title: Comparison of stochastic filtering methods for 3D tracking

abstract: In the recent years, the 3D visual research has gained momentum with publications appearing for all aspects of 3D including visual tracking. This paper presents a review of the literature published for 3D visual tracking over the past five years. The work particularly focuses on stochastic filtering techniques such as particle filter and Kalman filter. These two filters are extensively used for tracking due to their ability to consider uncertainties in the estimation. The improvement in computational power of computers and increasing interest in robust tracking algorithms lead to increase in the use of stochastic filters in visual tracking in general and 3D visual tracking in particular. Stochastic filters are used for numerous applications in the literature such as robot navigation, computer games and behavior analysis. Kalman filter is a linear estimator which approximates system's dynamics with Gaussian model while particle filter approximates system's dynamics using weighted samples. In this paper, we investigate the implementation of Kalman and particle filters in the published work and we provide comparison between these techniques qualitatively as well as quantitatively. The quantitative analysis is in terms of computational time and accuracy. The quantitative analysis has been implemented using four parameters of the tracked object which are object position, velocity, size of bounding ellipse and orientation angle.


title: Detecting Events and Key Actors in Multi-person Videos

abstract: Multi-person event recognition is a challenging task, often with many people active in the scene but only a small subset contributing to an actual event. In this paper, we propose a model which learns to detect events in such videos while automatically "attending" to the people responsible for the event. Our model does not use explicit annotations regarding who or where those people are during training and testing. In particular, we track people in videos and use a recurrent neural network (RNN) to represent the track features. We learn time-varying attention weights to combine these features at each time-instant. The attended features are then processed using another RNN for event detection/classification. Since most video datasets with multiple people are restricted to a small number of videos, we also collected a new basketball dataset comprising 257 basketball games with 14K event annotations corresponding to 11 event classes. Our model outperforms state-of-the-art methods for both event classification and detection on this new dataset. Additionally, we show that the attention mechanism is able to consistently localize the relevant players.


title: A case study in statistical image processing: positron-emission tomography

abstract: This paper traces the development of methods for producing estimates of the spatial distribution of a positron-emitting radionuclide within the body. a This work was supported by the National Institute of Health under grants RR01380, from the Division of Research Resources, and HL13851 and by the National Science Foundation under grants MIP-8722463 and ECE-8552518. b Washington University, St. Louis, Missouri 63130.


title: Detection of forest fires using machine learning technique: A perspective

abstract: Wireless Sensor Networks (WSN) has gained attention as it has been useful in warning about disasters. Predicting natural disasters like hailstorm, fire, rainfall etc. by WSN are infrequent and stochastic. This is an important topic of research. Detection of these disasters should be fast and accurate as they may cause damage and destruction at a large scale. In this paper, comparison of various machine learning techniques such as SVM, regression, decision trees, neural networks etc. has been done for prediction of forest fires. The proposed approach in this paper presents how regression works best for detection of forest fires with high accuracy by dividing the dataset. Fast detection of forest fires is done in this paper by taking less time as compared to other machine learning techniques.


A Generalization of Regenerating Codes for Clustered Storage Systems


title: The Influence of Attitudes on Behavior.

abstract: On September 11, 2001, a group of terrorists commandeered four airliners filled with passengers and fuel in a coordinated attack on the United States. Two airplanes were flown into the World Trade Center towers in New York City, one into the Pentagon, and the fourth crashed in Pennsylvania when passengers resisted the hijackers. Not only did the attack result in the collapse of the twin towers and in severe damage to the Pentagon—prominent symbols of American financial and military might—but thousands of people lost their lives, including several hundred police officers and firefighters who came to the aid of the victims. The response of the American people was inspiring. A wave of patriotism and national pride washed across the country. Public discussion turned from issues of little substance to serious matters of life and death. The increased solidarity was not limited to words; it found expression in a multitude of private and public deeds. The American flag was prominently displayed on homes, offices, and cars; police officers were cheered in the streets of New York; monetary donations flowed into relief funds; blood banks that had faced dwindling supplies were overwhelmed by volunteer donors; and even otherwise cynical politicians joined in a spontaneous singing of God Bless America on the steps of the Capitol in Washington, DC. Clearly, the dramatic events of September 11 had a profound impact on people’s beliefs and attitudes, and the enhanced pride in country, increased solidarity with fellow citizens, and heightened sense of purpose found expression in a variety of behavioral domains. In light of such evidence, few would question the proposition that people act in accordance with their attitudes. If further evidence were needed, one only need to consider the actions of the terrorists who were prepared to sacrifice their lives for their fundamentalist religious beliefs and extremist political ideology. Yet there was a time when many social psychologists were ready to abandon the attitude construct because they had become convinced that people’s attitudes had little to do with their actual behavior. In this chapter we discuss the role of attitudes in human social behavior. We will show that, in order to understand the influence of attitudes on behavior, we must distinguish between two types of attitude. The first type are general attitudes toward physical objects (Yosemite National Park, the Empire State Building); racial, ethnic, or other groups (African Americans, Jews,


Track Characterizing mapk signaling in different cancers Through large public datasets


title: AMTS: Advanced Movie Ticketing System

abstract: Mobiles phones play a vital role in our day-to-day life and their revolution has brought numerous changes not only in professional life but also personal lives of people across the globe. Initially, mobile phones were used for communication purpose, but with the advanced developments we are now using applications directly. This paper focuses and presents a novel application (app) named Advance Movie Ticketing System (AMTS) for online booking of movie tickets, followed by many other facilities also. Once the ticket is booked, a secured QR code is generated which is to be used at the ticket verification and authentication at various Cinema halls. By this application the physical transactions and the redundant physical checks may be reduced to some extent.


title: PUSHOVER ANALYSIS OF A 19-STORY CONCRETE SHEAR-WALL BUILDING

abstract: Pushover analysis was performed on a nineteen story, slender concrete tower building located in San Francisco with a gross area of 430,000 square feet. Lateral system of the building consists of concrete shear walls. The building is newly designed conforming to 1997 Uniform Building Code, and pushover analysis was performed to verify code's underlying intent of Life Safety performance under design earthquake. Procedure followed for carrying out the analysis and results are presented in this paper.


title: Comparative assessment of methods for aligning multiple genome sequences

abstract: Multiple sequence alignment is a difficult computational problem. There have been compelling pleas for methods to assess whole-genome multiple sequence alignments and compare the alignments produced by different tools. We assess the four ENCODE alignments, each of which aligns 28 vertebrates on 554 Mbp of total input sequence. We measure the level of agreement among the alignments and compare their coverage and accuracy. We find a disturbing lack of agreement among the alignments not only in species distant from human, but even in mouse, a well-studied model organism. Overall, the assessment shows that Pecan produces the most accurate or nearly most accurate alignment in all species and genomic location categories, while still providing coverage comparable to or better than that of the other alignments in the placental mammals. Our assessment reveals that constructing accurate whole-genome multiple sequence alignments remains a significant challenge, particularly for noncoding regions and distantly related species.


title: Towards a simplified ontology for better e-commerce search

abstract: Query Understanding is a semantic search method that can classify tokens in a customer's search query to entities such as Product, Brand, etc. This method can overcome the limitations of bag-of-words methods but requires an ontology. We show that current ontologies are not optimized for search and propose a simplified ontology framework designed specifically for e-commerce search and retrieval. We also present three methods for automatically extracting product classes for the proposed ontology and compare their performance relative to each other.


Developing a Mechanism to Study Code Trustworthiness


title: Ask Your Neurons: A Neural-Based Approach to Answering Questions about Images

abstract: We address a question answering task on real-world images that is set up as a Visual Turing Test. By combining latest advances in image representation and natural language processing, we propose Neural-Image-QA, an end-to-end formulation to this problem for which all parts are trained jointly. In contrast to previous efforts, we are facing a multi-modal problem where the language output (answer) is conditioned on visual and natural language input (image and question). Our approach Neural-Image-QA doubles the performance of the previous best approach on this problem. We provide additional insights into the problem by analyzing how much information is contained only in the language part for which we provide a new human baseline. To study human consensus, which is related to the ambiguities inherent in this challenging task, we propose two novel metrics and collect additional answers which extends the original DAQUAR dataset to DAQUAR-Consensus.


title: Semantically Conditioned LSTM-based Natural Language Generation for Spoken Dialogue Systems

abstract: © 2015 Association for Computational Linguistics. Natural language generation (NLG) is a critical component of spoken dialogue and it has a significant impact both on usability and perceived quality. Most NLG systems in common use employ rules and heuristics and tend to generate rigid and stylised responses without the natural variation of human language. They are also not easily scaled to systems covering multiple domains and languages. This paper presents a statistical language generator based on a semantically controlled Long Short-term Memory (LSTM) structure. The LSTM generator can learn from unaligned data by jointly optimising sentence planning and surface realisation using a simple cross entropy training criterion, and language variation can be easily achieved by sampling from output candidates. With fewer heuristics, an objective evaluation in two differing test domains showed the proposed method improved performance compared to previous methods. Human judges scored the LSTM system higher on informativeness and naturalness and overall preferred it to the other systems..


title: Keeping the vehicle on the road: A survey on on-road lane detection systems

abstract: The development of wireless sensor networks, such as researchers Advanced Driver Assistance Systems (ADAS) requires the ability to analyze the road scene just like a human does. Road scene analysis is an essential, complex, and challenging task and it consists of: road detection (which includes the localization of the road, the determination of the relative position between vehicle and road, and the analysis of the vehicle's heading direction) and obstacle detection (which is mainly based on localizing possible obstacles on the vehicle's path). The detection of the road borders, the estimation of the road geometry, and the localization of the vehicle are essential tasks in this context since they are required for the lateral and longitudinal control of the vehicle. Within this field, on-board vision has been widely used since it has many advantages (higher resolution, low power consumption, low cost, easy aesthetic integration, and nonintrusive nature) over other active sensors such as RADAR or LIDAR. At first glance the problem of detecting the road geometry from visual information seems simple and early works in this field were quickly rewarded with promising results. However, the large variety of scenarios and the high rates of success demanded by the industry have kept the lane detection research work alive. In this article a comprehensive review of vision-based road detection systems vision is presented.


title: Estimating Embedding Vectors for Queries

abstract: The dense vector representation of vocabulary terms, also known as word embeddings, have been shown to be highly effective in many natural language processing tasks. Word embeddings have recently begun to be studied in a number of information retrieval (IR) tasks. One of the main steps in leveraging word embeddings for IR tasks is to estimate the embedding vectors of queries. This is a challenging task, since queries are not always available during the training phase of word embedding vectors. Previous work has considered the average or sum of embedding vectors of all query terms (AWE) to model the query embedding vectors, but no theoretical justification has been presented for such a model. In this paper, we propose a theoretical framework for estimating query embedding vectors based on the individual embedding vectors of vocabulary terms. We then provide a number of different implementations of this framework and show that the AWE method is a special case of the proposed framework. We also introduce pseudo query vectors, the query embedding vectors estimated using pseudo-relevant documents. We further extrinsically evaluate the proposed methods using two well-known IR tasks: query expansion and query classification. The estimated query embedding vectors are evaluated via query expansion experiments over three newswire and web TREC collections as well as query classification experiments over the KDD Cup 2005 test set. The experiments show that the introduced pseudo query vectors significantly outperform the AWE method.


title: Advanced IEEE 802.11ax TCP aware scheduling under unreliable channels

abstract: In this paper we suggest advanced IEEE 802.11ax TCP-aware scheduling strategies for optimizing the AP operation under transmission of unidirectional TCP traffic. Our scheduling strategies optimize the performance using the capability for Multi User transmissions over the Uplink, first introduced in IEEE 802.11ax, together with Multi User transmissions over the Downlink. They are based on Transmission Opportunities (TXOP) and we suggest three scheduling strategies determining the TXOP formation parameters. In one of the strategies one can control the achieved Goodput vs. the delay. We also assume saturated WiFi transmission queues. We show that with minimal Goodput degradation one can avoid considerable delays.


title: Low voltage CMOS op-amp with rail-to-rail input/output swing

abstract: As the supply voltage to a standard CMOS op-amp is reduced, the input common mode range and the output swing get reduced drastically. Special biasing circuits have to be used to raise them up to rail-to-rail supply voltage. Three low voltage op-amps with new biasing circuits have been proposed in this paper and their performance evaluated. The op-amp design is focused on dynamic range and high drive capability.


title: SGD-QN: Careful Quasi-Newton Stochastic Gradient Descent

abstract: The SGD-QN algorithm is a stochastic gradient descent algorithm that makes careful use of second-order information and splits the parameter update into independently scheduled components. Thanks to this design, SGD-QN iterates nearly as fast as a first-order stochastic gradient descent but requires less iterations to achieve the same accuracy. This algorithm won the "Wild Track" of the first PASCAL Large Scale Learning Challenge (Sonnenburg et al., 2008).


title: Online analysis of information diffusion in twitter

abstract: The advent of social media has facilitated the study of information diffusion, user interaction and user influence over social networks. The research on analyzing information spreading focuses mostly on modeling, while analyses of real-life data have been limited to small, carefully cleaned datasets that are analyzed in an offline fashion. In this paper, we present an approach for online analysis of information diffusion in Twitter. We reconstruct so-called information cascades that model how information is being propagated from user to user from the stream of messages and the social graph. The results show that such an inference is feasible even on noisy, large-scale, rapidly produced data. We provide insights into the impact of incomplete data and the effect of different influence models on the cascades. The observed cascades show a significant amount of variety in scale and structure.


title: Access Control Mechanism for IoT Environments Based on Modelling Communication Procedures as Resources

abstract: Internet growth has generated new types of services where the use of sensors and actuators is especially remarkable. These services compose what is known as the Internet of Things (IoT). One of the biggest current challenges is obtaining a safe and easy access control scheme for the data managed in these services. We propose integrating IoT devices in an access control system designed for Web-based services by modelling certain IoT communication elements as resources. This would allow us to obtain a unified access control scheme between heterogeneous devices (IoT devices, Internet-based services, etc.). To achieve this, we have analysed the most relevant communication protocols for these kinds of environments and then we have proposed a methodology which allows the modelling of communication actions as resources. Then, we can protect these resources using access control mechanisms. The validation of our proposal has been carried out by selecting a communication protocol based on message exchange, specifically Message Queuing Telemetry Transport (MQTT). As an access control scheme, we have selected User-Managed Access (UMA), an existing Open Authorization (OAuth) 2.0 profile originally developed for the protection of Internet services. We have performed tests focused on validating the proposed solution in terms of the correctness of the access control system. Finally, we have evaluated the energy consumption overhead when using our proposal.


title: General Type-2 Fuzzy Logic Systems Made Simple: A Tutorial

abstract: The purpose of this tutorial paper is to make general type-2 fuzzy logic systems (GT2 FLSs) more accessible to fuzzy logic researchers and practitioners, and to expedite their research, designs, and use. To accomplish this, the paper 1) explains four different mathematical representations for general type-2 fuzzy sets (GT2 FSs); 2) demonstrates that for the optimal design of a GT2 FLS, one should use the vertical-slice representation of its GT2 FSs because it is the only one of the four mathematical representations that is parsimonious; 3) shows how to obtain set theoretic and other operations for GT2 FSs using type-1 (T1) FS mathematics (α- cuts play a central role); 4) reviews Mamdani and TSK interval type-2 (IT2) FLSs so that their mathematical operations can be easily used in a GT2 FLS; 5) provides all of the formulas that describe both Mamdani and TSK GT2 FLSs; 6) explains why center-of sets type-reduction should be favored for a GT2 FLS over centroid type-reduction; 7) provides three simplified GT2 FLSs (two are for Mamdani GT2 FLSs and one is for a TSK GT2 FLS), all of which bypass type reduction and are generalizations from their IT2 FLS counterparts to GT2 FLSs; 8) explains why gradient-based optimization should not be used to optimally design a GT2 FLS; 9) explains how derivative-free optimization algorithms can be used to optimally design a GT2 FLS; and 10) provides a three-step approach for optimally designing FLSs in a progressive manner, from T1 to IT2 to GT2, each of which uses a quantum particle swarm optimization algorithm, by virtue of which the performance for the IT2 FLS cannot be worse than that of the T1 FLS, and the performance for the GT2 FLS cannot be worse than that of the IT2 FLS.


title: A Hybrid Islanding Detection Technique Using Voltage Unbalance and Frequency Set Point

abstract: The phenomenon of unintentional islanding occurs when a distributed generator (DG) continues to feed power into the grid when power flow from the central utility source has been interrupted. This phenomenon can result in serious injury to the linemen who are trying to fix the power outage problem. Several techniques have been proposed in the past to avoid such an occurrence. This paper first gives an overview of the dominant islanding detection techniques. Then, the principles of two of the suggested techniques are combined to obtain a new hybrid islanding detection technique for synchronously rotating DGs. Simulation results show that the proposed hybrid technique is more effective than each of the techniques used. Simulation results are given for two testbeds to verify the advantages of the proposed hybrid islanding detection technique


title: Diagnosis and management of primary breast sarcoma

abstract: Primary sarcoma of the breast is an extremely rare and heterogeneous disease. The rarity of this tumour limits most studies to small retrospective case reviews and case reports and has made clinicopathological study difficult. This article reviews the current literature on the diagnosis and management of breast sarcoma. The optimal treatment of breast sarcoma involves a multidisciplinary team prior to the initiation of treatment. Patients with tumours less than 5 cm that are easily resectable should undergo complete resection to the extent required to provide negative surgical margins. Negative surgical margins are more important for local recurrence and overall survival than the extent of surgical resection. Thus, neoadjuvant chemotherapy should be considered in order to shrink the tumour and help obtain negative surgical margins. Whether chemotherapy is indicated is primarily determined by tumour size. There is evidence that tumours larger than 5 cm are associated with an elevated risk of systemic failure and a poor prognosis. After surgical resection, patients with chemosensitive tumours should undergo additional adjuvant chemotherapy to treat micrometastatic disease. Radiation therapy should be used to improve local control in cases in which the tumour is larger than 5 cm and in cases with positive surgical margins. We propose to treat the patients according to the clinical practice guidelines in use for soft tissue sarcomas and address them to a reference centre for sarcoma. The appropriate treatment of breast sarcoma requires a multidisciplinary team approach necessitating experienced sarcoma surgeons, pathologists, radiotherapists and medical oncologists. Treating rare tumours in the same place should permit us to standardise pathological data and to include patients into multicentric radiotherapy or chemotherapy protocols to improve overall survival.


title: Programmed population control by cell–cell communication and regulated killing

abstract: De novo engineering of gene circuits inside cells is extremely difficult, and efforts to realize predictable and robust performance must deal with noise in gene expression and variation in phenotypes between cells. Here we demonstrate that by coupling gene expression to cell survival and death using cell-cell communication, we can programme the dynamics of a population despite variability in the behaviour of individual cells. Specifically, we have built and characterized a 'population control' circuit that autonomously regulates the density of an Escherichia coli population. The cell density is broadcasted and detected by elements from a bacterial quorum-sensing system, which in turn regulate the death rate. As predicted by a simple mathematical model, the circuit can set a stable steady state in terms of cell density and gene expression that is easily tunable by varying the stability of the cell-cell communication signal. This circuit incorporates a mechanism for programmed death in response to changes in the environment, and allows us to probe the design principles of its more complex natural counterparts.


title: Collective dynamics of ‘small-world’ networks

abstract: Networks of coupled dynamical systems have been used to model biological oscillators1,2,3,4, Josephson junction arrays5,6, excitable media7, neural networks8,9,10, spatial games11, genetic control networks12 and many other self-organizing systems. Ordinarily, the connection topology is assumed to be either completely regular or completely random. But many biological, technological and social networks lie somewhere between these two extremes. Here we explore simple models of networks that can be tuned through this middle ground: regular networks ‘rewired’ to introduce increasing amounts of disorder. We find that these systems can be highly clustered, like regular lattices, yet have small characteristic path lengths, like random graphs. We call them ‘small-world’ networks, by analogy with the small-world phenomenon13,14 (popularly known as six degrees of separation15). The neural network of the worm Caenorhabditis elegans, the power grid of the western United States, and the collaboration graph of film actors are shown to be small-world networks. Models of dynamical systems with small-world coupling display enhanced signal-propagation speed, computational power, and synchronizability. In particular, infectious diseases spread more easily in small-world networks than in regular lattices.


title: Future Challenging Issues in Location based Services

abstract: The fast advances of mobile devices and positioning technologies has led to the flourish of Location-Based Services (LBS), in that people want to enjoy wireless services everywhere like in hotels, colleges, etc. LBS, the branch of computer program level services used in various fields and support, the application are broadly classified as Maps and Navigation, Information service, Tracking service, Social networking, Games, Vehicular navigation and Advertising etc. Now a days, LBSs attract millions of mobile users for example include POI finders such as Qype, which help the users to find the next POI such as bars or cinemas, and enrich the provided information. But during this communication, Security and Privacy of personal location information (of LBSs users) is becoming an increasingly important issue for future. So concerning it (privacy) as an important issue, discusses several privacy preserving location issues for vehicular (mobile) users (since knowledge of a vehicle‟s location can result in leakage of sensitive information). Location privacy for mobile users is mainly determined into two levels such as internally by a device or externally by systems and kind of networks with which the device interrelates. Users wish to maintain the vehicle„s information is known only to those legally authorized to have access to them and remain unknown to anybody unauthorized. Hence the purpose and contribution of this paper is to discussed about various privacy and challenges issues in LBSs increasing in future that have not been published in the any research journal so far.


title: Paper ladder: a rating scale to collect children's opinion in user studies

abstract: We present Paper Ladder, a low-cost, light-weight, and paper-based variant of the Sticky Ladder rating scale to collect children's product preferences in user studies. This paper version revamps the original, effective but arguably impractical evaluation method by making it more accessible to researchers. In this paper, we show its potential with preliminary results from a pilot study, where 45 grade-2 children used a Paper Ladder to rate their preference for different types of correctness feedback in a math app. Results suggest that Paper Ladder could be an effective and valuable instrument for conducting user studies with young children.


title: Direct and indirect effects of perceived social support on women's infertility-related stress

abstract: BACKGROUND ::: Social support can be a critical component of how a woman adjusts to infertility, yet few studies have investigated its impact on infertility-related coping and stress. We examined relationships between social support contexts and infertility stress domains, and tested if they were mediated by infertility-related coping strategies in a sample of infertile women. ::: ::: ::: METHODS ::: The Multidimensional Scale of Perceived Social Support, the Copenhagen Multi-centre Psychosocial Infertility coping scales and the Fertility Problem Inventory were completed by 252 women seeking treatment. Structural equation modeling analysis was used to test the hypothesized multiple mediation model. ::: ::: ::: RESULTS ::: The final model revealed negative effects from perceived partner support to relationship concern (β = -0.47), sexual concern (β = -0.20) and rejection of childfree lifestyle through meaning-based coping (β = -0.04). Perceived friend support had a negative effect on social concern through active-confronting coping (β = -0.04). Finally, besides a direct negative association with social concern (β = -0.30), perceived family support was indirectly and negatively related with all infertility stress domains (β from -0.04 to -0.13) through a positive effect of active-avoidance coping. The model explained between 12 and 66% of the variance of outcomes. ::: ::: ::: CONCLUSIONS ::: Despite being limited by a convenience sampling and cross-sectional design, results highlight the importance of social support contexts in helping women deal with infertility treatment. Health professionals should explore the quality of social networks and encourage seeking positive support from family and partners. Findings suggest it might prove useful for counselors to use coping skills training interventions, by retraining active-avoidance coping into meaning-based and active-confronting strategies.


title: Learning Steering Bounds for Parallel Autonomous Systems

abstract: Deep learning has been successfully applied to “end-to-end” learning of the autonomous driving task, where a deep neural network learns to predict steering control commands from camera data input. However, the learned representations do not support higher-level decision making required for autonomous navigation, nor the uncertainty estimates required for parallel autonomy, where vehicle control is shared between human and robot. This paper tackles the problem of learning a representation to predict a continuous control probability distribution, and thus steering control options and bounds for those options, which can be used for autonomous navigation. Each mode of the distribution encodes a possible macro-action that the system could execute at that instant, and the covariances of the modes place bounds on safe steering control values. Our approach has the added advantage of being trained on unlabeled data collected from inexpensive cameras. The deep neural network based algorithm generates a probability distribution over the space of steering angles, from which we leverage Variational Bayesian methods to extract a mixture model and compute the different possible actions in the environment. A bound, which the autonomous vehicle must respect in our parallel autonomy setting, is then computed for each of these actions. We evaluate our approach on a challenging dataset containing a wide variety of driving conditions, and show that our algorithm is capable of parameterizing Gaussian Mixture Models for possible actions, and extract steering bounds with a mean error of only 2 degrees. Additionally, we demonstrate our system working on a full scale autonomous vehicle and evaluate its ability to successful handle various different parallel autonomy situations.


title: Dual-Primal Graph Convolutional Networks

abstract: In recent years, there has been a surge of interest in developing deep learning methods for non-Euclidean structured data such as graphs. In this paper, we propose Dual-Primal Graph CNN, a graph convolutional architecture that alternates convolution-like operations on the graph and its dual. Our approach allows to learn both vertex- and edge features and generalizes the previous graph attention (GAT) model. We provide extensive experimental validation showing state-of-the-art results on a variety of tasks tested on established graph benchmarks, including CORA and Citeseer citation networks as well as MovieLens, Flixter, Douban and Yahoo Music graph-guided recommender systems.


Synthesizing cloth wrinkles by CNN-based geometry image superresolution


Feature Engineering for Second Language Acquisition Modeling


title: Circle grid fractal pattern for calibration at different camera zoom levels

abstract: Camera calibration patterns and fiducial markers are basic technique for 3D measurement to create Computer Graphics (CG) and Augmented Reality (AR). A checkerboard is widely used as a calibration pattern and a circle or ring grid provides higher precision [Datta et al. 2009], and matrix codes are common as AR markers.


title: Solving Influence Diagrams: Exact Algorithms

abstract: Influence diagrams were developed as a graphical representation for formulating a decision analysis model, facilitating communication between the decision maker and the analyst. We show several approaches for evaluating influence diagrams, determining the optimal strategy for the decision maker and the value or certain equivalent of the decision situation when that optimal strategy is applied. They can be converted into decision trees, evaluated directly as influence diagrams, or converted into another graphical model, a rooted cluster tree, for analysis. ::: ::: ::: Keywords: ::: ::: influence diagrams; ::: cluster trees; ::: junction trees; ::: exact algorithms; ::: decision analysis


title: A large-scale classification of English verbs

abstract: Lexical classifications have proved useful in supporting various natural language processing (NLP) tasks. The largest verb classification for English is Levin’s (1993) work which defines groupings of verbs based on syntactic and semantic properties. VerbNet (VN) (Kipper et al. 2000; Kipper-Schuler 2005)—an extensive computational verb lexicon for English—provides detailed syntactic-semantic descriptions of Levin classes. While the classes included are extensive enough for some NLP use, they are not comprehensive. Korhonen and Briscoe (2004) have proposed a significant extension of Levin’s classification which incorporates 57 novel classes for verbs not covered (comprehensively) by Levin. Korhonen and Ryant (unpublished) have recently proposed another extension including 53 additional classes. This article describes the integration of these two extensions into VN. The result is a comprehensive Levin-style classification for English verbs providing over 90% token coverage of the Proposition Bank data (Palmer et al. 2005) and thus can be highly useful for practical applications.


title: 3D Fingerprint Phantoms

abstract: One of the critical factors prior to deployment of any large scale biometric system is to have a realistic estimate of its matching performance. In practice, evaluations are conducted on the operational data to set an appropriate threshold on match scores before the actual deployment. These performance estimates, though, are restricted by the amount of available test data. To overcome this limitation, use of a large number of 2D synthetic fingerprints for evaluating fingerprint systems had been proposed. However, the utility of 2D synthetic fingerprints is limited in the context of testing end-to-end fingerprint systems which involve the entire matching process, from image acquisition to feature extraction and matching. For a comprehensive evaluation of fingerprint systems, we propose creating 3D fingerprint phantoms (phantoms or imaging phantoms are specially designed objects with known properties scanned or imaged to evaluate, analyze, and tune the performance of various imaging devices) with known characteristics (e.g., type, singular points and minutiae) by (i) projecting 2D synthetic fingerprints with known characteristics onto a generic 3D finger surface and (ii) printing the 3D fingerprint phantoms using a commodity 3D printer. Preliminary experimental results show that the captured images of the 3D fingerprint phantoms can be successfully matched to the 2D synthetic fingerprint images (from which the phantoms were generated) using a commercial fingerprint matcher. This demonstrates that our method preserves the ridges and valleys during the 3D fingerprint phantom creation process ensuring that the synthesized 3D phantoms can be utilized for comprehensive evaluations of fingerprint systems.


title: Modeling Semantic Expectation: Using Script Knowledge for Referent Prediction

abstract: Recent research in psycholinguistics has provided increasing evidence that humans predict upcoming content. Prediction also affects perception and might be a key to robustness in human language processing. In this paper, we investigate the factors that affect human prediction by building a computational model that can predict upcoming discourse referents based on linguistic knowledge alone vs. linguistic knowledge jointly with common-sense knowledge in the form of scripts. We find that script knowledge significantly improves model estimates of human predictions. In a second study, we test the highly controversial hypothesis that predictability influences referring expression type but do not find evidence for such an effect.


Unsupervised concept based entity extraction from scientific titles


Industrial Control System Simulation and Data Logging for Intrusion Detection System Research


title: Review of dimensioning and tolerancing: representation and processing

abstract: The paper surveys the current state of knowledge of techniques for representing, manipulating and analysing dimensioning and tolerancing data in computer-aided design and manufacturing. The use of solid models and variational geometry, and its implications for the successful integration of CAD and CAM, are discussed. The topics explored so far can be grouped into four categories: (a) the representation ot dimensioning and tolerancing (D& T), (b) the synthesis and analysis of D& T, (() tolerance control, and (d) the implications of D& T in CAM. The paper describes in detail the recent work in each group, and concludes with speculation on a general framework k)r future research.


title: Cube index for unstructured text analysis and mining

abstract: A Cube Index Model on multidimensional text database and effective study of Online Analytical Processing (OLAP) over such data had been experimented and found to provide good results. We had proposed a cube index model for unstructured text database derived from the text index structures. There are three kinds of hierarchies on it. They are term hierarchy and dimensional hierarchy. This paper proposes the document hierarchy. Two new operations scroll up and scroll down are discussed exclusively for the cube index. The implementation, OLAP execution and query processing on the index are studied. The performance study gives a good guarantee of the model to be used on unstructured text database.


title: Multi-aspects Review Summarization Based on Identification of Important Opinions and their Similarity

abstract: The development of the Web services lets many users easily provide their opinions recently. Automatic summarization of enormous sentiments has been expected. Intuitively, we can summarize a review with traditional document summarization methods. However, such methods have not well-discussed "aspects". Basically, a review consists of sentiments with various aspects. We summarize reviews for each aspect so that the summary presents information without biasing to a specific topic. In this paper, we propose a method for multi- aspects review summarization based on evaluative sentence extraction. We handle three fea- tures; ratings of aspects, the tf -idf value, and the number of mentions with a similar topic. For estimating the number of mentions, we apply a clustering algorithm. By integrating these features, we generate a more appropriate summary. The experiment results show the effectiveness of our method.


title: LightShop: interactive light field manipulation and rendering

abstract: Light fields can be used to represent an object's appearance with a high degree of realism. However, unlike their geometric counterparts, these image-based representations lack user control for manipulating them. We present a system that allows a user to interactively manipulate, composite and render multiple light fields. LightShop is a modular system consisting of three parts: 1) a set of functions that allow a user to model a scene containing multiple light fields, 2) a ray-shading language that describes how an image should be constructed from a set of light fields, and 3) a real-time light field rendering system in OpenGL that can plug into existing 3D engines as a GLSL shader. We show applications in digital photography and we demonstrate how to integrate light fields into a modern space-flight game using LightShop.


title: Pressure ulcer prevention and healing using alternating pressure mattress at home: the PARESTRY project.

abstract: Objective: Specialised pressure-relieving supports reduce or relieve the interface pressure between the skin and the support surface. The comparative effectiveness of dynamic support surfaces is debated. The aim of this study is to examine the impact of using an alternating pressure air mattress (APAM) on pressure ulcer (PU) incidence in patients receiving home-based care. A second aim was to determine the level of patient/family satisfaction with comfort and gain the views of the care team that used the APAM. Method: The PARESTRY study was a prospective observational study conducted in patients with a high risk of PUs (Braden score <15), discharged to hospital-care at home. The primary prevention groups consisted of patients with no PU at baseline who were in bed for at least 20 hours a day. Patients at baseline with a category 3 or 4 PU or a category 1 or 2 PU in association with poor general health or end-of-life status were included in the secondary prevention group. All patients were laid on an APAM....


title: A Compile-Time Optimization Method for WCET Reduction in Real-Time Embedded Systems through Block Formation

abstract: Compile-time optimizations play an important role in the efficient design of real-time embedded systems. Usually, compile-time optimizations are designed to reduce average-case execution time (ACET). While ACET is a main concern in high-performance computing systems, in real-time embedded systems, concerns are different and worst-case execution time (WCET) is much more important than ACET. Therefore, WCET reduction is more desirable than ACET reduction in many real-time embedded systems. In this article, we propose a compile-time optimization method aimed at reducing WCET in real-time embedded systems. In the proposed method, based on the predicated execution capability of embedded processors, program code blocks that are in the worst-case paths of the program are merged to increase instruction-level parallelism and opportunity for WCET reduction. The use of predicated execution enables merging code blocks from different worst-case paths that can be very effective in WCET reduction. The experimental results show that the proposed method can reduce WCET by up to 45p as compared to previous compile-time block formation methods. It is noteworthy that compared to previous works, while the proposed method usually achieves more WCET reduction, it has considerably less negative impact on ACET and code size.


title: Lessons Learned in Deploying the World’s Largest Scale Lustre File System

abstract: The Spider system at the Oak Ridge National Laboratory’s Leadership Computing Facility (OLCF) is the world’s largest scale Lustre parallel file system. Envisioned as a shared parallel file system capable of delivering both the bandwidth and capacity requirements of the OLCF’s diverse computational environment, the project had a number of ambitious goals. To support the workloads of the OLCF’s diverse computational platforms, the aggregate performance and storage capacity of Spider exceed that of our previously deployed systems by a factor of 6x 240 GB/sec, and 17x 10 Petabytes, respectively. Furthermore, Spider supports over 26,000 clients concurrently accessing the file system, which exceeds our previously deployed systems by nearly 4x. In addition to these scalability challenges, moving to a center-wide shared file system required dramatically improved resiliency and fault-tolerance mechanisms. This paper details our efforts in designing, deploying, and operating Spider. Through a phased approach of research and development, prototyping, deployment, and transition to operations, this work has resulted in a number of insights into large-scale parallel file system architectures, from both the design and the operational perspectives. We present in this paper our solutions to issues such as network congestion, performance baselining and evaluation, file system journaling overheads, and high availability in a system with tens of thousands of components. We also discuss areas of continued challenges, such as stressed metadata performance and the need for file system quality of service alongside with our efforts to address them. Finally, operational aspects of managing a system of this scale are discussed along with real-world data and observations.


title: A probabilistic approach to spatiotemporal theme pattern mining on weblogs

abstract: Mining subtopics from weblogs and analyzing their spatiotemporal patterns have applications in multiple domains. In this paper, we define the novel problem of mining spatiotemporal theme patterns from weblogs and propose a novel probabilistic approach to model the subtopic themes and spatiotemporal theme patterns simultaneously. The proposed model discovers spatiotemporal theme patterns by (1) extracting common themes from weblogs; (2) generating theme life cycles for each given location; and (3) generating theme snapshots for each given time period. Evolution of patterns can be discovered by comparative analysis of theme life cycles and theme snapshots. Experiments on three different data sets show that the proposed approach can discover interesting spatiotemporal theme patterns effectively. The proposed probabilistic model is general and can be used for spatiotemporal text mining on any domain with time and location information.


title: An educational perspective on database management systems and object-oriented methodology: a 12 year journey

abstract: Relational database management systems are an essential component of many data intensive applications. At USC, a course entitled "File and Database Management" introduces students to fundamental concepts in relational databases. Students are introduced to conceptual, logical and physical organization of data, use of both formal and commercial query languages, e.g., SQL, indexing techniques for efficient retrieval of data, the concept of a transaction, concurrency control and crash recovery techniques. This paper summarizes our experiences with this course and the challenges of educating students on use of object-oriented concepts and their mapping to tables.


title: Convolutional Neural Networks for Automatic State-Time Feature Extraction in Reinforcement Learning Applied to Residential Load Control

abstract: Direct load control of a heterogeneous cluster of residential demand flexibility sources is a high-dimensional control problem with partial observability. This work proposes a novel approach that uses a convolutional neural network to extract hidden state-time features to mitigate the curse of partial observability. More specific, a convolutional neural network is used as a function approximator to estimate the state-action value function or Q-function in the supervised learning step of fitted Q-iteration. The approach is evaluated in a qualitative simulation, comprising a cluster of thermostatically controlled loads that only share their air temperature, whilst their envelope temperature remains hidden. The simulation results show that the presented approach is able to capture the underlying hidden features and successfully reduce the electricity cost the cluster.


title: Practical and secure dynamic searchable encryption via oblivious access on distributed data structure

abstract: Dynamic Searchable Symmetric Encryption (DSSE) allows a client to perform keyword searches over encrypted files via an encrypted data structure. Despite its merits, DSSE leaks search and update patterns when the client accesses the encrypted data structure. These leakages may create severe privacy problems as already shown, for example, in recent statistical attacks on DSSE. While Oblivious Random Access Memory (ORAM) can hide such access patterns, it incurs significant communication overhead and, therefore, it is not yet fully practical for cloud computing systems. Hence, there is a critical need to develop private access schemes over the encrypted data structure that can seal the leakages of DSSE while achieving practical search/update operations. In this paper, we propose a new oblivious access scheme over the encrypted data structure for searchable encryption purposes, that we call D istributed O blivious D ata structure DSSE (DOD-DSSE). The main idea is to create a distributed encrypted incidence matrix on two non-colluding servers such that no arbitrary queries on these servers can be linked to each other. This strategy prevents not only recent statistical attacks on the encrypted data structure but also other potential threats exploiting query linkability. Our security analysis proves that DOD-DSSE ensures the unlink-ability of queries and, therefore, offers much higher security than traditional DSSE. At the same time, our performance evaluation demonstrates that DOD-DSSE is two orders of magnitude faster than ORAM-based techniques (e.g., Path ORAM), since it only incurs a small-constant number of communication overhead. That is, we deployed DOD-DSSE on geographically distributed Amazon EC2 servers, and showed that, a search/update operation on a very large dataset only takes around one second with DOD-DSSE, while it takes 3 to 13 minutes with Path ORAM-based methods.


SLR-Tool: A Tool for Performing Systematic Literature Reviews.


title: A hierarchy of evidence for assessing qualitative health research.

abstract: Abstract Objective The objective of this study is to outline explicit criteria for assessing the contribution of qualitative empirical studies in health and medicine, leading to a hierarchy of evidence specific to qualitative methods. Study Design and Setting This paper arose from a series of critical appraisal exercises based on recent qualitative research studies in the health literature. We focused on the central methodological procedures of qualitative method (defining a research framework, sampling and data collection, data analysis, and drawing research conclusions) to devise a hierarchy of qualitative research designs, reflecting the reliability of study conclusions for decisions made in health practice and policy. Results We describe four levels of a qualitative hierarchy of evidence-for-practice. The least likely studies to produce good evidence-for-practice are single case studies, followed by descriptive studies that may provide helpful lists of quotations but do not offer detailed analysis. More weight is given to conceptual studies that analyze all data according to conceptual themes but may be limited by a lack of diversity in the sample. Generalizable studies using conceptual frameworks to derive an appropriately diversified sample with analysis accounting for all data are considered to provide the best evidence-for-practice. Explicit criteria and illustrative examples are described for each level. Conclusion A hierarchy of evidence-for-practice specific to qualitative methods provides a useful guide for the critical appraisal of papers using these methods and for defining the strength of evidence as a basis for decision making and policy generation.


title: Performance analysis of Snort-based Intrusion Detection System

abstract: The most important purpose of intrusion detection system is to identify attacks against information systems. It is a security method attempting to identify various attacks. Snort is mostly used signature based IDS because it is an open source software. It is used world widely in intrusion detection and prevention domain. In this paper, we used IDEVAL data set we detect attacks using Snort on this dataset.


title: Towards Synthesizing Realistic Workload Traces for Studying the Hadoop Ecosystem

abstract: Designing cloud computing setups is a challenging task. It involves understanding the impact of a plethora of parameters ranging from cluster configuration, partitioning, networking characteristics, and the targeted applications' behavior. The design space, and the scale of the clusters, make it cumbersome and error-prone to test different cluster configurations using real setups. Thus, the community is increasingly relying on simulations and models of cloud setups to infer system behavior and the impact of design choices. The accuracy of the results from such approaches depends on the accuracy and realistic nature of the workload traces employed. Unfortunately, few cloud workload traces are available (in the public domain). In this paper, we present the key steps towards analyzing the traces that have been made public, e.g., from Google, and inferring lessons that can be used to design realistic cloud workloads as well as enable thorough quantitative studies of Hadoop design. Moreover, we leverage the lessons learned from the traces to undertake two case studies: (i) Evaluating Hadoop job schedulers, and (ii) Quantifying the impact of shared storage on Hadoop system performance.


title: Complex event processing over uncertain events: Techniques, challenges, and future directions

abstract: In the recent years, there is a massive surge in real-time data owing to the growing number of distributed software applications that continuously generates a large volume of data streams. These applications utilize new hardware technologies and data collection methods that inherently produce uncertain data. The Complex event processing (CEP) is a crucial software technology that manages highly dynamic event streams in a timely manner and determines the event patterns of interest. The CEP processes the massive data streams from different applications that depend on the reliability of data to produce accurate results. The business intelligence requires high-level of knowledge extracted from continuous streams. Ignoring uncertainty in event processing significantly degrades the performance and accuracy. Thus, efficient processing of uncertain data is vital to improving the efficiency and the accuracy of the CEP results for better decision making in business. This survey discusses the basics of uncertainty, explores models and query processing methods to deal with uncertainty. The primary contribution of this work is the survey of CEP regarding event query language and event query processing involved in identifying the complex event patterns over uncertain events. The challenges and the future directions for handling uncertain events in CEP are discussed in detail.


The Hadley Circulation of the Earth's Atmosphere.


title: Dynamic conditional random fields: factorized probabilistic models for labeling and segmenting sequence data

abstract: In sequence modeling, we often wish to represent complex interaction between labels, such as when performing multiple, cascaded labeling tasks on the same sequence, or when long-range dependencies exist. We present dynamic conditional random fields (DCRFs), a generalization of linear-chain conditional random fields (CRFs) in which each time slice contains a set of state variables and edges---a distributed state representation as in dynamic Bayesian networks (DBNs)---and parameters are tied across slices. Since exact inference can be intractable in such models, we perform approximate inference using several schedules for belief propagation, including tree-based reparameterization (TRP). On a natural-language chunking task, we show that a DCRF performs better than a series of linear-chain CRFs, achieving comparable performance using only half the training data. In addition to maximum conditional likelihood, we present two alternative approaches for training DCRFs: marginal likelihood training, for when we are primarily interested in predicting only a subset of the variables, and cascaded training, for when we have a distinct data set for each state variable, as in transfer learning. We evaluate marginal training and cascaded training on both synthetic data and real-world text data, finding that marginal training can improve accuracy when uncertainty exists over the latent variables, and that for transfer learning, a DCRF trained in a cascaded fashion performs better than a linear-chain CRF that predicts the final task directly.


title: ADAPTABLE FINGERPRINT MINUTIAE EXTRACTION ALGORITHM BASED -ON CROSSING NUMBER METHOD FOR HARDWARE IMPLEMENTATION USING FPGA DEVICE

abstract: In this article, a main perspective of developing and implementing fingerprint extraction and matching algorithms as a part of fingerprint recognition system is focused. First, developing a simple algorithm to extract fingerprint features and test this algorithm on PC. The second thing is implementing this algorithm into FPGA devices. The major research topics on which the proposed approach is developing and modifying fingerprint extraction feature algorithm. This development and modification are using crossing number method on pixel representation value ’0’. In this new proposed algorithm, it is no need a process concerning ROI segmentation and no trigonometry calculation. And specially in obtaining their parameters using Angle Calculation Block avoiding floating points calculation. As this method is local feature that usually involve with 60-100 minutiae points, makes the template is small in size. Providing FAR, FRR and EER, performs the performance evaluation of proposed algorithm. The result is an adaptable fingerprint minutiae extraction algorithm into hardware implementation with 14.05 % of EER, better than reference algorithm, which is 20.39 % .The computational time is 18 seconds less than a similar method, which takes 60-90 seconds just for pre-processing step. The first step of algorithm implementation in hardware environment (embedded) using FPGA Device by developing IP Core without using any soft processor is presented.


title: The University of Cambridge's Machine Translation Systems for WMT18

abstract: The University of Cambridge submission to the WMT18 news translation task focuses on the combination of diverse models of translation. We compare recurrent, convolutional, and self-attention-based neural models on German-English, English-German, and Chinese-English. Our final system combines all neural models together with a phrase-based SMT system in an MBR-based scheme. We report small but consistent gains on top of strong Transformer ensembles.


title: Semi-Supervised Learning on Graphs Based on Local Label Distributions

abstract: Most approaches that tackle the problem of node classification consider nodes to be similar, if they have shared neighbors or are close to each other in the graph. Recent methods for attributed graphs additionally take attributes of neighboring nodes into account. We argue that the class labels of the neighbors bear important information and considering them helps to improve classification quality. Two nodes which are similar based on class labels in their neighborhood do not need to be close-by in the graph and may even belong to different connected components. In this work, we propose a novel approach for the semi-supervised node classification. Precisely, we propose a new node embedding which is based on the class labels in the local neighborhood of a node. We show that this is a different setting from attribute-based embeddings and thus, we propose a new method to learn label-based node embeddings which can mirror a variety of relations between the class labels of neighboring nodes. Our experimental evaluation demonstrates that our new methods can significantly improve the prediction quality on real world data sets.


title: Image based search engine using deep learning

abstract: During previous couple of years, the World Wide Web (WWW) has become an extremely well-liked information source. To successfully utilize the vast quantity of information that the web provides, we want an effective way to explore it. Image data is much more voluminous than textual data, and visual information cannot be indexed by traditional strategies developed for indexing textual information. Therefore, Content-Based Image Retrieval (CBIR) has received an excellent deal of interest within the research community. A CBIR system operates on the visible features at low-level of a user's input image that makes it troublesome for the users to devise the input and additionally doesn't offer adequate retrieval results. In CBIR system, the study of the useful representation of features and appropriate similarity metrics is extremely necessary for improving the performance of retrieval task. Semantic gap has been the main issue which occurs between image pixels at low-level and semantics at high-level interpreted by humans. Among varied methods, machine learning (ML) has been explored as a feasible way to reduce the semantic gap. Inspired by the current success of deep learning methods for computer vision applications, in this paper, we aim to confront an advance deep learning method, known as Convolutional Neural Network (CNN), for studying feature representations and similarity measures. In this paper, we explored the applications of CNNs towards solving classification and retrieval problems. For retrieval of similar images, we agreed on using transfer learning to apply the GoogleNet deep architecture to our problem. Extracting the last-but-one fully connected layer from the retraining of GoogleNet CNN model served as the feature vectors for each image, computing Euclidean distances between these feature vectors and that of our query image to return the closest matches in the dataset.


title: Comparing SVM and convolutional networks for epileptic seizure prediction from intracranial EEG

abstract: Recent research suggests that electrophysiological changes develop minutes to hours before the actual clinical onset in focal epileptic seizures. Seizure prediction is a major field of neurological research, enabled by statistical analysis methods applied to features derived from intracranial Electroencephalographic (EEG) recordings of brain activity. However, no reliable seizure prediction method is ready for clinical applications. In this study, we use modern machine learning techniques to predict seizures from a number of features proposed in the literature. We concentrate on aggregated features that encode the relationship between pairs of EEG channels, such as cross-correlation, nonlinear interdependence, difference of Lyapunov exponents and wavelet analysis-based synchrony such as phase locking. We compare L1-regularized logistic regression, convolutional networks, and support vector machines. Results are reported on the standard Freiburg EEG dataset which contains data from 21 patients suffering from medically intractable focal epilepsy. For each patient, at least one method predicts 100% of the seizures on average 60 minutes before the onset, with no false alarm. Possible future applications include implantable devices capable of warning the patient of an upcoming seizure as well as implanted drug-delivery devices.


title: Competition for Customers

abstract: We consider an oligopolistic market with a given finite number of price setting firms. We study the dependence of the market share of a firm on its own price and give conditions on the distribution of consumers' characteristics such that the profit of a firm becomes a quasiconcave function of its price.


title: AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks

abstract: Click-through rate (CTR) prediction, which aims to predict the probability of a user clicking on an ad or an item, is critical to many online applications such as online advertising and recommender systems. The problem is very challenging since (1) the input features (e.g., the user id, user age, item id, item category) are usually sparse and high-dimensional, and (2) an effective prediction relies on high-order combinatorial features (a.k.a. cross features), which are very time-consuming to hand-craft by domain experts and are impossible to be enumerated. Therefore, there have been efforts in finding low-dimensional representations of the sparse and high-dimensional raw features and their meaningful combinations. In this paper, we propose an effective and efficient method called the AutoInt to automatically learn the high-order feature interactions of input features. Our proposed algorithm is very general, which can be applied to both numerical and categorical input features. Specifically, we map both the numerical and categorical features into the same low-dimensional space. Afterwards, a multi-head self-attentive neural network with residual connections is proposed to explicitly model the feature interactions in the low-dimensional space. With different layers of the multi-head self-attentive neural networks, different orders of feature combinations of input features can be modeled. The whole model can be efficiently fit on large-scale raw data in an end-to-end fashion. Experimental results on four real-world datasets show that our proposed approach not only outperforms existing state-of-the-art approaches for prediction but also offers good explainability. Code is available at: \urlhttps://github.com/DeepGraphLearning/RecommenderSystems.


title: SCARFF: a Scalable Framework for Streaming Credit Card Fraud Detection with Spark

abstract: Abstract The expansion of the electronic commerce, together with an increasing confidence of customers in electronic payments, makes of fraud detection a critical factor. Detecting frauds in (nearly) real time setting demands the design and the implementation of scalable learning techniques able to ingest and analyse massive amounts of streaming data. Recent advances in analytics and the availability of open source solutions for Big Data storage and processing open new perspectives to the fraud detection field. In this paper we present a Scalable Real-time Fraud Finder (SCARFF) which integrates Big Data tools (Kafka, Spark and Cassandra) with a machine learning approach which deals with imbalance, nonstationarity and feedback latency. Experimental results on a massive dataset of real credit card transactions show that this framework is scalable, efficient and accurate over a big stream of transactions.


title: Multi‐scale brain networks

abstract: The network architecture of the human brain has become a feature of increasing interest to the neuroscientific community, largely because of its potential to illuminate human cognition, its variation over development and aging, and its alteration in disease or injury. Traditional tools and approaches to study this architecture have largely focused on single scales -- of topology, time, and space. Expanding beyond this narrow view, we focus this review on pertinent questions and novel methodological advances for the multi-scale brain. We separate our exposition into content related to multi-scale topological structure, multi-scale temporal structure, and multi-scale spatial structure. In each case, we recount empirical evidence for such structures, survey network-based methodological approaches to reveal these structures, and outline current frontiers and open questions. Although predominantly peppered with examples from human neuroimaging, we hope that this account will offer an accessible guide to any neuroscientist aiming to measure, characterize, and understand the full richness of the brain's multiscale network structure -- irrespective of species, imaging modality, or spatial resolution.


title: Heuristic Methods for Solving Job-Shop Scheduling Problems

abstract: Solving scheduling problems with Constraint Satisfaction Problems (CSP’s) techniques implies a wide space search with a large number of variables, each one of them with a wide interpretation domain. This paper discusses the application of CSP heuristic techniques (based on the concept of slack of activities) for variable and value ordering on a special type of job-shop scheduling problems in which the operations must schedule inside of temporal windows. These techniques are improved by introducing the concepts of slack probability and the find-hole method. Thus, a more flexible heuristic technique is obtained, which improves empirical efficiency and allows early detection of unfeasible problems.


title: Towards Integrating Plot and Character for Interactive Drama

abstract: The authors are currently engaged in a three year collaboration to build an interactive story world integrating believable agents and interactive plot. This paper provides a brief description of the project goals and design requirements, discusses the problem of autonomy in the context of story-based believable agents, and describes an architecture that uses the dramatic beat as a s tructural principle to integrate plot and character.


title: GraPacc: a graph-based pattern-oriented, context-sensitive code completion tool

abstract: Code completion tool plays an important role in daily development activities. It helps developers by auto-completing tedious and detailed code during an editing session. However, existing code completion tools are limited to recommending only context-free code templates and a single method call of the variable under editing. We introduce GraPacc, an advanced, context-sensitive code completion tool that is based on frequent API usage patterns. It extracts the context-sensitive features from the code under editing, for example, the API elements on focus and the current editing point, and their relations to other code elements. It then ranks the relevant API usage patterns and auto-completes the current code with the proper elements according to the chosen pattern.


title: Speech Dereverberation Using Long Short-Term Memory

abstract: Recently, neural networks have been used for not only phone recognition but also denoising and dereverberation. However, the conventional denoising deep autoencoder (DAE) based on the feed-forward structure is not capable of handling very long speech frames of reverberation. LSTM can be effectively trained to reduce the average error between the enhanced signal and the original clean signal by considering the effect of the long past time frames. In this paper, we demonstrate that considering as long as the maximum reverberation time of the database is effective. Since the effect of reverberation varies depending on the phone-class of the whole speech context, we augment the input of the autoencoder with the phone-class information of the past frames as well as the current frame and call this version of the LSTM autoencoder pLSTM. In the speech recognition experiment using the data set of Reverb Challenge 2014, the LSTM front-end reduced the WER of the multicondition DNN-HMM by 14.5%, and the use of the phone class feature yielded in pLSTM further improvement of 7.5%. The performance with the pLSTM is comparable to that of pDAE, while the number of parameters is only 1/25-1/8. Index Terms: Speech Dereverberation, Long Short-Term Memory (LSTM), Deep Autoencoder (DAE)


Object classification Techniques using Machine Learning Model


title: Dynamically Testing GUIs Using Ant Colony Optimization (T)

abstract: In this paper we introduce a dynamic GUI test generator that incorporates ant colony optimization. We created two ant systems for generating tests. Our first ant system implements the normal ant colony optimization algorithm in order to traverse the GUI and find good event sequences. Our second ant system, called AntQ, implements the antq algorithm that incorporates Q-Learning, which is a behavioral reinforcement learning technique. Both systems use the same fitness function in order to determine good paths through the GUI. Our fitness function looks at the amount of change in the GUI state that each event causes. Events that have a larger impact on the GUI state will be favored in future tests. We compared our two ant systems to random selection. We ran experiments on six subject applications and report on the code coverage and fault finding abilities of all three algorithms.


title: Axial Flux Resolver Design Techniques for Minimizing Position Error Due to Static Eccentricities

abstract: This paper proposes a design solution for static eccentricity (SE) in axial flux resolvers (AFRs). There are two definitions for SE in AFRs. The first approach is based on the definition of SE in conventional radial flux resolvers, wherein the rotor axis does not coincide with the stator bore, but the rotor rotates around its own shaft. In other words, it is different in the angular inclination of the rotor and stator axis. Thus, the air gap of the motor is not uniform. In the second approach, which is more common in AFRs, when SE occurs, air-gap length remains uniform. Rotor axis remains parallel with the stator axis, although it does not coincide with the stator bore. This means that there is radial misalignment of the rotor and stator axis. In this paper, both definitions are considered, and an innovative design solution is proposed to decrease the effect of both types of SE in the accuracy of detected position. 3-D time stepping finite-element analysis is used to show the success of proposed designs. Finally, both models were evaluated using experimental results.


Gualzru's Path to the Advertisement World


title: A System Architecture for Autonomous Demand Side Load Management in Smart Buildings

abstract: This paper presents a system architecture for load management in smart buildings which enables autonomous demand side load management in the smart grid. Being of a layered structure composed of three main modules for admission control, load balancing, and demand response management, this architecture can encapsulate the system functionality, assure the interoperability between various components, allow the integration of different energy sources, and ease maintenance and upgrading. Hence it is capable of handling autonomous energy consumption management for systems with heterogeneous dynamics in multiple time-scales and allows seamless integration of diverse techniques for online operation control, optimal scheduling, and dynamic pricing. The design of a home energy manager based on this architecture is illustrated and the simulation results with Matlab/Simulink confirm the viability and efficiency of the proposed framework.


title: Immersive planar display using roughly aligned projectors

abstract: When a projector is oblique with respect to a planar display surface, it creates keystoning and the projected image is distorted. We present a rendering technique to display perspectively correct images for a moving user. This allows using roughly aligned projectors and eliminates the need for frequent electro-mechanical adjustments. The rendering process has no additional cost and can be implemented with traditional graphics hardware. We compute the collineation induced due to the display plane during preprocessing. The main idea of the paper is to use this collineation to render and warp the images of 3D scenes in a single pass via approximation of the depth buffer. We also describe how this method can be extended to display systems with multiple overlapping projectors. This technique can be easily used in CAVE, Immersive Workbenches and PowerWalls.


title: Non-determinism and overcount on modern hardware performance counter implementations

abstract: Ideal hardware performance counters provide exact deterministic results. Real-world performance monitoring unit (PMU) implementations do not always live up to this ideal. Events that should be exact and deterministic (such as retired instructions) show run-to-run variation and overcount on ×86_64 machines, even when run in strictly controlled environments. These effects are non-intuitive to casual users and cause difficulties when strict determinism is desirable, such as when implementing deterministic replay or deterministic threading libraries. We investigate eleven different x86 64 CPU implementations and discover the sources of divergence from expected count totals. Of all the counter events investigated, we find only a few that exhibit enough determinism to be used without adjustment in deterministic execution environments. We also briefly investigate ARM, IA64, POWER and SPARC systems and find that on these platforms the counter events have more determinism. We explore various methods of working around the limitations of the ×86_64 events, but in many cases this is not possible and would require architectural redesign of the underlying PMU.


title: Regression testing of GUIs

abstract: Although graphical user interfaces (GUIs) constitute a large part of the software being developed today and are typically created using rapid prototyping, there are no effective regression testing techniques for GUIs. The needs of GUI regression testing differ from those of traditional software. When the structure of a GUI is modified, test cases from the original GUI are either reusable or unusable on the modified GUI. Since GUI test case generation is expensive, our goal is to make the unusable test cases usable. The idea of reusing these unusable (a.k.a. obsolete) test cases has not been explored before. In this paper, we show that for GUIs, the unusability of a large number of test cases is a serious problem. We present a novel GUI regression testing technique that first automatically determines the usable and unusable test cases from a test suite after a GUI modification. It then determines which of the unusable test cases can be repaired so they can execute on the modified GUI. The last step is to repair the test cases. Our technique is integrated into a GUI testing framework that, given a test case, automatically executes it on the GUI. We implemented our regression testing technique and demonstrate for two case studies that our approach is effective in that many of the test cases can be repaired, and is practical in terms of its time performance.


title: Kinematic modeling and simulation of an economical SCARA manipulator by Pro-E and verification using MATLAB/Simulink

abstract: SCARA (Selective Compliance Assembly Robot Arm) manipulators are among the most extensively used manipulators in industry due to high precision and their inherent rigidity. This research demonstrates mechanical design process of an economical SCARA manipulator with unique and competing industrial specifications using Pro-E software. Inverse kinematic equations are also derived using algebraic and geometric method to control the manipulator movement. The design process includes the design of joints, links and controller as well as the selection of its electrical and mechanical components. The selection of the actuators and the dimensions of the whole mechanical structure are selected in such a way to direct its center of gravity towards base and also reduce the vibration and backlash in its mechanical structure. The main task was to use readily available components with an eye on keeping the costs down. Finally the performance of SCARA system is examined in Pro-E and verified manipulators movement with MATLAB/Simulink which exhibits the effectiveness of the proposed model.


title: A balanced High Voltage IGBT design with ultra dynamic ruggedness and area-efficient edge termination

abstract: In this paper, a balanced High Voltage (HV) IGBT is presented. The proposed HV IGBT is composed of three technologies: Wide Cell Pitch CSTBTTM(III) for cell structure, Partial P collector utilizing LPT(II) buffer for vertical structure, and a novel area-efficient edge termination design. We called the above edge termination design “Linearly-narrowed Field Limiting Ring (LNFLR)”. The experiment results of a balanced 4500 V class IGBT show that the device maintains an excellent dynamic ruggedness with a 50% cut in edge termination width comparing to the conventional Field Limiting Ring (FLR) design. Moreover, optimizing fabrication process can further widen the process window for LNFLR dose.


title: 3D Marking menu selection with freehand gestures

abstract: A controlled experiment was performed to evaluate the usability of freehand gestural target selection with different 3D marking menu layouts and target directions. We found that a rectangular layout was faster than an octagonal layout, with no significant increase in errors, and that our right-handed participants preferred to select to the right and forwards in a 3D marking menu. We propose an improved design for 3D marking menus based on our findings. The experimental results also suggest that designers should consider carefully whether or not findings from similar interaction techniques using hands-on devices can be carried over to the design of freehand gestural interaction.


title: Free for All! Assessing User Data Exposure to Advertising Libraries on Android

abstract: In this work, we systematically explore the potential reach of advertising libraries through these channels. We design a framework called Pluto that can be leveraged to analyze an app and discover whether it exposes targeted user data—such as contact information, interests, demographics, medical conditions and so on—-to an opportunistic ad library. We present a prototype implementation of Pluto, that embodies novel strategies for using natural language processing to illustrate what targeted data can potentially be learned from an ad network using files and user inputs. Pluto also leverages machine learning and data mining models to reveal what advertising networks can learn from the list of installed apps. We validate Pluto with a collection of apps for which we have determined ground truth about targeted data they may reveal, together with a data set derived from a survey we conducted that gives ground truth for targeted data and corresponding lists of installed apps for about 300 users. We use these to show that Pluto, and hence also opportunistic ad networks, can achieve 75% recall and 80% precision for selected targeted data coming from app files and inputs, and even better results for certain targeted data based on the list of installed apps. Pluto is the first tool that estimates the risk associated with integrating advertising in apps based on the four available channels and arbitrary sets of targeted data.


title: A Benchmark and Evaluation for Text Extraction from PDF

abstract: Extracting the body text from a PDF document is an important but surprisingly difficult task. The reason is that PDF is a layout-based format which specifies the fonts and positions of the individual characters rather than the semantic units of the text (e.g., words or paragraphs) and their role in the document (e.g., body text or caption). There is an abundance of extraction tools, but their quality and the range of their functionality are hard to determine. In this paper, we show how to construct a high-quality benchmark of principally arbitrary size from parallel TeX and PDF data. We construct such a benchmark of 12,098 scientific articles from arXiv.org and make it publicly available. We establish a set of criteria for a clean and independent assessment of the semantic abilities of a given extraction tool. We provide an extensive evaluation of 14 state-of-the-art tools for text extraction from PDF on our benchmark according to our criteria. We include our own method, Icecite, which significantly outperforms all other tools, but is still not perfect. We outline the remaining steps necessary to finally make text extraction from PDF a "solved problem".


title: NLSR: named-data link state routing protocol

abstract: This paper presents the design of the Named-data Link State Routing protocol (NLSR), a routing protocol for Named Data Networking (NDN). Since NDN uses names to identify and retrieve data, NLSR propagates reachability to name prefixes instead of IP prefixes. Moreover, NLSR differs from IP-based link-state routing protocols in two fundamental ways. First, NLSR uses Interest/Data packets to disseminate routing updates, directly benefiting from NDN's data authenticity. Second, NLSR produces a list of ranked forwarding options for each name prefix to facilitate NDN's adaptive forwarding strategies. In this paper we discuss NLSR's main design choices on (1) a hierarchical naming scheme for routers, keys, and routing updates, (2) a hierarchical trust model for routing within a single administrative domain, (3) a hop-by-hop synchronization protocol to replace the traditional network-wide flooding for routing update dissemination, and (4) a simple way to rank multiple forwarding options. Compared with IP-based link state routing, NLSR offers more efficient update dissemination, built-in update authentication, and native support of multipath forwarding.


title: Analysis of Hybrid Soft and Hard Computing Techniques for Forex Monitoring Systems

abstract: The need for intelligent monitoring systems has become a necessity to keep track of the complex forex market. The vast currency market is a foreign concept to the average individual. We attempt to compare the performance of hybrid soft computing and hard computing techniques to predict the average monthly forex rates one month ahead. The soft computing models considered are a neural network trained by the scaled conjugate gradient algorithm and a neurofuzzy model implementing a Takagi-Sugeno fuzzy inference system. We also considered multivariate adaptive regression splines (MARS), classification and regression trees (CART) and a hybrid CART-MARS technique. We considered the exchange rates of Australian dollar with respect to US dollar, Singapore dollar, New Zealand dollar, Japanese yen and United Kingdom pounds. The models were trained using 70% of the data and remaining was used for testing and validation purposes. It is observed that the proposed hybrid models could predict the forex rates more accurately than all the techniques when applied individually. Empirical results also reveal that the hybrid hard computing approach also improved some of our previous work using a neuro-fuzzy approach.


title: Combining Psychodynamic Psychotherapy and Pharmacotherapy

abstract: Many patients with depression, anxiety disorders, and other psychiatric disorders are treated with combinations of psychodynamic psychotherapy and medication. Whether this is better than monotherapy is an empirical question that requires much more extensive research than is currently available. When medications were first introduced to treat psychiatric illnesses, some psychopharmacologists insisted that it heralded a new area of “biological psychiatry” that would ultimately render psychotherapy obsolete. Psychodynamic theorists and practitioners, on the other hand, argued that psychopharmacology offered only a superficial approach to treatment. Fortunately, these battles are now largely supplanted by the belief that whatever treatment offers the patient the best outcome should be employed, regardless of the therapist's theoretical outlook. This should motivate more extensive study of the value of combination treatment. So far, the few studies that have been done suggest that the combination of psychodyna...


title: Semantic Social Network Analysis

abstract: Social Network Analysis (SNA) tries to understand and exploit the key features of social networks in order to manage their life cycle and predict their evolution. Increasingly popular web 2.0 sites are forming huge social network. Classical methods from social network analysis (SNA) have been applied to such online networks. In this paper, we propose leveraging semantic web technologies to merge and exploit the best features of each domain. We present how to facilitate and enhance the analysis of online social networks, exploiting the power of semantic social network analysis.


title: Specification and verification of concurrent programs by A∀automata

abstract: ∀-automata are non-deterministic finite-state automata over infinite sequences. They differ from conventional automata in that a sequence is accepted if all runs of the automaton over the sequence are accepting. These automata are suggested as a formalism for the specification and verification of temporal properties of concurrent programs. It is shown that they are as expressive as extended-temporal-logic (ETL), and in some cases provide a more compact representation of properties than temporal logic. A structured diagram notation is suggested for the graphical representation of these automata. A single sound and complete proof rule is presented for proving that all computations of a program have the property specified by a ∀-automaton.


title: The Enterprise Data Trust at Mayo Clinic: A semantically integrated warehouse of biomedical data

abstract: Mayo Clinic’s Enterprise Data Trust is a collection of data from patient care, education, research, and administrative transactional systems, organized to support information retrieval, business intelligence, and high-level decision making. Structurally it is a top-down, subject-oriented, integrated, time-variant, and non-volatile collection of data in support of Mayo Clinic’s analytic and decisionmaking processes. It is an interconnected piece of Mayo Clinic’s Enterprise Information Management initiative, which also includes Data Governance, Enterprise Data Modeling, the Enterprise Vocabulary System, and Metadata Management. These resources enable unprecedented organization of enterprise information about patient, genomic, and research data. While facile access for cohort definition or aggregate retrieval is supported, a high level of security, retrieval audit, and user authentication ensures privacy, confidentiality, and respect for the trust imparted by our patients for the respectful use of information about their conditions.


title: Large margin dags for multiclass classification

abstract: We present a new learning architecture: the Decision Directed Acyclic Graph (DDAG), which is used to combine many two-class classifiers into a multiclass classifier. For an N-class problem, the DDAG contains N(N - 1)/2 classifiers, one for each pair of classes. We present a VC analysis of the case when the node classifiers are hyperplanes; the resulting bound on the test error depends on N and on the margin achieved at the nodes, but not on the dimension of the space. This motivates an algorithm, DAGSVM, which operates in a kernel-induced feature space and uses two-class maximal margin hyperplanes at each decision-node of the DDAG. The DAGSVM is substantially faster to train and evaluate than either the standard algorithm or Max Wins, while maintaining comparable accuracy to both of these algorithms.


title: Multi-criteria of Bike Purchasing Using Fuzzy Choquet Integral

abstract: A bike purchasing is a multi-criteria decision-making problem including both quantitative and qualitative main and sub-criteria. This paper shows that when these criteria include interactions between each others, Choquet integral presents an excellent tool for the solution of this multi-criteria decision making problem. An example is given to illustrate the proposed model.


title: Articles Five Challenges for Intelligent Text Entry Methods

abstract: For text entry methods to be useful they have to deliver high entry rates and low error rates. At the same time they need to be easy-to-learn and provide effective means of correcting mistakes. Intelligent text entry methods combine AI techniques with HCI theory to enable users to enter text as efficiently and effortlessly as possible. Here I sample a selection of such techniques from the research literature and set them into their historical context. I then highlight five challenges for text entry methods that aspire to make an impact in our society: localization, error correction, editor support, feedback, and context of use.


The politics of prevention: anti-vaccinationism and public health in nineteenth-century England.


title: Global state evaluation in StarCraft

abstract: State evaluation and opponent modelling are important areas to consider when designing game-playing Artificial Intelligence. This paper presents a model for predicting which player will win in the real-time strategy game StarCraft. Model weights are learned from replays using logistic regression. We also present some metrics for estimating player skill which can be used a features in the predictive model, including using a battle simulation as a baseline to compare player performance against.


Distributed Firewalls


Real Time Wireless Health Monitoring Application Using Mobile Devices


title: DigiClay: An Interactive Installation for Virtual Pottery using Motion Sensing Technology

abstract: In this paper we present an interactive installation for virtual pottery, which adopts intuitive in-air hand interactions with the help of motion sensing technology. A Microsoft Kinect depth camera is used for motion capture in this installation, and a real-time deformation algorithm is applied to the mesh in Unity3D. Once a virtual pottery work is completed, it can be shared to a social network automatically. In this prototype, an automated publishing process will upload the screenshot of a user's work to Facebook, and the mesh data will be saved for future use, such as 3D printing. User behaviors are observed during an exhibition, and some issues are identified from the observation for further improvement. Our work, which eliminates the gap between the traditional ceramic art and everyday life, offers a way to the common audience to express themselves as artists. Our prototype can be further developed as commercial products in various fields, such as arts education, home entertainment and public space exhibitions.


Folk Tale, Fiction, and Saga in the Homeric Epics


title: Avoiding malar edema during midface/cheek augmentation with dermal fillers.

abstract: As dermal fillers have evolved, volume restoration and contour enhancement have become the objective of advanced injectors. The value of injections of dermal fillers into the midface is well documented in the literature. However, the midface, particularly the infraorbital hollow, is the facial area most prone to adverse events from filler treatment. Malar edema is a particularly significant and long-lasting untoward event that is frequently reported. This article reviews the anatomic basis for malar edema, relates it to filler injection technique, and presents the author's preferred method of injection to help ensure avoidance of this adverse event.


title: The Effects of Self-Regulated Learning Processes on E-Learning Outcomes in Organizational Settings

abstract: This paper focuses on employees' e-learning processes during online job training. A new categorization of self-regulated learning strategies, that is, personal versus social learning strategies, is proposed, and measurement scales are developed. The new measures were tested using data collected from employees in a large company. Our approach provides context-relevant insights into online training providers and employees themselves. The results suggest that learners adopt different self-regulated learning strategies resulting in different e-learning outcomes. Furthermore, the use of self-regulated learning strategies is influenced by individual factors such as virtual competence and goal orientation, and job and contextual factors such as intellectual demand and cooperative norms. The findings can 1 help e-learners obtain better learning outcomes through their active use of varied learning strategies, 2 provide useful information for organizations that are currently using or plan to use e-learning for training, and 3 inform software designers to integrate self-regulated learning strategy support in e-learning system design and development.


title: A Probabilistic Analysis of the Rocchio Algorithm with TFIDF for Text Categorization

abstract: Abstract : A probabilistic analysis of the Rocchio relevance feedback algorithm, one of the most popular learning methods from information retrieval, is presented in a text categorization framework. The analysis results in a probabilistic version of the Rocchio classifier and offers an explanation for the TFIDF word weighting heuristic. The Rocchio classifier, its probabilistic variant and a standard naive Bayes classifier are compared on three text categorization tasks. The results suggest that the probabilistic algorithms are preferable to the heuristic Rocchio classifier.