The Effects of Profile Pictures and Friends' Comments on Social Network Site Users' Body Image and Adherence to the Norm. This study sought to explore the effects of exposure to Facebook body ideal profile pictures and norm conforming comments on users' body image. In addition, the social identity and self-categorization theoretical frameworks were used to explore users' endorsement of a body ideal norm. A mock Facebook page was used to conduct a pretest posttest 2 × 2 between-group web-based experiment that featured body ideal profile pictures (body ideal vs. no body) and body ideal comments (conforming vs. nonconforming). Five hundred and one participants completed the experiment and passed all manipulation checks. Participants viewed pictures and comments on the status page and were able to leave their own comment before exiting. Results demonstrated no significant main effects. However, predispositional body satisfaction significantly moderated the relationship between body ideal pictures and body satisfaction. Most comments supported the body ideal norm. However, in support of self-categorization theory, participants exposed to nonconforming comments made nonconforming comments themselves significantly more than those exposed to conforming comments. The findings demonstrated the importance of continued body image research in social network sites, as well as the potential for self-categorization theory to guide such research.

Question: What is the difference between a profile picture and a status update?


Deep Reinforcement Learning for General Video Game AI. The General Video Game AI (GVGAI) competition and its associated software framework provides a way of benchmarking AI algorithms on a large number of games written in a domain-specific description language. While the competition has seen plenty of interest, it has so far focused on online planning, providing a forward model that allows the use of algorithms such as Monte Carlo Tree Search. In this paper, we describe how we interface GVGAI to the OpenAI Gym environment, a widely used way of connecting agents to reinforcement learning problems. Using this interface, we characterize how widely used implementations of several deep reinforcement learning algorithms fare on a number of GVGAI games. We further analyze the results to provide a first indication of the relative difficulty of these games relative to each other, and relative to those in the Arcade Learning Environment under similar conditions.

Question: How do deep reinforcement learning algorithms fare on a large number of games written in a domain-specific description language?


Iterative Thresholding Algorithm for Sparse Inverse Covariance Estimation. The `1-regularized maximum likelihood estimation problem has recently become a topic of great interest within the machine learning, statistics, and optimization communities as a method for producing sparse inverse covariance estimators. In this paper, a proximal gradient method (G-ISTA) for performing `1-regularized covariance matrix estimation is presented. Although numerous algorithms have been proposed for solving this problem, this simple proximal gradient method is found to have attractive theoretical and numerical properties. G-ISTA has a linear rate of convergence, resulting in an O(log ε) iteration complexity to reach a tolerance of ε. This paper gives eigenvalue bounds for the G-ISTA iterates, providing a closed-form linear convergence rate. The rate is shown to be closely related to the condition number of the optimal point. Numerical convergence results and timing comparisons for the proposed method are presented. G-ISTA is shown to perform very well, especially when the optimal point is well-conditioned.

Question: What is the iterative thresholding algorithm for sparse inverse covariance estimation?


Collective Opinion Target Extraction in Chinese Microblogs. Microblog messages pose severe challenges for current sentiment analysis techniques due to some inherent characteristics such as the length limit and informal writing style. In this paper, we study the problem of extracting opinion targets of Chinese microblog messages. Such fine-grained word-level task has not been well investigated in microblogs yet. We propose an unsupervised label propagation algorithm to address the problem. The opinion targets of all messages in a topic are collectively extracted based on the assumption that similar messages may focus on similar opinion targets. Topics in microblogs are identified by hashtags or using clustering algorithms. Experimental results on Chinese microblogs show the effectiveness of our framework and algorithms.

Question: How to extract opinion targets of microblog messages?


An automatic irrigation system using ZigBee in wireless sensor network. Wireless Sensing Technology is widely used everywhere in the current scientific world. As the technology is growing and changing rapidly, Wireless sensing Network (WSN) helps to upgrade the technology. In the research field of wireless sensor networks the power efficient time is a major issue. This problem can be overcome by using the ZigBee technology. The main idea of this is to understand how data travels through a wireless medium transmission using wireless sensor network and monitoring system. This paper design an irrigation system which is automated by using controllable parameter such as temperature, soil moisture and air humidity because they are the important factors to be controlled in PA(Precision Agricultures).

Question: What is the main idea of this paper?


Question: What is deep image prior?


Drugs of Abuse and Stress Trigger a Common Synaptic Adaptation in Dopamine Neurons. Drug seeking and drug self-administration in both animals and humans can be triggered by drugs of abuse themselves or by stressful events. Here, we demonstrate that in vivo administration of drugs of abuse with different molecular mechanisms of action as well as acute stress both increase strength at excitatory synapses on midbrain dopamine neurons. Psychoactive drugs with minimal abuse potential do not cause this change. The synaptic effects of stress, but not of cocaine, are blocked by the glucocorticoid receptor antagonist RU486. These results suggest that plasticity at excitatory synapses on dopamine neurons may be a key neural adaptation contributing to addiction and its interactions with stress and thus may be an attractive therapeutic target for reducing the risk of addiction.

Question: What is the molecular mechanism of the synaptic plasticity?


Child-based personas: need, ability and experience. Interactive technologies are becoming ubiquitous in many children's lives. From school to home, technologies are changing the way children live. However, current methods of designing these technologies do not adequately consider children's needs and developmental abilities. This paper describes and illustrates a new approach for creating user abstractions of children called the child-persona technique. Child-personas integrate theoretical concepts, empirically generated data and experiential goals. An analysis of the utility of this technique provides insights about how this technique can benefit designers by generating realistic child-user abstractions through a process which supports designers in child-centric design.

Question: How can we design for children?


Graded Entailment for Compositional Distributional Semantics. The categorical compositional distributional model of natural language provides a conceptually motivated procedure to compute the meaning of sentences, given grammatical structure and the meanings of its words. This approach has outperformed other models in mainstream empirical language processing tasks. However, until now it has lacked the crucial feature of lexical entailment – as do other distributional models of meaning. In this paper we solve the problem of entailment for categorical compositional distributional semantics. Taking advantage of the abstract categorical framework allows us to vary our choice of model. This enables the introduction of a notion of entailment, exploiting ideas from the categorical semantics of partial knowledge in quantum computation. The new model of language uses density matrices, on which we introduce a novel robust graded order capturing the entailment strength between concepts. This graded measure emerges from a general framework for approximate entailment, induced by any commutative monoid. Quantum logic embeds in our graded order. Our main theorem shows that entailment strength lifts compositionally to the sentence level, giving a lower bound on sentence entailment. We describe the essential properties of graded entailment such as continuity, and provide a procedure for calculating entailment strength.

Question: What is the meaning of the word "entailment"?


A unified distance measurement for orientation coding in palmprint verification. Orientation coding based palmprint verification methods, such as competitive code, palmprint orientation code and robust line orientation code, are state-of-the-art verification algorithms with fast matching speeds. Orientation code makes use of two types of distance measure, SUM_XOR (angular distance) and OR_XOR (Hamming distance), yet little is known about the similarities and differences both SUM_XOR and OR_XOR can be regarded as special cases, and provide some principles for determining the parameters of the unified distance. Experimental results show that, using the same feature extraction and coding methods, the unified distance measure gets lower equal error rates than the original distance measures. & 2009 Elsevier B.V. All rights reserved.

Question: What is the unified distance measure?


LoRea: A Backscatter Reader for Everyone!. Computational RFID (CRFID) platforms have enabled reconfigurable, battery-free applications for close to a decade. However, several factors have impeded their wide-spread adoption: low communication range, low throughput, and expensive infrastructure— CRFID readers usually cost upwards of $1000. This paper presents LoRea, a backscatter reader that achieves an order of magnitude higher range than existing CRFID readers, while costing a fraction of their price. LoRea achieves this by diverging from existing designs of CRFID readers and more specifically, how self-interference is tackled. LoRea builds on recent work that frequency-shifts backscatter transmissions away from the carrier signal to reduce selfinterference. LoRea also decouples the carrier generation from the reader, helping to further decrease self-interference. Decoupling carrier generation also enables the use of deployed infrastructure of smartphones and sensor nodes to provide the carrier signal. Together these methods reduce cost and complexity of the reader. LoRea also purposefully operates at lower bitrates than recent backscatter systems which enables high sensitivity and longer range. We evaluate LoRea experimentally and find that it achieves a communication range of up to 225 m in lineof-sight scenarios. In indoor environments, where the signal traverses several walls separating the reader and the backscatter tag, LoRea achieves a range of 30 m. These results illustrate how LoRea outperforms stateof-art backscatter systems and CRFID platforms.

Question: What is a backscatter reader?


Graph-based Cross Entropy method for solving multi-robot decentralized POMDPs. This paper introduces a probabilistic algorithm for multi-robot decision-making under uncertainty, which can be posed as a Decentralized Partially Observable Markov Decision Process (Dec-POMDP). Dec-POMDPs are inherently synchronous decision-making frameworks which require significant computational resources to be solved, making them infeasible for many real-world robotics applications. The Decentralized Partially Observable Semi-Markov Decision Process (Dec-POSMDP) was recently introduced as an extension of the Dec-POMDP that uses high-level macro-actions to allow large-scale, asynchronous decision-making. However, existing Dec-POSMDP solution methods have limited scalability or perform poorly as the problem size grows. This paper proposes a cross-entropy based Dec-POSMDP algorithm motivated by the combinatorial optimization literature. The algorithm is applied to a constrained package delivery domain, where it significantly outperforms existing Dec-POSMDP solution methods.

Question: What is a Dec-POSMDP?


An Effective High Threating Alarm Mining Method for Cloud Security Management. Security equipment such as intrusion prevention system is an important supplementary for security management. They reduce the difficulty of network management by giving alarms corresponding to different attacks instead of raw traffic packet inspection. But there are many false alarms due to their running mechanism, which greatly reduces its usability. In this paper, we develop a hierarchical framework to mine high threating alarms from the massive alarm logs, and aim to provide fundamental and useful information for administrators to design efficient management policy. First, the alarms are divided into two parts based on their attributes, the first part mainly includes several kinds of famous attacks which are critical for security management, we proposed a similar alarm mining method based on Choquet integral to cluster and rank the frequently occurred attacks. The rest alarms constitute the second part, which are caused by the potential threats attacks, also include many false alarms. To reduce the effect of false alarms and rank the potential threats, we employ the frequent pattern mining algorithm to mine correlation rules and then filter false alarms. Following, we proposed a self-adapting threat degree calculation method to qualify the threat degree of these alarms after filtering. To verity the methods developed, an experimental platform is constructed in the campus network of Xi'an Jiaotong University. Experimental results based on the data collected verify the efficiency of the developed methods. For the first kind of alarms, the similar alarms mining accuracy is higher than 97% and the alarms are ranked with different processing urgencies. For the rest alarms, the proposed methods have filtering accuracy above 80% and can rank the potential threats. Based on the ranking results, administrators can deal with the high threats with their limited time and energy, in turn, keep the

Question: What is the high threating alarm mining method for cloud security management?


Stereo-Based Autonomous Navigation and Obstacle Avoidance*. This paper presents a stereo vision-based autonomous navigation system using a GPS and a modified version of the VFH algorithm. In order to obtain a high-accuracy disparity map and meet the time constraints of the real time navigation system, this work proposes the use of a semi-global stereo method. By not suffering the same issues of the regularly used local stereo methods, the employed stereo technique enables the generation of a highly dense, efficient, and accurate disparity map. Obstacles are detected using a method that checks for relative slopes and heights differences. Experimental tests using an electric vehicle in an urban environment were performed to validate the proposed approach.

Question: What is a stereo vision-based autonomous navigation system?


English as a Formal Specification Language. PENG is a computer-processable controlled natural language designed for writing unambiguous and precise specifications. PENG covers a strict subset of standard English and is precisely defined by a controlled grammar and a controlled lexicon. In contrast to other controlled languages, the author does not need to know the grammatical restrictions explicitly. ECOLE, a look-ahead text editor, indicates the restrictions while the specification is written. The controlled lexicon contains domain-specific content words that can be defined by the author on the fly and predefined function words. Specifications written in PENG can be deterministically translated into discourse representations structures to cope with anaphora and presuppositions and also into first-order predicate logic. To test the formal properties of PENG, we reformulated Schubert's steamroller puzzle in PENG, translated the resulting specification via discourse representation structures into first-order predicate logic with equality, and proved the steamroller's conclusion with OTTER, a standard theorem prover.

Question: What is a controlled natural language?


The facial width-to-height ratio determines interpersonal distance preferences in the observer.. Facial width-to-height ratio (fWHR) is correlated with a number of aspects of aggressive behavior in men. Observers appear to be able to assess aggressiveness from male fWHR, but implications for interpersonal distance preferences have not yet been determined. This study utilized a novel computerized stop-distance task to examine interpersonal space preferences of female participants who envisioned being approached by a man; men's faces photographed posed in neutral facial expressions were shown in increasing size to mimic approach. We explored the effect of the men's fWHR, their behavioral aggression (measured previously in a computer game), and women's ratings of the men's aggressiveness, attractiveness, and masculinity on the preferred interpersonal distance of 52 German women. Hierarchical linear modelling confirmed the relationship between the fWHR and trait judgements (ratings of aggressiveness, attractiveness, and masculinity). There were effects of fWHR and actual aggression on the preferred interpersonal distance, even when controlling statistically for men's and the participants' age. Ratings of attractiveness, however, was the most influential variable predicting preferred interpersonal distance. Our results extend earlier findings on fWHR as a cue of aggressiveness in men by demonstrating implications for social interaction. In conclusion, women are able to accurately detect aggressiveness in emotionally neutral facial expressions, and adapt their social distance preferences accordingly.

Question: What is the facial width-to-height ratio?


ELDEN: Improved Entity Linking Using Densified Knowledge Graphs. Entity Linking (EL) systems aim to automatically map mentions of an entity in text to the corresponding entity in a Knowledge Graph (KG). Degree of connectivity of an entity in the KG directly affects an EL system's ability to correctly link mentions in text to the entity in KG. This causes many EL systems to perform well for entities well connected to other entities in KG, bringing into focus the role of KG density in EL. In this paper, we propose Entity Linking using Densified Knowledge Graphs (ELDEN). ELDEN is an EL system which first densifies the KG with co-occurrence statistics from a large text corpus, and then uses the densified KG to train entity embeddings. Entity similarity measured using these trained entity embeddings result in improved EL. ELDEN outperforms stateof-the-art EL system on benchmark datasets. Due to such densification, ELDEN performs well for sparsely connected entities in the KG too. ELDEN's approach is simple, yet effective. We have made ELDEN's code and data publicly available.

Question: What is the name of the entity linking system?


A benchmark for semantic image segmentation. Though quite a few image segmentation benchmark datasets have been constructed, there is no suitable benchmark for semantic image segmentation. In this paper, we construct a benchmark for such a purpose, where the ground-truths are generated by leveraging the existing fine granular ground-truths in Berkeley Segmentation Dataset (BSD) as well as using an interactive segmentation tool for new images. We also propose a percept-tree-based region merging strategy for dynamically adapting the ground-truth for evaluating test segmentation. Moreover, we propose a new evaluation metric that is easy to understand and compute, and does not require boundary matching. Experimental results show that, compared with BSD, the generated ground-truth dataset is more suitable for evaluating semantic image segmentation, and the conducted user study demonstrates that the proposed evaluation metric matches user ranking very well.

Question: What is a benchmark for semantic image segmentation?


Best of both worlds: Human-machine collaboration for object annotation. The long-standing goal of localizing every object in an image remains elusive. Manually annotating objects is quite expensive despite crowd engineering innovations. Current state-of-the-art automatic object detectors can accurately detect at most a few objects per image. This paper brings together the latest advancements in object detection and in crowd engineering into a principled framework for accurately and efficiently localizing objects in images. The input to the system is an image to annotate and a set of annotation constraints: desired precision, utility and/or human cost of the labeling. The output is a set of object annotations, informed by human feedback and computer vision. Our model seamlessly integrates multiple computer vision models with multiple sources of human input in a Markov Decision Process. We empirically validate the effectiveness of our human-in-the-loop labeling approach on the ILSVRC2014 object detection dataset.

Question: What is the best object detection model?


MPLS based hybridization in SDN. The new paradigm of Software Defined Networking (SDN) although has great potential to address the complex problems presented by enterprise networks, it has its own deployment and scalability issues. Further, a full SDN deployment has its own business and economic challenges. A smooth transition from legacy networks to SDN (disruption free, accommodating budget constraints, with progressive improvement in network management) requires a hybrid networking model as an inevitable intermediate step; that allows heterogeneous paradigms to function together while the full transition is realized in phases. Therefore, the need of the hour is to develop an incremental deployment strategy that caters to the needs of the organization. We present here a class-based hybrid SDN model for Multi Protocol Label Switching (MPLS) networks. We discuss the model, design, components, their interactions, advantages and drawbacks. We also present a n implementation and evaluation of a prototype. In legacy networks, MPLS architecture closely resembles SDN paradigm in terms of separation of control and data planes, flow-abstraction etc. Moreover, ISPs have preferred MPLS over the years due to benefits of virtual private networks and traffic engineering. The central idea is to partition traffic using forwarding equivalence classes at the ingress router, the rules of which can be updated via a centralized controller using OpenFlow. Therefore, we aim to use the standard MPLS data-plane together with a control-plane based on OpenFlow to come up with a systematic incremental deployment methodology as well as a hybrid operation model

Question: What is a hybrid SDN model for MPLS?


Research on continuous auditing: A bibliometric analysis. This paper presents the results of a bibliometric study about the evolution of research on Continuous Auditing. This study has as main motivation to find reasons for the very slow evolvement of research on this topic. In addition, Continuous Auditing is one of the features of the emerging concept of Continuous Assurance. Thus, considering that Continuous Assurance represents numerous advantages for the organizational performance, this study also intends to understand if there is a relation between the evolution of research on Continuous Auditing and the still very low maturity levels of continuous assurance solutions. This study shows that the number of publications is considerably reduced and that the slow growth of research on Continuous Auditing may be contributing to the lack of maturity of Continuous Assurance.

Question: What is the relation between continuous auditing and continuous assurance?


A Dataset for Lane Instance Segmentation in Urban Environments. Autonomous vehicles require knowledge of the surrounding road layout, which can be predicted by state-of-the-art CNNs. This work addresses the current lack of data for determining lane instances, which are needed for various driving manoeuvres. The main issue is the timeconsuming manual labelling process, typically applied per image. We notice that driving the car is itself a form of annotation. Therefore, we propose a semi-automated method that allows for efficient labelling of image sequences by utilising an estimated road plane in 3D based on where the car has driven and projecting labels from this plane into all images of the sequence. The average labelling time per image is reduced to 5 seconds and only an inexpensive dash-cam is required for data capture. We are releasing a dataset of 24,000 images and additionally show experimental semantic segmentation and instance segmentation results.

Question: How to segment lane instances in urban environments?


Microbial co-operation in the rhizosphere.. Soil microbial populations are immersed in a framework of interactions known to affect plant fitness and soil quality. They are involved in fundamental activities that ensure the stability and productivity of both agricultural systems and natural ecosystems. Strategic and applied research has demonstrated that certain co-operative microbial activities can be exploited, as a low-input biotechnology, to help sustainable, environmentally-friendly, agro-technological practices. Much research is addressed at improving understanding of the diversity, dynamics, and significance of rhizosphere microbial populations and their co-operative activities. An analysis of the co-operative microbial activities known to affect plant development is the general aim of this review. In particular, this article summarizes and discusses significant aspects of this general topic, including (i) the analysis of the key activities carried out by the diverse trophic and functional groups of micro-organisms involved in co-operative rhizosphere interactions; (ii) a critical discussion of the direct microbe-microbe interactions which results in processes benefiting sustainable agro-ecosystem development; and (iii) beneficial microbial interactions involving arbuscular mycorrhiza, the omnipresent fungus-plant beneficial symbiosis. The trends of this thematic area will be outlined, from molecular biology and ecophysiological issues to the biotechnological developments for integrated management, to indicate where research is needed in the future.

Question: What is the rhizosphere?


Visual Analysis of Public Utility Service Problems in a Metropolis. Issues about city utility services reported by citizens can provide unprecedented insights into the various aspects of such services. Analysis of these issues can improve living quality through evidence-based decision making. However, these issues are complex, because of the involvement of spatial and temporal components, in addition to having multi-dimensional and multivariate natures. Consequently, exploring utility service problems and creating visual representations are difficult. To analyze these issues, we propose a visual analytics process based on the main tasks of utility service management. We also propose an aggregate method that transforms numerous issues into legible events and provide visualizations for events. In addition, we provide a set of tools and interaction techniques to explore such issues. Our approach enables administrators to make more informed decisions.

Question: What is the main task of utility service management?


Device-to-Device Communication Underlaying a Finite Cellular Network Region. Underlay in-band device-to-device (D2D) communication can improve the spectrum efficiency of cellular networks. However, the coexistence of D2D and cellular users causes inter-cell and intra-cell interference. The former can be effectively managed through inter-cell interference coordination and, therefore, is not considered in this paper. Instead, we focus on the intra-cell interference and propose a D2D mode selection scheme to manage it inside a finite cellular network region. The potential D2D users are controlled by the base station (BS) to operate in D2D mode based on the average interference generated to the BS. Using stochastic geometry, we study the outage probability experienced at the BS and a D2D receiver, and spectrum reuse ratio, which quantifies the average fraction of successfully transmitting D2D users. The analysis shows that the outage probability at the D2D receiver varies for different locations. In addition, without impairing the performance at the BS, if the path-loss exponent on the cellular link is slightly lower than that on the D2D link, the spectrum reuse ratio can have negligible decrease, while the D2D users' average number of successful transmissions increases with increasing D2D node density. This indicates that an increasing level of D2D communication can be beneficial in future networks.

Question: How to manage the inter-cell interference?


Automatic White Balance for Digital Still Cameras. In recent years digital cameras have captured the camera market. Although the factors that consumer consider is the quality of the photographs produced. White balance is one of the methods used to improve the quality of the photographs. The basic white balance methods are, however powerless to handle all possible situations in the captured scene. In order to improve the white balance we divide this problem in three steps–white object purification, white point detection, and white point adjustment. The proposed method basically solves the problem of detecting the white point very well. The experimental results show that the proposed method can achieve superior result in both objective and subjective evaluative terms. The complexity of the proposed system is acceptable. The propose method can be easily applied to digital cameras to obtain good results.

Question: What is the white balance method?


Clustering with Deep Learning: Taxonomy and New Methods. Clustering methods based on deep neural networks have proven promising for clustering real-world data because of their high representational power. In this paper we propose a systematic taxonomy of clustering methods that utilize deep neural networks. We base our taxonomy on a comprehensive review of recent work and validate the taxonomy in a case study. In this case study, we show that the taxonomy enables researchers and practitioners to systematically create new clustering methods by selectively recombining and replacing distinct aspects of previous methods with the goal of overcoming their individual limitations. The experimental evaluation confirms this and shows that the method created for the case study achieves state-of-the-art clustering quality and surpasses it in some cases.

Question: What is a clustering method based on deep neural networks?


Modeling the ownership of source code topics. Exploring linguistic topics in source code is a program comprehension activity that shows promise in helping a developer to become familiar with an unfamiliar software system. Examining ownership in source code can reveal complementary information, such as who to contact with questions regarding a source code entity, but the relationship between linguistic topics and ownership is an unexplored area. In this paper we combine software repository mining and topic modeling to measure the ownership of linguistic topics in source code. We conduct an exploratory study of the relationship between linguistic topics and ownership in source code using 10 open source Java systems. We find that classes that belong to the same linguistic topic tend to have similar ownership characteristics, which suggests that conceptually related classes often share the same owner(s). We also find that similar topics tend to share the same ownership characteristics, which suggests that the same developers own related topics.

Question: What is the relationship between ownership and linguistic topics?


DeepFace: Closing the Gap to Human-Level Performance in Face Verification. In modern face recognition, the conventional pipeline consists of four stages: detect => align => represent => classify. We revisit both the alignment step and the representation step by employing explicit 3D face modeling in order to apply a piecewise affine transformation, and derive a face representation from a nine-layer deep neural network. This deep network involves more than 120 million parameters using several locally connected layers without weight sharing, rather than the standard convolutional layers. Thus we trained it on the largest facial dataset to-date, an identity labeled dataset of four million facial images belonging to more than 4, 000 identities. The learned representations coupling the accurate model-based alignment with the large facial database generalize remarkably well to faces in unconstrained environments, even with a simple classifier. Our method reaches an accuracy of 97.35% on the Labeled Faces in the Wild (LFW) dataset, reducing the error of the current state of the art by more than 27%, closely approaching human-level performance.

Question: How does deepface work?


Multi-Scale Patch-Based Image Restoration. Many image restoration algorithms in recent years are based on patch processing. The core idea is to decompose the target image into fully overlapping patches, restore each of them separately, and then merge the results by a plain averaging. This concept has been demonstrated to be highly effective, leading often times to the state-of-the-art results in denoising, inpainting, deblurring, segmentation, and other applications. While the above is indeed effective, this approach has one major flaw: the prior is imposed on intermediate (patch) results, rather than on the final outcome, and this is typically manifested by visual artifacts. The expected patch log likelihood (EPLL) method by Zoran and Weiss was conceived for addressing this very problem. Their algorithm imposes the prior on the patches of the final image, which in turn leads to an iterative restoration of diminishing effect. In this paper, we propose to further extend and improve the EPLL by considering a multi-scale prior. Our algorithm imposes the very same prior on different scale patches extracted from the target image. While all the treated patches are of the same size, their footprint in the destination image varies due to subsampling. Our scheme comes to alleviate another shortcoming existing in patch-based restoration algorithms-the fact that a local (patch-based) prior is serving as a model for a global stochastic phenomenon. We motivate the use of the multi-scale EPLL by restricting ourselves to the simple Gaussian case, comparing the aforementioned algorithms and showing a clear advantage to the proposed method. We then demonstrate our algorithm in the context of image denoising, deblurring, and super-resolution, showing an improvement in performance both visually and quantitatively.

Question: What is the difference between the EPLL and the multi-scale EPLL?


Vessel enhancement algorithm in digital retinal fundus microaneurysms filter for nonproliferative diabetic retinopathy classification. Diabetic Retinopathy is one of a complication of diabetes which can cause blindness. There are two levels of diabetic retinopathy which are nonproliferative diabetic retinopathy (NPDR) and proliferative diabetic retinopathy (PDR). The presence of microaneurysms in the eye is one of the early signs of diabetic retinopathy. Automated detection of microaneurysms in digital color fundus photographs is developed to help opthalmologist to detect the emergence of its early symptoms and determine the next action step for the patient. In this study we developed microaneurysms filter algorithm with the concept of vessel enhancement in order to extract the structure of microaneurysms in medical imagery, especially in the retinal image. Microaneurysms filter algorithm has been able to extract part of microaneurysms in the retinal image and detect pretty well. In addition, microaneurysms filters can also be developed to detect the next symptoms of diabetic retinopathy-hemorraghes (bleeding) that has features in common with microaneurysms which are larger and have irregular shape. Furthermore, the nonproliferative diabetic retinopathy (NPDR) classification system was developed based on the microaneurysms and hemorraghes detection. The system was tested against the patient data. Microanuerisyms detection results on DiaRetDB data has sensitivity value by 57,4% to ophthalmlogist 1, 35,37% for opthalmologist 2 and 62,68% for opthalmologist 3.

Question: What is the vessel enhancement algorithm in digital retinal fundus microaneurysms filter for nonproliferative diabetic retinopathy classification?


A comparative analysis of techniques for predicting academic performance. This paper compares the accuracy of decision tree and Bayesian network algorithms for predicting the academic performance of undergraduate and postgraduate students at two very different academic institutes: Can Tho University (CTU), a large national university in Viet Nam; and the Asian Institute of Technology (AIT), a small international postgraduate institute in Thailand that draws students from 86 different countries. Although the diversity of these two student populations is very different, the data-mining tools were able to achieve similar levels of accuracy for predicting student performance: 73/71% for {fail, fair, good, very good} and 94/93% for {fail, pass} at the CTU/AIT respectively. These predictions are most useful for identifying and assisting failing students at CTU (64% accurate), and for selecting very good students for scholarships at the AIT (82% accurate). In this analysis, the decision tree was consistently 3-12% more accurate than the Bayesian network. The results of these case studies give insight into techniques for accurately predicting student performance, compare the accuracy of data mining algorithms, and demonstrate the maturity of open source tools.

Question: What is the difference between a decision tree and a Bayesian network?


Development of a credible and integrated electronic voting machine based on contactless IC cards, biometrie fingerprint credentials and POS printer. In recent times there has been a decline in the confidence of common people over electronic voting machines (EVMs). More anticipated than what the reality actually is, today's automated vote casting methods have faced immense controversy related to being vulnerable to hacking and questions have been raised about their transparency and security. This paper proposes the design and development of a novel tamper resistant electronic voting system that aims to mitigate the recurring issues and flaws of existing voting machines of today. To elucidate the system in brief, multiple layered verification process would be carried out on a potential voter by the means of fingerprint recognition and a Near Field Communication (NFC) smart card entry in order to authenticate his or her identity. Subsequently, the person would cast the vote by pressing a button corresponding to a particular candidate which would be recorded in the system providing the vote caster a visual confirmation. The final vote would then be printed out spontaneously onto a ballot box using a POS (Point of Sales) printer for an added level of validation. This system not only prevents multiple vote casts, but also eliminates the discrepancies that commonly arise with a person claiming not to have voted, whereas his or her name is present in the list of vote casters. The project is an incremental extension of a previous research.

Question: What is a POS printer?


Detecting Image Splicing using Geometry Invariants and Camera Characteristics Consistency. Recent advances in computer technology have made digital image tampering more and more common. In this paper, we propose an authentic vs. spliced image classification method making use of geometry invariants in a semi-automatic manner. For a given image, we identify suspicious splicing areas, compute the geometry invariants from the pixels within each region, and then estimate the camera response function (CRF) from these geometry invariants. The cross-fitting errors are fed into a statistical classifier. Experiments show a very promising accuracy, 87%, over a large data set of 363 natural and spliced images. To the best of our knowledge, this is the first work detecting image splicing by verifying camera characteristic consistency from a single-channel image

Question: How to detect image splicing?


An End-to-End Signal Strength Model for Underwater Optical Communications. In this paper, we present a generic model of signal strength in underwater optical communications. The model includes light sources, detectors, amplifier and detector circuitry, optics, as well as a simple extinction model of the water channel. The end-to-end model provides insights into optimization approaches for underwater optical modems and enables relative pose estimation between underwater optical transmitters and receivers. We instantiate our model to the AquaOptical model by determining its parameters and verifying the model prediction in a suite of pool experiments.

Question: What is the end-to-end model for underwater optical communications?


A Random Sampling Scheme for Path Planning. Several randomized path planners have been proposed during the last few years. Their attractiveness stems from their applicability to virtually any type of robots, and their empirically observed success. In this paper we attempt to present a unifying view of these planners and to theoretically explain their success. First, we introduce a general planning scheme that consists of randomly sampling the robot's configuration space. We then describe two previously developed planners as instances of planners based on this scheme, but applying very different sampling strategies. These planners are probabilistically complete: if a path exists, they will find one with high probability, if we let them run long enough. Next, for one of the planners, we analyze the relation between the probability of failure and the running time. Under assumptions characterizing the "goodness" of the robot's free space, we show that the running time only grows as the absolute value of the logarithm of the probability of failure that we are willing to tolerate. We also show that it increases at a reasonable rate as the space goodness degrades. In the last section we suggest directions for future research.

Question: What is a randomized path planner?


Direct least squares fitting of ellipses. This work presents a new efficient method for fitting ellipses to scattered data. Previous algorithms either fitted general conics or were computationally expensive. By minimizing the algebraic distance subject to the constraint 4 2 1 the new method incorporates the ellipticity constraint into the normalization factor. The new method combines several advantages: (i) It is ellipse-specific so that even bad data will always return an ellipse; (ii) It can be solved naturally by a generalized eigensystem and (iii) it is extremely robust, efficient and easy to implement. We compare the proposed method to other approaches and show its robustness on several examples in which other non-ellipse-specific approaches would fail or require computationally expensive iterative refinements. Source code for the algorithm is supplied and a demonstration is available on ! " ! $#% '& () "*) & +, ./10 0 32, . 4) "*) "*) % 5* 0

Question: How to fit ellipses to scattered data?


Bridging Neural Machine Translation and Bilingual Dictionaries. Neural Machine Translation (NMT) has become the new state-of-the-art in several language pairs. However, it remains a challenging problem how to integrate NMT with a bilingual dictionary which mainly contains words rarely or never seen in the bilingual training data. In this paper, we propose two methods to bridge NMT and the bilingual dictionaries. The core idea behind is to design novel models that transform the bilingual dictionaries into adequate sentence pairs, so that NMT can distil latent bilingual mappings from the ample and repetitive phenomena. One method leverages a mixed word/character model and the other attempts at synthesizing parallel sentences guaranteeing massive occurrence of the translation lexicon. Extensive experiments demonstrate that the proposed methods can remarkably improve the translation quality, and most of the rare words in the test sentences can obtain correct translations if they are covered by the dictionary.

Question: How to bridge neural machine translation and bilingual dictionaries?


Error detecting and error correcting codes. When a message is transmitted, it has the potential to get scrambled by noise. This is certainly true of voice messages, and is also true of the digital messages that are sent to and from computers. Now even sound and video are being transmitted in this manner. A digital message is a sequence of 0's and 1's which encodes a given message. More data will be added to a given binary message that will help to detect if an error has been made in the transmission of the message; adding such data is called an error-detecting code. More data may also be added to the original message so that errors made in transmission may be detected, and also to figure out what the original message was from the possibly corrupt message that was received. This type of code is an error-correcting code.

Question: What is an error-correcting code?


Software Architecture Quality Measurement Stability and. Over the past years software architecture has become an important sub-field of software engineering. There has been substantial advancement in developing new technical approaches to start handling architectural design as an engineering discipline. Measurement is an essential part of any engineering discipline. Quantifying the quality attributes of the software architecture will reveal good insights about the architecture. It will also help architects and practioners to choose the best fit of alternative architectures that meets their needs. This work paves the way for researchers to start investigating ways to measure software architecture quality attributes. Measurement of these qualities is essential for this sub-field of software engineering. This work explores Stability and Understandability of software architecture, several metrics that affect them, and literature review of these qualities. Keywords—Software Engineering; Software Architecture; Quality Attributes; Stability; Understandability

Question: What is the stability of software architecture?


From Hashing to CNNs: Training Binary Weight Networks via Hashing. Deep convolutional neural networks (CNNs) have shown appealing performance on various computer vision tasks in recent years. This motivates people to deploy CNNs to realworld applications. However, most of state-of-art CNNs require large memory and computational resources, which hinders the deployment on mobile devices. Recent studies show that low-bit weight representation can reduce much storage and memory demand, and also can achieve efficient network inference. To achieve this goal, we propose a novel approach named BWNH to train Binary Weight Networks via Hashing. In this paper, we first reveal the strong connection between inner-product preserving hashing and binary weight networks, and show that training binary weight networks can be intrinsically regarded as a hashing problem. Based on this perspective, we propose an alternating optimization method to learn the hash codes instead of directly learning binary weights. Extensive experiments on CIFAR10, CIFAR100 and ImageNet demonstrate that our proposed BWNH outperforms current state-of-art by a large margin.

Question: How to train binary weight networks via hashing?


Pharmacological profile of lurasidone, a novel antipsychotic agent with potent 5-hydroxytryptamine 7 (5-HT7) and 5-HT1A receptor activity.. Lurasidone [(3aR,4S,7R,7aS)-2-[(1R,2R)-2-[4-(1,2-benzisothiazol-3-yl)piperazin-1-ylmethyl]cyclohexylmethyl]hexahydro-4,7-methano-2H-isoindole-1,3-dione hydrochloride; SM-13496] is an azapirone derivative and a novel antipsychotic candidate. The objective of the current studies was to investigate the in vitro and in vivo pharmacological properties of lurasidone. Receptor binding affinities of lurasidone and several antipsychotic drugs were tested under comparable assay conditions using cloned human receptors or membrane fractions prepared from animal tissue. Lurasidone was found to have potent binding affinity for dopamine D(2), 5-hydroxytryptamine 2A (5-HT(2A)), 5-HT(7), 5-HT(1A), and noradrenaline alpha(2C) receptors. Affinity for noradrenaline alpha(1), alpha(2A), and 5-HT(2C) receptors was weak, whereas affinity for histamine H(1) and muscarinic acetylcholine receptors was negligible. In vitro functional assays demonstrated that lurasidone acts as an antagonist at D(2) and 5-HT(7) receptors and as a partial agonist at the 5-HT(1A) receptor subtype. Lurasidone showed potent effects predictive of antipsychotic activity, such as inhibition of methamphetamine-induced hyperactivity and apomorphine-induced stereotyped behavior in rats, similar to other antipsychotics. Furthermore, lurasidone had only weak extrapyramidal effects in rodent models. In animal models of anxiety disorders and depression, treatment with lurasidone was associated with significant improvement. Lurasidone showed a preferential effect on the frontal cortex (versus striatum) in increasing dopamine turnover. Anti-alpha(1)-noradrenergic, anticholinergic, and central nervous system (CNS) depressant actions of lurasidone were also very weak. These results demonstrate that lurasidone possesses antipsychotic activity and antidepressant- or anxiolytic-like effects wi

Question: What is the pharmacological profile of lurasidone?


Software-Defined Wireless Networking Opportunities and Challenges for Internet-of-Things: A Review. With the emergence of Internet-of-Things (IoT), there is now growing interest to simplify wireless network controls. This is a very challenging task, comprising information acquisition, information analysis, decision-making, and action implementation on large scale IoT networks. Resulting in research to explore the integration of software-defined networking (SDN) and IoT for a simpler, easier, and strain less network control. SDN is a promising novel paradigm shift which has the capability to enable a simplified and robust programmable wireless network serving an array of physical objects and applications. This paper starts with the emergence of SDN and then highlights recent significant developments in the wireless and optical domains with the aim of integrating SDN and IoT. Challenges in SDN and IoT integration are also discussed from both security and scalability perspectives.

Question: What is the difference between SDN and IoT?


Return of the Devil in the Details: Delving Deep into Convolutional Nets. The latest generation of Convolutional Neural Networks (CNN) have achieved impressive results in challenging benchmarks on image recognition and object detection, significantly raising the interest of the community in these methods. Nevertheless, it is still unclear how different CNN methods compare with each other and with previous state-of-the-art shallow representations such as the Bag-of-Visual-Words and the Improved Fisher Vector. This paper conducts a rigorous evaluation of these new techniques, exploring different deep architectures and comparing them on a common ground, identifying and disclosing important implementation details. We identify several useful properties of CNN-based representations, including the fact that the dimensionality of the CNN output layer can be reduced significantly without having an adverse effect on performance. We also identify aspects of deep and shallow methods that can be successfully shared. In particular, we show that the data augmentation techniques commonly applied to CNN-based methods can also be applied to shallow methods, and result in an analogous performance boost. Source code and models to reproduce the experiments in the paper is made publicly available.

Question: What is the dimensionality of the output layer of a convolutional neural network?


Survey: Finite-state technology in natural language processing. In this survey, we will discuss current uses of finite-state information in several statistical natural language processing tasks. To this end, we will review standard approaches in tokenization, part-of-speech tagging, and parsing, and illustrate the utility of finite-state information and technology in these areas. The particular problems were chosen to allow a natural progression from simple prediction to structured prediction. We aim for a sufficiently formal presentation suitable for readers with a background in automata theory that allows to appreciate the contribution of finite-state approaches, but we will not discuss practical issues outside the core ideas. We provide instructive examples and pointers into the relevant literature for all constructions. We close with an outlook on finite-state technology in statistical machine translation.

Question: What is a finite-state machine?


The standard operating procedure of the DOE-JGI Microbial Genome Annotation Pipeline (MGAP v.4). The DOE-JGI Microbial Genome Annotation Pipeline performs structural and functional annotation of microbial genomes that are further included into the Integrated Microbial Genome comparative analysis system. MGAP is applied to assembled nucleotide sequence datasets that are provided via the IMG submission site. Dataset submission for annotation first requires project and associated metadata description in GOLD. The MGAP sequence data processing consists of feature prediction including identification of protein-coding genes, non-coding RNAs and regulatory RNA features, as well as CRISPR elements. Structural annotation is followed by assignment of protein product names and functions.

Question: What is the standard operating procedure of the DOE-JGI Microbial Genome Annotation Pipeline?


Computation of the characteristics of a claw pole alternator through the finite element method. The paper presents the analysis of a 3-D numerical model developed for a claw pole alternator. The complex structure of the claw-pole magnetic circuit required a 3D FEM model and a double scalar potential magnetic φ-φred formulation, in order to reduce computing time and memory. The no-load and magnetization characteristics and the e.m.f. time dependence have been calculated. Working characteristics and the induced voltage in the static winding can be calculated by knowing the 3D distribution of the field in stationary magnetic regime for successive positions of the rotor considering the stator.

Question: How to calculate the characteristics of a claw pole alternator?


Relational versus non-relational database systems for data warehousing. Relational database systems have been the dominating technology to manage and analyze large data warehouses. Moreover, the ER model, the standard in database design has a close relationship with the relational model. Recently, there has been a surge of alternative technologies for large scale analytic processing, most of which are not based on the relational model. Out of these proposals, distributed file systems together with MapReduce have become strong competitors to relational database systems to analyze large data sets, exploiting parallel processing. Moreover, there is progress on using MapReduce to evaluate relational queries. With that motivation in mind, this panel will compare pros and cons of each technology for data warehousing and will identify research issues, considering practical aspects like ease of use, programming flexibility and cost; as well as technical aspects like data modeling, storage, hardware, scalability, query processing, fault tolerance and data mining.

Question: What is the difference between relational and non-relational database systems?


Techniques of EMG signal analysis: detection, processing, classification and applications. Electromyography (EMG) signals can be used for clinical/biomedical applications, Evolvable Hardware Chip (EHW) development, and modern human computer interaction. EMG signals acquired from muscles require advanced methods for detection, decomposition, processing, and classification. The purpose of this paper is to illustrate the various methodologies and algorithms for EMG signal analysis to provide efficient and effective ways of understanding the signal and its nature. We further point up some of the hardware implementations using EMG focusing on applications related to prosthetic hand control, grasp recognition, and human computer interaction. A comparison study is also given to show performance of various EMG signal analysis methods. This paper provides researchers a good understanding of EMG signal and its analysis procedures. This knowledge will help them develop more powerful, flexible, and efficient applications.

Question: What is the purpose of this paper?


Consistent Hashing and Random Trees: Distributed Caching Protocols for Relieving Hot Spots on the World Wide Web. We describe a family of caching protocols for distrib-uted networks that can be used to decrease or eliminate the occurrence of hot spots in the network. Our protocols are particularly designed for use with very large networks such as the Internet, where delays caused by hot spots can be severe, and where it is not feasible for every server to have complete information about the current state of the entire network. The protocols are easy to implement using existing network protocols such as TCP/fF',and require very little overhead. The protocols work with local control, make efficient use of existing resources, and scale gracefully as the network grows. Our caching protocols are based on a special kind of hashing that we call consistent hashing. Roughly speaking, a consistent hash function is one which changes nr.inimaflyas the range of the function changes. Through the development of good consistent hash functions, we are able to develop caching protocols which do not require users to have a current or even consistent view of the network. We believe that consistent hash functions may eventually prove to be useful in other applications such as distributed name servers and/or quorum systems.

Question: What is a consistent hash function?


of the Thesis Classification of Imbalanced Data Using Synthetic OverSampling Techniques by. of the Thesis Classification of Imbalanced Data Using Synthetic Over-Sampling Techniques

Question: What is the thesis classification of imbalanced data?


Memory Leak Analysis by Contradiction. We present a novel leak detection algorithm. To prove the absence of a memory leak, the algorithm assumes its presence and runs a backward heap analysis to disprove this assumption. We have implemented this approach in a memory leak analysis tool and used it to analyze several routines that manipulate linked lists and trees. Because of the reverse nature of the algorithm, the analysis can locally reason about the absence of memory leaks. We have also used the tool as a scalable, but unsound leak detector for C programs. The tool has found several bugs in larger programs from the SPEC2000 suite.

Question: What is a memory leak?


A Scalable SIEM Correlation Engine and Its Application to the Olympic Games IT Infrastructure. The security event correlation scalability has become a major concern for security analysts and IT administrators when considering complex IT infrastructures that need to handle gargantuan amounts of events or wide correlation window spans. The current correlation capabilities of Security Information and Event Management (SIEM), based on a single node in centralized servers, have proved to be insufficient to process large event streams. This paper introduces a step forward in the current state of the art to address the aforementioned problems. The proposed model takes into account the two main aspects of this field: distributed correlation and query parallelization. We present a case study of a multiple-step attack on the Olympic Games IT infrastructure to illustrate the applicability of our approach.

Question: What is the olympic games?


Practical path planning and path following for a non-holonomic mobile robot based on visual servoing. In this paper, a practical real-time path planning and robot navigation algorithm for a non-holonomic indoor mobile robot based on visual servoing is implemented. The proposed algorithm is divided into three parts; the first part uses Multi-Stencils Fast Marching (MSFM) as a path planning method. But the generated path results from fast marching methods when used directly, not guaranteed to be safe and smooth. Subsequently, the robot can touch corners, walls and obstacles. The proposed algorithm uses image processing methods to solve this problem. The second part estimates the position and orientation of the robot, from the visual information, to follow the desired path with avoiding obstacles. The third part proposes a decentralized PD-like Fuzzy Logic Controller (FLC) to keep up the robot on the desired path. Experimental results show that the developed design is valid to estimate shortest-path by avoiding obstacles and able to guide the robot to follow the path in real-time.

Question: What is a non-holonomic mobile robot?


Character Recognition Using Convolutional Neural Networks. Pattern recognition is one of the traditional uses of neural networks. When trained with gradient-based learning methods, these networks can learn the classification of input data by example. An introduction to classifiers and gradient-based learning is given. It is shown how several perceptrons can be combined and trained gradient-based. Furthermore, an overview of convolutional neural networks, as well as a real-world example, are discussed.

Question: What is a perceptron?


An improved Opposition-Based Sine Cosine Algorithm for global optimization. Real life optimization problems require techniques that properly explore the search spaces to obtain the best solutions. In this sense, it is common that traditional optimization algorithms fail in local optimal values. The Sine Cosine Algorithms (SCA) has been recently proposed; it is a global optimization approach based on two trigonometric functions. SCA uses the sine and cosine functions to modify a set of candidate solutions; such operators create a balance between exploration and exploitation of the search space. However, like other similar approaches, SCA tends to be stuck into sub-optimal regions that it is reflected in the computational effort required to find the best values. This situation occurs due that the operators used for exploration do not work well to analyze the search space. This paper presents an improved version of SCA that considers the opposition based learning (OBL) as a mechanism for a better exploration of the search space generating more accurate solutions. OBL is a machine learning strategy commonly used to increase the performance of metaheuristic algorithms. OBL considers the opposite position of a solution in the search space. Based on the objective function value, the OBL selects the best element between the original solution and its opposite position; this task increases the accuracy of the optimization process. The hybridization of concepts from different fields is crucial in intelligent and expert systems; it helps to combine the advantages of algorithms to generate more efficient approaches. The proposed method is an example of this combination; it has been tested over several benchmark functions and engineering problems. Such results support the efficacy of the proposed approach to find the optimal solutions in complex search spaces. © 2017 Elsevier Ltd. All rights reserved. e w g t S f t ( fi b M a b

Question: What is the difference between a sine and a cosine function?


Smoothed Sarsa: Reinforcement learning for robot delivery tasks. Our goal in this work is to make high level decisions for mobile robots. In particular, given a queue of prioritized object delivery tasks, we wish to find a sequence of actions in real time to accomplish these tasks efficiently. We introduce a novel reinforcement learning algorithm called Smoothed Sarsa that learns a good policy for these delivery tasks by delaying the backup reinforcement step until the uncertainty in the state estimate improves. The state space is modeled by a Dynamic Bayesian Network and updated using a Region-based Particle Filter. We take advantage of the fact that only discrete (topological) representations of entity locations are needed for decision-making, to make the tracking and decision making more efficient. Our experiments show that policy search leads to faster task completion times as well as higher total reward compared to a manually crafted policy. Smoothed Sarsa learns a policy orders of magnitude faster than previous policy search algorithms. We demonstrate our results on the Player/Stage simulator and on the Pioneer robot.

Question: What is a dynamic bayesian network?


Predicting Fault Incidence Using Software Change History. ÐThis paper is an attempt to understand the processes by which software ages. We define code to be aged or decayed if its structure makes it unnecessarily difficult to understand or change and we measure the extent of decay by counting the number of faults in code in a period of time. Using change management data from a very large, long-lived software system, we explore the extent to which measurements from the change history are successful in predicting the distribution over modules of these incidences of faults. In general, process measures based on the change history are more useful in predicting fault rates than product metrics of the code: For instance, the number of times code has been changed is a better indication of how many faults it will contain than is its length. We also compare the fault rates of code of various ages, finding that if a module is, on the average, a year older than an otherwise similar module, the older module will have roughly a third fewer faults. Our most successful model measures the fault potential of a module as the sum of contributions from all of the times the module has been changed, with large, recent changes receiving the most weight. Index TermsÐFault potential, code decay, change management data, metrics, statistical analysis, generalized linear models.

Question: How do you predict the fault rate of a module?


Trust in Online Shopping: The Korean Student Experience. E-commerce has become an important part of business. In South Korea, the market size of online shopping malls was 13,460 billion Korean Won in 2006, and this figure keeps growing. Thus, gaining loyal customers has become a rising concern. In this study, we adopted Lewicki and Bunker's three different types of trust, namely, calculus-based trust, knowledge-based trust, and identification-based trust, in order to investigate their hierarchical relationships in e-commerce and their impacts on customer satisfaction and loyalty. A total of 104 responses from university students were analyzed to test the proposed model and its hypotheses using PLS. The results showed that hierarchical relationships between different types of trust exist in the online environment, and among them, knowledge-based trust has the strongest impact on customer satisfaction. This finding implied that practitioners should focus on developing an appropriate online strategy in terms of how to build up trust-based relationships with online customers.

Question: What is the impact of trust on customer satisfaction?


Sensorless vector control of asynchronous machine based on reduced order Kalman filter. The article describes sensorless vector control of the asynchronous machine based on Kalman filter. A control system block diagram is presented along with a functional scheme of the original speed observer using dynamic system state vector filter estimator. The synthesis of current regulator using machine parameters determined by inverse r model is shown. The method of a control matrix dimension reduction is proposed by removal the flux linkage calculations out of Kalman filter algorithms. Current and speed regulators, as well as a flux estimator, were implemented in the Mexbios Development Studio simulation environment. The robustness of a speed observer and the efficiency of the asynchronous machine sensorless control system were confirmed by the simulation results.

Question: How to implement a sensorless vector control of an asynchronous machine?


The International Exascale Software Project roadmap. Over the last 20 years, the open-source community has provided more and more software on which the world's highperformance computing systems depend for performance and productivity. The community has invested millions of dollars and years of effort to build key components. However, although the investments in these separate software elements have been tremendously valuable, a great deal of productivity has also been lost because of the lack of planning, coordination, and key integration of technologies necessary to make them work together smoothly and efficiently, both within individual petascale systems and between different systems. It seems clear that this completely uncoordinated development model will not provide the software needed to support the unprecedented parallelism required for peta/ exascale computation on millions of cores, or the flexibility required to exploit new hardware models and features, such as transactional memory, speculative execution, and graphics processing units. This report describes the work of the community to prepare for the challenges of exascale computing, ultimately combing their efforts in a coordinated International Exascale Software Project.

Question: What is the roadmap for exascale software?


A Survey on Device-to-Device Communication in Cellular Networks. Device-to-device (D2D) communications was initially proposed in cellular networks as a new paradigm for enhancing network performance. The emergence of new applications such as content distribution and location-aware advertisement introduced new user cases for D2D communications in cellular networks. The initial studies showed that D2D communications has advantages such as increased spectral efficiency and reduced communication delay. However, this communication mode introduces complications in terms of interference control overhead and protocols that are still open research problems. The feasibility of D2D communications in Long-Term Evolution Advanced is being studied by academia, industry, and standardization bodies. To date, there are more than 100 papers available on D2D communications in cellular networks, but there is no survey on this field. In this paper, we provide a taxonomy based on the D2D communicating spectrum and review the available literature extensively under the proposed taxonomy. Moreover, we provide new insights into the over-explored and under-explored areas that lead us to identify open research problems of D2D communications in cellular networks.

Question: What is the taxonomy of device-to-device communication?


The Analysis of the Disease Spectrum in China. Analysis of the related risks of disease provides a scientific basis for disease prevention and treatment, hospital management, and policy formulation by the changes in disease spectrum of patients in hospital. Retrospective analysis was made to the first diagnosis, age, gender, daily average cost of hospitalized patients, and other factors in the First Affiliated Hospital of Nanjing Medical University during 2006-2013. The top 4 cases were as follows: cardiovascular disease, malignant tumors, lung infections, and noninsulin dependent diabetes mellitus. By the age of disease analysis, we found a younger age trend of cardiovascular disease, and the age of onset of cancer or diabetes was somewhat postponed. The average daily cost of hospitalization and the average daily cost of the main noncommunicable diseases were both on the rise. Noncommunicable diseases occupy an increasingly important position in the constitution of the disease, and they caused an increasing medical burden. People should pay attention to health from the aspects of lifestyle changing. Hospitals should focus on building the appropriate discipline. On the other hand, an integrated government response is required to tackle key risks. Multiple interventions are needed to lower the burden of these diseases and to improve national health.

Question: What is the average daily cost of hospitalized patients?


Networks of the Brain. Models of Network Growth All networks, whether they are social, technological, or biological, are the result of a growth process. Many of these networks continue to grow for prolonged periods of time, continually modifying their connectivity structure throughout their entire existence. For example, the World Wide Web has grown from a small number of cross-linked documents in the early 1 990s to an estimated 30 billion indexed web pages in 2009.3 The extraordinary growth of the Web continues unabated and has occurred without any top-down design, yet the topology of its hyperlink structure exhibits characteristic statistical patterns (Pastor-Satorras and Vespig nani, 2004). Other technological networks such as the power grid, global transportation networks, or mobile communication networks continue to grow and evolve, each displaying characteristic patterns of expansion and elaboration. Growth and change in social and organizational

Question: What is the growth of the web?


Question: What is survey research?


Complete discrete 2-D Gabor transforms by neural networks for image analysis and compression. A three-layered neural network is described for transforming two-dimensional discrete signals into generalized nonorthogonal 2-D "Gabor" representations for image analysis, segmentation, and compression. These transforms are conjoint spat iahpectral representations [lo], [15], which provide a complete image description in terms of locally windowed 2-D spectral coordinates embedded within global 2-D spatial coordinates. Because intrinsic redundancies within images a re extracted, the resulting image codes can be very compact. However, these conjoint transforms are inherently difficult to compute because t e elementary expansion functions a re not orthogonal. One o r t h o g o n k i n g approach developed for 1-D signals by Bastiaans [SI, based on biorthonormal expansions, is restricted by constraints on the conjoint sampling rates and invariance of the windowing function, as well as by the fact that the auxiliary orthogonalizing functions are nonlocal infinite series. In the present "neural network" approach, based upon interlaminar interactions involving two layers with fixed weights and one layer with adjustable weights, the network finds coefficients for complete conjoint 2-D Gabor transforms without these restrictive conditions. For arbitrary noncomplete transforms, in which the coefficients might be interpreted simply as signifying the presence of certain features in the image, the network finds optimal coefficients in the sense of minimal mean-squared-error in representing the image. I n one algebraically complete scheme permitting exact reconstruction, the network finds expansion coefficients that reduce entropy from 7.57 in the pixel representation to 2.55 in the complete 2-D Gabor transform. In "wavelet" expansions based on a biologically inspired log-polar ensemble of dilations, rotations, and translations of a single underlying 2-D Gabor wavelet template, image co

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


Path-guided artificial potential fields with stochastic reachable sets for motion planning in highly dynamic environments. Highly dynamic environments pose a particular challenge for motion planning due to the need for constant evaluation or validation of plans. However, due to the wide range of applications, an algorithm to safely plan in the presence of moving obstacles is required. In this paper, we propose a novel technique that provides computationally efficient planning solutions in environments with static obstacles and several dynamic obstacles with stochastic motions. Path-Guided APF-SR works by first applying a sampling-based technique to identify a valid, collision-free path in the presence of static obstacles. Then, an artificial potential field planning method is used to safely navigate through the moving obstacles using the path as an attractive intermediate goal bias. In order to improve the safety of the artificial potential field, repulsive potential fields around moving obstacles are calculated with stochastic reachable sets, a method previously shown to significantly improve planning success in highly dynamic environments. We show that Path-Guided APF-SR outperforms other methods that have high planning success in environments with 300 stochastically moving obstacles. Furthermore, planning is achievable in environments in which previously developed methods have failed.

Question: How does path-guided artificial potential fields with stochastic reachable sets work?


Thermodynamics and NMR of internal G.T mismatches in DNA.. Thermodynamics of 39 oligonucleotides with internal G.T mismatches dissolved in 1 M NaCl were determined from UV absorbance versus temperature profiles. These data were combined with literature values of six sequences to derive parameters for 10 linearly independent trimer and tetramer sequences with G.T mismatches and Watson-Crick base pairs. The G.T mismatch parameters predict DeltaG degrees 37, DeltaH degrees , DeltaS degrees , and TM with average deviations of 5.1%, 7.5%, 8.0%, and 1.4 degrees C, respectively. These predictions are within the limits of what can be expected for a nearest-neighbor model. The data show that the contribution of a single G.T mismatch to helix stability is context dependent and ranges from +1.05 kcal/mol for AGA/TTT to -1.05 kcal/mol for CGC/GTG. Several tests of the applicability of the nearest-neighbor model to G.T mismatches are described. Analysis of imino proton chemical shifts show that structural perturbations from the G.T mismatches are highly localized. One-dimensional NOE difference spectra demonstrate that G.T mismatches form stable hydrogen-bonded wobble pairs in diverse contexts. Refined nearest-neighbor parameters for Watson-Crick base pairs are also presented.

Question: What is the nearest neighbor model?


Robust Sentiment Detection on Twitter from Biased and Noisy Data. In this paper, we propose an approach to automatically detect sentiments on Twitter messages (tweets) that explores some characteristics of how tweets are written and meta-information of the words that compose these messages. Moreover, we leverage sources of noisy labels as our training data. These noisy labels were provided by a few sentiment detection websites over twitter data. In our experiments, we show that since our features are able to capture a more abstract representation of tweets, our solution is more effective than previous ones and also more robust regarding biased and noisy data, which is the kind of data provided by these

Question: How to detect sentiment on twitter?


An Introduction to Boosting and Leveraging. We provide an introduction to theoretical and practical aspects of Boosting and Ensemble learning, providing a useful reference for researchers in the field of Boosting as well as for those seeking to enter this fascinating area of research. We begin with a short background concerning the necessary learning theoretical foundations of weak learners and their linear combinations. We then point out the useful connection between Boosting and the Theory of Optimization, which facilitates the understanding of Boosting and later on enables us to move on to new Boosting algorithms, applicable to a broad spectrum of problems. In order to increase the relevance of the paper to practitioners, we have added remarks, pseudo code, "tricks of the trade", and algorithmic considerations where appropriate. Finally, we illustrate the usefulness of Boosting algorithms by giving an overview of some existing applications. The main ideas are illustrated on the problem of binary classification, although several extensions are discussed. 1 A Brief History of Boosting The underlying idea of Boosting is to combine simple "rules" to form an ensemble such that the performance of the single ensemble member is improved, i.e. "boosted". Let h1, h2, . . . , hT be a set of hypotheses, and consider the composite ensemble hypothesis

Question: What is boosting?


Analyzing the Performance of Mutation Operators to Solve the Travelling Salesman Problem. The genetic algorithm includes some parameters that should be adjusted, so as to get reliable results. Choosing a representation of the problem addressed, an initial population, a method of selection, a crossover operator, mutation operator, the probabilities of crossover and mutation, and the insertion method creates a variant of genetic algorithms. Our work is part of the answer to this perspective to find a solution for this combinatorial problem. What are the best parameters to select for a genetic algorithm that creates a variety efficient to solve the Traveling Salesman Problem (TSP)? In this paper, we present a comparative analysis of different mutation operators, surrounded by a dilated discussion that justifying the relevance of genetic operators chosen to solving the TSP problem.

Question: What is the best parameters to select for a genetic algorithm that creates a variety efficient to solve the Traveling Salesman Problem (TSP)?


Microgrid power electronic converters: State of the art and future challenges. This paper presents a review of the state of the art of power electric converters used in microgrids. The paper focuses primarily on grid connected converters. Different topologies and control and modulation strategies for these specific converters are critically reviewed. Moreover, future challenges in respect of these converters are identified along with their potential solutions.

Question: What is a microgrid?


Credit Card Fraud Detection: A case study. In this research, a technique for `Credit Card Fraud Detection' is developed. As fraudsters are increasing day by day. And fallacious transactions are done by the credit card and there are various types of fraud. So to solve this problem combination of technique is used like Genetic Algorithm, Behavior Based Technique and Hidden Markov Model. By this transaction is tested individually and whatever suits the best is further proceeded. And the foremost goal is to detect fraud by filtering the above techniques to get better result.

Question: What is genetic algorithm?


The Pascal Visual Object Classes (VOC) Challenge. The Pascal Visual Object Classes (VOC) challenge is a benchmark in visual object category recognition and detection, providing the vision and machine learning communities with a standard dataset of images and annotation, and standard evaluation procedures. Organised annually from 2005 to present, the challenge and its associated dataset has become accepted as the benchmark for object detection. This paper describes the dataset and evaluation procedure. We review the state-of-the-art in evaluated methods for both classification and detection, analyse whether the methods are statistically different, what they are learning from the images (e.g. the object or its context), and what the methods find easy or confuse. The paper concludes with lessons learnt in the three year history of the challenge, and proposes directions for future improvement and extension.

Question: What is the Pascal Visual Object Classes (VOC) Challenge?


PV-OWL — Pharmacovigilance surveillance through semantic web-based platform for continuous and integrated monitoring of drug-related adverse effects in open data sources and social media. The recent EU regulation on Pharmacovigilance [Regulation (EU) 1235/2010, Directive 2010/84/EU] imposes both to Pharmaceutical companies and Public health agencies to maintain updated safety information of drugs, monitoring all available data sources. Here, we present our project aiming to develop a web platform for continuous monitoring of adverse effects of medicines (pharmacovigilance), by integrating information from public databases, scientific literature and social media. The project will start by scanning all available data sources concerning drug adverse events, both open (e.g., FAERS — FDA Adverse Event Reporting Systems, medical literature, social media, etc.) and proprietary data (e.g., discharge hospital records, drug prescription archives, electronic health records), that require agreement with respective data owners. Subsequent, pharmacovigilance experts will perform a semi-automatic mapping of codes identifying drugs and adverse events, to build the thesaurus of the web based platform. After these preliminary activities, signal generation and prioritization will be the core of the project. This task will result in risk confidence scores for each included data source and a comprehensive global score, indicating the possible association between a specific drug and an adverse event. The software framework MOMIS, an open source data integration system, will allow semi-automatic virtual integration of heterogeneous and distributed data sources. A web platform, based on MOMIS, able to merge many heterogeneous data sets concerning adverse events will be developed. The platform will be tested by external specialized subjects (clinical researchers, public or private employees in pharmacovigilance field). The project will provide a) an innovative way to link, for the first tim

Question: What is the name of the project?


Asynchronous Exceptions in Haskell. Asynchronous exceptions, such as timeouts are important for robust, modular programs, but are extremely difficult to program with — so much so that most programming languages either heavily restrict them or ban them altogether. We extend our earlier work, in which we added synchronous exceptions to Haskell, to support asynchronous exceptions too. Our design introduces scoped combinators for blocking and unblocking asynchronous interrupts, along with a somewhat surprising semantics for operations that can suspend. Uniquely, we also give a formal semantics for our system.

Question: What is the semantics of asynchronous exceptions in haskell?


DepSys: Dependency aware integration of cyber-physical systems for smart homes. As sensor and actuator networks mature, they become a core utility of smart homes like electricity and water and enable the running of many CPS applications. Like other Cyber-Physical Systems (CPSs), when a number of applications share physical world entities, it raises many systems of systems interdependency problems. Such problems arise in the cyber part mainly because each application has assumptions on the physical world entities without knowing how other applications work. In this work, we propose DepSys, a utility sensing and actuation infrastructure for smart homes that provides comprehensive strategies to specify, detect, and resolve conflicts in a home setting. Based on real home data, we demonstrate the severity of conflicts when multiple CPSs are integrated and the significant ability of detecting and resolving such conflicts using DepSys.

Question: What is a utility sensing and actuation infrastructure for smart homes?


Detecting hot topics from Twitter: A multiview approach. Twitter is widely used all over the world, and a huge number of hot topics are generated by Twitter users in real time. These topics are able to reflect almost every aspect of people's daily lives. Therefore, the detection of topics in Twitter can be used in many real applications, such as monitoring public opinion, hot product recommendation and incidence detection. However, the performance of traditional topic detection methods is still far from perfect largely owing to the tweets' features, such as their limited length and arbitrary abbreviations. To address these problems, we propose a novel framework (MVTD) for Twitter topic detection using multiview clustering, which can integrate multirelations among tweets, such as semantic relations, social tag relations and temporal relations. We also propose some methods for measuring relations among tweets. In particular, to better measure the semantic similarity of tweets, we propose a new document similarity measure based on a suffix tree (STVSM). In addition, a new keyword extraction method based on a suffix tree is proposed. Experiments on real datasets show that the performance of MVTD is much better than that of a single view, and it is useful for detecting topics from Twitter.

Question: How to detect hot topics from Twitter?


Fidelius Charm: Isolating Unsafe Rust Code. The Rust programming language has a safe memory model that promises to eliminate critical memory bugs. While the language is strong in doing so, its memory guarantees are lost when any unsafe blocks are used. Unsafe code is often needed to call library functions written in an unsafe language inside a Rust program. We present Fidelius Charm (FC), a system that protects a programmer-specified subset of data in memory from unauthorized access through vulnerable unsafe libraries. FC does this by limiting access to the program's memory while executing unsafe libraries. FC uses standard features of Rust and utilizes the Linux kernel as a trusted base for splitting the address space into a trusted privileged region under the control of functions written in Rust and a region available to unsafe external libraries. This paper presents our design and implementation of FC, presents two case studies for using FC in Rust TLS libraries, and reports on experiments showing its performance overhead is low for typical uses.

Question: How does rust protect memory from unsafe libraries?


Do Differences in Password Policies Prevent Password Reuse?. Password policies were originally designed to make users pick stronger passwords. However, research has shown that they often fail to achieve this goal. In a systematic audit of the top 100 web sites in Germany, we explore if diversity in current real-world password policies prevents password reuse. We found that this is not the case: we are the first to show that a single password could hypothetically fulfill 99% of the policies under consideration. This is especially problematic because password reuse exposes users to similar risks as weak passwords. We thus propose a new approach for policies that focuses on password reuse and respects other websites to determine if a password should be accepted. This re-design takes current user behavior into account and potentially boosts the usability and security of password-based authentication.

Question: How do password policies prevent password reuse?


Comparing Task Models for User Interface Design. Many task models, task analysis methods, and supporting tools have been introduced in the literature and are widely used in practice. With this comes need to understand their scopes and their differences. This chapter provides a thorough review of selected, significant task models along with their method and supporting tools. For this purpose, a meta-model of each task model is expressed as an Entity-Relationship-Attribute schema (ERA) and discussed. This leads to a comparative analysis of task models according to aims and goals, discipline, concepts and relationships, expressiveness of static and dynamic structures. Following is discussion of the model with respect to developing life cycle steps, tool support, advantages, and shortcomings. This comparative analysis provides a reference framework against which task models can be understood with respect to each other. The appreciation of the similarities and the differences allows practioners to identify a task model that fits a situation's given requirements. It shows how a similar concept or relationship translates in different task usage models.

Question: What is a task model?


The cyber threat landscape: Challenges and future research directions. Cyber threats are becoming more sophisticated with the blending of once distinct types of attack into more damaging forms. Increased variety and volume of attacks is inevitable given the desire of financially and criminally-motivated actors to obtain personal and confidential information, as highlighted in this paper. We describe how the Routine Activity Theory can be applied to mitigate these risks by reducing the opportunities for cyber crime to occur, making cyber crime more difficult to commit and by increasing the risks of detection and punishment associated with committing cyber crime. Potential research questions are also identified. a 2011 Elsevier Ltd. All rights reserved.

Question: How can we mitigate cyber threats?


Deep Learning for Time Series Modeling CS 229 Final Project Report. Demand forecasting is crucial to electricity providers because their ability to produce energy exceeds their ability to store it. Excess demand can cause "brown outs," while excess supply ends in waste. In an industry worth over $1 trillion in the U.S. alone [1], almost 9% of GDP [2], even marginal improvements can have a huge impact. Any plan toward energy efficiency should include enhanced utilization of existing production. Energy loads provide an interesting topic for Machine Learning techniques due to the availability of large datasets that exhibit fundamental nonlinear patterns. Using data from the Kaggle competition "Global Energy Forecasting Competition 2012 Load Forecasting" [3] we sought to use deep learning architectures to predict energy loads across different network grid areas, using only time and temperature data. Data included hourly demand for four and a half years from 20 different geographic regions, and similar hourly temperature readings from 11 zones. For most of our analysis we focused on short term load forecasting because this will aide online operational scheduling.

Question: What is the difference between deep learning and machine learning?


Best Practices for Convolutional Neural Networks Applied to Visual Document Analysis. Neural networks are a powerful technology for classification of visual inputs arising from documents. However, there is a confusing plethora of different neural network methods that are used in the literature and in industry. This paper describes a set of concrete best practices that document analysis researchers can use to get good results with neural networks. The most important practice is getting a training set as large as possible: we expand the training set by adding a new form of distorted data. The next most important practice is that convolutional neural networks are better suited for visual document tasks than fully connected networks. We propose that a simple "do-it-yourself" implementation of convolution with a flexible architecture is suitable for many visual document problems. This simple convolutional neural network does not require complex methods, such as momentum, weight decay, structuredependent learning rates, averaging layers, tangent prop, or even finely-tuning the architecture. The end result is a very simple yet general architecture which can yield state-of-the-art performance for document analysis. We illustrate our claims on the MNIST set of English digit

Question: What is the best way to train a convolutional neural network?


The role of emotion in computer-mediated communication: A review. It has been argued that the communication of emotions is more difficult in computer-mediated communication (CMC) than in face-to-face (F2F) communication. The aim of this paper is to review the empirical evidence in order to gain insight in whether emotions are communicated differently in these different modes of communication. We review two types of studies: (1) studies that explicitly examine discrete emotions and emotion expressions, and (2) studies that examine emotions more implicitly, namely as self-disclosure or emotional styles. Our conclusion is that there is no indication that CMC is a less emotional or less personally involving medium than F2F. On the contrary, emotional communication online and offline is surprisingly similar, and if differences are found they show more frequent and explicit emotion communication in CMC than in F2F. 2007 Elsevier Ltd. All rights reserved.

Question: What is the role of emotion in computer-mediated communication?


Custom Pictorial Structures for Re-identification. We propose a novel methodology for re-identification, based on Pictorial Structures (PS). Whenever face or other biometric information is missing, humans recognize an individual by selectively focusing on the body parts, looking for part-to-part correspondences. We want to take inspiration from this strategy in a re-identification context, using PS to achieve this objective. For single image re-identification, we adopt PS to localize the parts, extract and match their descriptors. When multiple images of a single individual are available, we propose a new algorithm to customize the fit of PS on that specific person, leading to what we call a Custom Pictorial Structure (CPS). CPS learns the appearance of an individual, improving the localization of its parts, thus obtaining more reliable visual characteristics for re-identification. It is based on the statistical learning of pixel attributes collected through spatio-temporal reasoning. The use of PS and CPS leads to state-of-the-art results on all the available public benchmarks, and opens a fresh new direction for research on re-identification.

Question: What is the difference between a pictorial structure and a pictorial structure?


Naive Bayes Word Sense Induction. We introduce an extended naive Bayes model for word sense induction (WSI) and apply it to a WSI task. The extended model incorporates the idea the words closer to the target word are more relevant in predicting its sense. The proposed model is very simple yet effective when evaluated on SemEval-2010 WSI data.

Question: What is the extended naive Bayes model for word sense induction?


Local energy-based shape histogram feature extraction technique for breast cancer diagnosis. 25 26 27 28 29 30 31 32 33 34 35 Article history: Available online xxxx

Question: What is the local energy-based shape histogram feature extraction technique for breast cancer diagnosis?