title: Learning to Predict the Wisdom of Crowds

abstract: The problem of \approximating the crowd" is that of estimating the crowd’s majority opinion by querying only a subset of it. Algorithms that approximate the crowd can intelligently stretch a limited budget for a crowdsourcing task. We present an algorithm, \CrowdSense," that works in an online fashion to dynamically sample subsets of labelers based on an exploration/exploitation criterion. The algorithm produces a weighted combination of a subset of the labelers’ votes that approximates the crowd’s opinion.


title: Real-Time Remote Health Monitoring Systems Using Body Sensor Information and Finger Vein Biometric Verification: A Multi-Layer Systematic Review

abstract: The development of wireless body area sensor networks is imperative for modern telemedicine. However, attackers and cybercriminals are gradually becoming aware in attacking telemedicine systems, and the black market value of protected health information has the highest price nowadays. Security remains a formidable challenge to be resolved. Intelligent home environments make up one of the major application areas of pervasive computing. Security and privacy are the two most important issues in the remote monitoring and control of intelligent home environments for clients and servers in telemedicine architecture. The personal authentication approach that uses the finger vein pattern is a newly investigated biometric technique. This type of biometric has many advantages over other types (explained in detail later on) and is suitable for different human categories and ages. This study aims to establish a secure verification method for real-time monitoring systems to be used for the authentication of patients and other members who are working in telemedicine systems. The process begins with the sensor based on Tiers 1 and 2 (client side) in the telemedicine architecture and ends with patient verification in Tier 3 (server side) via finger vein biometric technology to ensure patient security on both sides. Multilayer taxonomy is conducted in this research to attain the study’s goal. In the first layer, real-time remote monitoring studies based on the sensor technology used in telemedicine applications are reviewed and analysed to provide researchers a clear vision of security and privacy based on sensors in telemedicine. An extensive search is conducted to identify articles that deal with security and privacy issues, related applications are reviewed comprehensively and a coherent taxonomy of these articles is established. ScienceDirect, IEEE Xplore and Web of Science databases are checked for articles on mHealth in telemedicine based on sensors. A total of 3064 papers are collected from 2007 to 2017. The retrieved articles are filtered according to the security and privacy of telemedicine applications based on sensors. Nineteen articles are selected and classified into two categories. The first category, which accounts for 57.89% (n = 11/19), includes surveys on telemedicine articles and their applications. The second category, accounting for 42.1% (n = 8/19), includes articles on the three-tiered architecture of telemedicine. The collected studies reveal the essential need to construct another taxonomy layer and review studies on finger vein biometric verification systems. This map-matching for both taxonomies is developed for this study to go deeply into the sensor field and determine novel risks and benefits for patient security and privacy on client and server sides in telemedicine applications. In the second layer of our taxonomy, the literature on finger vein biometric verification systems is analysed and reviewed. In this layer, we obtain a final set of 65 articles classified into four categories. In the first category, 80% (n = 52/65) of the articles focus on development and design. In the second category, 12.30% (n = 8/65) includes evaluation and comparative articles. These articles are not intensively included in our literature analysis. In the third category, 4.61% (n = 3/65) includes articles about analytical studies. In the fourth category, 3.07% (n = 2/65) comprises reviews and surveys. This study aims to provide researchers with an up-to-date overview of studies that have been conducted on (user/patient) authentication to enhance the security level in telemedicine or any information system. In the current study, taxonomy is presented by explaining previous studies. Moreover, this review highlights the motivations, challenges and recommendations related to finger vein biometric verification systems and determines the gaps in this research direction (protection of finger vein templates in real time), which represent a new research direction in this area.


title: Towards Ubiquitous RE: A Perspective on Requirements Engineering in the Era of Digital Transformation

abstract: We are now living in the era of digital transformation: Innovative and digital business models are transforming the global business world and society. However, the authors of this paper have perceived barriers that prevent requirements engineers from contributing properly to the development of the software systems that underpin the digital transformation. We also realized that breaking down each of these barriers would contribute to requirements engineering (RE) becoming ubiquitous in certain dimensions: RE everywhere, with everyone, for everything, automated, accepting openness, and cross-domain. In this paper, we analyze each dimension of ubiquity in the scope of the interaction between requirements engineers and end users. In particular, we point out the transformation that is required to break down each barrier, present the perspective of the scientific community and our own practical perspective, and discuss our vision on how to achieve this dimension of ubiquity. Our goal is to raise the interest of the research community in providing approaches to address the barriers and move towards ubiquitous RE.


title: Fuzzy C-Means for Fraud Detection in Large Transaction Data Sets

abstract: Large, multinational corporations typically have to process hundreds and thousands of vendor invoices every day coming from hundreds or thousands of vendors in different currencies from many countries. This has forced the adoption of simple and fast rules of thumb to accept and pay invoices below a chosen value cutoff without further scrutiny. This has invited fraud, which is hard to detect and react to, as the transaction data sets are very large. Fuzzy C-means has turned out to be effective to identify and sort out transactions, which are similar to typical fraud cases.


title: Intelligent Performance Assessment System for Laparoscopic Surgical Box-Trainer

abstract: Laparoscopic surgeons should possess strong cognitive knowledge, case/problem management and manual dexterity skills. Such skills can be acquired through simulation using a basic, low-fidelity setup, such as the surgical box-trainer. The trainee’s performance is evaluated on the grounds of both precision and speed. However, no significant qualitative and quantitative information is measured and recorded electronically. The main objective of this research is to develop an intelligent box-trainer device which includes sensors and several high definition digital video cameras along with a two-layer fuzzy logic-based assessment system. The significance of the project is to create an intelligent performance assessment system that is much more objective than the ones which are currently being used. Using fuzzy logic to capture expert knowledge is a new approach in this area.


ONLY Three-dimensional modeling and fi nite element analysis in treatment planning for orthodontic tooth movement


title: Spectral performance and noise theory of nonlinear transmission line frequency multipliers

abstract: In this paper the theoretical and measured spectral performance of nonlinear transmission lines (NLTLs) and their applicability to frequency multiplication is presented. NLTLs, through nonlinear dispersion in large signal, create a sharp edge from a sinusoid that is rich in harmonics, making a very effective frequency multiplier. While the design of NLTLs has been widely published [1–2] for high harmonic content, prior to this work, the noise performance of NLTLs has only been published in [3]. The results presented in this paper demonstrate the near ideal 20·log 10 N phase noise multiplication as referenced to the additive phase noise of the fundamental with N being the multiplicative factor. Theoretical analysis of the noise properties of NLTLs show the effect of DC biasing and the overall change in phase shift of the NLTL in sum with AM source noise has the most dramatic influence in additive phase noise and is confirmed by measurement. An NLTL design with a −187dBc/Hz input referred phase noise is presented.


title: Design of Simple Multiband Patch Antenna for Mobile Communication Applications Using New E-Shape Fractal

abstract: In this study, a probe fed E-shape fractal patch antenna (EFPA) for heptads' band LTE/WWAN (GSM850/900/1800/1900/UMTS/LTE2300/2500) operation is proposed. Various iterations of this fractal antenna are compared, and an optimized design is presented. Exploitation of E-shape fractal notion in antenna design makes the patch antenna flexible in terms of generating resonances and bandwidth as an iteration order of the fractal is increased. Several properties of the antenna such as impedance bandwidth, radiation patterns, radiation efficiency, electric current distributions on the patch, and gain have been demonstrated numerically and empirically in detail. Experimental results have authorized the design procedure and confirm the gratification of the requirements for multistandard mobile terminal applications.


title: An ultra-wideband nested coaxial waveguide feed for reflector antenna applications

abstract: A 70:1 frequency bandwidth (0.6-42 GHz) four-cup ultra-wideband nested coaxial waveguide feed is designed and analyzed. The performance of the feed, when it illuminates an axially-symmetric paraboloidal reflector, is also presented and discussed. The numerical analysis is rigorously carried out using a body-of-revolution method of moments (MOM) computer code. A circularly polarized coaxial waveguide TE/sub 11/ mode is impressed at the shorted rear of each cup to provide excitation. In practice this circularly polarized TE(11), mode can be produced by four hybrid-excited probes in spatial quadrature and phase opposition.


title: Oversampling Method for Imbalanced Classification

abstract: Classification problem for imbalanced datasets is pervasive in a lot of data mining domains. Imbalanced classification has been a hot topic in the academic community. From data level to algorithm level, a lot of solutions have been proposed to tackle the problems resulted from imbalanced datasets. SMOTE is the most popular data-level method and a lot of derivations based on it are developed to alleviate the problem of class imbalance. Our investigation indicates that there are severe flaws in SMOTE. We propose a new oversampling method SNOCC that can compensate the defects of SMOTE. In SNOCC, we increase the number of seed samples and that renders the new samples not confine in the line segment between two seed samples in SMOTE. We employ a novel algorithm to find the nearest neighbors of samples, which is different to the previous ones. These two improvements make the new samples created by SNOCC naturally reproduce the distribution of original seed samples. Our experiment results show that SNOCC outperform SMOTE and CBSO (a SMOTE-based method).


title: Multiview Consensus Graph Clustering

abstract: A graph is usually formed to reveal the relationship between data points and graph structure is encoded by the affinity matrix. Most graph-based multiview clustering methods use predefined affinity matrices and the clustering performance highly depends on the quality of graph. We learn a consensus graph with minimizing disagreement between different views and constraining the rank of the Laplacian matrix. Since diverse views admit the same underlying cluster structure across multiple views, we use a new disagreement cost function for regularizing graphs from different views toward a common consensus. Simultaneously, we impose a rank constraint on the Laplacian matrix to learn the consensus graph with exactly $k$ connected components where $k$ is the number of clusters, which is different from using fixed affinity matrices in most existing graph-based methods. With the learned consensus graph, we can directly obtain the cluster labels without performing any post-processing, such as $k$ -means clustering algorithm in spectral clustering-based methods. A multiview consensus clustering method is proposed to learn such a graph. An efficient iterative updating algorithm is derived to optimize the proposed challenging optimization problem. Experiments on several benchmark datasets have demonstrated the effectiveness of the proposed method in terms of seven metrics.


title: Modelling empathy in social robotic companions

abstract: Empathy can be broadly defined as the ability to understand and respond appropriately to the affective states of others. In this paper, we present a scenario where a social robot acts as a chess companion for children, and describe our current efforts towards endowing such robot with empathic capabilities. A multimodal framework for modeling some of the user's affective states that combines visual and task-related features is presented. Using this model of the user, we personalise the learning environment by adapting the robot's empathic responses to the particular preferences of the child who is interacting with the robot. We also describe a preliminary study conducted in this scenario.


title: A performance prediction model for the CUDA GPGPU platform

abstract: The significant growth in computational power of modern Graphics Processing Units (GPUs) coupled with the advent of general purpose programming environments like NVIDIA's CUDA, has seen GPUs emerging as a very popular parallel computing platform. Till recently, there has not been a performance model for GPGPUs. The absence of such a model makes it difficult to definitively assess the suitability of the GPU for solving a particular problem and is a significant impediment to the mainstream adoption of GPUs as a massively parallel (super)computing platform. In this paper we present a performance prediction model for the CUDA GPGPU platform. This model encompasses the various facets of the GPU architecture like scheduling, memory hierarchy, and pipelining among others. We also perform experiments that demonstrate the effects of various memory access strategies. The proposed model can be used to analyze pseudo code for a CUDA kernel to obtain a performance estimate, in a way that is similar to performing asymptotic analysis. We illustrate the usage of our model and its accuracy with three case studies: matrix multiplication, list ranking, and histogram generation.


title: Three-Level ZVS Active Clamping PWM for the DC–DC Buck Converter

abstract: This paper presents the study of a dc-dc buck converter with three-level buck clamping (buck-buck), zero-voltage switching (ZVS), active clamping, and constant-frequency pulsewidth modulation (PWM). Other ZVS dc-dc converter topologies that employ three-level switching cells are introduced, and their steady-state dc gain is analyzed. This analysis shows that the buck-buck converter has characteristics that warrant a more detailed study. A feature that is common to all the introduced topologies is the theoretical reduction of the voltage stresses across the active semiconductors to 50% of the corresponding two-level converters. Accordingly, the switches of the buck-buck converter provide 50% of the blocking voltage of a ZVS two-level buck converter. The steady-state analysis of the converter is performed according to the description of the operation stages of the converter. Based on the performed analyses, a comparative discussion to other topologies is given. Furthermore, a topologic derivation of the circuit is presented, which provides ZVS operation to all semiconductors. Finally, a simplified design procedure is proposed, and used to design and build a prototype. Experimental results from a laboratory prototype are presented.


Prosthetic Control by an EEG-based Brain-Computer Interface ( BCI )


title: Frequency reconfigurable bandpass filter for IMT-A applications

abstract: A spiral resonator reconfigurable microstrip band pass filter is presented in this manuscript. The filter provides control variation in resonance frequency (ƒ c ). The variation in center frequency of the proposed design is noted from 3.40 – 3.77 GHz. The filter is designed to work at LTE-42 band to support IMT-A applications. The size of the proposed spiral structure is 14.6 × 5.5 mm2.


title: Blind deconvolution using alternating maximum a posteriori estimation with heavy-tailed priors

abstract: Single image blind deconvolution aims to estimate the unknown blur from a single observed blurred image and recover the original sharp image. Such task is severely ill-posed and typical approaches involve some heuristic or other steps without clear mathematical explanation to arrive at an acceptable solution. We show that a straightforward maximum a posteriory estimation combined with very sparse priors and an efficient numerical method can produce results, which compete with much more complicated state-of-the-art methods.


title: Deep kernel-SVM network

abstract: Deep learning techniques have claimed state-of-the-art results in a wide range of tasks, including classification. Despite the promising results, there are limitations for these large networks. In fact, deep neural networks have a poor generalisation performance on small data sets, such as biologic data. This paper describes a new machine learning algorithm for classification tasks. We introduce a Multi-Layer Multiple Kernel Learning (ML-MKL) framework. The input data are first transformed through a set of weighted non-linear kernel functions in a multilayer structure. Then, an SVM classifier is used to make the final decision. The proposed network is trained to minimize the error function. Indeed, we propose to optimize the network over an adaptive backpropagation algorithm. The generalization performance of the proposed method is compared over various state-of-the-art multiple kernel algorithms on several benchmark and two real world applications, including object recognition and spoken language recognition. Experimental results show that the ML-MKL generally outperforms existing kernel methods.


title: BitTorrent Locality and Transit TrafficReduction: When, Why, and at What Cost?

abstract: A substantial amount of work has recently gone into localizing BitTorrent traffic within an ISP in order to avoid excessive and often times unnecessary transit costs. Several architectures and systems have been proposed and the initial results from specific ISPs and a few torrents have been encouraging. In this work we attempt to deepen and scale our understanding of locality and its potential. Looking at specific ISPs, we consider tens of thousands of concurrent torrents, and thus capture ISP-wide implications that cannot be appreciated by looking at only a handful of torrents. Second, we go beyond individual case studies and present results for few thousands ISPs represented in our data set of up to 40K torrents involving more than 3.9M concurrent peers and more than 20M in the course of a day spread in 11K ASes. Finally, we develop scalable methodologies that allow us to process this huge data set and derive accurate traffic matrices of torrents. Using the previous methods we obtain the following main findings: i) Although there are a large number of very small ISPs without enough resources for localizing traffic, by analyzing the 100 largest ISPs we show that Locality policies are expected to significantly reduce the transit traffic with respect to the default random overlay construction method in these ISPs; ii) contrary to the popular belief, increasing the access speed of the clients of an ISP does not necessarily help to localize more traffic; iii) by studying several real ISPs, we have shown that soft speed-aware locality policies guarantee win-win situations for ISPs and end users. Furthermore, the maximum transit traffic savings that an ISP can achieve without limiting the number of inter-ISP overlay links is bounded by “unlocalizable” torrents with few local clients. The application of restrictions in the number of inter-ISP links leads to a higher transit traffic reduction but the QoS of clients downloading “unlocalizable” torrents would be severely harmed.


title: Frontex Perspectives on Biometrics for Border Checks.

abstract: A console having annunciator lights and a cigar lighter receptacle compatible telescoping plug is mounted in the cigar lighter receptacle of an automobile. The plug is inserted in the receptacle using the console as a handle and telescopes within the console until the console abuts the dash board. Temperature sensing switch elements contained in an encapsulating capsule are connected by conductors to the lights and the receptacle power connections. The capsule is inserted in the transmission dip stick tube and the conductors pass through an existing opening in the fire wall.


title: PhyloFinder: An intelligent search engine for phylogenetic tree databases

abstract: BackgroundBioinformatic tools are needed to store and access the rapidly growing phylogenetic data. These tools should enable users to identify existing phylogenetic trees containing a specified taxon or set of taxa and to compare a specified phylogenetic hypothesis to existing phylogenetic trees.ResultsPhyloFinder is an intelligent search engine for phylogenetic databases that we have implemented using trees from TreeBASE. It enables taxonomic queries, in which it identifies trees in the database containing the exact name of the query taxon and/or any synonymous taxon names, and it provides spelling suggestions for the query when there is no match. Additionally, PhyloFinder can identify trees containing descendants or direct ancestors of the query taxon. PhyloFinder also performs phylogenetic queries, in which it identifies trees that contain the query tree or topologies that are similar to the query tree.ConclusionPhyloFinder can enhance the utility of any tree database by providing tools for both taxonomic and phylogenetic queries as well as visualization tools that highlight the query results and provide links to NCBI and TBMap. An implementation of PhyloFinder using trees from TreeBASE is available from the web client application found in the availability and requirements section.


title: Software Engineering for Self-Adaptive Systems: A Second Research Roadmap

abstract: The goal of this roadmap paper is to summarize the state-of-the-art and identify research challenges when developing, deploying and managing self-adaptive software systems. Instead of dealing with a wide range of topics associated with the field, we focus on four essential topics of self-adaptation: design space for self-adaptive solutions, software engineering processes for self-adaptive systems, from centralized to decentralized control, and practical run-time verification & validation for self-adaptive systems. For each topic, we present an overview, suggest future directions, and focus on selected challenges. This paper complements and extends a previous roadmap on software engineering for self-adaptive systems published in 2009 covering a different set of topics, and reflecting in part on the previous paper. This roadmap is one of the many results of the Dagstuhl Seminar 10431 on Software Engineering for Self-Adaptive Systems, which took place in October 2010.


title: Sparse Models of Natural Language Text

abstract: In statistical text analysis, many learning problems can be formulated as a minimization of a sum of a loss function and a regularization function for a vector of parameters (feature coefficients). The loss function drives the model to learn generalizable patterns from the training data, whereas the regularizer plays two important roles: to prevent the models from capturing idiosyncrasies of the training data (overfitting) and to encode prior knowledge about the model parameters. When learning from high-dimensional data such as text, it has been empirically observed that relatively few dimensions are relevant to the predictive task (Forman, 2003). How can we capitalize on this insight and choose which dimensions are relevant in an informed and principled manner? Sparse regularizers provide a way to select relevant dimensions by means of regularization. However, past work rarely encodes non-trivial prior knowledge that yields sparse solutions through a regularization function. This thesis investigates the applications of sparse models—especially structured sparse models—as a medium to encode linguistically-motivated prior knowledge in textual models to advance NLP systems. We explore applications of sparse NLP models in temporal models of text, word embeddings, and text categorization. Sparse models come with their own challenges, since new instantiations of sparse models often require a specialized optimization method. This thesis also presents optimization methods for the proposed instantiations of sparse models. Therefore, the goals of this thesis are twofold: (i) to show how sparsity can be used to encode linguistic information in statistical text models, and (ii) to develop efficient learning algorithms to solve the resulting optimization problems.


title: Using uml to visualize role-based access control constraints

abstract: Organizations use Role-Based Access Control (RBAC) to protect information resources from unauthorized access. We propose an approach, based on the Unified Modeling Language (UML), that shows how RBAC policies can be systematically incorporated into an application design. We consider an RBAC model to be a pattern which we express using UML diagram templates; RBAC policies for an application conforming to this model can be generated by instantiating these templates with values obtained from the application. The constraints of the RBAC model are expressed using the Object Constraint Language (OCL). OCL constraints, based on first-order logic, are difficult to understand. To alleviate this problem, we show how violation of such constraints can be visually represented using object diagram templates. With adequate tool support, developers can use these to demonstrate constraint violations in their applications. Our approach is illustrated using a small banking application.


title: Beta Process Joint Dictionary Learning for Coupled Feature Spaces with Application to Single Image Super-Resolution

abstract: This paper addresses the problem of learning over-complete dictionaries for the coupled feature spaces, where the learned dictionaries also reflect the relationship between the two spaces. A Bayesian method using a beta process prior is applied to learn the over-complete dictionaries. Compared to previous couple feature spaces dictionary learning algorithms, our algorithm not only provides dictionaries that customized to each feature space, but also adds more consistent and accurate mapping between the two feature spaces. This is due to the unique property of the beta process model that the sparse representation can be decomposed to values and dictionary atom indicators. The proposed algorithm is able to learn sparse representations that correspond to the same dictionary atoms with the same sparsity but different values in coupled feature spaces, thus bringing consistent and accurate mapping between coupled feature spaces. Another advantage of the proposed method is that the number of dictionary atoms and their relative importance may be inferred non-parametrically. We compare the proposed approach to several state-of-the-art dictionary learning methods by applying this method to single image super-resolution. The experimental results show that dictionaries learned by our method produces the best super-resolution results compared to other state-of-the-art methods.


Financial accounting calculation in relation to nature


title: Detecting Spammers on Twitter Based on Content and Social Interaction

abstract: Twitter has become a target platform on which spammers spread large amounts of harmful information. These malicious spamming activities have seriously threatened normal users' personal privacy and information security. An effective method for detecting spammers is to learn a classifier based on user features and social network information. However, social spammers often change their spamming strategies for evading the detection system. To tackle this challenge, latent user features factorized by text matrix are adopted to capture the consistency of users' behavior. Also, a new social regularization based on users' interaction is introduced to distinguish different types of users. Finally, Supervised Spammer Detection method with Social Interaction is proposed, which jointly learn a classifier by using combine text content, social network information and labeled data. Experimental results on a real-world Twitter dataset confirm the effectiveness of the proposed method.


title: Indented Tree or Graph? A Usability Study of Ontology Visualization Techniques in the Context of Class Mapping Evaluation

abstract: Research effort in ontology visualization has largely focused on developing new visualization techniques. At the same time, researchers have paid less attention to investigating the usability of common visualization techniques that many practitioners regularly use to visualize ontological data. In this paper, we focus on two popular ontology visualization techniques: indented tree and graph. We conduct a controlled usability study with an emphasis on the effectiveness, efficiency, workload and satisfaction of these visualization techniques in the context of assisting users during evaluation of ontology mappings. Findings from this study have revealed both strengths and weaknesses of each visualization technique. In particular, while the indented tree visualization is more organized and familiar to novice users, subjects found the graph visualization to be more controllable and intuitive without visual redundancy, particularly for ontologies with multiple inheritance.


Fast and Efficient Browser Identification with JavaScript Engine Fingerprinting Technical Report TR-SBA-Research-0512-01


title: Chrome extensions: Threat analysis and countermeasures

abstract: The widely popular browser extensions now become one of the most commonly used malware attack vectors. The Google Chrome browser, which implements the principles of least privileges and privilege separation by design, offers a strong security mechanism to protect malicious websites from damaging the whole browser system via extensions. In this study, we however reveal that Chrome’s extension security model is not a panacea for all possible attacks with browser extensions. Through a series of practical bot-based attacks that can be performed even under typical settings, we demonstrate that malicious Chrome extensions pose serious threats, including both information dispersion and harvesting, to browsers. We further conduct an in-depth analysis of Chrome’s extension security model, and conclude that its vulnerabilities are rooted from the violation of the principles of least privileges and privilege separation. Following these principles, we propose a set of countermeasures that enforce the policies of microprivilege management and differentiating DOM elements. Using a prototype developed on the latest Chrome browser, we show that they can effectively mitigate the threats posed by malicious Chrome extensions with little effect on normal browsing experience.


title: A VHF class E DC-DC converter with self-oscillating gate driver

abstract: This paper describes the analysis and design of a DC-DC converter topology which is operational at frequencies in the Very High Frequency (VHF) band ranging from 30 MHz–300 MHz. The presented topology, which consists of a class E inverter, class E rectifier, and self-oscillating gate driver, is inherently resonant, and switching losses are greatly reduced by ensuring Zero Voltage Switching (ZVS) of the power semiconductor devices. A design method to ensure ZVS operation when combining the inverter, rectifier, and gate driver is provided. Several parasitic effects and their influence on converter operation are discussed, and measurement results of a 100 MHz prototype converter are presented and evaluated. The designed prototype converter verifies the described topology.


Data-Driven Morphological Analysis and Disambiguation for Morphologically Rich Languages and Universal Dependencies


title: Logical Structure Recovery in Scholarly Articles with Rich Document Features

abstract: Scholarly digital libraries increasingly provide analytics to information within documents themselves. This includes information about the logical document structure of use to downstream components, such as search, navigation, and summarization. In this paper, the authors describe SectLabel, a module that further develops existing software to detect the logical structure of a document from existing PDF files, using the formalism of conditional random fields. While previous work has assumed access only to the raw text representation of the document, a key aspect of this work is to integrate the use of a richer representation of the document that includes features from optical character recognition OCR, such as font size and text position. Experiments reveal that using such rich features improves logical structure detection by a significant 9 F1 points, over a suitable baseline, motivating the use of richer document representations in other digital library applications.


title: A Survey of Stealth Malware Attacks, Mitigation Measures, and Steps Toward Autonomous Open World Solutions

abstract: As our professional, social, and financial existences become increasingly digitized and as our government, healthcare, and military infrastructures rely more on computer technologies, they present larger and more lucrative targets for malware. Stealth malware in particular poses an increased threat because it is specifically designed to evade detection mechanisms, spreading dormant, in the wild for extended periods of time, gathering sensitive information or positioning itself for a high-impact zero-day attack. Policing the growing attack surface requires the development of efficient anti-malware solutions with improved generalization to detect novel types of malware and resolve these occurrences with as little burden on human experts as possible. In this paper, we survey malicious stealth technologies as well as existing solutions for detecting and categorizing these countermeasures autonomously. While machine learning offers promising potential for increasingly autonomous solutions with improved generalization to new malware types, both at the network level and at the host level, our findings suggest that several flawed assumptions inherent to most recognition algorithms prevent a direct mapping between the stealth malware recognition problem and a machine learning solution. The most notable of these flawed assumptions is the closed world assumption: that no sample belonging to a class outside of a static training set will appear at query time. We present a formalized adaptive open world framework for stealth malware recognition and relate it mathematically to research from other machine learning domains.


title: Hierarchical Representations for Efficient Architecture Search

abstract: We explore efficient neural architecture search methods and show that a simple yet powerful evolutionary algorithm can discover new architectures with excellent performance. Our approach combines a novel hierarchical genetic representation scheme that imitates the modularized design pattern commonly adopted by human experts, and an expressive search space that supports complex topologies. Our algorithm efficiently discovers architectures that outperform a large number of manually designed models for image classification, obtaining top-1 error of 3.6% on CIFAR-10 and 20.3% when transferred to ImageNet, which is competitive with the best existing neural architecture search approaches. We also present results using random search, achieving 0.3% less top-1 accuracy on CIFAR-10 and 0.1% less on ImageNet whilst reducing the search time from 36 hours down to 1 hour.


title: Physikit: Data Engagement Through Physical Ambient Visualizations in the Home

abstract: Internet of things (IoT) devices and sensor kits have the potential to democratize the access, use, and appropriation of data. Despite the increased availability of low cost sensors, most of the produced data is "black box" in nature: users often do not know how to access or interpret data. We propose a "human-data design" approach in which end-users are given tools to create, share, and use data through tangible and physical visualizations. This paper introduces Physikit, a system designed to allow users to explore and engage with environmental data through physical ambient visualizations. We report on the design and implementation of Physikit, and present a two-week field study which showed that participants got an increased sense of the meaning of data, embellished and appropriated the basic visualizations to make them blend into their homes, and used the visualizations as a probe for community engagement and social behavior.


title: IPSO Task Scheduling Algorithm for Large Scale Data in Cloud Computing Environment

abstract: Task scheduling is one of the essential techniques in the cloud computing environment. It is required for allocating tasks to the proper resources and optimizing the overall system performance. Particle swarm optimization (PSO) algorithm is one of the most popular scheduling algorithms, which is used to maximize resource utilization. However, the performance of the PSO scheduling algorithm decreases when the number of tasks is significant. In this paper, the improved PSO (IPSO) algorithm is proposed to provide the optimal allocation for a large number of tasks. This is achieved by splitting the submitted tasks into batches in a dynamic way. The resources utilization state is considered in each creation of batches. After getting a sub-optimal solution for each batch, the algorithm appends all the sub-optimal solutions for batches into a final allocation map. Finally, IPSO tries to balance the loads over the final allocation map. The proposed algorithm is compared with different scheduling algorithms, namely, honey bee, ant colony, and round-robin algorithms. The results of experiments show the efficiency of the proposed algorithm in terms of makespan, standard deviation of load, and degree of imbalance.


title: Detection of recurring software vulnerabilities

abstract: Software security vulnerabilities are discovered on an almost daily basis and have caused substantial damage. Aiming at supporting early detection and resolution for them, we have conducted an empirical study on thousands of vulnerabilities and found that many of them are recurring due to software reuse. Based on the knowledge gained from the study, we developed SecureSync, an automatic tool to detect recurring software vulnerabilities on the systems that reuse source code or libraries. The core of SecureSync includes two techniques to represent and compute the similarity of vulnerable code across different systems. The evaluation for 60 vulnerabilities on 176 releases of 119 open-source software systems shows that SecureSync is able to detect recurring vulnerabilities with high accuracy and to identify 90 releases having potentially vulnerable code that are not reported or fixed yet, even in mature systems. A couple of cases were actually confirmed by their developers.


title: A heuristic algorithm for nurse scheduling with balanced preference satisfaction

abstract: This paper tackles the nurse scheduling problem with balanced preference satisfaction which consists of generating an assignment of shifts to nurses over a given time horizon and ensuring that the satisfaction of nurses' personal preferences for shifts is as even as possible in order to ensure fairness. We propose a heuristic algorithm based on successive resolutions of the bottleneck assignment problem. The algorithm has two phases. In the first phase, the algorithm constructs an initial solution by solving successive bottleneck assignment problems. In the second phase, two improvement procedures based on reassignment steps are applied. Computational tests are carried out using instances from the standard benchmark dataset NSPLib. Our experiments indicate that the proposed method is effective and efficient, reducing discrepancies (hence improving fairness) between the individual rosters.


International Journal of Electrical and Computer Engineering 5:5 2010 A Comparative Study of P-I, I-P, Fuzzy and Neuro-Fuzzy Controllers for Speed Control of DC Motor Drive


Self-Improving Factory Simulation using Continuous-time Average-Reward Reinforcement Learning


title: Strain-induced perpendicular magnetic anisotropy and Gilbert damping of Tm3Fe5O12 thin films

abstract: In the attempt of implementing iron garnets with perpendicular magnetic anisotropy (PMA) in spintronics, the attention turned towards strain-grown iron garnets. One candidate is Tm3Fe5O12 (TmIG) which possesses an out-of-plane magnetic easy axis when grown under tensile strain. In this study, the effect of film thickness on the structural and magnetic properties of TmIG films including magnetic anisotropy, saturation magnetization, and Gilbert damping is investigated. TmIG films with thicknesses between 20 and 300 nm are epitaxially grown by pulsed laser deposition on substituted-Gd3Ga5O12(111) substrates. Structural characterization shows that films thinner than 200 nm show in-plane tensile strain, thus exhibiting PMA due to strain-induced magnetoelastic anisotropy. However, with increasing film thickness a relaxation of the unit cell is observed resulting in the rotation of the magnetic easy axis towards the sample plane due to the dominant shape anisotropy. Furthermore, the Gilbert damping parameter is found to be in the range of 0.02 ± 0.005.


title: The Be/X-ray transient 4U 0115+63/V635 Cassiopeiae - I. A consistent model

abstract: We present photometry and high SNR spectroscopy in the classification region of V635 Cas, the optical counterpart to the transient X-ray pulsar 4U 0115+63, taken at a time when the circumstellar envelope had disappeared. V635 Cas is classified as a B0.2Ve star at a distance of $7{-}8$ kpc. We use the physical parameters derived from these observations and the orbit derived from X-ray observations to elaborate a model of the system based on the theory of decretion discs around Be stars. We show that the disc surrounding the Be star is truncated by the tidal/resonant interaction with the neutron star and cannot be in a steady state. This explains many of the observed properties of 4U 0115+63. In particular, because of this effect, under normal circumstances the neutron star cannot accrete from the disc, which explains the lack of regular type I outbursts from the source.


title: Software-Defined Mobile Networks Security

abstract: The future 5G wireless is triggered by the higher demand on wireless capacity. With Software Defined Network (SDN), the data layer can be separated from the control layer. The development of relevant studies about Network Function Virtualization (NFV) and cloud computing has the potential of offering a quicker and more reliable network access for growing data traffic. Under such circumstances, Software Defined Mobile Network (SDMN) is presented as a promising solution for meeting the wireless data demands. This paper provides a survey of SDMN and its related security problems. As SDMN integrates cloud computing, SDN, and NFV, and works on improving network functions, performance, flexibility, energy efficiency, and scalability, it is an important component of the next generation telecommunication networks. However, the SDMN concept also raises new security concerns. We explore relevant security threats and their corresponding countermeasures with respect to the data layer, control layer, application layer, and communication protocols. We also adopt the STRIDE method to classify various security threats to better reveal them in the context of SDMN. This survey is concluded with a list of open security challenges in SDMN.


title: A randomised comparison of a four- and a five-point scale version of the Norwegian Function Assessment Scale

abstract: Background ::: There is variation in the number of response alternatives used within health-related questionnaires. This study compared a four-and a five-point scale version of the Norwegian Function Assessment Scale (NFAS) by evaluating data quality, internal consistency and validity.


title: Amygdala Response to Emotional Stimuli without Awareness: Facts and Interpretations

abstract: Over the past two decades, evidence has accumulated that the human amygdala exerts some of its functions also when the observer is not aware of the content, or even presence of the triggering emotional stimulus. Nevertheless, there is as of yet no consensus on the limits and conditions that affect the extent of amygdala’s response without focused attention or awareness. Here we review past and recent studies on this subject, examining neuroimaging literature on healthy participants as well as brain-damaged patients, and we comment on their strengths and limits. We propose a theoretical distinction between processes involved in attentional unawareness, wherein the stimulus is potentially accessible to enter visual awareness but fails to do so because attention is diverted, and in sensory unawareness, wherein the stimulus fails to enter awareness because its normal processing in the visual cortex is suppressed. We argue this distinction, along with data sampling amygdala responses with high temporal resolution, helps to appreciate the multiplicity of functional and anatomical mechanisms centred on the amygdala and supporting its role in non-conscious emotion processing. Separate, but interacting, networks relay visual information to the amygdala exploiting different computational properties of subcortical and cortical routes, thereby supporting amygdala functions at different stages of emotion processing. This view reconciles some apparent contradictions in the literature, as well as seemingly contrasting proposals, such as the dual stage and the dual route model. We conclude that evidence in favour of the amygdala response without awareness is solid, albeit this response originates from different functional mechanisms and is driven by more complex neural networks than commonly assumed. Acknowledging the complexity of such mechanisms can foster new insights on the varieties of amygdala functions without awareness and their impact on human behaviour.


Mathematical Expression for the Channel Capacity of a Power and Rate Simultaneous Adaptive Cellular DS / FFH-CDMA System in a Rayleigh Fading Channel


title: TextCatcher: a method to detect curved and challenging text in natural scenes

abstract: In this paper, we propose a text detection algorithm which is hybrid and multi-scale. First, it relies on a connected component-based approach: After the segmentation of the image, a classification step using a new wavelet descriptor spots the letters. A new graph modeling and its traversal procedure allow to form candidate text areas. Second, a texture-based approach discards the false positives. Finally, the detected text areas are precisely cut out and a new binarization step is introduced. The main advantage of our method is that few assumptions are put forward. Thus, "challenging texts" like multi-sized, multi-colored, multi-oriented or curved text can be localized. The efficiency of TextCatcher has been validated on three different datasets: Two come from the ICDAR competition, and the third one contains photographs we have taken with various daily life texts. We present both qualitative and quantitative results.


title: Improved English to Russian Translation by Neural Suffix Prediction

abstract: Neural machine translation (NMT) suffers a performance deficiency when a limited vocabulary fails to cover the source or target side adequately, which happens frequently when dealing with morphologically rich languages. To address this problem, previous work focused on adjusting translation granularity or expanding the vocabulary size. However, morphological information is relatively under-considered in NMT architectures, which may further improve translation quality. We propose a novel method, which can not only reduce data sparsity but also model morphology through a simple but effective mechanism. By predicting the stem and suffix separately during decoding, our system achieves an improvement of up to 1.98 BLEU compared with previous work on English to Russian translation. Our method is orthogonal to different NMT architectures and stably gains improvements on various domains.


title: Robust hex-dominant mesh generation using field-guided polyhedral agglomeration

abstract: We propose a robust and efficient field-aligned volumetric meshing algorithm that produces hex-dominant meshes, i.e. meshes that are predominantly composed of hexahedral elements while containing a small number of irregular polyhedra. The latter are placed according to the singularities of two optimized guiding fields, which allow our method to generate meshes with an exceptionally high amount of isotropy. The field design phase of our method relies on a compact quaternionic representation of volumetric octa-fields and a corresponding optimization that explicitly models the discrete matchings between neighboring elements. This optimization naturally supports alignment constraints and scales to very large datasets. We also propose a novel extraction technique that uses field-guided mesh simplification to convert the optimized fields into a hexdominant output mesh. Each simplification operation maintains topological validity as an invariant, ensuring manifold output. These steps easily generalize to other dimensions or representations, and we show how they can be an asset in existing 2D surface meshing techniques. Our method can automatically and robustly convert any tetrahedral mesh into an isotropic hex-dominant mesh and (with minor modifications) can also convert any triangle mesh into a corresponding isotropic quad-dominant mesh, preserving its genus, number of holes, and manifoldness. We demonstrate the benefits of our algorithm on a large collection of shapes provided in the supplemental material along with all generated results.


Architecture support for single address space operating systems


title: Multilevel minimum cross entropy threshold selection based on particle swarm optimization

abstract: Thresholding is one of the popular and fundamental techniques for conducting image segmentation. Many thresholding techniques have been proposed in the literature. Among them, the minimum cross entropy thresholding (MCET) have been widely adopted. Although the MCET method is effective in the bilevel thresholding case, it could be very time-consuming in the multilevel thresholding scenario for more complex image analysis. This paper first presents a recursive programming technique which reduces an order of magnitude for computing the MCET objective function. Then, a particle swarm optimization (PSO) algorithm is proposed for searching the near-optimal MCET thresholds. The experimental results manifest that the proposed PSO-based algorithm can derive multiple MCET thresholds which are very close to the optimal ones examined by the exhaustive search method. The convergence of the proposed method is analyzed mathematically and the results validate that the proposed method is efficient and is suited for real-time applications.


title: AMR-to-text generation as a Traveling Salesman Problem

abstract: The task of AMR-to-text generation is to generate grammatical text that sustains the semantic meaning for a given AMR graph. We at- tack the task by first partitioning the AMR graph into smaller fragments, and then generating the translation for each fragment, before finally deciding the order by solving an asymmetric generalized traveling salesman problem (AGTSP). A Maximum Entropy classifier is trained to estimate the traveling costs, and a TSP solver is used to find the optimized solution. The final model reports a BLEU score of 22.44 on the SemEval-2016 Task8 dataset.


title: Incidence of cleft lip, cleft palate, and cleft lip and palate among races: a review.

abstract: A review of the literature pertaining to the incidence of cleft lip, cleft palate, and cleft lip and palate in different races is presented. The studies have been evaluated according to the method used to record the incidence rate. Half of the studies include in their base population livebirths, stillbirths, and abortions, or livebirths and stillbirths to record the incidence rate. In addition, in most of the studies, clefts with associated malformations and possible syndromes are included in the reported incidence. There is evidence, however, to suggest that the risk of developing clefts in stillbirths and abortions is three times as frequent as in livebirths and that clefts with associated malformations behave differently epidemiologically from clefts without associated malformations. It is suggested, therefore, that the incidence of cleft lip, cleft palate, and cleft lip and palate should be studied separately for each group, namely for livebirths, stillbirths, and abortions and should be reported separately for clefts without associated malformations, clefts with associated malformations, and syndromes. More research is needed to study the risk of developing clefts among the various groups that exhibit different epidemiologic behavior for each race.


title: Multi-modal Spectral Image Super-Resolution

abstract: Recent advances have shown the great power of deep convolutional neural networks (CNN) to learn the relationship between low and high-resolution image patches. However, these methods only take a single-scale image as input and require large amount of data to train without the risk of overfitting. In this paper, we tackle the problem of multi-modal spectral image super-resolution while constraining ourselves to a small dataset. We propose the use of different modalities to improve the performance of neural networks on the spectral super-resolution problem. First, we use multiple downscaled versions of the same image to infer a better high-resolution image for training, we refer to these inputs as a multi-scale modality. Furthermore, color images are usually taken at a higher resolution than spectral images, so we make use of color images as another modality to improve the super-resolution network. By combining both modalities, we build a pipeline that learns to super-resolve using multi-scale spectral inputs guided by a color image. Finally, we validate our method and show that it is economic in terms of parameters and computation time, while still producing state-of-the-art results (Code at https://github.com/IVRL/Multi-Modal-Spectral-Image-Super-Resolution).


title: Competence-based Curriculum Learning for Neural Machine Translation

abstract: Current state-of-the-art NMT systems use large neural networks that are not only slow to train, but also often require many heuristics and optimization tricks, such as specialized learning rate schedules and large batch sizes. This is undesirable as it requires extensive hyperparameter tuning. In this paper, we propose a curriculum learning framework for NMT that reduces training time, reduces the need for specialized heuristics or large batch sizes, and results in overall better performance. Our framework consists of a principled way of deciding which training samples are shown to the model at different times during training, based on the estimated difficulty of a sample and the current competence of the model. Filtering training samples in this manner prevents the model from getting stuck in bad local optima, making it converge faster and reach a better solution than the common approach of uniformly sampling training examples. Furthermore, the proposed method can be easily applied to existing NMT models by simply modifying their input data pipelines. We show that our framework can help improve the training time and the performance of both recurrent neural network models and Transformers, achieving up to a 70% decrease in training time, while at the same time obtaining accuracy improvements of up to 2.2 BLEU.


title: Soft 3D reconstruction for view synthesis

abstract: We present a novel algorithm for view synthesis that utilizes a soft 3D reconstruction to improve quality, continuity and robustness. Our main contribution is the formulation of a soft 3D representation that preserves depth uncertainty through each stage of 3D reconstruction and rendering. We show that this representation is beneficial throughout the view synthesis pipeline. During view synthesis, it provides a soft model of scene geometry that provides continuity across synthesized views and robustness to depth uncertainty. During 3D reconstruction, the same robust estimates of scene visibility can be applied iteratively to improve depth estimation around object edges. Our algorithm is based entirely on O(1) filters, making it conducive to acceleration and it works with structured or unstructured sets of input views. We compare with recent classical and learning-based algorithms on plenoptic lightfields, wide baseline captures, and lightfield videos produced from camera arrays.


title: Competitive Collaboration: Joint Unsupervised Learning of Depth, Camera Motion, Optical Flow and Motion Segmentation

abstract: We address the unsupervised learning of several interconnected problems in low-level vision: single view depth prediction, camera motion estimation, optical flow, and segmentation of a video into the static scene and moving regions. Our key insight is that these four fundamental vision problems are coupled through geometric constraints. Consequently, learning to solve them together simplifies the problem because the solutions can reinforce each other. We go beyond previous work by exploiting geometry more explicitly and segmenting the scene into static and moving regions. To that end, we introduce Competitive Collaboration, a framework that facilitates the coordinated training of multiple specialized neural networks to solve complex problems. Competitive Collaboration works much like expectation-maximization, but with neural networks that act as both competitors to explain pixels that correspond to static or moving regions, and as collaborators through a moderator that assigns pixels to be either static or independently moving. Our novel method integrates all these problems in a common framework and simultaneously reasons about the segmentation of the scene into moving objects and the static background, the camera motion, depth of the static scene structure, and the optical flow of moving objects. Our model is trained without any supervision and achieves state-of-the-art performance among joint unsupervised methods on all sub-problems.


title: Millimeter-Wave Compact EBG Structure for Mutual Coupling Reduction Applications

abstract: A new millimeter-wave (MMW), electromagnetic band-gap (EBG) structure is presented. The proposed EBG structure without the use of metallic vias or vertical components is formed by etching two slots and adding two connecting bridges to a conventional uniplanar EBG unit-cell. The transmission characteristics of the proposed EBG structure are measured. Results show that the proposed EBG structure has a wide bandgap around the 60 GHz band. The size of the proposed EBG unit-cell is 78% less than a conventional uniplanar EBG, and 72% less than a uniplanar-compact EBG (UC-EBG) operating at the same frequency band. Moreover, and despite the fabrication limitations at the 60 GHz band, the proposed EBG unit-cell provides at least 12% more size reduction than any other planar EBG structures at microwave frequencies. Its enhanced performance and applicability to reduce mutual coupling in antenna arrays are then investigated. Results show a drastic decrease in the mutual coupling level. This EBG structure can find its applications in MMW wireless communication systems.


title: Adversarial Occlusion-aware Face Detection

abstract: Occluded face detection is a challenging detection task due to the large appearance variations incurred by various real-world occlusions. This paper introduces an Adversarial Occlusion-aware Face Detector (AOFD) by simultaneously detecting occluded faces and segmenting occluded areas. Specifically, we employ an adversarial training strategy to generate occlusion-like face features that are difficult for a face detector to recognize. Occlusion mask is predicted simultaneously while detecting occluded faces and the occluded area is utilized as an auxiliary instead of being regarded as a hindrance. Moreover, the supervisory signals from the segmentation branch will reversely affect the features, aiding in detecting heavily-occluded faces accordingly. Consequently, AOFD is able to find the faces with few exposed facial landmarks with very high confidences and keeps high detection accuracy even for masked faces. Extensive experiments demonstrate that AOFD not only significantly outperforms state-of-the-art methods on the MAFA occluded face detection dataset, but also achieves competitive detection accuracy on benchmark dataset for general face detection such as FDDB.


title: On the feasibility of real-time cyber attack attribution on the Internet

abstract: The capability to reliably and accurately identify the attacker has long been believed as one of the most effective deterrents to an attack. Ideally, the attribution of cyber attack should be automated from the attack target all the way toward the attack source on the Internet in real-time. Real-time, network-wide attack attribution, however, is every challenging, and many people have doubted whether it is feasible to have practical attack attribution on the Internet. In this paper, we look into the problem, challenges of real-time attack attribution on the Internet, and analyze what it takes to have the real-time attack attribution on the Internet. We show that it is indeed feasible and practical to attribute certain cyber attacks on the Internet in real-time. We build such a real-time attack attribution system upon the malware immunization and packet flow watermarking techniques we have developed. We demonstrate the unprecedented real-time attack attribution capability via live experiments on the Internet and Tor nodes all over the world.


title: Applications and Advances in Bioelectronic Noses for Odour Sensing

abstract: A bioelectronic nose, an intelligent chemical sensor array system coupled with bio-receptors to identify gases and vapours, resembles mammalian olfaction by which many vertebrates can sniff out volatile organic compounds (VOCs) sensitively and specifically even at very low concentrations. Olfaction is undertaken by the olfactory system, which detects odorants that are inhaled through the nose where they come into contact with the olfactory epithelium containing olfactory receptors (ORs). Because of its ability to mimic biological olfaction, a bio-inspired electronic nose has been used to detect a variety of important compounds in complex environments. Recently, biosensor systems have been introduced that combine nanoelectronic technology and olfactory receptors themselves as a source of capturing elements for biosensing. In this article, we will present the latest advances in bioelectronic nose technology mimicking the olfactory system, including biological recognition elements, emerging detection systems, production and immobilization of sensing elements on sensor surface, and applications of bioelectronic noses. Furthermore, current research trends and future challenges in this field will be discussed.


title: Seasonal subsidence and rebound in Las Vegas Valley, Nevada, observed by Synthetic Aperture Radar Interferometry

abstract: Analyses of areal variations in the subsidence and rebound occurring over stressed aquifer systems, in conjunction with measurements of the hydraulic head fluctuations causing these displacements, can yield valuable information about the compressibility and storage properties of the aquifer system. Historically, stress-strain relationships have been derived from paired extensometer/piezometer installations, which provide only point source data. Because of the general unavailability of spatially detailed deformation data, areal stress-strain relations and their variability are not commonly considered in constraining conceptual and numerical models of aquifer systems. Interferometric synthetic aperture radar (InSAR) techniques can map ground displacements at a spatial scale of tens of meters over 100 km wide swaths. InSAR has been used previously to characterize larger magnitude, generally permanent aquifer system compaction and land subsidence at yearly and longer timescales, caused by sustained drawdown of groundwater levels that produces intergranular stresses consistently greater than the maximum historical stress. We present InSAR measurements of the typically small-magnitude, generally recoverable deformations of the Las Vegas Valley aquifer system occurring at seasonal timescales. From these we derive estimates of the elastic storage coefficient for the aquifer system at several locations in Las Vegas Valley. These high-resolution measurements offer great potential for future investigations into the mechanics of aquifer systems and the spatial heterogeneity of aquifer system structure and material properties as well as for monitoring ongoing aquifer system compaction and land subsidence.


title: Contextual Operation for Recommender Systems

abstract: With the rapid growth of various applications on the Internet, recommender systems become fundamental for helping users alleviate the problem of information overload. Since contextual information is a significant factor in modeling the user behavior, various context-aware recommendation methods have been proposed recently. The state-of-the-art context modeling methods usually treat contexts as certain dimensions similar to those of users and items, and capture relevances between contexts and users/items. However, such kind of relevance has much difficulty in explanation. Some works on multi-domain relation prediction can also be used for the context-aware recommendation, but they have limitations in generating recommendations under a large amount of contextual information. Motivated by recent works in natural language processing, we represent each context value with a latent vector, and model the contextual information as a semantic operation on the user and item. Besides, we use the contextual operating tensor to capture the common semantic effects of contexts. Experimental results show that the proposed Context Operating Tensor (COT) model yields significant improvements over the competitive compared methods on three typical datasets. From the experimental results of COT, we also obtain some interesting observations which follow our intuition.


title: Password typos resilience in honey encryption

abstract: Honey encryption (HE) is a novel password-based encryption scheme which is secure against brute-force attack even if users' passwords have min-entropy. However, because decryption under a wrong key produces fake but valid-looking messages to everyone, typos in password may confuse even legitimate users in HE. This has been one of the most challenging problems in HE. In this paper, we propose two types of protocols that enable legitimate users to detect the typos in a password. We compare and analyze the performance and security of each scheme. The analysis results show that the proposed schemes can effectively solve the typos problem in HE while providing message recovery security.


title: What's Your Next Move: User Activity Prediction in Location-based Social Networks.

abstract: Location-based social networks have been gaining increasing popularity in recent years. To increase users’ engagement with location-based services, it is important to provide attractive features, one of which is geo-targeted ads and coupons. To make ads and coupon delivery more effective, it is essential to predict the location that is most likely to be visited by a user at the next step. However, an inherent challenge in location prediction is a huge prediction space, with millions of distinct check-in locations as prediction target. In this paper we exploit the check-in category information to model the underlying user movement pattern. We propose a framework which uses a mixed hidden Markov model to predict the category of user activity at the next step and then predict the most likely location given the estimated category distribution. The advantages of modeling the category level include a significantly reduced prediction space and a precise expression of the semantic meaning of user activities. Extensive experimental results show that, with the predicted category distribution, the number of location candidates for prediction is 5.45 times smaller, while the prediction accuracy is 13.21% higher.


title: A deep reinforcement learning based framework for power-efficient resource allocation in cloud RANs

abstract: Cloud Radio Access Networks (RANs) have become a key enabling technique for the next generation (5G) wireless communications, which can meet requirements of massively growing wireless data traffic. However, resource allocation in cloud RANs still needs to be further improved in order to reach the objective of minimizing power consumption and meeting demands of wireless users over a long operational period. Inspired by the success of Deep Reinforcement Learning (DRL) on solving complicated control problems, we present a novel DRL-based framework for power-efficient resource allocation in cloud RANs. Specifically, we define the state space, action space and reward function for the DRL agent, apply a Deep Neural Network (DNN) to approximate the action-value function, and formally formulate the resource allocation problem (in each decision epoch) as a convex optimization problem. We evaluate the performance of the proposed framework by comparing it with two widely-used baselines via simulation. The simulation results show it can achieve significant power savings while meeting user demands, and it can well handle highly dynamic cases.


title: Color texture classification by integrative Co-occurrence matrices

abstract: Abstract Integrative Co-occurrence matrices are introduced as novel features for color texture classification. The extended Co-occurrence notation allows the comparison between integrative and parallel color texture concepts. The information profit of the new matrices is shown quantitatively using the Kolmogorov distance and by extensive classification experiments on two datasets. Applying them to the RGB and the LUV color space the combined color and intensity textures are studied and the existence of intensity independent pure color patterns is demonstrated. The results are compared with two baselines: gray-scale texture analysis and color histogram analysis. The novel features improve the classification results up to 20% and 32% for the first and second baseline, respectively.


title: Multimedia event detection with multimodal feature fusion and temporal concept localization

abstract: We present a system for multimedia event detection. The developed system characterizes complex multimedia events based on a large array of multimodal features, and classifies unseen videos by effectively fusing diverse responses. We present three major technical innovations. First, we explore novel visual and audio features across multiple semantic granularities, including building, often in an unsupervised manner, mid-level and high-level features upon low-level features to enable semantic understanding. Second, we show a novel Latent SVM model which learns and localizes discriminative high-level concepts in cluttered video sequences. In addition to improving detection accuracy beyond existing approaches, it enables a unique summary for every retrieval by its use of high-level concepts and temporal evidence localization. The resulting summary provides some transparency into why the system classified the video as it did. Finally, we present novel fusion learning algorithms and our methodology to improve fusion learning under limited training data condition. Thorough evaluation on a large TRECVID MED 2011 dataset showcases the benefits of the presented system.


title: Data flow analysis techniques for test data selection

abstract: This paper examines a family of program test data selection criteria derived from data flow analysis techniques similar to those used in compiler optimization. It is argued that currently used path selection criteria which examine only the control flow of a program are inadequate. Our procedure associates with each point in a program at which a variable is defined, those points at which the value is used. Several related path criteria, which differ in the number of these associations needed to adequately test the program, are defined and compared.


title: Designing Gabor " lters for optimal texture separability

abstract: The discrimination of textures is a critical aspect of identi"cation in digital imagery. Texture features generated by Gabor "lters have been increasingly considered and applied to image analysis. Here, a comprehensive classi"cation and segmentation comparison of di!erent techniques used to produce texture features using Gabor "lters is presented. These techniques are based on existing implementations as well as new, innovative methods. The functional characterization of the "lters as well as feature extraction based on the raw "lter outputs are both considered. Overall, using the Gabor "lter magnitude response given a frequency bandwidth and spacing of one octave and orientation bandwidth and spacing of 303 augmented by a measure of the texture complexity generated preferred results. ( 2000 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.


title: Drug-induced myopathies

abstract: The most common drugs currently in use that may cause myopathies were reviewed using the Medline database (U.S. National Library of Medicine, Bethesda, Maryland). Our review included results from epidemiologic and database surveys, clinical trials, and case reports. The clinical spectrum is wide, and presentations range from asymptomatic elevations in serum creatine phosphokinase levels to severe life-threatening rhabdomyolysis. Management of suspected drug-induced myopathy should include immediate discontinuation of the offending agent, as well as supportive care when needed. Earlier diagnosis and drug discontinuation raises the likelihood of resolution and recovery.


title: Design and evaluation of MiMaze a multi-player game on the Internet

abstract: This paper describes the design, implementation, and evaluation of MiMaze, a distributed multiplayer game on the Internet. The major contribution of this work is to have designed and implemented a completely distributed communication architecture based on IP multicast. MiMaze uses multicast communication system based on RTP/UDP/IP, and distributed synchronization mechanisms to guarantee the consistency of the game, regardless of network delay. This paper provides evaluation results on the Mbone.


title: Gaussian Processes for Big Data

abstract: We introduce stochastic variational inference for Gaussian process models. This enables the application of Gaussian process (GP) models to data sets containing millions of data points. We show how GPs can be variationally decomposed to depend on a set of globally relevant inducing variables which factorize the model in the necessary manner to perform variational inference. Our approach is readily extended to models with non-Gaussian likelihoods and latent variable models based around Gaussian processes. We demonstrate the approach on a simple toy problem and two real world data sets.


Cyber Victimization: School Experience of Malaysian Cyberbullied Teenagers


Fuzzy Logic Based Intrusion Detection Scheme against DoS Attack in MANET


title: Novel switching control technique for bridgeless PFC to reduce both switching and conduction losses

abstract: The main theme of this paper is to propose a novel switching control method for bridgeless PFC converter to improve the efficiency by reducing both switching and conduction losses. Comparing to the conventional method, the power device is always on (no switching loss) to provide a current path via the turn-on resistor of MOSFET to reduce conduction losses under light load condition. As the load increases, the power device is always off (no switching loss) to provide a current path via the body diode of MOSFET to reduce conduction losses under heavy load condition. In this paper, both simulation and experimental results will be shown to confirm the effectiveness of the proposed switching control method. Test results derived from a digital-controlled 1.2 kW PFC show the efficiency improvement can go up to 0.73% and 0.57% under full load and light load conditions, respectively. It will also be confirmed this efficiency improvement has no impact on the power factor and just simply changing the switching control method. Both measured steady state and transient waveforms will be shown for confirmation.


title: An Actor-critic Algorithm for Learning Rate Learning

abstract: Stochastic gradient descent (SGD), which updates the model parameters by adding a local gradient times a learning rate at each step, is widely used in model training of machine learning algorithms such as neural networks. It is observed that the models trained by SGD are sensitive to learning rates and good learning rates are problem specific. To avoid manually searching of learning rates, which is tedious and inefficient, we propose an algorithm to automatically learn learning rates using actor-critic methods from reinforcement learning (RL). In particular, we train a policy network called actor to decide the learning rate at each step during training, and a value network called critic to give feedback about quality of the decision (e.g., the goodness of the learning rate outputted by the actor) that the actor made. Experiments show that our method leads to good convergence of SGD and can prevent overfitting to a certain extent, resulting in better performance than human-designed competitors.


title: Miniature implantable and wearable on-body antennas: towards the new era of wireless body-centric systems [antenna applications corner]

abstract: Wireless body-centric sensing systems have an important role in the fields of biomedicine, personal healthcare, safety, and security. Body-centric radio-frequency identification (RFID) technology provides a wireless and maintenance-free communication link between the human body and the surroundings through wearable and implanted antennas. This enables real-time monitoring of human vital signs everywhere. Seamlessly integrated wearable and implanted miniaturized antennas thus have the potential to revolutionize the everyday life of people, and to contribute to independent living. Low-cost and low-power system solutions will make widespread use of such technology become reality. The primary target applications for this research are body-centric sensing systems and the relatively new interdisciplinary field of wireless brain-machine interface (BMI) systems. Providing a direct wireless pathway between the brain and an external device, a wireless brain-machine interface holds an enormous potential for helping people suffering from severely disabling neurological conditions to communicate and manage their everyday life more independently. In this paper, we discuss RFID-inspired wireless brain-machine interface systems. We demonstrate that mm-size loop implanted antennas are capable of efficiently coupling to an external transmitting loop antenna through an inductive link. In addition, we focus on wearable antennas based on electrically conductive textiles and threads, and present design guidelines for their use as wearable-antenna conductive elements. Overall, our results constitute an important milestone in the development of wireless brain-machine interface systems, and a new era of wireless body-centric systems.


title: Automatic monitoring of cyberbullying on social networking sites: From technological feasibility to desirability

abstract: Abstract The automatic monitoring of cyberbullying on social networking sites has potential for signalling harmful messages, preventing these messages from remaining online and providing timely responses. Although technological advancements are made to optimise automatic cyberbullying detection systems, little is known about its desirability and requirements. Experts in the field of cyberbullying, as excellent sources of valuable insight into these issues, were solicited based on three open-ended questions relating to the desirability of automatic monitoring. Answers were examined through qualitative content analysis. Of the 179 experts contacted, 50 (28%) responded. Most of these experts favoured automatic monitoring, but specified clear conditions under which such systems should be implemented, including effective follow-up strategies, protecting the adolescents’ privacy and safeguarding their self-reliance. Follow-up strategies should focus on preventing future cyberbullying and empowering the parties involved. The majority of respondents suggested priorities for detection, including threats and the misuse of pictures. Despite generally positive opinions, several experts harboured doubts regarding desirability and feasibility. Appropriate follow-up strategies should be determined according to severity, and be tested for effectiveness. Future research should involve the views of adolescents and parents with regard to user desirability and prioritisation of cyberbullying detection, as well as views from social network providers.


abstract: Archaeological remote sensing is not a novel discipline. Indeed, there is already a suite of geoscientific techniques that are regularly used by practitioners in the field, according to standards and best practice guidelines. However, (i) the technological development of sensors for data capture; (ii) the accessibility of new remote sensing and Earth Observation data; and (iii) the awareness that a combination of different techniques can lead to retrieval of diverse and complementary information to characterize landscapes and objects of archaeological value and significance, are currently three triggers stimulating advances in methodologies for data acquisition, signal processing, and the integration and fusion of extracted information. The Special Issue “Remote Sensing and Geosciences for Archaeology” therefore presents a collection of scientific contributions that provides a sample of the state-of-the-art and forefront research in this field. Site discovery, understanding of cultural landscapes, augmented knowledge of heritage, condition assessment, and conservation are the main research and practice targets that the papers published in this Special Issue aim to address.


title: Angry , Disgusted , or Afraid ? Studies on the Malleability of Emotion Perception

abstract: Current theories of emotion perception posit that basic facial expressions signal categorically discrete emotions or affective dimensions of valence and arousal. In both cases, the information is thought to be directly “read out” from the face in a way that is largely immune to context. In contrast, the three studies reported here demonstrated that identical facial configurations convey strikingly different emotions and dimensional values depending on the affective context in which they are embedded. This effect is modulated by the similarity between the target facial expression and the facial expression typically associated with the context. Moreover, by monitoring eye movements, we demonstrated that characteristic fixation patterns previously thought to be determined solely by the facial expression are systematically modulated by emotional context already at very early stages of visual processing, even by the first time the face is fixated. Our results indicate that the perception of basic facial expres...


title: Percutaneous augmented instrumentation of unstable thoracolumbar burst fractures

abstract: Background ::: Internal fixation of unstable thoracolumbar spine fractures requires correction of the lacking anterior column support. This usually entails insertion of a vertebral body replacement strut through an anterior approach, or a long posterior construct spanning at least two vertebrae above and two vertebrae below the fracture. Posterior short-segment pedicle instrumentation (SSPI)—one vertebra above and below—is suitable for approximately 40% of fractures, but not for all.


title: A study of the impact of social media on consumers

abstract: Social media have provided new opportunities to consumers to engage in social interaction on the internet. Consumers use social media, such as online communities, to generate content and to network with other users. The study of social media can also identify the advantages to be gained by business. A multidisciplinary model, building on the technology acceptance model and relevant literature on trust and social media, has been devised. The model has been validated by SEM-PLS, demonstrating the role of social media in the development of e-commerce into social commerce. The data emerging from a survey show how social media facilitate the social interaction of consumers, leading to increased trust and intention to buy. The results also show that trust has a significant direct effect on intention to buy. The perceived usefulness (PU) of a site is also identified as a contributory factor. At the end of the paper, the author discusses the results, along with implications, limitations and recommended future research directions


title: Measuring dependency freshness in software systems

abstract: Modern software systems often make use of third-party components to speed-up development and reduce maintenance costs. In return, developers need to update to new releases of these dependencies to avoid, for example, security and compatibility risks. In practice, prioritizing these updates is difficult because the use of outdated dependencies is often opaque. In this paper we aim to make this concept more transparent by introducing metrics to quantify the use of recent versions of dependencies, i.e. the system's "dependency freshness". We propose and investigate a system-level metric based on an industry benchmark. We validate the usefulness of the metric using interviews, analyze the variance of the metric through time, and investigate the relationship between outdated dependencies and security vulnerabilities. The results show that the measurements are considered useful, and that systems using outdated dependencies four times as likely to have security issues as opposed to systems that are up-to-date.


title: Speed measurement algorithms for low-resolution incremental encoder equipped drives: a comparative analysis

abstract: Precise motion control requires high-accuracy and high- bandwidth feedback speed information, often calculated by direct measurement of the rotor position available through the incremental encoder equipping the drive. The resolution of the encoder heavily affects the cost of the drive and the accuracy and bandwidth of the calculated speed. The limitations introduced by a low resolution encoder can be partially reduced by a proper speed calculation (or estimation) algorithm, that therefore plays a key role in modern drive systems. In this paper a comparative analysis of state-of-art speed measurement and estimation algorithms suitable for low-resolution incremental encoder equipped drives is presented, aiming at highlighting the specific feature of each one, both from the performance point of view, and from the computational requirements needed for actual implementation. The paper proposes itself as a guide for engineers in the complex choice of the best solution for each application.


title: Real-time 2D+3D facial action and expression recognition

abstract: This paper presents a completely automated facial action and facial expression recognition system using 2D+3D images recorded in real-time by a structured light sensor. It is based on local feature tracking and rule-based classification of geometric, appearance and surface curvature measurements. Several experiments conducted under relatively non-controlled conditions demonstrate the accuracy and robustness of the approach.


title: Readability-based Sentence Ranking for Evaluating Text Simplification

abstract: We propose a new method for evaluating the readability of simplified sentences through pair-wise ranking. The validity of the method is established through in-corpus and cross-corpus evaluation experiments. The approach correctly identifies the ranking of simplified and unsimplified sentences in terms of their reading level with an accuracy of over 80%, significantly outperforming previous results. To gain qualitative insights into the nature of simplification at the sentence level, we studied the impact of specific linguistic features. We empirically confirm that both word-level and syntactic features play a role in comparing the degree of simplification of authentic data. To carry out this research, we created a new sentence-aligned corpus from professionally simplified news articles. The new corpus resource enriches the empirical basis of sentence-level simplification research, which so far relied on a single resource. Most importantly, it facilitates cross-corpus evaluation for simplification, a key step towards generalizable results.


Big Data Analysis: Apache Storm Perspective


title: Autopilots for Small Fixed-Wing Unmanned Air Vehicles: A Survey

abstract: This paper presents a survey of the autopilot systems for small fixed-wing unmanned air vehicles (UAVs). The UAV flight control basics are introduced first. The radio control system and autopilot control system are then explained from both hardware and software viewpoints. Several typical commercial off-the-shelf autopilot packages are compared in detail. In addition, some research autopilot systems are introduced. Finally, conclusions are made with a summary of the current autopilot market and a remark on the future development.This paper presents a survey of the autopilot systems for small fixed-wing unmanned air vehicles (UAVs). The UAV flight control basics are introduced first. The radio control system and autopilot control system are then explained from both hardware and software viewpoints. Several typical commercial off-the-shelf autopilot packages are compared in detail. In addition, some research autopilot systems are introduced. Finally, conclusions are made with a summary of the current autopilot market and a remark on the future development.


Data Mining Yelp Data-Predicting rating stars from review text


title: Distributed Cardinality Estimation of Set Operations with Differential Privacy

abstract: In this paper we study the problem of estimating the cardinality of pairwise set operations (union and intersection) over sets possessed by different data owners, while preserving differential privacy. In our problem setting, a data owner could only communicate with an untrusted server, and thus have to perturb its set data for privacy protection before sharing them with the server. This problem setting is relevant to diverse applications in practice, including sensor-based traffic monitoring, cross-domain data integration, and combining findings from multiple surveys. To tackle this problem, we first adopt existing randomized response technique to perturb the bit vector (to achieve differential privacy) and develop tools which the server can use to derive the cardinality of set operations from the randomized bit vectors. However, the variance of the union/intersection estimator grows linearly with the universe (bit-vector) size which is impractical for large universes. To keep the variance low we in addition propose to resort to Bloom filters instead of high-dimensional bit vectors to share set data with the server. The key insight is that in spite of inevitable collisions in BF by keeping its size small we can bound the variance of the union/intersection cardinality estimators. Finally, we show that investing a small part of the privacy budget into reporting (obfuscated) set cardinality can further reduce the estimator errors for up to 20%. Our empirical analysis reveals the impact of various parameters including privacy budget and Bloom filter size on the overall accuracy of the approach and demonstrates the utility of the proposed solution.


title: Industrial Software Developments Effort Estimation Model

abstract: Over the past decade, software has spread to most areas of our lives. This has led to increased demands on product quality and complexity. Industrial software which belong to a safety-critical area where high quality products are essential. Many processes and standards must be completed and met within stipulated deadlines. The complexity of the software and the boundary conditions of developing it dictate the product defect ratio. Requirements will be added and adjusted during the development lifecycle which will leads to changes in the software. All these changes need to be refined and tested to ensure product quality. Predicting software defects and changes is a major part of software engineering. The software defect count is used to assess quality and estimate the effort needed for software fixes, however, it is rarely accurate. Industrial development involves many standards and norms. This research models correction efforts from multiple perspectives. The effort needed for software fixes needs to be combined with overall product effort to ensure software quality. The most common approach is to update the software. Several software update will be applied with each release. Each defect has specific characteristics. These depend on the process and project parameters, with the parameters determining whether a defect exists and the probability of generating a new one. The software process model contained in this thesis will describe these parameters and illustrate their effect on the software defect rate. The software is developed in different phases during the development lifecycle. The effort needed to fix defects depends on the phases where they originated and were detected. This is modeled in this thesis as a defect cost flow. Describing the distribution of defects during these phases helps control quality assurance and limit the effort needed to increase product quality.


title: Efficient implementation of Marching Cubes' cases with topological guarantees

abstract: Abstract Marching Cubes methods first offered visual access to experimental and theoretical volumetric data. The implementation of this method usually relies on a small look-up table; many enhancements and optimizations of Marching Cubes still use it. However, this look-up table can lead to cracks and inconsistent topology. This paper introduces a full implementation of Chernyaev's technique to ensure a topologically correct result, i.e., a manifold mesh, for any input data. It completes the original paper for the ambiguity resolution and for the feasibility of the implementation. Moreover, the cube interpolation provided here can be used in a wider range of methods. The source code is available online.


title: The impact of project managers' leadership style on employees' job satisfaction, performance and turnover

abstract: Behavioral theory of leadership, divides leaders into two groups of "task-oriented" and "relationship-oriented" leaders. According to this theory, task-oriented leaders' priority is achieving projected results for current tasks, whereas relationship-oriented leaders focus on employees and try to improve them and take into consideration their ideas and suggestions. The current research intends to discover the extent to which employees' level of job satisfaction, quality of performance and rate turnover are affected by the leader's style. The employees of Iranian oil&gas projects were selected as the target population for this study. An online survey was selected as the method of data collection. 100 employees from different projects participated in the survey. The collected data were analyzed using independent sample t-test. The results of the analysis showed that in all the three research areas there was a significant difference between the employees of task-oriented project managers and those who work with relationship-oriented project managers, while the latter group scored higher. Keywords - Project manager, Leadership Style, Job Satisfaction, Performance, Turnover.


title: A New MIMO HF Data Link: Designing for High Data Rates and Backwards Compatibility

abstract: High frequency (HF) radios (the 3-30 MHz range) provide tactical communications with the ability to communicate over long distances and around large obstructions without supporting infrastructure (e.g., satellite links). Unfortunately, spectrum is scarce in the low HF operating frequencies. To improve spectral efficiency, commercial UHF/SHF systems have offered multiple antennas with intelligent signal processing (MIMO). MIMO not only amplifies spectral efficiency, but also reduces transmit power, rejects jamming, and increases link reliability. In a companion paper, we demonstrated HF MIMO feasibility with compact cross-polarized arrays through measurements. In this paper, we leverage those measurements to design a new MIMO HF physical layer based on the existing single-antenna MIL-STD-188-110C-Appendix-D wideband HF standard. We include simulation results based on our channel measurements to show that this new standard provides 116% improvement in overall throughput and a 15 dB signal-to-noise ratio (SNR) improvement over the highest-rate single-antenna modes due to greater reliability and reduced sensitivity to amplifier nonlinearities.