perform low-dimensional projection of the training data posteriors. The resulted enhanced posteriors are applied as soft targets for training better DNN acoustic model under the student-teacher framework. The present work advances this approach by studying incorporation of sequence discriminative training. We demonstrate how to combine the gains from eigenposterior based enhancement with sequence discrimination to improve ASR using semi-supervised training. Evaluation on AMI meeting corpus yields nearly 4% absolute reduction in word error rate (WER) compared to the baseline DNN trained with cross entropy objective. In this context, eigenposterior enhancement of the soft targets is crucial to enable additive improvement using out-of-domain untranscribed data.

Question: How to combine the gains from eigenposterior based enhancement with sequence discrimination to improve ASR using semi-supervised training.


LIP ACTIVITY DETECTION FOR TALKING FACES CLASSIFICATION IN TV-CONTENT. Our objective is to index people in a TV-Content. In this context, because of multi-face shots and non-speaking face shots, it is difficult to determine which face is speaking. There is no guaranteed synchronization between sequences of a person's appearance and sequences of his or her speech. In this work, we want to separate talking and non-talking faces by detecting lip motion. We propose a method to detect the lip motion by measuring the degree of disorder of pixel directions around the lip. Results of experiments on a TV-Show database show that a high correct classification rate can be achieved by the proposed method.

Question: How to detect lip motion?


Chrome Extensions: Threat Analysis and Countermeasures. The widely popular browser extensions now become one of the most commonly used malware attack vectors. The Google Chrome browser, which implements the principles of least privileges and privilege separation by design, offers a strong security mechanism to protect malicious websites from damaging the whole browser system via extensions. In this study, we however reveal that Chrome's extension security model is not a panacea for all possible attacks with browser extensions. Through a series of practical bot-based attacks that can be performed even under typical settings, we demonstrate that malicious Chrome extensions pose serious threats, including both information dispersion and harvesting, to browsers. We further conduct an in-depth analysis of Chrome's extension security model, and conclude that its vulnerabilities are rooted fro m the violation of the principles of least privileges and priv ilege separation. Following these principles, we propose a set of countermeasures that enforce the policies of microprivilege management and differentiating DOM elements. Using a prototype developed on the latest Chrome browser, we show that they can effectively mitigate the threats posed by malicious Chrome extensions with little effect on normal browsing experience.

Question: What is the difference between a browser extension and a browser add-on?


Skin force sensor using piezoresistive PEDOT:PSS with arabitol on flexible PDMS. In this paper, a flexible conducting polymer based force sensor array has been developed. A conducting polymer, namely poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate) (PEDOT:PSS) with additive arabitol, was utilized as the piezoresistive layer on a flexible polydimethylsiloxane (PDMS) substrate owing to its high conductivity and stretchability. A simplified strain sensor has been developed to demonstrate the properties of the arabitol-PEDOT:PSS. Suitable curing conditions and arabitol concentration have been established with design of experiment. The arabitol-PEDOT:PSS can be stretched up to 20% and it shows a significant gauge factor of 11.5. A flexible interdigital force sensor array has been developed to demonstrate the effectiveness of arabitol-PEDOT:PSS on PDMS as a force sensor.

Question: What is the difference between a piezoresistive and a piezoelectric sensor?


Implementation of potential field method for mobile robot navigation in greenhouse environment with WSN support. This paper deals with the implementation of mobile measuring station in greenhouse environment navigated using potential field method. The function of a greenhouse is to create the optimal growing conditions for the full life of the plants. Using autonomous measuring systems helps to monitor all the necessary parameters for creating the optimal environment in the greenhouse. The robot equipped with sensors is capable of driving to the end and back along crop rows inside the greenhouse. It introduces a wireless sensor network that was used for the purpose of measuring and controlling the greenhouse application. Continuous advancements in wireless technology and miniaturization have made the deployment of sensor networks to monitor various aspects of the environment increasingly flexible.

Question: How to implement a wireless sensor network in a greenhouse?


A Safe, Efficient Algorithm for Regression Test Selection. Regression testing is a necessary but costly maintenance activity aimed at demonstrating that code has not been adversely aaected by changes. A selective approach to regression testing selects tests for a modi-ed program from an existing test suite. We present a new technique for selective regression testing. Our algorithm constructs control dependence graphs for program versions, and uses these graphs to determine which tests from the existing test suite may exhibit changed behavior on the new version. Unlike most previous techniques for selective retest, our algorithm selects every test from the original test suite that might expose errors in the modiied program, and does this without prior knowledge of program modiications. Our algorithm handles all language constructs and program modiications, and is easily automated.

Question: What is a regression test?


Dynamics and control of a 4-dof wearable cable-driven upper arm exoskeleton. In this paper, we present the dynamics, control, and preliminary experiments on a wearable upper arm exoskeleton intended for human users with four degrees-of-freedom (dof), driven by six cables. The control of this cable-driven exoskeleton is complicated because the cables can transmit forces to the arm only under tension. The standard PD controllers or Computed torque controllers perform only moderately since the cables need to be in tension. Future efforts will seek to refine these control strategies and their implementations to improve functionality of a human user.

Question: What is a cable-driven exoskeleton?


Rule Extraction from Support Vector Machines: An Overview of Issues and Application in Credit Scoring. 1 Department of Decision Sciences and Information Management, K.U.Leuven Naamsestraat 69, B-3000 Leuven, Belgium {David.Martens;Johan.Huysmans; Bart.Baesens;Jan.Vanthienen}@econ.kuleuven.be 2 School of Computing, National University of Singapore, 3 Science Drive 2, Singapore 117543, Singapore rudys@comp.nus.edu.sg 3 University of Southampton, School of Management, Highfield Southampton, SO17 1BJ, UK Bart@soton.ac.uk

Question: What is the name of the paper?


Making Arabic PDF books accessible using gamification. Most of online Arabic books are not accessible to Arab people with visual impairments. They cannot read online books because they are usually scanned images of the original ones. There is also a problem in PDF encoding of some of the textual books. One of the solutions is to use an Arabic OCR to convert scanned books into text; however Arabic OCR is still in its early stages and suffers from many limitations. In this paper we propose the use of human recognition skills to replace OCR limitations by incorporating the concepts of crowdsourcing and gamification. Our proposed system is in the form of a mobile recall game that presents players with word images segmented from the books to be converted into text. The players' answers are checked using techniques similar to what is used in word spotting. We initially implemented two components of the system; the segmentation, and the feature extraction and matching components. For the feature extraction and matching component, which is used to verify the player's answers, we performed four tests to choose a similarity measure threshold for accepting an entered word as a correct answer. Future work will consider other means of input correctness assurance.

Question: How to make Arabic PDF books accessible using gamification?


Ratio estimators in simple random sampling. This study proposes ratio estimators by adapting the estimators type ofRay and Singh [J. Ind. Stat. Assoc. 19 (1981) 147] to traditional and the other ratio-type estimators in simple random sampling in literature. Theoretically, mean square error (MSE) equations of all proposed ratio estimators are obtained and compared with each other. By these comparisons the conditions, which make each proposed estimator more efficient than the others, are found. The theoretical results are supported by a numerical illustration. 2003 Elsevier Inc. All rights reserved.

Question: What is the ratio estimator?


High Level Sensor Data Fusion Approaches For Object Recognition In Road Environment. Application of high level fusion approaches demonstrate a sequence of significant advantages in multi sensor data fusion and automotive safety fusion systems are no exception to this. High level fusion can be applied to automotive sensor networks with complementary or/and redundant field of views. The advantage of this approach is that it ensures system modularity and allows benchmarking, as it does not permit feedbacks and loops inside the processing. In this paper two specific high level data fusion approaches are described including a brief architectural and algorithmic presentation. These approaches differ mainly in their data association part: (a) track level fusion approach solves it with the point to point association with emphasis on object continuity and multidimensional assignment, and (b) grid based fusion approach that proposes a generic way to model the environment and to perform sensor data fusion. The test case for these approaches is a multi sensor equipped PReVENT/ProFusion2 truck demonstrator vehicle.

Question: What is the difference between the track level fusion approach and the grid based fusion approach?


Auto-Calibration Around-View Monitoring System. This work is to establish an auto-calibration around-view monitoring system to avoid the inconsistent performance from deviations of camera installation. First, a vehicle is parked at a specific area which has a special pattern on the ground. Second, the captured images from four embedded cameras are compared with the reference pattern images to obtain the deviations in angle, X-axis and Y-axis. Finally, these deviations are used to compensate the coefficients of transformation matrices associated with view-point conversion and image stitching. Particularly, the adaptive edge recovery scheme is developed to help on precise alignments. The experimental results show that the proposed system implemented at the TI DaVinci DSP platform can accomplish auto-calibration of a vehicle within 60 seconds and have deviation rates smaller than 38% at stitched regions and 25% at the other regions. Additionally, ±2° deviation errors at angles, X-axis and Y-axis are allowed. Particularly, the proposed system can generate precise 2-meter around-view pictures at a vehicle speed of 30km/h. Therefore, the auto-calibration system proposed herein can be widely applied to vehicle manufacture, verification and repair.

Question: What is the purpose of the auto-calibration around-view monitoring system?


A mixed reality system for virtual glasses try-on. In this paper we present an augmented reality system for automatic try-on of 3D virtual eyeglasses. The user can select from various virtual models of eyeglasses for trying-on and the system will automatically fit the selected virtual glasses on the user's face. The user can see his/her face as in a mirror with the 3D virtual glasses fitted on it. We also propose a method for handling the occlusion problem, to display only those parts of the glasses that are not occluded by the face. This system can be used for online shopping, or short listing a large set of available models to a few before physical try-on at a retailer's site.

Question: How does a virtual try-on system work?


Using social network analysis to prevent money laundering.

Question: How to prevent money laundering?


MEMS Capacitive Pressure Sensors : A Review on Recent Development and Prospective. Recently MEMS Capacitive Pressure Sensor gains more advantage over micromachined piezoresistive pressure sensor due to high sensitivity, low power consumption, free from temperature effects, IC compatibility, etc,. The spectrum of capacitive pressure sensor application is increasing, hence it is essential to review the path of technological development and further prospective of micromachined capacitive pressure sensor. This paper focuses on the review of various types of capacitive pressure sensor principle, MEMS materials used in fabrication, procedures adopted in microfabrication for silicon and polymer material diaphragm, bonding and packaging techniques used. Selected result on capacitive sensitivity, effect of temperature on capacitive sensitivity was also presented. Finally, the development of smart sensor was discussed. MEMS Capacitive pressure sensor, Review on pressure sensor, CDPS, MEMS Fabrication, MEMS Material, pressure sensor, MEMS (Micro Electro Mechanical System)

Question: What is the difference between MEMS and microelectromechanical systems?


AUTOMATED IRRIGATION SYSTEM USING THERMOELECTRIC GENERATOR AS SOIL MOISTURE DETECTOR. This paper shows the design and fabrication of a Thermo-electric generator (TEG) and the implementation of an automated irrigation system using this TEG as a soil moisture detector. The TEG inserted in two heat exchangers is capable of finding the thermal difference between the air and the soil that establishes a relationship with the soil's moisture condition. Being able to obtain the soil moisture level from the TEG's output, a microcontroller is used to automate the irrigation system. The irrigation system adapts to the soil area's condition it irrigates based from the moisture it detects via the TEG. The water consumption of the soil is controlled by the automated irrigation system based on the soil's condition and therefore, promotes water conservation compared to that of the water consumption of manual irrigation system. It also optimizes plant growth in that it waters it to the correct moisture level at the right time.

Question: What is the difference between a TEG and a soil moisture detector?


Automated Image Annotation based on YOLOv3. A typical pedestrian protection system requires sophisticated hardware and robust detection algorithms. To solve these problems the existing systems use hybrid sensors where mono and stereo vision merged with active sensors. One of the most assuring pedestrian detection sensors is far infrared range camera. The classical pedestrian detection approach based on Histogram of oriented gradients is not robust enough to be applied in devices which consumers can trust. An application of deep neural network-based approach is able to perform with significantly higher accuracy. However, the deep learning approach requires a high number of labeled data examples. The investigation presented in this paper aimed the acceleration of pedestrian labeling in far-infrared image sequences. In order to accelerate pedestrian labeling in far-infrared camera videos, we have integrated the YOLOv3 object detector into labeling software. The verification of the pre-labeled results was around eleven times faster than manual labeling of every single frame.

Question: How to accelerate pedestrian labeling in far-infrared image sequences?


Sales Forecasting for Retail Chains. This paper presents a use case of data mining for sales forecasting in retail demand and sales prediction. In particular, the Extreme Gradient Boosting algorithm is used to design a prediction model to accurately estimate probable sales for retail outlets of a major European Pharmacy retailing company. The forecast of potential sales is based on a mixture of temporal and economical features including prior sales data, store promotions, retail competitors, school and state holidays, location and accessibility of the store as well as the time of year. The model building process was guided by common sense reasoning and by analytic knowledge discovered during data analysis and definitive conclusions were drawn. The performances of the XGBoost predictor were compared with those of more traditional regression algorithms like Linear Regression and Random Forest Regression. Findings not only reveal that the XGBoost algorithm outperforms the traditional modeling approaches with regard to prediction accuracy, but it also uncovers new knowledge that is hidden in data which help in building a more robust feature set and strengthen the sales prediction model.

Question: What is the difference between linear regression and random forest regression?


TweetCred: A Real-time Web-based System for Assessing Credibility of Content on Twitter. During large scale events, a large volume of content is posted on Twitter, but not all of this content is trustworthy. The presence of spam, advertisements, rumors and fake images reduces the value of information collected from Twitter, especially during sudden-onset crisis events where information from other sources is scarce. In this research work, we describe various facets of assessing the credibility of usergenerated content on Twitter during large scale events, and develop a novel real-time system to assess the credibility of tweets. Firstly, we develop a semi-supervised ranking model using SVM-rank for assessing credibility, based on training data obtained from six high-impact crisis events of 2013. An extensive set of forty-five features is used to determine the credibility score for each of the tweets. Secondly, we develop and deploy a system–TweetCred–in the form of a browser extension, a web application and an API at the link: http://twitdigest.iiitd.edu.in/TweetCred/. To the best of our knowledge, this is the first research work to develop a practical system for credibility on Twitter and evaluate it with real users. TweetCred was installed and used by 717 Twitter users within a span of three weeks. During this period, a credibility score was computed for more than 1.1 million unique tweets. Thirdly, we evaluated the real-time performance of TweetCred , observing that 84% of the credibility scores were displayed within 6 seconds. We report on the positive feedback that we received from the system's users and the insights we gained into improving the system for future iterations.

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


Component Model of Addiction Treatment: A Pragmatic Transdiagnostic Treatment Model of Behavioral and Substance Addictions. Behavioral addictions such as gambling, video games, sex, and shopping share many clinical features with substance use addictions including etiology, course, and neurobiology. Yet, the treatment of behavioral and substance use addictions tends to be separated. However, we argue that a more effective and efficient treatment approach is to conceptualize behavioral and substance use addictions as different expressions of a common underlying disorder and, in treatment, to address the underlying mechanisms common to both. To this end, the article presents a developing transdiagnostic treatment model of addictions that targets underlying similarities between behavioral and substance use addictions, called the component model of addiction treatment (CMAT). The CMAT is transdiagnostic in that it can be used in the treatment of both behavioral and substance use addictions. It is pragmatic in that it targets component vulnerabilities, which are enduring, yet malleable, individual psychological, cognitive, and neurobiological characteristics that are common to all addictive disorders and have been demonstrated to be modifiable. A working model of CMAT is presented, including proposed component vulnerabilities: lack of motivation, urgency, maladaptive expectancies, deficits in self-control, deficits in social support, and compulsivity, as well as their potential intervention possibilities. Future directions and potential implications of the CMAT are discussed.

Question: What is the component model of addiction treatment?