Heart Rate Monitoring as an Easy Way to Increase Engagement in Human-Agent Interaction. Physiological sensors are gaining the attention of manufacturers and users. As denoted by devices such as smartwatches or the newly released Kinect 2 – which can covertly measure heartbeats – or by the popularity of smartphone apps that track heart rate during fitness activities. Soon, physiological monitoring could become widely accessible and transparent to users. We demonstrate how one could take advantage of this situation to increase users' engagement and enhance user experience in human-agent interaction. We created an experimental protocol involving embodied agents – "virtual avatars". Those agents were displayed alongside a beating heart. We compared a condition in which this feedback was simply duplicating the heart rates of users to another condition in which it was set to an average heart rate. Results suggest a superior social presence of agents when they display feedback similar to users' internal state. This physiological "similarity-attraction" effect may lead, with little effort, to a better acceptance of agents and robots by the general public.

Question: How can we make agents more attractive?


Research Commentary: Desperately Seeking the "IT" in IT Research - A Call to Theorizing the IT Artifact. The field of information systems is premised on the centrality of information technology in everyday socio-economic life. Yet, drawing on a review of the full set of articles published in Information Systems Research (ISR) over the past ten years, we argue that the field has not deeply engaged its core subject matter—the information technology (IT) artifact. Instead, we find that IS researchers tend to give central theoretical significance to the context (within which some usually unspecified technology is seen to operate), the discrete processing capabilities of the artifact (as separable from its context or use), or the dependent variable (that which is posited to be affected or changed as technology is developed, implemented, and used). The IT artifact itself tends to disappear from view, be taken for granted, or is presumed to be unproblematic once it is built and installed. After discussing the implications of our findings, we propose a research direction for the IS field that begins to take technology as seriously as its effects, context, and capabilities. In particular, we propose that IS researchers begin to theorize specifically about IT artifacts, and then incorporate these theories explicitly into their studies. We believe that such a research direction is critical if IS research is to make a significant contribution to the understanding of a world increasingly suffused with ubiquitous, interdependent, and emergent information technologies. (Information Systems Research; Information Technology; IT Research; IT Theory; Technological Artifacts; Technology Change)

Question: What is the difference between information systems research and information technology research?


Background Prior-Based Salient Object Detection via Deep Reconstruction Residual. Detection of salient objects from images is gaining increasing research interest in recent years as it can substantially facilitate a wide range of content-based multimedia applications. Based on the assumption that foreground salient regions are distinctive within a certain context, most conventional approaches rely on a number of hand-designed features and their distinctiveness is measured using local or global contrast. Although these approaches have been shown to be effective in dealing with simple images, their limited capability may cause difficulties when dealing with more complicated images. This paper proposes a novel framework for saliency detection by first modeling the background and then separating salient objects from the background. We develop stacked denoising autoencoders with deep learning architectures to model the background where latent patterns are explored and more powerful representations of data are learned in an unsupervised and bottom-up manner. Afterward, we formulate the separation of salient objects from the background as a problem of measuring reconstruction residuals of deep autoencoders. Comprehensive evaluations of three benchmark datasets and comparisons with nine state-of-the-art algorithms demonstrate the superiority of this paper.

Question: What is the background of the image?


Analysis and validation of proteomic data generated by tandem mass spectrometry. The analysis of the large amount of data generated in mass spectrometry–based proteomics experiments represents a significant challenge and is currently a bottleneck in many proteomics projects. In this review we discuss critical issues related to data processing and analysis in proteomics and describe available methods and tools. We place special emphasis on the elaboration of results that are supported by sound statistical arguments.

Question: What is the largest proteomics project?


Category-aware Next Point-of-Interest Recommendation via Listwise Bayesian Personalized Ranking. Next Point-of-Interest (POI) recommendation has become an important task for location-based social networks (LBSNs). However, previous efforts suffer from the high computational complexity, besides the transition pattern between POIs has not been well studied. In this paper, we proposed a twofold approach for next POI recommendation. First, the preferred next category is predicted by using a third-rank tensor optimized by a Listwise Bayesian Personalized Ranking (LBPR) approach. Specifically we introduce two functions, namely PlackettLuce model and cross entropy, to generate the likelihood of a ranking list for posterior computation. Then POI candidates filtered by the predicated category are ranked based on the spatial influence and category ranking influence. The experiments on two real-world datasets demonstrate the significant improvements of our methods over several state-ofthe-art methods.

Question: What is the difference between PlackettLuce model and cross entropy?


The Cerebellum, Sensitive Periods, and Autism. Cerebellar research has focused principally on adult motor function. However, the cerebellum also maintains abundant connections with nonmotor brain regions throughout postnatal life. Here we review evidence that the cerebellum may guide the maturation of remote nonmotor neural circuitry and influence cognitive development, with a focus on its relationship with autism. Specific cerebellar zones influence neocortical substrates for social interaction, and we propose that sensitive-period disruption of such internal brain communication can account for autism's key features.

Question: What is the cerebellum?


Augmenting paper-based reading activity with direct access to digital materials and scaffolded questioning. Comprehension is the goal of reading. However, students often encounter reading difficulties due to the lack of background knowledge and proper reading strategy. Unfortunately, print text provides very limited assistance to one's reading comprehension through its static knowledge representations such as symbols, charts, and graphs. Integrating digital materials and reading strategy into paper-based reading activities may bring opportunities for learners to make meaning of the print material. In this study, QR codes were adopted in association with mobile technology to deliver supplementary materials and questions to support students' reading. QR codes were printed on paper prints to provide direct access to digital materials and scaffolded questions. Smartphones were used to scan the printed QR codes to fetch predesigned digital resources and scaffolded questions over the Internet. A quasi-experiment was conducted to evaluate the effectiveness of direct access to the digital materials prepared by the instructor using QR codes and that of scaffolded questioning in improving students' reading comprehension. The results suggested that direct access to digital resources using QR codes does not significantly influence students' reading comprehension; however, the reading strategy of scaffolded questioning significantly improves students' understanding about the text. The survey showed that most students agreed that the integrated print-and-digital-materialbased learning system benefits English reading comprehension but may not be as efficient as expected. The implications of the findings shed light on future improvement of the system. 2011 Elsevier Ltd. All rights reserved.

Question: How does the integrated print-and-digital-materialbased learning system benefit English reading comprehension?


The Role of ICT to Achieve the UN Sustainable Development Goals (SDG). This paper is aiming at illustrating the potential of ICT for achieving the Sustainable Development Goals which were declared by the United Nations in 2015 as binding for all nations of our planet addressing both developing and developed countries. ICT must play a significant role if the SDGs should be achieved as projected in 2030. The paper gives an overview of some of the existing efforts in this area and is written as an appeal to all professionals, scientists and IT-professional and their organization to take a holistic approach for all ICT-activities and projects to always include and monitor the effects of their work on the SDGs. The impacts of ICT on sustainability are twofold: On the one hand there might be negative effects on sustainability such as the generation of electronic waste, on the other hand ICT is definitely an enabler to more efficient resource usage, education and business operations which is critical success factor for achieving the SDGs.

Question: What is the role of ICT in achieving the UN Sustainable Development Goals?


Spinal curvature determination from scoliosis X-Ray image using sum of squared difference template matching. Scoliosis is a disorder in which there is a sideways curve of the spine. Curve are often S-shaped or C-shaped. One of the methods to ascertain a patient with scoliosis is through using Cobb angle measurement. The importance of the automatic spinal curve detection system is to detect any spinal disorder quicker and faster. This study is intended as a first step based on computer-aided diagnosis. The spinal detection method that we propose is using template matching based on Sum of Squared Difference (SSD). This method is used to estimate the location of the vertebra. By using polynomial curve fitting, a spinal curvature estimation can be done. This paper discusses the performance of SSD method used to detect a variety of data sources of X-Ray from numerous patients. The results from the implementation indicate that the proposed algorithm can be used to detect all the X-ray images. The best result in this experiment has 96.30% accuracy using 9-subdivisions poly 5 algorithm, and the average accuracy is 86.01%.

Question: How to detect spinal curvature using template matching?


CNN-based modulation classification in the complicated communication channel. Machine Learning are gradually applied in Automatic Modulation Classification (AMC) domain these years. However, in the past researches, the experiment conditions are mostly idealized or simplified. This paper proposes a modified structure of Convolutional Neural Network (CNN), which is proved to have strong capability of classification. The wavelet denoising technology is exploited to restrain the high frequency noise for input signal. As an analysis of the feasibility of this method for complicated channel, we compare the proposed method with the commonly used AMC methods through a large number of signal sequences, taking consider of various distractors. The result shows that the method proposed outperforms on most rugged conditions.

Question: CNN-based modulation classification in the complicated communication channel


Improving returns on stock investment through neural network selection. Artificial neural networks' (ANNs') generalization powers have in recent years received admiration of finance researchers and practitioners. Their usage in such areas as bankruptcy prediction, debt-risk assessment, and security-market applications has yielded promising results. With such intensive research and proven ability of the ANN in the area of security-market application and the growing importance of the role of equity securities in Singapore, it has motivated the conceptual development of this work in using the ANN in stock selection. With their proven generalization ability, neural networks are able to infer the characteristics of performing stocks from the historical patterns. The performance of stocks is reflective of the profitability and quality of management of the underlying company. Such information is reflected in financial and technical variables. As such, the ANN is used as a tool to uncover the intricate relationships between the performance of stocks and the related financial and technical variables. Historical data, such as financial variables (inputs) and performance of the stock (output) is used in this ANN application. Experimental results obtained thus far have been very encouraging. IDEA GROUP PUBLISHING This paper appears in the publication, Artificial Neural Networks in Finance and Manufacturing edited by Joarder Kamruzzaman, Rezaul Begg, and Ruhul Sarker© 2006, Idea Group Inc. 701 E. Chocolate Avenue, Suite 200, Hershey PA 17033-1240, USA Tel: 717/533-8845; Fax 717/533-8661; URL-http://www.idea-group.com ITB13014 Improving Returns on Stock Investment through Neural Network Selection 153 Copyright © 2006, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited. Introduction With the growing importance in the role of equities to both international and local investors, the selection of attractive

Question: How to improve returns on stock investment through neural network selection?


Robust solutions for network design under transportation cost and demand uncertainty. In many applications, the network design problem (NDP) faces significant uncertainty in transportation costs and demand, as it can be difficult to estimate current (and future values) of these quantities. In this paper, we present a robust optimization-based formulation for the NDP under transportation cost and demand uncertainty. We show that solving an approximation to this robust formulation of the NDP can be done efficiently for a network with single origin and destination per commodity and general uncertainty in transportation costs and demand that are independent of each other. For a network with path constraints, we propose an efficient column generation procedure to solve the linear programming relaxation. We also present computational results that show that the approximate robust solution found provides significant savings in the worst case while incurring only minor sub-optimality for specific instances of the uncertainty. Journal of the Operational Research Society advance online publication, 7 February 2007 doi:10.1057/palgrave.jors.2602362

Question: What is the approximate robust solution?


Training verified learners with learned verifiers. This paper proposes a new algorithmic framework, predictor-verifier training, to train neural networks that are verifiable, i.e., networks that provably satisfy some desired input-output properties. The key idea is to simultaneously train two networks: a predictor network that performs the task at hand, e.g., predicting labels given inputs, and a verifier network that computes a bound on how well the predictor satisfies the properties being verified. Both networks can be trained simultaneously to optimize a weighted combination of the standard data-fitting loss and a term that bounds the maximum violation of the property. Experiments show that not only is the predictor-verifier architecture able to train networks to achieve state of the art verified robustness to adversarial examples with much shorter training times (outperforming previous algorithms on small datasets like MNIST and SVHN), but it can also be scaled to produce the first known (to the best of our knowledge) verifiably robust networks for CIFAR-10.

Question: What is the first known verifiably robust network for CIFAR-10?


Synchronising movements with the sounds of a virtual partner enhances partner likeability. Previous studies have demonstrated that synchronising movements with other people can influence affiliative behaviour towards them. While research has focused on synchronisation with visually observed movement, synchronisation with a partner who is heard may have similar effects. We replicate findings showing that synchronisation can influence ratings of likeability of a partner, but demonstrate that this is possible with virtual interaction, involving a video of a partner. Participants performed instructed synchrony in time to sounds instead of the observable actions of another person. Results show significantly higher ratings of likeability of a partner after moving at the same time as sounds attributed to that partner, compared with moving in between sounds. Objectively quantified synchrony also correlated with ratings of likeability. Belief that sounds were made by another person was manipulated in Experiment 2, and results demonstrate that when sounds are attributed to a computer, ratings of likeability are not affected by moving in or out of time. These findings demonstrate that interaction with sound can be experienced as social interaction in the absence of genuine interpersonal contact, which may help explain why people enjoy engaging with recorded music.

Question: How can synchronising movements with a virtual partner enhance partner likeability?


EMI filter design and optimization for both AC and DC side in a DC-fed motor drive system. This paper proposes an EMI filter design and optimization method for both AC and DC side in a DC-fed motor drive system. Based on the noise generation and propagation mechanism, the analysis of common mode (CM) and differential mode (DM) EMI noise equivalent circuits is provided. Based on those equivalent circuits, this paper investigates the system EMI noise between AC and DC side, which shows the interaction between DM and CM noises and the interaction between adding AC and DC side filters. With these considerations, an optimized EMI filter design procedure is proposed to design CM and DM filters for both AC and DC sides. To minimize the impact on the EMI noise of one side caused by adding filter on the other side, certain order must be followed to design AC and DC CM and DM filters. Moreover, the EMI filter weight optimization method is also discussed to get the minimum weight of EMI filter and improve system power density. Simulation and experimental results verify the interaction between AC and DC filters and show that EMI filters can be designed to suppress both AC and DC EMI noise to meet the standard with the proposed EMI filter design method.

Question: How to design an EMI filter for a DC-fed motor drive system?


Using Three Machine Learning Techniques for Predicting Breast Cancer Recurrence. Introduction Breast cancer (BC) is the most common cancer in women, affecting about 10% of all women at some stages of their life. In recent years, the incidence rate keeps increasing and data show that the survival rate is 88% after five years from diagnosis and 80% after 10 years from diagnosis [1]. Early prediction of breast cancer is one of the most crucial works in the follow-up process. Data mining methods can help to reduce the number of false positive and false negative decisions [2,3]. Consequently, new methods such as knowledge discovery in databases (KDD) has become a popular research tool for medical researchers who try to identify and exploit patterns and relationships among large number of variables, and predict the outcome of a disease using historical cases stored in datasets [4].

Question: What is the most common cancer in women?


The genomic landscapes of human breast and colorectal cancers.. Human cancer is caused by the accumulation of mutations in oncogenes and tumor suppressor genes. To catalog the genetic changes that occur during tumorigenesis, we isolated DNA from 11 breast and 11 colorectal tumors and determined the sequences of the genes in the Reference Sequence database in these samples. Based on analysis of exons representing 20,857 transcripts from 18,191 genes, we conclude that the genomic landscapes of breast and colorectal cancers are composed of a handful of commonly mutated gene "mountains" and a much larger number of gene "hills" that are mutated at low frequency. We describe statistical and bioinformatic tools that may help identify mutations with a role in tumorigenesis. These results have implications for understanding the nature and heterogeneity of human cancers and for using personal genomics for tumor diagnosis and therapy.

Question: What is the difference between a mountain and a hill?


Bitcoin Market Return and Volatility Forecasting Using Transaction Network Flow Properties. Bit coin, as the foundation for a secure electronic payment system, has drawn broad interests from researchers in recent years. In this paper, we analyze a comprehensive Bit coin transaction dataset and investigate the interrelationship between the flow of Bit coin transactions and its price movement. Using network theory, we examine a few complexity measures of the Bit coin transaction flow networks, and we model the joint dynamic relationship between these complexity measures and Bit coin market variables such as return and volatility. We find that a particular complexity measure of the Bit coin transaction network flow is significantly correlated with the Bit coin market return and volatility. More specifically we document that the residual diversity or freedom of Bit coin network flow scaled by the total system throughput can significantly improve the predictability of Bit coin market return and volatility.

Question: What is the relationship between the bitcoin market and the bitcoin transaction network?


Diffusion Component Analysis: Unraveling Functional Topology in Biological Networks. Complex biological systems have been successfully modeled by biochemical and genetic interaction networks, typically gathered from high-throughput (HTP) data. These networks can be used to infer functional relationships between genes or proteins. Using the intuition that the topological role of a gene in a network relates to its biological function, local or diffusionbased "guilt-by-association" and graph-theoretic methods have had success in inferring gene functions. Here we seek to improve function prediction by integrating diffusion-based methods with a novel dimensionality reduction technique to overcome the incomplete and noisy nature of network data. In this paper, we introduce diffusion component analysis (DCA), a framework that plugs in a diffusion model and learns a low-dimensional vector representation of each node to encode the topological properties of a network. As a proof of concept, we demonstrate DCA's substantial improvement over state-of-the-art diffusion-based approaches in predicting protein function from molecular interaction networks. Moreover, our DCA framework can integrate multiple networks from heterogeneous sources, consisting of genomic information, biochemical experiments and other resources, to even further improve function prediction. Yet another layer of performance gain is achieved by integrating the DCA framework with support vector machines that take our node vector representations as features. Overall, our DCA framework provides a novel representation of nodes in a network that can be used as a plug-in architecture to other machine learning algorithms to decipher topological properties of and obtain novel insights into interactomes. This paper was selected for oral presentation at RECOMB 2015 and an abstract is published in the conference proceedings. ar X iv :1 50 4. 02 71 9v 1 [ qbi o. M N ] 1 0 A pr 2 01 5

Question: How does diffusion component analysis work?


Improved Relation Extraction with Feature-Rich Compositional Embedding Models. Compositional embedding models build a representation (or embedding) for a linguistic structure based on its component word embeddings. We propose a Feature-rich Compositional Embedding Model (FCM) for relation extraction that is expressive, generalizes to new domains, and is easy-to-implement. The key idea is to combine both (unlexicalized) handcrafted features with learned word embeddings. The model is able to directly tackle the difficulties met by traditional compositional embeddings models, such as handling arbitrary types of sentence annotations and utilizing global information for composition. We test the proposed model on two relation extraction tasks, and demonstrate that our model outperforms both previous compositional models and traditional feature rich models on the ACE 2005 relation extraction task, and the SemEval 2010 relation classification task. The combination of our model and a loglinear classifier with hand-crafted features gives state-of-the-art results. We made our implementation available for general use1.

Question: What is the relation extraction task?


A database of German emotional speech. The article describes a database of emotional speech. Ten actors (5 female and 5 male) simulated the emotions, producing 10 German utterances (5 short and 5 longer sentences) which could be used in everyday communication and are interpretable in all applied emotions. The recordings were taken in an anechoic chamber with high-quality recording equipment. In addition to the sound electro-glottograms were recorded. The speech material comprises about 800 sentences (seven emotions * ten actors * ten sentences + some second versions). The complete database was evaluated in a perception test regarding the recognisability of emotions and their naturalness. Utterances recognised better than 80% and judged as natural by more than 60% of the listeners were phonetically labelled in a narrow transcription with special markers for voice-quality, phonatory and articulatory settings and articulatory features. The database can be accessed by the public via the internet (http://www.expressive-speech.net/emodb/).

Question: What is the database of emotional speech?


Integrating Surface and Abstract Features for Robust Cross-Domain Chinese Word Segmentation. Current character-based approaches are not robust for cross domain Chin ese word segmentation. In this paper, we alleviate this problem by deriving a novel enhanced ch aracterbased generative model with a new abstract aggregate candidate-feature, which indicates if th given candidate prefers the corresponding position-tag of the longest dictionary matching wo rd. Since the distribution of the proposed feature is invariant across domains, our m del thus possesses better generalization ability. Open tests on CIPS-SIGHAN-2010 show that the enhance d gen rative model achieves robust cross-domain performance for various OOV cov erage rates and obtains the best performance on three out of four domains. The enhanced gen erative model is then further integrated with a discriminative model which also utilizes dictionary information . This integrated model is shown to be either superior or comparable to all other models repo rted in the literatur e on every domain of this task.

Question: How to integrate surface and abstract features for robust cross-domain Chinese word segmentation?


Information Extraction: Techniques and Challenges. This volume takes a broad view of information extraction as any method for ltering information from large volumes of text. This includes the retrieval of documents from collections and the tagging of particular terms in text. In this paper we shall use a narrower de nition: the identi cation of instances of a particular class of events or relationships in a natural language text, and the extraction of the relevant arguments of the event or relationship. Information extraction therefore involves the creation of a structured representation (such as a data base) of selected information drawn from the text. The idea of reducing the information in a document to a tabular structure is not new. Its feasibility for sublanguage texts was suggested by Zellig Harris in the 1950's, and an early implementation for medical texts was done at New York University by Naomi Sager[20]. However, the speci c notion of information extraction described here has received wide currency over the last decade through the series of Message Understanding Conferences [1, 2, 3, 4, 14]. We shall discuss these Conferences in more detail a bit later, and shall use simpli ed versions of extraction tasks from these Conferences as examples throughout this paper. Figure 1 shows a simpli ed example from one of the earlier MUC's, involving terrorist events (MUC-3) [1]. For each terrorist event, the system had to determine the type of attack (bombing, arson, etc.), the date, location, perpetrator (if stated), targets, and e ects on targets. Other examples of extraction tasks are international joint ventures (where the arguments included the partners, the new venture, its product or service, etc.) and executive succession (indicating who was hired or red by which company for which position). Information extraction is a more limited task than \full text understanding". In full text understanding, we aspire to represent in a explicit fashion all the information

Question: What is the difference between information extraction and full text understanding?


Participatory Air Pollution Monitoring Using Smartphones. Air quality monitoring is extremely important as air pollution has a direct impact on human health. In this paper we introduce a low-power and low-cost mobile sensing system for participatory air quality monitoring. In contrast to traditional stationary air pollution monitoring stations, we present the design, implementation, and evaluation of GasMobile, a small and portable measurement system based on off-the-shelf components and suited to be used by a large number of people. Vital to the success of participatory sensing applications is a high data quality. We improve measurement accuracy by (i) exploiting sensor readings near governmental measurement stations to keep sensor calibration up to date and (ii) analyzing the effect of mobility on the accuracy of the sensor readings to give user advice on measurement execution. Finally, we show that it is feasible to use GasMobile to create collective high-resolution air pollution maps.

Question: How to improve the accuracy of air pollution measurements?


A Look into 30 Years of Malware Development from a Software Metrics Perspective. During the last decades, the problem of malicious and unwanted software (malware) has surged in numbers and sophistication. Malware plays a key role in most of today's cyber attacks and has consolidated as a commodity in the underground economy. In this work, we analyze the evolution of malware since the early 1980s to date from a software engineering perspective. We analyze the source code of 151 malware samples and obtain measures of their size, code quality, and estimates of the development costs (effort, time, and number of people). Our results suggest an exponential increment of nearly one order of magnitude per decade in aspects such as size and estimated effort, with code quality metrics similar to those of regular software. Overall, this supports otherwise confirmed claims about the increasing complexity of malware and its production progressively becoming an industry.

Question: What is the size of the malware?


Contextualized Sarcasm Detection on Twitter. Sarcasm requires some shared knowledge between speaker and audience; it is a profoundly contextual phenomenon. Most computational approaches to sarcasm detection, however, treat it as a purely linguistic matter, using information such as lexical cues and their corresponding sentiment as predictive features. We show that by including extra-linguistic information from the context of an utterance on Twitter – such as properties of the author, the audience and the immediate communicative environment – we are able to achieve gains in accuracy compared to purely linguistic features in the detection of this complex phenomenon, while also shedding light on features of interpersonal interaction that enable sarcasm in conversation.

Question: What is sarcasm?


Cross-Domain Metric and Multiple Kernel Learning Based on Information Theory. Learning an appropriate distance metric plays a substantial role in the success of many learning machines. Conventional metric learning algorithms have limited utility when the training and test samples are drawn from related but different domains (i.e., source domain and target domain). In this letter, we propose two novel metric learning algorithms for domain adaptation in an information-theoretic setting, allowing for discriminating power transfer and standard learning machine propagation across two domains. In the first one, a cross-domain Mahalanobis distance is learned by combining three goals: reducing the distribution difference between different domains, preserving the geometry of target domain data, and aligning the geometry of source domain data with label information. Furthermore, we devote our efforts to solving complex domain adaptation problems and go beyond linear cross-domain metric learning by extending the first method to a multiple kernel learning framework. A convex combination of multiple kernels and a linear transformation are adaptively learned in a single optimization, which greatly benefits the exploration of prior knowledge and the description of data characteristics. Comprehensive experiments in three real-world applications (face recognition, text classification, and object categorization) verify that the proposed methods outperform state-of-the-art metric learning and domain adaptation methods.

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


CFGAN: A Generic Collaborative Filtering Framework based on Generative Adversarial Networks. Generative Adversarial Networks (GAN) have achieved big success in various domains such as image generation, music generation, and natural language generation. In this paper, we propose a novel GAN-based collaborative filtering (CF) framework to provide higher accuracy in recommendation. We first identify a fundamental problem of existing GAN-based methods in CF and highlight it quantitatively via a series of experiments. Next, we suggest a new direction of vector-wise adversarial training to solve the problem and propose our GAN-based CF framework, called CFGAN, based on the direction. We identify a unique challenge that arises when vector-wise adversarial training is employed in CF. We then propose three CF methods realized on top of our CFGAN that are able to address the challenge. Finally, via extensive experiments on real-world datasets, we validate that vector-wise adversarial training employed in CFGAN is really effective to solve the problem of existing GAN-based CF methods. Furthermore, we demonstrate that our proposed CF methods on CFGAN provide recommendation accuracy consistently and universally higher than those of the state-of-the-art recommenders.

Question: What is a GAN?


Manipulating the soil microbiome to increase soil health and plant fertility. A variety of soil factors are known to increase nutrient availability and plant productivity. The most influential might be the organisms comprising the soil microbial community of the rhizosphere, which is the soil surrounding the roots of plants where complex interactions occur between the roots, soil, and microorganisms. Root exudates act as substrates and signaling molecules for microbes creating a complex and interwoven relationship between plants and the microbiome. While individual microorganisms such as endophytes, symbionts, pathogens, and plant growth promoting rhizobacteria are increasingly featured in the literature, the larger community of soil microorganisms, or soil microbiome, may have more far-reaching effects. Each microorganism functions in coordination with the overall soil microbiome to influence plant health and crop productivity. Increasing evidence indicates that plants can shape the soil microbiome through the secretion of root exudates. The molecular communication fluctuates according to the plant development stage, proximity to neighboring species, management techniques, and many other factors. This review seeks to summarize the current knowledge on this topic.

Question: How do plants shape the soil microbiome?


Biodegradable polymeric nanoparticles based drug delivery systems.. Biodegradable nanoparticles have been used frequently as drug delivery vehicles due to its grand bioavailability, better encapsulation, control release and less toxic properties. Various nanoparticulate systems, general synthesis and encapsulation process, control release and improvement of therapeutic value of nanoencapsulated drugs are covered in this review. We have highlighted the impact of nanoencapsulation of various disease related drugs on biodegradable nanoparticles such as PLGA, PLA, chitosan, gelatin, polycaprolactone and poly-alkyl-cyanoacrylates.

Question: What is the impact of nanoencapsulation of various disease related drugs on biodegradable nanoparticles?


Essential Layers, Artifacts, and Dependencies of Enterprise Architecture. After a period where implementation speed was more important than integration, consistency and reduction of complexity, architectural considerations have become a key issue of information management in recent years again. Enterprise architecture is widely accepted as an essential mechanism for ensuring agility and consistency, compliance and efficiency. Although standards like TOGAF and FEAF have developed, however, there is no common agreement on which architecture layers, which artifact types and which dependencies constitute the essence of enterprise architecture. This paper contributes to the identification of essential elements of enterprise architecture by (1) specifying enterprise architecture as a hierarchical, multilevel system comprising aggregation hierarchies, architecture layers and views, (2) discussing enterprise architecture frameworks with regard to essential elements, (3) proposing interfacing requirements of enterprise architecture with other architecture models and (4) matching these findings with current enterprise architecture practice in several large companies.

Question: What is enterprise architecture?


Pedestrian intention recognition using Latent-dynamic Conditional Random Fields. We present a novel approach for pedestrian intention recognition for advanced video-based driver assistance systems using a Latent-dynamic Conditional Random Field model. The model integrates pedestrian dynamics and situational awareness using observations from a stereo-video system for pedestrian detection and human head pose estimation. The model is able to capture both intrinsic and extrinsic class dynamics. Evaluation of our method is performed on a public available dataset addressing scenarios of lateral approaching pedestrians that might cross the road, turn into the road or stop at the curbside. During experiments, we demonstrate that the proposed approach leads to better stability and class separation compared to state-of-the-art pedestrian intention recognition approaches.

Question: How to recognize pedestrian intention?


Deep Network Guided Proof Search. Deep learning techniques lie at the heart of several significant AI advances in recent years including object recognition and detection, image captioning, machine translation, speech recognition and synthesis, and playing the game of Go. Automated first-order theorem provers can aid in the formalization and verification of mathematical theorems and play a crucial role in program analysis, theory reasoning, security, interpolation, and system verification. Here we suggest deep learning based guidance in the proof search of the theorem prover E. We train and compare several deep neural network models on the traces of existing ATP proofs of Mizar statements and use them to select processed clauses during proof search. We give experimental evidence that with a hybrid, two-phase approach, deep learning based guidance can significantly reduce the average number of proof search steps while increasing the number of theorems proved. Using a few proof guidance strategies that leverage deep neural networks, we have found first-order proofs of 7.36% of the first-order logic translations of the Mizar Mathematical Library theorems that did not previously have ATP generated proofs. This increases the ratio of statements in the corpus with ATP generated proofs from 56% to 59%.

Question: What is the first-order logic of the mizar mathematical library?


Semantic analysis of soccer video using dynamic Bayesian network. Video semantic analysis is formulated based on the low-level image features and the high-level knowledge which is encoded in abstract, nongeometric representations. This paper introduces a semantic analysis system based on Bayesian network (BN) and dynamic Bayesian network (DBN). It is validated in the particular domain of soccer game videos. Based on BN/DBN, it can identify the special events in soccer games such as goal event, corner kick event, penalty kick event, and card event. The video analyzer extracts the low-level evidences, whereas the semantic analyzer uses BN/DBN to interpret the high-level semantics. Different from previous shot-based semantic analysis approaches, the proposed semantic analysis is frame-based for each input frame, it provides the current semantics of the event nodes as well as the hidden nodes. Another contribution is that the BN and DBN are automatically generated by the training process instead of determined by ad hoc. The last contribution is that we introduce a so-called temporal intervening network to improve the accuracy of the semantics output

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


The PASCAL Recognising Textual Entailment Challenge. This paper describes the PASCAL Network of Excellence first Recognising Textual Entailment (RTE-1) Challenge benchmark. The RTE task is defined as recognizing, given two text fragments, whether the meaning of one text can be inferred (entailed) from the other. This application-independent task is suggested as capturing major inferences about the variability of semantic expression which are commonly needed across multiple applications. The Challenge has raised noticeable attention in the research community, attracting 17 submissions from diverse groups, suggesting the generic relevance of the task.

Question: What is the PASCAL Recognising Textual Entailment Challenge?


Introduction Welcome to this early seminal work , Fostering Resiliency in Kids : Protective Factors in the Family , School and Community. The field of prevention, both research and practice, came a long way in the1980s: from short-term, even one-shot, individual-focused interventions in the school classroom to a growing awareness and beginning implementation of long-term, comprehensive, environmental-focused interventions expanding beyond the school to include the community. Furthermore, in the mid-1980s we finally started to hear preventionists talking about prevention strategies and programs based on research identifying the underlying risk factors for problems such as alcohol and other drug abuse, teen pregnancy, delinquency and gangs, and dropping out (Hawkins, Lishner, and Catalano, 1985). While certainly a giant step in the right direction, the identification of risks does not necessarily provide us with a clear sense of just what strategies we need to implement to reduce the risks. More recently, we are hearing preventionists talk about "protective factors," about building "resiliency" in youth, about basing our strategies on what research hast old us about the environmental factors that facilitate the development of youth who do not get involved in life-compromising problems (Benard, March 1987). What clearly becomes the challenge for the l990s is the implementation of prevention strategies that strengthen protective factors in our families, schools, an communities. As Gibbs and Bennett (1990) conceptualize the process, we must"turn the situation around by translating negative risk factors into positive action strategies" which are, in essence, protective factors. After a brief overview of the protective factor research phenomenon, this paper will discuss the major protective factors that research has identified as contributing to the development of resiliency in youth and the implications of this for building effective prevention programs.

Question: What are the major protective factors that research has identified as contributing to the development of resiliency in youth?


Health and well-being benefits of spending time in forests: systematic review. BACKGROUND Numerous studies have reported that spending time in nature is associated with the improvement of various health outcomes and well-being. This review evaluated the physical and psychological benefits of a specific type of exposure to nature, forest therapy.   METHOD A literature search was carried out using MEDLINE, PubMed, ScienceDirect, EMBASE, and ProQuest databases and manual searches from inception up to December 2016. Key words: "Forest" or "Shinrin -Yoku" or "Forest bath" AND "Health" or "Wellbeing". The methodological quality of each randomized controlled trials (RCTs) was assessed according to the Cochrane risk of bias (ROB) tool.   RESULTS Six RCTs met the inclusion criteria. Participants' ages ranged from 20 to 79 years. Sample size ranged from 18 to 99. Populations studied varied from young healthy university students to elderly people with chronic disease. Studies reported the positive impact of forest therapy on hypertension (n = 2), cardiac and pulmonary function (n = 1), immune function (n = 2), inflammation (n = 3), oxidative stress (n = 1), stress (n = 1), stress hormone (n = 1), anxiety (n = 1), depression (n = 2), and emotional response (n = 3). The quality of all studies included in this review had a high ROB.   CONCLUSION Forest therapy may play an important role in health promotion and disease prevention. However, the lack of high-quality studies limits the strength of results, rendering the evidence insufficient to establish clinical practice guidelines for its use. More robust RCTs are warranted.

Question: What is the impact of forest therapy on health?


AtlanticWave-SDX : An International SDX to Support Science Data Applications. New scientific instruments that are being designed and deployed in the coming years will dramatically increase the need for large, real-time data transfers among scientists throughout the world. One such instrument is the Large Synoptic Survey Telescope being built in Chile that will produce 6.4 GB images every 17 seconds. This paper describes an ongoing effort to meet the demands of these large data scientific instruments through the development of an international software defined exchange point (SDX) that will meet the provisioning needs for the scientific users. The specific planned and ongoing work in SDX architecture is described with specific consideration for policy specification and security. Keywords—Software Defined Exchange; Science Data

Question: What is the Large Synoptic Survey Telescope?


Author Identification Using Imbalanced and Limited Training Texts. This paper deals with the problem of author identification. The common N-grams (CNG) method [6] is a language-independent profile-based approach with good results in many author identification experiments so far. A variation of this approach is presented based on new distance measures that are quite stable for large profile length values. Special emphasis is given to the degree upon which the effectiveness of the method is affected by the available training text samples per author. Experiments based on text samples on the same topic from the Reuters Corpus Volume 1 are presented using both balanced and imbalanced training corpora. The results show that CNG with the proposed distance measures is more accurate when only limited training text samples are available, at least for some of the candidate authors, a realistic condition in author identification problems.

Question: How to identify authors using limited training texts?


Forensic Analysis of WhatsApp on Android Smartphones. ii Acknowledgements The research and writing of this thesis has been one of the most significant academic challenges I ever had to face. Without the support and guidance of the following people, this study would not have been completed. It is to them that I owe my deepest gratitude. I would sincerely like to thank my advisor, Dr. Golden G. Richard III for giving me an opportunity to conduct this thesis research under his excellent guidance. His exceptional knowledge, wisdom and understanding have inspired and motivated me. My professors, Dr. Shengru Tu and Dr. Adlai DePano for being on my thesis committee and mentoring me during my Master's degree in Computer Science at University of New Orleans. I am thankful to my friend Thomas Sires, for initiating my interest in Linux, making sure I keep it up and help me solve technical issues. I appreciate the help from Aisha Ali-Gombe, Joe Sylve and Andrew Case who are on our Information Assurance group, for their contributions in the Information Assurance field and answering my questions. Finally, I would like to thank my Parents and each member in my family and friends for their unconditional love and support. I specially want to thank my husband Mr. Naveen Singh, who has been my core source of strength through every phase of this effort and constantly encouraged me to do my best. His systematic approach and kind feedback towards my thesis has helped me improve. Words cannot express how grateful I am to you. My heartfelt gratitude to my precious daughter Navya Singh, who was born during my Master's program and has spent many days without my complete attention. With her never give up attitude she has taught me perseverance to work around a problem until I succeed. Thank you for all your love my little girl, it kept me going. Abstract Android forensics has evolved over time offering significant opportunities and exciting challenges. On one hand, being an open source platfo

Question: What is the difference between Android forensics and Android forensics?


Illuminating Light: An Optical Design Tool with a Luminous-Tangible Interface. We describe a novel system for rapid prototyping of laserbased optical and holographic layouts. Users of this optical prototyping tool – called the Illuminating Light system – move physical representations of various optical elements about a workspace, while the system tracks these components and projects back onto the workspace surface the simulated propagation of laser light through the evolving layout. This application is built atop the Luminous Room infrastructure, an aggregate of interlinked, computer-controlled projector-camera units called I/O Bulbs. Philosophically, the work embodies the emerging ideas of the Luminous Room and builds on the notions of 'graspable media'. We briefly introduce the I/O Bulb and Luminous Room concepts and discuss their current implementations. After an overview of the optical domain that the Illuminating Light system is designed to address, we present the overall system design and implementation, including that of an intermediary toolkit called voodoo which provides a general facility for object identification and tracking.

Question: What is the luminous room?


A Beginner's Guide to Interferometric SAR Concepts and Signal Processing [AESS Tutorial IV]. Interferometric synthetic aperture radar (IFSAR, also abbreviated as InSAR) employs pairs of high resolution SAR images to generate high quality terrain elevation maps using phase interferometry methods. IFSAR provides an all-weather, day/night capability to generate measurements of terrain elevation on a dense grid of sample points with accuracies of ones of meters. Both spaceborne and airborne IFSAR systems are in use. In this paper we present a tutorial introduction to the concepts, techniques, and applications of IFSAR. After a brief introduction to digital elevation models (DEMs) and digital terrain elevation data (DTED), the fundamental IFSAR equation relating interferometric phase measurements to terrain elevation is derived from simple geometric considerations. The central section of the paper describes the major algorithmic steps required to form an IFSAR terrain map. Finally, variations of IFSAR for mapping terrain elevation or reflectivity changes are briefly described. A Web site at users.ece.gatech.edu/~mrichard/AESSJFSAR.htm provides access to color versions of many of the IFSAR images included in this paper.

Question: What is an interferometric synthetic aperture radar?


An information theoretic approach to improve semantic similarity assessments across multiple ontologies. Semantic similarity has become, in recent years, the backbone of numerous knowledge-based applications dealing with textual data. From the different methods and paradigms proposed to assess semantic similarity, ontology-based measures and, more specifically, those based on quantifying the Information Content (IC) of concepts are the most widespread solutions due to their high accuracy. However, these measures were designed to exploit a single ontology. They thus cannot be leveraged in many contexts in which multiple knowledge bases are considered. In this paper, we propose a new approach to achieve accurate IC-based similarity assessments for concept pairs spread throughout several ontologies. Based on Information Theory, our method defines a strategy to accurately measure the degree of commonality between concepts belonging to different ontologies—this is the cornerstone for estimating their semantic similarity. Our approach therefore enables classic IC-based measures to be directly applied in a multiple ontology setting. An empirical evaluation, based on well-established benchmarks and ontologies related to the bio-medical domain, illustrates the accuracy of our approach, and demonstrates that similarity estimations provided by our approach are significantly more correlated with human ratings of similarity than those obtained via related works. Semantic similarity is a pillar of text understanding since it quantifies the degree of resemblance between the meanings of textual terms. In recent years, due to the marked increase in electronically available textual data, considerable effort has been focused on defining semantic measures, which have been extensively applied in various contexts such as information retrieval [54], information extraction [56], word sense disambiguation [32], data clustering [4,27], data privacy [6,45–47] and biomedicine (e.g. protein cla

Question: What is the difference between a measure and a similarity measure?


Robust Non-Intrusive Load Monitoring (NILM) with unknown loads. A Non-Intrusive Load Monitoring (NILM) method, robust even in the presence of unlearned or unknown appliances (UUAs) is presented in this paper. In the absence of such UUAs, this NILM algorithm is capable of accurately identifying each of the turned-ON appliances as well as their energy levels. However, when there is an UUA or set of UUAs are turned-ON during a particular time window, proposed NILM method detects their presence. This enables the operator to detect presence of anomalies or unlearned appliances in a household. This quality increases the reliability of the NILM strategy and makes it more robust compared to existing NILM methods. The proposed Robust NILM strategy (RNILM) works accurately with a single active power measurement taken at a low sampling rate as low as one sample per second. Here first, a unique set of features for each appliance was extracted through decomposing their active power signal traces into uncorrelated subspace components (SCs) via a high-resolution implementation of the Karhunen-Loeve (KLE). Next, in the appliance identification stage, through considering power levels of the SCs, the number of possible appliance combinations were rapidly reduced. Finally, through a Maximum a Posteriori (MAP) estimation, the turned-ON appliance combination and/or the presence of UUA was determined. The proposed RNILM method was validated using real data from two public databases: Reference Energy Disaggregation Dataset (REDD) and Tracebase. The presented results demonstrate the capability of the proposed RNILM method to identify, the turned-ON appliance combinations, their energy level disaggregation as well as the presence of UUAs accurately in real households.

Question: What is the difference between a robust non-intrusive load monitoring and a non-intrusive load monitoring?


Analysis on credit card fraud detection methods. Due to the rise and rapid growth of E-Commerce, use of credit cards for online purchases has dramatically increased and it caused an explosion in the credit card fraud. As credit card becomes the most popular mode of payment for both online as well as regular purchase, cases of fraud associated with it are also rising. In real life, fraudulent transactions are scattered with genuine transactions and simple pattern matching techniques are not often sufficient to detect those frauds accurately. Implementation of efficient fraud detection systems has thus become imperative for all credit card issuing banks to minimize their losses. Many modern techniques based on Artificial Intelligence, Data mining, Fuzzy logic, Machine learning, Sequence Alignment, Genetic Programming etc., has evolved in detecting various credit card fraudulent transactions. A clear understanding on all these approaches will certainly lead to an efficient credit card fraud detection system. This paper presents a survey of various techniques used in credit card fraud detection mechanisms and evaluates each methodology based on certain design criteria.

Question: What is a survey of credit card fraud detection methods?


Types of minority class examples and their influence on learning classifiers from imbalanced data. Many real-world applications reveal difficulties in learning classifiers from imbalanced data. Although several methods for improving classifiers have been introduced, the identification of conditions for the efficient use of the particular method is still an open research problem. It is also worth to study the nature of imbalanced data, characteristics of the minority class distribution and their influence on classification performance. However, current studies on imbalanced data difficulty factors have been mainly done with artificial datasets and their conclusions are not easily applicable to the real-world problems, also because the methods for their identification are not sufficiently developed. In our paper, we capture difficulties of class distribution in real datasets by considering four types of minority class examples: safe, borderline, rare and outliers. First, we confirm their occurrence in real data by exploring multidimensional visualizations of selected datasets. Then, we introduce a method for an identification of these types of examples, which is based on analyzing a class distribution in a local neighbourhood of the considered example. Two ways of modeling this neighbourhood are presented: with k-nearest examples and with kernel functions. Experiments with artificial datasets show that these methods are able to re-discover simulated types of examples. Next contributions of this paper include carrying out a comprehensive experimental study with 26 real world imbalanced datasets, where (1) we identify new data characteristics basing on the analysis of types of minority examples; (2) we demonstrate that considering the results of this analysis allow to differentiate classification performance of popular classifiers and pre-processing methods and to evaluate their areas of competence. Finally, we highlight directions of exploiting the results of our analy

Question: What is the difference between a minority class example and a rare class example?


Embedding a Mathematical OCR Module into OCRopus. This paper describes embedding a mathematical formula recognition module into the OCR system OCRopus aiming at developing a OCR system for scientific and technical documents which include mathematical formulas. OCRopus is a open source OCR system emphasizing modularity, easy extensibility, and reuse. This system has several basic components such as preprocessing, layout analysis, and text line recognition, so it is a challenging project to embed the mathematical formula recognition module into the OCRopus system. We have developed the math OCR module, then report how to embed our module into the OCRopus system in order to realize a math OCR which can deal with wide variety of documents including mathematical formulas.

Question: How to embed a mathematical formula recognition module into an OCR system?


Resolving constraint conflicts. In this paper, we define constraint conflicts and examine properties that may aid in guiding their resolution. A constraint conflict is an inconsistency between the access control policy and the constraints specified to limit that policy. For example, a policy that permits a high integrity subject to access low integrity data is in conflict with a Biba integrity constraint. Constraint conflicts differ from typical policy conflicts in that constraints are never supposed to be violated. That is, a conflict with a constraint results in a policy compilation error, whereas policy conflicts are resolved at runtime. As we have found in the past, when constraint conflicts occur in a specification a variety of resolutions are both possible and practical. In this paper, we detail some key formal properties of constraint conflicts and show how these are useful in guiding conflict resolution. We use the SELinux example policy for Linux 2.4.19 as the source of our constraint conflicts and resolution examples. The formal properties are used to guide the selection of resolutions and provide a basis for a resolution language that we apply to resolve conflicts in the SELinux example policy.

Question: What is the difference between a policy conflict and a constraint conflict?


Report on a general problem-solving program..

Question: What is a general problem-solving program?


Digital Image Forgeries and Passive Image Authentication Techniques: A Survey. Digital images are present everywhere on magazine covers, in newspapers, in courtrooms as evidences, and all over the Internet signifying one of the major ways for communication nowadays. The trustworthiness of digital images has been questioned, because of the ease with which these images can be manipulated in both its origin & content as a result of tremendous growth of digital image manipulation tools. Digital image forensics is the latest research field which intends to authorize the genuineness of images. This survey attempts to provide an overview of various digital image forgeries and the state of art passive methods to authenticate digital images.

Question: What is the state of art passive method to authenticate digital images?


A multi-scale target detection method for optical remote sensing images. Faster RCNN is a region proposal based object detection approach. It integrates the region proposal stage and classification stage into a single pipeline, which has both rapid speed and high detection accuracy. However, when the model is applied to the target detection of remote sensing imagery, faced with multi-scale targets, its performance is degraded. We analyze the influences of pooling operation and target size on region proposal, then a modified solution for region proposal is introduced to improve recall rate of multi-scale targets. To speed up the convergence of the region proposal networks, an improved generation strategy of foreground samples is proposed, which could suppresses the generation of non-effective foreground samples. Extensive evaluations on the remote sensing image dataset show that the proposed model can obviously improve detection accuracy for multi-scale targets, moreover the training of the model is rapid and high-efficient.

Question: What is the target detection method for optical remote sensing images?


Multiresolution genetic clustering algorithm for texture segmentation. This work plans to approach the texture segmentation problem by incorporating genetic algorithm and K-means clustering method within a multiresolution structure. As the algorithm descends the multiresolution structure, the coarse segmentation results are propagated down to the lower levels so as to reduce the inherent class–position uncertainty and to improve the segmentation accuracy. The procedure is described as follows. In the first step, a quad-tree structure of multiple resolutions is constructed. Sampling windows of different sizes are utilized to partition the underlying image into blocks at different resolution levels and texture features are extracted from each block. Based on the texture features, a hybrid genetic algorithm is employed to perform the segmentation. While the select and mutate operators of the traditional genetic algorithm are adopted in this work, the crossover operator is replaced with K-means clustering method. In the final step, the boundaries and the segmentation result of the current resolution level are propagated down to the next level to act as contextual constraints and the initial configuration of the next level, respectively. q 2003 Elsevier B.V. All rights reserved.

Question: What is the multiresolution genetic clustering algorithm for texture segmentation?


Self-stabilizing Systems in Spite of Distributed Control. The synchronization task between loosely coupled cyclic sequential processes (as can be distinguished in, for instance, operating systems) can be viewed as keeping the relation "the system is in a legitimate state" invariant. As a result, each individual process step that could possibly cause violation of that relation has to be preceded by a test deciding whether the process in question is allowed to proceed or has to be delayed. The resulting design is readily—and quite systematically—implemented if the different processes can be granted mutually exclusive access to a common store in which "the current system state" is recorded.

Question: What is a self-stabilizing system?


Extraction of Information Related to Adverse Drug Events from Electronic Health Record Notes: Design of an End-to-End Model Based on Deep Learning. BACKGROUND Pharmacovigilance and drug-safety surveillance are crucial for monitoring adverse drug events (ADEs), but the main ADE-reporting systems such as Food and Drug Administration Adverse Event Reporting System face challenges such as underreporting. Therefore, as complementary surveillance, data on ADEs are extracted from electronic health record (EHR) notes via natural language processing (NLP). As NLP develops, many up-to-date machine-learning techniques are introduced in this field, such as deep learning and multi-task learning (MTL). However, only a few studies have focused on employing such techniques to extract ADEs.   OBJECTIVE We aimed to design a deep learning model for extracting ADEs and related information such as medications and indications. Since extraction of ADE-related information includes two steps-named entity recognition and relation extraction-our second objective was to improve the deep learning model using multi-task learning between the two steps.   METHODS We employed the dataset from the Medication, Indication and Adverse Drug Events (MADE) 1.0 challenge to train and test our models. This dataset consists of 1089 EHR notes of cancer patients and includes 9 entity types such as Medication, Indication, and ADE and 7 types of relations between these entities. To extract information from the dataset, we proposed a deep-learning model that uses a bidirectional long short-term memory (BiLSTM) conditional random field network to recognize entities and a BiLSTM-Attention network to extract relations. To further improve the deep-learning model, we employed three typical MTL methods, namely, hard parameter sharing, parameter regularization, and task relation learning, to build three MTL models, called HardMTL, RegMTL, and LearnMTL, respectively.   RESULTS Since extraction of ADE-related information

Question: What is the difference between a deep learning model and a deep learning model?


Sequential Mining: Patterns and Algorithms Analysis. This paper presents and analysis the common existing sequential pattern mining algorithms. It presents a classifying study of sequential pattern-mining algorithms into five extensive classes. First, on the basis of Apriori-based algorithm, second on Breadth First Search-based strategy, third on Depth First Search strategy, fourth on sequential closed-pattern algorithm and five on the basis of incremental pattern mining algorithms. At the end, a comparative analysis is done on the basis of important key features supported by various algorithms. This study gives an enhancement in the understanding of the approaches of sequential

Question: What is sequential mining?


A probabilistic model for retrospective news event detection. Retrospective news event detection (RED) is defined as the discovery of previously unidentified events in historical news corpus. Although both the contents and time information of news articles are helpful to RED, most researches focus on the utilization of the contents of news articles. Few research works have been carried out on finding better usages of time information. In this paper, we do some explorations on both directions based on the following two characteristics of news articles. On the one hand, news articles are always aroused by events; on the other hand, similar articles reporting the same event often redundantly appear on many news sources. The former hints a generative model of news articles, and the latter provides data enriched environments to perform RED. With consideration of these characteristics, we propose a probabilistic model to incorporate both content and time information in a unified framework. This model gives new representations of both news articles and news events. Furthermore, based on this approach, we build an interactive RED system, HISCOVERY, which provides additional functions to present events, Photo Story and Chronicle.

Question: What is a probabilistic model for retrospective news event detection?


Computational Models of Reading: A Primer. This article brief ly reviews four computational models of reading and the types of empirical findings that they are designed to explain: (1) theDual-Route model of word identification (Coltheart et al., 2001); (2) the SimpleRecurrent Network model of sentence processing (Elman, 1990); (3) the Construction–Integration model of discourse representation (Kintsch, 1988); and (4) the E-Z Reader model of eye-movement control in reading (Reichle et al., 2012). These particular models are reviewed because they provide comprehensive accounts of a large number of empirical findings in each of their respective domains, and because they have advanced our understanding of the cognitive processes involved in reading by motivating new empirical studies. Future models of reading will build upon the success of their predecessors by integrating models from two or more of the aforementioned domains, thereby providing a means to examine the compatibility of their theoretical assumptions and more comprehensive accounts of the cognitive processes involved in reading. The perceptual, cognitive, and motor processes that support skilled reading are numerous, extremely complex, and work together in a highly coordinated manner. For those reasons, any deep understanding of what happens in the mind during reading requires the use of computational models that describe those processes and how they jointly determine both the on-line behavior that is observed during reading (e.g., the patterns of eye movements that readers make) and the products of that behavior (e.g., the mental representations that readers generate to understand the text). As will be discussed in this article, such models provide theoretical frameworks for understanding the mental processes involved in reading, but also advance our understanding by generating novel predictions that can be tested with experiments. In the remainder of this article, I will describe four existing models tha

Question: What is a computational model of reading?


Zoomorphic design. Zoomorphic shapes are man-made shapes that possess the form or appearance of an animal. They have desirable aesthetic properties, but are difficult to create using conventional modeling tools. We present a method for creating zoomorphic shapes by merging a man-made shape and an animal shape. To identify a pair of shapes that are suitable for merging, we use an efficient graph kernel based technique. We formulate the merging process as a continuous optimization problem where the two shapes are deformed jointly to minimize an energy function combining several design factors. The modeler can adjust the weighting between these factors to attain high-level control over the final shape produced. A novel technique ensures that the zoomorphic shape does not violate the design restrictions of the man-made shape. We demonstrate the versatility and effectiveness of our approach by generating a wide variety of zoomorphic shapes.

Question: How to create zoomorphic shapes?


Dissolvable films of silk fibroin for ultrathin conformal bio-integrated electronics.. Electronics that are capable of intimate, non-invasive integration with the soft, curvilinear surfaces of biological tissues offer important opportunities for diagnosing and treating disease and for improving brain/machine interfaces. This article describes a material strategy for a type of bio-interfaced system that relies on ultrathin electronics supported by bioresorbable substrates of silk fibroin. Mounting such devices on tissue and then allowing the silk to dissolve and resorb initiates a spontaneous, conformal wrapping process driven by capillary forces at the biotic/abiotic interface. Specialized mesh designs and ultrathin forms for the electronics ensure minimal stresses on the tissue and highly conformal coverage, even for complex curvilinear surfaces, as confirmed by experimental and theoretical studies. In vivo, neural mapping experiments on feline animal models illustrate one mode of use for this class of technology. These concepts provide new capabilities for implantable and surgical devices.

Question: What is the silk fibroin used for?


Defeasible Logic Programming: An Argumentative Approach. The work reported here introduces Defeasible Logic Programming (DeLP), a formalism that combines results of Logic Programming and Defeasible Argumentation. DeLP provides the possibility of representing information in the form of weak rules in a declarative manner, and a defeasible argumentation inference mechanism for warranting the entailed conclusions. In DeLP an argumentation formalism will be used for deciding between contradictory goals. Queries will be supported by arguments that could be defeated by other arguments. A query q will succeed when there is an argument A for q that is warranted, i. e. the argument A that supports q is found undefeated by a warrant procedure that implements a dialectical analysis. The defeasible argumentation basis of DeLP allows to build applications that deal with incomplete and contradictory information in dynamic domains. Thus, the resulting approach is suitable for representing agent's knowledge and for providing an argumentation based reasoning mechanism to agents.

Question: What is defeasible logic programming?


Planning algorithms for s-curve trajectories. Although numerous researches on s-curve motion profiles have been carried out, up to date, no systematic investigation on the general model of polynomial s-curve motion profiles is considered. In this paper, the model of polynomial s-curve motion profiles is generalized in a recursive form. Based on that, a general algorithm to design s-curve trajectory with jerk bounded and time-optimal consideration is proposed. In addition, a special strategy for planning s-curve motion profiles using a trigonometric model is also presented. The algorithms are implemented on a linear motor system. Experimental results show the effectiveness and promising application ability of the algorithms in s-curve motion profiling.

Question: How to design s-curve motion profiles?


A hybrid bipartite graph based recommendation algorithm for mobile games. With the rapid development of the mobile games, mobile game recommendation has become a core technique to mobile game marketplaces. This paper proposes a bipartite graph based recommendation algorithm PKBBR (Prior Knowledge Based for Bipartite Graph Rank). We model the user's interest in mobile game based on bipartite graph structure and use the users' mobile game behavior to describe the edge weights of the graph, then incorporate users' prior knowledge into the projection procedure of the bipartite graph to enrich the information among the nodes. Because the popular games have a great influence on mobile game marketplace, we design a hybrid recommendation algorithm to incorporate popularity recommendation based on users' behaviors. The experiment results show that this hybrid method could achieve a better performance than other approaches.

Question: What is a bipartite graph?


Ecological momentary assessment.. Assessment in clinical psychology typically relies on global retrospective self-reports collected at research or clinic visits, which are limited by recall bias and are not well suited to address how behavior changes over time and across contexts. Ecological momentary assessment (EMA) involves repeated sampling of subjects' current behaviors and experiences in real time, in subjects' natural environments. EMA aims to minimize recall bias, maximize ecological validity, and allow study of microprocesses that influence behavior in real-world contexts. EMA studies assess particular events in subjects' lives or assess subjects at periodic intervals, often by random time sampling, using technologies ranging from written diaries and telephones to electronic diaries and physiological sensors. We discuss the rationale for EMA, EMA designs, methodological and practical issues, and comparisons of EMA and recall data. EMA holds unique promise to advance the science and practice of clinical psychology by shedding light on the dynamics of behavior in real-world settings.

Question: What is ecological momentary assessment?


Bundle Adjustment - A Modern Synthesis. This paper is a survey of the theory and methods of photogrammetric bundle adjustment, aimed at potential implementors in the computer vision community. Bundle adjustment is the problem of refining a visual reconstruction to produce jointly optimal structure and viewing parameter estimates. Topics covered include: the choice of cost function and robustness; numerical optimization including sparse Newton methods, linearly convergent approximations, updating and recursive methods; gauge (datum) invariance; and quality control. The theory is developed for general robust cost functions rather than restricting attention to traditional nonlinear least squares.

Question: What is bundle adjustment?


Equivalent circuit modeling of LLC resonant converter. LLC resonant converter is widely used in industry. However, up to now, no simple and accurate small-signal equivalent circuit model is available. This paper proposes an equivalent circuit model of LLC resonant converter. The simple equivalent circuit model is derived based on modification and simplification of extended describing function method. The model can well predicts the small-signal behaviors observed in LLC resonant converter, whenever switching frequency is below, close to or above the resonant frequency. For the first time, analytical expressions for control to output voltage, input to output voltage, input impedance and output impedance are provided to aid close loop feedback design. Simplis simulation and experimental results are presented to prove the accuracy of the model.

Question: What is LLC resonant converter?


Annual Report Readability , Earnings , and Stock Returns ∗. This paper examines the relationship between annual report readability and firm performance. This is motivated by the Securities and Exchange Commission's plain English disclosure regulations that attempt to make corporate disclosures easier to read for ordinary investors. I measure the readability of public company annual reports using the Fog Index and the Kincaid Index from computational linguistics. I find that the annual reports of firms with lower earnings are harder to read. Moreover, the positive earnings of firms with annual reports that are easier to read are more persistent in the next one to four years. This suggests that, consistent with the motivation behind the plain English disclosure regulation of the Securities and Exchange Commission, managers may be opportunistically choosing the readability of annual reports to hide adverse information from investors. However, I do not find any correlation between annual report readability and future stock returns, suggesting that the market does impound the implications of disclosure readability into stock prices.

Question: What is the relationship between annual report readability and firm performance?


On the evolution of user interaction in Facebook. Online social networks have become extremely popular; numerous sites allow users to interact and share content using social links. Users of these networks often establish hundreds to even thousands of social links with other users. Recently, researchers have suggested examining the activity network - a network that is based on the actual interaction between users, rather than mere friendship - to distinguish between strong and weak links. While initial studies have led to insights on how an activity network is structurally different from the social network itself, a natural and important aspect of the activity network has been disregarded: the fact that over time social links can grow stronger or weaker. In this paper, we study the evolution of activity between users in the Facebook social network to capture this notion. We find that links in the activity network tend to come and go rapidly over time, and the strength of ties exhibits a general decreasing trend of activity as the social network link ages. For example, only 30% of Facebook user pairs interact consistently from one month to the next. Interestingly, we also find that even though the links of the activity network change rapidly over time, many graph-theoretic properties of the activity network remain unchanged.

Question: How do social networks evolve over time?


Maladaptive and adaptive emotion regulation through music: a behavioral and neuroimaging study of males and females. Music therapists use guided affect regulation in the treatment of mood disorders. However, self-directed uses of music in affect regulation are not fully understood. Some uses of music may have negative effects on mental health, as can non-music regulation strategies, such as rumination. Psychological testing and functional magnetic resonance imaging (fMRI) were used explore music listening strategies in relation to mental health. Participants (n = 123) were assessed for depression, anxiety and Neuroticism, and uses of Music in Mood Regulation (MMR). Neural responses to music were measured in the medial prefrontal cortex (mPFC) in a subset of participants (n = 56). Discharge, using music to express negative emotions, related to increased anxiety and Neuroticism in all participants and particularly in males. Males high in Discharge showed decreased activity of mPFC during music listening compared with those using less Discharge. Females high in Diversion, using music to distract from negative emotions, showed more mPFC activity than females using less Diversion. These results suggest that the use of Discharge strategy can be associated with maladaptive patterns of emotional regulation, and may even have long-term negative effects on mental health. This finding has real-world applications in psychotherapy and particularly in clinical music therapy.

Question: What is the difference between maladaptive and adaptive emotion regulation through music?


ioCane: A Smart-Phone and Sensor-Augmented Mobility Aid for the Blind. We present the design, implementation, and early results of ioCane, a mobility aid for blind cane users that uses detachable cane-mounted ultrasonic sensors connected to a circuit board to send contextual data wirelessly to an Android phone application. The system uses the built-in mobile phone modalities of vibrations and chimes to alert the user to object height and proximity. We believe this plug-and-play solution for visually impaired users has the potential to enhance user mobility and object avoidance with a minimal learning curve. A pilot study testing the performance of the ioCane with blind cane users showed a 47.3% improvement in obstacle avoidance. To our knowledge, the ioCane is the first sensor-based mobility assistance system to integrate natively with a mobile phone without any modifications to the phone or the system, as well as the first system of its kind to be evaluated by actual visually-impaired cane users. ACM Classification

Question: What is the classification of the ioCane?


Robust acoustic echo cancellation in the short-time fourier transform domain using adaptive crossband filters. This paper presents a robust acoustic echo cancellation (AEC) system in the short-time Fourier transform (STFT) domain using adaptive crossband filters. The STFT-domain AEC allows for a simpler system structure compared to the traditional frequency-domain AEC, which normally requires several applications of the discrete Fourier transform (DFT) and the inverse DFT, while the robust AEC (RAEC) allows for continuous and stable filter updates during double talk without freezing the adaptive filter. The RAEC and the STFT-domain AEC have been investigated in the past in separate studies. In this work we propose a novel algorithm that combines the advantages of both approaches for robust update of the adaptive crossband filters even during double talk. Experimental results confirm the benefit of incorporating the robustness constraint for the adaptive crossband filters and show improved performance in terms of the echo reduction and the predicted sound quality.

Question: Robust acoustic echo cancellation in the short-time fourier transform domain using adaptive crossband filters.


Digital Enterprise Architecture - Transformation for the Internet of Things. Excellence in IT is both a driver and a key enabler of the digital transformation. The digital transformation changes the way we live, work, learn, communicate, and collaborate. The Internet of Things (IoT) fundamentally influences today's digital strategies with disruptive business operating models and fast changing markets. New business information systems are integrating emerging Internet of Things infrastructures and components. With the huge diversity of Internet of Things technologies and products organizations have to leverage and extend previous Enterprise Architecture efforts to enable business value by integrating Internet of Things architectures. Both architecture engineering and management of current information systems and business models are complex and currently integrating beside the Internet of Things synergistic subjects, like Enterprise Architecture in context with services & cloud computing, semantic-based decision support through ontologies and knowledge-based systems, big data management, as well as mobility and collaboration networks. To provide adequate decision support for complex business/IT environments, we have to make transparent the impact of business and IT changes over the integral landscape of affected architectural capabilities, like directly and transitively impacted IoT-objects, business categories, processes, applications, services, platforms and infrastructures. The paper describes a new metamodel-based approach for integrating Internet of Things architectural objects, which are semi-automatically federated into a holistic Digital Enterprise Architecture environment.

Question: What is the difference between a metamodel and a model?


Prediction of heart disease using data mining techniques. The healthcare industry is a vast field with a plethora of data about patients,added to the huge medical records every passing day. In terms of science, this industry is 'information rich' yet 'knowledge poor'. However, data mining with its various analytical tools and techniques plays a major role in reducing the use of cumbersome tests used on patients to detect a disease. The aim of this paper is to employ and analyze different data mining techniques for the prediction of heart disease in a patient through extraction of interesting patterns from the dataset using vital parameters. This paper strives to bring out the methodology and implementation of these techniques-Artificial Neural Networks, Decision Tree and Naive Bayes and stress upon the results and conclusion induced on the basis of accuracy and time complexity. By far, the observations reveal that Artificial Neural Networks outperformed Naive Bayes and Decision Tree.

Question: What is the aim of this paper?


Identifiable Phenotyping using Constrained Non-Negative Matrix Factorization. This work proposes a new algorithm for automated and simultaneous phenotyping of multiple co–occurring medical conditions, also referred as comorbidities, using clinical notes from the electronic health records (EHRs). A basic latent factor estimation technique of non-negative matrix factorization (NMF) is augmented with domain specific constraints to obtain sparse latent factors that are anchored to a fixed set of chronic conditions. The proposed anchoring mechanism ensures a one-to-one identifiable and interpretable mapping between the latent factors and the target comorbidities. Qualitative assessment of the empirical results by clinical experts suggests that the proposed model learns clinically interpretable phenotypes while being predictive of 30 day mortality. The proposed method can be readily adapted to any non-negative EHR data across various healthcare institutions.

Question: How can we identify phenotypes from clinical notes?


0.9–10GHz low noise amplifier with capacitive cross coupling. This paper presents a 0.9GHz-10GHz Ultra Wideband Low Noise Amplifier (LNA) designed for software-defined-radios (SDR). Capacitive cross coupling (CCC) is used at both input stage and cascade stage for wideband input impedance matching and small noise figure (NF). A combination of inductor peaking load and series inductor between the cascade stages of LNA is employed for a flat gain and enhanced input matching. This LNA is implemented in a standard 0.18µm CMOS technology. For 0.9GHz-10GHz ultra wideband applications, it achieves a maximum gain of 14dB, 3.5dB∼4.1dB NF and +1.8dBm IIP3. It consumes only7.6mW from a 1.8V power supply.

Question: What is a low noise amplifier?


GraphGen: Exploring Interesting Graphs in Relational Data. Analyzing interconnection structures among the data through the use of graph algorithms and graph analytics has been shown to provide tremendous value in many application domains. However, graphs are not the primary choice for how most data is currently stored, and users who want to employ graph analytics are forced to extract data from their data stores, construct the requisite graphs, and then use a specialized engine to write and execute their graph analysis tasks. This cumbersome and costly process not only raises barriers in using graph analytics, but also makes it hard to explore and identify hidden or implicit graphs in the data. Here we demonstrate a system, called GRAPHGEN, that enables users to declaratively specify graph extraction tasks over relational databases, visually explore the extracted graphs, and write and execute graph algorithms over them, either directly or using existing graph libraries like the widely used NetworkX Python library. We also demonstrate how unifying the extraction tasks and the graph algorithms enables significant optimizations that would not be possible otherwise.

Question: What is graphgen?


An AUC-based permutation variable importance measure for random forests. The random forest (RF) method is a commonly used tool for classification with high dimensional data as well as for ranking candidate predictors based on the so-called random forest variable importance measures (VIMs). However the classification performance of RF is known to be suboptimal in case of strongly unbalanced data, i.e. data where response class sizes differ considerably. Suggestions were made to obtain better classification performance based either on sampling procedures or on cost sensitivity analyses. However to our knowledge the performance of the VIMs has not yet been examined in the case of unbalanced response classes. In this paper we explore the performance of the permutation VIM for unbalanced data settings and introduce an alternative permutation VIM based on the area under the curve (AUC) that is expected to be more robust towards class imbalance. We investigated the performance of the standard permutation VIM and of our novel AUC-based permutation VIM for different class imbalance levels using simulated data and real data. The results suggest that the new AUC-based permutation VIM outperforms the standard permutation VIM for unbalanced data settings while both permutation VIMs have equal performance for balanced data settings. The standard permutation VIM loses its ability to discriminate between associated predictors and predictors not associated with the response for increasing class imbalance. It is outperformed by our new AUC-based permutation VIM for unbalanced data settings, while the performance of both VIMs is very similar in the case of balanced classes. The new AUC-based VIM is implemented in the R package party for the unbiased RF variant based on conditional inference trees. The codes implementing our study are available from the companion website: http://www.ibe.med.uni-muenchen.de/organisation/mitarbeiter/070_drittmittel/janitza/index.html

Question: What is the difference between the standard permutation VIM and the AUC-based permutation VIM?


Automatic inspection of pavement cracking distress. 15. Supplementary Notes Project performed in cooperation with the Texas Department of Transportation and the Federal Highway Administration. Project Title: Implementation of Automated Pavement Distress Rating Systems 16. Abstract This paper presents the image-processing algorithm customized for high-speed, real-time inspection of pavement cracking. In the algorithm, a pavement image is divided into grid cells of 8 x 8 pixels, and each cell is classified as a non-crack or crack cell using the grayscale information of the border pixels. Whether a crack cell can be regarded as a basic element (or seed) depends on its contrast to the neighboring cells. A number of crack seeds can be called a crack cluster if they fall on a linear string. A crack cluster corresponds to a dark strip in the original image that may or may not be a section of a real crack. Additional conditions to verify a crack cluster include the requirements in the contrast, width, and length of the strip. If verified crack clusters are oriented in similar directions, they will be joined to become one crack. Because many operations are performed on crack seeds rather than on the original image, crack detection can be executed simultaneously when the frame grabber is forming a new image, which permits a real-time, online pavement survey. The trial test results show good repeatability and accuracy when multiple surveys were conducted in different driving conditions.

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


ICWSM - A Great Catchy Name: Semi-Supervised Recognition of Sarcastic Sentences in Online Product Reviews. Sarcasm is a sophisticated form of speech act widely used in online communities. Automatic recognition of sarcasm is, however, a novel task. Sarcasm recognition could contribute to the performance of review summarization and ranking systems. This paper presents SASI, a novel Semi-supervised Algorithm for Sarcasm Identification that recognizes sarcastic sentences in product reviews. SASI has two stages: semisupervised pattern acquisition, and sarcasm classification. We experimented on a data set of about 66000 Amazon reviews for various books and products. Using a gold standard in which each sentence was tagged by 3 annotators, we obtained precision of 77% and recall of 83.1% for identifying sarcastic sentences. We found some strong features that characterize sarcastic utterances. However, a combination of more subtle pattern-based features proved more promising in identifying the various facets of sarcasm. We also speculate on the motivation for using sarcasm in online communities and social networks.

Question: What is a sarcasm?