title: Multimedia Big Data Analytics: A Survey

abstract: With the proliferation of online services and mobile technologies, the world has stepped into a multimedia big data era. A vast amount of research work has been done in the multimedia area, targeting different aspects of big data analytics, such as the capture, storage, indexing, mining, and retrieval of multimedia big data. However, very few research work provides a complete survey of the whole pine-line of the multimedia big data analytics, including the management and analysis of the large amount of data, the challenges and opportunities, and the promising research directions. To serve this purpose, we present this survey, which conducts a comprehensive overview of the state-of-the-art research work on multimedia big data analytics. It also aims to bridge the gap between multimedia challenges and big data solutions by providing the current big data frameworks, their applications in multimedia analyses, the strengths and limitations of the existing methods, and the potential future directions in multimedia big data analytics. To the best of our knowledge, this is the first survey that targets the most recent multimedia management techniques for very large-scale data and also provides the research studies and technologies advancing the multimedia analyses in this big data era.


title: Exploring similarity between academic paper and patent based on Latent Semantic Analysis and Vector Space Model

abstract: With the development of network technology, the storage format of science and technology literature changes from paper to electronic version, and its size also is increasing. The academic papers and patents are important science and technology literature. To a certain extent, they represent the highest level of academic research and technical innovation. In this paper, we perform a study to measure the semantic similarity between academic papers and patents. The paper argues it's important to get similarity between single paper and single patent. To find linkage between them, four semantic similarity measurements are compared: Latent Semantic Analysis (LSA) based on words, LSA based on terms, Vector Space Model (VSM) based on words, VSM based on terms. A case study is conducted in the area of optical sensors. And result shows that the measurement method of terms based VSM is the best to find the similarity between single paper and single patent.


title: Nonlinear Disturbance Observer Design For Robotic Manipulators

abstract: Abstract Robotic manipulators are highly nonlinear and coupled systems that are subject to different types of disturbances such as joint frictions, unknown payloads, varying contact points, and unmodeled dynamics. These disturbances, when unaccounted for, adversely affect the performance of the manipulator. Employing a disturbance observer is a common method to reject such disturbances. In addition to disturbance rejection, disturbance observers can be used in force control applications. Recently, research has been done regarding the design of nonlinear disturbance observers (NLDOs) for robotic manipulators. In spite of good results in terms of disturbance tracking, the previously designed nonlinear disturbance observers can merely be used for planar serial manipulators with revolute joints [Chen, W. H., Ballance, D. J., Gawthorp, P. J., O'Reilly, J. (2000). A nonlinear disturbance observer for robotic manipulators. IEEE Transactions on Industrial Electronics, 47 (August (4)), 932–938; Nikoobin, A., Haghighi, R. (2009). Lyapunov-based nonlinear disturbance observer for serial n-link manipulators. Journal of Intelligent & Robotic Systems, 55 (July (2–3)), 135–153]. In this paper, a general systematic approach is proposed to solve the disturbance observer design problem for robotic manipulators without restrictions on the number of degrees-of-freedom (DOFs), the types of joints, or the manipulator configuration. Moreover, this design method does not need the exact dynamic model of the serial robotic manipulator. This method also unifies the previously proposed linear and nonlinear disturbance observers in a general framework. Simulations are presented for a 4-DOF SCARA manipulator to show the effectiveness of the proposed disturbance observer design method. Experimental results using a PHANToM Omni haptic device further illustrate the effectiveness of the design method.


Tabu Search - Part I


title: Changes in chemical properties during composting of spent pig litter at different moisture contents

abstract: Abstract An ordinary pig pen employing the pig-on-litter system was set-up and 40 pigs were placed inside. After 12 weeks, the pigs were removed from the pen and the spent litter was collected and piled up in an open shed for further composting and maturation. Three piles of the spent litter were set up with moisture content adjusted to 50% (Pile A), 60% (Pile B) and 70% (Pile C). During further composting, the changes in temperature and chemical properties of the piles were monitored. Results of the study revealed that moisture content affected the changes in temperature and chemical properties (i.e., concentrations of NH+4-N, [NO−3 + NO−2]-N, C:N ratio, cation-exchange capacity [CEC], total and water extractable K and pH value) of the spent litter. Pile C, the pile adjusted to 70% moisture, had a delay in reaching peak temperature values and the temperature never dropped back to the ambient level. Piles A and B, the piles adjusted to 50 and 60% moisture content respectively, had very similar changes in temperature and chemical properties and had faster decomposition rates than pile C suggesting that moisture content between 50 and 60% is the suitable moisture content for an efficient further composting of spent litter. The maturation of spent litter was accompanied by changes, with periods of composting in pH, total C, ash, various forms of N, CEC and C:N ratio. Results of the regression analysis showed a curvilinear relationship between these chemical parameters with period of composting and temperature. Moreover, changes in these chemical parameters were found to be either positively or negatively correlated with temperature.


title: Kinesthesia in a sustained-attention driving task

abstract: Abstract This study investigated the effects of kinesthetic stimuli on brain activities during a sustained-attention task in an immersive driving simulator. Tonic and phasic brain responses on multiple timescales were analyzed using time–frequency analysis of electroencephalographic (EEG) sources identified by independent component analysis (ICA). Sorting EEG spectra with respect to reaction times (RT) to randomly introduced lane-departure events revealed distinct effects of kinesthetic stimuli on the brain under different performance levels. Experimental results indicated that EEG spectral dynamics highly correlated with performance lapses when driving involved kinesthetic feedback. Furthermore, in the realistic environment involving both visual and kinesthetic feedback, a transitive relationship of power spectra between optimal-, suboptimal-, and poor-performance groups was found predominately across most of the independent components. In contrast to the static environment with visual input only, kinesthetic feedback reduced theta-power augmentation in the central and frontal components when preparing for action and error monitoring, while strengthening alpha suppression in the central component while steering the wheel. In terms of behavior, subjects tended to have a short response time to process unexpected events with the assistance of kinesthesia, yet only when their performance was optimal. Decrease in attentional demand, facilitated by kinesthetic feedback, eventually significantly increased the reaction time in the suboptimal-performance state. Neurophysiological evidence of mutual relationships between behavioral performance and neurocognition in complex task paradigms and experimental environments, presented in this study, might elucidate our understanding of distributed brain dynamics, supporting natural human cognition and complex coordinated, multi-joint naturalistic behavior, and lead to improved understanding of brain–behavior relations in operating environments.


title: A novel modified flower pollination algorithm for global optimization

abstract: The flower pollination algorithm (FPA) is a relatively new natural bio-inspired optimization algorithm that mimics the real-life processes of the flower pollination. Indeed, this algorithm is based globally on two main rules: the global pollination (biotic and cross-pollination) and the local pollination (abiotic and self-pollination). The random permutation between these latter allows to keep a permanent balance between intensification and diversification. However, this procedure causes an involuntary orientation toward a bad solution (local optima). In addition, FPA illustrates an inadequacy in terms of intensification and diversification of new solutions; this has become clear when the complexity of the treated problem is increased. Further, FPA has also another insufficiency, which is its slow convergence rate caused in principle by its weak intensification. In this paper, to overcome these weaknesses, we have introduced some modifications on the basic FPA algorithmic structure based on the two following improvements: (1) Generating a set of global orientations (toward global or local pollination) for all members of the population. Indeed, each element (global orientation) in this set is composed of a fixed number (equal to the population size) of sub-random orientation. Thus, the number of elements is fixed by the designer, which enhances significantly the diversification characteristic. (2) Constructing a set of best solution vectors relating to all generated global orientations. In fact, this set is compared at each iteration to a fixed number of actual solution vectors to select the best among them based on their fitness values. The proposed algorithm called novel modified FPA (NMFPA) with its novel algorithmic structure offers to researchers the opportunity to: (1) use it in their comparison study (e.g., with others FPA proposed variants) and (2) develop other new methods or techniques based on its novel integrated mechanisms. To demonstrate the performance of this new FPA variant, a set of 28 benchmark functions defined in IEEE-CEC’13 and a 15 real-world numerical optimization problems proposed in the IEEE-CEC’11 are employed. Compared with FPA, two its famous variants and other state-of-the-art evolutionary algorithms, NMFPA shows overall better performance.


title: Machine Learning under Attack: Vulnerability Exploitation and Security Measures

abstract: Learning to discriminate between secure and hostile patterns is a crucial problem for species to survive in nature. Mimetism and camouflage are well-known examples of evolving weapons and defenses in the arms race between predators and preys. It is thus clear that all of the information acquired by our senses should not be considered necessarily secure or reliable. In machine learning and pattern recognition systems, however, we have started investigating these issues only recently. This phenomenon has been especially observed in the context of adversarial settings like malware detection and spam filtering, in which data can be purposely manipulated by humans to undermine the outcome of an automatic analysis. As current pattern recognition methods are not natively designed to deal with the intrinsic, adversarial nature of these problems, they exhibit specific vulnerabilities that an attacker may exploit either to mislead learning or to evade detection. Identifying these vulnerabilities and analyzing the impact of the corresponding attacks on learning algorithms has thus been one of the main open issues in the novel research field of adversarial machine learning, along with the design of more secure learning algorithms. In the first part of this talk, I introduce a general framework that encompasses and unifies previous work in the field, allowing one to systematically evaluate classifier security against different, potential attacks. As an example of application of this framework, in the second part of the talk, I discuss evasion attacks, where malicious samples are manipulated at test time to evade detection. I then show how carefully-designed poisoning attacks can mislead some learning algorithms by manipulating only a small fraction of their training data. In addition, I discuss some defense mechanisms against both attacks in the context of real-world applications, including biometric identity recognition and computer security. Finally, I briefly discuss our ongoing work on attacks against clustering algorithms, and sketch some promising future research directions.


title: Statistical Arbitrage Mining for Display Advertising

abstract: We study and formulate arbitrage in display advertising. Real-Time Bidding (RTB) mimics stock spot exchanges and utilises computers to algorithmically buy display ads per impression via a real-time auction. Despite the new automation, the ad markets are still informationally inefficient due to the heavily fragmented marketplaces. Two display impressions with similar or identical effectiveness (e.g., measured by conversion or click-through rates for a targeted audience) may sell for quite different prices at different market segments or pricing schemes. In this paper, we propose a novel data mining paradigm called Statistical Arbitrage Mining (SAM) focusing on mining and exploiting price discrepancies between two pricing schemes. In essence, our SAMer is a meta-bidder that hedges advertisers' risk between CPA (cost per action)-based campaigns and CPM (cost per mille impressions)-based ad inventories; it statistically assesses the potential profit and cost for an incoming CPM bid request against a portfolio of CPA campaigns based on the estimated conversion rate, bid landscape and other statistics learned from historical data. In SAM, (i) functional optimisation is utilised to seek for optimal bidding to maximise the expected arbitrage net profit, and (ii) a portfolio-based risk management solution is leveraged to reallocate bid volume and budget across the set of campaigns to make a risk and return trade-off. We propose to jointly optimise both components in an EM fashion with high efficiency to help the meta-bidder successfully catch the transient statistical arbitrage opportunities in RTB. Both the offline experiments on a real-world large-scale dataset and online A/B tests on a commercial platform demonstrate the effectiveness of our proposed solution in exploiting arbitrage in various model settings and market environments.


title: Connecting databases with process mining: a meta model and toolset

abstract: Process Mining techniques require event logs which, in many cases, are obtained from databases. Obtaining these event logs is not a trivial task and requires substantial domain knowledge. In addition, the result is a single view on the database in the form of a specific event log. If we desire to change our view, e.g. to focus on another business process, and generate another event log, it is necessary to go back to the source of data. This paper proposes a meta model to integrate both process and data perspectives, relating one to the other and allowing to generate different views from it at any moment in a highly flexible way. This approach decouples the data extraction from the application of analysis techniques, enabling its use in different contexts.


title: A hundred impossibility proofs for distributed computing

abstract: Abstract : This talk is about impossibility results in the area of distributed computing. In this category, I include not just results that say that a particular task cannot be accomplished, but also lower bound results, which say that a task cannot be accomplished within a certain bound on cost. I started out with a simple plan for preparing this talk: I would spend a couple of weeks reading all the impossibility proofs in our fields, and would categorize them according to the ideas used. Then I would make wise and general observations, and try to predict where the future of this area is headed. That turned out to be a bit too ambitious; there are many more such results than I thought. Although it is often hard to say what constitutes ad different results, I managed to count over 100 such impossibility proofs And my search wasn't even very systematic or exhaustive. It's not quite as hopeless to understand this area as it might seem from the number of papers. Although there are 100 different results, there aren't 100 different ideas. I thought I could contribute something by identifying some of the commonality among the different results.


title: OrthoMCL: identification of ortholog groups for eukaryotic genomes.

abstract: The identification of orthologous groups is useful for genome annotation, studies on gene/protein evolution, comparative genomics, and the identification of taxonomically restricted sequences. Methods successfully exploited for prokaryotic genome analysis have proved difficult to apply to eukaryotes, however, as larger genomes may contain multiple paralogous genes, and sequence information is often incomplete. OrthoMCL provides a scalable method for constructing orthologous groups across multiple eukaryotic taxa, using a Markov Cluster algorithm to group (putative) orthologs and paralogs. This method performs similarly to the INPARANOID algorithm when applied to two genomes, but can be extended to cluster orthologs from multiple species. OrthoMCL clusters are coherent with groups identified by EGO, but improved recognition of "recent" paralogs permits overlapping EGO groups representing the same gene to be merged. Comparison with previously assigned EC annotations suggests a high degree of reliability, implying utility for automated eukaryotic genome annotation. OrthoMCL has been applied to the proteome data set from seven publicly available genomes (human, fly, worm, yeast, Arabidopsis, the malaria parasite Plasmodium falciparum, and Escherichia coli). A Web interface allows queries based on individual genes or user-defined phylogenetic patterns (http://www.cbil.upenn.edu/gene-family). Analysis of clusters incorporating P. falciparum genes identifies numerous enzymes that were incompletely annotated in first-pass annotation of the parasite genome.


title: CCFinder: a multilinguistic token-based code clone detection system for large scale source code

abstract: A code clone is a code portion in source files that is identical or similar to another. Since code clones are believed to reduce the maintainability of software, several code clone detection techniques and tools have been proposed. This paper proposes a new clone detection technique, which consists of the transformation of input source text and a token-by-token comparison. For its implementation with several useful optimization techniques, we have developed a tool, named CCFinder (Code Clone Finder), which extracts code clones in C, C++, Java, COBOL and other source files. In addition, metrics for the code clones have been developed. In order to evaluate the usefulness of CCFinder and metrics, we conducted several case studies where we applied the new tool to the source code of JDK, FreeBSD, NetBSD, Linux, and many other systems. As a result, CCFinder has effectively found clones and the metrics have been able to effectively identify the characteristics of the systems. In addition, we have compared the proposed technique with other clone detection techniques.


title: Lean principles, learning, and knowledge work: Evidence from a software services provider

abstract: In this paper, we examine the applicability of lean production to knowledge work by investigating the implementation of a lean production system at an Indian software services firm. We first discuss specific aspects of knowledge work—task uncertainty, process invisibility, and architectural ambiguity—that call into question the relevance of lean production in this setting. Then, combining a detailed case study and empirical analysis, we find that lean software projects perform better than non-lean software projects at the company for most performance outcomes. We document the influence of the lean initiative on internal processes and examine how the techniques affect learning by improving both problem identification and problem resolution. Finally, we extend the lean production framework by highlighting the need to (1) identify problems early in the process and (2) keep problems and solutions together in time, space, and person.


title: Age group classification in the wild with deep RoR architecture

abstract: Automatically predicting age group from face images acquired in unconstrained conditions is an important and challenging task in many real-world applications. Nevertheless, the conventional methods with manually-designed features on in-the-wild benchmarks are unsatisfactory because of incompetency to tackle large variations in unconstrained images. In this paper, we propose a new CNN based method for age group classification leveraging Residual Networks of Residual Networks (RoR), which exhibits better optimization ability for age group classification than other CNN architectures. Moreover, two modest mechanisms based on observation of the characteristics of age group are presented to further improve the performance of age estimation. Our experiments illustrate the effectiveness of RoR method for age estimation in the wild, where it achieves better performance than other CNN methods. Finally, the Pre-RoR-58+SD with two mechanisms achieves new state-of-the-art results on Adience benchmark.


title: Online convolutional dictionary learning for multimodal imaging

abstract: Computational imaging methods that can exploit multiple modalities have the potential to enhance the capabilities of traditional sensing systems. In this paper, we propose a new method that reconstructs multimodal images from their linear measurements by exploiting redundancies across different modalities. Our method combines a convolutional group-sparse representation of images with total variation (TV) regularization for high-quality multimodal imaging. We develop an online algorithm that enables the unsupervised learning of convolutional dictionaries on large-scale datasets that are typical in such applications. We illustrate the benefit of our approach in the context of joint intensity-depth imaging.


title: Design, Control, and Analysis of a Fault-Tolerant Soft-Switching DC–DC Converter for High-Power High-Voltage Applications

abstract: A modular isolated soft-switching dc–dc converter that can offer two levels of fault tolerance is proposed. A typical application is the wind energy conversion system used in offshore series-dc wind farm concept. The converter consists of input-parallel-output-series (IPOS) connected modules. Each module is a full-bridge dc–dc converter with an active rectifier, which can achieve zero voltage switching for all primary side switches and zero current switching for all secondary side switches and diodes. Under normal operation, the converter is operated with secondary phase-shifted modulation. When tolerable fault occurs in certain module, reconfiguration method ensures uninterrupted operation for the system. Additionally, modular structure provides another level of fault tolerance. More benefits of IPOS structure include reduced input current and output voltage of each module, module shedding capability, reduced ripple content due to interleaving, intrinsic balancing, and scalable control method. Both normal and faulty operations are simulated and also verified by scaled-down prototype experiment.


title: Anticipating Advanced Persistent Threat (APT) countermeasures using collaborative security mechanisms

abstract: Information and communication security has gained significant importance due to its wide spread use, increased sophistication and complexity in its deployment. On the other hand, more sophisticated and stealthy techniques are being practiced by the intruder's group to penetrate and exploit the technology and attack detection. One such treacherous threat to all critical assets of an organization is Advanced Persistent Threat (APT). Since APT attack vector is not previously known, consequently this can harm the organization's assets before the patch for this security flaw is released/available. This paper presents a preliminary research effort to counter the APT or zero day attacks at an early stage by detecting malwares. Open Source version of Security Information and Event Management (SIEM) is used to detect denial of service attack launched through remote desktop service. The framework presented in this paper also shows the efficiency of the technique and it can be enhanced with more sophisticated mechanisms for APT attack detection.


title: Bayesian Nonparametric Methods for Partially-Observable Reinforcement Learning

abstract: Making intelligent decisions from incomplete information is critical in many applications: for example, robots must choose actions based on imperfect sensors, and speech-based interfaces must infer a user’s needs from noisy microphone inputs. What makes these tasks hard is that often we do not have a natural representation with which to model the domain and use for choosing actions; we must learn about the domain’s properties while simultaneously performing the task. Learning a representation also involves trade-offs between modeling the data that we have seen previously and being able to make predictions about new data. This article explores learning representations of stochastic systems using Bayesian nonparametric statistics. Bayesian nonparametric methods allow the sophistication of a representation to scale gracefully with the complexity in the data. Our main contribution is a careful empirical evaluation of how representations learned using Bayesian nonparametric methods compare to other standard learning approaches, especially in support of planning and control. We show that the Bayesian aspects of the methods result in achieving state-of-the-art performance in decision making with relatively few samples, while the nonparametric aspects often result in fewer computations. These results hold across a variety of different techniques for choosing actions given a representation.


title: Representation learning for aspect category detection in online reviews

abstract: User-generated reviews are valuable resources for decision making. Identifying the aspect categories discussed in a given review sentence (e.g., "food" and "service" in restaurant reviews) is an important task of sentiment analysis and opinion mining. Given a predefined aspect category set, most previous researches leverage handcrafted features and a classification algorithm to accomplish the task. The crucial step to achieve better performance is feature engineering which consumes much human effort and may be unstable when the product domain changes. In this paper, we propose a representation learning approach to automatically learn useful features for aspect category detection. Specifically, a semi-supervised word embedding algorithm is first proposed to obtain continuous word representations on a large set of reviews with noisy labels. Afterwards, we propose to generate deeper and hybrid features through neural networks stacked on the word vectors. A logistic regression classifier is finally trained with the hybrid features to predict the aspect category. The experiments are carried out on a benchmark dataset released by SemEval-2014. Our approach achieves the state-of-the-art performance and outperforms the best participating team as well as a few strong baselines.


title: PAPER Special Section on Cyberworlds Immersive Multi-Projector Display on Hybrid Screens with

abstract: Many immersive displays developed in previous researches are strongly influenced by the design concept of the CAVE, which is the origin of the immersive displays. In the view of human-scale interactive system for virtual environment (VE), the existing immersive systems are not enough to use the potential of a human sense further extent. The displays require more complicated structure for flexible extension, and are more restrictive to user's movement. Therefore we propose a novel multi-projector display for immersive VE with haptic interface for more flexible and dynamic interaction. The display part of our system named D-vision has a hybrid curved screen which consist of compound prototype with flat and curve screen. This renders images seamlessly in real time, and generates high-quality stereovision by PC cluster and two-pass technology. Furthermore a human-scale string-based haptic device will integrate with the D-vision for more interactive and immersive VE. In this paper, we show an overview of the D-vision and technologies used for the human-scale haptic interface.


title: Modifying the attitude of quadruped robot body against disturbances via data fusion

abstract: The purpose of this paper is to modify the attitude of quadruped robot body against disturbances via sensory feedback. For this goal Inertial Measurement Unit (a 3-axis gyroscope and a 3-axis accelerometer) and a 3-axis magnetometer are used. In order to use the data provided by IMU and magnetometer, it is required to design a filter to provide an estimation with proper accuracy. A Switching Extended Kalman Filter (SEKF) is used to fuse the sensory information and estimate the quadruped robot bodY attitude along with uncompensated gyro bias. In this filter, switching rule is used to separate the measurement model of EKF in the accelerated or non-accelerated motion of the robot body. Then, the body deviation is compensated using support legs based on the outputs of observer. Through numerical simulations, SEKF is compared with a nonlinear observer and its efficiency is shown. Finally, the robot body attitude is modified on a platform under ramp disturbances.


title: An Automatic Digital Audio Authentication/Forensics System

abstract: With the continuous rise in ingenious forgery, a wide range of digital audio authentication applications are emerging as a preventive and detective control in real-world circumstances, such as forged evidence, breach of copyright protection, and unauthorized data access. To investigate and verify, this paper presents a novel automatic authentication system that differentiates between the forged and original audio. The design philosophy of the proposed system is primarily based on three psychoacoustic principles of hearing, which are implemented to simulate the human sound perception system. Moreover, the proposed system is able to classify between the audio of different environments recorded with the same microphone. To authenticate the audio and environment classification, the computed features based on the psychoacoustic principles of hearing are dangled to the Gaussian mixture model to make automatic decisions. It is worth mentioning that the proposed system authenticates an unknown speaker irrespective of the audio content i.e., independent of narrator and text. To evaluate the performance of the proposed system, audios in multi-environments are forged in such a way that a human cannot recognize them. Subjective evaluation by three human evaluators is performed to verify the quality of the generated forged audio. The proposed system provides a classification accuracy of 99.2% ± 2.6. Furthermore, the obtained accuracy for the other scenarios, such as text-dependent and text-independent audio authentication, is 100% by using the proposed system.


The First Fifteen Years?? Personal Experience with Innominate Osteotomy in the Treatment of Congenital Dislocation and Subluxation of the Hip


title: A comparison of piezoelectric transformer AC/DC converters with current doubler and voltage doubler rectifiers

abstract: The objective of this study was to develop recommendations for an optimal design of piezoelectric transformer (PT) AC/DC converters. The paper presents a comprehensive comparison of the two commonly used rectifier topologies in a PT based power converters: current doubler and voltage doubler rectifiers. The advantages and disadvantages of the two rectifiers were investigated and the range of their applications with respect to output current, voltage, power capability, load resistance etc.-was delineated. Generic parameters are proposed and used to derive normalized and closed form equations that can help choosing a PT for a given set of requirements. Simulation and experimental results were found to be in a good agreement with the derivation of the theoretical analysis.


title: Distributed analytics in fog computing platforms using tensorflow and kubernetes

abstract: Modern Internet-of-Things (IoT) applications produce large amount of data and require powerful analytics approaches, such as Deep Learning to extract useful information. Existing IoT applications transmit the data to resource-rich data centers for analytics. However, it may congest networks, overload data centers, and increase security vulnerability. In this paper, we implement a platform, which integrates resources from data centers (servers) to end devices (IoT devices). We launch distributed analytics applications among the devices without sending everything to the data centers. We analyze challenges to implement such a platform and carefully adopt popular open-source projects to overcome the challenges. We then conduct comprehensive experiments on the implemented platform. The results show: (i) the benefits/limitations of distributed analytics, (ii) the importance of decisions on distributing an application across multiple devices, and (iii) the overhead caused by different components in our platform.


title: LINKKI: A planar linkage-based kinetic toy as a tool for education and design

abstract: This paper presents LINKKI, a kinetic construction toy based on a planar linkage mechanism with which users can design movements, make kinetic arts, and learn basic STEM subjects through hands-on play. Consisting of a modular box, bars and circles, and active/interactive modules, LINKKI simplifies the technical construction toy to such an extent as to look minimal but still retains the versatility of construction blocks as an educational tool and designer's prototyping tool. The simplicity of LINKKI not only allows intuitive play, but also enables users to easily tweak and create custom parts for themselves, thus making it easy to incorporate raw material in addition to the existing parts for prototyping use. Paying attention to the aesthetic side, it is also intended to encourage a demographic with possibly less exposure to technical toys (casual users, young girls, etc.) to be engaged in motion design and STEM subjects. In this paper, I describe the features of a functioning LINKKI prototype and discuss the adaptability of the toy through diverse usage examples and a user study.


title: The Mediterranean diet: science and practice

abstract: OBJECTIVE ::: To provide an overview of research relevant to the Mediterranean diet. ::: ::: ::: DESIGN ::: Personal perspectives. ::: ::: ::: SETTING ::: International. ::: ::: ::: SUBJECT ::: Populations in Europe, North America, Asia. ::: ::: ::: RESULTS ::: Approximately 50 years ago, Keys and colleagues described strikingly low rates of coronary heart disease in the Mediterranean region, where fat intake was relatively high but largely from olive oil. Subsequent controlled feeding studies have shown that compared to carbohydrate, both monounsaturated and polyunsaturated fats reduce LDL and triglycerides and increase HDL cholesterol. Importantly, these beneficial metabolic effects are greater in the presence of underlying insulin resistance. In a detailed analysis within the Nurses' Health Study, trans fat from partially hydrogenated vegetable oils (absent in traditional Mediterranean diets) was most strongly related to risk of heart disease, and both polyunsaturated and monounsaturated fat were inversely associated with risk. Epidemiologic evidence has also supported beneficial effects of higher intakes of fruits and vegetables, whole grains, fish, and daily consumption of moderate amounts of alcohol. Together with regular physical activity and not smoking, our analyses suggest that over 80% of coronary heart disease, 70% of stroke, and 90% of type 2 diabetes can be avoided by healthy food choices that are consistent with the traditional Mediterranean diet. ::: ::: ::: CONCLUSION ::: Both epidemiologic and metabolic studies suggest that individuals can benefit greatly by adopting elements of Mediterranean diets.


title: Haptic classification and recognition of objects using a tactile sensing forearm

abstract: In this paper, we demonstrate data-driven inference of mechanical properties of objects using a tactile sensor array (skin) covering a robot's forearm. We focus on the mobility (sliding vs. fixed), compliance (soft vs. hard), and identity of objects in the environment, as this information could be useful for efficient manipulation and search. By using the large surface area of the forearm, a robot could potentially search and map a cluttered volume more efficiently, and be informed by incidental contact during other manipulation tasks. Our approach tracks a contact region on the forearm over time in order to generate time series of select features, such as the maximum force, contact area, and contact motion. We then process and reduce the dimensionality of these time series to generate a feature vector to characterize the contact. Finally, we use the k-nearest neighbor algorithm (k-NN) to classify a new feature vector based on a set of previously collected feature vectors. Our results show a high cross-validation accuracy in both classification of mechanical properties and object recognition. In addition, we analyze the effect of taxel resolution, duration of observation, feature selection, and feature scaling on the classification accuracy.


title: 15 Years of Semantic Web: An Incomplete Survey

abstract: It has been 15 years since the first publications proposed the use of ontologies as a basis for defining information semantics on the Web starting what today is known as the Semantic Web Research Community. This work undoubtedly had a significant influence on AI as a field and in particular the knowledge representation and Reasoning Community that quickly identified new challenges and opportunities in using Description Logics in a practical setting. In this survey article, we will try to give an overview of the developments the field has gone through in these 15 years. We will look at three different aspects: the evolution of Semantic Web Language Standards, the evolution of central topics in the Semantic Web Community and the evolution of the research methodology.


title: A soft frequent pattern mining approach for textual topic detection

abstract: Textual topic detection methods that work by clustering terms according to their cooccurrence patterns are called feature-pivot methods. Typically, the similarity measure that is used for such clustering methods takes into account the cooccurrence patterns of only pairs of items. In this work, we argue that examining the simultaneous cooccurrence patterns of a larger number of terms, is a better option when the corpus contains a set of closely related fine-grained topics. To this end, we treat the topic detection problem as a Frequent Pattern Mining problem and propose a novel algorithm for "soft" Frequent Pattern Mining. We test the proposed approach using three annotated datasets collected from Twitter and compare it to a set of algorithms that includes a graph-based feature-pivot approach that takes into account only cooccurrence patterns, a standard Frequent Pattern Mining algorithm and Latent Dirichlet Allocation. The results indicate that SFPM is performing better than the other tested methods and show a clear improvement over the standard FPM approach.


title: Routing a mixed fleet of electric and conventional vehicles

abstract: In this paper, we propose the Electric Vehicle Routing Problem with Time Windows and Mixed Fleet (E-VRPTWMF) to optimize the routing of a mixed fleet of electric commercial vehicles (ECVs) and conventional internal combustion commercial vehicles (ICCVs). Contrary to existing routing models for ECVs, which assume energy consumption to be a linear function of traveled distance, we utilize a realistic energy consumption model that incorporates speed, gradient and cargo load distribution. This is highly relevant in the context of ECVs because energy consumption determines the maximal driving range of ECVs and the recharging times at stations. To address the problem, we develop an Adaptive Large Neighborhood Search algorithm that is enhanced by a local search for intensification. In numerical studies on newly designed E-VRPTWMF test instances, we investigate the effect of considering the actual load distribution on the structure and quality of the generated solutions. Moreover, we study the influence of different objective functions on solution attributes and on the contribution of ECVs to the overall routing costs. Finally, we demonstrate the performance of the developed algorithm on benchmark instances of the related problems VRPTW and E-VRPTW.


HCVRD: a benchmark for large-scale Human-Centered Visual Relationship Detection


title: Listening to polyphonic music recruits domain-general attention and working memory circuits

abstract: Polyphonic music combines multiple auditory streams to create complex auditory scenes, thus providing a tool for investigating the neural mechanisms that orient attention in natural auditory contexts. Across two fMRI experiments, we varied stimuli and task demands in order to identify the cortical areas that are activated during attentive listening to real music. In individual experiments and in a conjunction analysis of the two experiments, we found bilateral blood oxygen level dependent (BOLD) signal increases in temporal (the superior temporal gyrus), parietal (the intraparietal sulcus), and frontal (the precentral sulcus, the inferior frontal sulcus and gyrus, and the frontal operculum) areas during selective and global listening, as compared with passive rest without musical stimulation. Direct comparisons of the listening conditions showed significant differences between attending to single timbres (instruments) and attending across multiple instruments, although the patterns that were observed depended on the relative demands of the tasks being compared. The overall pattern of BOLD signal increases indicated that attentive listening to music recruits neural circuits underlying multiple forms of working memory, attention, semantic processing, target detection, and motor imagery. Thus, attentive listening to music appears to be enabled by areas that serve general functions, rather than by music-specific cortical modules.


title: PaperVis: literature review made easy

abstract: Reviewing literatures for a certain research field is always important for academics. One could use Googlelike information seeking tools, but oftentimes he/she would end up obtaining too many possibly related papers, as well as the papers in the associated citation network. During such a process, a user may easily get lost after following a few links for searching or cross-referencing. It is also difficult for the user to identify relevant/important papers from the resulting huge collection of papers. Our work, called PaperVis, endeavors to provide a user-friendly interface to help users quickly grasp the intrinsic complex citation-reference structures among a specific group of papers. We modify the existing Radial Space Filling (RSF) and Bullseye View techniques to arrange involved papers as a node-link graph that better depicts the relationships among them while saving the screen space at the same time. PaperVis applies visual cues to present node attributes and their transitions among interactions, and it categorizes papers into semantically meaningful hierarchies to facilitate ensuing literature exploration. We conduct experiments on the InfoVis 2004 Contest Dataset to demonstrate the effectiveness of PaperVis.


title: Adversarial Connective-exploiting Networks for Implicit Discourse Relation Classification

abstract: Implicit discourse relation classification is of great challenge due to the lack of connectives as strong linguistic cues, which motivates the use of annotated implicit connectives to improve the recognition. We propose a feature imitation framework in which an implicit relation network is driven to learn from another neural network with access to connectives, and thus encouraged to extract similarly salient features for accurate classification. We develop an adversarial model to enable an adaptive imitation scheme through competition between the implicit network and a rival feature discriminator. Our method effectively transfers discriminability of connectives to the implicit features, and achieves state-of-the-art performance on the PDTB benchmark.


title: DisCo: Display-Camera Communication Using Rolling Shutter Sensors

abstract: We present DisCo, a novel display-camera communication system. DisCo enables displays and cameras to communicate with each other while also displaying and capturing images for human consumption. Messages are transmitted by temporally modulating the display brightness at high frequencies so that they are imperceptible to humans. Messages are received by a rolling shutter camera that converts the temporally modulated incident light into a spatial flicker pattern. In the captured image, the flicker pattern is superimposed on the pattern shown on the display. The flicker and the display pattern are separated by capturing two images with different exposures. The proposed system performs robustly in challenging real-world situations such as occlusion, variable display size, defocus blur, perspective distortion, and camera rotation. Unlike several existing visible light communication methods, DisCo works with off-the-shelf image sensors. It is compatible with a variety of sources (including displays, single LEDs), as well as reflective surfaces illuminated with light sources. We have built hardware prototypes that demonstrate DisCo’s performance in several scenarios. Because of its robustness, speed, ease of use, and generality, DisCo can be widely deployed in several applications, such as advertising, pairing of displays with cell phones, tagging objects in stores and museums, and indoor navigation.


title: Audio-based context recognition

abstract: The aim of this paper is to investigate the feasibility of an audio-based context recognition system. Here, context recognition refers to the automatic classification of the context or an environment around a device. A system is developed and compared to the accuracy of human listeners in the same task. Particular emphasis is placed on the computational complexity of the methods, since the application is of particular interest in resource-constrained portable devices. Simplistic low-dimensional feature vectors are evaluated against more standard spectral features. Using discriminative training, competitive recognition accuracies are achieved with very low-order hidden Markov models (1-3 Gaussian components). Slight improvement in recognition accuracy is observed when linear data-driven feature transformations are applied to mel-cepstral features. The recognition rate of the system as a function of the test sequence length appears to converge only after about 30 to 60 s. Some degree of accuracy can be achieved even with less than 1-s test sequence lengths. The average reaction time of the human listeners was 14 s, i.e., somewhat smaller, but of the same order as that of the system. The average recognition accuracy of the system was 58% against 69%, obtained in the listening tests in recognizing between 24 everyday contexts. The accuracies in recognizing six high-level classes were 82% for the system and 88% for the subjects.


title: Cyber security exercises and competitions as a platform for cyber security experiments

abstract: This paper discusses the use of cyber security exercises and competitions to produce data valuable for security research. Cyber security exercises and competitions are primarily arranged to train participants and/or to offer competence contests for those with a profound interest in security. This paper discusses how exercises and competitions can be used as a basis for experimentation in the security field. The conjecture is that (1) they make it possible to control a number of variables of relevance to security and (2) the results can be used to study several topics in the security field in a meaningful way. Among other things, they can be used to validate security metrics and to assess the impact of different protective measures on the security of a system.


title: Mathematical formulas extraction

abstract: As a universal technical language, mathematics hasbeen widely applied in many fields, and it is more accuratethan any other languages in describing information.Therefore, numerous mathematical formulas exist in allkinds of documents. There is no doubt that automaticmathematical formulas processing is very important andnecessary, of which extract formulas from documentimages is the first step. In this paper, formulas extractionmethods which are not based on recognition results arepresented: isolated formulas are extracted based onParzen window and embedded expressions are extractedbased on 2-D structures detection. Experiments show thatour methods are very effective in formulas extraction.


title: Recognizing Daily and Sports Activities in Two Open Source Machine Learning Environments Using Body-Worn Sensor Units

abstract: This study provides a comparative assessment on the different techniques of classifying human activities performed while wearing inertial and magnetic sensor units on the chest, arms and legs. The gyroscope, accelerometer and the magnetometer in each unit are tri-axial. Naive Bayesian classifier, artificial neural networks (ANNs), dissimilarity-based classifier, three types of decision trees, Gaussian mixture models (GMMs) and support vector machines (SVMs) are considered.A feature set extracted from the raw sensor data using principal component analysis is used for classification. Three different cross-validation techniques are employed to validate the classifiers. A performance comparison of the classifiers is provided in terms of their correct differentiation rates, confusion matrices and computational cost. The highest correct differentiation rates are achieved with ANNs (99.2%), SVMs (99.2%) and a GMM (99.1%). GMMs may be preferable because of their lower computational requirements. Regarding the position of sensor units on the body, those worn on the legs are the most informative. Comparing the different sensor modalities indicates that if only a single sensor type is used, the highest classification rates are achieved with magnetometers, followed by accelerometers and gyroscopes. The study also provides a comparison between two commonly used open source machine learning environments (WEKA and PRTools) in terms of their functionality, manageability, classifier performance and execution times.


title: Relationship between self-reported shoulder function/quality of life, body mass index, and other contributing factors in patients awaiting rotator cuff repair surgery.

abstract: HYPOTHESES ::: We hypothesized that body mass index (BMI) would be negatively associated with self-reported shoulder function/pain. A second objective was to investigate the significance of other potential contributing factors: age, sex, smoking status, tear size, and workers' compensation (WC) involvement. ::: ::: ::: METHODS ::: In this retrospective cohort study, consecutive patients who underwent arthroscopic rotator cuff repair surgery between 2002 and 2007 were reviewed. Preoperative American Shoulder and Elbow Society (ASES) and Simple Shoulder Test (SST) scores were compared with regard to age, sex, height, weight, smoking status, WC status, and rotator cuff tear size. ::: ::: ::: RESULTS ::: Fifty-four patients were enrolled in this study. At a level of significance of P < .05, BMI showed no correlation with preoperative SST or ASES scores. WC status was negatively correlated with ASES scores. Female sex, WC status, and smoking were each negatively correlated with SST scores. The regression model predictive of preoperative SST scores included sex, WC status, and smoking status. WC status was predictive of ASES scores. ::: ::: ::: DISCUSSION ::: To our knowledge, this is the first study to examine BMI as a contributing factor in the magnitude of pain and level of function in patients with chronic rotator cuff tears. Our data suggest that there is no impact of BMI on function/pain. The inter-relationships between factors such as BMI, sex, age, and others are complex, and further study is needed to fully establish that there is no effect. ::: ::: ::: CONCLUSION ::: Our hypothesis that BMI is negatively related to magnitude of pain/level of function was not supported. Factors found to be related were sex, WC involvement, and smoking.


title: Urdu text classification

abstract: This paper compares statistical techniques for text classification using Naive Bayes and Support Vector Machines, in context of Urdu language. A large corpus is used for training and testing purpose of the classifiers. However, those classifiers cannot directly interpret the raw dataset, so language specific preprocessing techniques are applied on it to generate a standardized and reduced-feature lexicon. Urdu language is morphological rich language which makes those tasks complex. Statistical characteristics of corpus and lexicon are measured which show satisfactory results of text preprocessing module. The empirical results show that Support Vector Machines outperform Naive Bayes classifier in terms of classification accuracy.


title: ElectriSense: single-point sensing using EMI for electrical event detection and classification in the home

abstract: This paper presents ElectriSense, a new solution for automatically detecting and classifying the use of electronic devices in a home from a single point of sensing. ElectriSense relies on the fact that most modern consumer electronics and fluorescent lighting employ switch mode power supplies (SMPS) to achieve high efficiency. These power supplies continuously generate high frequency electromagnetic interference (EMI) during operation that propagates throughout a home's power wiring. We show both analytically and by in-home experimentation that EMI signals are stable and predictable based on the device's switching frequency characteristics. Unlike past transient noise-based solutions, this new approach provides the ability for EMI signatures to be applicable across homes while still being able to differentiate between similar devices in a home. We have evaluated our solution in seven homes, including one six-month deployment. Our results show that ElectriSense can identify and classify the usage of individual devices with a mean accuracy of 93.82%.


title: Bringing computational thinking to K-12: what is Involved and what is the role of the computer science education community?

abstract: The process of increasing student exposure to computational thinking in K-12 is complex, requiring systemic change, teacher engagement, and development of signifi cant resources. Collaboration with the computer science education community is vital to this effort.


title: Design and analysis of a cable-driven manipulator with variable stiffness

abstract: A manipulator with variable stiffness allows the manipulator to adjust its stiffness to fulfill different task requirements. In this paper, a cable-driven manipulator with the ability to significantly regulate its stiffness through tension manipulation is introduced. Variable stiffness is achieved by attaching a novel variable stiffness device along each driving cable, in which the stiffness of the device is a function the cable tension. As cable-driven manipulator has actuation redundancy, the tension distribution can be manipulated even at a stationary pose. Such property allows the cable-driven manipulator to adjust the stiffness of each variable stiffness device, thereby changing the stiffness of the manipulator. The design and analysis of the variable stiffness device is presented. The variable stiffness device uses commercial torsion springs, and has a compact and light-weight design. Experimental and simulation results verified that cable-driven manipulator with such variable stiffness devices is able to achieve significant stiffness regulation.


title: A variable stiffness soft robotic gripper with low-melting-point alloy

abstract: In this paper, we present the design, fabrication and the function of a soft actuator embedded with Low-Melting-Point Alloy (LMPA). By melting the metal via joule heating, the phase of the metal transformed from the solid state to the liquid state, by which the stiffness of the actuator changed over nearly an order of magnitude. Through a reheating-recrystallizing circle, the actuator can self-healing and recovered from the crack state. The melting speed under different electrical currents was measured. Besides, two experiments were conducted to investigate the self-healing property and stiffness enhancement of the actuator. The LMPA could be melted within 40 seconds. By using three actuators embedding with LMPA, we implemented a soft gripper with variable mechanical stiffness. Experimental results show that the actuator not only has the self-healing property but also enhance the mechanical stiffness compared to the no-LMPA actuator (control). While the LMPA was under solid (recrystallized) state, the bending stiffness of the actuator increased up to 3.5 times over that of the control; while the pull-off force of the gripper increased 6.5 times. The LMPA provide the soft robots with capacities of variable mechanical stiffness, and the self-healing provide a more robust capability for the system.


title: Wideband Electromagnetic Energy Harvesting from a Rotating Wheel

abstract: Harvesting kinetic energy from the environment is an ideal alternative to traditional electrochemical batteries as a power source for low-power electronics such as pressure sensors and thermometers. A tire pressure monitoring system (TPMS) is a key device in preventing a flat tire, which is one of the most frequent causes of car accidents. The U.S.A., European Union, and a number of Asian countries are currently considering the implementation of TPMSrelated legislation. Existing TPMS are powered by a battery, but there are drawbacks to these batteries, such as their limited durability and environmental pollution upon disposal. For this reason, this chapter is aimed at developing a TPMS energy harvester in a rotating wheel.


title: Active Subspace: Toward Scalable Low-Rank Learning

abstract: We address the scalability issues in low-rank matrix learning problems. Usually these problems resort to solving nuclear norm regularized optimization problems (NNROPs), which often suffer from high computational complexities if based on existing solvers, especially in large-scale settings. Based on the fact that the optimal solution matrix to an NNROP is often low rank, we revisit the classic mechanism of low-rank matrix factorization, based on which we present an active subspace algorithm for efficiently solving NNROPs by transforming large-scale NNROPs into small-scale problems. The transformation is achieved by factorizing the large solution matrix into the product of a small orthonormal matrix (active subspace) and another small matrix. Although such a transformation generally leads to nonconvex problems, we show that a suboptimal solution can be found by the augmented Lagrange alternating direction method. For the robust PCA (RPCA) (Candes, Li, Ma, & Wright, 2009) problem, a typical example of NNROPs, theoretical results verify the suboptimality of the solution produced by our algorithm. For the general NNROPs, we empirically show that our algorithm significantly reduces the computational complexity without loss of optimality.


title: Sentence Modeling with Gated Recursive Neural Network

abstract: Recently, neural network based sentence modeling methods have achieved great progress. Among these methods, the recursive neural networks (RecNNs) can effectively model the combination of the words in sentence. However, RecNNs need a given external topological structure, like syntactic tree. In this paper, we propose a gated recursive neural network (GRNN) to model sentences, which employs a full binary tree (FBT) structure to control the combinations in recursive structure. By introducing two kinds of gates, our model can better model the complicated combinations of features. Experiments on three text classification datasets show the effectiveness of our model.


title: Classification of Twitter Accounts into Automated Agents and Human Users

abstract: Online social networks (OSNs) have seen a remarkable rise in the presence of surreptitious automated accounts. Massive human user-base and business-supportive operating model of social networks (such as Twitter) facilitates the creation of automated agents. In this paper we outline a systematic methodology and train a classifier to categorise Twitter accounts into 'automated' and 'human' users. To improve classification accuracy we employ a set of novel steps. First, we divide the dataset into four popularity bands to compensate for differences in types of accounts. Second, we create a large ground truth dataset using human annotations and extract relevant features from raw tweets. To judge accuracy of the procedure we calculate agreement among human annotators as well as with a bot detection research tool. We then apply a Random Forests classifier that achieves an accuracy close to human agreement. Finally, as a concluding step we perform tests to measure the efficacy of our results.


title: Traversing Knowledge Graphs in Vector Space

abstract: Path queries on a knowledge graph can be used to answer compositional questions such as "What languages are spoken by people living in Lisbon?". However, knowledge graphs often have missing facts (edges) which disrupts path queries. Recent models for knowledge base completion impute missing facts by embedding knowledge graphs in vector spaces. We show that these models can be recursively applied to answer path queries, but that they suffer from cascading errors. This motivates a new "compositional" training objective, which dramatically improves all models' ability to answer path queries, in some cases more than doubling accuracy. On a standard knowledge base completion task, we also demonstrate that compositional training acts as a novel form of structural regularization, reliably improving performance across all base models (reducing errors by up to 43%) and achieving new state-of-the-art results.


title: Parallel active dictionary attack on WPA2-PSK Wi-Fi networks

abstract: Wi-Fi network offers an inexpensive and convenient way to access the Internet. It becomes even more important nowadays as we are moving from the traditional computer age to the current mobile devices and Internet-of-Things age. Wi-Fi Protected Access II (WPA2) - Pre-shared key (PSK) is the current security standard used to protect small 802.11 wireless networks. Most of the available dictionary password-guessing attacks on WPA2-PSK are based on capturing the four-way handshaking frames between an authorized wireless client and the Access Point (AP). These attacks will fail if an attacker is unable to capture the four-way handshaking frames of a legitimate client. An attacker also can apply an active dictionary attack by sending a pass-phrase to the AP and waiting for the response. However, this attack approach could only achieve a low attack intensity of testing a few pass-phrases per minute. In this paper, we develop a new scheme to speed up the active pass-phrase guessing trials intensity based on two novel ideas: First, the scheme mimics multiple Wi-Fi clients connecting to the AP at the same time—each emulated Wi-Fi client has its own spoofed MAC address; Second, each emulated Wi-Fi client could try many pass-phrases using a single wireless session without the need to pass the 802.11 authentication and association stages for every pass-phrase guess. We have developed a working prototype and our experiments show that the proposed scheme can improve active dictionary pass-phrase guessing speed by 100-fold compared to the traditional single client attack.


title: Dynamic User Task Scheduling for Mobile Robots

abstract: We present our efforts to deploy mobile robots in office environments, focusing in particular on the challenge of planning a schedule for a robot to accomplish user-requested actions. We concretely aim to make our CoBot mobile robots available to execute navigational tasks requested by users, such as telepresence, and picking up and delivering messages or objects at different locations. We contribute an efficient web-based approach in which users can request and schedule the execution of specific tasks. The scheduling problem is converted to a mixed integer programming problem. The robot executes the scheduled tasks using a synthetic speech and touch-screen interface to interact with users, while allowing users to follow the task execution online. Our robot uses a robust Kinect-based safe navigation algorithm, moves fully autonomously without the need to be chaperoned by anyone, and is robust to the presence of moving humans, as well as non-trivial obstacles, such as legged chairs and tables. Our robots have already performed 15km of autonomous service tasks.


title: The Web Engineering Security (WES) Methodology

abstract: The World Wide Web has had a significant impact on basic operational economical components in global information rich civilizations. This impact is forcing organizations to provide justification for security from a business case perspective and to focus on security from a web application development environment perspective. This increased focus on security was the basis of a business case discussion and led to the acquisition of empirical evidence gathered from a high level Web survey and more detailed industry surveys to analyse security in the Web application development environment. Along with this information, a collection of evidence from relevant literature was also gathered. Individual aspects of the data gathered in the previously mentioned activities contributed to the proposal of the Essential Elements (EE) and the Security Criteria for Web Application Development (SCWAD). The Essential Elements present the idea that there are essential, basic organizational elements that need to be identified, defined and addressed before examining security aspects of a Web Engineering Development process. The Security Criteria for Web Application Development identifies criteria that need to be addressed by a secure Web Engineering process. Both the EE and SCWAD are presented in detail along with relevant justification of these two elements to Web Engineering. SCWAD is utilized as a framework to evaluate the security of a representative selection of recognized software engineering processes used in Web Engineering application development. The software engineering processes appraised by SCWAD include: the Waterfall Model, the Unified Software Development Process (USD), Dynamic Systems Development Method (DSDM) and eXtreme Programming (XP). SCWAD is also used to assess existing security methodologies which are comprised of the Orion Strategy; Survivable / Viable IS approaches; Comprehensive Lightweight Application Security Process (CLASP) and Microsoft’s Trust Worthy Computing Security Development Lifecycle. The synthesis of information provided by both the EE and SCWAD were used to develop the Web Engineering Security (WES) methodology. WES is a proactive, flexible, process neutral security methodology with customizable components that is based on empirical evidence and used to explicitly integrate security throughout an organization’s chosen application development process. In order to evaluate the practical application of the EE, SCWAD and the WES methodology, two case studies were conducted during the course of this research. The first case study describes the application of both the EE and SCWAD to the Hunterian Museum and Art Gallery’s Online Photo Library (HOPL) Internet application project. The second case study presents the commercial implementation of the WES methodology within a Global Fortune 500 financial service sector organization. The assessment of the WES methodology within the organization consisted of an initial survey establishing current security practices, a follow-up survey after changes were implemented and an overall analysis of the security conditions assigned to projects throughout the life of the case study.


title: CLOUD REQUIREMENT FRAMEWORK: REQUIREMENTS AND EVALUATION CRITERIA TO ADOPT CLOUD SOLUTIONS

abstract: Due to the fast growth, Cloud Computing has become a non-transparent market with providers and customers willing to adopt it. Furthermore, many offers only partially meet customers’ requirements and it is not clear how exactly Cloud Computing influences the IT. That makes it difficult for customers to plan migration projects and implement sustainable Cloud solutions. There are important factors and considerations for the decision to adopt Cloud Computing. The current studies and research in this field can be summarized to focus around the questions why adoption of Cloud Computing would occur, how much adoption would take place or how it would be adopted. But the adoption requirements covering all three service models (SaaS, PaaS, IaaS) have barely been discussed in literature so far. A detailed understanding of Cloud requirements enables customers to adopt Cloud solutions efficiently. Therefore this paper aims to contribute a framework addressing the adoption and selection of Cloud services. A Cloud Requirement Framework (CRF) was developed, concentrating on relevant requirements for adopting Cloud services targeting all three service models. To develop this framework we followed a design science approach and conducted a systematic literature review, an extensive market analysis and an evaluation based on expert interviews.


title: Co-creation: Towards a taxonomy and an integrated research perspective

abstract: Enabled by the Internet-Web compound, co-creation of value by consumers has emerged as a major force in the marketplace. In sponsored co-creation, which takes place at the behest of producers, the activities of consumers drive or support the producers' business models. Autonomous co-creation is a wide range of consumer activities that amount to consumer-side production of value. Thus, individuals and communities have become a significant, and growing, productive force in e-commerce. To recognize co-creation, so broadly understood, as a fundamental area of e-commerce research, it is necessary to attain an integrated research perspective on this greatly varied, yet cohering, domain. The enabling information technology needs to be developed to suit the context. Toward these ends, the paper analyzes the intellectual space underlying co-creation research and proposes an inclusive taxonomy of Web-based co-creation, informed both by the extant multidisciplinary research and by results obtained in the natural laboratory of the Web. The essential directions of co-creation research are outlined, and some promising avenues of future work discussed. The taxonomic framework and the research perspective lay a foundation for the future development of co-creation theory and practice. The certainty of turbulent developments in e-commerce means that the taxonomic framework will require ongoing revision and expansion, as will any future framework.


title: The Effect of Noise on Sofware Engineers' Performance

abstract: Background: Noise, defined as an unwanted sound, is one of the commonest factors that could affect people's performance in their daily work activities. The software engineering research community has marginally investigated the effects of noise on software engineers' performance. Aims: We studied if noise affects software engineers' performance in (i) comprehending functional requirements and (ii) fixing faults in the source code. Method: We conducted two experiments with final-year undergraduate students in Computer Science. In the first experiment, we asked 55 students to comprehend functional requirements exposing them or not to noise, while in the second experiment 42 students were asked to fix faults in Java code. Results: The participants in the second experiment, when exposed to noise, had significantly worse performance in fixing faults in the source code. On the other hand, we did not observe any statistically significant difference in the first experiment. Conclusions: Fixing faults in source code seems to be more vulnerable to noise than comprehending functional requirements.


title: Global-Local Temporal Saliency Action Prediction

abstract: Action prediction on a partially observed action sequence is a very challenging task. To address this challenge, we first design a global-local distance model, where a global-temporal distance compares subsequences as a whole and local-temporal distance focuses on individual segment. Our distance model introduces temporal saliency for each segment to adapt its contribution. Finally, a global-local temporal action prediction model is formulated in order to jointly learn and fuse these two types of distances. Such a prediction model is capable of recognizing action of: 1) an on-going sequence and 2) a sequence with arbitrarily frames missing between the beginning and end (known as gap-filling). Our proposed model is tested and compared with related action prediction models on BIT, UCF11, and HMDB data sets. The results demonstrated the effectiveness of our proposal. In particular, we showed the benefit of our proposed model on predicting unseen action types and the advantage on addressing the gapfilling problem as compared with recently developed action prediction models.


Realtime Ray Tracing and its use for . . .


title: Random Matrix Theory and ζ(1/2 + it)

abstract: We study the characteristic polynomials Z(U, θ) of matrices U in the Circular Unitary Ensemble (CUE) of Random Matrix Theory. Exact expressions for any matrix size N are derived for the moments of |Z| and Z/Z *, and from these we obtain the asymptotics of the value distributions and cumulants of the real and imaginary parts of log Z as N→∞. In the limit, we show that these two distributions are independent and Gaussian. Costin and Lebowitz [15] previously found the Gaussian limit distribution for Im log Z using a different approach, and our result for the cumulants proves a conjecture made by them in this case. We also calculate the leading order N→∞ asymptotics of the moments of |Z| and Z/Z *. These CUE results are then compared with what is known about the Riemann zeta function ζ (s) on its critical line Re s= 1/2, assuming the Riemann hypothesis. Equating the mean density of the non-trivial zeros of the zeta function at a height T up the critical line with the mean density of the matrix eigenvalues gives a connection between N and T. Invoking this connection, our CUE results coincide with a theorem of Selberg for the value distribution of log ζ(1/2+iT) in the limit T→∞. They are also in close agreement with numerical data computed by Odlyzko [29] for large but finite T. This leads us to a conjecture for the moments of |ζ(1/2+it) |. Finally, we generalize our random matrix results to the Circular Orthogonal (COE) and Circular Symplectic (CSE) Ensembles.


title: Bit Fusion: Bit-Level Dynamically Composable Architecture for Accelerating Deep Neural Network

abstract: Hardware acceleration of Deep Neural Networks (DNNs) aims to tame their enormous compute intensity. Fully realizing the potential of acceleration in this domain requires understanding and leveraging algorithmic properties of DNNs. This paper builds upon the algorithmic insight that bitwidth of operations in DNNs can be reduced without compromising their accuracy. However, to prevent accuracy loss, the bitwidth varies significantly across DNNs and it may even be adjusted for each layer individually. Thus, a fixed-bitwidth accelerator would either offer limited benefits to accommodate the worst-case bitwidth, or inevitably lead to a degradation in final accuracy. To alleviate these deficiencies, this work introduces dynamic bit-level fusion/decomposition as a new dimension in the design of DNN accelerators. We explore this dimension by designing Bit Fusion, a bit-flexible accelerator, that constitutes an array of bit-level processing elements that dynamically fuse to match the bitwidth of individual DNN layers. This flexibility in the architecture minimizes the computation and the communication at the finest granularity possible with no loss in accuracy. We evaluate the benefits of Bit Fusion using eight real-world feed-forward and recurrent DNNs. The proposed microarchitecture is implemented in Verilog and synthesized in 45 nm technology. Using the synthesis results and cycle accurate simulation, we compare the benefits of Bit Fusion to two state-of-the-art DNN accelerators, Eyeriss and Stripes. In the same area, frequency, and technology node, Bit Fusion offers 4.3x speedup and 9.6x energy savings over Eyeriss. Bit Fusion provides 2.4x speedup and 4.1x energy reduction over Stripes at 45 nm node when Bit Fusion area and frequency are set to those of Stripes. Compared to Jetson-TX2, Bit Fusion offers 4.3x speedup and almost matches the performance of TitanX, which is 4.6x faster than TX2.


NEW DIMENSIONS IN THE ASSESSMENT OF VIRTUAL REALITY INDUCED SIDE EFFECTS EXPLORING NEW DIMENSIONS IN THE ASSESSMENT OF VIRTUAL REALITY INDUCED SIDE EFFECTS


title: A look at challenges and opportunities of Big Data analytics in healthcare

abstract: Big Data analytics can revolutionize the healthcare industry. It can improve operational efficiencies, help predict and plan responses to disease epidemics, improve the quality of monitoring of clinical trials, and optimize healthcare spending at all levels from patients to hospital systems to governments. This paper provides an overview of Big Data, applicability of it in healthcare, some of the work in progress and a future outlook on how Big Data analytics can improve overall quality in healthcare systems.


Heterogeneous Multi-Agent Deep Reinforcement Learning for Traffic Lights Control


title: Cutting and packing

abstract: Over a decade ago, the European Journal of Operational Research published its first special issue on Cutting and Packing [1] in conjunction with the founding of SICUP, the Special Interest Group on Cutting and Packing. The majority of the papers in the first special issue were focused on cutting problems in the aluminium, paper, and canvas industry, as well as container loading problems. In 1995, a second special issue appeared [2]. As before, the issue included a number of papers on traditional oneand two-dimensional cutting stock problems and container/pallet loading problems, but it also reported new research in nesting problems and introduced the use of metaheuristics such as simulated annealing and genetic algorithms for solving packing problems. Interest in solving these problems continues to grow: an increasing number of papers are being published in the literature each year on a range of cutting, packing, loading, and layout problems. Following the trend, this third special issue of EJOR reports current research that addresses a variety of cutting, packing and related problems where classical, as well heuristic approaches are applied. A first cluster of papers is dedicated to one-dimensional cutting and packing. De Carvalho reviews several models for problems of the cutting stock and bin packing type and analyses the relationship between them as grounds for the development of branch-and-price algorithms. In the two succeeding papers, new methods for the generation of integer solutions to the one-dimensional cutting stock problem with different standard lengths are presented. Belov and Scheithauer introduced an exact approach which uses Chvatal– Gomory cutting planes to tighten up the continuous relaxation of the classic model formulation. Despite the impressive computational results given, the suggested method may still not be applicable to all practical problems due to its computational requirements. In such cases, the heuristic solution method developed and evaluated by Holthaus may represent a feasible alternative. Important aspects of many real-world problems are considered in another two papers. Zak looks at cutting processes which extend over several stages. He proposes a solution method which is based on the classic column-generation procedure by Gilmore and Gomory and dynamically generates both rows (intermediate sizes) and columns (cutting patterns). However, the method does not guarantee that an optimal solution is found. In practice, the question of how to cut down orders from stock lengths is often interconnected with the problem of sequencing the cutting patterns in the most economical way. Armbruster describes such a problem at a steel service centre and suggests a European Journal of Operational Research 141 (2002) 239–240


Social desirability and ego resiliency.


title: Churn Prediction in MMORPGs Using Player Motivation Theories and an Ensemble Approach

abstract: In this paper, we investigate the problem of churn prediction in Massively multiplayer online role-playing games (MMORPGs) from a social science perspective and develop models incorporating theories of player motivation. The ability to predict player churn can be a valuable resource to game developers designing customer retention strategies. The results from our theory-driven model significantly outperform a diffusion-based churn prediction model on the same dataset. We describe the synthesis between a theory-driven approach and a data-driven approach to a problem and examine the trade-offs involved between the two approaches in terms of prediction accuracy, interpretability and model complexity. We observe that even though the theory-driven model is not as accurate as the data-driven one, the theory-driven model itself can be more interpretable to the domain experts and hence, more preferable over a complex data-driven model. We perform lift analysis of the two models and find that if a marketing effort is restricted in the number of customers it can contact, the theory-driven model would offer much better return-on-investment by identifying more customers among that restricted set who have the highest probability of churn. Finally, we use a clustering technique to partition the dataset and then build an ensemble on the partitioned dataset for better performance. Experiment results show that the ensemble performs notably better than the single classifier in terms of its recall value, which is a highly desirable property in the churn prediction problem.


title: Emotion regulation as a transdiagnostic treatment construct across anxiety, depression, substance, eating and borderline personality disorders: A systematic review.

abstract: Abstract A large body of research has implicated difficulties in emotion regulation as central to the development and maintenance of psychopathology. Emotion regulation has therefore been proposed as a transdiagnostic construct or an underlying mechanism in psychopathology. The transdiagnostic role of emotion regulation has yet to be systematically examined within the psychological treatment outcome literature. It can be proposed that if emotion regulation is indeed a transdiagnostic construct central to the maintenance of psychopathology, then changes in emotion regulation difficulties will occur after effective treatment and this will occur for different disorders. We conducted a systematic review, identifying 67 studies that measured changes in both emotion regulation and symptoms of psychopathology following a psychological intervention for anxiety, depression, substance use, eating pathology or borderline personality disorder. Results demonstrated that regardless of the intervention or disorder, both maladaptive emotion regulation strategy use and overall emotion dysregulation were found to significantly decrease following treatment in all but two studies. Parallel decreases were also found in symptoms of anxiety, depression, substance use, eating pathology and borderline personality disorder. These results contribute to the growing body of evidence supporting the conceptualization of emotion regulation as a transdiagnostic construct. The present study discusses the important implications of these findings for the development of unified treatments that target emotion regulation for individuals who present with multiple disorders.


title: Adaptive Stochastic Gradient Langevin Dynamics: Taming Convergence and Saddle Point Escape Time

abstract: In this paper, we propose a new adaptive stochastic gradient Langevin dynamics (ASGLD) algorithmic framework and its two specialized versions, namely adaptive stochastic gradient (ASG) and adaptive gradient Langevin dynamics(AGLD), for non-convex optimization problems. All proposed algorithms can escape from saddle points with at most $O(\log d)$ iterations, which is nearly dimension-free. Further, we show that ASGLD and ASG converge to a local minimum with at most $O(\log d/\epsilon^4)$ iterations. Also, ASGLD with full gradients or ASGLD with a slowly linearly increasing batch size converge to a local minimum with iterations bounded by $O(\log d/\epsilon^2)$, which outperforms existing first-order methods.


title: Deformable wheel robot based on origami structure

abstract: Origami is the traditional Japanese art of paper folding. Due to its fascinating properties, several attempts are being actively made to expand applications of origami-inspired designs in engineering. This paper presents the design of a deformable wheel based on an origami structure that was integrated with a small-scale mobile robot. The wheel of the robot employs an origami structure proposed by Guest et al. All segments of the structure are connected by links-i.e., folding lines-and this linked structure provides advantages in terms of maintaining geometry and force transmissibility. These two advantages enable control of the shape or size of the wheel by activating a certain portion of the structure. With this property, the wheel diameter of the robot is reduced from 11 cm to 4 cm by four SMA coil spring actuators. Two plate springs are embedded in the wheel to maintain stiffness and allow the wheel to recover from contraction. With the deformable wheel, the robot can pass through a 5 cm gap despite having an 11 cm wheel in its normal state. This deformable wheel concept can be used to build mobile robots that can move quickly with large wheels and move through small gaps when required.


A Wireless Battery Charger for Mobile Device


title: E-BANKING SERVICES - FEATURES, CHALLENGES AND BENEFITS

abstract: Today traditional banking services, based on lending and deposit-taking operations, are only part of banking activities. Due to the emergence of a knowledge-based economy and society as information and communication technology advanced, banking services have undergone profound changes during the past decades. In order to improve the quality of customer service delivery and reduce transaction cost, banks have invested to a great extent in ICT and have adopted ICT networks for delivering a wide range of banking products and services. Banks all over the world have embraced innovative banking technologies and e-banking services in recent years. In this context, the paper aims to provide an overview of the electronic banking service highlighting various aspects of e-banking. Although e-banking offers many advantages both to corporate and individual clients, electronic banking is not without certain challenges and issues in terms of security and interest of customers.


title: Vehicle velocity estimation using nonlinear observers

abstract: Nonlinear observers for estimation of lateral and longitudinal velocity of automotive vehicles are proposed. The observers are based on a sensor suite that is standard in many new cars, consisting of acceleration and yaw rate measurements in addition to wheel speed and steering angle measurements. Two approaches are considered: first, a modular approach where the estimated longitudinal velocity is used as input to the observer for lateral velocity, and second, a combined approach where all states are estimated in the same observer. Both approaches use a tire-road friction model, which is assumed to be known. It is also assumed that the road is flat. Stability of the observers is proven in the form of input-to-state stability of the observer error dynamics, under a structural assumption on the friction model. The assumption on the friction model is discussed in detail, and the observers are validated on experimental data from cars.


title: Predicting one’s own forgetting: The role of experience-based and theory-based processes

abstract: The authors examined the hypothesis that judgments of learning (JOL), if governed by processing fluency during encoding, should be insensitive to the anticipated retention interval. Indeed, neither item-by-item nor aggregate JOLs exhibited “forgetting” unless participants were asked to estimate recall rates for several different retention intervals, in which case their estimates mimicked closely actual recall rates. These results and others reported suggest that participants can access their knowledge about forgetting but only when theory-based predictions are made, and then only when the notion of forgetting is accentuated either by manipulating retention interval within individuals or by framing recall predictions in terms of forgetting rather than remembering. The authors interpret their findings in terms of the distinction between experience-based and theory-based JOLs.


title: Similarity measurement based on cloud models for time series prediction

abstract: Time series prediction has been extensively used for decision-making in many areas such as economics, engineering and medicine. And the useful data can be excavated by similarity measure of time series from a mass of historical data for predicting. The collected data from the real world is often uncertain, and the cloud model can be a good solution for the problem of uncertainty. This paper proposes a method based on the similarity degree of cloud model and combines it with back propagation network for prediction. In addition to the sequence itself, the trend of the sequence is used as another index of similarity. The neighbour set of query sequence from the training set is selected by similarity measure. Based on the neighbour set, a back propagation network is trained and used for prediction. Experimental results from the six time series show that the proposed method obtains better prediction accuracies than the comparative methods, which reveal its effectiveness.


title: In‐Plane 2H‐1T′ MoTe2 Homojunctions Synthesized by Flux‐Controlled Phase Engineering

abstract: The fabrication of in-plane 2H-1T′ MoTe2 homojunctions by the flux-controlled, phase-engineering of few-layer MoTe2 from Mo nanoislands is reported. The phase of few-layer MoTe2 is controlled by simply changing Te atomic flux controlled by the temperature of the reaction vessel. Few-layer 2H MoTe2 is formed with high Te flux, while few-layer 1T′ MoTe2 is obtained with low Te flux. With medium flux, few-layer in-plane 2H-1T′ MoTe2 homojunctions are synthesized. As-synthesized MoTe2 is characterized by Raman spectroscopy and X-ray photoelectron spectroscopy. Kelvin probe force microscopy and Raman mapping confirm that in-plane 2H-1T′ MoTe2 homojunctions have abrupt interfaces between 2H and 1T′ MoTe2 domains, possessing a potential difference of about 100 mV. It is further shown that this method can be extended to create patterned metal–semiconductor junctions in MoTe2 in a two-step lithographic synthesis. The flux-controlled phase engineering method could be utilized for the large-scale controlled fabrication of 2D metal–semiconductor junctions for next-generation electronic and optoelectronic devices.


title: Handwriting beautification using token means

abstract: In this paper, we propose a general purpose approach to handwriting beautification using online input from a stylus. Given a sample of writings, drawings, or sketches from the same user, our method improves a user's strokes in real-time as they are drawn. Our approach relies on one main insight. The appearance of the average of multiple instances of the same written word or shape is better than most of the individual instances. We utilize this observation using a two-stage approach. First, we propose an efficient real-time method for finding matching sets of stroke samples called tokens in a potentially large database of writings from a user. Second, we refine the user's most recently written strokes by averaging them with the matching tokens. Our approach works without handwriting recognition, and does not require a database of predefined letters, words, or shapes. Our results show improved results for a wide range of writing styles and drawings.


title: The Effects of Two Types of Sleep Deprivation on Visual Working Memory Capacity and Filtering Efficiency

abstract: Sleep deprivation has adverse consequences for a variety of cognitive functions. The exact effects of sleep deprivation, though, are dependent upon the cognitive process examined. Within working memory, for example, some component processes are more vulnerable to sleep deprivation than others. Additionally, the differential impacts on cognition of different types of sleep deprivation have not been well studied. The aim of this study was to examine the effects of one night of total sleep deprivation and 4 nights of partial sleep deprivation (4 hours in bed/night) on two components of visual working memory: capacity and filtering efficiency. Forty-four healthy young adults were randomly assigned to one of the two sleep deprivation conditions. All participants were studied: 1) in a well-rested condition (following 6 nights of 9 hours in bed/night); and 2) following sleep deprivation, in a counter-balanced order. Visual working memory testing consisted of two related tasks. The first measured visual working memory capacity and the second measured the ability to ignore distractor stimuli in a visual scene (filtering efficiency). Results showed neither type of sleep deprivation reduced visual working memory capacity. Partial sleep deprivation also generally did not change filtering efficiency. Total sleep deprivation, on the other hand, did impair performance in the filtering task. These results suggest components of visual working memory are differentially vulnerable to the effects of sleep deprivation, and different types of sleep deprivation impact visual working memory to different degrees. Such findings have implications for operational settings where individuals may need to perform with inadequate sleep and whose jobs involve receiving an array of visual information and discriminating the relevant from the irrelevant prior to making decisions or taking actions (e.g., baggage screeners, air traffic controllers, military personnel, health care providers).


title: Drive-by-wire vehicle stabilization and yaw regulation: a hybrid Model Predictive Control design

abstract: Electronic Stability Control (ESC) and Active Front Steering (AFS) have been introduced in production vehicles in recent years, due to improved vehicle maneuverability and the effects in reducing single vehicle accident. We propose a hybrid Model Predictive Control (MPC) design for coordinated control of AFS and ESC. By formulating the vehicle dynamics with respect to the front and rear tire slip angles and by approximating the tire-force characteristics by piecewise affine functions, the vehicle dynamics are formulated as a linear hybrid dynamical system. This model is used to design a hybrid model predictive controller. The proposed model formulation allows one to visually analyze the stability region of the closed-loop system and to assess the stabilizing capability of the hybrid MPC controller. Simulations of the controller in closed-loop with an accurate nonlinear model are presented.


title: Structure Grid for Directional Stippling

abstract: This paper presents a novel method to convert a photograph into a stipple illustration. Our method addresses directional stippling, where the collective flows of dots are directed parallel and/or orthogonal to the local feature orientations. To facilitate regular and directional spacing of dots, we introduce the notion of a structure grid, which is extracted from the smoothed feature orientation field. We represent a structure grid as a 2D texture and develop an efficient construction algorithm that outperforms conventional Lloyd’s method in terms of the rigor of dot alignment. Moreover, the criss-crossing nature of a structure grid allows for the inclusion of line primitives, providing effective description of dark tone. Given a structure grid, we determine the appropriate positions and attributes of primitives in the final illustration via rapid pixel-based primitive rendering. Experimental results show that our directional stippling method nicely reproduces features and tones of various input images.