title: Automated Transplantation and Differential Testing for Clones

abstract: Code clones are common in software. When applying similar edits to clones, developers often find it difficult to examine the runtime behavior of clones. The problem is exacerbated when some clones are tested, while their counterparts are not. To reuse tests for similar but not identical clones, G rafter transplants one clone to its counterpart by (1) identifying variations in identifier names, types, and method call targets, (2) resolving compilation errors caused by such variations through code transformation, and (3) inserting stub code to transfer input data and intermediate output values for examination. To help developers examine behavioral differences between clones, G rafter supports fine-grained differential testing at both the test outcome level and the intermediate program state level. In our evaluation on three open source projects, G rafter successfully reuses tests in 94% of clone pairs without inducing build errors, demonstrating its automated code transplantation capability. To examine the robustness of G rafter , we systematically inject faults using a mutation testing tool, M ajor , and detect behavioral differences induced by seeded faults. Compared with a static cloning bug finder, G rafter detects 31% more mutants using the test-level comparison and almost 2X more using the state-level comparison. This result indicates that G rafter should effectively complement static cloning bug finders.


title: Cryptography and Machine Learning

abstract: This paper gives a survey of the relationship between the fields of cryptography and machine learning, with an emphasis on how each field has contributed ideas and techniques to the other. Some suggested directions for future cross-fertilization are also proposed.


title: Learning concept embeddings for query expansion by quantum entropy minimization

abstract: In web search, users queries are formulated using only few terms and term-matching retrieval functions could fail at retrieving relevant documents. Given a user query, the technique of query expansion (QE) consists in selecting related terms that could enhance the likelihood of retrieving relevant documents. Selecting such expansion terms is challenging and requires a computational framework capable of encoding complex semantic relationships. In this paper, we propose a novel method for learning, in a supervised way, semantic representations for words and phrases. By embedding queries and documents in special matrices, our model disposes of an increased representational power with respect to existing approaches adopting a vector representation. We show that our model produces high-quality query expansion terms. Our expansion increase IR measures beyond expansion from current word-embeddings models and well-established traditional QE methods.


title: Biomechanical walking mechanisms underlying the metabolic reduction caused by an autonomous exoskeleton

abstract: BackgroundAnkle exoskeletons can now reduce the metabolic cost of walking in humans without leg disability, but the biomechanical mechanisms that underlie this augmentation are not fully understood. In this study, we analyze the energetics and lower limb mechanics of human study participants walking with and without an active autonomous ankle exoskeleton previously shown to reduce the metabolic cost of walking.MethodsWe measured the metabolic, kinetic and kinematic effects of wearing a battery powered bilateral ankle exoskeleton. Six participants walked on a level treadmill at 1.4 m/s under three conditions: exoskeleton not worn, exoskeleton worn in a powered-on state, and exoskeleton worn in a powered-off state. Metabolic rates were measured with a portable pulmonary gas exchange unit, body marker positions with a motion capture system, and ground reaction forces with a force-plate instrumented treadmill. Inverse dynamics were then used to estimate ankle, knee and hip torques and mechanical powers.ResultsThe active ankle exoskeleton provided a mean positive power of 0.105 ± 0.008 W/kg per leg during the push-off region of stance phase. The net metabolic cost of walking with the active exoskeleton (3.28 ± 0.10 W/kg) was an 11 ± 4 % (p = 0.019) reduction compared to the cost of walking without the exoskeleton (3.71 ± 0.14 W/kg). Wearing the ankle exoskeleton significantly reduced the mean positive power of the ankle joint by 0.033 ± 0.006 W/kg (p = 0.007), the knee joint by 0.042 ± 0.015 W/kg (p = 0.020), and the hip joint by 0.034 ± 0.009 W/kg (p = 0.006).ConclusionsThis study shows that the ankle exoskeleton does not exclusively reduce positive mechanical power at the ankle joint, but also mitigates positive power at the knee and hip. Furthermore, the active ankle exoskeleton did not simply replace biological ankle function in walking, but rather augmented the total (biological + exoskeletal) ankle moment and power. This study underscores the need for comprehensive models of human-exoskeleton interaction and global optimization methods for the discovery of new control strategies that optimize the physiological impact of leg exoskeletons.


RESEARCHING CORPORATE ANNUAL REPORTS: AN ANALYSIS OF PERSPECTIVES USED.


title: Models for Multidimensional Tests and Hierarchically Structured Training Materials. Final Report.

abstract: Abstract : Work on item response theory was extended to include two areas that had not been extensively researched previously. They include models for test items that require more than one ability for a correct response and models for the interaction between modules of instruction that have a hierarchical relationship. For both of these types of models, estimation procedures were developed for model parameters and extensive work was done to determine the appropriate interpretation of the parameter values. This report is a summary of work performed on these modules over a three year period. Keywords: Item response theory; Latent trait theory; Learning hierarchies; Multidimensional models.


title: The Viterbi Algorithm

abstract: This paper is a tutorial introduction to the Viterbi Algorithm, this is reinforced by an example use of the Viterbi Algorithm in the area of error correction in communications channels. Some extensions to the basic algorithm are also discussed briefly. Some of the many application areas where the Viterbi Algorithm has been used are considered, including it''s use in communications. target tracking and pattern recognition problems. A proposal for further research into the use of the Viterbi Algorithm in Signature Verification is then presented, and is the area of present research at the moment.


' s personal copy A comparative study on feature reduction approaches in Hindi and Bengali named entity recognition


title: A fast edge-oriented algorithm for image interpolation

abstract: This paper introduces a fast algorithm for image interpolation. By using this method, real-time enlargement of video images is accessible. The basic idea of the algorithm is to partition digital images into homogeneous and edge areas based on the analysis of the local structure on the images. In addition, in order to have better performance on interpolating images, specified algorithms are assigned to interpolate each classified areas, respectively. Experimental results show that the subjective quality of the interpolated images is substantially improved by using the proposed algorithm compared with that of using conventional interpolation algorithms. The computational complexity of the proposed algorithm is much lower than those of others mentioned in this paper. We also successfully implemented real-time enlargement of QCIF video sequences to CIF sequences with better image quality in low bit-rate environment.


title: TrackAdvisor: Taking Back Browsing Privacy from Third-Party Trackers

abstract: Even though most web users assume that only the websites that they visit directly become aware of the visit, this belief is incorrect. Many website display contents hosted externally by third-party websites, which can track users and become aware of their web-surfing behavior. This phenomenon is called third-party tracking, and although such activities violate no law, they raise privacy concerns because the tracking is carried out without users’ knowledge or explicit approval. Our work provides a systematic study of the third-party tracking phenomenon. First, we develop TrackAdvisor, arguably the first method that utilizes Machine Learning to identify the HTTP requests carrying sensitive information to third-party trackers with very high accuracy (100 % Recall and 99.4 Precision). Microsoft’s Tracking Protection Lists, which is a widely-used third-party tracking blacklist achieves only a Recall of 72.2 %. Second, we quantify the pervasiveness of the third-party tracking phenomenon: 46 % of the home pages of the websites in Alexa Global Top 10,000 have at least one third-party tracker, and Google, using third-party tracking, monitors 25 % of these popular websites. Our overarching goal is to measure accurately how widespread third-party tracking is and hopefully would raise the public awareness to its potential privacy risks.


title: OFDM and Its Wireless Applications: A Survey

abstract: Orthogonal frequency-division multiplexing (OFDM) effectively mitigates intersymbol interference (ISI) caused by the delay spread of wireless channels. Therefore, it has been used in many wireless systems and adopted by various standards. In this paper, we present a comprehensive survey on OFDM for wireless communications. We address basic OFDM and related modulations, as well as techniques to improve the performance of OFDM for wireless communications, including channel estimation and signal detection, time- and frequency-offset estimation and correction, peak-to-average power ratio reduction, and multiple-input-multiple-output (MIMO) techniques. We also describe the applications of OFDM in current systems and standards.


title: Detecting Phishing Attacks Using Natural Language Processing and Machine Learning

abstract: Phishing attacks are one of the most common and least defended security threats today. We present an approach which uses natural language processing techniques to analyze text and detect inappropriate statements which are indicative of phishing attacks. Our approach is novel compared to previous work because it focuses on the natural language text contained in the attack, performing semantic analysis of the text to detect malicious intent. To demonstrate the effectiveness of our approach, we have evaluated it using a large benchmark set of phishing emails.


title: QUANTITATIVE PERFORMANCE ANALYSIS OF HAPTIC DEVICES WITH PARALLELOGRAM SUBSYSTEMS

abstract: Parallel-architecture haptic devices offer significant advantages over serial-architecture counterparts in applications requiring high stiffness and high accuracy. To this end, many haptic devices have been created and deployed by modularly piecing together several serial-chain arms to form an in-parallel system. Furthermore, recent haptic devices design such as the Sensable’s PHANToM Premium line of haptic devices and Quanser’s High Definition Haptic Device (HD)2 placed the 2nd actuated joint (of a 2-DOF RR serial manipulator) at the base of the device that allowed the control of the 2nd joint through a parallelogram/fourbar structure. This design is favorable from the view point of reducing the overall weight that the first motor has to carry. However, such design choices can affect the overall system performance which depends both on the nature of the individual arms as well as their interactions. In this paper, we build on the rich theoretical background of constrained articulated mechanical systems to provide a systematic framework for formulation of system-level kinematic performance from individual-arm characteristics. Specifically, we discuss: (i) development of pertinent symbolic equations; (ii) generalization to arbitrary architectures; and (iii) combined symbolic/numeric analyses of performance, focusing on manipulability and stiffness. These aspects are illustrated using the example of Quanser High Definition Haptic Device (HD)2 — an in-parallel haptic device formed by coupling two 3-link PHANToM 1.5 type serial chain manipulators with appropriate passive joints.© 2011 ASME


title: Non-Iterative SLAM: A Fast Dense Method for Inertial-Visual SLAM.

abstract: The goal of this paper is to create a new framework of Visual SLAM that is light enough for Micro Unmanned Aerial Vehicle (MUAV). Feature-based and direct methods are two mainstreams in Visual SLAM. Both methods minimize photometric or reprojection error by iterative solutions, which are always computationally expensive. To overcome this problem, we propose a non-iterative approach to match point clouds directly. In particular, a new inertial-visual framework is proposed to reduce computational requirement in two steps. First, the attitude and heading reference system (AHRS) and axonometric projection are utilized to decouple the 6 Degree-of-Freedom (DoF) data, so that point clouds can be matched in independent spaces respectively. Second, the matching process is carried out in frequency domain by Fourier transformation, which reduces computational requirements dramatically and provides a closed-form non-iterative solution. In this manner, the time complexity can be reduced to $\mathcal{O}(n \log{n})$ where $n$ is the number of matched points in each frame. ::: To the best of our knowledge, this method is the first non-iterative approach for data association in Visual SLAM. Compared with the state of the arts, it runs at faster speed and obtains 3D maps with higher resolution yet still with comparable accuracy.


title: A deep learning framework for modeling structural features of RNA-binding protein targets

abstract: RNA-binding proteins (RBPs) play important roles in the post-transcriptional control of RNAs. Identifying RBP binding sites and characterizing RBP binding preferences are key steps toward understanding the basic mechanisms of the post-transcriptional gene regulation. Though numerous computational methods have been developed for modeling RBP binding preferences, discovering a complete structural representation of the RBP targets by integrating their available structural features in all three dimensions is still a challenging task. In this paper, we develop a general and flexible deep learning framework for modeling structural binding preferences and predicting binding sites of RBPs, which takes (predicted) RNA tertiary structural information into account for the first time. Our framework constructs a unified representation that characterizes the structural specificities of RBP targets in all three dimensions, which can be further used to predict novel candidate binding sites and discover potential binding motifs. Through testing on the real CLIP-seq datasets, we have demonstrated that our deep learning framework can automatically extract effective hidden structural features from the encoded raw sequence and structural profiles, and predict accurate RBP binding sites. In addition, we have conducted the first study to show that integrating the additional RNA tertiary structural features can improve the model performance in predicting RBP binding sites, especially for the polypyrimidine tract-binding protein (PTB), which also provides a new evidence to support the view that RBPs may own specific tertiary structural binding preferences. In particular, the tests on the internal ribosome entry site (IRES) segments yield satisfiable results with experimental support from the literature and further demonstrate the necessity of incorporating RNA tertiary structural information into the prediction model. The source code of our approach can be found in https://github.com/thucombio/deepnet-rbp.


abstract: Learning to Understand Figurative Language: From Similes to Metaphors to Irony Tony Veale (TONY.VEALE@UCD.ie) School of Computer Science and Informatics, University College Dublin Belfield, Dublin 4, Ireland Yanfen Hao (YANFEN.HAO@UCD.ie) School of Computer Science and Informatics, University College Dublin Belfield, Dublin 4, Ireland equivalent categorization. Indeed, Roncero et al. (2006) note that similes found on the internet are far more likely than the equivalent metaphors to be accompanied by an explicit explanation, which suggests that similes are less constrained by norms of category structure, and thus less likely than metaphors to be implicitly explained by these norms. Hanks (2004) goes as far as to argue that this non- reliance on category norms makes simile a freer and more creative form of expression than metaphor, since similes can serve as dynamic “triggers for the imagination” without having to appeal either to linguistic conventions or experiential gestalts. Chiappe et al. (2003) demonstrate that metaphoric expressions of a relationship are preferred when the relationship is an apt one, which suggests that it should follow more obviously from the corresponding categorization. These authors also find that aptness correlates strongly with ease of comprehension, and indeed, similes can enhance both their aptness and their comprehensibility by opting for explicit self-explanation: when one says “my left tire is as bald as a bowling ball”, there is no ambiguity whatsoever as to the property that is shared by topic and vehicle, even if baldness is not a literally sensible property of artifacts. Bowdle and Gentner (2005) argue, in a hypothesis they call the career of metaphor, that as metaphors become more conventionalized, they are more likely to be processed as categorizations than as comparisons. This suggests that increased familiarity with a particular metaphoric vehicle allows for greater competence in how the vehicle category is applied and extended to include new members (following Glucksberg, 2001). If, as the career of metaphor hypothesis suggests, there is an “evolutionary path …from comparison to categorization”, it is consistent to also argue for an evolutionary path between simile and metaphor. Certainly, explicit similes of the form “X is as P as Y” indicate that P is a highly salient property of Y, salient enough that Y can be used to exemplify P-ness. If exposed to enough similes of this form, or similes with accompanying explanations (like those reported by Roncero et al., 2006), a cognitive agent can build a detailed conceptual picture of the features P i that define a category Y. Since these will be the most salient and diagnostic features of Y, they can be used to build a category representation of Y that can subsequently be used Abstract Simile is widely viewed as a less sophisticated conceptual device than metaphor, not least because similes are explicitly marked and are frequently more obvious about the meanings they carry. Nonetheless, this lack of sophistication makes simile an ideal basis for acquiring the category-specific knowledge required to understand metaphor. In this paper we describe a computational approach to simile and metaphor that takes the career-of-metaphor hypothesis of Bowdle and Gentner (2005) as its starting point. We describe how the category-defining knowledge required by metaphor can be acquired from exposure to explicit similes, and demonstrate that this knowledge offers a richer and more diagnostic picture of category structure than that acquired from alternate sources. Keywords: metaphor; simile; irony; salient property; category representation. Introduction Figurative language can range from the sublime and the enigmatic to the banal and the obvious. Metaphor, for instance, is widely considered to be the epitome of creative expression, for metaphors often transcend the merely descriptive to yield profoundly enlightening insights; metaphors can be richly allusive, playful and challenging, and open to constant re-interpretation by new readers in new contexts (e.g., see Gibbs, 1994). Because metaphor allows us to view one concept through the prism of another, it is an inherently asymmetric device in which the meaning of a juxtaposition depends crucially on the direction of the information flow (Ortony, 1979). Similes, in contrast, seem an altogether more humble form of expression. The use of the hedge words “like” or “as” marks simile as a diffident figure of speech, which (unlike metaphor) stops short of ascribing category membership to merely draw attention to certain shared properties. So while the metaphor “drug dealers are vampires” challenges our conception of vampires and the criteria needed for membership in this category (see Glucksberg, 2001), the corresponding simile, “drug dealers are like vampires” merely enjoins us to look for common properties which in themselves may be insufficient to support category inclusion. This reluctance to categorize marks simile as a symmetric form of comparison. The hedging and diffident nature of similes might also be said to signal a lack of confidence in the aptness of the


title: Stepsize Selection in Explicit Runge-Kutta Methods for Moderately Stiff Problems

abstract: We present an algorithm for determining the stepsize in an explicit Runge-Kutta method that is suitable when solving moderately stiff differential equations. The algorithm has a geometric character, and is based on a pair of semicircles that enclose the boundary of the stability region in the left half of the complex plane. The algorithm includes an error control device. We describe a vectorized form of the algorithm, and present a corresponding MATLAB code. Numerical examples for Runge-Kutta methods of third and fourth order demonstrate the properties and capabilities of the algorithm.


title: Context-aware Path Ranking for Knowledge Base Completion

abstract: Knowledge base (KB) completion aims to infer missing facts from existing ones in a KB. Among various approaches, path ranking (PR) algorithms have received increasing attention in recent years. PR algorithms enumerate paths between entity pairs in a KB and use those paths as features to train a model for missing fact prediction. Due to their good performances and high model interpretability, several methods have been proposed. However, most existing methods suffer from scalability (high RAM consumption) and feature explosion (trains on an exponentially large number of features) problems. This paper proposes a Context-aware Path Ranking (C-PR) algorithm to solve these problems by introducing a selective path exploration strategy. C-PR learns global semantics of entities in the KB using word embedding and leverages the knowledge of entity semantics to enumerate contextually relevant paths using bidirectional random walk. Experimental results on three large KBs show that the path features (fewer in number) discovered by C-PR not only improve predictive performance but also are more interpretable than existing baselines.


Foldable Joints for Foldable Robots


Neural Machine Translation for English-Tamil


title: Real-time Classification of Malicious URLs on Twitter using Machine Activity Data

abstract: Massive online social networks with hundreds of millions of active users are increasingly being used by Cyber criminals to spread malicious software (malware) to exploit vulnerabilities on the machines of users for personal gain. Twitter is particularly susceptible to such activity as, with its 140 character limit, it is common for people to include URLs in their tweets to link to more detailed information, evidence, news reports and so on. URLs are often shortened so the endpoint is not obvious before a person clicks the link. Cyber criminals can exploit this to propagate malicious URLs on Twitter, for which the endpoint is a malicious server that performs unwanted actions on the person's machine. This is known as a drive-by-download. In this paper we develop a machine classification system to distinguish between malicious and benign URLs within seconds of the URL being clicked (i.e. 'real-time'). We train the classifier using machine activity logs created while interacting with URLs extracted from Twitter data collected during a large global event -- the Superbowl -- and test it using data from another large sporting event -- the Cricket World Cup. The results show that machine activity logs produce precision performances of up to 0.975 on training data from the first event and 0.747 on a test data from a second event. Furthermore, we examine the properties of the learned model to explain the relationship between machine activity and malicious software behaviour, and build a learning curve for the classifier to illustrate that very small samples of training data can be used with only a small detriment to performance.


title: A review on locomotion robophysics: the study of movement at the intersection of robotics, soft matter and dynamical systems

abstract: Discovery of fundamental principles which govern and limit effective locomotion (self-propulsion) is of intellectual interest and practical importance. Human technology has created robotic moving systems that excel in movement on and within environments of societal interest: paved roads, open air and water. However, such devices cannot yet robustly and efficiently navigate (as animals do) the enormous diversity of natural environments which might be of future interest for autonomous robots; examples include vertical surfaces like trees and cliffs, heterogeneous ground like desert rubble and brush, turbulent flows found near seashores, and deformable/flowable substrates like sand, mud and soil. In this review we argue for the creation of a physics of moving systems-a 'locomotion robophysics'-which we define as the pursuit of principles of self-generated motion. Robophysics can provide an important intellectual complement to the discipline of robotics, largely the domain of researchers from engineering and computer science. The essential idea is that we must complement the study of complex robots in complex situations with systematic study of simplified robotic devices in controlled laboratory settings and in simplified theoretical models. We must thus use the methods of physics to examine both locomotor successes and failures using parameter space exploration, systematic control, and techniques from dynamical systems. Using examples from our and others' research, we will discuss how such robophysical studies have begun to aid engineers in the creation of devices that have begun to achieve life-like locomotor abilities on and within complex environments, have inspired interesting physics questions in low dimensional dynamical systems, geometric mechanics and soft matter physics, and have been useful to develop models for biological locomotion in complex terrain. The rapidly decreasing cost of constructing robot models with easy access to significant computational power bodes well for scientists and engineers to engage in a discipline which can readily integrate experiment, theory and computation.


title: Automobile seat comfort: occupant preferences vs. anthropometric accommodation.

abstract: Automobile seat design specifications cannot be established without considering the comfort expectations of the target population. This contention is supported by published literature, which suggests that ergonomics criteria, particularly those related to physiology, do not satisfy consumer comfort. The objective of this paper is to challenge ergonomics criteria related to anthropometry in the same way. In this context, 12 subjects, representing a broad range of body sizes, evaluated five different compact car seats during a short-term seating session. Portions of a reliable and valid survey were used for this purpose. The contour and geometry characteristics of the five seats were quantified and compared to the survey information. Discrepancies were discovered between published anthropometric accommodation criteria and subject-preferred lumbar height, seatback width, cushion length, and cushion width. Based on this finding, it was concluded that automobile seat comfort is a unique science. Ergonomics criteria, while serving as the basis for this science, cannot be applied blindly for they do not ensure comfortable automobile seats.


MICR ICROARRA ARRAYTechnologies echnologies Benchmark TM4: A Free, Open-Source System for Microarray Data Management and Analysis


title: Cloud forensics: Technical challenges, solutions and comparative analysis

abstract: Cloud computing is arguably one of the most significant advances in information technology (IT) services today. Several cloud service providers (CSPs) have offered services that have produced various transformative changes in computing activities and presented numerous promising technological and economic opportunities. However, many cloud customers remain reluctant to move their IT needs to the cloud, mainly due to their concerns on cloud security and the threat of the unknown. The CSPs indirectly escalate their concerns by not letting customers see what is behind virtual wall of their clouds that, among others, hinders digital investigations. In addition, jurisdiction, data duplication and multi-tenancy in cloud platform add to the challenge of locating, identifying and separating the suspected or compromised targets for digital forensics. Unfortunately, the existing approaches to evidence collection and recovery in a non-cloud (traditional) system are not practical as they rely on unrestricted access to the relevant system and user data; something that is not available in the cloud due its decentralized data processing. In this paper we systematically survey the forensic challenges in cloud computing and analyze their most recent solutions and developments. In particular, unlike the existing surveys on the topic, we describe the issues in cloud computing using the phases of traditional digital forensics as the base. For each phase of the digital forensic process, we have included a list of challenges and analysis of their possible solutions. Our description helps identifying the differences between the problems and solutions for non-cloud and cloud digital forensics. Further, the presentation is expected to help the investigators better understand the problems in cloud environment. More importantly, the paper also includes most recent development in cloud forensics produced by researchers, National Institute of Standards and Technology and Amazon.


title: Churn prediction methodologies in the telecommunications sector: A survey

abstract: Predicting customer churn is a critical requirement of many if not all companies dependent on customer subscription services. The telecommunication sector is especially impacted due to the rival competition being very high and since tariff rates are maintained at a lower level. This paper describes the efforts made by researchers to build successful churn prediction models highlighting their special characteristics.


title: Does hotel attribute importance differ by hotel? Focusing on hotel star-classifications and customers' overall ratings

abstract: We conduct an exploratory case study with reviews from four hotels on TripAdvisor.We use a conjoint analysis to examine hotel attribute importance differences by hotel.A 2i?2 matrix with hotel segment and overall rating is designed.Four major and eight minor propositions are suggested for future empirical research.This study helps hotel managers to better prepare for hotel guests' needs. Hotel guests' expectations and actual experiences on hotel service quality often fail to coincide due to guests' unusually high anticipations, hotels' complete breakdowns in delivering their standard, or the combination of both. Moreover, this disconfirmation could be augmented contingent upon the level of hotel segment (hotel star-classification) and the overall rating manifested by previous guests. By incorporating a 2i?2 matrix design in which a hotel star-classification configures one dimension (2 versus 4 stars) and a customers' overall rating (lower versus higher overall ratings) configures the other, this explorative multiple case study uses conjoint analyses to examine the differences in the comparative importance of the six hotel attributes (value, location, sleep quality, rooms, cleanliness, and service) among four prominent hotel chain brands located in the United States. Four major and eight minor propositions are suggested for future empirical research based on the results of the four combined studies. Through the analysis of online data, this study may enlighten hotel managers with various ways to accommodate hotel guests' needs.


title: Making Vehicles Transparent Through V2V Video Streaming

abstract: The advent of infrastructureless vehicle-to-vehicle (V2V) communication has opened the opportunity to design driver-assistance systems that collect information from sensors residing in neighboring vehicles. Windshield-installed cameras are one example of a sensor that is becoming common in modern vehicles. Remotely accessing real-time images of these cameras using V2V communication enables a significant increase in the visual awareness of each driver. In this paper, we propose and evaluate the performance of a driver-assistance system that leverages on V2V communication and windshield-installed cameras to transform vision-obstructing vehicles into transparent tubular objects. This cooperative system is able to increase the visibility of drivers intending to overtake, thus making such critical maneuvers safer. Our system uses an augmented reality human-machine interface that is able to convey the increased visibility perspective in a straightforward fashion. We also show that the required latency for this intervehicle communication can be obtained using the Dedicated Short-Range Communications (DSRC) proposed for vehicular environments.


title: SeqViews2SeqLabels: Learning 3D Global Features via Aggregating Sequential Views by RNN With Attention

abstract: Learning 3D global features by aggregating multiple views has been introduced as a successful strategy for 3D shape analysis. In recent deep learning models with end-to-end training, pooling is a widely adopted procedure for view aggregation. However, pooling merely retains the max or mean value over all views, which disregards the content information of almost all views and also the spatial information among the views. To resolve these issues, we propose Sequential Views To Sequential Labels (SeqViews2SeqLabels) as a novel deep learning model with an encoder–decoder structure based on recurrent neural networks (RNNs) with attention. SeqViews2SeqLabels consists of two connected parts, an encoder-RNN followed by a decoder-RNN, that aim to learn the global features by aggregating sequential views and then performing shape classification from the learned global features, respectively. Specifically, the encoder-RNN learns the global features by simultaneously encoding the spatial and content information of sequential views, which captures the semantics of the view sequence. With the proposed prediction of sequential labels, the decoder-RNN performs more accurate classification using the learned global features by predicting sequential labels step by step. Learning to predict sequential labels provides more and finer discriminative information among shape classes to learn, which alleviates the overfitting problem inherent in training using a limited number of 3D shapes. Moreover, we introduce an attention mechanism to further improve the discriminative ability of SeqViews2SeqLabels. This mechanism increases the weight of views that are distinctive to each shape class, and it dramatically reduces the effect of selecting the first view position. Shape classification and retrieval results under three large-scale benchmarks verify that SeqViews2SeqLabels learns more discriminative global features by more effectively aggregating sequential views than state-of-the-art methods.


title: A Dual Linear Polarization Highly Isolated Crossed Dipole Antenna for MPAR Application

abstract: The design of a dual-polarization antenna with high isolation between ports has always been a challenge in the antenna community. To overcome this issue, a pair of printed collocated bent dipoles with a unique excitation method, is introduced in this letter. A balun and parallel transmission line are concurrently designed for multifunction phased array radar in the frequency bandwidth of 2.7–3 GHz. To maximize the polarization purity of the antenna and eliminate the parasitic radiation of the balun, the radiating element is isolated from the balun by a set of ground planes. A better than 10-dB return loss and a measured port-to-port isolation of 52 dB over the whole bandwidth are achieved. The cross-polarization pattern remains 40 dB below the copolarization peak in the principal planes. Having a compact geometry, the proposed antenna can readily be extended to form an array.


title: W-band AlGaN/GaN MMIC PA with 3.1W output power

abstract: A balanced four-stage W-band GaN MMIC PA with Lange couplers and micro-strips matching elements is reported. Electron-beam lithography has been used to produce a 100 nm T-shaped gate on the AlGaN/GaN HEMT structure with ultrahigh aluminum content. The MMIC PA offers a small signal gain of greater than 18 dB and an output power of greater than 2.5W from 88GHz to 93GHz with an associated power gain of greater than 15 dB. Moreover, it achieves a peak output power of 3.1W (34.9dBm) at 91GHz in continuous-wave mode, with an associated power density of 3.23W/mm. Most notably, it is the first time that the peak output power and power density exceed 3W and 3W/mm in AlGaN/GaN HEMT at W-band simultaneously.


Dominant Eigen Vector Based Feature Selection Using Singular Value Decomposition in Automatic Modulation Classification


title: Riemannian Geometry Applied to BCI Classification

abstract: In brain-computer interfaces based on motor imagery, covariance matrices are widely used through spatial filters computation and other signal processing methods. Covariance matrices lie in the space of Symmetric Positives-Definite (SPD) matrices and therefore, fall within the Riemannian geometry domain. Using a differential geometry framework, we propose different algorithms in order to classify covariance matrices in their native space.


title: A Robust Algorithm for the Detection of Vehicle Turn Signals and Brake Lights

abstract: Robust and lightweight detection of alert signals of front vehicle, such as turn signals and brake lights, is extremely critical, especially in autonomous vehicle applications. Even with cars that are driven by human beings, automatic detection of these signals can aid in the prevention of otherwise deadly accidents. This paper presents a novel, robust and lightweight algorithm for detecting brake lights and turn signals both at night and during the day. The proposed method employs a Kalman filter to reduce the processing load. Much research is focused only on the detection of brake lights at night, but our algorithm is able to detect turn signals as well as brake lights under any lighting conditions with high accuracy rates.


title: Five-Point Fundamental Matrix Estimation for Uncalibrated Cameras

abstract: We aim at estimating the fundamental matrix in two views from five correspondences of rotation invariant features obtained by e.g. the SIFT detector. The proposed minimal solver1 first estimates a homography from three correspondences assuming that they are co-planar and exploiting their rotational components. Then the fundamental matrix is obtained from the homography and two additional point pairs in general position. The proposed approach, combined with robust estimators like Graph-Cut RANSAC, is superior to other state-of-the-art algorithms both in terms of accuracy and number of iterations required. This is validated on synthesized data and 561 real image pairs. Moreover, the tests show that requiring three points on a plane is not too restrictive in urban environment and locally optimized robust estimators lead to accurate estimates even if the points are not entirely co-planar. As a potential application, we show that using the proposed method makes two-view multi-motion estimation more accurate.


title: How to Use Bitcoin to Incentivize Correct Computations

abstract: We study a model of incentivizing correct computations in a variety of cryptographic tasks. For each of these tasks we propose a formal model and design protocols satisfying our model's constraints in a hybrid model where parties have access to special ideal functionalities that enable monetary transactions. We summarize our results: Verifiable computation. We consider a setting where a delegator outsources computation to a worker who expects to get paid in return for delivering correct outputs. We design protocols that compile both public and private verification schemes to support incentivizations described above. Secure computation with restricted leakage. Building on the recent work of Huang et al. (Security and Privacy 2012), we show an efficient secure computation protocol that monetarily penalizes an adversary that attempts to learn one bit of information but gets detected in the process. Fair secure computation. Inspired by recent work, we consider a model of secure computation where a party that aborts after learning the output is monetarily penalized. We then propose an ideal transaction functionality FML and show a constant-round realization on the Bitcoin network. Then, in the FML-hybrid world we design a constant round protocol for secure computation in this model. Noninteractive bounties. We provide formal definitions and candidate realizations of noninteractive bounty mechanisms on the Bitcoin network which (1) allow a bounty maker to place a bounty for the solution of a hard problem by sending a single message, and (2) allow a bounty collector (unknown at the time of bounty creation) with the solution to claim the bounty, while (3) ensuring that the bounty maker can learn the solution whenever its bounty is collected, and (4) preventing malicious eavesdropping parties from both claiming the bounty as well as learning the solution. All our protocol realizations (except those realizing fair secure computation) rely on a special ideal functionality that is not currently supported in Bitcoin due to limitations imposed on Bitcoin scripts. Motivated by this, we propose validation complexity of a protocol, a formal complexity measure that captures the amount of computational effort required to validate Bitcoin transactions required to implement it in Bitcoin. Our protocols are also designed to take advantage of optimistic scenarios where participating parties behave honestly.


title: Efficient Oblivious Data Structures for Database Services on the Cloud

abstract: Database-as-a-service (DBaaS) allows the client to store and manage structured data on the cloud remotely. Despite its merits, DBaaS also brings significant privacy issues. Existing encryption techniques (e.g., SQL-aware encryption) can mitigate privacy concerns, but they still leak information through access patterns which are vulnerable to statistical inference attacks. Oblivious Random Access Machine (ORAM) can seal such leakages, but the recent studies showed significant challenges on the integration of ORAM into databases. Specifically, the direct usage of ORAM on databases is not only costly but also permits very limited query functionalities. We propose new oblivious data structures called Oblivious Matrix Structure (OMAT) and Oblivious Tree Structure (OTREE), which allow tree-based ORAM to be integrated into database systems in a more efficient manner with diverse query functionalities supported. OMAT provides special ORAM packaging strategies for table structures, which not only offers a significantly better performance but also enables a broad range of query types that may not be practical in existing frameworks. OTREE allows oblivious conditional queries to be deployed on tree-indexed databases more efficient than existing techniques. We fully implemented our proposed techniques and evaluated their performance on a real cloud database with various metrics, compared with state-of-the-art counterparts.


title: The Social Regulation of Emotion: An Integrative, Cross-Disciplinary Model

abstract: Research in emotion regulation has largely focused on how people manage their own emotions, but there is a growing recognition that the ways in which we regulate the emotions of others also are important. Drawing on work from diverse disciplines, we propose an integrative model of the psychological and neural processes supporting the social regulation of emotion. This organizing framework, the ‘social regulatory cycle', specifies at multiple levels of description the act of regulating another person's emotions as well as the experience of being a target of regulation. The cycle describes the processing stages that lead regulators to attempt to change the emotions of a target person, the impact of regulation on the processes that generate emotions in the target, and the underlying neural systems.


title: A study of mixture models for collaborative filtering

abstract: Collaborative filtering is a general technique for exploiting the preference patterns of a group of users to predict the utility of items for a particular user. Three different components need to be modeled in a collaborative filtering problem: users, items, and ratings. Previous research on applying probabilistic models to collaborative filtering has shown promising results. However, there is a lack of systematic studies of different ways to model each of the three components and their interactions. In this paper, we conduct a broad and systematic study on different mixture models for collaborative filtering. We discuss general issues related to using a mixture model for collaborative filtering, and propose three properties that a graphical model is expected to satisfy. Using these properties, we thoroughly examine five different mixture models, including Bayesian Clustering (BC), Aspect Model (AM), Flexible Mixture Model (FMM), Joint Mixture Model (JMM), and the Decoupled Model (DM). We compare these models both analytically and experimentally. Experiments over two datasets of movie ratings under different configurations show that in general, whether a model satisfies the proposed properties tends to be correlated with its performance. In particular, the Decoupled Model, which satisfies all the three desired properties, outperforms the other mixture models as well as many other existing approaches for collaborative filtering. Our study shows that graphical models are powerful tools for modeling collaborative filtering, but careful design is necessary to achieve good performance.


title: OSPRay - A CPU Ray Tracing Framework for Scientific Visualization

abstract: Scientific data is continually increasing in complexity, variety and size, making efficient visualization and specifically rendering an ongoing challenge. Traditional rasterization-based visualization approaches encounter performance and quality limitations, particularly in HPC environments without dedicated rendering hardware. In this paper, we present OSPRay, a turn-key CPU ray tracing framework oriented towards production-use scientific visualization which can utilize varying SIMD widths and multiple device backends found across diverse HPC resources. This framework provides a high-quality, efficient CPU-based solution for typical visualization workloads, which has already been integrated into several prevalent visualization packages. We show that this system delivers the performance, high-level API simplicity, and modular device support needed to provide a compelling new rendering framework for implementing efficient scientific visualization workflows.


title: TEXT EXTRACTION FROM IMAGES: A SURVEY

abstract: Text extraction is one of the key tasks in document image analysis. Automatic text extraction without characters recognition capabilities is to extract regions just contains text. The text extraction process includes detection, localization, segmentation and enhancement of the text from the given input image. In this paper we present a comparative study and performance evaluation of various text extraction techniques.


What Makes online Content Viral?


title: Power Electronic Traction Transformer-Low Voltage Prototype

abstract: Recently, a world's first ever power electronic traction transformer (PETT) for 15 kV, 16 2/3Hz railway grid, has been newly developed, commissioned, and installed on the locomotive, where it is presently in use. This marks an important milestone in the traction world. The design and development of the PETT are described in this paper, where a low-voltage (LV) PETT prototype is presented. It has been designed for the purposes of control hardware and software commissioning, thus serving a role of an analogue simulator. In this paper, emphasis is placed on the overall system requirements, from where control system has been developed, implemented, and successfully commissioned. The development of a 1.2MVA medium-voltage PETT prototype will be reported separately in accompanying paper.


title: The structure of interlayer water in a hydrated 2:1 clay

abstract: Abstract Neutron diffraction in conjunction with substitution of deuterium (D) for hydrogen (H) has been used to study the structure of water in a hydrated crystal of Ni-vermiculite. The intensities of the (00 l ) Bragg reflections were measured as a function of the H/D content, up to l =30. Difference analysis was then used to obtain separate density profiles, ϱ( z ), for the hydrogen and the oxygen plus the clay sheets. We have confirmed the existence of interlayer Ni 2+ (H 2 O) 6 complexes, in which the water molecules are strongly hydrogen bonded to the surface. More importantly, we have found that within the limits of the experiments all the additional water is located near the hexagonal rings of SiO 4 and AlO 4 − tetrahedra, which comprise the surface.


title: Training Visual Imagery: Improvements of Metacognition, but not Imagery Strength

abstract: Visual imagery has been closely linked to brain mechanisms involved in perception. Can visual imagery, like visual perception, improve by means of training? Previous research has demonstrated that people can reliably evaluate the vividness of single episodes of sensory imagination – might the metacognition of imagery also improve over the course of training? We had participants imagine colored Gabor patterns for an hour a day, over the course of five consecutive days, and again two weeks after training. Participants rated the subjective vividness and effort of their mental imagery on each trial. The influence of imagery on subsequent binocular rivalry dominance was taken as our measure of imagery strength. We found no overall effect of training on imagery strength. Training did, however, improve participant’s metacognition of imagery. Trial-by-trial ratings of vividness gained predictive power on subsequent rivalry dominance as a function of training. These data suggest that, while imagery strength might be immune to training in the current context, people’s metacognitive understanding of mental imagery can improve with practice.


title: A Method Based on Multi-Sensor Data Fusion for Fault Detection of Planetary Gearboxes

abstract: Studies on fault detection and diagnosis of planetary gearboxes are quite limited compared with those of fixed-axis gearboxes. Different from fixed-axis gearboxes, planetary gearboxes exhibit unique behaviors, which invalidate fault diagnosis methods that work well for fixed-axis gearboxes. It is a fact that for systems as complex as planetary gearboxes, multiple sensors mounted on different locations provide complementary information on the health condition of the systems. On this basis, a fault detection method based on multi-sensor data fusion is introduced in this paper. In this method, two features developed for planetary gearboxes are used to characterize the gear health conditions, and an adaptive neuro-fuzzy inference system (ANFIS) is utilized to fuse all features from different sensors. In order to demonstrate the effectiveness of the proposed method, experiments are carried out on a planetary gearbox test rig, on which multiple accelerometers are mounted for data collection. The comparisons between the proposed method and the methods based on individual sensors show that the former achieves much higher accuracies in detecting planetary gearbox faults.


title: Adverse Health Effects of Marijuana Use

abstract: As marijuana use becomes legal in some states, the dominant public opinion is that marijuana is a harmless source of mood alteration. Although the harms associated with marijuana use have not been well studied, enough information is available to cause concern.


title: Secure FoTA Object for IoT

abstract: Internet of Things (IoT) is slowly taking over the world where its predicted that by 2025, it will grow to 1-3 trillion connected machines/devices. With large scale deployments, maintaining these devices is a seeming burden on the operations. Also, with huge deployments of IoT devices, most concerns center around privacy and security. To reduce the efforts of on-field maintenance and to perform such tasks remotely and securely, Firmware over The Air (FoTA) can be adopted. FoTA is largely adopted in the mobile world, standardized by Open Mobile Alliance (OMA). In an IoT device ecosystem firmware of nodes/gateways can be updated remotely over the air for feature updates, functional updates, device behaviour updates or more importantly compromised/corrupted firmware, however to avoid the problem of malicious updates, FoTA itself needs to be secure. This paper presents a FoTA procedure for an IoT device ecosystem and defines a new secure object. This proposed Firmware Object Signing and Encryption FOSE is a secure object format in which the FoTA payload is encoded using a secure representation/format such as JOSE (JSON Object signing Encryption). This paper also proposes a simple procedure for over-the-air updates.


title: Hand Gesture Recognition Using Micro-Doppler Signatures With Convolutional Neural Network

abstract: In this paper, we investigate the feasibility of recognizing human hand gestures using micro-Doppler signatures measured by Doppler radar with a deep convolutional neural network (DCNN). Hand gesture recognition using radar can be applied to control electronic appliances. Compared with an optical recognition system, radar can work regardless of light conditions and it can be embedded in a case. We classify ten different hand gestures, with only micro-Doppler signatures on spectrograms without range information. The ten gestures, which included swiping from left to right, swiping from right to left, rotating clockwise, rotating counterclockwise, pushing, double pushing, holding, and double holding, were measured using Doppler radar and their spectrograms investigated. A DCNN was employed to classify the spectrograms, with 90% of the data utilized for training and the remaining 10% for validation. After five-fold validation, the classification accuracy of the proposed method was found to be 85.6%. With seven gestures, the accuracy increased to 93.1%.


title: Dynamic analysis of Thomson coil actuator for fast switch of HVDC circuit breaker

abstract: In this paper, the analysis is performed on Thomson coil actuator and the dynamic characteristics is presented, which is necessary to the fast switch of HVDC circuit breaker. For analysis, electromagnetic and dynamic equations are simultaneously solved using commercial code. Whole geometry with a drive coil is simplified and applied to analysis by considering the cylindrical shape of the actuator for simulation. In calculation of current flowing through the coil as the component of electric power circuit for operation of the actuator, the resistance and the inductance are taken into account, including stray component. The calculated current is used to predict the repulsive force on the plate, and the stroke of the actuator is deduced from solving the dynamic equation by considering the forces by spring and the gravity besides friction. Through simulation, dynamic variables are calculated such as inductance of a drive coil, force on a repulsive plate, and so on. Also, flux lines, induced current density, and magnetic flux density are predicted to time. In order to confirm the simulation results, experiments are performed for the prototypes with simple contacts and a vacuum interrupter, which is designed for fast switch. Experimental results are compared with simulation and the characteristics are discussed.


title: Pretty Bad Privacy: Pitfalls of DNS Encryption

abstract: As awareness for privacy of Domain Name System (DNS) is increasing, a number of mechanisms for encryption of DNS packets were proposed. We study the prominent defences, focusing on the privacy guarantees, interoperability with the DNS infrastructure, and the efficiency overhead. In particular: •We explore dependencies in DNS and show techniques that utilise side channel leaks, due to transitive trust, allowing to infer information about the target domain in an encrypted DNS packet. •We examine common DNS servers configurations and show that the proposals are expected to encounter deployment obstacles with (at least) 38% of 50K-top Alexa domains and (at least) 12% of the top-level domains (TLDs), and will disrupt the DNS functionality and availability for clients. •We show that due to the non-interoperability with the caches, the proposals for end-to-end encryption may have a prohibitive traffic overhead on the name servers. Our work indicates that further study may be required to adjust the proposals to stand up to their security guarantees, and to make them suitable for the common servers' configurations in the DNS infrastructure. Our study is based on collection and analysis of the DNS traffic of 50K-top Alexa domains and 568 TLDs.


title: (α, k)-anonymous data publishing

abstract: Privacy preservation is an important issue in the release of data for mining purposes. The k-anonymity model has been introduced for protecting individual identification. Recent studies show that a more sophisticated model is necessary to protect the association of individuals to sensitive information. In this paper, we propose an (α, k)-anonymity model to protect both identifications and relationships to sensitive information in data. We discuss the properties of (α, k)-anonymity model. We prove that the optimal (α, k)-anonymity problem is NP-hard. We first present an optimal global-recoding method for the (α, k)-anonymity problem. Next we propose two scalable local-recoding algorithms which are both more scalable and result in less data distortion. The effectiveness and efficiency are shown by experiments. We also describe how the model can be extended to more general cases.


REHABILITATION FOR ANTERIOR KNEE PAIN EVALUATION AND REHABILITATION FOR ANTERIOR KNEE PAIN


title: A Comparative Evaluation of Implicit Authentication Schemes

abstract: Implicit authentication (IA) schemes use behavioural biometrics to continuously and transparently authenticate mobile device users. Several IA schemes have been proposed by researchers which employ different behavioural features and provide reasonable detection accuracy. While these schemes work in principle, it is difficult to comprehend from these individual efforts which schemes work best (in terms of detection accuracy, detection delay and processing complexity) under different operating conditions (in terms of attack scenarios and availability of training and classification data). Furthermore, it is critical to evaluate these schemes on unbiased, real-world datasets to determine their efficacy in realistic operating conditions. In this paper, we evaluate six diverse IA schemes on four independently collected datasets from over 300 participants. We first evaluate these schemes in terms of: accuracy; training time and delay on real-world datasets; detection delay; processing and memory complexity for feature extraction, training and classification operations; vulnerability to mimicry attacks; and deployment issues on mobile platforms. We also leverage our real-world device usage traces to determine the proportion of time these schemes are able to afford protection to device owners. Based on our evaluations, we identify: 1) promising IA schemes with high detection accuracy, low performance overhead, and near real-time detection delays, 2) common pitfalls in contemporary IA evaluation methodology, and 3) open challenges for IA research. Finally, we provide an open source implementation of the IA schemes evaluated in this work that can be used for performance benchmarking by future IA research.


title: Synthetic aperture radar (SAR)-based mapping of wildfire burn severity and recovery

abstract: New radar based techniques for efficient identification of forest damage caused by wildfire and subsequent recovery are applied to data acquired over the 2002 Rodeo-Chediski, Arizona and 1988 Yellowstone National Park wildfire complexes. Fully polarimetric C-, L- and P-band airborne synthetic aperture radar data were acquired in approximately east-west and north-south swaths over the northern half of the Rodeo-Chediski wildfire scar on August 1, 2002, 25 days after the last active fire was contained. The AIRSAR instrument also acquired fully polarimetric data in C-, L- and P-band over Yellowstone on October 11, 1994, approximately 6 years after the event. We combine single frequency polarimetric parameters in composite images for optimal identification of fire-induced damage to the forests. For the Rodeo-Chediski fire, we demonstrate that polarimetric parameters - such as average scattering mechanism - readily identify bare surfaces, pine trees with intact needles, trees with exposed branches, and trees with residual burned trunks. In particular, a single-pass of polarimetric SAR provides identification of the perimeters and within-burn variability. This work shows that fully polarimetric SAR is sensitive to scattering changes wrought by wildfire in two types of predominantly coniferous forests. By extension, this technique should help quantify different degrees of burn severity in ecosystems where the canopy is altered. We anticipate that these results will provide a new method of responding to wildfires, perhaps even at the tactical fire-fighting level.


title: Embedded Binarized Neural Networks

abstract: We study embedded Binarized Neural Networks (eBNNs) with the aim of allowing current binarized neural networks (BNNs) in the literature to perform feedforward inference efficiently on small embedded devices. We focus on minimizing the required memory footprint, given that these devices often have memory as small as tens of kilobytes (KB). Beyond minimizing the memory required to store weights, as in a BNN, we show that it is essential to minimize the memory used for temporaries which hold intermediate results between layers in feedforward inference. To accomplish this, eBNN reorders the computation of inference while preserving the original BNN structure, and uses just a single floating-point temporary for the entire neural network. All intermediate results from a layer are stored as binary values, as opposed to floating-points used in current BNN implementations, leading to a 32x reduction in required temporary space. We provide empirical evidence that our proposed eBNN approach allows efficient inference (10s of ms) on devices with severely limited memory (10s of KB). For example, eBNN achieves 95\% accuracy on the MNIST dataset running on an Intel Curie with only 15 KB of usable memory with an inference runtime of under 50 ms per sample. To ease the development of applications in embedded contexts, we make our source code available that allows users to train and discover eBNN models for a learning task at hand, which fit within the memory constraint of the target device.


title: 5G D2D Networks: Techniques, Challenges, and Future Prospects

abstract: The increasing number of mobile users has given impetus to the demand for high data rate proximity services. The fifth-generation (5G) wireless systems promise to improve the existing technology according to the future demands and provide a road map for reliable and resource-efficient solutions. Device-to-device (D2D) communication has been envisioned as an allied technology of 5G wireless systems for providing services that include live data and video sharing. A D2D communication technique opens new horizons of device-centric communications, i.e., exploiting direct D2D links instead of relying solely on cellular links. Offloading traffic from traditional network-centric entities to D2D network enables low computational complexity at the base station besides increasing the network capacity. However, there are several challenges associated with D2D communication. In this paper, we present a survey of the existing methodologies related to aspects such as interference management, network discovery, proximity services, and network security in D2D networks. We conclude by introducing new dimensions with regard to D2D communication and delineate aspects that require further research.


Generating Questions for Reading Comprehension using Coherence Relations


title: A Double Jaw Hand Designed for Multi-Object Assembly

abstract: This paper presents a double jaw hand for industrial assembly. The hand comprises two orthogonal parallel grippers with different mechanisms. The inner gripper is made of a crank-slider mechanism which is compact and able to firmly hold objects like shafts. The outer gripper is made of a parallelogram that has large stroke to hold big objects like pulleys. The two grippers are connected by a prismatic joint along the hand's approaching vector. The hand is able to hold two objects and perform in-hand manipulation like pull-in (insertion) and push-out (ejection). This paper presents the detailed design and implementation of the hand, and demonstrates the advantages by performing experiments on two sets of peg-in-multi-hole assembly tasks as parts of the World Robot Challenge (WRC) 2018 using a bimanual robot.


title: Internet Use and Well‐Being in Adolescence

abstract: Previous research suggests that Internet use may be associated with decreases in well-being among adolescents. However, there has been little investigation of the relationship between well-being and social aspects of Internet use. In the present study, 130 7th graders from a middle-class public school in California completed dispositional measures of well-being, and on three subsequent evenings they responded to questions regarding their Internet use (including detailed logs of instant messages) and daily well-being. Time spent on-line was not associated with dispositional or daily well-being. However, as suggested by intimacy theory, the closeness of instant message communication partners was associated with daily social anxiety and loneliness in school, above and beyond the contribution of dispositional measures.


title: Early diagnosis of Alzheimer's disease with deep learning

abstract: All the neuroimaging data obtained from ANDI database were registered to the ICBM_152 template using Image Registration Toolkit (IRTK). Numerical anatomical measurements (volume, shape, curvature, etc.) are extracted from neuroimaging data (MRI and PET) accompanied with CSF measurements. • significantly boosts the classification performance • capable of multi-class classification • reduce the reliance on prior knowledge • semi-supervised learning • easy to examine the features


title: Three Methods for Making of Character Facial Animation based on Game Engine Focused on Scene Composition of Machinima Game 'Walking Dead'

abstract: Machinima is digital visuals created with game engines that build real-time 3D CG (computer graphic) environment. Composition of scene and facial animation play important role in composition of Machinima’s videos. Character facial animation is frequently used in scene composition that mainly displays face and upper body of the character. Expressing character’s emotion is an important factor in describing story of the animation. This paper provides three methods for character facial animation based on game engine, and applied them to real facial animation of Machinima characters. Through this process, we could match the most suitable facial animation to three types of scene composition by analyzing scene composition of the most renowned game of Machinima, ‘Walking dead-episode1’. Morph animation is suitable for medium close up and medium shot. Motion capture is efficient for over shoulder shot. However, during the process of combining body motion and facial animation, Motion capture was more difficult to reuse resources than morph animation and animated texture. When making pipelines for an independent video content, these findings could contribute to establishment of efficient resource production process based on real time rendering technology, which is a strength of game engine. In addition, this research provides guidelines of suitable production method best suitable to special traits of character animation.


title: Neural correlates of experimentally induced flow experiences

abstract: Abstract Flow refers to a positive, activity-associated, subjective experience under conditions of a perceived fit between skills and task demands. Using functional magnetic resonance perfusion imaging, we investigated the neural correlates of flow in a sample of 27 human subjects. Experimentally, in the flow condition participants worked on mental arithmetic tasks at challenging task difficulty which was automatically and continuously adjusted to individuals' skill level. Experimental settings of “boredom” and “overload” served as comparison conditions. The experience of flow was associated with relative increases in neural activity in the left anterior inferior frontal gyrus (IFG) and the left putamen. Relative decreases in neural activity were observed in the medial prefrontal cortex (MPFC) and the amygdala (AMY). Subjective ratings of the flow experience were significantly associated with changes in neural activity in the IFG, AMY, and, with trend towards significance, in the MPFC. We conclude that neural activity changes in these brain regions reflect psychological processes that map on the characteristic features of flow: coding of increased outcome probability (putamen), deeper sense of cognitive control (IFG), decreased self-referential processing (MPFC), and decreased negative arousal (AMY).


title: Discrete Sequential Prediction of Continuous Actions for Deep RL

abstract: It has long been assumed that high dimensional continuous control problems cannot be solved effectively by discretizing individual dimensions of the action space due to the exponentially large number of bins over which policies would have to be learned. In this paper, we draw inspiration from the recent success of sequence-to-sequence models for structured prediction problems to develop policies over discretized spaces. Central to this method is the realization that complex functions over high dimensional spaces can be modeled by neural networks that use next step prediction. Specifically, we show how Q-values and policies over continuous spaces can be modeled using a next step prediction model over discretized dimensions. With this parameterization, it is possible to both leverage the compositional structure of action spaces during learning, as well as compute maxima over action spaces (approximately). On a simple example task we demonstrate empirically that our method can perform global search, which effectively gets around the local optimization issues that plague DDPG and NAF. We apply the technique to off-policy (Q-learning) methods and show that our method can achieve the state-of-the-art for off-policy methods on several continuous control tasks.


title: Information systems for sustainable competitive advantage

abstract: Abstract There has been an increase in the attention paid to the strategic potential of information systems and a new willingness to accept the possibility that information systems can be the source of strategic gains. This belief is reflected in a host of publications, from the popular press to respected journals. Much of this has been supported by a very limited set of prominent and well publicized success stories, principally involving marketing and distribution, financial services, and the airlines. Unfortunately, there has been little attempt at an analysis that abstracts from these experiences to determine factors that determine strategic success. This can be attributed in part to the absence of attention paid to unsuccessful ventures in the use of information technology for competitive advantage. Although this paper relies on the same anecdotes, it augments them with data on a few unsuccessful attempts to exploit information technology and with economic theory where appropriate. General conditions that appear necessary for sustainable competitive advantage are developed.


title: Neural Monkey: An Open-source Tool for Sequence Learning

abstract: Abstract In this paper, we announce the development of Neural Monkey – an open-source neural machine translation (NMT) and general sequence-to-sequence learning system built over the TensorFlow machine learning library. The system provides a high-level API tailored for fast prototyping of complex architectures with multiple sequence encoders and decoders. Models’ overall architecture is specified in easy-to-read configuration files. The long-term goal of the Neural Monkey project is to create and maintain a growing collection of implementations of recently proposed components or methods, and therefore it is designed to be easily extensible. Trained models can be deployed either for batch data processing or as a web service. In the presented paper, we describe the design of the system and introduce the reader to running experiments using Neural Monkey.


title: An intelligent-agent-based fuzzy group decision making model for financial multicriteria decision support: The case of credit scoring

abstract: Credit risk analysis is an active research area in financial risk management and credit scoring is one of the key analytical techniques in credit risk evaluation. In this study, a novel intelligent-agent-based fuzzy group decision making (GDM) model is proposed as an effective multicriteria decision analysis (MCDA) tool for credit risk evaluation. In this proposed model, some artificial intelligent techniques, which are used as intelligent agents, are first used to analyze and evaluate the risk levels of credit applicants over a set of pre-defined criteria. Then these evaluation results, generated by different intelligent agents, are fuzzified into some fuzzy opinions on credit risk level of applicants. Finally, these fuzzification opinions are aggregated into a group consensus and meantime the fuzzy aggregated consensus is defuzzified into a crisp aggregated value to support final decision for decision-makers of credit-granting institutions. For illustration and verification purposes, a simple numerical example and three real-world credit application approval datasets are presented.


title: Data Mining Application in Customer Relationship Management Of Credit Card Business

abstract: First, we classify the selected customers into clusters using RFM model to identify high-profit, gold customers. Subsequently, we carry out data mining using association rules algorithm. We measure the similarity, difference and modified difference of mined association rules based on three rules, i.e. emerging pattern rule, unexpected change rule, and added/perished rule. In the meantime, we use rule matching threshold to derive all types of rules and explore the rules with significant change based on the degree of change measured. In this paper, we employ data mining tools and effectively discover the current spending pattern of customers and trends of behavioral change, which allow management to detect in a large database potential changes of customer preference, and provide as early as possible products and services desired by the customers to expand the clientele base and prevent customer attrition.


title: Exploring the Long Tail of Social Media Tags

abstract: There are millions of users who tag multimedia content, generating a large vocabulary of tags. Some tags are frequent, while other tags are rarely used following a long tail distribution. For frequent tags, most of the multimedia methods that aim to automatically understand audio-visual content, give excellent results. It is not clear, however, how these methods will perform on rare tags. In this paper we investigate what social tags constitute the long tail and how they perform on two multimedia retrieval scenarios, tag relevance and detector learning. We show common valuable tags within the long tail, and by augmenting them with semantic knowledge, the performance of tag relevance and detector learning improves substantially.


title: The minimal preprocessing pipelines for the Human Connectome Project

abstract: Abstract The Human Connectome Project (HCP) faces the challenging task of bringing multiple magnetic resonance imaging (MRI) modalities together in a common automated preprocessing framework across a large cohort of subjects. The MRI data acquired by the HCP differ in many ways from data acquired on conventional 3 Tesla scanners and often require newly developed preprocessing methods. We describe the minimal preprocessing pipelines for structural, functional, and diffusion MRI that were developed by the HCP to accomplish many low level tasks, including spatial artifact/distortion removal, surface generation, cross-modal registration, and alignment to standard space. These pipelines are specially designed to capitalize on the high quality data offered by the HCP. The final standard space makes use of a recently introduced CIFTI file format and the associated grayordinate spatial coordinate system. This allows for combined cortical surface and subcortical volume analyses while reducing the storage and processing requirements for high spatial and temporal resolution data. Here, we provide the minimum image acquisition requirements for the HCP minimal preprocessing pipelines and additional advice for investigators interested in replicating the HCP's acquisition protocols or using these pipelines. Finally, we discuss some potential future improvements to the pipelines.


title: From Revolution to Evolution: The Glutamate Hypothesis of Schizophrenia and its Implication for Treatment

abstract: Glutamate is the primary excitatory neurotransmitter in mammalian brain. Disturbances in glutamate-mediated neurotransmission have been increasingly documented in a range of neuropsychiatric disorders including schizophrenia, substance abuse, mood disorders, Alzheimer's disease, and autism-spectrum disorders. Glutamatergic theories of schizophrenia are based on the ability of N-methyl-D-aspartate receptor (NMDAR) antagonists to induce schizophrenia-like symptoms, as well as emergent literature documenting disturbances of NMDAR-related gene expression and metabolic pathways in schizophrenia. Research over the past two decades has highlighted promising new targets for drug development based on potential pre- and postsynaptic, and glial mechanisms leading to NMDAR dysfunction. Reduced NMDAR activity on inhibitory neurons leads to disinhibition of glutamate neurons increasing synaptic activity of glutamate, especially in the prefrontal cortex. Based on this mechanism, normalizing excess glutamate levels by metabotropic glutamate group 2/3 receptor agonists has led to potential identification of the first non-monoaminergic target with comparable efficacy as conventional antipsychotic drugs for treating positive and negative symptoms of schizophrenia. In addition, NMDAR has intrinsic modulatory sites that are active targets for drug development, several of which show promise in preclinical/early clinical trials targeting both symptoms and cognition. To date, most studies have been done with orthosteric agonists and/or antagonists at specific sites. However, allosteric modulators, both positive and negative, may offer superior efficacy with less danger of downregulation.


title: Design of Chopper-Stabilized Amplifiers With Reduced Offset for Sensor Applications

abstract: Offset error mechanisms in a single-ended chopper-stabilized amplifier are investigated. The error models and their prediction equations are given. This work also presents a new analytical approach for estimating the switch error in a four-transistor chopping network. A new resistance balancing circuit technique is also introduced, which permits further reduction of DC offsets in conventional chopping operational amplifier (op-amp) or chopping differential difference amplifier (DDA). The HSPICE simulation results have validated the proposed technique and identified dominant error sources using Level-49 BSIM3 model in a standard 0.6-mum CMOS technology. Applying the technique to the fabricated DDA chips at a noninverting gain of ten and a single 3-V supply, the measured results have shown that 40% of the ten samples display no more than 3- and 5-muV offsets at the chopping frequency of 10 and 64 kHz, respectively. The proposed technique offers a potential advantage for improving the yield of low-offset amplifiers in sensory systems.


title: A Standardised Procedure for Evaluating Creative Systems: Computational Creativity Evaluation Based on What it is to be Creative

abstract: Computational creativity is a flourishing research area, with a variety of creative systems being produced and developed. Creativity evaluation has not kept pace with system development with an evident lack of systematic evaluation of the creativity of these systems in the literature. This is partially due to difficulties in defining what it means for a computer to be creative; indeed, there is no consensus on this for human creativity, let alone its computational equivalent. This paper proposes a Standardised Procedure for Evaluating Creative Systems (SPECS). SPECS is a three-step process: stating what it means for a particular computational system to be creative, deriving and performing tests based on these statements. To assist this process, the paper offers a collection of key components of creativity, identified empirically from discussions of human and computational creativity. Using this approach, the SPECS methodology is demonstrated through a comparative case study evaluating computational creativity systems that improvise music.


title: Impact of Different Data Types on Classifier Performance of Random Forest, Naïve Bayes, and K-Nearest Neighbors Algorithms

abstract: This study aims to evaluate impact of three different data types (Text only, Numeric Only and Text + Numeric) on classifier performance (Random Forest, k-Nearest Neighbor (kNN) and Naive Bayes (NB) algorithms). The classification problems in this study are explored in terms of mean accuracy and the effects of varying algorithm parameters over different types of datasets. This content analysis has been examined through eight different datasets taken from UCI to train models for all three algorithms. The results obtained from this study clearly show that RF and kNN outperform NB. Furthermore, kNN and RF perform relatively the same in terms of mean accuracy nonetheless kNN takes less time to train a model. The changing numbers of attributes in datasets have no effect on Random Forest, whereas Naive Bayes mean accuracy fluctuates up and down that leads to a lower mean accuracy, whereas, kNN mean accuracy increases and ends with higher accuracy. Additionally, changing number of trees has no significant effects on mean accuracy of the Random forest, however, the time to train the model has increased greatly. Random Forest and k-Nearest Neighbor are proved to be the best classifiers for any type of dataset. Thus, Naive Bayes can outperform other two algorithms if the feature variables are in a problem space and are independent. Besides Random forests, it takes highest computational time and Naive Bayes takes lowest. The k-Nearest Neighbor requires finding an optimal number of k for improved performance at the cost of computation time. Similarly, changing the number of attributes that effect Naive Bayes and k-Nearest Neighbor performance nevertheless not the Random forest. This study can be extended by researchers who use the parametric method to analyze results.


title: Proximal socket shield for interimplant papilla preservation in the esthetic zone.

abstract: Managing the interimplant papilla is one of the most challenging tasks in anterior implant esthetics, especially when replacing a failing tooth adjacent to an existing implant restoration. This article describes the maintenance of the interimplant papilla when replacing a failing tooth adjacent to an implant restoration using the proximal socket shield procedure in conjunction with immediate implant placement and provisionalization.


title: Sesamoids and accessory ossicles of the foot: anatomical variability and related pathology

abstract: Sesamoids and accessory ossicles seen in the foot vary widely in their prevalence and appearance. Occasionally, these bones may be associated with painful syndromes, due to various pathologies, including trauma, infection, inflammation, degeneration and others. However, symptomatic accessory and sesamoid bones are rare, and search for additional pathology should be performed. Although the clinical significance of these osseous structures is probably minor, clinicians very commonly ask about these bones, which may originate an unnecessary work-up. Therefore, knowledge of their presence and morphological variations is important to prevent misinterpreting them as fractures—a common error. Finally, it may be very difficult to distinguish between incidental variants and truly symptomatic ones. Radiological studies provide insight regarding the presence and pathology involving these bones. This review describes an overview of the anatomy of sesamoids and accessory ossicles in the foot, and provides a pictorial review of their pathological conditions, including trauma, sesamoiditis, osteomyelitis, osteoarthritis and pain syndromes. Radiological studies including radiography, ultrasound, scintigraphy, computed tomography (CT) and magnetic resonance imaging (MRI) provide useful information which should be used in concert with clinical findings to guide patient management. Teaching points • Sesamoids and accessory ossicles seen in the foot vary widely in their prevalence and appearance. • Pathology of these bones includes trauma, sesamoiditis, infection, osteoarthritis and pain syndromes. • Radiography, ultrasound, scintigraphy, CT and MRI provide information regarding the pathology of these bones.


BinPro: A tool for binary backdoor accountability in code audits


title: On Pre-Trained Image Features and Synthetic Images for Deep Learning

abstract: Deep Learning methods usually require huge amounts of training data to perform at their full potential, and often require expensive manual labeling. Using synthetic images is therefore very attractive to train object detectors, as the labeling comes for free, and several approaches have been proposed to combine synthetic and real images for training. In this paper, we evaluate if ‘freezing’ the layers responsible for feature extraction to generic layers pre-trained on real images, and training only the remaining layers with plain OpenGL rendering may allow for training with synthetic images only. Our experiments with very recent deep architectures for object recognition (Faster-RCNN, R-FCN, Mask-RCNN) and image feature extractors (InceptionResnet and Resnet) show this simple approach performs surprisingly well.


Creating Artistic Compositions Using Coalition-Forming Intelligent Agents


On the bipolarity in argumentation frameworks


title: A Hybrid Microgrid With DC Connection at Back to Back Converters

abstract: The necessity of an AC or DC microgrid is governed by available micro sources and connected loads. A hybrid structure can ensure a sustainable configuration blending both the forms. In this paper, a hybrid microgrid structure for a grid connected microgrid with DC connection at back to back (B2B) converters is proposed. While a B2B connection between two AC systems could bestow a reliable, isolated and efficient coupling, an extra DC bus connection can facilitate use of the DC micro sources. The DC bus can supply the local DC loads and can also trade part of the power with the AC grids. The voltage support at the DC link (of the B2B converters) can be used for the DC bus formation. Different power management strategies with fixed power references or decentralized power distribution in AC/DC sides are proposed and validated with simulations in PSCAD.


title: Meta path-based collective classification in heterogeneous information networks

abstract: Collective classification approaches exploit the dependencies of a group of linked objects whose class labels are correlated and need to be predicted simultaneously. In this paper, we focus on studying the collective classification problem in heterogeneous networks, which involves multiple types of data objects interconnected by multiple types of links. Intuitively, two objects are correlated if they are linked by many paths in the network. By considering different linkage paths in the network, one can capture the subtlety of different types of dependencies among objects. We introduce the concept of meta-path based dependencies among objects, where a meta path is a path consisting a certain sequence of linke types. We show that the quality of collective classification results strongly depends upon the meta paths used. To accommodate the large network size, a novel solution, called HCC (meta-path based Heterogenous Collective Classification), is developed to effectively assign labels to a group of instances that are interconnected through different meta-paths. The proposed HCC model can capture different types of dependencies among objects with respect to different meta paths. Empirical studies on real-world networks demonstrate that effectiveness of the proposed meta path-based collective classification approach.


title: BitValue Inference: Detecting and Exploiting Narrow Bitwidth Computations

abstract: We present a compiler algorithm called BitValue, which can discover both unused and constant bits in dusty-deck C programs. Bit-Value uses forward and backward dataflow analyses, generalizing constant-folding and dead-code detection at the bit-level. This algorithm enables compiler optimizations which target special processor architectures for computing on non-standard bitwidths. ::: ::: Using this algorithm we show that up to 31% oft he computed bytes are thrown away (for programs from SpecINT95 and Mediabench). A compiler for reconfigurable hardware uses this algorithm to achieve substantial reductions (up to 20-fold) in the size of the synthesized circuits.


title: Detecting and resolving privacy conflicts for collaborative data sharing in online social networks

abstract: We have seen tremendous growth in online social networks (OSNs) in recent years. These OSNs not only offer attractive means for virtual social interactions and information sharing, but also raise a number of security and privacy issues. Although OSNs allow a single user to govern access to her/his data, they currently do not provide any mechanism to enforce privacy concerns over data associated with multiple users, remaining privacy violations largely unresolved and leading to the potential disclosure of information that at least one user intended to keep private. In this paper, we propose an approach to enable collaborative privacy management of shared data in OSNs. In particular, we provide a systematic mechanism to identify and resolve privacy conflicts for collaborative data sharing. Our conflict resolution indicates a tradeoff between privacy protection and data sharing by quantifying privacy risk and sharing loss. We also discuss a proof-of-concept prototype implementation of our approach as part of an application in Facebook and provide system evaluation and usability study of our methodology.


title: Smart decisions: an architectural design game

abstract: Architecture design is notoriously difficult to teach and to learn. Most competent architects in industry have deep knowledge won from long years of experience. But if we want architecture design to be methodical and repeatable, we need better methods for teaching it. Simply waiting for an aspiring architect to accumulate 10 or 20 years of experience is not acceptable if we believe that software engineering is a true engineering discipline. In this paper we describe our experiences with the development of a game that aids in teaching architecture design, specifically design employing the Attribute-Driven Design method. We discuss our approach to creating the game, and the "design concepts catalog" that provides the knowledge base for the game. Finally, we report on our experiences with deploying the game, and the (enthusiastic) assessments and feedback that we have received from industrial and academic participants.


E XPERIMENTAL E VALUATION OF N O SQL D ATABASES


title: The program dependence graph and its use in optimization

abstract: In this paper we present an intermediate program representation, called the program dependence graph ( PDG ), that makes explicit both the data and control dependences for each operation in a program. Data dependences have been used to represent only the relevant data flow relationships of a program. Control dependences are introduced to analogously represent only the essential control flow relationships of a program. Control dependences are derived from the usual control flow graph. Many traditional optimizations operate more efficiently on the PDG. Since dependences in the PDG connect computationally related parts of the program, a single walk of these dependences is sufficient to perform many optimizations. The PDG allows transformations such as vectorization, that previously required special treatment of control dependence, to be performed in a manner that is uniform for both control and data dependences. Program transformations that require interaction of the two dependence types can also be easily handled with our representation. As an example, an incremental approach to modifying data dependences resulting from branch deletion or loop unrolling is introduced. The PDG supports incremental optimization, permitting transformations to be triggered by one another and applied only to affected dependences.


title: Automated Variational Inference for Gaussian Process Models

abstract: We develop an automated variational method for approximate inference in Gaussian process (GP) models whose posteriors are often intractable. Using a mixture of Gaussians as the variational distribution, we show that (i) the variational objective and its gradients can be approximated efficiently via sampling from univariate Gaussian distributions and (ii) the gradients wrt the GP hyperparameters can be obtained analytically regardless of the model likelihood. We further propose two instances of the variational distribution whose covariance matrices can be parametrized linearly in the number of observations. These results allow gradient-based optimization to be done efficiently in a black-box manner. Our approach is thoroughly verified on five models using six benchmark datasets, performing as well as the exact or hard-coded implementations while running orders of magnitude faster than the alternative MCMC sampling approaches. Our method can be a valuable tool for practitioners and researchers to investigate new models with minimal effort in deriving model-specific inference algorithms.


title: Toward a visualization-supported workflow for cyber alert management using threat models and human-centered design

abstract: Cyber network analysts follow complex processes in their investigations of potential threats to their network. Much research is dedicated to providing automated decision support in the effort to make their tasks more efficient, accurate, and timely. Support tools come in a variety of implementations from machine learning algorithms that monitor streams of data to visual analytic environments for exploring rich and noisy data sets. Cyber analysts, however, need tools which help them merge the data they already have and help them establish appropriate baselines against which to compare anomalies. Furthermore, existing threat models that cyber analysts regularly use to structure their investigation are not often leveraged in support tools. We report on our work with cyber analysts to understand the analytic process and how one such model, the MITRE ATT&CK Matrix [42], is used to structure their analytic thinking. We present our efforts to map specific data needed by analysts into this threat model to inform our visualization designs. We leverage this expert knowledge elicitation to identify a capability gaps that might be filled with visual analytic tools. We propose a prototype visual analytic-supported alert management workflow to aid cyber analysts working with threat models.


title: Unsupervised classification of polarimetric synthetic aperture Radar images using fuzzy clustering and EM clustering

abstract: Five clustering techniques are compared by classifying a polarimetric synthetic aperture radar image. The pixels are complex covariance matrices, which are known to have the complex Wishart distribution. Two techniques are fuzzy clustering algorithms based on the standard /spl lscr//sub 1/ and /spl lscr//sub 2/ metrics. Two others are new, combining a robust fuzzy C-means clustering technique with a distance measure based on the Wishart distribution. The fifth clustering technique is an application of the expectation-maximization algorithm assuming the data are Wishart. The clustering algorithms that are based on the Wishart are demonstrably more effective than the clustering algorithms that appeal only to the /spl lscr//sub p/ norms. The results support the conclusion that the pixel model is more important than the clustering mechanism.