title: A fast MST-inspired kNN-based outlier detection method

abstract: Today's real-world databases typically contain millions of items with many thousands of fields. As a result, traditional distribution-based outlier detection techniques have more and more restricted capabilities and novel k-nearest neighbors based approaches have become more and more popular. However, the problems with these k-nearest neighbors based methods are that they are very sensitive to the value of k, may have different rankings for top n outliers, are very computationally expensive for large datasets, and doubts exist in general whether they would work well for high dimensional datasets. To partially circumvent these problems, we propose in this paper a new global outlier factor and a new local outlier factor and an efficient outlier detection algorithm developed upon them that is easy to implement and can provide competing performances with existing solutions. Experiments performed on both synthetic and real data sets demonstrate the efficacy of our method. A new k-nearest neighbors (kNN) based outlier detection scheme is proposed.It is built upon two new MST-inspired outlier scores, a global one and a local one.A set of state-of-the-art outlier detectors are applied to some high dimensional data.A fast approximate kNN search algorithm is used to accelerate the mining process.The proposed method can provide competing performances with existing solutions.


title: Moving Vehicle Registration and Super-Resolution

abstract: We describe a method for registering and super-resolving moving vehicles from aerial surveillance video. The challenge of vehicle super-resolution lies in the fact that vehicles may be very small and thus frame-to-frame registration does not offer enough constraints to yield registration with sub-pixel accuracy. To overcome this, we first register the large-scale image backgrounds and then, relative to the background registration, register the small-scale moving vehicle over all frames simultaneously using a vehicle motion model. To solve for the vehicle motion parameters we optimize a cost function that incorporates both vehicle appearance and background appearance consistency. Once this process accurately registers a moving vehicle, it is super-resolved. We apply both a frequency domain and a spatial domain approach. The frequency domain approach can be used when the final registered vehicle motion is well approximated by shifts in the image plane. The robust regularized spatial domain approach handles all cases of vehicle motion.


title: An effective evaluation measure for clustering on evolving data streams

abstract: Due to the ever growing presence of data streams, there has been a considerable amount of research on stream mining algorithms. While many algorithms have been introduced that tackle the problem of clustering on evolving data streams, hardly any attention has been paid to appropriate evaluation measures. Measures developed for static scenarios, namely structural measures and ground-truth-based measures, cannot correctly reflect errors attributable to emerging, splitting, or moving clusters. These situations are inherent to the streaming context due to the dynamic changes in the data distribution. In this paper we develop a novel evaluation measure for stream clustering called Cluster Mapping Measure (CMM). CMM effectively indicates different types of errors by taking the important properties of evolving data streams into account. We show in extensive experiments on real and synthetic data that CMM is a robust measure for stream clustering evaluation.


title: Semantic trajectory-based high utility item recommendation system

abstract: The topic on recommendation systems for mobile users has attracted a lot of attentions in recent years. However, most of the existing recommendation techniques were developed based only on geographic features of mobile users’ trajectories. In this paper, we propose a novel approach for recommending items for mobile users based on both the geographic and semantic features of users’ trajectories. The core idea of our recommendation system is based on a novel cluster-based location prediction strategy, namely TrajUtiRec, to improve items recommendation model. Our proposed cluster-based location prediction strategy evaluates the next location of a mobile user based on the frequent behaviors of similar users in the same cluster determined by analyzing users’ common behaviors in semantic trajectories. For each location, high utility itemset mining algorithm is performed for discovering high utility itemset. Accordingly, we can recommend the high utility itemset which is related to the location the user might visit. Through a comprehensive evaluation by experiments, our proposal is shown to deliver excellent performance.


title: Reliability of the Spinal Instability Neoplastic Score (SINS) among radiation oncologists: an assessment of instability secondary to spinal metastases

abstract: BackgroundThe Spinal Instability Neoplastic Score (SINS) categorizes tumor related spinal instability. It has the potential to streamline the referral of patients with established or potential spinal instability to a spine surgeon. This study aims to define the inter- and intra-observer reliability and validity of SINS among radiation oncologists.MethodsThirty-three radiation oncologists, across ten international sites, rated 30 neoplastic spinal disease cases. For each case, the total SINS (0-18 points), three clinical categories (stable: 0-6 points, potentially unstable: 7-12 points, and unstable: 13-18 points), and a binary scale (‘stable’: 0-6 points and ‘current or possible instability’; surgical consultation recommended: 7-18 points) were recorded. Evaluation was repeated 6-8 weeks later. Inter-observer agreement and intra-observer reproducibility were calculated by means of the kappa statistic and translated into levels of agreement (slight, fair, moderate, substantial, and excellent). Validity was determined by comparing the ratings against a spinal surgeon’s consensus standard.ResultsRadiation oncologists demonstrated substantial (κ = 0.76) inter-observer and excellent (κ = 0.80) intra-observer reliability when using the SINS binary scale (‘stable’ versus ‘current or possible instability’). Validity of the binary scale was also excellent (κ = 0.85) compared with the gold standard. None of the unstable cases was rated as stable by the radiation oncologists ensuring all were appropriately recommended for surgical consultation.ConclusionsAmong radiation oncologists SINS is a highly reliable, reproducible, and valid assessment tool to address a key question in tumor related spinal disease: Is the spine ‘stable’ or is there ‘current or possible instability’ that warrants surgical assessment?


title: Lightweight prevention of architectural erosion

abstract: Avoiding architectural erosion helps extend the lifetime of an evolving software system. Erosion can be reduced by ensuring that (i) developers share a good understanding of a system's architecture; (ii) alignment is preserved between the architectural description and its implementation at all stages of system construction and maintenance; and (iii) architectural changes are treated with the same care and attention as the production of the initial design. Through the metaphor of 'agile development' we present a lightweight approach to the control of architectural erosion. In particular, it covers the representation of an architectural description and the management of alignment between description and implementation during system evolution. A prototype support tool, ArchAngel, is introduced. This maintains an architectural design description, identifies when changes occur with respect to that description, and reports these changes for evaluation.


title: A Possible Extra-Ovarian Site for Synthesis of Lipovitellin During Vitellogenesis in Artemia sp. (Crustacea; Anostraca)*

abstract: Summary Lipovitellin samples, extracted from yolk platelets of cysts, were applied to SDS-PAGE. A female specific antiserum was raised against the high molecular weight apoprotein lipovitellin alpha-1 (LV-α1) of the lipovitellin complex. This anti-LV-α1 was used in the peroxidase-anti-peroxidase staining method with frontal paraffin sections (of 4μm) of whole embedded Artemia. Females were studied during a complete vitellogenic cycle. The presence of exogenous yolk precursors in the fat storage cells of the thoracopods of female Artemia was demonstrated. The amount of the female specific yolk polypeptides and the number of positively stained cells changes during the vitellogenic cycle. In vitro experiments with 35S-radiolabelled methionine show the synthesis of lipovitellin-like substances in the fat storage cells of vitellogenic females.


title: Bayesian optimization for conditional hyperparameter spaces

abstract: Hyperparameter optimization is now widely applied to tune the hyperparameters of learning algorithms. The hyperparameters can have structure, resulting in hyperparameters depending on conditions, or on the values of other hyperparameters. We target the problem of combined algorithm selection and hyperparameter optimization, which includes at least one conditional hyperparameter: the choice of the learning algorithm. In this work, we show that Bayesian optimization with Gaussian processes can be used for the optimization of conditional spaces with the injection of knowledge concerning conditions in the kernel. We propose and examine the behavior of two kernels, a conditional kernel which forces the similarity of two samples from different condition branches to be zero, and the Laplace kernel, based on similarities with Mondrian processes and random forests. We show the benefit of using such kernels, as well as proper imputation of inactive hyperparameters, on a benchmark of scikit-learn models.


title: Scientific research activity and communication measured with cybermetrics indicators

abstract: To test feasibility of cybermetric indicators for describing and ranking university activities as shown in their Web sites, a large set of 9,330 institutions worldwide was compiled and analyzed. Using search engines' advanced features, size (number of pages), visibility (number of external inlinks), and number of rich files (pdf, ps, doc, ppt, and xls formats) were obtained for each of the institutional domains of the universities. We found a statistically significant correlation between a Web ranking built on a combination of Webometric data and other university rankings based on bibliometric and other indicators. Results show that cybermetric measures could be useful for reflecting the contribution of technologically oriented institutions, increasing the visibility of developing countries, and improving the rankings based on Science Citation Index (SCI) data with known biases.


title: Three-dimensional integrated circuits

abstract: Three-dimensional (3D) integrated circuits (ICs), which contain multiple layers of active devices, have the potential to dramatically enhance chip performance, functionality, and device packing density. They also provide for microchip architecture and may facilitate the integration of heterogeneous materials, devices, and signals. However, before these advantages can be realized, key technology challenges of 3D ICs must be addressed. More specifically, the processes required to build circuits with multiple layers of active devices must be compatible with current state-of-the-art silicon processing technology. These processes must also show manufacturability, i.e., reliability, good yield, maturity, and reasonable cost. To meet these requirements, IBM has introduced a scheme for building 3D ICs based on the layer transfer of functional circuits, and many process and design innovations have been implemented. This paper reviews the process steps and design aspects that were developed at IBM to enable the formation of stacked device layers. Details regarding an optimized layer transfer process are presented, including the descriptions of 1) a glass substrate process to enable through-wafer alignment; 2) oxide fusion bonding and wafer bow compensation methods for improved alignment tolerance during bonding; 3) and a single-damascene patterning and metallization method for the creation of high-aspect-ratio (6:1 108 vias/cm2), and extremely aggressive wafer-to-wafer alignment (submicron) capability.


title: Robosemantics: How Stanley the Volkswagen Represents the World

abstract: One of the most impressive feats in robotics was the 2005 victory by a driverless Volkswagen Touareg in the DARPA Grand Challenge. This paper discusses what can be learned about the nature of representation from the car's successful attempt to navigate the world. We review the hardware and software that it uses to interact with its environment, and describe how these techniques enable it to represent the world. We discuss robosemantics, the meaning of computational structures in robots. We argue that the car constitutes a refutation of semantic arguments against the possibility of strong artificial intelligence.


title: Triple inverted pendulum system implementation using a new ARM/FPGA control platform

abstract: The paper deals with hardware implementation of triple inverted pendulum on a cart physical model.


title: Visualizing Dynamic Hierarchies in Graph Sequences

abstract: Graphs are used to model relations between objects, where these objects can be grouped hierarchically based on their connectivity. In many applications, the relations change over time and so does the hierarchical group structure. We developed a visualization technique that supports the analysis of the topology and the hierarchical group structure of a dynamic graph and the tracking of changes over time. Each graph of a sequence is visualized by an adjacency matrix, where the hierarchical group structure is encoded within the matrix using indentation and nested contours, complemented by icicle plots attached to the matrices. The density within and between subgroups of the hierarchy is represented within the matrices using a gray scale. To visualize changes, transitions and dissimilarities between the hierarchically structured graphs are shown using a flow metaphor and color coding. The design of our visualization technique allows us to show more than one hierarchical group structure of the same graph by stacking the sequences, where hierarchy comparison is supported not only within but also between sequences. To improve the readability, we minimize the number of crossing curves within and between sequences based on a sorting algorithm that sweeps through the sequences of hierarchies.


title: Understanding User Requirements for Music Information Services

abstract: User studies in the music information retrieval and music digital library fields have been gradually increasing in recent years, but large-scale studies that can help detect common user behaviors are still lacking. We have conducted a large-scale user survey in which we asked numerous questions related to users’ music needs, uses, seeking, and management behaviors. In this paper, we present our preliminary findings, specifically focusing on the responses to questions of users’ favorite music related websites/applications and the reasons why they like them. We provide a list of popular music services, as well as an analysis of how these services are used, and what qualities are valued. Our findings suggest several trends in the types of music services people like: an increase in the popularity of music streaming and mobile music consumption, the emergence of new functionality, such as music identification and cloud music services, an appreciation of music videos, serendipitous discovery of music, and customizability, as well as users’ changing expectations of particular types of music information. Understanding what kinds of music information services people use, how they use them, and what they expect from them is critical in designing successful services. We have seen a gradual increase in different types of user and usability studies in recent years. However, many of these studies are based on a limited number of subjects, and tend to employ analysis of qualitative research methods, like in-depth interviews or focus groups. While these kinds of studies can help uncover rich data about music users, large-scale user studies are also necessary in order to test the generalizability of results and to complement the insights obtained from smaller qualitative studies. To fill this gap, we have conducted a large-scale user survey questioning people’s music needs, uses, and music seeking and management behaviors. This survey is an extension of previous research conducted in 2004 by Lee and Downie [7]. The information we acquired through this new study can help improve our general understanding of music users and their behaviors, as well as how they have changed as compared to the 2004 survey results. 2. LITERATURE REVIEW


title: Disease Forecasting System Using Data Mining Methods

abstract: The healthcare industry collects large amounts of healthcare information which cannot be mined to find unknown information for efficient evaluation. Discovery of buried patterns frequently goes unexploited. Heart disease is a term for defining a huge amount of healthcare conditions that are related to the heart. This medicinal condition defines the unpredicted health conditions that directly control all the parts of the heart. Different data mining techniques such as association rule mining, classification, clustering are used to predict the heart disease in health care industry. The heart disease database is preprocessed to make the mining process more efficient. The preprocessed data is clustered using clustering algorithms like K-means to cluster relevant data in database. Maximal Frequent Item set Algorithm (MAFIA) is used for mining maximal frequent patterns in heart disease database. The frequent patterns can be classified using C4.5 algorithm as training algorithm using the concept of information entropy. The results showed that the designed prediction system is capable of predicting the heart attack successfully.


title: Efficient gradient-domain compositing using quadtrees

abstract: We describe a hierarchical approach to improving the efficiency of gradient-domain compositing, a technique that constructs seamless composites by combining the gradients of images into a vector field that is then integrated to form a composite. While gradient-domain compositing is powerful and widely used, it suffers from poor scalability. Computing an n pixel composite requires solving a linear system with n variables; solving such a large system quickly overwhelms the main memory of a standard computer when performed for multi-megapixel composites, which are common in practice. In this paper we show how to perform gradient-domain compositing approximately by solving an O(p) linear system, where p is the total length of the seams between image regions in the composite; for typical cases, p is O(√n). We achieve this reduction by transforming the problem into a space where much of the solution is smooth, and then utilize the pattern of this smoothness to adaptively subdivide the problem domain using quadtrees. We demonstrate the merits of our approach by performing panoramic stitching and image region copy-and-paste in significantly reduced time and memory while achieving visually identical results.


title: Passivity-based variable structure control of two-link underactuated robot

abstract: We present a method for position control of planar two-links robots with a passive rotational last joint. This underactuated mechanical system, which is subject to nonholonomic second-order constraints, are controlled to gain a desired position by the dynamic coupling. This objective is achieved in the absence of gravity. Most controllers based on the precise dynamic or kinematic equations aren't practical because we can't get the exact data of the systems. We proposed a passivity-based variable structure controller (PBVSC) to provide the robot robustness to the parameter uncertainty.


title: Does Your Model Know the Digit 6 Is Not a Cat? A Less Biased Evaluation of"Outlier"Detectors

abstract: In the real world, a learning system could receive an input that looks nothing like anything it has seen during training, and this can lead to unpredictable behaviour. We thus need to know whether any given input belongs to the population distribution of the training data to prevent unpredictable behaviour in deployed systems. A recent surge of interest on this problem has led to the development of sophisticated techniques in the deep learning literature. However, due to the absence of a standardized problem formulation or an exhaustive evaluation, it is not evident if we can rely on these methods in practice. What makes this problem different from a typical supervised learning setting is that we cannot model the diversity of out-of-distribution samples in practice. The distribution of outliers used in training may not be the same as the distribution of outliers encountered in the application. Therefore, classical approaches that learn inliers vs. outliers with only two datasets can yield optimistic results. We introduce OD-test, a three-dataset evaluation scheme as a practical and more reliable strategy to assess progress on this problem. The OD-test benchmark provides a straightforward means of comparison for methods that address the out-of-distribution sample detection problem. We present an exhaustive evaluation of a broad set of methods from related areas on image classification tasks. Furthermore, we show that for realistic applications of high-dimensional images, the existing methods have low accuracy. Our analysis reveals areas of strength and weakness of each method.


title: Comments on the spontaneous strain and polarization of polycrystalline ferroelectric ceramics

abstract: A framework to calculate the spontaneous strain and polarization of a polycrystalline ferroelectric is presented, and various applications are discussed.


title: Bacillus thuringiensis: mechanism of action, resistance, and new applications: a review

abstract: AbstractSince the first report by Ishiwata in 1902 of a Bombyx mori infection, followed by the description by Berliner, Bacillus thuringiensis (Bt) has become the main microorganism used in biological control. The application of Bt to combat invertebrates of human interest gained momentum with the growing demand for food free of chemical pesticides and with the implementation of agriculture methods that were less damaging to the environment. However, the mechanisms of action of these products have not been fully elucidated. There are two proposed models: the first is that Bt causes an osmotic imbalance in response to the formation of pores in a cell membrane, and the second is that it causes an opening of ion channels that activate the process of cell death. There are various ways in which Bt resistance can develop: changes in the receptors that do not recognize the Cry toxin, the synthesis of membrane transporters that eliminate the peptides from the cytosol and the development of regulatory mechanisms t...


title: A Framework for Effective Big data Analytics for Decision Support Systems

abstract: Supporting decision makers requires a good understanding of the various elements that affect the outcomes of a decision. Decision Support Systems have provided decision makers with such insights throughout its history of usage with varying degrees of success. The availability of data sources was a main limitation to what decision support systems can do. Therefore, with the advent of improved analytical methods for Big data sources new opportunities have emerged that can possibly enhance how decision makers analyze their problem and arrive at decisions using information systems. This paper analyzed current related works on both Big data and decision support systems to identify clear elements and factors relevant to the subject and identifying possible ways to enhance their joint usage. Finally, the paper proposes a framework that integrates the key components needed to ensure the quality and relevance of data being analyzed by decision support systems while providing the benefits of insights generated over time from past decisions and positive recommendations.


title: Force control of a jumping musculoskeletal robot with pneumatic artificial muscles

abstract: This paper introduces a method of force control for a jumping biped robot to correctly control the ground reaction force during continuous jumping. Assuming dynamic motion, such as jumping and running, the attitude of the robot depends on the dynamics in response to the ground reaction force. Therefore, controlling the ground reaction force is necessary for stabilizing the robot's motion. However, control of the ground reaction force involves a fundamental problem: feedback control does not work properly against the impact force owing to limitations in the control bandwidth. This study introduces a method for stiffness ellipse control that utilizes three antagonist pairs of six pneumatic artificial muscles. When the tip of the robot makes contact with the ground, an external force is induced in the direction of lower stiffness. By utilizing this property of the stiffness ellipse, it is possible to control the ground reaction force by feedforward control, which is not dependent on the control bandwidth. Based on this idea, impact force control at landing and jumping force control at takeoff were proposed to correctly control the ground reaction force during the continuous jumping of the robot. The results of several experiments conducted convince us that the relationship between the ground reaction force and the stiffness ellipse is almost linear, and that the ground reaction force can be controlled with high reproducibility by adjusting the stiffness ellipse.


title: Effects of X Capacitors on EMI Filter Effectiveness

abstract: The suppression mechanism of the differential-mode noise of an X capacitor in offline power supplies is, for the first time, attributed to two distinct concepts: 1) impedance mismatch (regarding a line impedance stabilization network or mains and the equivalent power supply noise source impedance) and 2) C(dv/dt) noise current balancing (to suppress mix-mode noise). The effectiveness of X capacitors is investigated with this theory, along with experimental supports. Understanding of the two aforementioned mechanisms gives better insight into filter effectiveness, which may lead to a more compact filter design.


title: Artificial Neural Networks and Support Vector Machines for water demand time series forecasting

abstract: Water plays a pivotal role in many physical processes, and most importantly in sustaining human life, animal life and plant life. Water supply entities therefore have the responsibility to supply clean and safe water at the rate required by the consumer. It is therefore necessary to implement mechanisms and systems that can be employed to predict both short-term and long-term water demands. The increasingly growing field of computational intelligence techniques has been proposed as an efficient tool in the modelling of dynamic phenomena. The primary objective of this paper is to compare the efficiency of two computational intelligence techniques in water demand forecasting. The techniques under comparison are Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs). In this study it was observed that ANNs perform better than SVMs. This performance is measured against the generalisation ability of the two.


title: Fully automated esophagus segmentation with a hierarchical deep learning approach

abstract: Segmentation of organs at risk in CT volumes is a prerequisite for radiotherapy treatment planning. In this paper we focus on esophagus segmentation, a challenging problem since the walls of the esophagus have a very low contrast in CT images. Making use of Fully Convolutional Networks (FCN), we present several extensions that improve the performance, including a new architecture that allows to use low level features with high level information, effectively combining local and global information for improving the localization accuracy. Experiments demonstrate competitive performance on a dataset of 30 CT scans.


title: New folded configuration of rectangular waveguide filters with asymmetrical transmission zeros

abstract: This paper presents a novel filter topology for the implementation of asymmetric responses with transmission zeros (TZs) in rectangular waveguide technology. It is based on a compact folded E-plane arrangement where adjacent resonators are capacitively coupled through rectangular slots, and non-adjacent resonators are coupled through simple inductive windows. The new folded configuration allows the introduction of transmission zeros above the passband, which can be easily controlled. High order filters can be designed by cascading an arbitrary number of resonators in a folded layout. Components based on this novel configuration are amenable to simple manufacturing processes and can be used in high-power environments. A triplexer containing filters implemented with the proposed topology has been designed. Measurements of a manufactured prototype are included to validate the use of this topology in practical applications.


title: CHIRP: a new classifier based on composite hypercubes on iterated random projections

abstract: We introduce a classifier based on the L-infinity norm. This classifier, called CHIRP, is an iterative sequence of three stages (projecting, binning, and covering) that are designed to deal with the curse of dimensionality, computational complexity, and nonlinear separability. CHIRP is not a hybrid or modification of existing classifiers; it employs a new covering algorithm. The accuracy of CHIRP on widely-used benchmark datasets exceeds the accuracy of competitors. Its computational complexity is sub-linear in number of instances and number of variables and subquadratic in number of classes.


title: Unsupervised Learning of Narrative Schemas and their Participants

abstract: We describe an unsupervised system for learning narrative schemas, coherent sequences or sets of events (arrested(POLICE, SUSPECT), convicted(JUDGE, SUSPECT)) whose arguments are filled with participant semantic roles defined over words (Judge = {judge, jury, court}, Police = {police, agent, authorities}). Unlike most previous work in event structure or semantic role learning, our system does not use supervised techniques, hand-built knowledge, or predefined classes of events or roles. Our unsupervised learning algorithm uses coreferring arguments in chains of verbs to learn both rich narrative event structure and argument roles. By jointly addressing both tasks, we improve on previous results in narrative/frame learning and induce rich frame-specific semantic roles.


title: Agile distributed software development: enacting control through media and context

abstract: While face-to-face interaction is fundamental in agile software development, distributed environments must rely extensively on mediated interactions. Practicing agile principles in distributed environments therefore poses particular control challenges related to balancing fixed vs. evolving quality requirements and people vs. process-based collaboration. To investigate these challenges, we conducted an in-depth case study of a successful agile distributed software project with participants from a Russian firm and a Danish firm. Applying Kirsch's elements of control framework, we offer an analysis of how control was enacted through the project context and in the participants' mediated communication. The analysis reveals that formal measurement and evaluation control were persistently enacted through mediated communication. These formal control practices were, however, predominantly carried out in conjunction with informal roles and relationships such as clan-like control inherent in agile development. Overall, the study demonstrates that, if appropriately applied, communication technologies can significantly support distributed, agile practices by allowing concurrent enactment of both formal and informal controls. The paper discusses these findings as they relate to previous research and concludes with their implications for future research. © 2012 Wiley Periodicals, Inc.


title: 3D Human Sensing

abstract: The development of 3D video in recent years realizes 3D surface capturing of human in motion as is. In this paper, we introduce 3D human sensing algorithms based on 3D video. Since 3D video capturing does not require the object to attach special markers, we can capture the original information such as body motion or viewing directions without any disturbance caused by the sensing system itself.


title: Running head: INTRODUCTION TO LATENT SEMANTIC ANALYSIS An Introduction to Latent Semantic Analysis

abstract: Latent Semantic Analysis (LSA) is a theory and method for extracting and representing the contextual‐usage meaning of words by statistical computations applied to a large corpus of text (Landauer & Dumais, 1997). The underlying idea is that the aggregate of all the word contexts in which a given word does and does not appear provides a set of mutual constraints that largely determines the similarity of meaning of words and sets of words to each other. The adequacy of LSA's reflection of human knowledge has been established in a variety of ways. For example, its scores overlap those of humans on standard vocabulary and subject matter tests; it mimics human word sorting and category judgments; it simulates word‐word and passage‐word lexical priming data; and, as reported in 3 following articles in this issue, it accurately estimates passage coherence, learnability of passages by individual students, and the quality and quantity of knowledge contained in an essay.


title: 5-GHz In-Phase Coupled Oscillators with 39% Tuning Range

abstract: We show that when in LC oscillator a large tuning range is required, the phase noise performance worsen. When both a wide frequency band and tight noise performance are required, a typical solution is to increase the tank capacitance, thus the power consumption. That can lead to unpractical values for the tank’s components. To overcome the problem we propose a circuit realized by coupling four identical oscillators. The circuit, fabricated in an existing 0.5~ SiGe BiCMOS technology, achieves 39% tuning range. Phase noise and power consumption vary respectively between -1051-95 dBclHz @ 100 kHz, and 2001100 mW, with a 3V power supply. The noise improvement due to the coupling is quantitatively analyzed and experimentally verified.


Initial Experimental Study on Dynamic Interaction Between an Amputee and a Powered Ankle-Foot Prostheses


title: Examining multi-dimensional trust and multi-faceted risk in initial acceptance of emerging technologies: An empirical study of mobile banking services

abstract: The factors affecting rejection or acceptance of an emerging IT artifact such as mobile banking have piqued interest among IS researchers and remain unknown due in part to consumers' trust and risk perceptions in the wireless platform. This study extends this line of research by conjointly examining multi-dimensional trust and multi-faceted risk perceptions in the initial adoption stage of the wireless Internet platform. Results of this study indicate that risk perception, derived from eight different facets, is a salient antecedent to innovative technology acceptance. Beyond prior studies, the results also provide empirical support for employing personal trait factors in analyzing acceptance of emerging IT artifacts.


PARAGEN : PERFORMATIVE EXPLORATION OF GENERATIVE SYSTEMS


title: D numbers theory: a generalization of Dempster-Shafer theory

abstract: Dempster-Shafer theory is widely applied to uncertainty modelling and knowledge reasoning due to its ability of expressing uncertain information. However, some conditions, such as exclusiveness hypothesis and completeness constraint, limit its development and application to a large extend. To overcome these shortcomings in Dempster-Shafer theory and enhance its capability of representing uncertain information, a novel theory called D numbers theory is systematically proposed in this paper. Within the proposed theory, uncertain information is expressed by D numbers, reasoning and synthesization of information are implemented by D numbers combination rule. The proposed D numbers theory is an generalization of Dempster-Shafer theory, which inherits the advantage of Dempster-Shafer theory and strengthens its capability of uncertainty modelling.


title: Automated arabic text classification with P-Stemmer, machine learning, and a tailored news article taxonomy

abstract: Arabic news articles in electronic collections are difficult to study. Browsing by category is rarely supported. Although helpful machine-learning methods have been applied successfully to similar situations for English news articles, limited research has been completed to yield suitable solutions for Arabic news. In connection with a Qatar National Research Fund QNRF-funded project to build digital library community and infrastructure in Qatar, we developed software for browsing a collection of about 237,000 Arabic news articles, which should be applicable to other Arabic news collections. We designed a simple taxonomy for Arabic news stories that is suitable for the needs of Qatar and other nations, is compatible with the subject codes of the International Press Telecommunications Council, and was enhanced with the aid of a librarian expert as well as five Arabic-speaking volunteers. We developed tailored stemming i.e., a new Arabic light stemmer called P-Stemmer and automatic classification methods the best being binary Support Vector Machines classifiers to work with the taxonomy. Using evaluation techniques commonly used in the information retrieval community, including 10-fold cross-validation and the Wilcoxon signed-rank test, we showed that our approach to stemming and classification is superior to state-of-the-art techniques.


title: Contributions to the study of SMS spam filtering: new collection and results

abstract: The growth of mobile phone users has lead to a dramatic increasing of SMS spam messages. In practice, fighting mobile phone spam is difficult by several factors, including the lower rate of SMS that has allowed many users and service providers to ignore the issue, and the limited availability of mobile phone spam-filtering software. On the other hand, in academic settings, a major handicap is the scarcity of public SMS spam datasets, that are sorely needed for validation and comparison of different classifiers. Moreover, as SMS messages are fairly short, content-based spam filters may have their performance degraded. In this paper, we offer a new real, public and non-encoded SMS spam collection that is the largest one as far as we know. Moreover, we compare the performance achieved by several established machine learning methods. The results indicate that Support Vector Machine outperforms other evaluated classifiers and, hence, it can be used as a good baseline for further comparison.


title: The Relation of Attachment Security Status to Effortful Self-Regulation: A Meta-Analysis

abstract: Secure attachment relationships have been described as having a regulatory function in regard to children's emotions, social cognition, and behavior. Although some theorists and researchers have argued that attachment affects children's self-regulation, most attachment theorists have not strongly emphasized this association. The goal of the current meta-analysis was to determine the magnitude of the relation between attachment security status and effortful control (EC)/top-down self-regulation in children up to 18 years of age. One hundred six papers met the inclusion criteria and 101 independent samples were used in analyses. When secure attachment status was compared with insecure attachment status, a significant relation (effect size [ES]) with EC favoring children with a secure attachment was found (100 studies; 20,350 participants; r = .20). A stronger relation was found when the same coder evaluated attachment than when the coder was different and when the measure of attachment was continuous; other moderators were not significant. Securely attached children were higher in EC than their avoidant (r = .10) or resistant (r = .17) counterparts. Children with organized attachments were higher in EC than those with disorganized attachments (r = .17), although this finding could be due to publication bias. For some comparisons of subgroups (B vs. A, B vs. C, and/or D vs. all others), moderation was found by source of information (higher ES for same reporter), age at assessment of EC and/or attachment (higher ES at older ages), method of attachment (lower ES for observational measures), time difference between assessments or research design (higher ESs for smaller time differences and concurrent findings), and published versus unpublished studies (higher ES for unpublished studies for A vs. B). (PsycINFO Database Record


title: Fine-Grained Sentiment Analysis on Financial Microblogs and News Headlines

abstract: Sentiment analysis in the financial domain is quickly becoming a prominent research topic as it provides a powerful method to predict market dynamics. In this work, we leverage advances in Semantic Web area to develop a fine-grained approach to predict real-valued sentiment scores. We compare several classifiers trained on two different datasets. The first dataset consists of microblog messages focusing on stock market events, while the second one consists of financially relevant news headlines crawled from different sources on the Internet. We test our approach using several feature sets including lexical features, semantic features and a combination of lexical and semantic features. Experimental results show that the proposed approach allows achieving an accuracy level of more than \(72\%\).


title: Model-Based Planning in Discrete Action Spaces

abstract: Planning actions using learned and differentiable forward models of the world is a general approach which has a number of desirable properties, including improved sample complexity over model-free RL methods, reuse of learned models across different tasks, and the ability to perform efficient gradient-based optimization in continuous action spaces. However, this approach does not apply straightforwardly when the action space is discrete, which may have limited its adoption. In this work, we introduce two discrete planning tasks inspired by existing question-answering datasets and show that it is in fact possible to effectively perform planning via backprop in discrete action spaces using two simple yet principled modifications. Our experiments show that this approach can significantly outperform model-free RL based methods and supervised imitation learners.


title: Detecting abnormal events in video using Narrowed Motion Clusters.

abstract: We formulate the abnormal event detection problem as an outlier detection task and we propose a two-stage algorithm based on k-means clustering and one-class Support Vector Machines (SVM) to eliminate outliers. After extracting motion features from the training video containing only normal events, we apply k-means clustering to find clusters representing different types of motion. In the first stage, we consider that clusters with fewer samples (with respect to a given threshold) contain only outliers and we eliminate these clusters altogether. In the second stage, we shrink the borders of the remaining clusters by training a one-class SVM model on each cluster. To detected abnormal events in the test video, we analyze each test sample and consider its maximum normality score provided by the trained one-class SVM models, based on the intuition that a test sample can belong to only one cluster of normal motion. If the test sample does not fit well in any narrowed cluster, than it is labeled as abnormal. We also combine our approach based on motion features with a recent approach based on deep appearance features extracted with pre-trained convolutional neural networks (CNN). We combine our two-stage algorithm with the deep framework using a late fusion strategy, keeping the pipelines of the two approaches independent. We compare our method with several state-of-the-art supervised and unsupervised methods on four benchmark data sets. The empirical results indicate that our abnormal event detection framework can achieve better results in most cases, while processing the test video in real-time at 30 frames per second on CPU.


title: Latent Attention Networks

abstract: Deep neural networks are able to solve tasks across a variety of domains and modalities of data. Despite many empirical successes, we lack the ability to clearly understand and interpret the learned internal mechanisms that contribute to such effective behaviors or, more critically, failure modes. In this work, we present a general method for visualizing an arbitrary neural network's inner mechanisms and their power and limitations. Our dataset-centric method produces visualizations of how a trained network attends to components of its inputs. The computed "attention masks" support improved interpretability by highlighting which input attributes are critical in determining output. We demonstrate the effectiveness of our framework on a variety of deep neural network architectures in domains from computer vision, natural language processing, and reinforcement learning. The primary contribution of our approach is an interpretable visualization of attention that provides unique insights into the network's underlying decision-making process irrespective of the data modality.


title: Dual-band circularly polarized microstrip antenna

abstract: A new dual-band circularly polarized microstrip antenna is presented experimentally. In this paper, using dual-radiator a new design dual-band antenna can be obtained. The frequency ratio of this design is flexible which more than 1.4.


title: Preventing Privilege Escalation

abstract: We introduce a system that eliminates the need to run programs in privileged process contexts. Using our system, programs run unprivileged but may execute certain operations with elevated privileges as determined by a configurable policy eliminating the need for suid or sgid binaries. We present the design and analysis of the "Systrace" facility which supports fine grained process confinement, intrusion detection, auditing and privilege elevation. It also facilitates the often difficult process of policy generation. With Systrace, it is possible to generate policies automatically in a training session or generate them interactively during program execution. The policies describe the desired behavior of services or user applications on a system call level and are enforced to prevent operations that are not explicitly permitted. We show that Systrace is efficient and does not impose significant performance penalties.


title: Modeling and Verifying Google File System

abstract: Google File System (GFS) is a distributed file system developed by Google for massive data-intensive applications. Its high aggregate performance of delivering massive data to many clients but the inexpensiveness of commodity hardware facilitate GFS to successfully meet the massive storage needs and be widely used in industries. In this paper, we first present a formal model of Google File System in terms of Communicating Sequential Processes (CSP#), which precisely describes the un-derlying read/write behaviors of GFS. On that basis, both relaxed consistency and eventually consistency guaranteed by GFS may be revealed in our framework. Furthermore, the suggested CSP# model is encoded in Process Analysis Toolkit (PAT), thus several properties such as starvation-free and deadlock-free could be automatically checked and verified in the framework of formal methods.


title: Deep Learning Anthropomorphic 3D Point Clouds from a Single Depth Map Camera Viewpoint

abstract: In footwear, fit is highly dependent on foot shape, which is not fully captured by shoe size. Scanners can be used to acquire better sizing information and allow for more personalized footwear matching, however when scanning an object, many images are usually needed for reconstruction. Semantics such as knowing the kind of object in view can be leveraged to determine the full 3D shape given only one input view. Deep learning methods have been shown to be able to reconstruct 3D shape from limited inputs in highly symmetrical objects such as furniture and vehicles. We apply a deep learning approach to the domain of foot scanning, and present a method to reconstruct a 3D point cloud from a single input depth map. Anthropomorphic body parts can be challenging due to their irregular shapes, difficulty for parameterizing and limited symmetries. We train a view synthesis based network and show that our method can produce foot scans with accuracies of 1.55 mm from a single input depth map.


title: Sequence Classification for Credit-Card Fraud Detection

abstract: Abstract Due to the growing volume of electronic payments, the monetary strain of credit-card fraud is turning into a substantial challenge for financial institutions and service providers, thus forcing them to continuously improve their fraud detection systems. However, modern data-driven and learning-based methods, despite their popularity in other domains, only slowly find their way into business applications. In this paper, we phrase the fraud detection problem as a sequence classification task and employ Long Short-Term Memory (LSTM) networks to incorporate transaction sequences. We also integrate state-of-the-art feature aggregation strategies and report our results by means of traditional retrieval metrics. A comparison to a baseline random forest (RF) classifier showed that the LSTM improves detection accuracy on offline transactions where the card-holder is physically present at a merchant. Both the sequential and non-sequential learning approaches benefit strongly from manual feature aggregation strategies. A subsequent analysis of true positives revealed that both approaches tend to detect different frauds, which suggests a combination of the two. We conclude our study with a discussion on both practical and scientific challenges that remain unsolved.


title: Towards robust SVM training from weakly labeled large data sets

abstract: Learning from large data sets that contain samples of unknown or incorrect labels becomes increasingly important. Such problems are inherent to many big data scenarios, hence there is a need for developing robust generic approaches to learning from difficult data. In this paper, we propose a new memetic algorithm that evolves samples and labels to select a training set for support vector machines from large, weakly-labeled sets. Our extensive experimental study confirmed that the new method presents high robustness against weakly-labeled data and outperforms other state-of-the-art algorithms.


title: The Price of Power: Power Seeking and Backlash Against Female Politicians:

abstract: Two experimental studies examined the effect of power-seeking intentions on backlash toward women in political office. It was hypothesized that a female politician’s career progress may be hindered by the belief that she seeks power, as this desire may violate prescribed communal expectations for women and thereby elicit interpersonal penalties. Results suggested that voting preferences for female candidates were negatively influenced by her power-seeking intentions (actual or perceived) but that preferences for male candidates were unaffected by power-seeking intentions. These differential reactions were partly explained by the perceived lack of communality implied by women’s power-seeking intentions, resulting in lower perceived competence and feelings of moral outrage. The presence of moral-emotional reactions suggests that backlash arises from the violation of communal prescriptions rather than normative deviations more generally. These findings illuminate one potential source of gender bias in politics.


title: CuLDA_CGS: solving large-scale LDA problems on GPUs

abstract: GPUs have benefited many ML algorithms. However, we observe that the performance of existing Latent Dirichlet Allocation(LDA) solutions on GPUs are not satisfying. We present CuLDA_CGS, an efficient approach to accelerate large-scale LDA problems. We delicately design workload partition and synchronization mechanism to exploit multiple GPUs. We also optimize the algorithm from the sampling algorithm, parallelization, and data compression perspectives. Experiment evaluations show that compared with the state-of-the-art LDA solutions, CuLDA_CGS outperforms them by a large margin (up to 7.3X) on a single GPU.


title: SegICP: Integrated Deep Semantic Segmentation and Pose Estimation

abstract: Recent robotic manipulation competitions have highlighted that sophisticated robots still struggle to achieve fast and reliable perception of task-relevant objects in complex, realistic scenarios. To improve these systems' perceptive speed and robustness, we present SegICP, a novel integrated solution to object recognition and pose estimation. SegICP couples convolutional neural networks and multi-hypothesis point cloud registration to achieve both robust pixel-wise semantic segmentation as well as accurate and real-time 6-DOF pose estimation for relevant objects. Our architecture achieves 1 cm position error and < 5° angle error in real time without an initial seed. We evaluate and benchmark SegICP against an annotated dataset generated by motion capture.


title: Globally convergent modified Perry's conjugate gradient method

abstract: Abstract Conjugate gradient methods are probably the most famous iterative methods for solving large scale optimization problems in scientific and engineering computation, characterized by the simplicity of their iteration and their low memory requirements. In this paper, we propose a new conjugate gradient method which is based on the MBFGS secant condition by modifying Perry’s method. Our proposed method ensures sufficient descent independent of the accuracy of the line search and it is globally convergent under some assumptions. Numerical experiments are also presented.


title: Digital Data Streams: Creating Value from the Real-Time Flow of Big Data

abstract: There is no escaping the Big Data hype. Vendors are peddling Big Data solutions; consulting firms employ Big Data specialists; Big Data conferences are aplenty. There is a rush to extract golden nuggets (of insight) from mountains (of data). By focusing merely on the mountain (of Big Data), these adventurers are overlooking the source of the revolution—namely, the many digital data streams (DDSs) that create Big Data—and the opportunity to improve real-time decision making. This article discusses the characteristics of DDSs, describes their common structure, and offers guidelines to enable firms to profit from their untapped potential.


Hardware Implementations of SVM on FPGA: A State-of-the-Art Review of Current Practice


title: The factors influencing members ' continuance intentions in professional virtual communities a

abstract: The advance of internet technology has stimulated the rise of professional virtual communities (PVCs). The objective of PVCs is to encourage people to exploit or explore knowledge through websites. However, many virtual communities have failed due to the reluctance of members to continue their participation in these PVCs. Motivated by such concerns, this study formulates and tests a theoretical model to explain the factors influencing individuals' intention to continue participating in PVCs' knowledge activities. Drawing from the information system and knowledge management literatures, two academic perspectives related to PVC continuance are incorporated in the integrated model. This model posits that an individual's intention to stay in a professional virtual community is influenced by a contextual factor and technological factors. Specifically, the antecedents of PVC members' intention to continue sharing knowledge include social interaction ties capital and satisfaction at post-usage stage. These variables, in turn, are adjusted based on the confirmation of pre-usage expectations. A longitudinal study is conducted with 360 members of a professional virtual community. Results indicate that the contextual factor and technological factors both exert significant impacts on PVC participants' continuance intentions.


title: A malware detection system based on intermediate language

abstract: Malware remains a serious problem for economic corporations, government agencies, and individuals. With the evolution of malware, the malware detection method based on signature, reputation or anomaly show the lack of detection ability. In this paper, we proposed a novel malware detection system, which use the feature extract from intermediate representation and utilizes multiple evaluation methods to detect the malware. We extract the feature from intermediate representation with fine-grained that retain the functional sequence of malware. Then, we use LSTM (Long Sort Term Memory) to learn the pattern from the training set and build a detection system that contain many antivirus software's results to detect malware and classify it. Our approach is evaluated on more than 300,000 samples. Experimental results show that our system can effectively detect unknown malware and classify it.


title: Effects of a robot-assisted training of grasp and pronation/supination in chronic stroke: a pilot study

abstract: BackgroundRehabilitation of hand function is challenging, and only few studies have investigated robot-assisted rehabilitation focusing on distal joints of the upper limb. This paper investigates the feasibility of using the HapticKnob, a table-top end-effector device, for robot-assisted rehabilitation of grasping and forearm pronation/supination, two important functions for activities of daily living involving the hand, and which are often impaired in chronic stroke patients. It evaluates the effectiveness of this device for improving hand function and the transfer of improvement to arm function.MethodsA single group of fifteen chronic stroke patients with impaired arm and hand functions (Fugl-Meyer motor assessment scale (FM) 10-45/66) participated in a 6-week 3-hours/week rehabilitation program with the HapticKnob. Outcome measures consisted primarily of the FM and Motricity Index (MI) and their respective subsections related to distal and proximal arm function, and were assessed at the beginning, end of treatment and in a 6-weeks follow-up.ResultsThirteen subjects successfully completed robot-assisted therapy, with significantly improved hand and arm motor functions, demonstrated by an average 3.00 points increase on the FM and 4.55 on the MI at the completion of the therapy (4.85 FM and 6.84 MI six weeks post-therapy). Improvements were observed both in distal and proximal components of the clinical scales at the completion of the study (2.00 FM wrist/hand, 2.55 FM shoulder/elbow, 2.23 MI hand and 4.23 MI shoulder/elbow). In addition, improvements in hand function were observed, as measured by the Motor Assessment Scale, grip force, and a decrease in arm muscle spasticity. These results were confirmed by motion data collected by the robot.ConclusionsThe results of this study show the feasibility of this robot-assisted therapy with patients presenting a large range of impairment levels. A significant homogeneous improvement in both hand and arm function was observed, which was maintained 6 weeks after end of the therapy.


title: Reconfigurable quarter-mode SIW antenna employing a fluidically switchable via

abstract: A new method to fluidically switch through via posts is proposed for the first time. The reconfigurable via post is used toward designing a switchable QMSIW antenna. The switching method is based on filling/emptying a non-plated via hole with/from a liquid metal alloy. A QMSIW antenna is designed to initially work at ∼3.2 GHz. Connecting the fluidically switchable via post at the corner of the QMSIW antenna shifts up the operating frequency. This translates into a switching range of 3.2–4.7 GHz. The Polydimethylsiloxane (PDMS) structures including the micro-channels are bonded to the QMSIW circuit board using a unique fabrication technique.


title: Modeling Deep Temporal Dependencies with Recurrent Grammar Cells

abstract: We propose modeling time series by representing the transformations that take a frame at time t to a frame at time t+1. To this end we show how a bi-linear model of transformations, such as a gated autoencoder, can be turned into a recurrent network, by training it to predict future frames from the current one and the inferred transformation using backprop-through-time. We also show how stacking multiple layers of gating units in a recurrent pyramid makes it possible to represent the "syntax" of complicated time series, and that it can outperform standard recurrent neural networks in terms of prediction accuracy on a variety of tasks.


title: Customer Brand Engagement on Online Social Media Platforms: A Conceptual Model and Empirical Analysis

abstract: The aim of this paper is to contribute to the knowledge of a newly emerged concept of customer engagement with brands in the context of online social media platforms. Drawing on the overview of currently available academic literature and the results of a quantitative consumer study, the paper delivers a thorough investigation of the concept and proposes a conceptual model of customer brand engagement on online social media platforms. The study suggests that customer brand relationship related factors will influence the level of customer engagement, which in turn will affect the levels of behavioral loyalty and intention to recommend the brand. Hence, this paper is an important contribution to the academic marketing literature in the field of customer engagement, which also provides useful managerial insights for marketing practitioners.


Emerging applications of exosomes in cancer therapeutics and diagnostics


title: Notification Volume Control and Optimization System at Pinterest

abstract: Notifications (including emails, mobile / desktop push notifications, SMS, etc.) are very effective channels for online services to engage with users and drive user engagement metrics and other business metrics. One of the most important and challenging problems in a production notification system is to decide the right frequency for each user. In this paper, we propose a novel machine learning approach to decide notification volume for each user such that long term user engagement is optimized. We will also discuss a few practical issues and design choices we have made. The new system has been deployed to production at Pinterest in mid 2017 and significantly reduced notification volume and improved CTR of notifications and site engagement metrics compared with the previous machine learning approach.


title: Deep Recursive Network Embedding with Regular Equivalence

abstract: Network embedding aims to preserve vertex similarity in an embedding space. Existing approaches usually define the similarity by direct links or common neighborhoods between nodes, i.e. structural equivalence. However, vertexes which reside in different parts of the network may have similar roles or positions, i.e. regular equivalence, which is largely ignored by the literature of network embedding. Regular equivalence is defined in a recursive way that two regularly equivalent vertexes have network neighbors which are also regularly equivalent. Accordingly, we propose a new approach named Deep Recursive Network Embedding (DRNE) to learn network embeddings with regular equivalence. More specifically, we propose a layer normalized LSTM to represent each node by aggregating the representations of their neighborhoods in a recursive way. We theoretically prove that some popular and typical centrality measures which are consistent with regular equivalence are optimal solutions of our model. This is also demonstrated by empirical results that the learned node representations can well predict the indexes of regular equivalence and related centrality scores. Furthermore, the learned node representations can be directly used for end applications like structural role classification in networks, and the experimental results show that our method can consistently outperform centrality-based methods and other state-of-the-art network embedding methods.


title: Customized Regression Model for Airbnb Dynamic Pricing

abstract: This paper describes the pricing strategy model deployed at Airbnb, an online marketplace for sharing home and experience. The goal of price optimization is to help hosts who share their homes on Airbnb set the optimal price for their listings. In contrast to conventional pricing problems, where pricing strategies are applied to a large quantity of identical products, there are no "identical" products on Airbnb, because each listing on our platform offers unique values and experiences to our guests. The unique nature of Airbnb listings makes it very difficult to estimate an accurate demand curve that's required to apply conventional revenue maximization pricing strategies. Our pricing system consists of three components. First, a binary classification model predicts the booking probability of each listing-night. Second, a regression model predicts the optimal price for each listing-night, in which a customized loss function is used to guide the learning. Finally, we apply additional personalization logic on top of the output from the second model to generate the final price suggestions. In this paper, we focus on describing the regression model in the second stage of our pricing system. We also describe a novel set of metrics for offline evaluation. The proposed pricing strategy has been deployed in production to power the Price Tips and Smart Pricing tool on Airbnb. Online A/B testing results demonstrate the effectiveness of the proposed strategy model.


title: Natural Language Guided Visual Relationship Detection

abstract: Reasoning about the relationships between object pairs in images is a crucial task for holistic scene understanding. Most of the existing works treat this task as a pure visual classification task: each type of relationship or phrase is classified as a relation category based on the extracted visual features. However, each kind of relationships has a wide variety of object combination and each pair of objects has diverse interactions. Obtaining sufficient training samples for all possible relationship categories is difficult and expensive. In this work, we propose a natural language guided framework to tackle this problem. We propose to use a generic bi-directional recurrent neural network to predict the semantic connection between the participating objects in the relationship from the aspect of natural language. The proposed simple method achieves the state-of-the-art on the Visual Relationship Detection (VRD) and Visual Genome datasets, especially when predicting unseen relationships (e.g. recall improved from 76.42% to 89.79% on VRD zero-shot testing set).


title: Deep Machine Learning - A New Frontier in Artificial Intelligence Research [Research Frontier]

abstract: This article provides an overview of the mainstream deep learning approaches and research directions proposed over the past decade. It is important to emphasize that each approach has strengths and "weaknesses, depending on the application and context in "which it is being used. Thus, this article presents a summary on the current state of the deep machine learning field and some perspective into how it may evolve. Convolutional Neural Networks (CNNs) and Deep Belief Networks (DBNs) (and their respective variations) are focused on primarily because they are well established in the deep learning field and show great promise for future work.


title: A low-power wide dynamic range envelope detector

abstract: We report a 75-dB 2.8-/spl mu/W 100-Hz-10-kHz envelope detector in a 1.5-/spl mu/m 2.8-V CMOS technology. The envelope detector performs input dc insensitive voltage-to-current converting rectification followed by novel nanopower current-mode peak detection. The use of a subthreshold wide linear range transconductor allows greater than 1.7-V/sub pp/ input voltage swings. We show theoretically that the optimal performance of this circuit is technology independent for the given topology and may be improved only by spending more power due to thermal noise rectification limits. A novel circuit topology is used to perform 140-nW peak detection with controllable attack and release time constants. We demonstrate good agreement of experimentally measured results with theory. The envelope detector is useful in low-power bionic implants for the deaf, hearing aids, and speech-recognition front-ends.


title: A Two-Layered Permission-Based Android Malware Detection Scheme

abstract: Android platform has become the main target of the malware developers in the past few years. One of Android's main defense mechanisms against malicious apps is a permission-based access control mechanism. It is a feasible approach to detect a potential malicious application based on the permissions it requested. In this paper, we proposed a two-layered permission based detection scheme for detecting malicious Android applications. Comparing with previous researches, we consider the apps requested permission pairs as the additional condition, and we also consider used permissions to improve detection accuracy. The result of an evaluation, performed using 28548 benign apps and 1536 malicious apps, indicates that a two-layered permission-based detector has high detection rate of malware.


Password Creation in the Presence of Blacklists


title: Top-down Tree Long Short-Term Memory Networks

abstract: Long Short-Term Memory (LSTM) networks, a type of recurrent neural network with a more complex computational unit, have been successfully applied to a variety of sequence modeling tasks. In this paper we develop Tree Long Short-Term Memory (TreeLSTM), a neural network model based on LSTM, which is designed to predict a tree rather than a linear sequence. TreeLSTM defines the probability of a sentence by estimating the generation probability of its dependency tree. At each time step, a node is generated based on the representation of the generated sub-tree. We further enhance the modeling power of TreeLSTM by explicitly representing the correlations between left and right dependents. Application of our model to the MSR sentence completion challenge achieves results beyond the current state of the art. We also report results on dependency parsing reranking achieving competitive performance.


title: Water-Assisted Organocatalysis: An Enantioselective Green Protocol for the Henry Reaction

abstract: We report an enantioselective Henry (nitroaldol) reaction catalysed by an organocatalyst using water as solvent. The enantioselective synthesis of β-nitroalcohols was achieved by using a neutral chiral organocatalyst, through strong hydrogen bonding, which results in the formation of corresponding products in excellent yield and enantioselectivity at room temperature. Other attractive features of the method are the eco-friendly, non-hazardous, and mild reaction conditions, inexpensive catalyst, and simple work up conditions.


title: Using the Fluhrer, Mantin, and Shamir Attack to Break WEP

abstract: We implemented an attack against WEP, the link-layer security protocol for 802.11 networks. The attack was described in a recent paper by Fluhrer, Mantin, and Shamir. With our implementation, and permission of the network administrator, we were able to recover the 128 bit secret key used in a production network, with a passive attack. The WEP standard uses RC4 IVs improperly, and the attack exploits this design failure. This paper describes the attack, how we implemented it, and some optimizations to make the attack more efficient. We conclude that 802.11 WEP is totally insecure, and we provide some recommendations.


Focused Depth-first Proof Number Search using Convolutional Neural Networks for the Game of Hex


title: System software for persistent memory

abstract: Emerging byte-addressable, non-volatile memory technologies offer performance within an order of magnitude of DRAM, prompting their inclusion in the processor memory subsystem. However, such load/store accessible Persistent Memory (PM) has implications on system design, both hardware and software. In this paper, we explore system software support to enable low-overhead PM access by new and legacy applications. To this end, we implement PMFS, a light-weight POSIX file system that exploits PM's byte-addressability to avoid overheads of block-oriented storage and enable direct PM access by applications (with memory-mapped I/O). PMFS exploits the processor's paging and memory ordering features for optimizations such as fine-grained logging (for consistency) and transparent large page support (for faster memory-mapped I/O). To provide strong consistency guarantees, PMFS requires only a simple hardware primitive that provides software enforceable guarantees of durability and ordering of stores to PM. Finally, PMFS uses the processor's existing features to protect PM from stray writes, thereby improving reliability. Using a hardware emulator, we evaluate PMFS's performance with several workloads over a range of PM performance characteristics. PMFS shows significant (up to an order of magnitude) gains over traditional file systems (such as ext4) on a RAMDISK-like PM block device, demonstrating the benefits of optimizing system software for PM.


title: Making Arabic PDF books accessible using gamification

abstract: Most of online Arabic books are not accessible to Arab people with visual impairments. They cannot read online books because they are usually scanned images of the original ones. There is also a problem in PDF encoding of some of the textual books. One of the solutions is to use an Arabic OCR to convert scanned books into text; however Arabic OCR is still in its early stages and suffers from many limitations. In this paper we propose the use of human recognition skills to replace OCR limitations by incorporating the concepts of crowdsourcing and gamification. Our proposed system is in the form of a mobile recall game that presents players with word images segmented from the books to be converted into text. The players' answers are checked using techniques similar to what is used in word spotting. We initially implemented two components of the system; the segmentation, and the feature extraction and matching components. For the feature extraction and matching component, which is used to verify the player's answers, we performed four tests to choose a similarity measure threshold for accepting an entered word as a correct answer. Future work will consider other means of input correctness assurance.


title: Ratings meet reviews, a combined approach to recommend

abstract: Most existing recommender systems focus on modeling the ratings while ignoring the abundant information embedded in the review text. In this paper, we propose a unified model that combines content-based filtering with collaborative filtering, harnessing the information of both ratings and reviews. We apply topic modeling techniques on the review text and align the topics with rating dimensions to improve prediction accuracy. With the information embedded in the review text, we can alleviate the cold-start problem. Furthermore, our model is able to learn latent topics that are interpretable. With these interpretable topics, we can explore the prior knowledge on items or users and recommend completely "cold"' items. Empirical study on 27 classes of real-life datasets show that our proposed model lead to significant improvement compared with strong baseline methods, especially for datasets which are extremely sparse where rating-only methods cannot make accurate predictions.


title: Security in Software-Defined Networking: Threats and Countermeasures

abstract: In recent years, Software-Defined Networking (SDN) has been a focus of research. As a promising network architecture, SDN will possibly replace traditional networking, as it brings promising opportunities for network management in terms of simplicity, programmability, and elasticity. While many efforts are currently being made to standardize this emerging paradigm, careful attention needs to be also paid to security at this early design stage. This paper focuses on the security aspects of SDN. We begin by discussing characteristics and standards of SDN. On the basis of these, we discuss the security features as a whole and then analyze the security threats and countermeasures in detail from three aspects, based on which part of the SDN paradigm they target, i.e., the data forwarding layer, the control layer and the application layer. Countermeasure techniques that could be used to prevent, mitigate, or recover from some of such attacks are also described, while the threats encountered when developing these defensive mechanisms are highlighted.


title: Security and privacy for multimedia database management systems

abstract: This paper describes security and privacy issues for multimedia database management systems. Multimedia data includes text, images, audio and video. It describes access control for multimedia database management systems and describes security policies and security architectures for such systems. Privacy problems that result from multimedia data mining are also discussed.


title: Implementation of MPPT for PMSG-based small-scale wind turbine

abstract: Using a Boost chopper to implement the Maximum Power Point Tracking (MPPT) for a PMSG-based small-scale wind turbine is described in this paper. By analyzing the characteristics of the Boost chopper, the optimal duty ratio was theoretically determined by the equivalent resistance of the load. Then the duty ratio was used as a reference instruction to control the output power of the Permanent Magnet Synchronous Generator(PMSG). As a result, when the reference wind speed increases linearly from 80 to 120 r/min or other given speed is selected, the generator rotational speed could follow the reference speed accurately and rapidly, also when the duty ratio was changed from 0 to about 0.9, the maximum power point could be always tracked.


title: Developmental Test of Visual-Motor Integration (VMI): An Effective Outcome Measure for Handwriting Interventions for Kindergarten, First-Grade, and Second-Grade Students?

abstract: We determined whether a widely used assessment of visual-motor skills, the Beery-Buktenica Developmental Test of Visual-Motor Integration (VMI), is appropriate for use as an outcome measure for handwriting interventions. A two-group pretest-posttest design was used with 207 kindergarten, first-grade, and second-grade students. Two well-established handwriting measures and the VMI were administered pre- and postintervention. The intervention group participated in the Size Matters Handwriting Program for 40 sessions, and the control group received standard instruction. Paired and independent-samples t tests were used to analyze group differences. The intervention group demonstrated significant improvements on the handwriting measures, with change scores having mostly large effect sizes. We found no significant difference in change scores on the VMI, t(202)=1.19, p=.23. Results of this study suggest that the VMI may not detect changes in handwriting related to occupational therapy intervention.


title: Identification of CDKN2A variants in breast cancer patients in Pakistan

abstract: The role of cyclin-dependent kinase inhibitor 2A gene (CDKN2A) variants in breast cancer is not well understood, here we investigated their possible effects on breast cancer in Pakistani women attending the NORI Hospital, Islamabad. Direct DNA sequencing of CDKN2A identified an already known polymorphism in the 3 UTR, c.*29G>C (rs11515), in 5.88% patients and two novel variants. One, a deep intronic substitution (c.458-554T>G) in 1.96% patients, is also detected as a compound heterozygous form along with c.*29G>C in 1.96% patients (c.[458-554T>G; *29G>C]). The other is a novel deletion (c.458-82delG) occurring as a compound variant with two other identified variants c.[458-554T>G; 458-82delG; *29G>C] in 1.96% patients. In silico pathogenicity prediction analyses did not predict pathogenic effects on breast cancer for these individual variants. We conclude that variations in CDKN2A are not the major genetic cause of breast cancer in the enrolled Pakistani patients.


title: Crunch Time: The Reasons and Effects of Unpaid Overtime in the Games Industry

abstract: The games industry is notorious for its intense work ethics with uncompensated overtime and weekends at the office, also known as crunch or crunch time. Since crunch time is so common within the industry, is it possible that the benefits of crunch time outweigh the disadvantages? By studying postmortems and conducting interviews with employees in the industry, we aim to characterise crunch time and discover its effects on the industry. We provide a classification of crunch, i.e., four types of crunch which all have distinct characteristics and affect the product, employees and schedule in various ways. One of the crunch types stands out from the others by only having positive effects on product and schedule. A characteristic that all of the types have in common is an increase in stress levels amongst the employees. We identify a set of reasons for crunch and show that crunch is less pronounced in game studios where prioritisation of features is a regular practice.


title: Experience Replay Using Transition Sequences

abstract: Experience replay is one of the most commonly used approaches to improve the sample efficiency of reinforcement learning algorithms. In this work, we propose an approach to select and replay sequences of transitions in order to accelerate the learning of a reinforcement learning agent in an off-policy setting. In addition to selecting appropriate sequences, we also artificially construct transition sequences using information gathered from previous agent-environment interactions. These sequences, when replayed, allow value function information to trickle down to larger sections of the state/state-action space, thereby making the most of the agent's experience. We demonstrate our approach on modified versions of standard reinforcement learning tasks such as the mountain car and puddle world problems and empirically show that it enables faster, and more accurate learning of value functions as compared to other forms of experience replay. Further, we briefly discuss some of the possible extensions to this work, as well as applications and situations where this approach could be particularly useful.


title: Learning Deep CNN Denoiser Prior for Image Restoration

abstract: Model-based optimization methods and discriminative learning methods have been the two dominant strategies for solving various inverse problems in low-level vision. Typically, those two kinds of methods have their respective merits and drawbacks, e.g., model-based optimization methods are flexible for handling different inverse problems but are usually time-consuming with sophisticated priors for the purpose of good performance, in the meanwhile, discriminative learning methods have fast testing speed but their application range is greatly restricted by the specialized task. Recent works have revealed that, with the aid of variable splitting techniques, denoiser prior can be plugged in as a modular part of model-based optimization methods to solve other inverse problems (e.g., deblurring). Such an integration induces considerable advantage when the denoiser is obtained via discriminative learning. However, the study of integration with fast discriminative denoiser prior is still lacking. To this end, this paper aims to train a set of fast and effective CNN (convolutional neural network) denoisers and integrate them into model-based optimization method to solve other inverse problems. Experimental results demonstrate that the learned set of denoisers can not only achieve promising Gaussian denoising results but also can be used as prior to deliver good performance for various low-level vision applications.


title: Lazy snapping

abstract: In this paper, we present Lazy Snapping, an interactive image cutout tool. Lazy Snapping separates coarse and fine scale processing, making object specification and detailed adjustment easy. Moreover, Lazy Snapping provides instant visual feedback, snapping the cutout contour to the true object boundary efficiently despite the presence of ambiguous or low contrast edges. Instant feedback is made possible by a novel image segmentation algorithm which combines graph cut with pre-computed over-segmentation. A set of intuitive user interface (UI) tools is designed and implemented to provide flexible control and editing for the users. Usability studies indicate that Lazy Snapping provides a better user experience and produces better segmentation results than the state-of-the-art interactive image cutout tool, Magnetic Lasso in Adobe Photoshop.


title: The different effects of online consumer reviews on consumers' purchase intentions depending on trust in online shopping malls

abstract: Purpose – With the increasing influence of online consumer reviews (OCRs) on a consumer's decision making, online sellers have begun to embed the OCRs in their advertisements (OEAs). This study has the following two research objectives: first, to investigate the effects of two types of (OCRs vs OEAs) on consumers' purchase intention from an informational influence perspective; second, to investigate the effects of OCRs from a credibility perspective.Design/methodology/approach – The data for this study are obtained from a two‐way factorial experimental research design. The factors included are the type of OCRs and the trust level of online shopping malls. In addition, PLS test is used to understand the underlying effects of trust in online shopping malls, credibility of OCRs/OEAs, and consumers' purchase intentions.Findings – The results show that OCRs are more influenced by trust in online shopping malls than OEAs. The greater the perceived credibility of OCRs among potential consumers, the higher is the...


title: A Review of the State of the Art of Power Electronics for Wind Turbines

abstract: This paper reviews the power electronic applications for wind energy systems. Various wind turbine systems with different generators and power electronic converters are described, and different technical features are compared. The electrical topologies of wind farms with different wind turbines are summarized and the possible uses of power electronic converters with wind farms are shown. Finally, the possible methods of using the power electronic technology for improving wind turbine performance in power systems to meet the main grid connection requirements are discussed.


title: A Formal Security Analysis of ERTMS Train to Trackside Protocols

abstract: This paper presents a formal analysis of the train to trackside communication protocols used in the European Railway Traffic Management System (ERTMS) standard, and in particular the EuroRadio protocol. This protocol is used to secure important commands sent between train and trackside, such as movement authority and emergency stop messages. We perform our analysis using the applied pi-calculus and the ProVerif tool. This provides a powerful and expressive framework for protocol analysis and allows to check a wide range of security properties based on checking correspondence assertions. We show how it is possible to model the protocol’s counter-style timestamps in this framework. We define ProVerif assertions that allow us to check for secrecy of long and short term keys, authenticity of entities, message insertion, deletion, replay and reordering. We find that the protocol provides most of these security features, however it allows undetectable message deletion and the forging of emergency messages. We discuss the relevance of these results and make recommendations to further enhance the security of ERTMS.


title: CloudID: Trustworthy cloud-based and cross-enterprise biometric identification

abstract: In biometric identification systems, the biometric database is typically stored in a trusted server, which is also responsible for performing the identification process. However, a standalone server may not be able to provide enough storage and processing power for large databases. Nowadays, cloud computing and storage solutions have provided users and enterprises with various capabilities to store and process their data in third-party data centers. However, maintenance of the confidentiality and integrity of sensitive data requires trustworthy solutions for storage and processing of data with proven zero information leakage. In this paper, we present CloudID, a privacy-preserving cloud-based and cross-enterprise biometric identification solution. It links the confidential information of the users to their biometrics and stores it in an encrypted fashion. Making use of a searchable encryption technique, biometric identification is performed in encrypted domain to make sure that the cloud provider or potential attackers do not gain access to any sensitive data or even the contents of the individual queries. In order to create encrypted search queries, we propose a k-d tree structure in the core of the searchable encryption. This helps not only in handling the biometrics variations in encrypted domain, but also in improving the overall performance of the system. Our proposed approach is the first cloud-based biometric identification system with a proven zero data disclosure possibility. It allows different enterprises to perform biometric identification on a single database without revealing any sensitive information. Our experimental results show that CloudID performs the identification of clients with high accuracy and minimal overhead and proven zero data disclosure.