title: Evaluating Search Result Diversity using Intent Hierarchies

abstract: Search result diversification aims at returning diversified document lists to cover different user intents for ambiguous or broad queries. Existing diversity measures assume that user intents are independent or exclusive, and do not consider the relationships among the intents. In this paper, we introduce intent hierarchies to model the relationships among intents. Based on intent hierarchies, we propose several hierarchical measures that can consider the relationships among intents. We demonstrate the feasibility of hierarchical measures by using a new test collection based on TREC Web Track 2009-2013 diversity test collections. Our main experimental findings are: (1) Hierarchical measures are generally more discriminative and intuitive than existing measures using flat lists of intents; (2) When the queries have multilayer intent hierarchies, hierarchical measures are less correlated to existing measures, but can get more improvement in discriminative power; (3) Hierarchical measures are more intuitive in terms of diversity or relevance. The hierarchical measures using the whole intent hierarchies are more intuitive than only using the leaf nodes in terms of diversity and relevance.


title: Millimeter-Wave Photonic Components for Broadband Wireless Systems

abstract: We report on advanced millimeter-wave (mm-wave) photonic components for broadband radio transmission. We have developed self-pulsating 60-GHz range quantum-dash Fabry-Perot mode-locked laser diodes (MLLD) for passive, i.e., unlocked, photonic mm-wave generation with comparably low-phase noise level of -76 dBc/Hz @ 100-kHz offset from a 58.8-GHz carrier. We further report on high-frequency 1.55-μm waveguide photodiodes (PD) with partially p-doped absorber for broadband operation (f3dB ~70-110 GHz) and peak output power levels up to +4.5 dBm @ 110 GHz as well as wideband antenna integrated photomixers for operation within 30-300 GHz and peak output power levels of -11 dBm @ 100 GHz and 6-mA photocurrent. We further present compact 60-GHz wireless transmitter and receiver modules for wireless transmission of uncompressed 1080p (2.97 Gb/s) HDTV signals utilizing the developed MLLD and mm-wave PD. Error-free (BER = 10-9, 231 - 1 PRBS, NRZ) outdoor wireless transmission of 3 Gb/s over 25 m is demonstrated, as well as wireless transmission of uncompressed HDTV signals in the 60-GHz band. Finally, an advanced 60-GHz photonic wireless system offering record data throughputs and spectral efficiencies is presented. For the first time, we demonstrate photonic wireless transmission of data throughputs up to 27.04 Gb/s (EVM 17.6%) using a 16-QAM OFDM modulation format resulting in a spectral efficiency as high as 3.86 b/s/Hz. Wireless experiments were carried out within the regulated 57-64-GHz band in a lab environment with a maximum transmit power of - 1 dBm and 23 dBi gain antennas for a wireless span of 2.5 m. This span can be extended to some 100 m when using high-gain antennas and higher transmit power levels.


title: A black-box testing tool for detecting SQL injection vulnerabilities

abstract: Web applications vulnerabilities allow attackers to perform malicious actions that range from gaining unauthorized account access to obtaining sensitive data. The number of web application vulnerabilities in last decade is growing constantly. Improper input validation and sanitization are reasons for most of them. The most important of these vulnerabilities based on improper input validation and sanitization is SQL injection (SQLI) vulnerability. The primary focus of our research was to develop a reliable black-box vulnerability scanner for detecting SQLI vulnerability - SQLIVDT (SQL Injection Vulnerability Detection Tool). The black-box approach is based on simulation of SQLI attacks against web applications. Thus, the scope of analysis is limited to HTTP responses and HTML pages received from the application server. In order to achieve efficient SQLI vulnerability detection, an efficient algorithm for HTML page similarity detection is used. The proposed tool showed promising results as compared to six well-known web application scanners.


title: Suffocation and poisoning—the hard-hitting side of Munchausen syndrome by proxy

abstract: Munchausen syndrome by proxy (MSBP) is a severe and difficult to diagnose form of child abuse characterised by the simulation, aggravation or production of symptoms of illness in a child by an adult. MSBP often leads to multiple hospitalisations and has a high mortality and long-term morbidity. This study describes the cases of 5 families with 8 children affected who presented with unexplained neurological or gastrointestinal symptoms or even loss of consciousness. All were victims of poisoning or suffocation by their mothers. Two of those children died and were initially diagnosed as SIDS or natural death, respectively. They were only recognised as MSBP victims after another sibling had fallen ill with similar symptoms. The cases are discussed in consideration of the relevant literature. In addition warning signs of this forensically relevant syndrome and a strategy for the management of suspected MSBP cases are described.


title: Determination of Protein Concentration Using Bradford Microplate Protein Quantification Assay

abstract: Background: Bradford protein assay is popular due to its ease of performance and relative sensitivity. Many researchers and laboratories in Iran use standard assay of Bradford by cuvette. No commercial kit was available for Bradford microplate assay in Iran. Meanwhile, imported Bradford commercial kits are very expensive and have a long delivery time in Iran. Till now no study or document on Bradford microplate protein quantification assay was reported in Iran, so this study aimed to design and carried out this assay. Methods: In the current study, antigen B of hydatid cyst fluid was used as sample and the assay was performed in microplate wells. The absorbance values were measured at 595 nm and standard curve was generated by Microsoft Office Excel software. The protein concentration of sample was calculated using the equation of the standard curve. Results: Average protein concentration of the sample was 1175 µg/ml. The total time needed for reading of absorbance was two minutes approximately. Conclusion: Bradford microplate protein assay is a fast and suitable method. This method could be replacing the time consuming method with cuvette. In addition, if this assay produces as a low price kit it could have many benefits for students and laboratories that need to determine protein concentration by Bradford assay.


title: Gait Generation With Smooth Transition Using CPG-Based Locomotion Control for Hexapod Walking Robot

abstract: This paper presents a locomotion control method based on central pattern generator (CPG) for hexapod walking robot to achieve gait generation with smooth transition. By deriving an analytical limit cycle approximation of the Van der Pol oscillator, a simple diffusive coupling scheme is proposed to construct a ring-shape CPG network with phase-locked behavior. The stability of the proposed network is proved using synchronization analysis with guaranteed uniform ultimate boundedness of the synchronous errors. In contrast to conventional numerical methods in tuning parameters of the CPG network, our method provides an explicit result that could analytically determine the network parameters to yield the rhythmic waveforms with prescribed frequency, amplitude, and phase relationship among neurons. Employing the proposed network to govern the swing/stance phase according to the profile of the resulting CPG signals, a locomotion control strategy for the hexapod robot is further developed to manipulate the leg movements during the gait cycle. By simply adjusting the phase lags of the CPG network, the proposed control strategy is capable of generating stable walking gaits for hexapod robots and achieving smooth transition among the generated gaits. The simulation and experimental results have demonstrated the effectiveness of the proposed locomotion control method.


title: Multi-tenant multi-objective bandwidth allocation in datacenters using stacked congestion control

abstract: In datacenter networks, flows can have different performance objectives. We use a tenant-objective division to denote all flows of a tenant that share the same objective. Bandwidth allocation in datacenters should support not only performance isolation among divisions but also objective-oriented scheduling among flows within the same division. This paper studies the Multi-Tenant Multi-Objective (MT-MO) bandwidth allocation problem. To our best knowledge, no existing practical work support performance isolation and objective scheduling simultaneously. We propose Stacked Congestion Control (SCC), a distributed host-based bandwidth allocation design, where an underlay congestion control (UCC) layer handles contention among divisions, and a private congestion control (PCC) layer for each division optimizes its performance objective. Via the tenant-objective tunnel abstraction, SCC achieves weighted bandwidth sharing for each division in a distributed and transparent way. By adding a rate-limiting send queue in the ingress of each tunnel, mechanisms between performance isolation and objective scheduling are completely decoupled. We evaluate SCC both on a small-scale testbed and with large-scale NS-2 simulations. Compared to the direct coexistence cases, SCC reduces latency by up to 40% for Latency-Sensitive flows, deadline miss ratio by up to 3.2× for Deadline-Sensitive flows, and average flow-completion-time by up to 53% for Completion-Sensitive flows.


title: Self-Compensation of the Commutation Angle Based on DC-Link Current for High-Speed Brushless DC Motors With Low Inductance

abstract: As low-resolution position sensors, a high placement accuracy of Hall-effect sensors is hard to achieve. Accordingly, a commutation angle error is generated. The commutation angle error will inevitably increase the loss of the low inductance motor and even cause serious consequence, which is the abnormal conduction of a freewheeling diode in the unexcited phase especially at high speed. In this paper, the influence of the commutation angle error on the power loss for the high-speed brushless dc motor with low inductance and nonideal back electromotive force in a magnetically suspended control moment gyro (MSCMG) is analyzed in detail. In order to achieve low steady-state loss of an MSCMG for space application, a straightforward method of self-compensation of commutation angle based on dc-link current is proposed. Both simulation and experimental results confirm the feasibility and effectiveness of the proposed method.


title: Social body comparison and internalization: mediators of social influences on men's muscularity-oriented body dissatisfaction.

abstract: Abstract Research suggests that body dissatisfaction (BD) is prevalent among males and associated with unhealthy outcomes. Consistent with the well-validated Tripartite Influence Model, internalization of an ideal body figure and social body comparison have received support as predictors of men's dissatisfaction with their muscularity. However, empirical investigations with males that include both constructs are lacking. The current study examined the associations of both constructs with muscularity-oriented BD in a sample of college men ( n = 204). Results from hierarchical regression analyses revealed that internalization and social body comparisons had unique relations with men's muscularity-oriented BD. A structural equation model indicated that both constructs mediated the relationship between social influences and muscularity BD. Preliminary data on targets of male body comparisons also were presented. These findings provide evidence for extension of the Tripartite Influence Model to males, particularly when constructs are assessed in ways that are reliable and valid for this population.


title: CrowdsouRS: A crowdsourced reputation system for identifying deceptive online contents

abstract: In recent years, accelerated web-based technologies have revolutionized content generation and broadcast mecha-nisms through the Internet. Social media, blogs, e-newspaper, auction sites facilitate the creation and exchange of user-generated contents, which rarely go through any fact-finding mechanism or rigorous editorial process. This has fuelled the creation and publication of fake news in the web. The proliferation of social networks has been exploited to accelerate the distribution and propagation of such fake news at an unprecedented level, creating a major concern for the web. There have been several efforts undertaken to rectify this problem, unfortunately, none seems to be effective to root out this concerning issue. In this paper, we present CrowdsouRS, a Crowd-sourced Reputation System, implemented as a browser extension, for the web that leverages the wisdom of the crowd to identify and tag deceptive online contents. It aggregates reputation scores for a web page from multiple users, which is then visualized in order to help other users to determine if the contents of the web page are deceptive. We have evaluated the usability and effectiveness of CrowdsouRS with a number of users and our evaluations suggest that users find the tool useful in serving its purpose.


title: Maximum common subgraph mining: A fast and effective approach towards feature generation

abstract: There exists a wide variety of local graph mining approaches that search for frequent, correlated or closed patterns in graphs. These methods typically return very large sets of patterns which can then be used as features to build classiers. Here we take a dierent approach: rather than mining for all local patterns, we randomly sample from the set of maximum common subgraphs. The advantages are that maximum common subgraphs are easier to compute than frequent or correlated patterns, and that the resulting features lead to classication models that achieve signicantly better predictive performance than models built on the patterns returned by traditional mining approaches.


title: Legal Texts as Discourses

abstract: Legal scholarship and discourse analysis have much in common. They both concern reading and interpreting texts, both are preoccupied with the meaning of texts, and both seem to assume that texts have a life of their own that is independent of their authors. Law is, after all, an inherently societal discourse, albeit quite a specific one. Therefore, within sociology of law it seems appropriate to look at those sociological methods that analyse how language is used by, and in relation to, law and its institutions. Nevertheless, it has taken sociology of law a long time to become interested in discourse analysis as a method of research. This lack of interest has been partly caused by the general orientation of research within sociology of law, which privileges the more traditional forms of empirical research, and mainstream sociological theorising about law, which remains under the influence of various forms of structural functionalism.1 We would like to argue that another reason for this reluctance has been that discourse analysis approaches texts in a fundamentally different way than legal science. The aim of this chapter is to discuss the application of the methods of discourse analysis to legal texts. By using examples from our own work we wish to show that this approach is fruitful for the analysis of legal discourse, for an understanding of the relationship between what is normative and what is factual and for grasping the relationship between social and legal theory. Our interest in discourse analytic reading of legal texts arises out of our research on legal issues related to violence against women.2 It is common knowledge in the social sciences that violence against women is a widespread and persistent problem and that it often is either neglected or ignored by the criminal justice system.3 For example, the


title: Spinal Muscular Atrophy in the Neonate

abstract: Spinal muscular atrophy (SMA) type I is an autosomal recessive disorder characterized by loss of lower motor neurons in the spinal cord. This severe hereditary neurodegenerative disorder is an important cause of morbidity in the neonate and the leading hereditary cause of infant mortality. The characteristic degeneration of anterior horn cells in the spinal cord leads to progressive muscular weakness and atrophy of the skeletal muscles. In SMA type I, the most severe form of SMA, death usually ensues by 2 years of age from respiratory failure or infection. Accurate diagnosis is now available through genetic testing, and progress is being made toward the development of therapy based on understanding of the disease mechanism. The neonatal nurse plays a pivotal role in identifying and caring for these medically fragile infants and in providing support and education for parents and families.


title: Parameters Identification and Modeling of High-Frequency Current Transducer for Partial Discharge Measurements

abstract: Rogowski coil (RC) is a low-cost, air-cored, and flexible induction sensor for nonintrusive condition monitoring and thus can be used in a variety of applications. In this paper, a lumped parameter model of RC is presented and an experiment-based methodology is developed to determine its parameters. The performance of the RC is analyzed for detection and measurement of high-frequency (pulsed) signals such as partial discharge (PD) current pulses. A simple and efficient technique of numerical integration is adopted to avoid the conventional type of expensive and complex design analogue integrators. RC is modeled and simulated in the alternative transient program-electromagnetic transient program environment. The designed coil is tested to measure PDs in the laboratory. Simulated and experimental performance of RC is compared with a high-frequency current transformer. This comparison shows a good match and, hence, validates the design of RC for PD applications.


title: Usage of lowpass filters for miniaturization of microstrip branch-line hybrid couplers

abstract: The article presents the ability of using low-pass filters for creating the miniaturized microstrip line based microwave devices. Numerical simulation and experimental research were made for branch-line hybrid coupler with 3 dB power division at 2000 MHz. Isolation bandwidth with the level of 20 dB is 745 MHz. Dimensions of the miniaturized device are 15.5 mm × 23.9 mm = 370.45 mm2, that is 66% less than of the conventional one.


title: Machine Learning for Reading Order Detection in Document Image Understanding

abstract: Summary. Document image understanding refers to logical and semantic analysis of document images in order to extract information understandable to humans and codify it into machine-readable form. Most of the studies on document image understanding have targeted the specific problem of associating layout components with logical labels, while less attention has been paid to the problem of extracting relationships between logical components, such as cross-references. In this chapter, we investigate the problem of detecting the reading order relationship between components of a logical structure. The domain specific knowledge required for this task is automatically acquired from a set of training examples by applying a machine learning method. The input of the learning method is the description of “chains” of layout components defined by the user. The output is a logical theory which defines two predicates, fi rst to read/ 1a ndsucc in reading/2, useful for consistently reconstructing all chains in the training set. Only spatial information on the page layout is exploited for both single and multiple chain reconstruction. The proposed approach has been evaluated on a set of document images processed by the system WISDOM++. Documents are characterized by two important structures: the layout structure and the logical structure. Both are the results of repeatedly dividing the content of a document into increasingly smaller parts, and are typically represented by means of a tree structure. The difference between them is the criteria adopted for structuring the document content: the layout structure is based on the presentation of the content, while the logical structure is based on the human-perceptible meaning of the content. The extraction of the layout structures from images of scanned paper documents is a complex process, typically denoted as document layout analysis, which involves several steps including preprocessing, page decomposition (or segmentation), classification of segments according to content type (e.g., text, graphics, pictures) and hierarchical organization on the basis of perceptual


title: Indoor Person Identification through Footstep Induced Structural Vibration

abstract: Person identification is crucial in various smart building applications, including customer behavior analysis, patient monitoring, etc. Prior works on person identification mainly focused on access control related applications. They achieve identification by sensing certain biometrics with specific sensors. However, these methods and apparatuses can be intrusive and not scalable because of instrumentation and sensing limitations. In this paper, we introduce our indoor person identification system that utilizes footstep induced structural vibration. Because structural vibration can be measured without interrupting human activities, our system is suitable for many ubiquitous sensing applications. Our system senses floor vibration and detects the signal induced by footsteps. Then the system extracts features from the signals that represent characteristics of each person's gait pattern. With the extracted features, the system conducts hierarchical classification at an individual step level and then at a trace (i.e., collection of consecutive steps) level. Our system achieves over 83% identification accuracy on average. Furthermore, when the application requires different levels of accuracy, our system can adjust confidence level threshold to discard uncertain traces. For example, at a threshold that allows only most certain 50% traces for classification, the identification accuracy increases to 96.5%.


title: First Person vs. Third Person Perspective in Digital Games: Do Player Preferences Affect Immersion?

abstract: Contemporary digital game developers offer a variety of games for the diverse tastes of their customers. Although the gaming experience often depends on one's preferences, the same may not apply to the level of their immersion. It has been argued whether the player perspective can influence the level of player's involvement with the game. The aim of this study was to research whether interacting with a game in first person perspective is more immersive than playing in the third person point of view (POV). The set up to test the theory involved participants playing a role-playing game in either mode, naming their preferred perspective, and subjectively evaluating their immersive experience. The results showed that people were more immersed in the game play when viewing the game world through the eyes of the character, regardless of their preferred perspectives.


title: Data + Intuition: A Hybrid Approach to Developing Product North Star Metrics

abstract: "You make what you measure" is a familiar mantra at data-driven companies. Accordingly, companies must be careful to choose North Star metrics that create a better product. Metrics fall into two general categories: direct count metrics such as total revenue and monthly active users, and nuanced quality metrics regarding value or other aspects of the user experience. Count metrics, when used exclusively as the North Star, might inform product decisions that harm user experience. Therefore, quality metrics play an important role in product development. We present a five-step framework for developing quality metrics using a combination of machine learning and product intuition. Machine learning ensures that the metric accurately captures user experience. Product intuition makes the metric interpretable and actionable. Through a case study of the Endorsements product at LinkedIn, we illustrate the danger of optimizing exclusively for count metrics, and showcase the successful application of our framework toward developing a quality metric. We show how the new quality metric has driven significant improvements toward creating a valuable, user-first product.


Security Issues in Mobile Payment Systems


title: Method of intrusion detection using deep neural network

abstract: In this study, an artificial intelligence (AI) intrusion detection system using a deep neural network (DNN) was investigated and tested with the KDD Cup 99 dataset in response to ever-evolving network attacks. First, the data were preprocessed through data transformation and normalization for input to the DNN model. The DNN algorithm was applied to the data refined through preprocessing to create a learning model, and the entire KDD Cup 99 dataset was used to verify it. Finally, the accuracy, detection rate, and false alarm rate were calculated to ascertain the detection efficacy of the DNN model, which was found to generate good results for intrusion detection.


title: Kinetic and dimensional optimization for a tendon-driven gripper

abstract: We design, optimize and demonstrate the behavior of a tendon-driven robotic gripper performing fingertip and enveloping grasps. The gripper consists of two fingers, each with two links, and is actuated using a single active tendon. During unobstructed closing, the distal links remain parallel, creating exact fingertip grasps. Conversely, if the proximal links are stopped by contact with an object, the distal links start flexing, creating a stable enveloping grasp. We optimize the route of the active tendon and the parameters of the springs providing passive extension forces in order to achieve this behavior. We show how an additional passive tendon can be used as a constraint preventing the gripper from entering undesirable parts of the joint workspace. Finally, we introduce a method for optimizing the dimensions of the links in order to achieve enveloping grasps of a large range of objects, and apply it to a set of common household objects.


title: Patent overlay mapping: Visualizing technological distance

abstract: We thank Kevin Boyack, Loet Leydesdorff, and Antoine Schoen for open and fruitful discussions about this paper. This research was undertaken largely at Georgia Tech drawing on support from the U.S. National Science Foundation (NSF) through the Center for Nanotechnology in Society (Arizona State University; Award No. 0531194); and NSF Award No. 1064146 ("Revealing Innovation Pathways: Hybrid Science Maps for Technology Assessment and Foresight"). Part of this research was also undertaken in collaboration with the Center for Nanotechnology in Society, University of California Santa Barbara (NSF Awards No. 0938099 and No. 0531184). The findings and observations contained in this paper are those of the authors and do not necessarily reflect the views of the US National Science Foundation.


title: The dynamics of transitions in socio-technical systems: a multi-level analysis of the transition pathway from horse-drawn carriages to automobiles (1860-1930)

abstract: This article investigates transitions at the level of societal functions (e.g. transport, communication, housing). Societal functions are fulfilled by socio-technical systems, which consist of a cluster of aligned elements, e.g. artefacts, knowledge, user practices and markets, regulation, cultural meaning, infrastructure, maintenance networks and supply networks. To understand how transitions from one socio-technical system to another come about, the article describes a conceptual multi-level perspective. The perspective is illustrated with a historical case study: the transition from horse-drawn carriages to automobiles in the USA (1860-1930). The case study shows that technological substitution approaches to this transition are too simple, because they neglect the electric tram and bicycle, which acted as important stepping stones. The case study also corrects another mistake, namely that the gasoline car won by chance from steam and electric automobiles. It will be shown that particular niches played a crucial role in this competition, as well as the wider socio-technical context. The case study deviates on three points from the multi-level perspective. These deviations are used to conceptualize a particular transition pathway, called 'de-alignment and re-alignment'.


title: Experiences in teaching quality attribute scenarios

abstract: The concept of the quality attribute scenario was introduced in 2003 to support the development of software architectures. This concept is useful because it provides an operational means to represent the quality requirements of a system. It also provides a more concrete basis with which to teach software architecture. Teaching this concept however has some unexpected issues. In this paper, I present my experiences of teaching quality attribute scenarios and outline Bus Tracker, a case study I have developed to support my teaching.


title: Detection of new drug indications from electronic medical records

abstract: Drug repositioning — detection of new uses of existing drugs — is an emerging trend in pharmaceutical industry. It essentially is a multiple aspect process of analyzing large-scale heterogeneous data for exploiting advantage of off-targets of the existing drugs. Three kinds of omics, phenomic and drug data are often integrated and used to study drug repositioning. The recent prevalence of electronic medical records (EMRs) makes it become an extremely significant resource of phenomic data for drug repositioning in the post-market stage. However, there is still no generic process and method to this end. This work aims to establish such a process and method. The paper addresses the solution of the first two problems in this complex process.


title: Instance-Based Question Answering: A Data-Driven Approach

abstract: Anticipating the availability of large questionanswer datasets, we propose a principled, datadriven Instance-Based approach to Question Answering. Most question answering systems incorporate three major steps: classify questions according to answer types, formulate queries for document retrieval, and extract actual answers. Under our approach, strategies for answering new questions are directly learned from training data. We learn models of answer type, query content, and answer extraction from clusters of similar questions. We view the answer type as a distribution, rather than a class in an ontology. In addition to query expansion, we learn general content features from training data and use them to enhance the queries. Finally, we treat answer extraction as a binary classification problem in which text snippets are labeled as correct or incorrect answers. We present a basic implementation of these concepts that achieves a good performance on TREC test data.


title: Automatic Algorithm Recognition Based on Programming Schemas and Beacons - A Supervised Machine Learning Classification Approach

abstract: Aalto University, P.O. Box 11000, FI-00076 Aalto www.aalto.fi Author Ahmad Taherkhani Name of the doctoral dissertation Automatic Algorithm Recognition Based on Programming Schemas and Beacons: A Supervised Machine Learning Classification Approach Publisher Aalto University School of Science Unit Department of Computer Science and Engineering Series Aalto University publication series DOCTORAL DISSERTATIONS 17/2013 Field of research Software Systems Manuscript submitted 11 September 2012 Date of the defence 8 March 2013 Permission to publish granted (date) 18 December 2012 Language English Monograph Article dissertation (summary + original articles) Abstract In this thesis, we present techniques to recognize basic algorithms covered in computer science education from source code. The techniques use various software metrics, language constructs and other characteristics of source code, as well as the concept of schemas and beacons from program comprehension models. Schemas are high level programming knowledge with detailed knowledge abstracted out. Beacons are statements that imply specific structures in a program. Moreover, roles of variables constitute an important part of the techniques. Roles are concepts that describe the behavior and usage of variables in a program. They have originally been introduced to help novices learn programming. We discuss two methods for algorithm recognition. The first one is a classification method based on a supervised machine learning technique. It uses the vectors of characteristics and beacons automatically computed from the algorithm implementations of a training set to learn what characteristics and beacons can best describe each algorithm. Based on these observed instance-class pairs, the system assigns a class to each new input algorithm implementation according to its characteristics and beacons. We use the C4.5 algorithm to generate a decision tree that performs the task. In the second method, the schema detection method, algorithms are defined as schemas that exist in the knowledge base of the system. To identify an algorithm, the method searches the source code to detect schemas that correspond to those predefined schemas. Moreover, we present a method that combines these two methods: it first applies the schema detection method to extract algorithmic schemas from the given program and then proceeds to the classification method applied to the schema parts only. This enhances the reliability of the classification method, as the characteristics and beacons are computed only from the algorithm implementation code, instead of the whole given program. We discuss several empirical studies conducted to evaluate the performance of the methods. Some results are as follows: evaluated by leave-one-out cross-validation, the estimated classification accuracy for sorting algorithms is 98,1%, for searching, heap, basic tree traversal and graph algorithms 97,3% and for the combined method (on sorting algorithms and their variations from real student submissions) 97,0%. For the schema detection method, the accuracy is 88,3% and 94,1%, respectively. In addition, we present a study for categorizing student-implemented sorting algorithms and their variations in order to find problematic solutions that would allow us to give feedback on them. We also explain how these variations can be automatically recognized.In this thesis, we present techniques to recognize basic algorithms covered in computer science education from source code. The techniques use various software metrics, language constructs and other characteristics of source code, as well as the concept of schemas and beacons from program comprehension models. Schemas are high level programming knowledge with detailed knowledge abstracted out. Beacons are statements that imply specific structures in a program. Moreover, roles of variables constitute an important part of the techniques. Roles are concepts that describe the behavior and usage of variables in a program. They have originally been introduced to help novices learn programming. We discuss two methods for algorithm recognition. The first one is a classification method based on a supervised machine learning technique. It uses the vectors of characteristics and beacons automatically computed from the algorithm implementations of a training set to learn what characteristics and beacons can best describe each algorithm. Based on these observed instance-class pairs, the system assigns a class to each new input algorithm implementation according to its characteristics and beacons. We use the C4.5 algorithm to generate a decision tree that performs the task. In the second method, the schema detection method, algorithms are defined as schemas that exist in the knowledge base of the system. To identify an algorithm, the method searches the source code to detect schemas that correspond to those predefined schemas. Moreover, we present a method that combines these two methods: it first applies the schema detection method to extract algorithmic schemas from the given program and then proceeds to the classification method applied to the schema parts only. This enhances the reliability of the classification method, as the characteristics and beacons are computed only from the algorithm implementation code, instead of the whole given program. We discuss several empirical studies conducted to evaluate the performance of the methods. Some results are as follows: evaluated by leave-one-out cross-validation, the estimated classification accuracy for sorting algorithms is 98,1%, for searching, heap, basic tree traversal and graph algorithms 97,3% and for the combined method (on sorting algorithms and their variations from real student submissions) 97,0%. For the schema detection method, the accuracy is 88,3% and 94,1%, respectively. In addition, we present a study for categorizing student-implemented sorting algorithms and their variations in order to find problematic solutions that would allow us to give feedback on them. We also explain how these variations can be automatically recognized.


title: Deep Auxiliary Learning for Visual Localization and Odometry

abstract: Localization is an indispensable component of a robot's autonomy stack that enables it to determine where it is in the environment, essentially making it a precursor for any action execution or planning. Although convolutional neural networks have shown promising results for visual localization, they are still grossly outperformed by state-of-the-art local feature-based techniques. In this work, we propose VLocNet, a new convolutional neural network architecture for 6-DoF global pose regression and odometry estimation from consecutive monocular images. Our multitask model incorporates hard parameter sharing, thus being compact and enabling real-time inference, in addition to being end-to-end trainable. We propose a novel loss function that utilizes auxiliary learning to leverage relative pose information during training, thereby constraining the search space to obtain consistent pose estimates. We evaluate our proposed VLocNet on indoor as well as outdoor datasets and show that even our single task model exceeds the performance of state-of-the-art deep architectures for global localization, while achieving competitive performance for visual odometry estimation. Furthermore, we present extensive experimental evaluations utilizing our proposed Geometric Consistency Loss that show the effectiveness of multitask learning and demonstrate that our model is the first deep learning technique to be on par with, and in some cases outperforms state-of-the-art SIFT-based approaches.


title: Mesenteric volvulus in the dog: a retrospective study of 12 cases

abstract: Mesenteric volvulus was diagnosed in 12 dogs over a nine-year period. Each case was presented with abdominal distension and shock. Haematochezia, which is frequently reported in association with mesenteric volvulus, was present in only two of the dogs. The diagnosis, which in all cases was based on radiography, was followed by immediate treatment for shock and surgical intervention whenever possible. In five of the 12 cases, the volvulus was treated successfully and these patients survived. This indicates that the prognosis for mesenteric volvulus might be better than is currently believed, and immediate laparotomy is recommended if mesenteric volvulus is suspected.


title: Hyperspectral Remote Sensing of Agriculture

abstract: Remote sensing is being increasingly used in different agricultural applications. Hyperspectral remote sensing in large continuous narrow wavebands provides significant advancement in understanding the subtle changes in biochemical and biophysical attributes of the crop plants and their different physiological processes, which otherwise are indistinct in multispectral remote sensing. This article describes spectral properties of vegetation both in the optical and thermal range of the electromagnetic spectrum as affected by its attributes. Different methods have been discussed to reduce data dimension and minimize the information redundancy. Potential applications of hyperspectral remote sensing in agriculture, i.e. spectral discrimination of crops and their genotypes, quantitative estimation of different biophysical and biochemical parameters through empirical and physical modelling, assessing abiotic and biotic stresses as developed by different researchers in India and abroad are described.


title: Model-Free segmentation and grasp selection of unknown stacked objects

abstract: We present a novel grasping approach for unknown stacked objects using RGB-D images of highly complex real-world scenes. Specifically, we propose a novel 3D segmentation algorithm to generate an efficient representation of the scene into segmented surfaces (known as surfels) and objects. Based on this representation, we next propose a novel grasp selection algorithm which generates potential grasp hypotheses and automatically selects the most appropriate grasp without requiring any prior information of the objects or the scene. We tested our algorithms in real-world scenarios using live video streams from Kinect and publicly available RGB-D object datasets. Our experimental results show that both our proposed segmentation and grasp selection algorithms consistently perform superior compared to the state-of-the-art methods.


title: Aspect based sentiment analysis for review rating prediction

abstract: Aspect based sentiment analysis consists of aspect and sentiment extraction, and determination of the sentiment's orientation. In this paper, we propose a system to extract the aspect sentiment pair and compute the rating for each grouped aspect. Our approach starts with selecting the subjective sentences in the reviews. Then, it extracts aspects and opinions from the sentences, and determine the orientation of the sentiment. Aspects are clustered together by using WordNet with the prior knowledge of the aspect categories. Finally, it measures the rating for each aspect category. We evaluate our approach by three criteria: precision, recall, and F1-Measure. The subjectivity classification model and extraction model has F1-Measure value of 0.783 and 0.794 respectively.


title: TrustLogin: Securing Password-Login on Commodity Operating Systems

abstract: With the increasing prevalence of Web 2.0 and cloud computing, password-based logins play an increasingly important role on user-end systems. We use passwords to authenticate ourselves to countless applications and services. However, login credentials can be easily stolen by attackers. In this paper, we present a framework, TrustLogin, to secure password-based logins on commodity operating systems. TrustLogin leverages System Management Mode to protect the login credentials from malware even when OS is compromised. TrustLogin does not modify any system software in either client or server and is transparent to users, applications, and servers. We conduct two study cases of the framework on legacy and secure applications, and the experimental results demonstrate that TrustLogin is able to protect login credentials from real-world keyloggers on Windows and Linux platforms. TrustLogin is robust against spoofing attacks. Moreover, the experimental results also show TrustLogin introduces a low overhead with the tested applications.


title: Diversified Visual Attention Networks for Fine-Grained Object Classification

abstract: Fine-grained object classification attracts increasing attention in multimedia applications. However, it is a quite challenging problem due to the subtle interclass difference and large intraclass variation. Recently, visual attention models have been applied to automatically localize the discriminative regions of an image for better capturing critical difference, which have demonstrated promising performance. Unfortunately, without consideration of the diversity in attention process, most of existing attention models perform poorly in classifying fine-grained objects. In this paper, we propose a diversified visual attention network (DVAN) to address the problem of fine-grained object classification, which substantially relieves the dependency on strongly supervised information for learning to localize discriminative regions com-pared with attention-less models. More importantly, DVAN explicitly pursues the diversity of attention and is able to gather discriminative information to the maximal extent. Multiple attention canvases are generated to extract convolutional features for attention. An LSTM recurrent unit is employed to learn the attentiveness and discrimination of attention canvases. The proposed DVAN has the ability to attend the object from coarse to fine granularity, and a dynamic internal representation for classification is built up by incrementally combining the information from different locations and scales of the image. Extensive experiments conducted on CUB-2011, Stanford Dogs, and Stanford Cars datasets have demonstrated that the pro-posed DVAN achieves competitive performance compared to the state-of-the-art approaches, without using any prior knowledge, user interaction, or external resource in training and testing.


title: Citation Metadata Extraction via Deep Neural Network-based Segment Sequence Labeling

abstract: Citation metadata extraction plays an important role in academic information retrieval and knowledge management. Current works on this task generally use rule-based, template-based or learning-based approaches but these methods usually either rely on handcrafted features or are limited with domains. Recently, neural networks have shown strong ability in addressing sequence labeling tasks. In this paper, we propose a sequence labeling model for citation metadata extraction, called segment sequence labeling. Instead of inferring at word level, the input sequence is first divided into segments, and then features of the segments are computed to infer the label sequence of the segments. We first run experiments to validate the effectiveness of different parts of the model by comparing it with a CRF-based model and a neural network-based model. Experimental results show our model beats both models on most fields. Besides, our model is evaluated on public datasets UMass and Cora and has achieved significant performance improvement. Our model was trained on the data which were generated from BibTeX files collected on the Web and annotated automatically.


title: Memristor-based neural networks

abstract: The synapse is a crucial element in biological neural networks, but a simple electronic equivalent has been absent. This complicates the development of hardware that imitates biological architectures in the nervous system. Now, the recent progress in the experimental realization of memristive devices has renewed interest in artificial neural networks. The resistance of a memristive system depends on its past states and exactly this functionality can be used to mimic the synaptic connections in a (human) brain. After a short introduction to memristors, we present and explain the relevant mechanisms in a biological neural network, such as long-term potentiation and spike time-dependent plasticity, and determine the minimal requirements for an artificial neural network. We review the implementations of these processes using basic electric circuits and more complex mechanisms that either imitate biological systems or could act as a model system for them. (Some figures may appear in colour only in the online journal)


title: Beyond student perceptions: Issues of interaction, presence, and performance in an online course

abstract: The research literature on Web-based learning supports the assumption that interaction is important for a successful course, yet questions exist regarding the nature and extent of the interaction and its effects on student performance. Much of the research is based on student perceptions of the quality and quantity of their interactions and how much they have learned in an online course. The purpose of this study is to examine performance in an online course in relationship to student interaction and sense of presence in the course. Data on multiple independent (measures of interaction and presence) and dependent (measures of performance) variables were collected and subjected to analysis. An attempt was made to go beyond typical institutional performance measures such as grades and withdrawal rates and to examine measures specifically related to course objectives.


title: Identifying Document Topics Using the Wikipedia Category Network

abstract: In the last few years the size and coverage of Wikipe- dia, a freely available on-line encyclopedia has reached the point where it can be utilized similar to an ontology or tax- onomy to identify the topics discussed in a document. In this paper we will show that even a simple algorithm that exploits only the titles and categories of Wikipedia articles can characterize documents by Wikipedia categories sur- prisingly well. We test the reliability of our method by pre- dicting categories ofWikipedia articles themselves based on their bodies, and by performing classification and cluster- ing on 20 Newsgroups and RCV1, representing documents by their Wikipedia categories instead of their texts.


title: A Visual and Textual Recurrent Neural Network for Sequential Prediction

abstract: Sequential prediction is a fundamental task for Web applications. Due to the insufficiency of user feedbacks, sequential prediction usually suffers from the cold start problem. There are two kinds of popular approaches based on matrix factorization (MF) and Markov chains (MC) for item prediction. MF methods factorize the user-item matrix to learn general tastes of users. MC methods predict the next behavior based on recent behaviors. However, they have limitations. MF methods can merge additional information to address cold start but could not capture dynamic properties of user's interest, and MC based sequential methods have difficulty in addressing cold start and has a strong Markov assumption that the next state only depends on the last state. In this work, to deal with the cold start problem of sequential prediction, we propose a RNN model adopting visual and textual content of items, which is named as $\mathbf{V}$isual and $\mathbf{T}$extual $\mathbf{R}$ecurrent $\mathbf{N}$eural $\mathbf{N}$etwork ($\mathbf{VT}$-$\mathbf{RNN}$). We can simultaneously learn the sequential latent vectors that dynamically capture the user's interest, as well as content-based representations that contribute to address the cold start. Experiments on two real-world datasets show that our proposed VT-RNN model can effectively generate the personalized ranking list and significantly alleviate the cold start problem.


title: Spatiotemporal analysis of event-related fMRI data using partial least squares

abstract: Partial least squares (PLS) has proven to be a important multivariate analytic tool for positron emission tomographic and, more recently, event-related potential (ERP) data. The application to ERP incorporates the ability to analyze space and time together, a feature that has obvious appeal for event-related functional magnetic resonance imaging (fMRI) data. This paper presents the extension of spatiotemporal PLS (ST-PLS) to fMRI, explaining the theoretical foundation and application to an fMRI study of auditory and visual perceptual memory. Analysis of activation effects with ST-PLS was compared with conventional univariate random effects analysis, showing general consensus for both methods, but several unique observations by ST-PLS, including enhanced statistical power. The application of ST-PLS for assessment of task-dependent brain-behavior relationships is also presented. Singular features of ST-PLS include (1) no assumptions about the shape of the hemodynamic response functions (HRFs); (2) robust statistical assessment at the image level through permutation tests; (3) protection against outlier influences at the voxel level through bootstrap resampling; (4) flexible analytic configurations that allow assessment of activation difference, brain-behavior relations, and functional connectivity. These features enable ST-PLS to act as an important complement to other multivariate and univariate approaches used in neuroimaging research.


title: Material Classification under Natural Illumination Using Reflectance Maps

abstract: Research on visual material recognition has traditionally been based on texture analysis. Whereas older work has focused on uncluttered scenes, more recent contributions allowed for material recognition 'in the wild'. Quite some objects have untextured surfaces, however. Especially man-made examples are legion. The most obvious cue to use in such cases would be reflection information. Yet, methods to that effect are lacking. It is clear that more than an estimate of a scalar albedo is needed, and a more complete reflectance model has to be derived. Rather than using an extensive lab setup, we propose a system that only requires the 3D shape of objects and a regular, commercial camera to capture their appearance in a single image, to perform material classification under unknown illumination. To this end, we rely on a Gaussian Process Latent Variable Model (GPLVM) with a discriminative prior to learn a low-dimensional manifold suitable for material classification of reflectance maps, i.e. from a 2D image of a singlematerial sphere under natural illumination. We evaluated our method based on experiments generated from synthetic and real-life data. Although recognizing materials without texture (or object recognition) is not a trivial problem, our method achieves about 75% recognition accuracy, about 27% higher than human performance.


title: High-Efficiency W-Band Electroforming Slot Array Antenna

abstract: A W-band $8 \times 8$ slot array antenna is proposed and fabricated by electroforming for high-precision manufacturing. The external mutual coupling between radiating shunt slots is compensated for input impedance matching and a uniform field distribution. The overall antenna aperture area is $473.1\,\rm{mm}^2$ with slot spacings of 2.8 and 2.64 mm in the transverse and longitudinal direction, respectively. The measured maximum gain is 26.8 dBi and the corresponding antenna efficiency is 81.9% at 94 GHz. The measured impedance bandwidth when the VSWR is less than 2.0 ( $- 10 \;\rm{dB}$ ) is 8.3%, with a range from 89.9 to 97.6 GHz.


title: ATP-dependent Permeability of Membrane Barriers in Penicillium cyclopium Westling

abstract: Summary The noncatalytic permeability of the protoplasmic membrane in conidiospores and hyphae of Penicillium cyclopium is drastically increased by treatment with azide or 2,4-dinitrophenol (1 mM). The number of cells which are sensitive to this permeabilization depends on the age of the cultures and the nutrient supply. Under optimal conditions 30% of the conidiospores and about 55% of the hyphae become fully permeable for low molecular compounds. The increase in permeability starts after the inhibitor-triggered depletion of about 70% of the cellular ATP and can be reversed by supply or by net synthesis of ATP. A method for metabolic permeabilization is based on this effect


title: A Review on Kombucha Tea-Microbiology, Composition, Fermentation, Beneficial Effects, Toxicity, and Tea Fungus

abstract: Fermentation of sugared tea with a symbiotic culture of acetic acid bacteria and yeast (tea fungus) yields kombucha tea which is consumed worldwide for its refreshing and beneficial properties on human health. Important progress has been made in the past decade concerning research findings on kombucha tea and reports claiming that drinking kombucha can prevent various types of cancer and cardiovascular diseases, promote liver functions, and stimulate the immune system. Considering the widespread reports on kombucha, we recognized the need to review and update the research conducted in relation to kombucha tea, its products and tea fungus. Existing reports have suggested that the protective effects of kombucha tea are as good as those of black tea, however, more studies on kombucha tea and its composition are needed before final conclusions can be made.


A Convolutional Architecture for Word Sequence Prediction


title: Realtime Ray Tracing on GPU with BVH-based Packet Traversal

abstract: Recent GPU ray tracers can already achieve performance competitive to that of their CPU counterparts. Nevertheless, these systems can not yet fully exploit the capabilities of modern GPUs and can only handle medium-sized, static scenes. In this paper we present a BVH-based GPU ray tracer with a parallel packet traversal algorithm using a shared stack. We also present a fast, CPU-based BVH construction algorithm which very accurately approximates the surface area heuristic using streamed binning while still being one order of magnitude faster than previously published results. Furthermore, using a BVH allows us to push the size limit of supported scenes on the GPU: We can now ray trace the 12.7 million triangle Power Plant at 1024 times 1024 image resolution with 3 fps, including shading and shadows.


title: Diagonal rnns in symbolic music modeling

abstract: In this paper, we propose a new Recurrent Neural Network (RNN) architecture. The novelty is simple: We use diagonal recurrent matrices instead of full. This results in better test likelihood and faster convergence compared to regular full RNNs in most of our experiments. We show the benefits of using diagonal recurrent matrices with popularly used LSTM and GRU architectures as well as with the vanilla RNN architecture, on four standard symbolic music datasets.


title: Multiclass support vector machines using adaptive directed acyclic graph

abstract: Presents a method of extending support vector machines (SVMs) for dealing with multiclass problems. Motivated by the decision directed acyclic graph (DDAG), we propose the adaptive DAG (ADAG): a modified structure of the DDAG that has a lower number of decision levels and reduces the dependency on the sequence of nodes. Thus, the ADAG improves the accuracy of the DDAG while maintaining low computational requirement.


title: Achieving Ultra-Low Latency in 5G Millimeter Wave Cellular Networks

abstract: The IMT 2020 requirements of 20 Gb/s peak data rate and 1 ms latency present significant engineering challenges for the design of 5G cellular systems. Systems that make use of the mmWave bands above 10 GHz ---where large regions of spectrum are available --- are a promising 5G candidate that may be able to rise to the occasion. However, although the mmWave bands can support massive peak data rates, delivering these data rates for end-to-end services while maintaining reliability and ultra-low-latency performance to support emerging applications and use cases will require rethinking all layers of the protocol stack. This article surveys some of the challenges and possible solutions for delivering end-to-end, reliable, ultra-low-latency services in mmWave cellular systems in terms of the MAC layer, congestion control, and core network architecture.


title: Multimodal summarization of complex sentences

abstract: In this paper, we introduce the idea of automatically illustrating complex sentences as multimodal summaries that combine pictures, structure and simplified compressed text. By including text and structure in addition to pictures, multimodal summaries provide additional clues of what happened, who did it, to whom and how, to people who may have difficulty reading or who are looking to skim quickly. We present ROC-MMS, a system for automatically creating multimodal summaries (MMS) of complex sentences by generating pictures, textual summaries and structure. We show that pictures alone are insufficient to help people understand most sentences, especially for readers who are unfamiliar with the domain. An evaluation of ROC-MMS in the Wikipedia domain illustrates both the promise and challenge of automatically creating multimodal summaries.


title: Neural and behavioral responses to attractiveness in adult and infant faces

abstract: Facial attractiveness provides a very powerful motivation for sexual and parental behavior. We therefore review the importance of faces to the study of neurobiological control of human reproductive motivations. For heterosexual individuals there is a common brain circuit involving the nucleus accumbens, the medial prefrontal, dorsal anterior cingulate and the orbitofrontal cortices that is activated more by attractive than unattractive faces, particularly for faces of the opposite sex. Behavioral studies indicate parallel effects of attractiveness on incentive salience or willingness to work to see faces. There is some evidence that the reward value of opposite sex attractiveness is more pronounced in men than women, perhaps reflecting the greater importance assigned to physical attractiveness by men when evaluating a potential mate. Sex differences and similarities in response to facial attractiveness are reviewed. Studies comparing heterosexual and homosexual observers indicate the orbitofrontal cortex and mediodorsal thalamus are more activated by faces of the desired sex than faces of the less-preferred sex, independent of observer gender or sexual orientation. Infant faces activate brain regions that partially overlap with those responsive to adult faces. Infant faces provide a powerful stimulus, which also elicits sex differences in behavior and brain responses that appear dependent on sex hormones. There are many facial dimensions affecting perceptions of attractiveness that remain unexplored in neuroimaging, and we conclude by suggesting that future studies combining parametric manipulation of face images, brain imaging, hormone assays and genetic polymorphisms in receptor sensitivity are needed to understand the neural and hormonal mechanisms underlying reproductive drives.


title: Assessment of Mercury Intake through Consumption of Yellowfin Seabream (Acanthopagrus latus) from Musa Estuary

abstract: Fish constitute a major part of people diet in southern Iranian cities due to the neighborhood of those cities to the Persian Gulf. Previous studies have demonstrated that Musa Estuary is polluted by mercury. Yellow sea bream is well distributed in this water body. To evaluate mercury level in this economically important fish a total number of 50 Yellowfin seabream were caught from 5 different creeks in Musa estuary including Zangi, Jafari, Petroshimi, Ghazaleh and Majidieh. The muscle tissues of the fish were digested and their Hg content was analyzed using atomic absorption spectrophotometer equipped with cold vapor system. Hg concentration in samples ranged from 0.44 to 1.46 mg/kg. In more than 88% of cases Hg concentration in fish exceeded the FAO/WHO permissible limit. The weekly mercury intake via fish consumption was estimated as 4.87 to 12.07 and 1.01 to 2.50 μg/kg body weight for children and adults respectively. The Estimated Weekly Intake (EWI) of mercury was higher than the Provisional Tolerable Weekly Intake (PTWI) for children. In addition, the calculated consumption limits of Yellowfin seabream for children and adults were 3 and 15 times per year respectively.


title: Predicting 30-Day Risk and Cost of "All-Cause" Hospital Readmissions

abstract: The hospital readmission rate of patients within 30 days after discharge is broadly accepted as a healthcare quality measure and cost driver in the United States. The ability to estimate hospitalization costs alongside 30 day risk-stratification for such readmissions provides additional benefit for accountable care, now a global issue and foundation for the U.S.~government mandate under the Affordable Care Act. Recent data mining efforts either predict healthcare costs or risk of hospital readmission, but not both. In this paper we present a dual predictive modeling effort that utilizes healthcare data to predict the risk and cost of any hospital readmission (``all-cause''). For this purpose, we explore machine learning algorithms to do accurate predictions of healthcare costs and risk of 30-day readmission.Results on risk prediction for ``all-cause'' readmission compared to the standardized readmission tool (LACE) are promising, and the proposed techniques for cost prediction consistently outperform baseline models and demonstrate substantially lower mean absolute error (MAE).


title: Dialogue Act Recognition via CRF-Attentive Structured Network

abstract: Dialogue Act Recognition (DAR) is a challenging problem in dialogue interpretation, which aims to attach semantic labels to utterances and characterize the speaker's intention. Currently, many existing approaches formulate the DAR problem ranging from multi-classification to structured prediction, which suffer from handcrafted feature extensions and attentive contextual structural dependencies. In this paper, we consider the problem of DAR from the viewpoint of extending richer Conditional Random Field (CRF) structural dependencies without abandoning end-to-end training. We incorporate hierarchical semantic inference with memory mechanism on the utterance modeling. We then extend structured attention network to the linear-chain conditional random field layer which takes into account both contextual utterances and corresponding dialogue acts. The extensive experiments on two major benchmark datasets Switchboard Dialogue Act (SWDA) and Meeting Recorder Dialogue Act (MRDA) datasets show that our method achieves better performance than other state-of-the-art solutions to the problem. It is a remarkable fact that our method is nearly close to the human annotator's performance on SWDA within 2% gap.


title: Soft-neuromorphic artificial touch for applications in neuro-robotics

abstract: We propose an artificial mechanotransduction system based on a 2×2 MEMS array touch sensor, and evaluate a neural model which is designed to convert raw sensor outputs into neural spike-trains. We show that core tactile information is preserved in the neural representation, and that the resulting modulation via spikes can be used in surface discrimination tasks. In this research study the first neural stage (i.e., at mechanoreceptor level) of somatosensory system was mimicked in a soft-neuromorphic fashion, while future works will target the implementation of the 2nd order stage (Cuneate neurons) to further understand the biological mechanisms underlying maximum transfer and fast processing of tactile information.


What Can I Not Do? Towards an Architecture for Reasoning about and Learning Affordances.


title: Heterogeneous Multi-task Semantic Feature Learning for Classification

abstract: Multi-task Learning (MTL) aims to learn multiple related tasks simultaneously instead of separately to improve generalization performance of each task. Most existing MTL methods assumed that the multiple tasks to be learned have the same feature representation. However, this assumption may not hold for many real-world applications. In this paper, we study the problem of MTL with heterogeneous features for each task. To address this problem, we first construct an integrated graph of a set of bipartite graphs to build a connection among different tasks. We then propose a multi-task nonnegative matrix factorization (MTNMF) method to learn a common semantic feature space underlying different heterogeneous feature spaces of each task. Finally, based on the common semantic features and original heterogeneous features, we model the heterogenous MTL problem as a multi-task multi-view learning (MTMVL) problem. In this way, a number of existing MTMVL methods can be applied to solve the problem effectively. Extensive experiments on three real-world problems demonstrate the effectiveness of our proposed method.


title: Methodological considerations for blood flow restricted resistance exercise

abstract: “KAATSU Training”, was first made available for public use in Japan.1 Since then, numerous studies have examined the effects of BFR resistance exercise on skeletal muscle hypertrophy and strength gains, as well as on neural, endocrine, and cardiovascular responses. The novelty of BFR resistance exercise is that relatively low exercise intensities (e.g. 20% of one-repetition maximum, 1-RM) can be utilized to elicit skeletal muscle hypertrophy and increase muscular strength. Even low-intensity aerobic exercise (e.g. walking, cycling), when combined with blood flow restriction, may be effective for increasing strength and hypertrophy.2 Thus, BFR exercise has a wide range of practical applications, from performance enhancement in athletes to combating muscle atrophy in clinical populations. To restrict blood flow during resistance exercise, a restrictive cuff or band (typically a pneumatic cuff) is placed on the most proximal portion of the exercising limb which reduces arterial blood inflow to the working muscle and occludes venous return resulting in the pooling of venous blood around the exercising muscle. Although there are likely many mechanisms involved in the adaptations to BFR exercise, optimizing muscle fiber recruitment appears to be critical. Thus, low-load BFR resistance exercise serves as an effective way to reach the same endpoint as reached with other forms of resistance exercise. However, BFR resistance training introduces more variables than standard training. Individual differences (e.g. limb size) as well as methodological differences (e.g. restrictive cuff size and pressure and resistance exercise protocol) are variables that must be considered with BFR resistance exercise. Manipulation of these variables will affect the degree to which blood flow is restricted during exercise. This may affect the acute responses and ultimately training adaptations to this type of exercise. Therefore, the purpose of this article is to summarize the different methodologies used in BFR resistance training studies to provide a resource for individuals who may be interested in utilizing this training technique and for researchers who are interested in studying the acute and chronic effects of BFR resistance exercise.


title: Three-level active NPC converter: PWM strategies and loss distribution

abstract: The 3L-NPC (Neutral-Point-Clamped) is the most popular multilevel converter used in high-power medium-voltage applications. An important disadvantage of this structure is the unequal distribution of losses among the switches. The performances of 3L-NPC structure were improved by developing the 3L-ANPC (Active-NPC) converter which has more degrees of freedom. In this paper the switching states and the loss distribution problem are studied for different PWM strategies in a STATCOM application. The PSIM simulation results are shown in order to validate the PWM strategies studied for 3L-ANPC converter.


title: Evaluation of SiC BJTs for High-Power DC–DC Converters

abstract: The design of a 200-A, all-SiC power-module-based on bipolar junction transistor devices is described, and the impact of the module is assessed on the performance of a 50-kW dc-dc converter for electric vehicle applications, particularly the overall weight and efficiency. Using a hard-switching dual-interleaved topology, which has proven high efficiency and high-power density capability, the operation of a 50-kW, 75-kHz all-SiC converter is compared with that of an insulated-gate bipolar transistor-based silicon converter, switching at 25 kHz, each providing 600-V output. The results show that the total losses are reduced by almost 40%, whilst the overall weight is reduced by 27%, achieving a power density of 10.5 kW/kg. Experimental results of the SiC converter operating at 220-600 V, 52.8 kW are provided, showing an efficiency of 97%.


title: Bitcoin Transaction Graph Analysis

abstract: Bitcoins have recently become an increasingly popular cryptocurrency through which users trade electronically and more anonymously than via traditional electronic transfers. Bitcoin's design keeps all transactions in a public ledger. The sender and receiver for each transaction are identified only by cryptographic public-key ids. This leads to a common misconception that it inherently provides anonymous use. While Bitcoin's presumed anonymity offers new avenues for commerce, several recent studies raise user-privacy concerns. We explore the level of anonymity in the Bitcoin system. Our approach is two-fold: (i) We annotate the public transaction graph by linking bitcoin public keys to "real" people - either definitively or statistically. (ii) We run the annotated graph through our graph-analysis framework to find and summarize activity of both known and unknown users.


Urban network analysis. A new toolbox for ArcGIS


title: Design and fabrication of flexible rod meshes

abstract: We present a computational tool for fabrication-oriented design of flexible rod meshes. Given a deformable surface and a set of deformed poses as input, our method automatically computes a printable rod mesh that, once manufactured, closely matches the input poses under the same boundary conditions. The core of our method is formed by an optimization scheme that adjusts the cross-sectional profiles of the rods and their rest centerline in order to best approximate the target deformations. This approach allows us to locally control the bending and stretching resistance of the surface with a single material, yielding high design flexibility and low fabrication cost.


title: Audio enhancing with DNN autoencoder for speaker recognition

abstract: In this paper we present a design of a DNN-based autoencoder for speech enhancement and its use for speaker recognition systems for distant microphones and noisy data. We started with augmenting the Fisher database with artificially noised and reverberated data and trained the autoencoder to map noisy and reverberated speech to its clean version. We use the autoencoder as a preprocessing step in the later stage of modelling in state-of-the-art text-dependent and text-independent speaker recognition systems. We report relative improvements up to 50% for the text-dependent system and up to 48% for the text-independent one. With text-independent system, we present a more detailed analysis on various conditions of NIST SRE 2010 and PRISM suggesting that the proposed preprocessig is a promising and efficient way to build a robust speaker recognition system for distant microphone and noisy data.


title: Hawkes Processes with Stochastic Excitations

abstract: We propose an extension to Hawkes processes by treating the levels of self-excitation as a stochastic differential equation. Our new point process allows better approximation in application domains where events and intensities accelerate each other with correlated levels of contagion. We generalize a recent algorithm for simulating draws from Hawkes processes whose levels of excitation are stochastic processes, and propose a hybrid Markov chain Monte Carlo approach for model fitting. Our sampling procedure scales linearly with the number of required events and does not require stationarity of the point process. A modular inference procedure consisting of a combination between Gibbs and Metropolis Hastings steps is put forward. We recover expectation maximization as a special case. Our general approach is illustrated for contagion following geometric Brownian motion and exponential Langevin dynamics.


title: Ranking-based clustering of heterogeneous information networks with star network schema

abstract: A heterogeneous information network is an information network composed of multiple types of objects. Clustering on such a network may lead to better understanding of both hidden structures of the network and the individual role played by every object in each cluster. However, although clustering on homogeneous networks has been studied over decades, clustering on heterogeneous networks has not been addressed until recently. A recent study proposed a new algorithm, RankClus, for clustering on bi-typed heterogeneous networks. However, a real-world network may consist of more than two types, and the interactions among multi-typed objects play a key role at disclosing the rich semantics that a network carries. In this paper, we study clustering of multi-typed heterogeneous networks with a star network schema and propose a novel algorithm, NetClus, that utilizes links across multityped objects to generate high-quality net-clusters. An iterative enhancement method is developed that leads to effective ranking-based clustering in such heterogeneous networks. Our experiments on DBLP data show that NetClus generates more accurate clustering results than the baseline topic model algorithm PLSA and the recently proposed algorithm, RankClus. Further, NetClus generates informative clusters, presenting good ranking and cluster membership information for each attribute object in each net-cluster.


title: Prospecting the Career Development of Talents: A Survival Analysis Perspective

abstract: The study of career development has become more important during a time of rising competition. Even with the help of newly available big data in the field of human resources, it is challenging to prospect the career development of talents in an effective manner, since the nature and structure of talent careers can change quickly. To this end, in this paper, we propose a novel survival analysis approach to model the talent career paths, with a focus on two critical issues in talent management, namely turnover and career progression. Specifically, for modeling the talent turnover behaviors, we formulate the prediction of survival status at a sequence of time intervals as a multi-task learning problem by considering the prediction at each time interval as a task. Also, we impose the ranking constraints to model both censored and uncensored data, and capture the intrinsic properties exhibited in general lifetime modeling with non-recurrent and recurrent events. Similarly, for modeling the talent career progression, each task concerns the prediction of a relative occupational level at each time interval. The ranking constraints imposed on different occupational levels can help to reduce the prediction error. Finally, we evaluate our approach with several state-of-the-art baseline methods on real-world talent data. The experimental results clearly demonstrate the effectiveness of the proposed models for predicting the turnover and career progression of talents.


title: Using body-measurement indices and wrist-type photoplethysmography signals to categorize consumer electronic users' health state through a smartwatch application

abstract: The development of wearable technologies has been boosted considerably. On one hand, due to Internet of Things market expansion, and on the other, owing to health awareness on users demanding the convergence of wearable technology with applications that track their activity during the day, providing feedback on how to improve their consuming experience employing human vital signs. The availability of sensors on most of the wrist smartwatches and fitness bands, make this convergence a precondition for consumer devices industry and stakeholders. This article presents an analytical exploitation of this valuable data, and uses the embedded heart rate sensor from an attainable smartwatch to meet the prior requirements, parting the users according to the level of physical activity in pursuance of make a suitable recommendation from a specific consumer device edible catalog, according to the number of calories recommended for a healthy state.


title: Semantic Image Segmentation via Deep Parsing Network

abstract: This paper addresses semantic image segmentation by incorporating rich information into Markov Random Field (MRF), including high-order relations and mixture of label contexts. Unlike previous works that optimized MRFs using iterative algorithm, we solve MRF by proposing a Convolutional Neural Network (CNN), namely Deep Parsing Network (DPN), which enables deterministic end-toend computation in a single forward pass. Specifically, DPN extends a contemporary CNN architecture to model unary terms and additional layers are carefully devised to approximate the mean field algorithm (MF) for pairwise terms. It has several appealing properties. First, different from the recent works that combined CNN and MRF, where many iterations of MF were required for each training image during back-propagation, DPN is able to achieve high performance by approximating one iteration of MF. Second, DPN represents various types of pairwise terms, making many existing works as its special cases. Third, DPN makes MF easier to be parallelized and speeded up in Graphical Processing Unit (GPU). DPN is thoroughly evaluated on the PASCAL VOC 2012 dataset, where a single DPN model yields a new state-of-the-art segmentation accuracy of 77.5%.


title: KeyXtract Twitter Model - An Essential Keywords Extraction Model for Twitter Designed using NLP Tools

abstract: Since a tweet is limited to 140 characters, it is ambiguous and difficult for traditional Natural Language Processing (NLP) tools to analyse. This research presents KeyXtract which enhances the machine learning based Stanford CoreNLP Part-of-Speech (POS) tagger with the Twitter model to extract essential keywords from a tweet. The system was developed using rule-based parsers and two corpora. The data for the research was obtained from a Twitter profile of a telecommunication company. The system development consisted of two stages. At the initial stage, a domain specific corpus was compiled after analysing the tweets. The POS tagger extracted the Noun Phrases and Verb Phrases while the parsers removed noise and extracted any other keywords missed by the POS tagger. The system was evaluated using the Turing Test. After it was tested and compared against Stanford CoreNLP, the second stage of the system was developed addressing the shortcomings of the first stage. It was enhanced using Named Entity Recognition and Lemmatization. The second stage was also tested using the Turing test and its pass rate increased from 50.00% to 83.33%. The performance of the final system output was measured using the F1 score. Stanford CoreNLP with the Twitter model had an average F1 of 0.69 while the improved system had a F1 of 0.77. The accuracy of the system could be improved by using a complete domain specific corpus. Since the system used linguistic features of a sentence, it could be applied to other NLP tools.


title: ClusterVis: visualizing nodes attributes in multivariate graphs

abstract: Many computing applications imply dealing with network data, for example, social networks, communications and computing networks, epidemiological networks, among others. These applications are usually based on multivariate graphs, i.e., graphs in which nodes and edges have multiple attributes. Most of the visualization techniques described in the literature for dealing with multivariate graphs focus either on problems associated with the visualization of topology or on problems associated with the visualization of the nodes' attributes. The integration of these two components (topology and multiple attributes) in a single visualization is a challenge due to the necessity of simultaneously representing the connections and attributes, possibly generating overlapping elements. Among usual strategies to overcome this legibility problem we find filtering and aggregation, which make possible a simplified representation providing a general view with reduced size and lower density. However, this simplification may lead to a reduction of the amount of information being displayed, while in several applications the graph details still need to be represented in order to allow in-depth data analysis. In face of that, we propose ClusterVis, a visualization technique aiming at exploring nodes attributes pertaining to sub-graphs, which are either obtained from clustering algorithms or some user-defined criteria. The technique allows comparing attributes of nodes while keeping the representation of the relationships among them. The technique was implemented within a visualization framework and evaluated by potential users.


title: FCFraud: Fighting Click-Fraud from the User Side

abstract: Pay-Per-Click (PPC) ad networks charge advertisers for every click on their ads. Click-fraud happens when a user or an automated software clicks on an ad with a malicious intent and advertisers need to pay for those valueless clicks. Click-fraud has been proved to be a serious problem for the online advertisement industry. Although it has attracted much attention from the security community, the direct victims of click-fraud, the advertisers, still lack confidence in the click-fraud detection techniques. Among many forms of click-fraud, botnets with the automated clickers are the most severe ones. In this paper, we present a technique for detecting automated clickers from the user side. Normal internet users are victimized by malicious attackers (e.g., bot-master of a botnet) and the attackers infect and use their machines to defraud advertisers. We propose a technique to Fight Click-Fraud, FCFraud, which can be integrated into the operating system. Since most modern operating systems already provide some kind of anti-malware service, our proposed technique can be implemented with a negligible overhead. We believe that an effective protection at the operating system level can save billions of dollars of the advertisers. Experiments show that FCFraud is 99.6% accurate in classifying ad requests from all user processes and it is 100% successful in finding the fraudulent processes.


title: SDN and Virtualization Solutions for the Internet of Things: A Survey

abstract: The imminent arrival of the Internet of Things (IoT), which consists of a vast number of devices with heterogeneous characteristics, means that future networks need a new architecture to accommodate the expected increase in data generation. Software defined networking (SDN) and network virtualization (NV) are two technologies that promise to cost-effectively provide the scale and versatility necessary for IoT services. In this paper, we survey the state of the art on the application of SDN and NV to IoT. To the best of our knowledge, we are the first to provide a comprehensive description of every possible IoT implementation aspect for the two technologies. We start by outlining the ways of combining SDN and NV. Subsequently, we present how the two technologies can be used in the mobile and cellular context, with emphasis on forthcoming 5G networks. Afterward, we move to the study of wireless sensor networks, arguably the current foremost example of an IoT network. Finally, we review some general SDN-NV-enabled IoT architectures, along with real-life deployments and use-cases. We conclude by giving directions for future research on this topic.


Credit Card Fraud Identification Using Artificial Neural Networks


title: Trypanosoma cruzi: adaptation to its vectors and its hosts

abstract: American trypanosomiasis is a parasitic zoonosis that occurs throughout Latin America. The etiological agent, Trypanosoma cruzi, is able to infect almost all tissues of its mammalian hosts and spreads in the environment in multifarious transmission cycles that may or not be connected. This biological plasticity, which is probably the result of the considerable heterogeneity of the taxon, exemplifies a successful adaptation of a parasite resulting in distinct outcomes of infection and a complex epidemiological pattern. In the 1990s, most endemic countries strengthened national control programs to interrupt the transmission of this parasite to humans. However, many obstacles remain to the effective control of the disease. Current knowledge of the different components involved in elaborate system that is American trypanosomiasis (the protozoan parasite T. cruzi, vectors Triatominae and the many reservoirs of infection), as well as the interactions existing within the system, is still incomplete. The Triatominae probably evolve from predatory reduvids in response to the availability of vertebrate food source. However, the basic mechanisms of adaptation of some of them to artificial ecotopes remain poorly understood. Nevertheless, these adaptations seem to be associated with a behavioral plasticity, a reduction in the genetic repertoire and increasing developmental instability.


title: A Bayesian method for reducing bias in neural representational similarity analysis

abstract: In neuroscience, the similarity matrix of neural activity patterns in response to different sensory stimuli or under different cognitive states reflects the structure of neural representational space. Existing methods derive point estimations of neural activity patterns from noisy neural imaging data, and the similarity is calculated from these point estimations. We show that this approach translates structured noise from estimated patterns into spurious bias structure in the resulting similarity matrix, which is especially severe when signal-to-noise ratio is low and experimental conditions cannot be fully randomized in a cognitive task. We propose an alternative Bayesian framework for computing representational similarity in which we treat the covariance structure of neural activity patterns as a hyper-parameter in a generative model of the neural data, and directly estimate this covariance structure from imaging data while marginalizing over the unknown activity patterns. Converting the estimated covariance structure into a correlation matrix offers a much less biased estimate of neural representational similarity. Our method can also simultaneously estimate a signal-to-noise map that informs where the learned representational structure is supported more strongly, and the learned covariance matrix can be used as a structured prior to constrain Bayesian estimation of neural activity patterns. Our code is freely available in Brain Imaging Analysis Kit (Brainiak) (https://github.com/IntelPNI/brainiak).


title: Precision curved micro hemispherical resonator shells fabricated by poached-egg micro-molding

abstract: This paper presents a new technique for the fabrication of high resolution non-planar precision microshells for hemispherical resonating gyros via a poached-egg micromolding (PEM) method. In PEM, precision ball lenses are used as starting molds. Hemispherical shells are formed on the lenses using five major steps: (1) isotropic coating with sacrificial and shell layers, (2) stencil transfer of coated lenses to substrate, (3) shell top removal by anisotropic etching, (4) sacrificial layer etching and (5) release of the ball lens molds. Using PEM, we fabricated posted sputtered ultra-low-expansion glass hemispherical shells of 1 mm diameter and 1.2 μm thickness. The microshells have better than 120 ppm uniformity in thickness and less than ±0.125 μm deviation from a perfect sphere. The measured microshell resonant frequency at 100 mTorr was 17.32 kHz and the Q was ∼ 20,000.


title: e-Government adoption model based on theory of planned behavior: Empirical validation

abstract: Abstract The e-Government phenomenon has become more important with the ever increasing number of implementations worldwide. A model explaining the e-Government adoption and the related measurement instrument – a survey – had been developed and validated in this study. In a post Technology Acceptance Model (TAM) approach, theory of planned behavior (TPB) was extended to fit the requirements of e-Government context. The adoption of student loans service of the higher education student loans and accommodation association of Turkey (KYK) was investigated to obtain data for empirical validation. The instrument was administered to over four-hundred students and partial least squares path modeling was employed to analyze the data. The results indicate that the model was an improvement over TAM in terms of predictive power. The constructs trust, perceived behavioral control and attitudes successfully explained the intention to use an e-Government service. The findings presented in this study provide useful insights for researchers and policy makers when dealing with e-Government services.


title: (Online) Subgradient Methods for Structured Prediction

abstract: Promising approaches to structured learning problems have recently been developed in the maximum margin framework. Unfortunately, algorithms that are computationally and memory efficient enough to solve large scale problems have lagged behind. We propose using simple subgradient-based techniques for optimizing a regularized risk formulation of these problems in both online and batch settings, and analyze the theoretical convergence, generalization, and robustness properties of the resulting techniques. These algorithms are are simple, memory efficient, fast to converge, and have small regret in the online setting. We also investigate a novel convex regression formulation of structured learning. Finally, we demonstrate the benefits of the subgradient approach on three structured prediction problems.


title: Where is the data? Why you cannot debate CPU vs. GPU performance without the answer

abstract: General purpose GPU Computing (GPGPU) has taken off in the past few years, with great promises for increased desktop processing power due to the large number of fast computing cores on high-end graphics cards. Many publications have demonstrated phenomenal performance and have reported speedups as much as 1000× over code running on multi-core CPUs. Other studies have claimed that well-tuned CPU code reduces the performance gap significantly. We demonstrate that this important discussion is missing a key aspect, specifically the question of where in the system data resides, and the overhead to move the data to where it will be used, and back again if necessary. We have benchmarked a broad set of GPU kernels on a number of platforms with different GPUs and our results show that when memory transfer times are included, it can easily take between 2 to 50× longer to run a kernel than the GPU processing time alone. Therefore, it is necessary to either include memory transfer overhead when reporting GPU performance, or to explain why this is not relevant for the application in question. We suggest a taxonomy for future CPU/GPU comparisons, and we argue that this is not only germane for reporting performance, but is important to heterogeneous scheduling research in general.


title: Enriching Word Vectors with Subword Information

abstract: Continuous word representations, trained on large unlabeled corpora are useful for many natural language processing tasks. Popular models to learn such representations ignore the morphology of words, by assigning a distinct vector to each word. This is a limitation, especially for languages with large vocabularies and many rare words. In this paper, we propose a new approach based on the skipgram model, where each word is represented as a bag of character n-grams. A vector representation is associated to each character n-gram, words being represented as the sum of these representations. Our method is fast, allowing to train models on large corpora quickly and allows to compute word representations for words that did not appear in the training data. We evaluate our word representations on nine different languages, both on word similarity and analogy tasks. By comparing to recently proposed morphological word representations, we show that our vectors achieve state-of-the-art performance on these tasks.


title: Hubs, authorities, and communities

abstract: This has been a computer scientist's revolution; but we all share in its results. Only a few years ago, the World Wide Web was known just to a small research community; it is hard to remember that the voluminous content we see on the WWW, expanding by an estimated million pages each day, has grown up around us in so short a time. Researchers now release their results to the Web before they appear in print; corporations list their URLs alongside their toll-free numbers; news media and entertainment companies vie for the attention of a browsing audience. The Web has become the most visible manifestation of a new medium: a global, populist hypertext.


title: A dynamic model of top management team effectiveness: Managing unstructured task streams. Leadership Quarterly 14(3

abstract: Abstract Leadership research relating top management team (TMT) demographics to firm performance has produced mixed empirical results. This article suggests a new explanation for these inconsistencies. We first note that a given TMT is likely to face a variety of different situations over time. Thus, while TMT demographic composition is relatively stable, the TMT task is dynamic and variable. In some situations, team members have similar information and interests (a symmetric distribution); in others, information or interests diverge (an asymmetric distribution). Based on team effectiveness theory, we argue that, unless group process is managed accordingly, asymmetric distributions of situation-specific information and interests will reduce TMT decision-making effectiveness. We then develop leader process choices to mitigate the potentially harmful effect of these asymmetries. These arguments form the basis of a theoretical model of TMT effectiveness that integrates insights from research on leadership, TMTs, small group process, and negotiation, and has practical implications for how leaders of senior teams can improve team effectiveness through appropriate process choices.


Clustering Student Sequential Trajectories Using Dynamic Time Wrapping