Detecting and Classifying Crimes from Arabic Twitter Posts using Text Mining Techniques. Crime analysis has become a critical area for helping law enforcement agencies to protect civilians. As a result of a rapidly increasing population, crime rates have increased dramatically, and appropriate analysis has become a timeconsuming effort. Text mining is an effective tool that may help to solve this problem to classify crimes in effective manner. The proposed system aims to detect and classify crimes in Twitter posts that written in the Arabic language, one of the most widespread languages today. In this paper, classification techniques are used to detect crimes and identify their nature by different classification algorithms. The experiments evaluate different algorithms, such as SVM, DT, CNB, and KNN, in terms of accuracy and speed in the crime domain. Also, different features extraction techniques are evaluated, including rootbased stemming, light stemming, n-gram. The experiments revealed the superiority of n-gram over other techniques. Specifically, the results indicate the superiority of SVM with trigram over other classifiers, with a 91.55% accuracy. Keywords—Crimes; text mining; classification; features extraction techniques; arabic posts; twitter

Question: How to detect and classify crimes from Arabic Twitter posts?


Computerized classification of intraductal breast lesions using histopathological images. In the diagnosis of preinvasive breast cancer, some of the intraductal proliferations pose a special challenge. The continuum of intraductal breast lesions includes the usual ductal hyperplasia (UDH), atypical ductal hyperplasia (ADH), and ductal carcinoma in situ (DCIS). The current standard of care is to perform percutaneous needle biopsies for diagnosis of palpable and image-detected breast abnormalities. UDH is considered benign and patients diagnosed UDH undergo routine follow-up, whereas ADH and DCIS are considered actionable and patients diagnosed with these two subtypes get additional surgical procedures. About 250 000 new cases of intraductal breast lesions are diagnosed every year. A conservative estimate would suggest that at least 50% of these patients are needlessly undergoing unnecessary surgeries. Thus, improvement in the diagnostic reproducibility and accuracy is critically important for effective clinical management of these patients. In this study, a prototype system for automatically classifying breast microscopic tissues to distinguish between UDH and actionable subtypes (ADH and DCIS) is introduced. This system automatically evaluates digitized slides of tissues for certain cytological criteria and classifies the tissues based on the quantitative features derived from the images. The system is trained using a total of 327 regions of interest (ROIs) collected across 62 patient cases and tested with a sequestered set of 149 ROIs collected across 33 patient cases. An overall accuracy of 87.9% is achieved on the entire test data. The test accuracy of 84.6% is obtained with borderline cases (26 of the 33 test cases) only, when compared against the diagnostic accuracies of nine pathologists on the same set (81.2% average), indicates that the system is highly competitive with the expert pathologists as a stand-alone diagnostic tool and has a great potential in imp

Question: What is the difference between a computer and a human?


DESH: Database evaluation system with hibernate ORM framework. Relational databases have been the predominant choice for back-ends in enterprise applications for several decades. JDBC - a Java API - that is used for developing such applications and persisting data on the back-end requires enormous time and effort. JDBC makes the application logic to become tightly coupled with the database and consequently is inadequate for building enterprise applications that need to adopt to dynamic requirements. Hence, ORM frameworks such as Hibernate, became prominent. However, even with ORM, the relational back-end often faces a drawback of lack of scalability and flexibility. In this context, NoSQL databases are increasingly gaining popularity. Existing research works have either benchmarked Hibernate with an RDBMS or with one of the NoSQL databases. However, it has not been attempted in the literature to test both an RDBMS and a NoSQL database for their performance within the context of a single application developed using the features of Hibernate. This kind of a study will provide an insight that using Hibernate ORM solution would help us to develop database-independent applications and how much performance gain can be achieved when an application is ported from an RDBMS back-end to a NoSQL database backend. The objective of this work is to develop a business application using Hibernate framework that can individually communicate with an RDBMS as well as a specific NoSQL database and to evaluate the performance of both these databases.

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


Single image haze removal with WLS-based edge-preserving smoothing filter. Images captured under hazy conditions have low contrast and poor color. This is primarily due to air-light which degrades image quality according to the transmission map. The approach to enhance these hazy images we introduce here is based on the `Dark-Channel Prior' method with image refinement by the `Weighted Least Square' based edge-preserving smoothing. Local contrast is further enhanced by multi-scale tone manipulation. The proposed method improves the contrast, color and detail for the entire image domain effectively. In the experiment, we compare the proposed method with conventional methods to validate performance.

Question: How to remove haze from images?


Service quality delivery through Web sites : A critical review of extant know. Evidence exists that service quality delivery through Web sites is an essential strategy to success, possibly more important than low price and Web presence. To deliver superior service quality, managers of companies with Web presences must first understand how customers perceive and evaluate online customer service. Information on this topic is beginning to emerge from both academic and practitioner sources, but this information has not yet been examined as a whole. The goals of this article are to review and synthesize the literature about service quality delivery through Web sites, describe what is known about the topic, and develop an agenda for needed research.

Question: What is the difference between service quality and service delivery?


Sentiment Classification Using Machine Learning Techniques with Syntax Features. Sentiment classification has adopted machine learning techniques to improve its precision and efficiency. However, the features are always produced by basic words-bag methods without much consideration for words' syntactic properties, which could play an important role in the judgment of sentiment meanings. To remedy this, we firstly generate syntax trees of the sentences, with the analysis of syntactic features of the sentences. Then we introduce multiple sentiment features into the basic words-bag features. Such features were trained on movie reviews as data, with machine learning methods (Naive Bayes and support vector machines). The features and factors introduced by syntax tree were examined to generate a more accurate solution for sentiment classification.

Question: How to generate syntax trees of the sentences?


X-ray: Automating Root-Cause Diagnosis of Performance Anomalies in Production Software. Troubleshooting the performance of production software is challenging. Most existing tools, such as profiling, tracing, and logging systems, reveal what events occurred during performance anomalies. However, users of such tools must infer why these events occurred; e.g., that their execution was due to a root cause such as a specific input request or configuration setting. Such inference often requires source code and detailed application knowledge that is beyond system administrators and end users. This paper introduces performance summarization, a technique for automatically diagnosing the root causes of performance problems. Performance summarization instruments binaries as applications execute. It first attributes performance costs to each basic block. It then uses dynamic information flow tracking to estimate the likelihood that a block was executed due to each potential root cause. Finally, it summarizes the overall cost of each potential root cause by summing the per-block cost multiplied by the cause-specific likelihood over all basic blocks. Performance summarization can also be performed differentially to explain performance differences between two similar activities. X-ray is a tool that implements performance summarization. Our results show that X-ray accurately diagnoses 17 performance issues in Apache, lighttpd, Postfix, and PostgreSQL, while adding 2.3% average runtime overhead.

Question: What is X-ray?


Reverse Engineering of Embedded Software Using Syntactic Pattern Recognition. When a secure component executes sensitive operations, the information carried by the power consumption can be used to recover secret information. Many different techniques have been developped to recover this secret, but only few of them focus on the recovering of the executed code itself. Indeed, the code knowledge acquired through this step of Simple Power Analysis (SPA) can help to identify implementation weaknesses and to improve further kinds of attacks. In this paper we present a new approach improving the SPA based on a pattern recognition methodology, that can be used to automatically identify the processed instructions that leak through power consumption. We firstly process a geometrical classification with chosen instructions to enable the automatic identification of any sequence of instructions. Such an analysis is used to reverse general purpose code executions of a recent secure component.

Question: How to reverse engineer embedded software?


Multi-factor Authentication Framework for Cloud Computing. Cloud computing is a new paradigm to deliver services over the Internet. Data Security is the most critical issues in a cloud computing environment. Authentication is a key technology for information security, which is a mechanism to establish proof of identities to get access of information in the system. Traditional password authentication does not provide enough security for information in cloud computing environment to the most modern means of attacks. In this paper, we propose a new multi-factor authentication framework for cloud computing. The proposed framework provides a feasible and a most efficient mechanism which can closely integrate with the traditional authentication system.The proposed framework is verified by developing Cloud Access Management (CAM) system which authenticates the user based on multiple factors. Also using secret-splitting and encrypted value of arithmetic captcha is innovative factor for user authentication for cloud computing environment. Prototype model for cloud computing own cloud server is implemented using open sources technology. The proposed framework shows the close agreement with the standard criteria for security.

Question: What is cloud computing?


Radiomics: extracting more information from medical images using advanced feature analysis.. Solid cancers are spatially and temporally heterogeneous. This limits the use of invasive biopsy based molecular assays but gives huge potential for medical imaging, which has the ability to capture intra-tumoural heterogeneity in a non-invasive way. During the past decades, medical imaging innovations with new hardware, new imaging agents and standardised protocols, allows the field to move towards quantitative imaging. Therefore, also the development of automated and reproducible analysis methodologies to extract more information from image-based features is a requirement. Radiomics--the high-throughput extraction of large amounts of image features from radiographic images--addresses this problem and is one of the approaches that hold great promises but need further validation in multi-centric settings and in the laboratory.

Question: What is the difference between radiomics and radiogenomics?


Beyond Bowling Together : SocioTechnical Capital. Social resources like trust and shared identity make it easier for people to work and play together. Such social resources are sometimes referred to as social capital. Thirty years ago, Americans built social capital as a side effect of participation in civic organizations and social activities, including bowling leagues. Today, they do so far less frequently (Putnam 2000). HCI researchers and practitioners need to find new ways for people to interact that will generate even more social capital than bowling together does. A new theoretical construct, SocioTechnical Capital, provides a framework for generating and evaluating technology-mediated social relations.

Question: What is the difference between social capital and socio-technical capital?


Writing a narrative biomedical review: considerations for authors, peer reviewers, and editors. Review articles comprehensively covering a specific topic are crucial for successful research and academic projects. Most editors consider review articles for special and regular issues of journals. Writing a review requires deep knowledge and understanding of a field. The aim of this review is to analyze the main steps in writing a narrative biomedical review and to consider points that may increase the chances of success. We performed a comprehensive search through MEDLINE, EMBASE, Scopus, and Web of Science using the following keywords: review of the literature, narrative review, title, abstract, authorship, ethics, peer review, research methods, medical writing, scientific writing, and writing standards. Opinions expressed in the review are also based on personal experience as authors, peer reviewers, and editors.

Question: How to write a review article?


Smart Agents in Industrial Cyber–Physical Systems. Future industrial systems can be realized using the cyber-physical systems (CPSs) that advocate the coexistence of cyber and physical counterparts in a network structure to perform the system's functions in a collaborative manner. Multiagent systems share common ground with CPSs and can empower them with a multitude of capabilities in their efforts to achieve complexity management, decentralization, intelligence, modularity, flexibility, robustness, adaptation, and responsiveness. This work surveys and analyzes the current state of the industrial application of agent technology in CPSs, and provides a vision on the way agents can effectively enable emerging CPS challenges.

Question: What is the future of industrial cyber-physical systems?


Regulation of wound healing by growth factors and cytokines.. Cutaneous wound healing is a complex process involving blood clotting, inflammation, new tissue formation, and finally tissue remodeling. It is well described at the histological level, but the genes that regulate skin repair have only partially been identified. Many experimental and clinical studies have demonstrated varied, but in most cases beneficial, effects of exogenous growth factors on the healing process. However, the roles played by endogenous growth factors have remained largely unclear. Initial approaches at addressing this question focused on the expression analysis of various growth factors, cytokines, and their receptors in different wound models, with first functional data being obtained by applying neutralizing antibodies to wounds. During the past few years, the availability of genetically modified mice has allowed elucidation of the function of various genes in the healing process, and these studies have shed light onto the role of growth factors, cytokines, and their downstream effectors in wound repair. This review summarizes the results of expression studies that have been performed in rodents, pigs, and humans to localize growth factors and their receptors in skin wounds. Most importantly, we also report on genetic studies addressing the functions of endogenous growth factors in the wound repair process.

Question: What is the role of growth factors in wound healing?


A Comparison of Sender-Initiated and Receiver-Initiated Reliable Multicast Protocols. Sender-initiated reliable multicast protocols, based on the use of positive acknowledgments (ACKs), lead to an ACK implosion problem at the sender as the number of receivers increases. Briefly, the ACK implosion problem refers to the significant overhead incurred by the sending host due to the processing of ACKs from each receiver. A potential solution to this problem is to shift the burden of providing reliable data transfer to the receivers—thus resulting in a receiver-initiated multicast error control protocol based on the use of negative acknowledgments (NAKs). In this paper we determine the maximum throughputs of the sending and receiving hosts for generic sender-initiated and receiver-initiated protocols. We show that the receiver-initiated error control protocols provide substantially higher throughputs than their sender-initiated counterparts. We further demonstrate that the introduction of random delays prior to generating NAKs coupled with the multicasting of NAKs to all receivers has the potential for an additional substantial increase in the throughput of receiver-initiated error control protocols over sender-initiated protocols.

Question: What is the maximum throughput of a sender-initiated reliable multicast protocol?


A scaling normalization method for differential expression analysis of RNA-seq data. The fine detail provided by sequencing-based transcriptome surveys suggests that RNA-seq is likely to become the platform of choice for interrogating steady state RNA. In order to discover biologically important changes in expression, we show that normalization continues to be an essential step in the analysis. We outline a simple and effective method for performing normalization and show dramatically improved results for inferring differential expression in simulated and publicly available data sets.

Question: What is the difference between RNA-seq and microarray?


Device-to-device communication in 5G cellular networks: challenges, solutions, and future directions. In a conventional cellular system, devices are not allowed to directly communicate with each other in the licensed cellular bandwidth and all communications take place through the base stations. In this article, we envision a two-tier cellular network that involves a macrocell tier (i.e., BS-to-device communications) and a device tier (i.e., device-to-device communications). Device terminal relaying makes it possible for devices in a network to function as transmission relays for each other and realize a massive ad hoc mesh network. This is obviously a dramatic departure from the conventional cellular architecture and brings unique technical challenges. In such a two-tier cellular system, since the user data is routed through other users' devices, security must be maintained for privacy. To ensure minimal impact on the performance of existing macrocell BSs, the two-tier network needs to be designed with smart interference management strategies and appropriate resource allocation schemes. Furthermore, novel pricing models should be designed to tempt devices to participate in this type of communication. Our article provides an overview of these major challenges in two-tier networks and proposes some pricing schemes for different types of device relaying.

Question: What is device-to-device communication?


Question: How to transfer textures in Blender?


Feeling cybervictims' pain-The effect of empathy training on cyberbullying.. As the world's population increasingly relies on the use of modern technology, cyberbullying becomes an omnipresent risk for children and adolescents and demands counteraction to prevent negative (online) experiences. The classroom-based German preventive intervention "Medienhelden" (engl.: "Media Heroes") builds on previous knowledge about links between cognitive empathy, affective empathy, and cyberbullying, among others. For an evaluation study, longitudinal data were available from 722 high school students aged 11-17 years (M = 13.36, SD = 1.00, 51.8% female) before and six months after the implementation of the program. A 10-week version and a 1-day version were conducted and compared with a control group (controlled pre-long-term-follow-up study). Schools were asked to randomly assign their participating classes to the intervention conditions. Multi-group structural equation modeling (SEM) showed a significant effect of the short intervention on cognitive empathy and significant effects of the long intervention on affective empathy and cyberbullying reduction. The results suggest the long-term intervention to be more effective in reducing cyberbullying and promoting affective empathy. Without any intervention, cyberbullying increased and affective empathy decreased across the study period. Empathy change was not generally directly linked to change in cyberbullying behavior. "Media Heroes" provides effective teaching materials and empowers schools to address the important topic of cyberbullying in classroom settings without costly support from the outside.

Question: How does cyberbullying affect cybervictims?


RGB-H-CbCr skin colour model for human face detection. While the RGB, HSV and YUV (YCbCr) are standard models used in various colour imaging applications, not all of their information are necessary to classify skin colour. This paper presents a novel skin colour model, RGB-H-CbCr for the detection of human faces. Skin regions are extracted using a set of bounding rules based on the skin colour distribution obtained from a training set. The segmented face regions are further classified using a parallel combination of simple morphological operations. Experimental results on a large photo data set have demonstrated that the proposed model is able to achieve good detection success rates for near-frontal faces of varying orientations, skin colour and background environment. The results are also comparable to that of the AdaBoost face classifier.

Question: What is the skin colour model for human face detection?


Ship Surveillance With TerraSAR-X. Ship detection is an important application of global monitoring of environment and security. In order to overcome the limitations by other systems, surveillance with satellite synthetic aperture radar (SAR) is used because of its possibility to provide ship detection at high resolution over wide swaths and in all weather conditions. A new X-band radar onboard the TerraSAR-X (TS-X) satellite gives access to spatial resolution as fine as 1 m. In this paper, first results on the combined use of TS-X ship detection, automatic identification system (AIS), and satellite AIS (SatAIS) is presented. The AIS system is an effective terrestrial method for tracking vessels in real time typically up to 40 km off the coast. SatAIS, as a space-based system, allows almost global coverage for monitoring of ships since not all ships operate their AIS and smaller ships are not equipped with AIS. The system is considered to be of cooperative nature. In this paper, the quality of TS-X images with respect to ship detection is evaluated, and a first assessment of its performance for ship detection is given. The velocity of a moving ship is estimated using complex TS-X data. As test cases, images were acquired over the North Sea, Baltic Sea, Atlantic Ocean, and Pacific Ocean in Stripmap mode with a resolution of 3 m at a coverage of 30 km 100 km. Simultaneous information on ship positions was available from TS-X and terrestrial as well as SatAIS. First results on the simultaneous superposition of SatAIS and high-resolution radar images are presented.

Question: What is the resolution of the radar?


What Is an Ontology?. The word "ontology" is used with different senses in different communities. The most radical difference is perhaps between the philosophical sense, which has of course a well-established tradition, and the computational sense, which emerged in the recent years in the knowledge engineering community, starting from an early informal definition of (computational) ontologies as "explicit specifications of conceptualizations". In this paper we shall revisit the previous attempts to clarify and formalize such original definition, providing a detailed account of the notions of conceptualization and explicit specification, while discussing at the same time the importance of shared explicit specifications.

Question: What is an ontology?


Attitudes toward Vegans and Vegetarians : The Role of Anticipated Moral Reproach and Dissonance. This study attempted to determine if anticipated moral reproach and cognitive dissonance explain negative attitudes toward vegans and vegetarians, and if vegans are subject to more negative attitudes than vegetarians. The sample consisted of 65 participants (women =41, men =24) who were randomly assigned into one of four conditions. Each participant read a vignette about a person who consumed either a vegan, vegetarian, plant-based or omnivore diet. They then completed a series of questionnaires that recorded their attitudes towards the person in the vignette, anticipated moral reproach, and cognitive dissonance. The results indicated that for the observed general attitudes for the vegan, vegetarian and plant-based diet conditions were more negative than for the omnivore condition, with plant-based diet being the most negative. For moral reproach the results indicated that higher levels of moral reproach contributed to more negative attitudes, but only in the omnivore condition. Whereas the liner regression indicated that cognitive dissonance was only significant predictor of attitudes in the vegan condition. The findings of this study indicated that diet can have an impact on attitudes, and that the diet its self may have a larger impact than the labels applied. DIET AND ATTITUDES 3 Attitudes Toward Vegans and Vegetarians: The Role of Anticipated Moral Reproach and Dissonance Although the majority of North Americans eat an omnivore diet, veganism and vegetarianism have in recent years experienced an increase in popularity (Duchene & Jackson, 2017). As a result, omnivores are believed to be more likely to discuss their attitudes towards vegans and vegetarians, due to the increased exposure to the groups, both in public dialogue and interpersonally among acquaintances. Cole (2011) examined all British newspapers published in the year 2007 that mentioned the words vegan, v

Question: What is the difference between a vegan and a vegetarian?


Predicting short-term interests using activity-based search context. A query considered in isolation offers limited information about a searcher's intent. Query context that considers pre-query activity (e.g., previous queries and page visits), can provide richer information about search intentions. In this paper, we describe a study in which we developed and evaluated user interest models for the current query, its context (from pre-query session activity), and their combination, which we refer to as intent. Using large-scale logs, we evaluate how accurately each model predicts the user's short-term interests under various experimental conditions. In our study we: (i) determine the extent of opportunity for using context to model intent; (ii) compare the utility of different sources of behavioral evidence (queries, search result clicks, and Web page visits) for building predictive interest models, and; (iii) investigate optimally combining the query and its context by learning a model that predicts the context weight for each query. Our findings demonstrate significant opportunity in leveraging contextual information, show that context and source influence predictive accuracy, and show that we can learn a near-optimal combination of the query and context for each query. The findings can inform the design of search systems that leverage contextual information to better understand, model, and serve searchers' information needs.

Question: What is the difference between a query and a search?


The Second International Chinese Word Segmentation Bakeoff. The second international Chinese word segmentation bakeoff was held in the summer of 2005 to evaluate the current state of the art in word segmentation. Twenty three groups submitted 130 result sets over two tracks and four different corpora. We found that the technology has improved over the intervening two years, though the out-of-vocabulary problem is still or paramount importance.

Question: What is the second international Chinese word segmentation bakeoff?


A simulation study of the number of events per variable in logistic regression analysis.. We performed a Monte Carlo study to evaluate the effect of the number of events per variable (EPV) analyzed in logistic regression analysis. The simulations were based on data from a cardiac trial of 673 patients in which 252 deaths occurred and seven variables were cogent predictors of mortality; the number of events per predictive variable was (252/7 =) 36 for the full sample. For the simulations, at values of EPV = 2, 5, 10, 15, 20, and 25, we randomly generated 500 samples of the 673 patients, chosen with replacement, according to a logistic model derived from the full sample. Simulation results for the regression coefficients for each variable in each group of 500 samples were compared for bias, precision, and significance testing against the results of the model fitted to the original sample. For EPV values of 10 or greater, no major problems occurred. For EPV values less than 10, however, the regression coefficients were biased in both positive and negative directions; the large sample variance estimates from the logistic model both overestimated and underestimated the sample variance of the regression coefficients; the 90% confidence limits about the estimated values did not have proper coverage; the Wald statistic was conservative under the null hypothesis; and paradoxical associations (significance in the wrong direction) were increased. Although other factors (such as the total number of events, or sample size) may influence the validity of the logistic model, our findings indicate that low EPV can lead to major problems.

Question: How many events per variable is needed to avoid bias in logistic regression?


Line-start synchronous reluctance motors: Design guidelines and testing via active inertia emulation. Line-Start Synchronous Reluctance (LS-SyR) Motors are a viable solution to reach IE4 efficiency class with the frame size of induction motors having standard efficiency. The proposed paper reviews the guidelines for the design of LS-SyR machines and introduces a new technique for testing their capability of load synchronization. Design wise, the efficiency at synchronous speed and the load pull-in capability must be traded off. The key design principles of LS-SyR machines are reviewed in this sense, and two manufacturing solutions are proposed for comparison. A new experimental method for the evaluation of the pull-in characteristic is proposed. The test fixture consists of a torque-controlled ac drive load, equipped with a shaft accelerometer. The load applied to the line-start machine under test is augmented with a component proportional to the acceleration measurement, to emulate additional inertia.

Question: What is the difference between a line-start synchronous reluctance motor and a line-start synchronous reluctance motor?


Modeling attention to salient proto-objects. Selective visual attention is believed to be responsible for serializing visual information for recognizing one object at a time in a complex scene. But how can we attend to objects before they are recognized? In coherence theory of visual cognition, so-called proto-objects form volatile units of visual information that can be accessed by selective attention and subsequently validated as actual objects. We propose a biologically plausible model of forming and attending to proto-objects in natural scenes. We demonstrate that the suggested model can enable a model of object recognition in cortex to expand from recognizing individual objects in isolation to sequentially recognizing all objects in a more complex scene.

Question: How can we attend to objects before they are recognized?


Adaptive bootstrapping of recommender systems using decision trees. Recommender systems perform much better on users for which they have more information. This gives rise to a problem of satisfying users new to a system. The problem is even more acute considering that some of these hard to profile new users judge the unfamiliar system by its ability to immediately provide them with satisfying recommendations, and may quickly abandon the system when disappointed. Rapid profiling of new users by a recommender system is often achieved through a bootstrapping process - a kind of an initial interview - that elicits users to provide their opinions on certain carefully chosen items or categories. The elicitation process becomes particularly effective when adapted to users' responses, making best use of users' time by dynamically modifying the questions to improve the evolving profile. In particular, we advocate a specialized version of decision trees as the most appropriate tool for this task. We detail an efficient tree learning algorithm, specifically tailored to the unique properties of the problem. Several extensions to the tree construction are also introduced, which enhance the efficiency and utility of the method. We implemented our methods within a movie recommendation service. The experimental study delivered encouraging results, with the tree-based bootstrapping process significantly outperforming previous approaches.

Question: What is a recommender system?


Modeling Human Motion Using Binary Latent Variables. We propose a non-linear generative model for human motion data that uses an undirected model with binary latent variables and real-valued "visible" variables that represent joint angles. The latent and visible variables at each time step receive directed connections from the visible variables at the last few time-steps. Such an architecture makes on-line inference efficient and allows us to use a simple approximate learning procedure. After training, the model finds a single set of parameters that simultaneously capture several different kinds of motion. We demonstrate the power of our approach by synthesizing various motion sequences and by performing on-line filling in of data lost during motion capture. Website: http://www.cs.toronto.edu/ ∼gwtaylor/publications/nips2006mhmublv/

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


Learning Sparsely Used Overcomplete Dictionaries. We consider the problem of learning sparsely used overcomplete dictionaries, where each observation is a sparse combination of elements from an unknown overcomplete dictionary. We establish exact recovery when the dictionary elements are mutually incoherent. Our method consists of a clustering-based initialization step, which provides an approximate estimate of the true dictionary with guaranteed accuracy. This estimate is then refined via an iterative algorithm with the following alternating steps: 1) estimation of the dictionary coefficients for each observation through l1 minimization, given the dictionary estimate, and 2) estimation of the dictionary elements through least squares, given the coefficient estimates. We establish that, under a set of sufficient conditions, our method converges at a linear rate to the true dictionary as well as the true coefficients for each observation.

Question: What is the difference between a dictionary and a matrix?


Autotagger: A Model for Predicting Social Tags from Acoustic Features on Large Music Databases. Social tags are user-generated keywords associated with some resource on the Web. In the case of music, social tags have become an important component of "Web 2.0" recommender systems, allowing users to generate playlists based on use-dependent terms such as chill or jogging that have been applied to particular songs. In this paper, we propose a method for predicting these social tags directly from MP3 files. Using a set of 360 classifiers trained using the online ensemble learning algorithm FilterBoost, we map audio features onto social tags collected from the Web. The resulting automatic tags (or autotags) furnish information about music that is otherwise untagged or poorly tagged, allowing for insertion of previously unheard music into a social recommender. This avoids the "cold-start problem" common in such systems. Autotags can also be used to smooth the tag space from which similarities and recommendations are made by providing a set of comparable baseline tags for all tracks in a recommender system. Because the words we learn are the same as those used by people who label their music collections, it is easy to integrate our predictions into existing similarity and prediction methods based on web data.

Question: What is the difference between a social tag and a tag?


Brain–machine interfaces: past, present and future. Since the original demonstration that electrical activity generated by ensembles of cortical neurons can be employed directly to control a robotic manipulator, research on brain-machine interfaces (BMIs) has experienced an impressive growth. Today BMIs designed for both experimental and clinical studies can translate raw neuronal signals into motor commands that reproduce arm reaching and hand grasping movements in artificial actuators. Clearly, these developments hold promise for the restoration of limb mobility in paralyzed subjects. However, as we review here, before this goal can be reached several bottlenecks have to be passed. These include designing a fully implantable biocompatible recording device, further developing real-time computational algorithms, introducing a method for providing the brain with sensory feedback from the actuators, and designing and building artificial prostheses that can be controlled directly by brain-derived signals. By reaching these milestones, future BMIs will be able to drive and control revolutionary prostheses that feel and act like the human arm.

Question: What is the future of brain-machine interfaces?


Coordinated Charging of Plug-In Hybrid Electric Vehicles to Minimize Distribution System Losses. As the number of plug-in hybrid vehicles (PHEVs) increases, so might the impacts on the power system performance, such as overloading, reduced efficiency, power quality, and voltage regulation particularly at the distribution level. Coordinated charging of PHEVs is a possible solution to these problems. In this work, the relationship between feeder losses, load factor, and load variance is explored in the context of coordinated PHEV charging. From these relationships, three optimal charging algorithms are developed which minimize the impacts of PHEV charging on the connected distribution system. The application of the algorithms to two test systems verifies these relationships approximately hold independent of system topology. They also show the additional benefits of reduced computation time and problem convexity when using load factor or load variance as the objective function rather than system losses. This is important for real-time dispatching of PHEVs.

Question: How can we minimize the impact of plug-in hybrid vehicles on the power system?


Local Monotonic Attention Mechanism for End-to-End Speech And Language Processing. Recently, encoder-decoder neural networks have shown impressive performance on many sequence-related tasks. The architecture commonly uses an attentional mechanism which allows the model to learn alignments between the source and the target sequence. Most attentional mechanisms used today is based on a global attention property which requires a computation of a weighted summarization of the whole input sequence generated by encoder states. However, it is computationally expensive and often produces misalignment on the longer input sequence. Furthermore, it does not fit with monotonous or left-to-right nature in several tasks, such as automatic speech recognition (ASR), grapheme-to-phoneme (G2P), etc. In this paper, we propose a novel attention mechanism that has local and monotonic properties. Various ways to control those properties are also explored. Experimental results on ASR, G2P and machine translation between two languages with similar sentence structures, demonstrate that the proposed encoderdecoder model with local monotonic attention could achieve significant performance improvements and reduce the computational complexity in comparison with the one that used the standard global attention architecture.

Question: What is the difference between the standard global attention and the proposed local monotonic attention?


New approach for clustering of big data: DisK-means. The exponential growth in the amount of data gathered from various sources has resulted in the need for more efficient algorithms to quickly analyze large datasets. Clustering techniques, like K-Means are useful in analyzing data in a parallel fashion. K-Means largely depends upon a proper initialization to produce optimal results. K-means++ initialization algorithm provides solution based on providing an initial set of centres to the K-Means algorithm. However, its inherent sequential nature makes it suffer from various limitations when applied to large datasets. For instance, it makes k iterations to find k centres. In this paper, we present an algorithm that attempts to overcome the drawbacks of previous algorithms. Our work provides a method to select a good initial seeding in less time, facilitating fast and accurate cluster analysis over large datasets.

Question: What is the difference between k-means and k-means++?


A Secured System for Information Hiding in Image Steganography using Genetic Algorithm and Cryptography. Encryption is used to securely communicate data in open networks. Each type of data has its own structures; therefore according to the data the different techniques should be used to defend confidential data. Among them digital images are also very popular to carry confidential information in untrusted networks. . For pleasing the defense of data hiding and communication over network, the proposed system uses cryptographic algorithm along with Steganography. In the proposed system, the file which user want to make secure is firstly compressed to shrink in size and then the compressed data is altered into cipher text by using AES cryptographic algorithm and then the encrypted data is concealed in the image. In order to hide the information over the image in complex manner the genetic algorithm based technique is implemented which is used to evaluate the valuable pixels where the data can be hide in a secure manner. In addition of that, for hiding the information in images, the LSB (least significant bits) based steganographic method is used after the selection of eligible pixels. The implementation of the anticipated technique is performed using JAVA technology and for performance evaluation the time and space complexity is computed. In addition of that a

Question: What is the size of the image?


Identification of a DNA-binding site for the transcription factor Haa1, required for Saccharomyces cerevisiae response to acetic acid stress. The transcription factor Haa1 is the main player in reprogramming yeast genomic expression in response to acetic acid stress. Mapping of the promoter region of one of the Haa1-activated genes, TPO3, allowed the identification of an acetic acid responsive element (ACRE) to which Haa1 binds in vivo. The in silico analysis of the promoter regions of the genes of the Haa1-regulon led to the identification of an Haa1-responsive element (HRE) 5'-GNN(G/C)(A/C)(A/G)G(A/G/C)G-3'. Using surface plasmon resonance experiments and electrophoretic mobility shift assays it is demonstrated that Haa1 interacts with high affinity (K(D) of 2 nM) with the HRE motif present in the ACRE region of TPO3 promoter. No significant interaction was found between Haa1 and HRE motifs having adenine nucleotides at positions 6 and 8 (K(D) of 396 and 6780 nM, respectively) suggesting that Haa1p does not recognize these motifs in vivo. A lower affinity of Haa1 toward HRE motifs having mutations in the guanine nucleotides at position 7 and 9 (K(D) of 21 and 119 nM, respectively) was also observed. Altogether, the results obtained indicate that the minimal functional binding site of Haa1 is 5'-(G/C)(A/C)GG(G/C)G-3'. The Haa1-dependent transcriptional regulatory network active in yeast response to acetic acid stress is proposed.

Question: What is the minimal functional binding site of Haa1?


Bangladeshi banknote recognition by neural network with axis symmetrical masks. Automated banknote recognition system can be a very good utility in banking systems and other field of commerce. It can also aid visually impaired people. Although in Bangladesh, bill money recognition machines are not common but it is used in other countries. In this paper, for the first time, we have proposed a Neural Network based recognition scheme for Bangladeshi banknotes. The scheme can efficiently be implemented in cheap hardware which may be very useful in many places. The recognition system takes scanned images of banknotes which are scanned by low cost optoelectronic sensors and then fed into a Multilayer Perceptron, trained by Backpropagation algorithm, for recognition. Axis Symmetric Masks are used in preprocessing stage which reduces the network size and guarantees correct recognition even if the note is flipped. Experimental results are presented which show that this scheme can recognize currently available 8 notes (1, 2, 5, 10, 20, 50, 100 & 500 Taka) successfully with an average accuracy of 98.57%.

Question: How does the neural network recognize the banknotes?


Theory and implementation of dielectric resonator antenna excited by a waveguide slot. Excitation of dielectric resonator antennas (DRAs) by waveguide slots is proposed as an alternative to traditionally used excitation mechanisms in order to enhance the frequency bandwidth of slotted waveguide radiators and to control the power coupled to the DRA. The analysis is based on the numerical solution of coupled integral equations discretized using the method of moments (MoM). The dielectric resonator (DR) is modeled as a body-of-revolution based on the integral equation formulation for the equivalent electric and magnetic surface current densities. The analysis of an infinite or a semi-infinite waveguide containing longitudinal or transverse narrow slots uses the appropriate dyadic Green's function resulting in closed-form analytical expressions of the MoM matrix. The scattering parameters for a slotted waveguide loaded with a dielectric resonator antenna disk are calculated and compared with finite-difference time-domain results. Bandwidth enhancement is achieved by the proper selection for the antenna parameters.

Question: What is the difference between a waveguide and a waveguide slot?


Signal Space CoSaMP for Sparse Recovery With Redundant Dictionaries. Compressive sensing (CS) has recently emerged as a powerful framework for acquiring sparse signals. The bulk of the CS literature has focused on the case where the acquired signal has a sparse or compressible representation in an orthonormal basis. In practice, however, there are many signals that cannot be sparsely represented or approximated using an orthonormal basis, but that do have sparse representations in a redundant dictionary. Standard results in CS can sometimes be extended to handle this case provided that the dictionary is sufficiently incoherent or well conditioned, but these approaches fail to address the case of a truly redundant or overcomplete dictionary. In this paper, we describe a variant of the iterative recovery algorithm CoSaMP for this more challenging setting. We utilize the \mbi D-RIP, a condition on the sensing matrix analogous to the well-known restricted isometry property. In contrast to prior work, the method and analysis are "signal-focused"; that is, they are oriented around recovering the signal rather than its dictionary coefficients. Under the assumption that we have a near-optimal scheme for projecting vectors in signal space onto the model family of candidate sparse signals, we provide provable recovery guarantees. Developing a practical algorithm that can provably compute the required near-optimal projections remains a significant open problem, but we include simulation results using various heuristics that empirically exhibit superior performance to traditional recovery algorithms.

Question: What is the difference between a dictionary and a model family?


Random Forests and VGG-NET: An Algorithm for the ISIC 2017 Skin Lesion Classification Challenge. This manuscript briefly describes an algorithm developed for the ISIC 2017 Skin Lesion Classification Competition. In this task, participants are asked to complete two independent binary image classification tasks that involve three unique diagnoses of skin lesions (melanoma, nevus, and seborrheic keratosis). In the first binary classification task, participants are asked to distinguish between (a) melanoma and (b) nevus and seborrheic keratosis. In the second binary classification task, participants are asked to distinguish between (a) seborrheic keratosis and (b) nevus and melanoma. The other phases of the competition are not considered. Our proposed algorithm consists of three steps: preprocessing, classification using VGG-NET [2] and Random Forests [3], and calculation of a final score.

Question: What is the difference between a random forest and a vgg net?


Comparison of linear, nonlinear, and feature selection methods for EEG signal classification. The reliable operation of brain-computer interfaces (BCIs) based on spontaneous electroencephalogram (EEG) signals requires accurate classification of multichannel EEG. The design of EEG representations and classifiers for BCI are open research questions whose difficulty stems from the need to extract complex spatial and temporal patterns from noisy multidimensional time series obtained from EEG measurements. The high-dimensional and noisy nature of EEG may limit the advantage of nonlinear classification methods over linear ones. This paper reports the results of a linear (linear discriminant analysis) and two nonlinear classifiers (neural networks and support vector machines) applied to the classification of spontaneous EEG during five mental tasks, showing that nonlinear classifiers produce only slightly better classification results. An approach to feature selection based on genetic algorithms is also presented with preliminary results of application to EEG during finger movement.

Question: What is the difference between linear and nonlinear classifiers?


Adaptively generating high quality fixes for atomicity violations. It is difficult to fix atomicity violations correctly. Existing gate lock algorithm (GLA) simply inserts gate locks to serialize exe-cutions, which may introduce performance bugs and deadlocks. Synthesized context-aware gate locks (by Grail) require complex source code synthesis. We propose Fixer to adaptively fix ato-micity violations. It firstly analyses the lock acquisitions of an atomicity violation. Then it either adjusts the existing lock scope or inserts a gate lock. The former addresses cases where some locks are used but fail to provide atomic accesses. For the latter, it infers the visibility (being global or a field of a class/struct) of the gate lock such that the lock only protects related accesses. For both cases, Fixer further eliminates new lock orders to avoid introducing deadlocks. Of course, Fixer can produce both kinds of fixes on atomicity violations with locks. The experi-mental results on 15 previously used atomicity violations show that: Fixer correctly fixed all 15 atomicity violations without introducing deadlocks. However, GLA and Grail both intro-duced 5 deadlocks. HFix (that only targets on fixing certain types of atomicity violations) only fixed 2 atomicity violations and introduced 4 deadlocks. Fixer also provides an alternative way to insert gate locks (by inserting gate locks with proper visibility) considering fix acceptance.

Question: How to fix atomicity violations?


A Deep Policy Inference Q-Network for Multi-Agent Systems. We present DPIQN, a deep policy inference Q-network that targets multi-agent systems composed of controllable agents, collaborators, and opponents that interact with each other. We focus on one challenging issue in such systems—modeling agents with varying strategies—and propose to employ "policy features" learned from raw observations (e.g., raw images) of collaborators and opponents by inferring their policies. DPIQN incorporates the learned policy features as a hidden vector into its own deep Q-network (DQN), such that it is able to predict better Q values for the controllable agents than the state-of-the-art deep reinforcement learningmodels. We further propose an enhanced version of DPIQN, called deep recurrent policy inference Q-network (DRPIQN), for handling partial observability. Both DPIQN and DRPIQN are trained by an adaptive training procedure, which adjusts the network's attention to learn the policy features and its own Q-values at different phases of the training process. We present a comprehensive analysis of DPIQN and DRPIQN, and highlight their effectiveness and generalizability in various multi-agent settings. Our models are evaluated in a classic soccer game involving both competitive and collaborative scenarios. Experimental results performed on 1 vs. 1 and 2 vs. 2 games show that DPIQN and DRPIQN demonstrate superior performance to the baseline DQN and deep recurrent Q-network (DRQN) models. We also explore scenarios in which collaborators or opponents dynamically change their policies, and show that DPIQN and DRPIQN do lead to better overall performance in terms of stability and mean scores.

Question: What is a deep policy inference Q-network?


Service-Oriented Computing: State of the Art and Research Challenges. Service-oriented computing promotes the idea of assembling application components into a network of services that can be loosely coupled to create flexible, dynamic business processes and agile applications that span organizations and computing platforms. An SOC research road map provides a context for exploring ongoing research activities.

Question: What is service-oriented computing?


The Causal Foundations of Structural Equation Modeling. The role of causality in SEM research is widely perceived to be, on the one hand, of pivotal methodological importance and, on the other hand, confusing, enigmatic and controversial. The confusion is vividly portrayed, for example, in the influential report of Wilkinson and Task Force's (1999) on "Statistical Methods in Psychology Journals: Guidelines and Explanations." In discussing SEM, the report starts with the usual warning: "It is sometimes thought that correlation does not prove causation but 'causal modeling' does. [Wrong! There are] dangers in this practice." But then ends with a startling conclusion: "The use of complicated causal-modeling software [read SEM] rarely yields any results that have any interpretation as causal effects." The implication being that the entire enterprise of causal modeling, from Sewell Wright (1921) to Blalock (1964) and Duncan (1975), the entire literature in econometric research, including modern advances in graphical and nonparametric structural models have all been misguided, for they have been chasing parameters that have no causal interpretation. The motives for such overstatements notwithstanding, readers may rightly ask: "If SEM methods do not 'prove' causation, how can they yield results that have causal interpretation?" Put another way, if the structural coefficients that SEM researchers labor to estimate can legitimately be interpreted as causal effects then, unless these parameters are grossly misestimated, why deny SEM researchers the honor of "establishing causation" or at least of deriving some useful claims about causation. The answer is that a huge logical gap exists between "establishing causation," which requires careful manipulative experiments, and "interpreting parameters as causal effects," which may be based on firm scientific knowledge or on previously conducted experiments, perhaps by other researchers. One can legitimately be in a possession of a p

Question: What is the difference between establishing causation and interpreting parameters as causal effects?


Recovery of Interdependent Networks.. Recent network research has focused on the cascading failures in a system of interdependent networks and the necessary preconditions for system collapse. An important question that has not been addressed is how to repair a failing system before it suffers total breakdown. Here we introduce a recovery strategy for nodes and develop an analytic and numerical framework for studying the concurrent failure and recovery of a system of interdependent networks based on an efficient and practically reasonable strategy. Our strategy consists of repairing a fraction of failed nodes, with probability of recovery γ, that are neighbors of the largest connected component of each constituent network. We find that, for a given initial failure of a fraction 1 - p of nodes, there is a critical probability of recovery above which the cascade is halted and the system fully restores to its initial state and below which the system abruptly collapses. As a consequence we find in the plane γ - p of the phase diagram three distinct phases. A phase in which the system never collapses without being restored, another phase in which the recovery strategy avoids the breakdown, and a phase in which even the repairing process cannot prevent system collapse.

Question: How to repair a failing system before it collapses?


Improving Fraud and Abuse Detection in General Physician Claims: A Data Mining Study.. BACKGROUND We aimed to identify the indicators of healthcare fraud and abuse in general physicians' drug prescription claims, and to identify a subset of general physicians that were more likely to have committed fraud and abuse.   METHODS We applied data mining approach to a major health insurance organization dataset of private sector general physicians' prescription claims. It involved 5 steps: clarifying the nature of the problem and objectives, data preparation, indicator identification and selection, cluster analysis to identify suspect physicians, and discriminant analysis to assess the validity of the clustering approach.   RESULTS Thirteen indicators were developed in total. Over half of the general physicians (54%) were 'suspects' of conducting abusive behavior. The results also identified 2% of physicians as suspects of fraud. Discriminant analysis suggested that the indicators demonstrated adequate performance in the detection of physicians who were suspect of perpetrating fraud (98%) and abuse (85%) in a new sample of data.   CONCLUSION Our data mining approach will help health insurance organizations in low-and middle-income countries (LMICs) in streamlining auditing approaches towards the suspect groups rather than routine auditing of all physicians.

Question: How to identify physicians who are more likely to commit fraud and abuse?


How to stop spread of misinformation on social media: Facebook plans vs. right-click authenticate approach. One of the key features of social networks is that users are able to share information, and through cascades of sharing information, this information may reach a large number of individuals. The high availability of user-provided contents on online social media facilitates people aggregation around shared beliefs, interests, worldviews and narratives. With lack of means to verify information, social media has been accused of becoming a hot bed for sharing of misinformation. Facebook, as one of the largest social networking services, has been facing widespread criticism on how its newsfeed algorithm is designed thus amplifying dissemination of misinformation. In late 2016, Facebook revealed plans to address fake news on Facebook newsfeeds. In this work, we study the methods Facebook has proposed to combat the spread of misinformation and compare it with our previously proposed approach called 'Right-click Authenticate'. By analyzing the Business Process Modeling and Notation of both approaches, this paper suggests some key weaknesses and improvements social media companies need to consider when tackling the spread of misinformation online.

Question: How to stop spread of misinformation on social media: Facebook plans vs. right-click authenticate approach.


Learning from mistakes: towards a correctable learning algorithm. Many learning algorithms generate complex models that are difficult for a human to interpret, debug, and extend. In this paper, we address this challenge by proposing a new learning paradigm called correctable learning, where the learning algorithm receives external feedback about which data examples are incorrectly learned. We define a set of metrics which measure the correctability of a learning algorithm. We then propose a simple and efficient correctable learning algorithm which learns local models for different regions of the data space. Given an incorrect example, our method samples data in the neighborhood of that example and learns a new, more correct local model over that region. Experiments over multiple classification and ranking datasets show that our correctable learning algorithm offers significant improvements over the state-of-the-art techniques.

Question: What is a learning algorithm?


Probabilistic neural networks. -By replacing the sigmoid activation function often used in neural networks with an exponential function, a probabilistic neural network ( PNN) that can compute nonlinear decision boundaries which approach the Bayes optimal is formed. Alternate activation functions having similar properties are also discussed. A fourlayer neural network of the type proposed can map any input pattern to any number of classifications. The decision boundaries can be modified in real-time using new data as they become available, and can be implemented using artificial hardware "'neurons" that operate entirely in parallel. Provision is also made for estimating the probability and reliability of a classification as well as making the decision. The technique offers a tremendous speed advantage for problems in which the incremental adaptation time of back propagation is a significant fraction of the total computation time. For one application, the PNN paradigm was 200,000 times faster than back-propagation. Keywords--Neural network, Probability density function, Parallel processor, "Neuron", Pattern recognition, Parzen window, Bayes strategy, Associative memory.

Question: What is a probabilistic neural network?


The WHOI micro-modem: an acoustic communications and navigation system for multiple platforms. The micro-modem is a compact, low-power, underwater acoustic communications and navigation subsystem. It has the capability to perform low-rate frequency-hopping frequency-shift keying (FH-FSK), variable rate phase-coherent keying (PSK), and two different types of long base line navigation, narrow-band and broadband. The system can be configured to transmit in four different bands from 3 to 30 kHz, with a larger board required for the lowest frequency. The user interface is based on the NMEA standard, which is a serial port specification. The modem also includes a simple built-in networking capability which supports up to 16 units in a polled or random-access mode and has an acknowledgement capability which supports guaranteed delivery transactions. The paper contains a detailed system description and results from several tests are also presented

Question: What is the micro-modem?


Data Fusion by Matrix Factorization. For most problems in science and engineering we can obtain data sets that describe the observed system from various perspectives and record the behavior of its individual components. Heterogeneous data sets can be collectively mined by data fusion. Fusion can focus on a specific target relation and exploit directly associated data together with contextual data and data about system's constraints. In the paper we describe a data fusion approach with penalized matrix tri-factorization (DFMF) that simultaneously factorizes data matrices to reveal hidden associations. The approach can directly consider any data that can be expressed in a matrix, including those from feature-based representations, ontologies, associations and networks. We demonstrate the utility of DFMF for gene function prediction task with eleven different data sources and for prediction of pharmacologic actions by fusing six data sources. Our data fusion algorithm compares favorably to alternative data integration approaches and achieves higher accuracy than can be obtained from any single data source alone.

Question: What is a data fusion approach?


A comparative study of HTM and other neural network models for online sequence learning with streaming data. Online sequence learning from streaming data is one of the most challenging topics in machine learning. Neural network models represent promising candidates for sequence learning due to their ability to learn and recognize complex temporal patterns. In this paper, we present a comparative study of Hierarchical Temporal Memory (HTM), a neurally-inspired model, and other feedforward and recurrent artificial neural network models on both artificial and real-world sequence prediction algorithms. HTM and long-short term memory (LSTM) give the best prediction accuracy. HTM additionally demonstrates many other features that are desirable for real-world sequence learning, such as fast adaptation to changes in the data stream, robustness to sensor noise and fault tolerance. These features make HTM an ideal candidate for online sequence learning problems.

Question: What is the best neural network model for online sequence learning?


You Only Look Once: Unified, Real-Time Object Detection. We present YOLO, a new approach to object detection. Prior work on object detection repurposes classifiers to perform detection. Instead, we frame object detection as a regression problem to spatially separated bounding boxes and associated class probabilities. A single neural network predicts bounding boxes and class probabilities directly from full images in one evaluation. Since the whole detection pipeline is a single network, it can be optimized end-to-end directly on detection performance. Our unified architecture is extremely fast. Our base YOLO model processes images in real-time at 45 frames per second. A smaller version of the network, Fast YOLO, processes an astounding 155 frames per second while still achieving double the mAP of other real-time detectors. Compared to state-of-the-art detection systems, YOLO makes more localization errors but is less likely to predict false positives on background. Finally, YOLO learns very general representations of objects. It outperforms other detection methods, including DPM and R-CNN, when generalizing from natural images to other domains like artwork.

Question: What is the unified architecture?


Autoregressive Attention for Parallel Sequence Modeling. We introduce an autoregressive attention mechanism for parallelizable characterlevel sequence modeling. We use this method to augment a neural model consisting of blocks of causal convolutional layers connected by highway network skip connections. We denote the models with and without the proposed attention mechanism respectively as Highway Causal Convolution (Causal Conv) and Autoregressive-attention Causal Convolution (ARA-Conv). The autoregressive attention mechanism crucially maintains causality in the decoder, allowing for parallel implementation. We demonstrate that these models, compared to their recurrent counterparts, enable fast and accurate learning in character-level NLP tasks. In particular, these models outperform recurrent neural network models in natural language correction and language modeling tasks, and run in a fraction of the time.

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


The Self-Serv Environment for Web Services Composition. The composition of Web services to handle complex transactions such as finance, billing, and traffic information services is gaining considerable momentum as a way to enable business-to-business (B2B) collaborations.1 Web services allow organizations to share costs, skills, and resources by joining their applications and systems.2 Although current technologies provide the foundation for building composite services, several issues still need to be addressed:

Question: What is the self-serv environment for web services composition?


Negotiation and Cooperation in Multi-Agent Environments. Automated intelligent agents inhabiting a shared environment must coordinate their activities Cooperation not merely coordination may improve the performance of the individual agents or the overall behavior of the system they form Research in Distributed Arti cial Intelligence DAI addresses the problem of designing automated intelligent systems which interact e ectively DAI is not the only eld to take on the challenge of understanding cooperation and coordination There are a variety of other multi entity environments in which the entities coordinate their activity and cooperate Among them are groups of people animals particles and computers We argue that in order to address the challenge of building coordinated and collaborated intelligent agents it is bene cial to combine AI techniques with methods and techniques from a range of multi entity elds such as game theory operations research physics and philosophy To support this claim we describe some of our projects where we have successfully taken an interdisciplinary approach We demonstrate the bene ts in applying multi entity methodologies and show the adaptations modi cations and extensions necessary for solving the DAI problems This is an extended version of a lecture presented upon receipt of the Computers and Thought Award at the th International Joint Conference on Arti cial Intelligence in Montreal Canada August

Question: What is the difference between negotiation and cooperation?


Python for Microwave and RF Engineers [Application Notes]. Python is a powerful programming language for handling engineering and scientific computational tasks efficiently [1]-[5]. Used by companies such as Google and Intel and by organizations including NASA and Los Alamos National Laboratory, it offers an extremely wide selection of tools for tasks such as scientific computing, signal processing, Web site construction, database programming, and graphical user interface (GUI) design. The language is platform independent with most programs running on Linux, Microsoft Windows, or MAC OS virtually unchanged. Python has been distributed as a part of Open Source Initiative, and most versions are General Public License (GPL) compatible. In microwave and radio frequency (RF) engineering, it can be used for numerical programming, automated RF testing, automated monolithic microwave integrated-circuit (MMIC) layout generation, automated netlist generation and simulator sequencing, and other tasks. In this article, we are going to provide examples demonstrating the application areas of Python in microwave and RF engineering. We are also going to give a brief introduction to the language highlighting its salient features.

Question: What is the difference between python and python?


Virtualizing I/O Devices on VMware Workstation's Hosted Virtual Machine Monitor. Virtual machines were developed by IBM in the 1960's to provide concurrent, interactive access to a mainframe computer. Each virtual machine is a replica of the underlying physical machine and users are given the illusion of running directly on the physical machine. Virtual machines also provide benefits like isolation and resource sharing, and the ability to run multiple flavors and configurations of operating systems. VMware Workstation brings such mainframe-class virtual machine technology to PC-based desktop and workstation computers. This paper focuses on VMware Workstation's approach to virtualizing I/O devices. PCs have a staggering variety of hardware, and are usually pre-installed with an operating system. Instead of replacing the pre-installed OS, VMware Workstation uses it to host a user-level application (VMApp) component, as well as to schedule a privileged virtual machine monitor (VMM) component. The VMM directly provides high-performance CPU virtualization while the VMApp uses the host OS to virtualize I/O devices and shield the VMM from the variety of devices. A crucial question is whether virtualizing devices via such a hosted architecture can meet the performance required of high throughput, low latency devices. To this end, this paper studies the virtualization and performance of an Ethernet adapter on VMware Workstation. Results indicate that with optimizations, VMware Workstation's hosted virtualization architecture can match native I/O throughput on standard PCs. Although a straightforward hosted implementation is CPU-limited due to virtualization overhead on a 733 MHz Pentium R III system on a 100 Mb/s Ethernet, a series of optimizations targeted at reducing CPU utilization allows the system to match native network throughput. Further optimizations are discussed both within and outside a hosted architecture.

Question: How does VMware Workstation virtualize I/O devices?


MultiSE: multi-path symbolic execution using value summaries. Dynamic symbolic execution (DSE) has been proposed to effectively generate test inputs for real-world programs. Unfortunately, DSE techniques do not scale well for large realistic programs, because often the number of feasible execution paths of a program increases exponentially with the increase in the length of an execution path. In this paper, we propose MultiSE, a new technique for merging states incrementally during symbolic execution, without using auxiliary variables. The key idea of MultiSE is based on an alternative representation of the state, where we map each variable, including the program counter, to a set of guarded symbolic expressions called a value summary. MultiSE has several advantages over conventional DSE and conventional state merging techniques: value summaries enable sharing of symbolic expressions and path constraints along multiple paths and thus avoid redundant execution. MultiSE does not introduce auxiliary symbolic variables, which enables it to 1) make progress even when merging values not supported by the constraint solver, 2) avoid expensive constraint solver calls when resolving function calls and jumps, and 3) carry out most operations concretely. Moreover, MultiSE updates value summaries incrementally at every assignment instruction, which makes it unnecessary to identify the join points and to keep track of variables to merge at join points. We have implemented MultiSE for JavaScript programs in a publicly available open-source tool. Our evaluation of MultiSE on several programs shows that 1) value summaries are an eective technique to take advantage of the sharing of value along multiple execution path, that 2) MultiSE can run significantly faster than traditional dynamic symbolic execution and, 3) MultiSE saves a substantial number of state merges compared to conventional state-merging techniques.

Question: What is the difference between a value summary and a value?


Hippocampal Neurogenesis Regulates Forgetting During Adulthood and Infancy. Throughout life, new neurons are continuously added to the dentate gyrus. As this continuous addition remodels hippocampal circuits, computational models predict that neurogenesis leads to degradation or forgetting of established memories. Consistent with this, increasing neurogenesis after the formation of a memory was sufficient to induce forgetting in adult mice. By contrast, during infancy, when hippocampal neurogenesis levels are high and freshly generated memories tend to be rapidly forgotten (infantile amnesia), decreasing neurogenesis after memory formation mitigated forgetting. In precocial species, including guinea pigs and degus, most granule cells are generated prenatally. Consistent with reduced levels of postnatal hippocampal neurogenesis, infant guinea pigs and degus did not exhibit forgetting. However, increasing neurogenesis after memory formation induced infantile amnesia in these species.

Question: How do we forget?


Robot Pose Estimation in Unknown Environments by Matching 2D Range Scans. A mobile robot exploring an unknown environment has no absolute frame of reference for its position, other than features it detects through its sensors. Using distinguishable landmarks is one possible approach, but it requires solving the object recognition problem. In particular, when the robot uses two-dimensional laser range scans for localization, it is di cult to accurately detect and localize landmarks in the environment (such as corners and occlusions) from the range scans. In this paper, we develop two new iterative algorithms to register a range scan to a previous scan so as to compute relative robot positions in an unknown environment, that avoid the above problems. The rst algorithm is based on matching data points with tangent directions in two scans and minimizing a distance function in order to solve the displacement between the scans. The second algorithm establishes correspondences between points in the two scans and then solves the point-to-point least-squares problem to compute the relative pose of the two scans. Our methods work in curved environments and can handle partial occlusions by rejecting outliers.

Question: How to detect and localize landmarks in an unknown environment from range scans?


Four Text Classification Algorithms Compared on a Dutch Corpus. We describe an experiment in applying text classification algorithms to Dutch texts. Four well-known learning algorithms, Rocchio's algorithm, the Simple Bayesian Classifier (SBC), the Sleeping Experts (SE) and Winnow were implemented. They were tested on a corpus of articles from the Dutch newspaper NRC, pre-classified into four categories. The algorithms are compared on learning speed and error rate. We also investigated the effect of discarding terms, using either a dynamic stoplist or the Winnow heuristic.

Question: What is the difference between the four algorithms?


From checking on to checking in: designing for low socio-economic status older adults. In this paper we describe the design evolution of a novel technology that collects and displays presence information to be used in the homes of older adults. The first two iterations, the Ambient Plant and Presence Clock, were designed for higher socio-economic status (SES) older adults, whereas the Check-In Tree was designed for low SES older adults. We describe how feedback from older adult participants drove our design decisions, and give an in-depth account of how the Check-In Tree evolved from concept to a final design ready for in situ deployment.

Question: How did the design of the check-in tree evolve?


Reduce The Wastage of Data During Movement in Data Warehouse. In this research paper so as to handle Data in warehousing as well as reduce the wastage of data and provide a better results which takes more and more turn into a focal point of the data source business. Data warehousing and on-line analytical processing (OLAP) are vital fundamentals of resolution hold, which has more and more become a focal point of the database manufacturing. Lots of marketable yield and services be at the present accessible, and the entire primary database management organization vendor nowadays have contributions in the area assessment hold up spaces some quite dissimilar necessities on record technology compare to conventional on-line transaction giving out application. This article gives a general idea of data warehousing and OLAP technologies, with the highlighting on top of their latest necessities. So tools which is used for extract, clean-up and load information into back end of a information warehouse; multidimensional data model usual of OLAP; front end client tools for querying and data analysis; server extension for proficient query processing; and tools for data managing and for administration the warehouse. In adding to survey the circumstances of the art, this article also identify a number of capable research issue, a few which are interrelated to

Question: What is the data warehousing and OLAP?


Hard Words. How do children acquire the meaning of words? And why are words such as know harder for learners to acquire than words such as dog or jump? We suggest that the chief limiting factor in acquiring the vocabulary of natural languages consists not in overcoming conceptual difficulties with abstract word meanings but rather in mapping these meanings onto their corresponding lexical forms. This opening premise of our position, while controversial, is shared with some prior approaches. The present discussion moves forward from there to a detailed proposal for how the mapping problem for the lexicon is solved, as well as a presentation of experimental findings that support thisaccount.Wedescribeanoverlappingseriesofsteps throughwhichnovicesmove in representing the lexical forms and phrase structures of the exposure language, a probabilistic multiple-cue learning process known as syntactic bootstrapping. The machinery is set inmotionbyword-to-worldpairing, aprocedureavailable tonovices fromthe LANGUAGE LEARNING AND DEVELOPMENT, 1(1), 23–64 Copyright © 2005, Lawrence Erlbaum Associates, Inc.

Question: How do children acquire the meaning of words?


Spike-Based Synaptic Plasticity in Silicon: Design, Implementation, Application, and Challenges. The ability to carry out signal processing, classification, recognition, and computation in artificial spiking neural networks (SNNs) is mediated by their synapses. In particular, through activity-dependent alteration of their efficacies, synapses play a fundamental role in learning. The mathematical prescriptions under which synapses modify their weights are termed synaptic plasticity rules. These learning rules can be based on abstract computational neuroscience models or on detailed biophysical ones. As these rules are being proposed and developed by experimental and computational neuroscientists, engineers strive to design and implement them in silicon and en masse in order to employ them in complex real-world applications. In this paper, we describe analog very large-scale integration (VLSI) circuit implementations of multiple synaptic plasticity rules, ranging from phenomenological ones (e.g., based on spike timing, mean firing rates, or both) to biophysically realistic ones (e.g., calcium-dependent models). We discuss the application domains, weaknesses, and strengths of various representative approaches proposed in the literature, and provide insight into the challenges that engineers face when designing and implementing synaptic plasticity rules in VLSI technology for utilizing them in real-world applications.

Question: What is the difference between phenomenological and biophysically realistic synaptic plasticity rules?


A Performance Comparison of Open-Source Stream Processing Platforms. Distributed stream processing platforms is a new class of real-time monitoring systems that analyze and extracts knowledge from large continuous streams of data. This type of systems is crucial for providing high throughput and low latency required by Big Data or Internet of Things monitoring applications. This paper describes and analyzes three main open-source distributed stream- processing platforms: Storm Flink, and Spark Streaming. We analyze the system architectures and we compare their main features. We carry out two experiments concerning anomaly detection on network traffic to evaluate the throughput efficiency and the resilience to node failures. Results show that the performance of native stream processing systems, Storm and Flink, is up to 15 times higher than the micro-batch processing system, Spark Streaming. On the other hand, Spark Streaming is more robust to node failures and provides recovery without losses.

Question: What is the difference between the three stream processing platforms?


Towards IP over LPWANs technologies: LoRaWAN, DASH7, NB-IoT. In this paper, we discuss a set of solutions that are proposed in order to run IPv6 over IoT and we investigate its applicability over the three Low Power Wide Area Networks (LPWANs) technologies: LoRaWAN, DASH7, NB-IoT. LPWANs are wireless technologies that are used to connect things to the Internet. These technologies are characterized by their large coverage area, long battery operation, low bandwidth, small frame payload size, and the use of asymmetric and non-synchronized communication. Based on this investigation, we highlight the schemes that can be adopted for IP-based LPWANs technologies.

Question: What are the advantages of using LPWANs?


Development and Applications of CRISPR-Cas9 for Genome Engineering. Recent advances in genome engineering technologies based on the CRISPR-associated RNA-guided endonuclease Cas9 are enabling the systematic interrogation of mammalian genome function. Analogous to the search function in modern word processors, Cas9 can be guided to specific locations within complex genomes by a short RNA search string. Using this system, DNA sequences within the endogenous genome and their functional outputs are now easily edited or modulated in virtually any organism of choice. Cas9-mediated genetic perturbation is simple and scalable, empowering researchers to elucidate the functional organization of the genome at the systems level and establish causal linkages between genetic variations and biological phenotypes. In this Review, we describe the development and applications of Cas9 for a variety of research or translational applications while highlighting challenges as well as future directions. Derived from a remarkable microbial defense system, Cas9 is driving innovative applications from basic biology to biotechnology and medicine.

Question: What is the difference between CRISPR-Cas9 and CRISPR-Cas12a?


Description of interest regions with local binary patterns. This paper presents a novel method for interest region description. We adopted the idea that the appearance of an interest region can be well characterized by the distribution of its local features. The most well-known descriptor built on this idea is the SIFT descriptor that uses gradient as the local feature. Thus far, existing texture features are not widely utilized in the context of region description. In this paper, we introduce a new texture feature called center-symmetric local binary pattern (CS-LBP) that is a modified version of the well-known local binary pattern (LBP) feature. To combine the strengths of the SIFT and LBP, we use the CS-LBP as the local feature in the SIFT algorithm. The resulting descriptor is called the CS-LBP descriptor. In the matching and object category classification experiments, our descriptor performs favorably compared to the SIFT. Furthermore, the CS-LBP descriptor is computationally simpler than the SIFT.

Question: What is the difference between the SIFT and the CS-LBP?


Power Control in Ad-Hoc Networks: Theory, Architecture, Algorithm and Implementation of the COMPOW Protocol. We present a new protocol for power control in ad hoc networks. We describe the issues in conceptualizing the power control problem, and provide an architecturally simple as well as theoretically well founded solution. The solution is shown to simultaneously satisfy the three objectives of maximizing the traffic carrying capacity of the entire network, extending battery life through providing low power routes, and reducing the contention at the MAC layer. Further, the protocol has the plug and play feature that it can be employed in conjunction with any routing protocol that pro-actively maintains a routing table. The protocol, called COMPOW, has been implemented in the Linux kernel and we describe the software architecture and implementation details.

Question: What is the power control problem?


Semantic Audiovisual Data Fusion for Automatic Emotion Recognition. The paper describes a novel technique for the recognition of emotions from multimodal data. We focus on the recognition of the six prototypic emotions. The results from the facial expression recognition and from the emotion recognition from speech are combined using a bi-modal multimodal semantic data fusion model that determines the most probable emotion of the subject. Two types of models based on geometric face features for facial expression recognition are being used, depending on the presence or absence of speech. In our approach we define an algorithm that is robust to changes of face shape that occur during regular speech. The influence of phoneme generation on the face shape during speech is removed by using features that are only related to the eyes and the eyebrows. The paper includes results from testing the presented models.

Question: What is the most probable emotion of the subject?


Beagle: Automated Extraction and Interpretation of Visualizations from the Web. "How common is interactive visualization on the web?" "What is the most popular visualization design?" "How prevalent are pie charts really?" These questions intimate the role of interactive visualization in the real (online) world. In this paper, we present our approach (and findings) to answering these questions. First, we introduce Beagle, which mines the web for SVG-based visualizations and automatically classifies them by type (i.e., bar, pie, etc.). With Beagle, we extract over 41,000 visualizations across five different tools and repositories, and classify them with 85% accuracy, across 24 visualization types. Given this visualization collection, we study usage across tools. We find that most visualizations fall under four types: bar charts, line charts, scatter charts, and geographic maps. Though controversial, pie charts are relatively rare for the visualization tools that were studied. Our findings also suggest that the total visualization types supported by a given tool could factor into its ease of use. However this effect appears to be mitigated by providing a variety of diverse expert visualization examples to users.

Question: What is the most popular visualization design?


A concise introduction to autonomic computing. School of Computing and Mathematics, Faculty of Engineering, University of Ulster at Jordanstown, Shore Road, Newtownabbey, County Antrim BT37 0QB, Northern Ireland, UK The Applied Software Systems Laboratory (TASSL), Electrical and Computer Engineering, Rutgers, The State University of New Jersey, 94 Brett Road, Piscataway, NJ 08854-8058, USA School of Computing and Mathematical Sciences, The SRIF/SHEFC Centre for Virtual Organization Technology Enabling Research (VOTER), Glasgow Caledonian University, 70 Cowcaddens Road, Glasgow G4 0BA, UK Institute for Computer Science and Business Information Systems (ICB), University of Duisburg-Essen, Schützenbahn 70, 45117 Essen, Germany

Question: What is a concise introduction to autonomic computing?


Modified Caesar Cipher for Better Security Enhancement. Encryption is the process of scrambling a message so that only the intended recipient can read it. With the fast progression of digital data exchange in electronic way, Information Security is becoming much more important in data storage and transmission. Caesar cipher is a mono alphabetic cipher. It is a type of substitution cipher in which each letter in the plaintext is replaced by a letter. In this paper, author modified the traditional Caesar cipher and fixed the key size as one. Another thing alphabet index is checked if the alphabet index is even then increase the value by one else alphabet index is odd decrease the key value by one. Encryption and scrambling of the letters in the Cipher Text.

Question: How to encrypt a message?


Death Due to Decapitation in Two Motorcyclists: A Description of a Unique Case and a Brief Review of the Literature.. Deaths due to decapitation, especially related to traffic accidents, are rarely found in forensic practice. The present case involves a man and a woman who died from decapitation due to an unusual mechanism while they were riding on a motorbike down a mountain road. The autopsy, which was completed as a physics study, allowed the accident to be reconstructed as follows: A plastic cable that had detached from a timber-transporting machine whipped the road and hit the two motorcyclists. The impact resulted in the complete severing of both riders' heads. Involving different scientists in this accident investigation was crucial to understanding the dynamics of the accident. In fact, in addition to scene inspection and autopsy, a physics study was carried out on the cable and the clamp involved, which led to an explanation for the abrupt movement of the cable and, thus, to a thorough reconstruction of the accident.

Question: What is the difference between a physics study and an autopsy?


Analysis on 2-element insertion sort algorithm. This paper proposes a 2-element insertion sort algorithm, an improved algorithm on the direct insertion sort, gives the algorithm design idea and the description in C. After analyzes comparatively the time complexity and space complexity of the algorithms, the paper summarizes the pros and cons of the 2-element insertion sort algorithm compared with the original one, so as to giving a theoretical basis for the direct insertion sort algorithm optimization, and playing a guiding role in teaching the relevant chapters in "Data Structure" curriculum.

Question: What is the time complexity of the 2-element insertion sort algorithm?


A dual-networks architecture of top-down control. Complex systems ensure resilience through multiple controllers acting at rapid and slower timescales. The need for efficient information flow through complex systems encourages small-world network structures. On the basis of these principles, a group of regions associated with top-down control was examined. Functional magnetic resonance imaging showed that each region had a specific combination of control signals; resting-state functional connectivity grouped the regions into distinct 'fronto-parietal' and 'cingulo-opercular' components. The fronto-parietal component seems to initiate and adjust control; the cingulo-opercular component provides stable 'set-maintenance' over entire task epochs. Graph analysis showed dense local connections within components and weaker 'long-range' connections between components, suggesting a small-world architecture. The control systems of the brain seem to embody the principles of complex systems, encouraging resilient performance.

Question: What is a small-world network?


Enabling network programmability in LTE/EPC architecture using OpenFlow. Nowadays, mobile operators face the challenge to sustain the future data tsunami. In fact, today's increasing data and control traffic generated by new kinds of network usage puts strain on mobile operators', without creating any corresponding equivalent revenue. In our previous work, we analyzed the 3GPP LTE/EPC architecture and showed that a redesign of this architecture is needed to suit future network usages and to provide new revenue generating services. Moreover, we proposed a new control plane based on the OpenFlow (OF) protocol for the LTE/EPC architecture that enables flexibility and programmability aspects. In this paper, we are interested in the programmability aspect. We show how the data plane can be easily configured thanks to OF. In addition, we evaluate the signaling load of our proposed architecture and compare it to that of 3GPP LTE/EPC architecture. The preliminary findings suggest that managing the data plane with OF has little impact on the signaling load while the network programmability is improved.

Question: How can we enable network programmability in LTE/EPC architecture using OpenFlow?


Inspired by distraction: mind wandering facilitates creative incubation.. Although anecdotes that creative thoughts often arise when one is engaged in an unrelated train of thought date back thousands of years, empirical research has not yet investigated this potentially critical source of inspiration. We used an incubation paradigm to assess whether performance on validated creativity problems (the Unusual Uses Task, or UUT) can be facilitated by engaging in either a demanding task or an undemanding task that maximizes mind wandering. Compared with engaging in a demanding task, rest, or no break, engaging in an undemanding task during an incubation period led to substantial improvements in performance on previously encountered problems. Critically, the context that improved performance after the incubation period was associated with higher levels of mind wandering but not with a greater number of explicitly directed thoughts about the UUT. These data suggest that engaging in simple external tasks that allow the mind to wander may facilitate creative problem solving.

Question: How can we improve our creativity?


Detecting and Naming Actors in Movies Using Generative Appearance Models. We introduce a generative model for learning person and costume specific detectors from labeled examples. We demonstrate the model on the task of localizing and naming actors in long video sequences. More specifically, the actor's head and shoulders are each represented as a constellation of optional color regions. Detection can proceed despite changes in view-point and partial occlusions. We explain how to learn the models from a small number of labeled key frames or video tracks, and how to detect novel appearances of the actors in a maximum likelihood framework. We present results on a challenging movie example, with 81% recall in actor detection (coverage) and 89% precision in actor identification (naming).

Question: How to detect actors in movies?