title: A Comparison of Methods for Multiclass Support Vector Machines

abstract: Support vector machines (SVMs) were originally designed for binary classification. How to effectively extend it for multiclass classification is still an ongoing research issue. Several methods have been proposed where typically we construct a multiclass classifier by combining several binary classifiers. Some authors also proposed methods that consider all classes at once. As it is computationally more expensive to solve multiclass problems, comparisons of these methods using large-scale problems have not been seriously conducted. Especially for methods solving multiclass SVM in one step, a much larger optimization problem is required so up to now experiments are limited to small data sets. In this paper we give decomposition implementations for two such "all-together" methods. We then compare their performance with three methods based on binary classifications: "one-against-all," "one-against-one," and directed acyclic graph SVM (DAGSVM). Our experiments indicate that the "one-against-one" and DAG methods are more suitable for practical use than the other methods. Results also show that for large problems methods by considering all data at once in general need fewer support vectors.


The Design and Implementation of the Database File System


title: LOMAC: Low Water-Mark integrity protection for COTS environments

abstract: We hypothesize that a form of kernel-resident access control based integrity protection can gain widespread acceptance in commercial off-the-shelf (COTS) environments, provided that it couples some useful protection with a high degree of compatibility with existing software, configurations, and practices. To test this hypothesis, we have developed a highly compatible free open-source prototype called LOMAC, and released it on the Internet. LOMAC is a dynamically loadable extension for COTS Linux kernels that provides integrity protection based on Low Water-Mark access control. We present a classification of existing access control models with regard to compatibility, concluding that models similar to Low Water-Mark are especially well suited to high-compatibility solutions. We also describe our practical strategies for dealing with the pathological cases in the Low Water-Mark model's behavior which include a small extension of the model, and an unusual application of its concepts.


title: From image parsing to painterly rendering

abstract: We present a semantics-driven approach for stroke-based painterly rendering, based on recent image parsing techniques [Tu et al. 2005; Tu and Zhu 2006] in computer vision. Image parsing integrates segmentation for regions, sketching for curves, and recognition for object categories. In an interactive manner, we decompose an input image into a hierarchy of its constituent components in a parse tree representation with occlusion relations among the nodes in the tree. To paint the image, we build a brush dictionary containing a large set (760) of brush examples of four shape/appearance categories, which are collected from professional artists, then we select appropriate brushes from the dictionary and place them on the canvas guided by the image semantics included in the parse tree, with each image component and layer painted in various styles. During this process, the scene and object categories also determine the color blending and shading strategies for inhomogeneous synthesis of image details. Compared with previous methods, this approach benefits from richer meaningful image semantic information, which leads to better simulation of painting techniques of artists using the high-quality brush dictionary. We have tested our approach on a large number (hundreds) of images and it produced satisfactory painterly effects.


title: MORPHOLOGICAL SUFFIXES IN THE ARTICLE OF THE JAKARTA POST ONLINE NEWSPAPER

abstract: This study was designed in the form of descriptive qualitative research with the aim at describing the most frequently used suffix in the article entitled “B r itish Company to Invest $200m in West Java waste-to-fuel Plants” from online newspaper The Jakarta Post that published on 4th of March 2019. Documentation is the techniques of data collecting used in this study by reading, analyzing the text, gathering, and counting the suffixes used. The result showed that there are 83 data of suffixes found with 62 data are inflectional suffixes consist of 37% suffix –s (31 data), 18% suffix –ed (15 data), 18% suffix –ing (15 data), and 1% suffix –er (1 data). The rest of 21 data found are derivational suffixes, consist of 1% suffix –able, –ant, –ate, –ation, –ition, –in, –or, –ship, and –ty with each suffix consist of 1 data. The other forms of derivational suffixes found are 5% suffix –ion (4 data), 5% suffix –ly (4 data), 3% suffix –ment (2 data), and 3% suffix –y (2 data). Thus, the writers concluded that inflectional suffixes with the form of suffix –s is the most frequently used suffix in the article.


title: Dwarf germplasm: the key to giant Cannabis hempseed and cannabinoid crops

abstract: After a century of banishment, both euphoric (“marijuana”) and non-euphoric (“industrial hemp”) classes of Cannabis sativa are attracting billions of dollars of investment as new legitimate crops. Most domesticated C. sativa is very tall, a phenotype that is desirable only for hemp fibre obtained from the stems. However, because the principal demands today are for chemicals from the inflorescence and oilseeds from the infructescence, an architecture maximizing reproductive tissues while minimizing stems is appropriate. Such a design was the basis of the greatest short-term increases in crop productivity in the history of agriculture: the creation of short-stature (“semi-dwarf”), high-harvest-index grain cultivars, especially by ideotype breeding, as demonstrated during the “Green Revolution.” This paradigm has considerable promise for C. sativa. The most critical dwarfing character for breeding such productivity into C. sativa is contraction of internodes. This reduces stem tissues (essentially a waste product except for fibre hemp) and results in compact inflorescences (which, on an area basis, maximize cannabinoid chemicals) and infructescences (which maximize oilseed production), as well as contributing to ease of harvesting and efficiency of production on an area basis. Four sources of germplasm useful for breeding semi-dwarf biotypes deserve special attention: (1) Naturally short northern Eurasian wild plants (often photoperiodically day-neutral, unlike like most biotypes) adapted to the stress of very short seasons by maximizing relative development of reproductive tissues. (2) Short, high-harvest-index, oilseed plants selected in northern regions of Eurasia. (3) “Indica type” marijuana, an ancient semi-dwarf cultigen tracing to the Afghanistan-Pakistan area. (4) Semi-dwarf strains of marijuana bred illegally in recent decades to avoid detection when grown clandestinely indoors for the black market. Although the high THC content in marijuana strains limits their usage as germplasm for low-THC cultivars, modern breeding techniques can control this variable. The current elimination of all marijuana germplasm from breeding of hemp cultivars is short-sighted because marijuana biotypes possess a particularly wide range of genes. There is an urgent need to develop public gene bank collections of Cannabis.


title: On the Relationships Between Stability and Bug-Proneness of Code Clones: An Empirical Study

abstract: Exact or similar copies of code fragments in a code base are known as code clones. Code clones are considered as one of the serious code smells. Stability is a widely investigated perspective of assessing the impacts of clones on software systems. A number of existing studies show that clones are often less stable than non-cloned code. This suggests that clones change more frequently than non-cloned code and thus may require comparatively more maintenance efforts. Again, frequent changes to clones may increase the likelihood of missing change propagation to the co-change candidates leading to inconsistencies or bugs. However, none of the existing studies investigate whether stability of clones is related to the bug-proneness. In this paper, we present an empirical study that analyzes the relationships between stability and bug-proneness of clones. We identify bug-fix commits by analyzing the commit messages from software repositories. We then identify the clones those are changed in the bug-fix commits as bug-prone clones. We then compare the stability of buggy and non-buggy clones considering the fine-grained syntactic change types and their significance.,,Our experimental results based on five open-source Java systems of different size and application domains show that (1) stability and bug-proneness of code clones are related and this relationship is statistically significant, (2) for both exact (Type 1) and near-miss (Type 2 and Type 3) clones, buggy clones tend to have higher frequency of changes than non-buggy clones, (3) the bug-proneness of Type 2 and Type 3 clones tend to be strongly related with their stability compared to Type 1 clones, and (4) the relation between the stability and the bug-proneness of clones with respect to fine-grained change types is likely to be influenced by the changes of low to medium significance. We believe that our findings are important and potentially useful in identifying and prioritizing candidate clones for management.


title: Relevance Feedback � A Power Tool for Interactive Content Based Image Retrieval

abstract: Content-based image retrieval (CBIR) has become one of the most active research areas in the past few years. Many visual feature representations have been explored and many systems built. While these research efforts establish the basis of CBIR, the usefulness of the proposed approaches is limited. Specifically, these efforts have relatively ignored two distinct characteristics of CBIR systems: (1) the gap between high-level concepts and low-level features, and (2) the subjectivity of human perception of visual content. This paper proposes a relevance feedback based interactive retrieval approach, which effectively takes into account the above two characteristics in CBIR. During the retrieval process, the user's high-level query and perception subjectivity are captured by dynamically updated weights based on the user's feedback. The experimental results over more than 70000 images show that the proposed approach greatly reduces the user's effort of composing a query, and captures the user's information need more precisely.


title: Attachment Theory as a Guide to Understanding and Working With Transference and the Real Relationship in Psychotherapy

abstract: Recent decades have witnessed an extraordinary amount of conceptual and empirical work on attachment theory in psychology and psychotherapy. Attachment theory is discussed in the present article as a way of understanding and fostering therapeutic work with 2 other key relationship constructs that have been theorized to be elements of all psychotherapies: client transference and the real relationship existing between the therapist and patient. Fundamental features of attachment, transference, and the real relationship are summarized. Particular emphasis is given to the role of the therapist as a secure base and a safe haven within the real relationship, and to the patient's internal working model as it relates to transference. A case of long-term psychodynamic psychotherapy conducted by the first author is presented to illuminate the 3 main constructs. The case demonstrates both the usefulness of attachment theory and the fact that any single theory cannot explain all of the complex features of a given treatment.


title: Game theory in supply chain analysis

abstract: Game theory (hereafter GT) is a powerful tool for analyzing situations in which the decisions of multiple agents affect each agent’s payoff. As such, GT deals with interactive optimization problems. While many economists in the past few centuries have worked on what can be considered game-theoretic models, John von Neumann and Oskar Morgenstern are formally credited as the fathers of modern game theory. Their classic book “Theory of Games and Economic Behavior”, von Neumann and Morgenstern (1944), summarizes the basic concepts existing at that time. GT has since enjoyed an explosion of developments, including the concept of equilibrium by Nash (1950), games with imperfect information by Kuhn (1953), cooperative games by Aumann (1959) and Shubik (1962) and auctions by Vickrey (1961), to name just a few. Citing Shubik (2002), “In the 50s ... game theory was looked upon as a curiosum not to be taken seriously by any behavioral scientist. By the late 1980s, game theory in the new industrial organization has taken over ... game theory has proved its success in many disciplines.”


title: Discovering Block-Structured Process Models From Event Logs- A Constructive Approach

abstract: Process discovery is the problem of, given a log of observed behaviour, finding a process model that 'best' describes this behaviour. A large variety of process discovery algorithms has been proposed. However, no existing algorithm guarantees to return a fitting model (i.e., able to reproduce all observed behaviour) that is sound (free of deadlocks and other anomalies) in finite time. We present an extensible framework to discover from any given log a set of block-structured process models that are sound and fit the observed behaviour. In addition we characterise the minimal information required in the log to rediscover a particular process model. We then provide a polynomial-time algorithm for discovering a sound, fitting, block-structured model from any given log; we give sufficient conditions on the log for which our algorithm returns a model that is language-equivalent to the process model underlying the log, including unseen behaviour. The technique is implemented in a prototypical tool.


title: Yagi slot antenna based on substrate integrated waveguide cavity for wider bandwidth applications

abstract: To design, develop and study the substrate integrated waveguide using slot antennas with Yagi configurations and analyze its performance at s-band frequency. Compactness, light weight and improved performance are the major objective of this project. The main focus is towards achieving miniaturized form of waveguide based slot antenna suitable for mobile application and air-borne requirements.


title: Current status of research on optimum sizing of stand-alone hybrid solar-wind power generation systems

abstract: Solar and wind energy systems are omnipresent, freely available, environmental friendly, and they are considered as promising power generating sources due to their availability and topological advantages for local power generations. Hybrid solar-wind energy systems, uses two renewable energy sources, allow improving the system efficiency and power reliability and reduce the energy storage requirements for stand-alone applications. The hybrid solar-wind systems are becoming popular in remote area power generation applications due to advancements in renewable energy technologies and substantial rise in prices of petroleum products. This paper is to review the current state of the simulation, optimization and control technologies for the stand-alone hybrid solar-wind energy systems with battery storage. It is found that continued research and development effort in this area is still needed for improving the systems' performance, establishing techniques for accurately predicting their output and reliably integrating them with other renewable or conventional power generation sources.


title: The effect of negative online consumer reviews on product attitude: An information processing view

abstract: Online consumer reviews provide product information and recommendations from the customer perspective. This study investigates the effects of negative online consumer reviews on consumer product attitude. In particular, it examines the proportion and quality of negative online consumer reviews from the perspective of information processing. The elaboration likelihood model is used to explain the persuasive effect of the proportion and quality depending on product involvement. A high proportion of negative online consumer reviews elicits a conformity effect. As the proportion of negative online consumer reviews increases, high-involvement consumers tend to conform to the perspective of reviewers, depending on the quality of the negative online consumer reviews; in contrast, low-involvement consumers tend to conform to the perspective of reviewers regardless of the quality of the negative online consumer reviews. The experiment in this study uses 248 college students in Korea. The proposed hypotheses are tested by three-way analysis of covariance.


title: Development Pattern Recognition Model for the Classification of Circuit Probe Wafer Maps on Semiconductors

abstract: Spatial defect patterns generated during integrated circuit (IC) manufacturing contain valuable information on the fabrication process and can help engineers identify the root causes of any defect. Classification of these defect patterns is crucial to improving reliability and yield during IC manufacturing. Accurate classification requires good feature selection in order to assist in identifying the defect cluster types. In this paper, we demonstrate that the linear Hough transformation, the circular Hough transformation incorporating the cover ratio approach, and the zone ratio approach, when used as feature-extraction techniques, are able to distinguish lines, various solid circle-like cluster patterns such as blobs and bull's-eyes, and various hollow circle-like cluster patterns such as rings and edges. On the basis of these features, in this paper we provide a comprehensive evaluation of several data-mining classification approaches in terms of performance and accuracy. The results obtained using both artificial and real manufacturing data demonstrate the potential of this approach for analyzing general defect patterns that are generated during the IC fabrication process.


title: Optimal pay-as-you-go social security with endogenous retirement

abstract: This paper considers an overlapping-generations model with pay-as-you-go social security and retirement decision making by an old agent. In addition, the paper assumes that labor productivity depreciates. Under this setting, socially optimal allocations are examined. The first-best allocation is an allocation that maximizes welfare when a social planner distributes resources and forces an old agent to work and retire as she wants. The second-best allocation is an allocation that maximizes welfare when she can use only pay-as-you-go social security in a decentralized economy. The paper finds a range of an old agent’s labor productivity such that the first-best allocation is achieved in the decentralized economy. This differs from the finding in Micheland Pestieau [“Social security and early retirement in an overlapping-generations growth model”, Annals of Economics & Finance, 2013] that the first-best allocation cannot be achieved in the decentralized economy.


title: Context-Sensitive Fencing: Securing Speculative Execution via Microcode Customization

abstract: This paper describes context-sensitive fencing (CSF), a microcode-level defense against multiple variants of Spectre. CSF leverages the ability to dynamically alter the decoding of the instruction stream, to seamlessly inject new micro-ops, including fences, only when dynamic conditions indicate they are needed. This enables the processor to protect against the attack, but with minimal impact on the efficacy of key performance features such as speculative execution. This research also examines several alternative fence implementations, and introduces three new types of fences which allow most dynamic reorderings of loads and stores, but in a way that prevents speculative accesses from changing visible cache state. These optimizations reduce the performance overhead of the defense mechanism, compared to state-of-the-art software-based fencing mechanisms by a factor of six.


title: Voronoi treemaps

abstract: Treemaps are a well known method for the visualization of attributed hierarchical data. Previously proposed treemap layout algorithms are limited to rectangular shapes, which cause problems with the aspect ratio of the rectangles as well as with identifying the visualized hierarchical structure. The approach of Voronoi treemaps presented in this paper eliminates these problems through enabling subdivisions of and in polygons. Additionally, this allows for creating treemap visualizations within areas of arbitrary shape, such as triangles and circles, thereby enabling a more flexible adaptation of treemaps for a wider range of applications.


title: Systems of Global Governance in the Era of Human-Machine Convergence

abstract: Technology is increasingly shaping our social structures and is becoming a driving force in altering human biology. Besides, human activities already proved to have a significant impact on the Earth system which in turn generates complex feedback loops between social and ecological systems. Furthermore, since our species evolved relatively fast from small groups of hunter-gatherers to large and technology-intensive urban agglomerations, it is not a surprise that the major institutions of human society are no longer fit to cope with the present complexity. In this note we draw foundational parallelisms between neurophysiological systems and ICT-enabled social systems, discussing how frameworks rooted in biology and physics could provide heuristic value in the design of evolutionary systems relevant to politics and economics. In this regard we highlight how the governance of emerging technology (i.e. nanotechnology, biotechnology, information technology, and cognitive science), and the one of climate change both presently confront us with a number of connected challenges. In particular: historically high level of inequality; the co-existence of growing multipolar cultural systems in an unprecedentedly connected world; the unlikely reaching of the institutional agreements required to deviate abnormal trajectories of development. We argue that wise general solutions to such interrelated issues should embed the deep understanding of how to elicit mutual incentives in the socio-economic subsystems of Earth system in order to jointly concur to a global utility function (e.g. avoiding the reach of planetary boundaries and widespread social unrest). We leave some open questions on how techno-social systems can effectively learn and adapt with respect to our understanding of geopolitical complexity.


title: FCN and LSTM Based Computer Vision System for Recognition of Vehicle Type, License Plate Number, and Registration Country

abstract: We propose an advanced Automatic number-plate recognition (ANPR) system, which not only recognizes the number and the issuing state, but also the type and location of the vehicle in the input image. The system is based on a combination of existing methods, modifications to neural network architectures and improvements in the training process. The proposed system uses machine-learning approach and consists of three main parts: segmentation of input image by Fully Convolutional Network for localization of license plate and determination of vehicle type; recognition of the characters of the localized plate by a Maxout CNN and LSTM; determination of the state that has issued the license plate by a CNN. The training of these neural network models is accomplished using a manually labeled custom dataset, which is expanded with data augmented techniques. The resulting system is capable of localizing and classifying multiple types of vehicles (including motorcycles and emergency vehicles) as well as their license plates. The achieved precision of the localization is 99.5%. The whole number recognition accuracy is 96.7% and character level recognition accuracy is 98.8%. The determination of issuing state is precise in 92.8% cases.


title: Artificial Immune Systems

abstract: The biological immune system is a robust, complex, adaptive system that defends the body from foreign pathogens. It is able to categorize all cells (or molecules) within the body as self-cells or non-self cells. It does this with the help of a distributed task force that has the intelligence to take action from a local and also a global perspective using its network of chemical messengers for communication. There are two major branches of the immune system. The innate immune system is an unchanging mechanism that detects and destroys certain invading organisms, whilst the adaptive immune system responds to previously unknown foreign cells and builds a response to them that can remain in the body over a long period of time. This remarkable information processing biological system has caught the attention of computer science in recent years. A novel computational intelligence technique, inspired by immunology, has emerged, called Artificial Immune Systems. Several concepts from the immune have been extracted and applied for solution to real world science and engineering problems. In this tutorial, we briefly describe the immune system metaphors that are relevant to existing Artificial Immune Systems methods. We will then show illustrative real-world problems suitable for Artificial Immune Systems and give a step-by-step algorithm walkthrough for one such problem. A comparison of the Artificial Immune Systems to other well-known algorithms, areas for future work, tips&tricks and a list of resources will round this tutorial off. It should be noted that as Artificial Immune Systems is still a young and evolving field, there is not yet a fixed algorithm template and hence actual implementations might differ somewhat from time to time and from those examples given here.


A data model for supporting on-line analytical processing


title: Making public transport financially sustainable

abstract: Over the past two decades, Germany has improved the quality of its public transport services and attracted more passengers while increasing productivity, reducing costs, and cutting subsidies. Public transport systems reduced their costs through organizational restructuring and outsourcing to newly founded subsidiaries; cutting employee benefits and freezing salaries; increasing work hours, using part-time employees, expanding job tasks, and encouraging retirement of older employees; cooperation with other agencies to share employees, vehicles, and facilities; cutting underutilized routes and services; and buying new vehicles with lower maintenance costs and greater passenger capacity per driver. Revenues were increased through fare hikes for single tickets while maintaining deep discounts for monthly, semester, and annual tickets; and raising passenger volumes by improved quality of service, and full regional coordination of timetables, fares, and services. Those efforts by public transport agencies were enhanced by the increasing costs and restrictions on car use in German cities. Although the financial performance of German public transport has greatly improved, there are concerns of inequitable burdens on labor, since many of the cost reduction measures involved reducing wages or benefits of workers.


title: Embedding Lexical Features via Low-Rank Tensors

abstract: Modern NLP models rely heavily on engineered features, which often combine word and contextual information into complex lexical features. Such combination results in large numbers of features, which can lead to over-fitting. We present a new model that represents complex lexical features---comprised of parts for words, contextual information and labels---in a tensor that captures conjunction information among these parts. We apply low-rank tensor approximations to the corresponding parameter tensors to reduce the parameter space and improve prediction speed. Furthermore, we investigate two methods for handling features that include $n$-grams of mixed lengths. Our model achieves state-of-the-art results on tasks in relation extraction, PP-attachment, and preposition disambiguation.


title: The Venn of Identity: Options and Issues in Federated Identity Management

abstract: Federated identity management lets users dynamically distribute identity information across security domains, increasing the portability of their digital identities. It also raises new architectural challenges and significant security and privacy issues.


title: Profit Maximization for Viral Marketing in Online Social Networks: Algorithms and Analysis

abstract: Information can be disseminated widely and rapidly through Online Social Networks (OSNs) with “word-of-mouth” effects. Viral marketing is such a typical application in which new products or commercial activities are advertised by some seed users in OSNs to other users in a cascading manner. The selection of initial seed users yields a tradeoff between the expense and reward of viral marketing. In this paper, we define a general profit metric that naturally combines the benefit of influence spread with the cost of seed selection in viral marketing. We carry out a comprehensive study on finding a set of seed nodes to maximize the profit of viral marketing. We show that the profit metric is significantly different from the influence metric in that it is no longer monotone. This characteristic differentiates the profit maximization problem from the traditional influence maximization problem. We develop new seed selection algorithms for profit maximization with strong approximation guarantees. We also derive several upper bounds to benchmark the practical performance of an algorithm on any specific problem instance. Experimental evaluations with real OSN datasets demonstrate the effectiveness of our algorithms and techniques.


title: Grating Coupler Design for Reduced Back-Reflections

abstract: A grating coupler having asymmetric grating trenches for low back reflections is experimentally demonstrated. Conventional and asymmetric-trench grating couplers have been fabricated on a silicon nitride waveguide platform. Both grating couplers have fully etched trenches, which normally result in higher back reflections than shallow-etched trenches. For evaluating the back reflection characteristics, test structures based on a 3-dB multimode interference power splitter have been measured and the backreflection has been extracted from each grating coupler using an equivalent optical circuit. The designed grating coupler has no critical penalty (<0.2 dB) in coupling efficiency and ~5 dB lower back reflections than a conventional grating coupler design. Using ray transfer matrix modeling, further improvements to the back reflection characteristics of the asymmetric grating coupler are expected.


title: Intelligent Sensorless ABS for In-Wheel Electric Vehicles

abstract: The design of electric vehicles (EVs) is increasingly based upon using the in-wheel technology. In this design, the use of a separate electric machine at each corner of the vehicle provides unique opportunities for innovative vehicle control strategies. In this paper, a sensorless antilock braking system (ABS) is proposed that eliminates the need for the installation of separate conventional ABS sensors and saves the costs associated with the installation and maintenance of those sensors for in-wheel EVs. The proposed ABS exploits the information carried by the back electromotive force (EMF) of the electric machines of the in-wheel vehicle to obtain accurate wheel speed estimation at each wheel and conduct road identification simultaneously. A wavelet-packet denoising method is used to maintain the accuracy of wheel speed estimation in the presence of noise. In addition to sensorless wheel speed estimation, the proposed ABS is capable of road identification by analyzing the back EMF signal using discrete wavelet transforms. The design was realized and fully tested using actual ABS hardware. The results of the sensorless technique were compared with a commercial ABS sensor. The experimental results showed that the sensorless ABS can adequately replace the conventional ABS sensor in in-wheel EVs and significantly improve the performance of the ABS.


title: A Compact Dual-Band Rectenna Using Slot-Loaded Dual Band Folded Dipole Antenna

abstract: A compact dual-band rectenna operating at 915 MHz and 2.45 GHz is presented. The rectenna consists of a slot-loaded dual-band folded dipole antenna and a dual-band rectifier. The length of the proposed antenna is only 36.6% of the half-wavelength $(\lambda_{0}/2)$ dipole antenna at 915 MHz while keeping dual-band property at 915 MHz and 2.45 GHz. The rectifier circuit is optimized for low input power densities using harmonic balance (HB) simulation. The efficiencies of the rectifier are evaluated with both single- and dual-frequency input signals. The measured results show an efficiency of 37% and 30% at 915 MHz and at 2.45 GHz when illuminated by a microwave signal of available power of $-$ 9 dBm for a load resistor of 2.2 k $\Omega$ .


title: Detecting Group Review Spam

abstract: It is well-known that many online reviews are not written by genuine users of products, but by spammers who write fake reviews to promote or demote some target products. Although some existing works have been done to detect fake reviews and individual spammers, to our knowledge, no work has been done on detecting spammer groups. This paper focuses on this task and proposes an effective technique to detect such groups.


title: Acquired amnesia in childhood: A single case study

abstract: We report the case of C.L., an 8-year-old child who, following the surgical removal of an ependymoma from the left cerebral ventricle at the age of 4 years, developed significant difficulties in retaining day-to-day events and information. A thorough neuropsychological analysis documented in C.L. a severe anterograde amnesic syndrome, characterised by normal short-term memory, but poor performance on episodic long-term memory tests. In particular, C.L. demonstrated virtually no ability to recollect new verbal information several minutes after the presentation. As for semantic memory, C.L. demonstrated general semantic competencies, which, depending on the test, ranged from the level of a 6-year-old girl to a level corresponding to her actual chronological age. Finding a patient who, despite being severely impaired in the ability to recollect new episodic memories, still demonstrates at least partially preserved abilities to acquire new semantic knowledge suggests that neural circuits implicated in the memorisation of autobiographical events and factual information do not overlap completely. This case is examined in the light of growing literature concerned with the dissociation between episodic and semantic memory in childhood amnesia.


title: An evaluation of the google chrome extension security architecture

abstract: Vulnerabilities in browser extensions put users at risk by providing a way for website and network attackers to gain access to users' private data and credentials. Extensions can also introduce vulnerabilities into the websites that they modify. In 2009, Google Chrome introduced a new extension platform with several features intended to prevent and mitigate extension vulnerabilities: strong isolation between websites and extensions, privilege separation within an extension, and an extension permission system. We performed a security review of 100 Chrome extensions and found 70 vulnerabilities across 40 extensions. Given these vulnerabilities, we evaluate how well each of the security mechanisms defends against extension vulnerabilities. We find that the mechanisms mostly succeed at preventing direct web attacks on extensions, but new security mechanisms are needed to protect users from network attacks on extensions, website metadata attacks on extensions, and vulnerabilities that extensions add to websites. We propose and evaluate additional defenses, and we conclude that banning HTTP scripts and inline scripts would prevent 47 of the 50 most severe vulnerabilities with only modest impact on developers.


Complementarity, F-score, and NLP Evaluation.


title: Radiation necrosis of the pons after radiotherapy for nasopharyngeal carcinoma: diagnosis and treatment.

abstract: We report a case of radiation necrosis in an unusual location, the pons, in a patient who had received chemoradiation for nasopharyngeal carcinoma (NPC) over one year prior to presentation. This patient presented with subacute onset of ataxic hemiparesis and slurred speech. Initial magnetic resonance imaging (MRI) studies showed two 1-2 cm peripherally contrast-enhancing lesions in the pons with extensive surrounding edema. Proton magnetic resonance spectroscopy (MRS) played a key role in narrowing the differential diagnosis to radiation necrosis. The patient underwent biweekly bevacizumab therapy and has remained clinically stable with radiologic improvement of his lesion. In addition to this case, we present an overview of the use of advanced neuroimaging in distinguishing radiation necrosis of the central nervous system (CNS) from other entities as well as the role of bevacizumab in treatment.


title: Model Predictive Control of PV Sources in a Smart DC Distribution System: Maximum Power Point Tracking and Droop Control

abstract: In a dc distribution system, where multiple power sources supply a common bus, current sharing is an important issue. When renewable energy resources are considered, such as photovoltaic (PV), dc/dc converters are needed to decouple the source voltage, which can vary due to operating conditions and maximum power point tracking (MPPT), from the dc bus voltage. Since different sources may have different power delivery capacities that may vary with time, coordination of the interface to the bus is of paramount importance to ensure reliable system operation. Further, since these sources are most likely distributed throughout the system, distributed controls are needed to ensure a robust and fault tolerant control system. This paper presents a model predictive control-based MPPT and model predictive control-based droop current regulator to interface PV in smart dc distribution systems. Back-to-back dc/dc converters control both the input current from the PV module and the droop characteristic of the output current injected into the distribution bus. The predictive controller speeds up both of the control loops, since it predicts and corrects error before the switching signal is applied to the respective converter.


title: MedLDA: Maximum margin supervised topic models for regression and classification

abstract: A supervised topic model can use side information such as ratings or labels associated with documents or images to discover more predictive low dimensional topical representations of the data. However, existing supervised topic models predominantly employ likelihood-driven objective functions for learning and inference, leaving the popular and potentially powerful max-margin principle unexploited for seeking predictive representations of data and more discriminative topic bases for the corpus. In this paper, we propose the maximum entropy discrimination latent Dirichlet allocation (MedLDA) model, which integrates the mechanism behind the max-margin prediction models (e.g., SVMs) with the mechanism behind the hierarchical Bayesian topic models (e.g., LDA) under a unified constrained optimization framework, and yields latent topical representations that are more discriminative and more suitable for prediction tasks such as document classification or regression. The principle underlying the MedLDA formalism is quite general and can be applied for jointly max-margin and maximum likelihood learning of directed or undirected topic models when supervising side information is available. Efficient variational methods for posterior inference and parameter estimation are derived and extensive empirical studies on several real data sets are also provided. Our experimental results demonstrate qualitatively and quantitatively that MedLDA could: 1) discover sparse and highly discriminative topical representations; 2) achieve state of the art prediction performance; and 3) be more efficient than existing supervised topic models, especially for classification.


title: PivotPaths: Strolling through Faceted Information Spaces

abstract: We present PivotPaths, an interactive visualization for exploring faceted information resources. During both work and leisure, we increasingly interact with information spaces that contain multiple facets and relations, such as authors, keywords, and citations of academic publications, or actors and genres of movies. To navigate these interlinked resources today, one typically selects items from facet lists resulting in abrupt changes from one subset of data to another. While filtering is useful to retrieve results matching specific criteria, it can be difficult to see how facets and items relate and to comprehend the effect of filter operations. In contrast, the PivotPaths interface exposes faceted relations as visual paths in arrangements that invite the viewer to `take a stroll' through an information space. PivotPaths supports pivot operations as lightweight interaction techniques that trigger gradual transitions between views. We designed the interface to allow for casual traversal of large collections in an aesthetically pleasing manner that encourages exploration and serendipitous discoveries. This paper shares the findings from our iterative design-and-evaluation process that included semi-structured interviews and a two-week deployment of PivotPaths applied to a large database of academic publications.


title: Evaluating stream buffers as a secondary cache replacement

abstract: Today's commodity microprocessors require a low latency memory system to achieve high sustained performance. The conventional high-performance memory system provides fast data access via a large secondary cache. But large secondary caches can be expensive, particularly in large-scale parallel systems with many processors (and thus many caches).We evaluate a memory system design that can be both cost-effective as well as provide better performance, particularly for scientific workloads: a single level of (on-chip) cache backed up only by Jouppi's stream buffers [10] and a main memory. This memory system requires very little hardware compared to a large secondary cache and doesn't require modifications to commodity processors. We use trace-driven simulation of fifteen scientific applications from the NAS and PERFECT suites in our evaluation. We present two techniques to enhance the effectiveness of Jouppi's original stream buffers: filtering schemes to reduce their memory bandwidth requirement and a scheme that enables stream buffers to prefetch data being accessed in large strides. Our results show that, for the majority of our benchmarks, stream buffers can attain hit rates that are comparable to typical hit rates of secondary caches. Also, we find that as the data-set size of the scientific workload increases the performance of streams typically improves relative to secondary cache performance, showing that streams are more scalable to large data-set sizes.


title: Bridging the Gaps Between Residual Learning, Recurrent Neural Networks and Visual Cortex

abstract: We discuss relations between Residual Networks (ResNet), Recurrent Neural Networks (RNNs) and the primate visual cortex. We begin with the observation that a shallow RNN is exactly equivalent to a very deep ResNet with weight sharing among the layers. A direct implementation of such a RNN, although having orders of magnitude fewer parameters, leads to a performance similar to the corresponding ResNet. We propose 1) a generalization of both RNN and ResNet architectures and 2) the conjecture that a class of moderately deep RNNs is a biologically-plausible model of the ventral stream in visual cortex. We demonstrate the effectiveness of the architectures by testing them on the CIFAR-10 dataset.


title: In utero and postnatal imaging findings of parasitic conjoined twins (ischiopagus parasiticus tetrapus)

abstract: Conjoined twins are a rare developmental anomaly with a reported prevalence of 1.47 per 100,000 births. We present an uncommon case of a parasitic ischiopagus tetrapus with a parasitic ischiopagus partial twin joined to the complete fetus at the level of the ischium diagnosed in utero by fetal MRI. The correct prenatal diagnosis led to birth by caesarean section. Prenatal MRI findings are presented and corroborated by postnatal imaging delineating the full extent and associated anomalies of this rare malformation. Differential diagnosis of duplicated lower extremities is discussed.


title: Private Stochastic Multi-arm Bandits: From Theory to Practice

abstract: In this paper we study the problem of private stochastic multi-arm bandits. Our notion of privacy is the same as some of the earlier works in the general area of private online learning (Dwork et al., 2010; Jain et al., 2012; Smith and Thakurta, 2013). We design algorithms that are i) differentially private, and ii) have regret guarantees that (almost) match the regret guarantees for the best non-private algorithms (e.g., upper confidence bound sampling). Moreover, through our experiments on simulated and real-world data sets, we empirically show the effectiveness of our algorithms. Note: A version of this paper with the technical details appears in the appendix.


title: An assessment of overt malicious activity manifest in residential networks

abstract: While conventional wisdom holds that residential users experience a high degree of compromise and infection, this presumption has seen little validation in the way of an in-depth study. In this paper we present a first step towards an assessment based on monitoring network activity (anonymized for user privacy) of 20,000 residential DSL customers in a European urban area, roughly 1,000 users of a community network in rural India, and several thousand dormitory users at a large US university. Our study focuses on security issues that overtly manifest in such data sets, such as scanning, spamming, payload signatures, and contact to botnet rendezvous points. We analyze the relationship between overt manifestations of such activity versus the "security hygiene" of the user populations (anti-virus and OS software updates) and potential risky behavior (accessing blacklisted URLs). We find that hygiene has little correlation with observed behavior, but risky behavior--which is quite prevalent--more than doubles the likelihood that a system will manifest security issues.


title: Azapropazone in arthritis: a long-term treatment

abstract: SummaryA long-term open assessment of azapropazone (900 mg to 1200 mg daily) was carried out in 51 patients suffering from osteoarthritis for periods of up to 1 year. Of these patients, 70% completed 9 to 12 months of treatment. Clinical assessments were made before and during the study of pain on motion or weight bearing, night pain, junctional activity, morning stiffness, doctor's overall assessment, patients' overall assessment, and details of side-effects recorded. There were statistically significant improvements in the mean rating scores for each of the clinical assessments and these became apparent at 1 month, maximal at 3 to 6 months and persisted at the same level to the end of the trial. There were no side-effects recorded at any period of the trial in 30 (59%) patients. When side-effects did occur they were mild in 12 (24%). moderate in 3 (6%) and severe in 6 (12%) patients. The most frequent side-effects were related to the gastro-intestinal tract and gastric pain occurred in 7 (14%) patients....


title: The effect of vendor managed inventory (VMI) dynamics on the Bullwhip Effect in supply chains

abstract: The paper compares the expected performance of a vendor managed inventory (VMI) supplychain with a traditional “serially linked” supply chain. The emphasis of this investigation is the impact these two alternative structures have on the “BullwhipEffect” generated in the supply chain. We pay particular attention to the manufacturer's production ordering activities via a simulation model based on difference equations. VMI is thereby shown to be significantly better at responding to volatile changes in demand such as those due to discounted ordering or price variations. Inventory recovery as measured by the integral of time×absolute error performance metric is also substantially improved via VMI. Noise bandwidth, that is a measure of capacity requirements, is then used to estimate the order rate variance in response to random customer demand. Finally, the paper simulates the VMI and traditional supply chain response to a representative retail sales pattern. The results are in accordance with “rich picture” performance predictions made from deterministic inputs.


title: Modeling documents by combining semantic concepts with unsupervised statistical learning. The Semantic Web-ISWC 2008

abstract: Human-defined concepts are fundamental building-blocks in constructing knowledge bases such as ontologies. Statistical learning techniques provide an alternative automated approach to concept definition, driven by data rather than prior knowledge. In this paper we propose a probabilistic modeling framework that combines both human-defined concepts and data-driven topics in a principled manner. The methodology we propose is based on applications of statistical topic models (also known as latent Dirichlet allocation models). We demonstrate the utility of this general framework in two ways. We first illustrate how the methodology can be used to automatically tag Web pages with concepts from a known set of concepts without any need for labeled documents. We then perform a series of experiments that quantify how combining human-defined semantic knowledge with data-driven techniques leads to better language models than can be obtained with either alone.


title: Visual Reasoning by Progressive Module Networks

abstract: Humans learn to solve tasks of increasing complexity by building on top of previously acquired knowledge. Typically, there exists a natural progression in the tasks that we learn - most do not require completely independent solutions, but can be broken down into simpler subtasks. We propose to represent a solver for each task as a neural module that calls existing modules (solvers for simpler tasks) in a functional program-like manner. Lower modules are a black box to the calling module, and communicate only via a query and an output. Thus, a module for a new task learns to query existing modules and composes their outputs in order to produce its own output. Our model effectively combines previous skill-sets, does not suffer from forgetting, and is fully differentiable. We test our model in learning a set of visual reasoning tasks, and demonstrate improved performances in all tasks by learning progressively. By evaluating the reasoning process using human judges, we show that our model is more interpretable than an attention-based baseline.


title: Beyond Asimov: The Three Laws of Responsible Robotics

abstract: Asimov's three laws of robotics have been inculcated so successfully into our culture that they now appear to shape expectations as to how robots should act around humans. However, there has been little serious discussion as to whether the laws really do provide a framework for human-robot interactions. Asimov actually used his laws as a literary device to explore the lack of resilience in the interplay between people and robots in a range of situations. This paper briefly reviews some of the practical shortcomings of each of Asimov's laws for framing the relationships between people and robots, including reminders about what robots can't do. The main focus of the paper is to propose an alternative, parallel set of laws of responsible robotics as a means to stimulate debate about the accountability relationships for robots when their actions can result in harm to people or human interests. The alternative laws emphasize (1) systems safety in terms of the responsibilities of those who develop and deploy robotic systems, (2) robots' responsiveness as they participate in dynamic social and cognitive relationships, and (3) smooth transfer of control as a robot encounters and initially responds to disruptions, impasses, or opportunities in context.


title: Principal components analysis based control of a multi-dof underactuated prosthetic hand

abstract: BackgroundFunctionality, controllability and cosmetics are the key issues to be addressed in order to accomplish a successful functional substitution of the human hand by means of a prosthesis. Not only the prosthesis should duplicate the human hand in shape, functionality, sensorization, perception and sense of body-belonging, but it should also be controlled as the natural one, in the most intuitive and undemanding way. At present, prosthetic hands are controlled by means of non-invasive interfaces based on electromyography (EMG). Driving a multi degrees of freedom (DoF) hand for achieving hand dexterity implies to selectively modulate many different EMG signals in order to make each joint move independently, and this could require significant cognitive effort to the user.MethodsA Principal Components Analysis (PCA) based algorithm is used to drive a 16 DoFs underactuated prosthetic hand prototype (called CyberHand) with a two dimensional control input, in order to perform the three prehensile forms mostly used in Activities of Daily Living (ADLs). Such Principal Components set has been derived directly from the artificial hand by collecting its sensory data while performing 50 different grasps, and subsequently used for control.ResultsTrials have shown that two independent input signals can be successfully used to control the posture of a real robotic hand and that correct grasps (in terms of involved fingers, stability and posture) may be achieved.ConclusionsThis work demonstrates the effectiveness of a bio-inspired system successfully conjugating the advantages of an underactuated, anthropomorphic hand with a PCA-based control strategy, and opens up promising possibilities for the development of an intuitively controllable hand prosthesis.


title: Multiphysics Modeling of a Permanent Magnet Synchronous Machine by Using Lumped Models

abstract: This paper describes the modeling of a permanent magnet synchronous machine (PMSM) by using lumped models (LMs). Designing electrical machines necessarily involves several fields of physics, such as electromagnetics, thermics, mechanics, and acoustics. Magnetic, electrical, electronic, and thermal parts are represented by LMs, whereas vibro-acoustic and mechanical parts are represented by analytical models. The aim of this study is to build a design model of a PMSM for traction applications. Each model is parameterized to optimize the machine. The method of taking into account saturation and movement is described. These fast, LMs make it possible to couple the software used with optimization tools. Simulation results are presented and compared with the finite-element method and the experiments performed.


The Impact of Federal and State Notification Laws on Security Breach Announcements


title: Change-blindness as a result of 'mudsplashes'.

abstract: Change-blindness1,2 occurs when large changes are missed under natural viewing conditions because they occur simultaneously with a brief visual disruption, perhaps caused by an eye movement3,4, a flicker5, a blink6, or a camera cut in a film sequence7. We have found that this can occur even when the disruption does not cover or obscure the changes. When a few small, high-contrast shapes are briefly spattered over a picture, like mudsplashes on a car windscreen, large changes can be made simultaneously in the scene without being noticed. This phenomenon is potentially important in driving, surveillance or navigation, as dangerous events occurring in full view can go unnoticed if they coincide with even very small, apparently innocuous, disturbances. It is also important for understanding how the brain represents the world.


Learning Partially Contracting Dynamical Systems from Demonstrations.


title: Adaptive Artificial Potential Field Approach for Obstacle Avoidance Path Planning

abstract: This paper presents an adaptive artificial potential field method for robot's obstacle avoidance path planning. Despite the obstacle avoidance path planning based on the artificial potential field method is very popular, but there is local minima problem with this approach. As a result, this paper proposes an improved obstacle potential field function model considering for the size of the robot and the obstacles and changes the weight of the obstacle potential field function adaptively to make the robot escape from the local minima. Three simulations have been done and the simulation results show: the improved algorithm can make the robot escape from the local minima and accomplish the robot collision avoidance path planning well.


title: Implementation of parallel multi-objective artificial bee colony algorithm based on spark platform

abstract: The highly parallel framework of Spark enables it to have outstanding advantages in accelerating computing speed. At the same time, artificial bee colony (ABC) algorithm needs to improve its performance because of its high time complexity. In this paper, we combine ABC algorithm with Spark platform. First, we extend ABC algorithm to the multi-objective artificial bee colony (MOABC) algorithm; then we implement the parallel MOABC algorithm based on Spark platform. We conduct several experiments using our proposed algorithm on Spark platform, and the experimental results show that parallel MOABC algorithm produces a better performance.


title: From checking on to checking in: designing for low socio-economic status older adults

abstract: In this paper we describe the design evolution of a novel technology that collects and displays presence information to be used in the homes of older adults. The first two iterations, the Ambient Plant and Presence Clock, were designed for higher socio-economic status (SES) older adults, whereas the Check-In Tree was designed for low SES older adults. We describe how feedback from older adult participants drove our design decisions, and give an in-depth account of how the Check-In Tree evolved from concept to a final design ready for in situ deployment.


title: Spatial Path Generation by the Complementary Use of Nominal Path Data and Sensory Path Data and its Application to a Seam Tracking Robot

abstract: A spatial path generation algorithm and its application to the highreliability seam tracking robotic system which can operate even under poor sensing conditions are proposed. First, the concept of the robotic system with a wrist mounted high performance vision sensor is presented, then an accurate spatial path generation algorithm based on the complementary use of nominal path data and sensory data of an actual path with a reliability coefficient is described. In the algorithm, the modification matrix that maps a nominal path to an actual path is generated by comparing nominal path data with actual path data acquired by a sensor located ahead of an end-effector at certain points. Then an actual path is restored by modifying nominal path data with the modification matrix. Computer simulations and experiments on a spatial path show that the proposed system operates effectively even under disturbances such as burst sensory data missing caused by joint configuration abnormalities like tack welding beads often appearing in practical applications.


title: Do social media foster or curtail adolescents' empathy? A longitudinal study

abstract: Recently, concerns have been raised that adolescents' prolific social media use may cause them to become less empathic. However, direct empirical evidence is missing and research suggests that social media use can also be beneficial for adolescents' psychosocial development. The present study aims to investigate whether and how social media use influences empathy. We surveyed 942 Dutch adolescents (10-14 years) twice, with a one-year interval. The results showed that social media use is related to an increase in cognitive and affective empathy over time. Specifically, adolescents' social media use improved both their ability to understand (cognitive empathy) and share the feelings of their peers (affective empathy). Social media use is related to an increase in empathy over a 1-year period.This effect holds for both the affective and the cognitive component of empathy.Sympathy is not related to social media use.


The maturing of gamification research


title: Reversing Paralysis Technology - Remarkable Breakthrough for the Treatment of Incurable Paralysis

abstract: This research has been developed to study on the Brain Computer Interface (BCI) technology - which is called a Reversing Paralysiss Technology. It is a momentous innovation of medical sector for the treatment of Incurable Paralysis. The purpose of this study was to provide a high level of evidences of the effectiveness and the safety of this technology. The methodology of this study is using a qualitative systematic review method. It was followed by data collection and specific keywords based data searching method. Two renowned research papers indexing database have been employed for locating peer reviewed scientific papers such as Pub-Med Database and Google Scholar Database. Besides this database searching, the Google Search also performed for collecting scientific review news and informations regarding this technology. Researchers analysed all keyword based from the selected literatures, from the clinical trial results and from the new sources; and then summarizes the findings in respect of this technology. In this technology, the scientists have hypothesized that reversing of the paralyzed state of a patient is possible by creating Brain Computer Interfaces (BCIs) in the patient’s body through two brain implants. One implant is placed on the surface of the brain or attached to the brain's cortex. Then, another one is implanted in the spinal cord by passing the injured lesion of spine. These two implants will act as a bridge between the command center (the brain) and the control center (the spine). When the regular communication is established between the brain and the spine, then the paralyzed condition will reverse. The researchers, till to this date have analysed thirty seven (37) peer reviewed literatures, twelve (12) scientific/tech based news review sources and four (4) authentic clinical trial results which had been published in this context. Scientists claimed that this theory is effective and is safe for the treatment of paralysis based on successful clinical trials. Thus, from this extensive qualitative systematic review study, it established a theory that Reversing Paralysis Technology is working through Brain Computer Interfaces - Implants mechanism; and it is an effective and a safe process for patients’ paralysis treatment.


title: The stochastic CRB for array processing: a textbook derivation

abstract: The stochastic Cramer-Rao bound (CRB) for direction estimation in array processing applications was indirectly derived some ten years ago as the (asymptotic) covariance matrix of the maximum likelihood (ML) estimator. Attempts to obtain the stochastic CRB directly via the CRB theory fell short of providing a simple derivation and consequently, no direct derivation of this useful performance bound was available in the open literature. we correct this situation by providing a textbook-like direct derivation of the stochastic CRB.


title: Development of New a-Si/c-Si Heterojunction Solar Cells: ACJ-HIT (Artificially Constructed Junction-Heterojunction with Intrinsic Thin-Layer)

abstract: A new type of a-Si/c-Si heterojunction solar cell, called the HIT (Heterojunction with Intrinsic Thin-layer) solar cell, has been developed based on ACJ (Artificially Constructed Junction) technology. A conversion efficiency of more than 18% has been achieved, which is the highest ever value for solar cells in which the junction was fabricated at a low temperature (<200°C).


Key Issues in Domestic Violence


title: Development of a small quadruped robot with bi-articular muscle-tendon complex

abstract: Hydraulic and pneumatic actuators are used as actuators of robots. They have large capabilities of instantaneous output, but with problems of increase in size and mass, and difficulty for precise control. In contrast, electromagnetic motors have better controllability, lower cost, and smaller size. However, in order to actuate robots, they are usually used with reducers which have high reduction ratio, and it is difficult to realize creature-like dynamic motions such as fast running and high jumping, due to low backdrivability of joints. To solve the problem, we have developed leg mechanisms, which consist of a spring and a damper inspired by bi-articular muscle-tendon complex of animals. The final target is to develop a quadruped robot which can walk, run fast and jump highly like a cat. A cat mainly uses its hind legs in jumping and front legs in landing. It implies that the hind legs play an important role in jumping, and that the front legs do in landing. For this reason, it is necessary to design different leg structures for front and hind legs. In this paper, we develop a new front leg mechanism suitable to a hind leg mechanism which was already made by our group, and make a small quadruped robot. As the result of experiments for dynamic motions, stable running trot at a speed of 3.5 kilometers per hour and forward jumping of 1 body length per jump have been realized by the robot.


title: Comprehensive Review of Neural Network-Based Prediction Intervals and New Advances

abstract: This paper evaluates the four leading techniques proposed in the literature for construction of prediction intervals (PIs) for neural network point forecasts. The delta, Bayesian, bootstrap, and mean-variance estimation (MVE) methods are reviewed and their performance for generating high-quality PIs is compared. PI-based measures are proposed and applied for the objective and quantitative assessment of each method's performance. A selection of 12 synthetic and real-world case studies is used to examine each method's performance for PI construction. The comparison is performed on the basis of the quality of generated PIs, the repeatability of the results, the computational requirements and the PIs variability with regard to the data uncertainty. The obtained results in this paper indicate that: 1) the delta and Bayesian methods are the best in terms of quality and repeatability, and 2) the MVE and bootstrap methods are the best in terms of low computational load and the width variability of PIs. This paper also introduces the concept of combinations of PIs, and proposes a new method for generating combined PIs using the traditional PIs. Genetic algorithm is applied for adjusting the combiner parameters through minimization of a PI-based cost function subject to two sets of restrictions. It is shown that the quality of PIs produced by the combiners is dramatically better than the quality of PIs obtained from each individual method.


title: ARGoS: a modular, parallel, multi-engine simulator for multi-robot systems

abstract: We present a novel multi-robot simulator named ARGoS. ARGoS is designed to simulate complex experiments involving large swarms of robots of different types. ARGoS is the first multi-robot simulator that is at the same time both efficient (fast performance with many robots) and flexible (highly customizable for specific experiments). Novel design choices in ARGoS have enabled this breakthrough. First, in ARGoS, it is possible to partition the simulated space into multiple sub-spaces, managed by different physics engines running in parallel. Second, ARGoS’ architecture is multi-threaded, thus designed to optimize the usage of modern multi-core CPUs. Finally, the architecture of ARGoS is highly modular, enabling easy addition of custom features and appropriate allocation of computational resources. We assess the efficiency of ARGoS and showcase its flexibility with targeted experiments. Experimental results demonstrate that simulation run-time increases linearly with the number of robots. A 2D-dynamics simulation of 10,000 e-puck robots can be performed in 60 % of the time taken by the corresponding real-world experiment. We show how ARGoS can be extended to suit the needs of an experiment in which custom functionality is necessary to achieve sufficient simulation accuracy. ARGoS is open source software licensed under GPL3 and is downloadable free of charge.


title: Health and reproduction of red mullet, Mullus barbatus, in the western Mediterranean Sea

abstract: The reproductive and general health of exploited fish stocks is an essential element of sustainable and profitable fisheries. The main purpose of this study was to assess the relationships between reproduction and two important parameters of fish health (parasitism and energy reserves) in female specimens of red mullet, Mullus barbatus, from the western Mediterranean Sea. We present new data for this species on (i) the prevalence and intensity of infection by metazoan parasites; (ii) the total lipid content in muscle and gonads as a measure of condition and (iii) fecundity and egg quality as a measure of their reproductive capacity. The results show that M. barbatus is a batch spawner with an income breeding strategy, an asynchronous development of oocytes and indeterminate fecundity. The results also indicate that the three most abundant and prevalent parasites significantly affect the condition and reproduction of M. barbatus. Specifically, the digenean, Opecoeloides furcatus, causes a reduction in the female’s energy reserves, while the nematodes, Hysterothylacium fabri and H. aduncum, produce a rise in egg production but impair egg quality. These implications of the relationships between parasitism, fish health and fish reproduction should be taken into consideration in the assessment and management of exploited species.


title: Are there distinct cognitive types

abstract: A few previous attempts to isolate cognitive types—groups of individuals with similar profiles of cognitive skills—have been reported in the literature. For the most part, they have not arrived at the same types. The present study sought to identify cognitive types via cluster analysis in an available sample of adolescent twins. Analyses were carried out using one randomly selected twin from each of 16-year-old Australian twin pairs who had taken a five-scale cognitive test battery (N = 677 pairs—a further sample of 628 pairs was reserved for replication purposes). Three levels of tightness of clusters were examined—minimum mutual intercorrelations among cluster members of 0.10, 0.30 and 0.50. The higher intercorrelations led to more clusters—18 at the 0.10 criterion, 25 at 0.30, and 36 at 0.50. The 0.10 level was most similar to the number of clusters reported in previous studies, and was chosen for further analysis. Clustering at this level yielded 18 clusters of individuals from the random twins, and 17 from their co-twins. Fifteen of these matched, as evidenced by a correlation of 0.80 or more between the cluster means. A second method of clustering based on a different approach, SPSS Cluster, gave similar results—15 clusters matched those from the original analysis. Agreement for cluster membership was compared for identical and fraternal twins. A greater agreement for identical twin pairs was found, and interpreted as evidence of a genetic contribution to the clustering. Most of the above analyses were successfully replicated on the reserved sample of twin pairs. Thus there was evidence for stability of initial clustering in this population. However there was not much evidence across studies for discrete and dependable cognitive types.


title: Motion Perception in Reinforcement Learning with Dynamic Objects

abstract: In dynamic environments, learned controllers are supposed to take motion into account when selecting the action to be taken. However, in existing reinforcement learning works motion is rarely treated explicitly; it is rather assumed that the controller learns the necessary motion representation from temporal stacks of frames implicitly. In this paper, we show that for continuous control tasks learning an explicit representation of motion improves the quality of the learned controller in dynamic scenarios. We demonstrate this on common benchmark tasks (Walker, Swimmer, Hopper), on target reaching and ball catching tasks with simulated robotic arms, and on a dynamic single ball juggling task. Moreover, we find that when equipped with an appropriate network architecture, the agent can, on some tasks, learn motion features also with pure reinforcement learning, without additional supervision. Further we find that using an image difference between the current and the previous frame as an additional input leads to better results than a temporal stack of frames.


title: Mobile Encrypted Traffic Classification Using Deep Learning

abstract: The massive adoption of hand-held devices has led to the explosion of mobile traffic volumes traversing home and enterprise networks, as well as the Internet. Procedures for inferring (mobile) applications generating such traffic, known as Traffic Classification (TC), are the enabler for highly-valuable profiling information while certainly raise important privacy issues. The design of accurate classifiers is however exacerbated by the increasing adoption of encrypted protocols (such as TLS), hindering the applicability of highly-accurate approaches, such as deep packet inspection. Additionally, the (daily) expanding set of apps and the moving-target nature of mobile traffic makes design solutions with usual machine learning, based on manually-and expert-originated features, outdated. For these reasons, we suggest Deep Learning (DL) as a viable strategy to design traffic classifiers based on automatically-extracted features, reflecting the complex mobile-traffic patterns. To this end, different state-of-the-art DL techniques from TC are here reproduced, dissected, and set into a systematic framework for comparison, including also a performance evaluation workbench. Based on three datasets of real human users' activity, performance of these DL classifiers is critically investigated, highlighting pitfalls, design guidelines, and open issues of DL in mobile encrypted TC.


title: A Survey of Internet of Things and Big Data integrated Solutions for Industrie 4.0

abstract: The industrial internet of things (IIoT) is growing at an exponential rate generating massive amounts of industrial data. This data must be leveraged to support business and operational goals. As a result, there is an urgent need for adopting big data technologies to enable data analytics in industrial automation. This paper explores interrelations between IIoT and big data technologies and how they work together to generate business insights from industrial data. Additionally, requirements for cloud-based solutions are derived from the Industrie 4.0 use case scenario value-based-services, focusing on condition monitoring and predictive maintenance services. A survey of selected cloud-based platforms is conducted to examine how these platforms meet the requirements derived from the use case. Results show that existing general cloud platforms should adopt more IIoT applications and platforms, while existing industrial cloud platforms should add big data frameworks to their portfolio. Finally, an architecture for integrating cloud-based IIoT and big data solutions is introduced and issues regarding the use of public cloud for IIoT applications are discussed.


title: Virtual Communities of Practice: Bridging Research and Practice Using Web 2.0.

abstract: A significant dilemma for the health and human service professions continues to be the question of how best to bridge the divide between academic research and practice. Communities of practice have traditionally been a vehicle for collaborative research and for information exchange (Moore, 2008). Through collaboration, communities of practice have the ability to disseminate knowledge and transmit best practices in these disciplines (Barry, 2007). However, organizational constraints on time can impose barriers to professional partnerships (i.e., Le May, Mulhall, & Alexander, 1998). Increasingly restrictive research agendas of faculty pose an additional barrier (Colbeck, 2000). Web 2.0 technologies may offer a solution to this challenge. According to Boulos and Wheeler (2007), Web 2.0 technologies “represent a quite revolutionary way of managing and repurposing/remixing online information and knowledge repositories, including clinical and research information, in comparison with the traditional Web 1.0 mode...


title: An exact algorithm for the elementary shortest path problem with resource constraints: Application to some vehicle routing problems

abstract: In this article, we propose a solution procedure for the Elementary Shortest Path Problem with Resource Constraints (ESPPRC). A relaxed version of this problem in which the path does not have to be elementary has been the backbone of a number of solution procedures based on column generation for several important problems, such as vehicle routing and crew pairing. In many cases relaxing the restriction of an elementary path resulted in optimal solutions in a reasonable computation time. However, for a number of other problems, the elementary path restriction has too much impact on the solution to be relaxed or might even be necessary. We propose an exact solution procedure for the ESPPRC, which extends the classical label correcting algorithm originally developed for the relaxed (nonelementary) path version of this problem. We present computational experiments of this algorithm for our specific problem and embedded in a column generation scheme for the classical Vehicle Routing Problem with Time Windows. © 2004 Wiley Periodicals, Inc. NETWORKS, Vol. 44(3), 216–229 2004


title: Fingerprint Image Reconstruction from Standard Templates

abstract: A minutiae-based template is a very compact representation of a fingerprint image, and for a long time, it has been assumed that it did not contain enough information to allow the reconstruction of the original fingerprint. This work proposes a novel approach to reconstruct fingerprint images from standard templates and investigates to what extent the reconstructed images are similar to the original ones (that is, those the templates were extracted from). The efficacy of the reconstruction technique has been assessed by estimating the success chances of a masquerade attack against nine different fingerprint recognition algorithms. The experimental results show that the reconstructed images are very realistic and that, although it is unlikely that they can fool a human expert, there is a high chance to deceive state-of-the-art commercial fingerprint recognition systems.


title: NVSim: A Circuit-Level Performance, Energy, and Area Model for Emerging Nonvolatile Memory

abstract: Various new nonvolatile memory (NVM) technologies have emerged recently. Among all the investigated new NVM candidate technologies, spin-torque-transfer memory (STT-RAM, or MRAM), phase-change random-access memory (PCRAM), and resistive random-access memory (ReRAM) are regarded as the most promising candidates. As the ultimate goal of this NVM research is to deploy them into multiple levels in the memory hierarchy, it is necessary to explore the wide NVM design space and find the proper implementation at different memory hierarchy levels from highly latency-optimized caches to highly density- optimized secondary storage. While abundant tools are available as SRAM/DRAM design assistants, similar tools for NVM designs are currently missing. Thus, in this paper, we develop NVSim, a circuit-level model for NVM performance, energy, and area estimation, which supports various NVM technologies, including STT-RAM, PCRAM, ReRAM, and legacy NAND Flash. NVSim is successfully validated against industrial NVM prototypes, and it is expected to help boost architecture-level NVM-related studies.


title: How are we searching the World Wide Web? A comparison of nine search engine transaction logs

abstract: The Web and especially major Web search engines are essential tools in the quest to locate online information for many people. This paper reports results from research that examines characteristics and changes in Web searching from nine studies of five Web search engines based in the US and Europe. We compare interactions occurring between users and Web search engines from the perspectives of session length, query length, query complexity, and content viewed among the Web search engines. The results of our research shows (1) users are viewing fewer result pages, (2) searchers on US-based Web search engines use more query operators than searchers on European-based search engines, (3) there are statistically significant differences in the use of Boolean operators and result pages viewed, and (4) one cannot necessary apply results from studies of one particular Web search engine to another Web search engine. The wide spread use of Web search engines, employment of simple queries, and decreased viewing of result pages may have resulted from algorithmic enhancements by Web search engine companies. We discuss the implications of the findings for the development of Web search engines and design of online content.


title: A Compact Multiband Monopole Antenna With a Defected Ground Plane

abstract: A compact multiband antenna is proposed that consists of a printed circular disc monopole antenna with an L-shaped slot cut out of the ground, forming a defected ground plane. Analysis of the current distribution on the antenna reveals that at low frequencies the addition of the slot creates two orthogonal current paths, which are responsible for two additional resonances in the response of the antenna. By virtue of the orthogonality of these modes the antenna exhibits orthogonal pattern diversity, while enabling the adjacent resonances to be merged, forming a wideband low-frequency response and maintaining the inherent wideband high-frequency response of the monopole. The antenna exhibits a measured -10 dB S 11 bandwidth of 600 MHz from 2.68 to 3.28 GHz, and a bandwidth of 4.84 GHz from 4.74 to 9.58 GHz, while the total size of the antenna is only 24 times 28.3 mm. The efficiency is measured using a modified Wheeler cap method and is verified using the gain comparison method to be approximately 90% at both 2.7 and 5.5 GHz.


title: An evaluation of image feature detectors and descriptors for robot navigation

abstract: The detection and matching of feature points is crucial in many computer vision systems. Successful establishing of points correspondences between concurrent frames is important in such tasks as visual odometry, structure from motion or simultaneous localization and mapping. This paper compares of the performance of the well established, single scale detectors and descriptors and the increasingly popular, multiscale approaches.


The English Works of Raja Rammohun Roy


A Practical Tutorial on Conducting Meta-Analysis in R


title: Biosynthesis of aspyrone, a metabolite of Aspergillus malleus. Incorporation studies with 14C- and 3H-labelled acetates and malonate

abstract: Incorporation studies with 14C-labelled acetates and malonate confirm the polyketide origin of aspyrone (1), and identify the chain starter unit. Five carbons are derived from the methyl group of acetate, and the remaining four from the carboxy group. The pattern of incorporation of tritium from [2-3H]acetate is inconsistent with a biosynthesis from aromatic precursors of the mellein type. Possibly advanced precursors containing a 2-methylchromone nucleus were not incorporated. The evidence suggests that aromatic precursors are not involved in aspyrone biosynthesis, and that the carbon skeleton is produced, like that of the co-metabolite asperlactone (6), by decarboxylation and rearrangement of a linear penta-ketide intermediate.


title: Active Zero-Shot Learning

abstract: In multi-label classification in the big data age, the number of classes can be in thousands, and obtaining sufficient training data for each class is infeasible. Zero-shot learning aims at predicting a large number of unseen classes using only labeled data from a small set of classes and external knowledge about class relations. However, previous zero-shot learning models passively accept labeled data collected beforehand, relinquishing the opportunity to select the proper set of classes to inquire labeled data and optimize the performance of unseen class prediction. To resolve this issue, we propose an active class selection strategy to intelligently query labeled data for a parsimonious set of informative classes. We demonstrate two desirable probabilistic properties of the proposed method that can facilitate unseen classes prediction. Experiments on 4 text datasets demonstrate that the active zero-shot learning algorithm is superior to a wide spectrum of baselines. We indicate promising future directions at the end of this paper.


title: Analysis of Multiple Plasmodium falciparum Infections in Tanzanian Children during the Phase III Trial of the Malaria Vaccine SPf66

abstract: In the first phase III efficacy trial of the malaria vaccine SPf66 in Africa, MOIs in SPf66- and placebo-vaccinated children were analyzed by polymerase chain reaction ‐ restriction fragment length polymorphism of the Plasmodium falciparum merozoite surface antigen 2 (MSA2). MOIs were significantly reduced in asymptomatic vaccine recipients compared with those in asymptomatic placebo recipients; however, no differences were observed among symptomatic children in the vaccine and control groups. These results show that immunization with SPf66 modulates the course of naturally occurring infections, as reflected by reduced MOIs. In placebo recipients, however, there was a significant negative correlation between numbers of infecting genotypes, as identified by MSA2, and morbidity. Asymptomatic placebo recipients had an average of 5 concurrent infections, whereas children with clinical cases had an average of 3.4 infections. These data provide further evidence that premunition from concurrent infections is important in immunity against clinical malaria. No such effect of multiple infections was found in the vaccinated group.


title: Fast trajectory clustering using Hashing methods

abstract: There has been an explosion in the usage of trajectory data. Clustering is one of the simplest and most powerful approaches for knowledge discovery from trajectories. In order to produce meaningful clusters, well-defined metrics are required to capture the essence of similarity between trajectories. One such distance function is Dynamic Time Warping (DTW), which aligns two trajectories together in order to determine similarity. DTW has been widely accepted as a very good distance measure for trajectory data. However, trajectory clustering is very expensive due to the complexity of the similarity functions, for example, DTW has a high computational cost O(n ::: 2 ::: ), where n is the average length of the trajectory, which makes the clustering process very expensive. In this paper, we propose the use of hashing techniques based on Distance-Based Hashing (DBH) and Locality Sensitive Hashing (LSH) to produce approximate clusters and speed up the clustering process.


title: Improving Back-Propagation by Adding an Adversarial Gradient

abstract: The back-propagation algorithm is widely used for learning in artificial neural networks. A challenge in machine learning is to create models that generalize to new data samples not seen in the training data. Recently, a common flaw in several machine learning algorithms was discovered: small perturbations added to the input data lead to consistent misclassification of data samples. Samples that easily mislead the model are called adversarial examples. Training a"maxout"network on adversarial examples has shown to decrease this vulnerability, but also increase classification performance. This paper shows that adversarial training has a regularizing effect also in networks with logistic, hyperbolic tangent and rectified linear units. A simple extension to the back-propagation method is proposed, that adds an adversarial gradient to the training. The extension requires an additional forward and backward pass to calculate a modified input sample, or mini batch, used as input for standard back-propagation learning. The first experimental results on MNIST show that the"adversarial back-propagation"method increases the resistance to adversarial examples and boosts the classification performance. The extension reduces the classification error on the permutation invariant MNIST from 1.60% to 0.95% in a logistic network, and from 1.40% to 0.78% in a network with rectified linear units. Results on CIFAR-10 indicate that the method has a regularizing effect similar to dropout in fully connected networks. Based on these promising results, adversarial back-propagation is proposed as a stand-alone regularizing method that should be further investigated.


title: Adaptive fuzzy approach to estimate supplier's competitiveness in open e-bidding

abstract: This paper presents an adaptive fuzzy approach to estimate the competition of the suppliers in an open electronic bidding. The competition is formulated as the supply curve relating pricing against requested quantity. A set of supply curves, each from a competitor, is formulated into fuzzy expression to allow flexibility for a supplier to prepare an optimal pricing policy that deals with the fact that other competitors are also adapting at some unknown rate. The pricing policy is formulated into a mathematical expression that is easily adjusted as new information on the competitors becomes available. A revision of pricing policy for a supplier to compete on the pricing basis while maintaining an optimal profit margin is derived as an application. Computer simulations are provided to demonstrate the workability of this approach


title: Visualizing Musical Structure and Rhythm via Self-Similarity

abstract: This paper presents a novel approach to visualizing the time structure of musical waveforms. The acoustic similarity between any two instants of an audio recording is displayed in a static 2D representation, which makes structural and rhythmic characteristics visible. Unlike practically all prior work, this method characterizes self-similarity rather than specific audio attributes such as pitch or spectral features. Examples are presented for classical and popular music.


title: Liquid crystalline spinning of spider silk

abstract: Spider silk has outstanding mechanical properties despite being spun at close to ambient temperatures and pressures using water as the solvent. The spider achieves this feat of benign fibre processing by judiciously controlling the folding and crystallization of the main protein constituents, and by adding auxiliary compounds, to create a composite material of defined hierarchical structure. Because the ‘spinning dope’ (the material from which silk is spun) is liquid crystalline, spiders can draw it during extrusion into a hardened fibre using minimal forces. This process involves an unusual internal drawdown within the spider’s spinneret that is not seen in industrial fibre processing, followed by a conventional external drawdown after the dope has left the spinneret. Successful copying of the spider’s internal processing and precise control over protein folding, combined with knowledge of the gene sequences of its spinning dopes, could permit industrial production of silk-based fibres with unique properties under benign conditions.


title: A Neural Network-Powered Cognitive Method of Identifying Semantic Entities in Earth Science Papers

abstract: In the current era of knowledge explosion, it is becoming increasingly critical to help researchers quickly grasp the core ideas and methods used in the sea of published articles. As a first step toward the aim, this paper proposes a novel approach that simulates the cognitive process of how human beings read Earth science articles, and automatically identifies semantic entities from the articles. Among others, one major objective is to identify the datasets studied in articles. Oftentimes, however, researchers do not explicitly cite the datasets used. Thus, we propose a profile-matching method strengthened by a neural network-based method to identify implicitly cited dataset entities based on the context. Our experiments have demonstrated the effectiveness of our approaches.


title: Addressing the fundamental tension of PCGML with discriminative learning

abstract: Procedural content generation via machine learning (PCGML) is typically framed as the task of fitting a generative model to full-scale examples of a desired content distribution. This approach presents a fundamental tension: the more design effort expended to produce detailed training examples for shaping a generator, the lower the return on investment from applying PCGML in the first place. In response, we propose the use of discriminative models, which capture the validity of a design rather the distribution of the content, trained on positive and negative example design fragments. Through a modest modification of WaveFunctionCollapse, a commercially-adopted PCG approach that we characterize as using elementary machine learning, we demonstrate a new mode of control for learning-based generators. We demonstrate how an artist might craft a focused set of additional positive and negative design fragments by critique of the generator's previous outputs. This interaction mode bridges PCGML with mixed-initiative design assistance tools by working with a machine to define a space of valid designs rather than just one new design.


title: Machine learning approach on synchronization for FEC enabled channels

abstract: Within the problem of modern synchronization systems there are is a task of system synchronization based on structure of information being transmitted over the noisy channel. Here is given a relevant example for the channel with forward error correction codes (FEC enabled channel). The method being redefined here is based on FEC data structure of the transmitted information which includes redundant symbols, and machine learning approach which is using this redundant information to detect synchronization and synchronize the whole system with less spare date being transmitted over the channel, which usually being used for synchronization purposes. Suggested redefined method of synchronization takes more computation efforts and more memory for computation, but on the other hand it set an area for its implementation in terms of complexity of synchronizing machine versus data transmission rate: both being pushed by the level of noise. Implication of machine learning mechanism is giving a great improvement on top of the produced result, providing much better performance for the whole end-to-end system, dramatically decreasing delays of taking decision on synchronization, or extending the range of the system use by improving applicability of the system over higher levels of noise. For wired telecommunication systems this leads to better quality, but for wireless systems it leads to wide range or use or/and better quality. Use of reviewed approach for FEC enabled channels leads to new result for synchronization problems solving. Moreover, proposed approach might provide better decoding level due to soft decisions being forward from synchronization module into decoding module of receiving system.


title: Sparse Topical Coding

abstract: We present sparse topical coding (STC), a non-probabilistic formulation of topic models for discovering latent representations of large collections of data. Unlike probabilistic topic models, STC relaxes the normalization constraint of admixture proportions and the constraint of defining a normalized likelihood function. Such relaxations make STC amenable to: 1) directly control the sparsity of inferred representations by using sparsity-inducing regularizers; 2) be seamlessly integrated with a convex error function (e.g., SVM hinge loss) for supervised learning; and 3) be efficiently learned with a simply structured coordinate descent algorithm. Our results demonstrate the advantages of STC and supervised MedSTC on identifying topical meanings of words and improving classification accuracy and time efficiency.


Impact of Nurse Managers' Leadership Styles on Staff Nurses' Intent to Turnover


title: Optimal resource management for a model driven LTE protocol stack on a multicore platform

abstract: The Long Term Evolution (LTE) is the successor technology of the 3G wireless system. The high data rates enabled by LTE can benefit from a strong computational power provided by today's high-performance embedded processors. In this work we therefore utilize a multicore processor to increase the LTE system throughput in the mobile terminal. We investigate the dynamic memory allocation scheme for the LTE protocol stack, modeled using Specification and Description Language (SDL), as the underlying issue with migrating from single to multiple cores. We discover that, under some schemes, multicore performance becomes inferior to a single-core, especially in case of intensive dynamic memory allocation and deallocation. By modifying the SDL system's run time kernel we implement a static memory management scheme. This is supplemented by a selective usage of resource protection in single- and dual-core situations. As a result, an increase of the system throughput by about 75% can be observed when migrating from one core to two cores.


title: Beyond relevance in marketplace search

abstract: In this paper we study diversity and its relations to search relevance in the context of an online marketplace. We conduct a large-scale log-based study using click-stream data from a leading eCommerce site. We introduce 3 main metrics -- selection (diversity), trust, and value. In our analysis we also show how these interact with relevance in different ways. We study the benefits of diversity and also show why guaranteeing diversity is important.