title: Design of High Power Permanent Magnet Motor With Segment Rectangular Copper Wire and Closed Slot Opening on Electric Vehicles*

abstract: Brushless permanent magnet DC motors are being manufactured and used increasingly in everything from home appliances to automobiles due to their inherent advantages. The merits of these motors could be enhanced further according to another design approach method which alters the typically used winding parts for low to medium power range motors. This paper deals with the high power brushless DC motor used for traction in a 48 V golf cart system. The stator is designed with segment rectangular copper winding to maximize output power within its limited dimensions. In the design process, the closed slot opening and slot-pole combination are investigated and compared in terms of efficiency and input current density. The developed motor obtained about 120% greater power density per volume compared to a commercial motor with round winding coils at similar efficiency and variable speed range.


title: Towards better decoding and language model integration in sequence to sequence models

abstract: The recently proposed Sequence-to-Sequence (seq2seq) framework advocates replacing complex data processing pipelines, such as an entire automatic speech recognition system, with a single neural network trained in an end-to-end fashion. In this contribution, we analyse an attention-based seq2seq speech recognition system that directly transcribes recordings into characters. We observe two shortcomings: overconfidence in its predictions and a tendency to produce incomplete transcriptions when language models are used. We propose practical solutions to both problems achieving competitive speaker independent word error rates on the Wall Street Journal dataset: without separate language models we reach 10.6% WER, while together with a trigram language model, we reach 6.7% WER.


title: Never-Ending Learning for Open-Domain Question Answering over Knowledge Bases

abstract: Translating natural language questions to semantic representations such as SPARQL is a core challenge in open-domain question answering over knowledge bases (KB-QA). Existing methods rely on a clear separation between an offline training phase, where a model is learned, and an online phase where this model is deployed to answer users’ questions. Two major shortcomings are that such methods require access to a large training set, which is not always readily available, and that they fail on questions from domains not seen during training. To overcome these limitations, this paper presents a continuous learning paradigm for KB-QA, called NEQA. Offline, NEQA automatically learns templates from a small number of training question-answer pairs. Once deployed, continuous learning is triggered on cases where templates are insufficient. Using a semantic similarity function between questions and by judicious invocation of non-expert user feedback, NEQA learns new templates that capture previously-unseen syntactic structures. This way, NEQA gradually extends and improves its template repository. NEQA periodically re-trains its underlying models, allowing it to adapt to the language used after deployment. Our experiments demonstrate NEQA’s practical viability, with steady improvement in answering quality over time, and the gains on questions from previously-unobserved domains.


title: A bit-serial approximate min-sum LDPC decoder and FPGA implementation

abstract: We propose a bit-serial LDPC decoding scheme to reduce interconnect complexity in fully-parallel low-density parity-check decoders. Bit-serial decoding also facilitates efficient implementation of wordlength-programmable LDPC decoding which is essential for gear shift decoding. To simplify the implementation of bit-serial decoding we propose a new approximation to the check update function in the min-sum decoding algorithm. The new check update rule computes only the absolute minimum and applies a correction to outgoing messages if required. We present a 650-Mbps bit-serial (480, 355) RS-based LDPC decoder implemented on a single Altera Stratix EP1S80 FPGA device. To our knowledge, this is the fastest FPGA-based LDPC decoder reported in the literature.


title: Forthcoming as a target paper in Behavioral and Brain Sciences, 32 (2009) How we know our own minds: the relationship between mindreading and metacognition

abstract: Four different accounts of the relationship between third-person mindreading and first-person metacognition are compared and evaluated. While three of them endorse the existence of introspection for propositional attitudes, the fourth (defended here) claims that our knowledge of our own attitudes results from turning our mindreading capacities upon ourselves. Section 1 of this target article introduces the four accounts. Section 2 develops the "mindreading is prior" model in more detail, showing how it predicts introspection for perceptual and quasi-perceptual (e.g., imagistic) mental events while claiming that metacognitive access to our own attitudes always results from swift unconscious self-interpretation. This section also considers the model's relationship to the expression of attitudes in speech. Section 3 argues that the commonsense belief in the existence of introspection should be given no weight. Section 4 argues briefly that data from childhood development are of no help in resolving this debate. Section 5 considers the evolutionary claims to which the different accounts are committed, and argues that the three introspective views make predictions that are not borne out by the data. Section 6 examines the extensive evidence that people often confabulate when self-attributing attitudes. Section 7 considers "two systems" accounts of human thinking and reasoning, arguing that although there are introspectable events within System 2, there are no introspectable attitudes. Section 8 examines alleged evidence of "unsymbolized thinking". Section 9 considers the claim that schizophrenia exhibits a dissociation between mindreading and metacognition. Finally, section 10 evaluates the claim that autism presents a dissociation in the opposite direction, of metacognition without mindreading.


title: Driven-right-leg circuit design

abstract: The driven-right-leg circuit is often used with biopotential differential amplifiers to reduce common mode voltage. We analyze this circuit and show that high loop gains can cause instability. We present equations that can be used to design circuits that minimize common mode voltage without instability. We also show that it is important to consider the reduction of high-frequency interference from fluorescent lights when determining the bandwidth of the drivenright-leg circuit.


title: Design, Analysis, and Prototyping of an Axial-Flux Permanent Magnet Motor Based on Genetic Algorithm and Finite-Element Analysis

abstract: This paper presents an axial-flux permanent magnet motor optimized by the genetic algorithm (GA)-based sizing equation and the finite-element analysis (FEA). The designed slotted TORUS motor produces sinusoidal back-EMF waveform, maximum power density, and reduced cogging torque. The GA obtained the dimensions of motors with different numbers of slots and the highest possible power density. Electromagnetic field analysis of the candidate motors obtained from GA with various dimensions is subjected through FEA to obtain the motors' characteristics. Based on the GA and FEA results, a candidate motor design is introduced and subjected to FEA for reoptimization and finalization of the motor design. Techniques like modifying winding configuration and permanent magnet skewing are investigated to attain the most sinusoidal back-EMF waveform and reduced cogging torque. FEA and GA simulation results are compared and agreed well to the flux density in various areas of the designed motor at no-load condition. The final designed motor (1 kW, 50 Hz, four poles) is fabricated and tested. Experimental results agree with those of the simulation, both satisfying the desired technical specifications.


title: Lighting Deep G-Bu↵ers: Single-Pass, Layered Depth Images with Minimum Separation Applied to Indirect Illumination

abstract: Weintroduceanewmethodforcomputing two-levelLayeredDepth Images (LDIs) [Shade et al. 1998] that is designed for modern GPUs. The method is order-independent, can guarantee a minimum separation distance between the layers, operates within small, bounded memory, and requires no explicit sorting. Critically, it also operates in a single pass over scene geometry. This is important because the cost of streaming geometry through a modern game engine pipeline can be high due to work expansion (from patches to triangles to pixels), matrix-skinning for animation, and the relative scarcity of main memory bandwidth compared to caches and registers. We apply the new LDI method to create Deep Geometry Buffers for deferred shading and show that two layers with a minimum separation distance make a variety of screen-space illumination effects surprisingly robust. We specifically demonstrate improved robustness for Scalable Ambient Obsurance [McGuire et al. 2012b], an extended n-bounce screen-space radiosity [Soler et al. 2009], and screen-space reflection ray tracing. All of these produce results that are necessarily view-dependent, but in a manner that is plausible based on visible geometry and more temporally coherent than results without layers.


title: Estimating the Support of a High-Dimensional Distribution

abstract: Suppose you are given some data set drawn from an underlying probability distribution P and you want to estimate a "simple" subset S of input space such that the probability that a test point drawn from P lies outside of S equals some a priori specified value between 0 and 1. We propose a method to approach this problem by trying to estimate a function f that is positive on S and negative on the complement. The functional form of f is given by a kernel expansion in terms of a potentially small subset of the training data; it is regularized by controlling the length of the weight vector in an associated feature space. The expansion coefficients are found by solving a quadratic programming problem, which we do by carrying out sequential optimization over pairs of input patterns. We also provide a theoretical analysis of the statistical performance of our algorithm. The algorithm is a natural extension of the support vector algorithm to the case of unlabeled data.


title: k-fingerprinting: a Robust Scalable Website Fingerprinting Technique

abstract: Website fingerprinting enables an attacker to infer which web page a client is browsing through encrypted or anonymized network connections. We present a new website fingerprinting technique based on random decision forests and evaluate performance over standard web pages as well as Tor hidden services, on a larger scale than previous works. Our technique, k-fingerprinting, performs better than current state-of-the-art attacks even against website fingerprinting defenses, and we show that it is possible to launch a website fingerprinting attack in the face of a large amount of noisy data. We can correctly determine which of 30 monitored hidden services a client is visiting with 85% true positive rate (TPR), a false positive rate (FPR) as low as 0.02%, from a world size of 100,000 unmonitored web pages. We further show that error rates vary widely between web resources, and thus some patterns of use will be predictably more vulnerable to attack than others.


title: Deep Learning 3D Shape Surfaces Using Geometry Images

abstract: Surfaces serve as a natural parametrization to 3D shapes. Learning surfaces using convolutional neural networks (CNNs) is a challenging task. Current paradigms to tackle this challenge are to either adapt the convolutional filters to operate on surfaces, learn spectral descriptors defined by the Laplace-Beltrami operator, or to drop surfaces altogether in lieu of voxelized inputs. Here we adopt an approach of converting the 3D shape into a ‘geometry image’ so that standard CNNs can directly be used to learn 3D shapes. We qualitatively and quantitatively validate that creating geometry images using authalic parametrization on a spherical domain is suitable for robust learning of 3D shape surfaces. This spherically parameterized shape is then projected and cut to convert the original 3D shape into a flat and regular geometry image. We propose a way to implicitly learn the topology and structure of 3D shapes using geometry images encoded with suitable features. We show the efficacy of our approach to learn 3D shape surfaces for classification and retrieval tasks on non-rigid and rigid shape datasets.


title: PACMan: Coordinated memory caching for parallel jobs

abstract: Data-intensive analytics on large clusters is important for modern Internet services. As machines in these clusters have large memories, in-memory caching of inputs is an effective way to speed up these analytics jobs. The key challenge, however, is that these jobs run multiple tasks in parallel and a job is sped up only when inputs of all such parallel tasks are cached. Indeed, a single task whose input is not cached can slow down the entire job. To meet this "all-or-nothing" property, we have built PACMan, a caching service that coordinates access to the distributed caches. This coordination is essential to improve job completion times and cluster efficiency. To this end, we have implemented two cache replacement policies on top of PACMan's coordinated infrastructure fb-LIFE that minimizes average completion time by evicting large incomplete inputs, and LFU-F that maximizes cluster efficiency by evicting less frequently accessed inputs. Evaluations on production workloads from Facebook and Microsoft Bing show that PACMan reduces average completion time of jobs by 53% and 51% (small interactive jobs improve by 77%), and improves efficiency of the cluster by 47% and 54%, respectively.


title: Commentary on: SMAS Fusion Zones Determine the Subfacial and Subcutaneous Anatomy of the Human Face: Fascial Spaces, Fat Compartments, and Models of Facial Aging.

abstract: What's in a name? That which we call a rose by any other name would smell as sweet. ::: ::: William Shakespeare ::: ::: As the title suggests, this contribution by Dr Pessa1 differs from the usual focused plastic surgery articles in this journal. It is closer to a thesis, being several papers in one on related aspects of this anatomy, to which the reader will likely return. Unfortunately, as is the anatomy, the paper is not easy to understand and I regret this brief Commentary cannot do justice to many aspects worthy of comment. It is the culmination of Dr Pessa's anatomical contributions to plastic surgery over more than 20 years that have followed a continuous theme, being largely based on dye injection studies.2,3 The earlier studies focused on the superficial anatomy, whereas the recent progression into unfamiliar territory of the deeper facial anatomy has been more difficult for plastic surgeons to follow.4 ::: ::: The difficulty is defined in the opening paragraph “the sub-divisions of these layers, the terminology that defines them, and their boundaries have remained a source of controversy throughout medical history.”1 Some good clinical correlations and excellent dissections are included to explain the messages. Having spent considerable time studying the illustrations and their legends on several occasions. I concur with the overall message, despite some disappointing errors of detail in the text. So the focus is to understand the relationship between the superficial and deeper facial anatomy. ::: ::: A sound understanding of basic anatomical principles provides the framework on which to understand the detail described. The cheek is not homogenous, but consists of several functionally, and therefore structurally, different regions. In simplest terms the cheek can be considered as having a central part, the cheek proper, and a peripheral part that can be likened to a …


title: Understanding individual human mobility patterns

abstract: The mapping of large-scale human movements is important for urban planning, traffic forecasting and epidemic prevention. Work in animals had suggested that their foraging might be explained in terms of a random walk, a mathematical rendition of a series of random steps, or a Levy flight, a random walk punctuated by occasional larger steps. The role of Levy statistics in animal behaviour is much debated — as explained in an accompanying News Feature — but the idea of extending it to human behaviour was boosted by a report in 2006 of Levy flight-like patterns in human movement tracked via dollar bills. A new human study, based on tracking the trajectory of 100,000 cell-phone users for six months, reveals behaviour close to a Levy pattern, but deviating from it as individual trajectories show a high degree of temporal and spatial regularity: work and other commitments mean we are not as free to roam as a foraging animal. But by correcting the data to accommodate individual variation, simple and predictable patterns in human travel begin to emerge. The cover photo (by Cesar Hidalgo) captures human mobility in New York's Grand Central Station. This study used a sample of 100,000 mobile phone users whose trajectory was tracked for six months to study human mobility patterns. Displacements across all users suggest behaviour close to the Levy-flight-like pattern observed previously based on the motion of marked dollar bills, but with a cutoff in the distribution. The origin of the Levy patterns observed in the aggregate data appears to be population heterogeneity and not Levy patterns at the level of the individual. Despite their importance for urban planning1, traffic forecasting2 and the spread of biological3,4,5 and mobile viruses6, our understanding of the basic laws governing human motion remains limited owing to the lack of tools to monitor the time-resolved location of individuals. Here we study the trajectory of 100,000 anonymized mobile phone users whose position is tracked for a six-month period. We find that, in contrast with the random trajectories predicted by the prevailing Levy flight and random walk models7, human trajectories show a high degree of temporal and spatial regularity, each individual being characterized by a time-independent characteristic travel distance and a significant probability to return to a few highly frequented locations. After correcting for differences in travel distances and the inherent anisotropy of each trajectory, the individual travel patterns collapse into a single spatial probability distribution, indicating that, despite the diversity of their travel history, humans follow simple reproducible patterns. This inherent similarity in travel patterns could impact all phenomena driven by human mobility, from epidemic prevention to emergency response, urban planning and agent-based modelling.


title: Can Scientific Impact Be Predicted?

abstract: A widely used measure of scientific impact is citations. However, due to their heavy-tailed distribution, citations are fundamentally difficult to predict. Instead, to characterize scientific impact, we address two analogous questions asked by many scientific researchers: “How will my h -index evolve over time, and which of my previously or newly published papers will contribute to it?” To answer these questions, we perform two related tasks. First, we develop a model to predict authors’ future h -indices based on their current scientific impact. Second, we examine the factors that drive papers—either previously or newly published—to increase their authors’ predicted future h -indices. By leveraging relevant factors, we can predict an author's h -index in five years with an $R^2$ value of 0.92 and whether a previously (newly) published paper will contribute to this future h -index with an $F_1$ score of 0.99 (0.77). We find that topical authority and publication venue are crucial to these effective predictions, while topic popularity is surprisingly inconsequential. Further, we develop an online tool that allows users to generate informed h -index predictions. Our work demonstrates the predictability of scientific impact, and can help researchers to effectively leverage their scholarly position of “standing on the shoulders of giants.”


title: Change in normal haematological values of camels (Camelus dromedarius): influence of age and sex

abstract: This study was conducted to determine the longitudinal variations in normal values of haematological parameters in camels (Camelus dromedarius) of different age and sex. Three hundred and two camels of either sex were used to estimate the changes from 1 to 5 and above years of age. The haematological variables estimated were white blood cell counts, red blood cell counts, haemoglobin, packed cell volume, mean corpuscular volume, mean corpuscular haemoglobin and mean corpuscular haemoglobin concentration. The mean and standard deviation (SD) of different variables have been calculated and results are discussed in relation to previous reports. Significant variation for white blood cell counts, red blood cell counts, haemoglobin, packed cell volume and mean corpuscular haemoglobin was observed for males with age, while difference in white blood cell counts, red blood cell counts, haemoglobin, packed cell volume and mean corpuscular volume was observed for females.


title: Gaussian Processes and Kernel Methods: A Review on Connections and Equivalences

abstract: This paper is an attempt to bridge the conceptual gaps between researchers working on the two widely used approaches based on positive definite kernels: Bayesian learning or inference using Gaussian processes on the one side, and frequentist kernel methods based on reproducing kernel Hilbert spaces on the other. It is widely known in machine learning that these two formalisms are closely related; for instance, the estimator of kernel ridge regression is identical to the posterior mean of Gaussian process regression. However, they have been studied and developed almost independently by two essentially separate communities, and this makes it difficult to seamlessly transfer results between them. Our aim is to overcome this potential difficulty. To this end, we review several old and new results and concepts from either side, and juxtapose algorithmic quantities from each framework to highlight close similarities. We also provide discussions on subtle philosophical and theoretical differences between the two approaches.


title: UAV-assisted disaster management: Applications and open issues

abstract: The fast-paced development of Unmanned Aerial Vehicles (UAVs) and their use in different domains, opens a new paradigm on their use in natural disaster management. In UAV-assisted disaster management applications, UAVs not only survey the affected area but also assist in establishing the communication network between the disaster survivors, rescue teams and nearest available cellular infrastructure. This paper identifies main disaster management applications of UAV networks and discusses open research issues related to UAV-assisted disaster management.


Fear and Logging in the Internet of Things


title: Edge preserving image enhancement for color and gray scale images using local mean and local standard deviation

abstract: The aim of image enhancement is to produce a processed image which is more suitable than the original image for specific application. Application can be edge detection, boundary detection, image fusion, segmentation etc. In this paper different types of image enhancement algorithms in spatial domain are presented for gray scale as well as for color images. Quantitative analysis like AMBE (Absolute mean brightness error), MSE (Mean square error) and PSNR (Peak signal to noise ratio) for the different algorithms are evaluated. For gray scale image Weighted histogram equalization, Linear contrast stretching (LCS), Non linear contrast stretching logarithmic (NLLCS), Non linear contrast stretching exponential (NLECS), Bi Histogram Equalization (BHE) algorithms are discussed and compared. For color image (RGB) Linear contrast stretching, Non linear contrast stretching logarithmic and Non linear contrast stretching exponential algorithms are discussed. During result analysis, it has been observed that some algorithms does give considerably highly distinct values(MSE or AMBE) for different images. To stabilize these parameters, had proposed the new enhancement scheme Local mean and local standard deviation(LMLS) which will take care of these issues. By experimental analysis It has been observed that proposed method gives better AMBE (should be less) and PSNR (should be high) values compared with other algorithms, also these values are not highly distinct for different images.


title: SoftNet: A software defined decentralized mobile network architecture toward 5G

abstract: Mobile cloud computing and other new emerging communication paradigms such as mobile social networking and Internet-of-Things, have brought challenges in flexibility, efficiency, and scalability to the current LTE network. Inspired by thinking of the fundamental mechanisms in LTE as reasons causing those problems, SoftNet, a software defined decentralized mobile network architecture toward 5G, is proposed in this article following principles proposed for designing an efficient and scalable network. The analysis of the working mechanisms of SoftNet, including its dynamically defined architecture, decentralized mobility management, distributed data forwarding, and multi-RATs coordination, show that SoftNet has improved system capacity and performance. Further, simulations are conducted to demonstrate that signaling cost in SoftNet, as an important performance metric, can be decreased significantly compared with LTE networks.


title: Δ9-Tetrahydrocannabinolic acid synthase: The application of a plant secondary metabolite enzyme in biocatalytic chemical synthesis.

abstract: Δ(9)-Tetrahydrocannabinolic acid synthase (THCAS) from the secondary metabolism of Cannabis sativa L. catalyzes the oxidative formation of an intramolecular CC bond in cannabigerolic acid (CBGA) to synthesize Δ(9)-tetrahydrocannabinolic acid (THCA), which is the direct precursor of Δ(9)-tetrahydrocannabinol (Δ(9)-THC). Aiming on a biotechnological production of cannabinoids, we investigated the potential of the heterologously produced plant oxidase in a cell-free system on preparative scale. THCAS was characterized in an aqueous/organic two-liquid phase setup in order to solubilize the hydrophobic substrate and to allow in situ product removal. Compared to the single phase aqueous setup the specific activity decreased by a factor of approximately 2 pointing to a substrate limitation of CBGA in the two-liquid phase system. However, the specific activity remained stable for at least 3h illustrating the benefit of the two-liquid phase setup. In a repeated-batch setup, THCAS showed only a minor loss of specific activity in the third batch pointing to a high intrinsic stability and high solvent tolerance of the enzyme. Maximal space-time-yields of 0.121gL(-1)h(-1) were reached proving the two-liquid phase concept suitable for biotechnological production of cannabinoids.


title: Best proximity point theorems via fixed point theorems for multivalued mappings

abstract: It is well known that the concept of a best proximity point includes that of a fixed point as a special case. In this paper, we show that the best proximity point theorems of Basha and Shahzad (Fixed Point Theory Appl. 2012:42, 2012) and of Fernandez-Leon (J. Nonlinear Convex Anal. 15(2):313-324, 2014) can be regarded as a fixed point theorem for multivalued mappings which is modified as regards the results of Mizoguchi and Takahashi (J. Math. Anal. Appl. 141(1):177-188, 1989) and of Kada et al. (Math. Jpn. 44(2):381-391, 1996).


title: Artful thinking and Augmented Reality in kindergarten: technology contributions to the inclusion of socially underprivileged children in creative activities

abstract: This paper presents a set of creative thinking activities which took place in a low income area kindergarten of Piraeus, complying with the principles of Harvard's Artful Thinking Program. The aim of this research study is to examine the contribution of the Augmented Reality application Aurasma to the children's participation levels. All data are based on class observation of learner behavior and attitudes before/after the introduction of AR and preliminary results reveal that the use of the application encouraged learner involvement in creating stories, attracted more student interest in consequent similar activities and increased participation levels of 'quiet' children, especially those who face learning challenges due to their poor socio-economic background, allowing to hope that AR implementation promotes the inclusion of socially underprivileged children in activities leading to the development of high level thinking.


title: Cyber Threat Metrics

abstract: Threats are generally much easier to list than to describe, and much easier to describe than to measure. As a result, many organizations list threats. Fewer describe them in useful terms, and still fewer measure them in meaningful ways. This is particularly true in the dynamic and nebulous domain of cyber threats - a domain that tends to resist easy measurement and, in some cases, appears to defy any measurement. We believe the problem is tractable. In this report we describe threat metrics and models for characterizing threats consistently and unambiguously. The purpose of this report is to support the Operational Threat Assessment (OTA) phase of risk and vulnerability assessment. To this end, we focus on the task of characterizing cyber threats using consistent threat metrics and models. In particular, we address threat metrics and models for describing malicious cyber threats to US FCEB agencies and systems.


title: Inferring monopartite projections of bipartite networks: an entropy-based approach

abstract: Bipartite networks are currently regarded as providing a major insight into the organization of many real-world systems, unveiling the mechanisms driving the interactions occurring between distinct groups of nodes. One of the most important issues encountered when modeling bipartite networks is devising a way to obtain a (monopartite) projection on the layer of interest, which preserves as much as possible the information encoded into the original bipartite structure. In the present paper we propose an algorithm to obtain statistically-validated projections of bipartite networks, according to which any two nodes sharing a statistically-significant number of neighbors are linked. Since assessing the statistical significance of nodes similarity requires a proper statistical benchmark, here we consider a set of four null models, defined within the exponential random graph framework. Our algorithm outputs a matrix of link-specific p-values, from which a validated projection is straightforwardly obtainable, upon running a multiple hypothesis testing procedure. Finally, we test our method on an economic network (i.e. the countries-products World Trade Web representation) and a social network (i.e. MovieLens, collecting the users' ratings of a list of movies). In both cases non-trivial communities are detected: while projecting the World Trade Web on the countries layer reveals modules of similarly-industrialized nations, projecting it on the products layer allows communities characterized by an increasing level of complexity to be detected; in the second case, projecting MovieLens on the films layer allows clusters of movies whose affinity cannot be fully accounted for by genre similarity to be individuated.


title: Integrating Web Services With Competitive Strategies: The Balanced Scorecard Approach

abstract: The significance of aligning IT with corporate strategy is widely recognized, but the lack of appropriate methodologies prevented practitioners from integrating IT projects with competitive strategies effectively. This article addresses the issue of deploying Web services strategically using the concept of a widely accepted management tool, the balanced scorecard. A framework is developed to match potential benefits of Web services with corporate strategy in four business dimensions: innovation and learning, internal business process, customer, and financial. It is argued that the strategic benefits of implementing Web services can only be realized if the Web services initiatives are planned and implemented within the framework of an IT strategy that is designed to support the business strategy of a firm.


title: On Multiple-Input Multiple-Output OFDM with Index Modulation for Next Generation Wireless Networks

abstract: Multiple-input multiple-output orthogonal frequency division multiplexing with index modulation (MIMO-OFDM-IM) is a novel multicarrier transmission technique which has been proposed recently as an alternative to classical MIMO-OFDM. In this scheme, OFDM with index modulation (OFDM-IM) concept is combined with MIMO transmission to take advantage of the benefits of these two techniques. In this paper, we shed light on the implementation and error performance analysis of the MIMO-OFDM-IM scheme for next generation 5G wireless networks. Maximum likelihood (ML), near-ML, simple minimum mean square error (MMSE) and ordered successive interference cancellation (OSIC) based MMSE detectors of MIMO-OFDM-IM are proposed and their theoretical performance is investigated. It has been shown via extensive computer simulations that MIMO-OFDM-IM scheme provides an interesting trade-off between error performance and spectral efficiency as well as it achieves considerably better error performance than classical MIMO-OFDM using different type detectors and under realistic conditions.


title: Analyzing the oversampling of different classes and types of examples in multi-class imbalanced datasets

abstract: Canonical machine learning algorithms assume that the number of objects in the considered classes are roughly similar. However, in many real-life situations the distribution of examples is skewed since the examples of some of the classes appear much more frequently. This poses a difficulty to learning algorithms, as they will be biased towards the majority classes. In recent years many solutions have been proposed to tackle imbalanced classification, yet they mainly concentrate on binary scenarios. Multi-class imbalanced problems are far more difficult as the relationships between the classes are no longer straightforward. Additionally, one should analyze not only the imbalance ratio but also the characteristics of the objects within each class. In this paper we present a study on oversampling for multi-class imbalanced datasets that focuses on the analysis of the class characteristics. We detect subsets of specific examples in each class and fix the oversampling for each of them independently. Thus, we are able to use information about the class structure and boost the more difficult and important objects. We carry an extensive experimental analysis, which is backed-up with statistical analysis, in order to check when the preprocessing of some types of examples within a class may improve the indiscriminate preprocessing of all the examples in all the classes. The results obtained show that oversampling concrete types of examples may lead to a significant improvement over standard multi-class preprocessing that do not consider the importance of example types. Graphical abstractDisplay Omitted HighlightsA thorough analysis of oversampling for handling multi-class imbalanced datasets.Proposition to detect underlying structures and example types in considered classes.Smart oversampling based on extracted knowledge about imbalance distribution types.In-depth insight into the importance of selecting proper examples for oversampling.Guidelines that allow to design efficient classifiers for multi-class imbalanced data.


title: Nutritional Guidelines and Fermented Food Frameworks

abstract: This review examines different nutritional guidelines, some case studies, and provides insights and discrepancies, in the regulatory framework of Food Safety Management of some of the world’s economies. There are thousands of fermented foods and beverages, although the intention was not to review them but check their traditional and cultural value, and if they are still lacking to be classed as a category on different national food guides. For understanding the inconsistencies in claims of concerning fermented foods among various regulatory systems, each legal system should be considered unique. Fermented foods and beverages have long been a part of the human diet, and with further supplementation of probiotic microbes, in some cases, they offer nutritional and health attributes worthy of recommendation of regular consumption. Despite the impact of fermented foods and beverages on gastro-intestinal wellbeing and diseases, their many health benefits or recommended consumption has not been widely translated to global inclusion in world food guidelines. In general, the approach of the legal systems is broadly consistent and their structures may be presented under different formats. African traditional fermented products are briefly mentioned enhancing some recorded adverse effects. Knowing the general benefits of traditional and supplemented fermented foods, they should be a daily item on most national food guides.


3D Reconstruction of Incomplete Archaeological Objects Using a Generative Adversarial Network


title: Sustainable Solutions for Municipal Solid Waste Management in Thailand

abstract: General as well as the MSW management in Thailand is reviewed in this paper. Topics include the MSW generation, sources, composition, and trends. The review, then, moves to sustainable solutions for MSW management, sustainable alternative approaches with an emphasis on an integrated MSW management. Information of waste in Thailand is also given at the beginning of this paper for better understanding of later contents. It is clear that no one single method of MSW disposal can deal with all materials in an environmentally sustainable way. As such, a suitable approach in MSW management should be an integrated approach that could deliver both environmental and economic sustainability. With increasing environmental concerns, the integrated MSW management system has a potential to maximize the useable waste materials as well as produce energy as a by-product. In Thailand, the compositions of waste (86%) are mainly organic waste, paper, plastic, glass, and metal. As a result, the waste in Thailand is suitable for an integrated MSW management. Currently, the Thai national waste management policy starts to encourage the local administrations to gather into clusters to establish central MSW disposal facilities with suitable technologies and reducing the disposal cost based on the amount of MSW generated.


title: Alphabet Soup Blurring the Distinctions Between p’s and �’s in Psychological Research

abstract: Confusion over the reporting and interpretation of results of commonly employed classical statistical tests is recorded in a sample of 1,645 papers from 12 psychology journals for the period 1990 through 2002. The confusion arises because researchers mistakenly believe that their interpretation is guided by a single unified theory of statistical inference. But this is not so: classical statistical testing is a nameless amalgamation of the rival and often contradictory approaches developed by Ronald Fisher, on the one hand, and Jerzy Neyman and Egon Pearson, on the other. In particular, there is extensive failure to acknowledge the incompatibility of Fisher’s evidential pvalue with the Type I error rate, •, of Neyman–Pearson statistical orthodoxy. The distinction between evidence (p’s) and errors (•’s) is not trivial. Rather, it reveals the basic differences underlying Fisher’s ideas on significance testing and inductive inference, and Neyman–Pearson views on hypothesis testing and inductive behavior. So c...


title: Independence of color and luminance edges in natural scenes

abstract: Form vision is traditionally regarded as processing primarily achromatic information. Previous investigations into the statistics of color and luminance in natural scenes have claimed that luminance and chromatic edges are not independent of each other and that any chromatic edge most likely occurs together with a luminance edge of similar strength. Here we computed the joint statistics of luminance and chromatic edges in over 700 calibrated color images from natural scenes. We found that isoluminant edges exist in natural scenes and were not rarer than pure luminance edges. Most edges combined luminance and chromatic information but to varying degrees such that luminance and chromatic edges were statistically independent of each other. Independence increased along successive stages of visual processing from cones via postreceptoral color-opponent channels to edges. The results show that chromatic edge contrast is an independent source of information that can be linearly combined with other cues for the proper segmentation of objects in natural and artificial vision systems. Color vision may have evolved in response to the natural scene statistics to gain access to this independent information.


title: Simulation of Bewley's lattice diagram using MATLAB

abstract: Bewley's lattice diagram is a graphical method that has been widely used for determining value of a travelling wave (TW) in transient analysis. The method provides a very simple representation for voltage and current surge reflections as it travels along the transmission system. From an extensive literature survey it was concluded that till date use of digital computers by developing a program for the same computation has not been done. This paper aims at an approach for the development of a program for this method using MATLAB. Coding has been done for few specific practical cases of transmission system. Results for all those cases have been presented in this paper. And finally with the achieved results a discussion has been done on the possible future work in this direction.


title: VII Workshop de Visão Computacional – WVC 2011 Focus of Expansion Estimation for Motion Segmentation from a Single Camera

abstract: This paper proposes a new approach to motion segmentation from video sequences acquired using a single camera, whose aim is to identify which components are due to pure egomotion and which components are due to independent moving objects within the observed motion field. The model has three main steps, namely computation of the optical flow field, estimation of the focus of expansion and classification under constraints. Preliminary results show that estimation of the focus of expansion followed by simple clustering techniques is promising for the achievement of motion segmentation when using a single camera with no a priori information about the environment.


Parental Stress and Coping in Families of Children With and Without Developmental Delays


title: Better key sizes (and attacks) for LWE-based encryption

abstract: We analyze the concrete security and key sizes of theoretically sound lattice-based encryption schemes based on the "learning with errors" (LWE) problem. Our main contributions are: (1) a new lattice attack on LWE that combines basis reduction with an enumeration algorithm admitting a time/success tradeoff, which performs better than the simple distinguishing attack considered in prior analyses; (2) concrete parameters and security estimates for an LWE-based cryptosystem that is more compact and efficient than the well-known schemes from the literature. Our new key sizes are up to 10 times smaller than prior examples, while providing even stronger concrete security levels.


title: Deconvolution Method for View Interpolation Using Multiple Images of Circular Camera Array

abstract: This paper deals with a view interpolation problem using multiple images captured with a circular camera array. A novel deconvolution method for reconstructing a virtual image at the center of the camera array is presented and discussed in a framework of image restoration in the frequency domain. The reconstruction filter does not depend on the scene structure; therefore the presented method requires no depth estimation. The simulation result using synthetic images shows that increasing the number of the cameras improves the quality of the virtual image.


title: Comparative study of Principal Component Analysis based Intrusion Detection approach using machine learning algorithms

abstract: This paper induces the prominence of variegated machine learning techniques adapted so far for the identifying different network attacks and suggests a preferable Intrusion Detection System (IDS) with the available system resources while optimizing the speed and accuracy. With booming number of intruders and hackers in todays vast and sophisticated computerized world, it is unceasingly challenging to identify unknown attacks in promising time with no false positive and no false negative. Principal Component Analysis (PCA) curtails the amount of data to be compared by reducing their dimensions prior to classification that results in reduction of detection time. In this paper, PCA is adopted to reduce higher dimension dataset to lower dimension dataset. It is accomplished by converting network packet header fields into a vector then PCA applied over high dimensional dataset to reduce the dimension. The reduced dimension dataset is tested with Support Vector Machines (SVM), K-Nearest Neighbors (KNN), J48 Tree algorithm, Random Forest Tree classification algorithm, Adaboost algorihm, Nearest Neighbors generalized Exemplars algorithm, Navebayes probabilistic classifier and Voting Features Interval classification algorithm. Obtained results demonstrates detection accuracy, computational efficiency with minimal false alarms, less system resources utilization. Experimental results are compared with respect to detection rate and detection time and found that TREE classification algorithms achieved superior results over other algorithms. The whole experiment is conducted by using KDD99 data set.


title: BRAIN TUMOR DETECTION AND SEGMENTATION USING WATERSHED SEGMENTATION AND MORPHOLOGICAL OPERATION

abstract: In the field of medical image processing, detection of brain tumor from magnetic resonance image (MRI) brain scan has become one of the most active research. Detection of the tumor is the main objective of the system. Detection plays a critical role in biomedical imaging. In this paper, MRI brain image is used to tumor detection process. This system includes test the brain image process, image filtering, skull stripping, segmentation, morphological operation, calculation of the tumor area and determination of the tumor location. In this system, morphological operation of erosion algorithm is applied to detect the tumor. The detailed procedures are implemented using MATLAB. The proposed method extracts the tumor region accurately from the MRI brain image. The experimental results indicate that the proposed method efficiently detected the tumor region from the brain image. And then, the equation of the tumor region in this system is effectively applied in any shape of the tumor region.


title: Common mode EMI reduction technique for interleaved MHz critical mode PFC converter with coupled inductor

abstract: Coupled inductor has been widely adopted in VR applications for many years because of its benefits such as reducing current ripple and improving transient performance. In this presentation, coupled inductor concept is applied to interleaved MHz totem-pole CRM PFC converter with GaN devices. The coupled inductor in CRM PFC converter can reduce switching frequency variation, help achieving ZVS and reduce circulating energy. Therefore the coupled inductor can improve the efficiency of PFC converter. In addition, balance technique is applied to help minimize CM noise. This paper will introduce how to achieve balance with coupled inductor. The novel PCB winding inductor design will be provided. The PCB winding coupled inductor will have similar loss with litz wire inductor but much less labor in manufacture.


title: Derivation and implementation of a semantic GIS data model informed by principles of cognition

abstract: Abstract The purpose of this research is to develop a new kind of semantic GIS data model that is better able to represent users’ conceptual models of geographic domains than the conventional vector and raster data models. To this end, I look to the principles of cognition, how humans represent geographic information in their minds, to inform the development of this semantic data model. A three-stage methodology for the derivation and implementation of the semantic data model is presented. First, a conceptual framework of geographic cognition is developed. This framework incorporates principles of cognitive categorization and ‘top-down’ and ‘bottom-up’ information processing. Second, by replacing those elements of cognition with their database representation counterparts, often drawing from the object-oriented modeling paradigm, a semantic data model is derived. Finally, a prototype implementation of the semantic data model is presented using the Java programming language and the object-oriented database Poet as a development platform. This implementation utilizes a rule-based approach for representing categorical information and for the extraction of semantic entities from observational data.


title: Affordances in HCI: toward a mediated action perspective

abstract: Interpretations of the concept of "affordances" in HCI are becoming increasingly diverse, extending well beyond the original Gibsonian meaning. We discuss some of the key analyses of affordances in HCI research and make three related claims. First, we argue that many current interpretations of the concept are essentially incompatible with Gibson. Second, we hold that the Gibsonian concept of affordances, conceptualized as interaction between animals and their environments, provides some important insights, but is, in the end, of limited relevance to HCI research. Third, we call for adopting a mediated action perspective on affordances as an alternative to Gibson's ecological psychology. We outline a view of technology affordances as possibilities for human action mediated by cultural means conceived as a relational property of a three-way interaction between the person, mediational means, and environment. We conclude with a discussion of prospects for future conceptual and empirical explorations of the meditational perspective in HCI research.


title: Detecting Wash Trade in Financial Market Using Digraphs and Dynamic Programming

abstract: A wash trade refers to the illegal activities of traders who utilize carefully designed limit orders to manually increase the trading volumes for creating a false impression of an active market. As one of the primary formats of market abuse, a wash trade can be extremely damaging to the proper functioning and integrity of capital markets. The existing work focuses on collusive clique detections based on certain assumptions of trading behaviors. Effective approaches for analyzing and detecting wash trade in a real-life market have yet to be developed. This paper analyzes and conceptualizes the basic structures of the trading collusion in a wash trade by using a directed graph of traders. A novel method is then proposed to detect the potential wash trade activities involved in a financial instrument by first recognizing the suspiciously matched orders and then further identifying the collusions among the traders who submit such orders. Both steps are formulated as a simplified form of the knapsack problem, which can be solved by dynamic programming approaches. The proposed approach is evaluated on seven stock data sets from the NASDAQ and the London Stock Exchange. The experimental results show that the proposed approach can effectively detect all primary wash trade scenarios across the selected data sets.


title: Effect of seaweed mixture intake on plasma lipid and antioxidant profile of hyperholesterolaemic rats

abstract: Cardiovascular disease (CVD) is the leading cause of death in many countries. Hypercholesterolaemia is a recurring risk factor in CVD leading to coronary atherosclerosis, stroke and ischemic heart disease. Previous research has proven that seaweeds are highly nutritious, providing a good source of dietary fibre, minerals, proteins and vitamins as well as being high in antioxidants. Antioxidants have been known to retard low-density lipoprotein (LDL) oxidation to reduce CVD risk in hypercholesterolaemia. However, there is yet to be a study on the effect of a mixture of different seaweed species on cholesterol lowering properties. Therefore, this study was designed to investigate the effects of a mixture of extracts from two seaweed species, red seaweed Kappaphycus alvarezii and brown seaweed Sargassum polycystum on plasma lipid and antioxidant profiles of rats fed high-cholesterol diet. S. polycystum extract significantly decreased plasma cholesterol by 37.52 % over an 8-week treatment period compared to K. alvarezii and mixture groups. However, S. polycystum showed an increase in plasma triglyceride (TG) levels by 16.66 %. K. alvarezii extract most effectively decreased TG levels by 40.11 % and the mixture extract most effectively increased high-density lipoprotein cholesterol by 56.71 %. All treatment groups were able to reduce LDL cholesterol levels compared to the high-cholesterol group, with no significant differences between them. K. alvarezii and S. polycystum mixture extract had the best atherogenic index, which is an indicator of lipid disorder in coronary diseases, among treatment and high-cholesterol-fed groups. All treatment groups were able to restore enzyme antioxidant levels (superoxide dismutase and catalase) to normal.


title: Broadband millimetre-wave passive spatial combiner based on coaxial waveguide

abstract: A broadband millimetre-wave passive spatial combiner using a microstrip probe array and an oversized coaxial waveguide is successfully designed and tested. The equivalent circuit approach of transverse electromagnetic (TEM) lines is adopted to synthesise the coaxial stepped impedance transformer from a K connector with the oversized coaxial waveguide and to analyse the microstrip probe array. The equivalent susceptances of the step discontinuities for the power divider are calculated. A four-way coaxial waveguide passive power combining circuit operating at the entire Ka-band is designed, fabricated and measured. Experiments on the four-way passive combiner show that a minimum insertion loss of 0.7 dB has been achieved at about 29.5 GHz. The combiner has shown a bandwidth of 26.5-40 GHz with 10 dB return loss and less than 2.5 dB insertion loss.


title: Choosing words in computer-generated weather forecasts

abstract: One of the main challenges in automatically generating textual weather forecasts is choosing appropriate English words to communicate numeric weather data. A corpus-based analysis of how humans write forecasts showed that there were major differences in how individual writers performed this task, that is, in how they translated data into words. These differences included both different preferences between potential near-synonyms that could be used to express information, and also differences in the meanings that individual writers associated with specific words. Because we thought these differences could confuse readers, we built our SumTime-Mousam weather-forecast generator to use consistent data-to-word rules, which avoided words which were only used by a few people, and words which were interpreted differently by different people. An evaluation by forecast users suggested that they preferred SumTime-Mousam's texts to human-generated texts, in part because of better word choice; this may be the first time that an evaluation has shown that nlg texts are better than human-authored texts.


title: 7.5% Optical-to-Terahertz Conversion Efficiency Offered by Photoconductive Emitters With Three-Dimensional Plasmonic Contact Electrodes

abstract: We present a photoconductive terahertz emitter that incorporates three-dimensional plasmonic contact electrodes to offer record high optical-to-terahertz power conversion efficiencies. By use of three-dimensional plasmonic contact electrodes the majority of photocarriers are generated within nanoscale distances from the photoconductor contact electrodes and drifted to the terahertz radiating antenna in a sub-picosecond time-scale to efficiently contribute to terahertz radiation. We experimentally demonstrate 105 μW of broadband terahertz radiation in the 0.1-2 THz frequency range in response to a 1.4 mW optical pump, exhibiting a record high optical-to-terahertz power conversion efficiency of 7.5%.


title: A survey on learning to rank

abstract: Ranking is the key problem for information retrieval and other text applications. Recently, the ranking methods based on machine learning approaches, called learning to rank, become the focus for researchers and practitioners. The main idea of these methods is to apply the various existing and effective algorithms on machine learning to ranking. However, as a learning problem, ranking is different from other classical ones such as classification and regression. In this paper, we investigate the important papers in this direction; the cons and pros of the recent-proposed framework and algorithms for ranking are analyzed, and the relationships among them are discussed. Finally, the promising directions in practice are also pointed out.


title: SFC-Checker: Checking the correct forwarding behavior of Service Function chaining

abstract: Network middleboxes are difficult to manage and troubleshoot, due to their proprietary monolithic design. Moving towards Network Functions Virtualization (NFV), virtualized middlebox appliances can be more flexibly instantiated and dynamically chained, making troubleshooting even more difficult. To guarantee carrier-grade availability and minimize outages, operators need ways to automatically verify that the deployed network and middlebox configurations obey higher level network policies. In this paper, we first define and identify the key challenges for checking the correct forwarding behavior of Service Function Chains (SFC). We then design and develop a network diagnosis framework that aids network administrators in verifying the correctness of SFC policy enforcement. Our prototype - SFC-Checker can verify stateful service chains efficiently, by analyzing the switches' forwarding rules and the middleboxes' stateful forwarding behavior. Built on top of the network function models we proposed, we develop a diagnosis algorithm that is able to check the stateful forwarding behavior of a chain of network service functions.


title: Learning how to learn: an adaptive dialogue agent for incrementally learning visually grounded word meanings

abstract: We present an optimised multi-modal dialogue agent for interactive learning of visually grounded word meanings from a human tutor, trained on real human-human tutoring data. Within a life-long interactive learning period, the agent, trained using Reinforcement Learning (RL), must be able to handle natural conversations with human users and achieve good learning performance (accuracy) while minimising human effort in the learning process. We train and evaluate this system in interaction with a simulated human tutor, which is built on the BURCHAK corpus -- a Human-Human Dialogue dataset for the visual learning task. The results show that: 1) The learned policy can coherently interact with the simulated user to achieve the goal of the task (i.e. learning visual attributes of objects, e.g. colour and shape); and 2) it finds a better trade-off between classifier accuracy and tutoring costs than hand-crafted rule-based policies, including ones with dynamic policies.


title: No-Reference Image Quality Assessment in the Spatial Domain

abstract: We propose a natural scene statistic-based distortion-generic blind/no-reference (NR) image quality assessment (IQA) model that operates in the spatial domain. The new model, dubbed blind/referenceless image spatial quality evaluator (BRISQUE) does not compute distortion-specific features, such as ringing, blur, or blocking, but instead uses scene statistics of locally normalized luminance coefficients to quantify possible losses of "naturalness" in the image due to the presence of distortions, thereby leading to a holistic measure of quality. The underlying features used derive from the empirical distribution of locally normalized luminances and products of locally normalized luminances under a spatial natural scene statistic model. No transformation to another coordinate frame (DCT, wavelet, etc.) is required, distinguishing it from prior NR IQA approaches. Despite its simplicity, we are able to show that BRISQUE is statistically better than the full-reference peak signal-to-noise ratio and the structural similarity index, and is highly competitive with respect to all present-day distortion-generic NR IQA algorithms. BRISQUE has very low computational complexity, making it well suited for real time applications. BRISQUE features may be used for distortion-identification as well. To illustrate a new practical application of BRISQUE, we describe how a nonblind image denoising algorithm can be augmented with BRISQUE in order to perform blind image denoising. Results show that BRISQUE augmentation leads to performance improvements over state-of-the-art methods. A software release of BRISQUE is available online: http://live.ece.utexas.edu/research/quality/BRISQUE_release.zip for public use and evaluation.


title: Comparative research of the common face detection methods

abstract: Face detection is an important part of the face recognition, in this article the common face detection algorithms are summarized and classified, and several representative face detection algorithm are compared, respectively, the principle, advantages and disadvantages of these methods is described and finally the development trends and challenges of face detection method are presented.


title: The presence of pinworms ( Enterobius sp.) in the mesenteric lymph nodes, liver and lungs of a chimpanzee, Pan troglodytes

abstract: This report describes a case of fatal enterobiasis caused by Enterobius sp. in the mesenteric lymph nodes, lymphatic vessels of mesentery, blood vessels of the liver and lungs of a chimpanzee from Qingdao Zoo, China. Based on pathological findings, it is likely that the pinworms obtained access to these organs via the lymphatic or haematogenous pathway. As far as we are aware, this is the first reported case of Enterobius sp. in the mesenteric lymph nodes and blood vessels of the lungs and veins of the hepatic triad in a chimpanzee.


The Support Vector Machine for Nonlinear Spatio-Temporal Regression


title: Prevalence of interpersonal trauma exposure and trauma-related disorders in severe mental illness

abstract: Background: Interpersonal trauma exposure and trauma-related disorders in people with severe mental illness (SMI) are often not recognized in clinical practice. Objective: To substantiate the prevalence of interpersonal trauma exposure and trauma-related disorders in people with SMI. Methods: We conducted a systematic review of four databases (1980� 2010) and then described and analysed 33 studies in terms of primary diagnosis and instruments used to measure trauma exposure and traumarelated disorders. Results: Population-weighted mean prevalence rates in SMI were physical abuse 47% (range 25� 72%), sexual abuse 37% (range 24� 49%), and posttraumatic stress disorder (PTSD) 30% (range 20� 47%). Compared to men, women showed a higher prevalence of sexual abuse in schizophrenia spectrum disorder, bipolar disorder, and mixed diagnosis groups labelled as having SMI. Conclusions: Prevalence rates of interpersonal trauma and trauma-related disorders were significantly higher in SMI than in the general population. Emotional abuse and neglect, physical neglect, complex PTSD, and dissociative disorders have been scarcely examined in SMI.


title: A Feature Subset Selection Method based on Conditional Mutual Information and Ant Colony Optimization

abstract: Feature subset selection is one of the important problems in a number of fields namely data mining, machine learning, pattern recognition. It refers to the problem of opting for useful features that are neither irrelevant nor redundant. Since most of the data acquired through different sources are not in a proper shape to mine useful patterns from it therefore feature selection is applied over this data to filter out useless features. But since feature selection is a combinatorial optimization problem therefore exhaustively generating and evaluating all possible subsets is intractable in terms of computational cost, memory usage and processing time. Hence such a mechanism is required that intelligently searches for useful set of features in a polynomial time. In this study a feature subset selection algorithm based on conditional mutual information and ant colony optimization is proposed. The proposed method is a pure filter based feature subset selection technique that incurs less computational cost and proficient in terms of classification accuracy. Moreover, along with high accuracy it opts for less number of features. Extensive experimentation is performed based on thirteen benchmark datasets over a number of well known classification algorithms. Empirical results endorse efficiency and effectiveness of the proposed method.


title: Prospective trial of intensive decongestive physiotherapy for upper extremity lymphedema.

abstract: BACKGROUND ::: The aim of this study was to evaluate the effects of kinesiophobia, quality of life, and home exercise programs on women with upper extremity lymphedema. ::: ::: ::: METHODS ::: A total of 62 women with lymphedema after breast cancer treatments were provided a protocol of complete decongestive therapy (CDT). This protocol involved manual lymphatic drainage (MLD), compression garments, skin care, and remedial exercises. The women were taken to a 12-week therapy program once per day, 3 days per week. A home program, consisting of compression bandage exercises, skin care and walking was recommended. Absolute volume and percentage of volume of the lymphedema were compared before and after treatment. The kinesiophobia, quality of life, and home-based program were assessed before and after physiotherapy. ::: ::: ::: RESULTS ::: Strong correlations were found between the severity of edema and fear of movement. There was a significant negative relationship among the fear of movement, quality of life, and home-based exercises program. Mean initial lymphedema volume was 925 ml, and the percentage of lymphedema was 47.1%. After decongestive physiotherapy, the lymphedema volume and percentage were 510 ml and 21.3% (P < 0.05), respectively. There was also a trend toward improvement in general well-being (P < 0.05). ::: ::: ::: CONCLUSION ::: In upper extremity lymphedema, the use of complex physiotherapy programs (CDP) can decrease edema and fear of activity, and increase the quality of life.


title: Modeling Human Motion with Quaternion-based Neural Networks

abstract: Previous work on predicting or generating 3D human pose sequences regresses either joint rotations or joint positions. The former strategy is prone to error accumulation along the kinematic chain, as well as discontinuities when using Euler angles or exponential maps as parameterizations. The latter requires re-projection onto skeleton constraints to avoid bone stretching and invalid configurations. This work addresses both limitations. QuaterNet represents rotations with quaternions and our loss function performs forward kinematics on a skeleton to penalize absolute position errors instead of angle errors. We investigate both recurrent and convolutional architectures and evaluate on short-term prediction and long-term generation. For the latter, our approach is qualitatively judged as realistic as recent neural strategies from the graphics literature. Our experiments compare quaternions to Euler angles as well as exponential maps and show that only a very short context is required to make reliable future predictions. Finally, we show that the standard evaluation protocol for Human3.6M produces high variance results and we propose a simple solution.


title: Visual speech features representation for automatic lip-reading

abstract: A fundamental task in the pattern recognition field is to find a suitable representation for a feature. We present a new visual speech feature representation approach that combines hypercolumn model (HCM) with HMM to perform a complete lip-reading system. We use HCM to extract visual speech features from the input image. The extracted features are modeled by Gaussian distributions using HMM. The proposed lip-reading system can work under varying lip positions and sizes. All images were captured in a natural environment without using special lighting or lip markers. Experimental results are shown to compare favourably with the results of two reported systems, SOM and DCT based systems. HCM provides better performance than both of these systems.


title: Effort Estimation in Agile Software Development using Story Points

abstract: Agile software development has been gaining popularity and replacing the traditional methods of developing software. However, estimating the size and effort in Agile Software development still remains a challenge. This paper gives an overview of the different size estimation methods traditionally used and discusses in details the method based on Story Points, which is at present the most widely used estimation technique in Agile Software Development. The paper describes the steps followed in Story Point based method and highlights the area which needs to be looked into further.


title: Security Analysis on Cyber-physical System Using Attack Tree

abstract: Cyber-Physical System (CPS) is a system of system which integrates physical system with cyber capability in order to improve the physical performance. It is being widely used in areas closely related to national economy and people's livelihood, therefore CPS security problems have drawn a global attention and an appropriate risk assessment for CPS is in urgent need. Existing risk assessment for CPS always focuses on the reliability assessment, using Probability Risk Assessment (PRA). In this way, the assessment of physical part and cyber part is isolated as PRA is difficult to quantify the risks from the cyber world. Methodologies should be developed to assess the both parts as a whole system, considering this integrated system has a high coupling between the physical layer and cyber layer. In this paper, a risk assessment idea for CPS with the use of attack tree is proposed. Firstly, it presents a detailed description about the threat and vulnerability attributes of each leaf in an attack tree and tells how to assign value to its threat and vulnerability vector. Then this paper focuses on calculating the threat and vulnerability vector of an attack path with the use of the leaf vector values. Finally, damage is taken into account and an idea to calculate the risk value of the whole attack path is given.


title: Mobile Clinical Decision Support Systems and Applications: A Literature and Commercial Review

abstract: The latest advances in eHealth and mHealth have propitiated the rapidly creation and expansion of mobile applications for health care. One of these types of applications are the clinical decision support systems, which nowadays are being implemented in mobile apps to facilitate the access to health care professionals in their daily clinical decisions. The aim of this paper is twofold. Firstly, to make a review of the current systems available in the literature and in commercial stores. Secondly, to analyze a sample of applications in order to obtain some conclusions and recommendations. Two reviews have been done: a literature review on Scopus, IEEE Xplore, Web of Knowledge and PubMed and a commercial review on Google play and the App Store. Five applications from each review have been selected to develop an in-depth analysis and to obtain more information about the mobile clinical decision support systems. Ninety-two relevant papers and 192 commercial apps were found. Forty-four papers were focused only on mobile clinical decision support systems. One hundred seventy-one apps were available on Google play and 21 on the App Store. The apps are designed for general medicine and 37 different specialties, with some features common in all of them despite of the different medical fields objective. The number of mobile clinical decision support applications and their inclusion in clinical practices has risen in the last years. However, developers must be careful with their interface or the easiness of use, which can impoverish the experience of the users.


title: OLED Electrical Equivalent Device for Driver Topology Design

abstract: In this paper, a hardware equivalent of an organic light-emitting diode (OLED) was designed and investigated. This substitution OLED device is based on a circuit-equivalent OLED model and can be used to design and test OLED dedicated drivers. Indeed, OLEDs are available on the market, but they are still very expensive and hard to obtain. Compared to a real OLED, the substitution device is cheap and robust and can be easily duplicated. Moreover, a photodetector is not required to measure the light output waveform. This can be simply done by measuring a voltage across a resistance. This model can be used, for instance, to simulate a large OLED panel made of several associated single OLEDs for various series/parallel connection strategies. It can also be used to simulate aging phenomena by changing the values of some of its components. This might be useful for the definition of strategies to compensate aging effects like luminous flux depreciation. Another advantage of such a device is its use for power supply tests as it could serve as a substitution load, at maximum deviation from standard OLED electrical characteristics. We discuss the theoretical model that was used as a basis for developing the device. The accuracy of the model was then evaluated, particularly in pulsewidth-modulation dimming conditions. Then, the hardware equivalent device was compared to a real OLED. Finally, an example of the potential use of this substitution device is given: It was successfully used to investigate the “overdrive” technique in order to increase OLED light output rise time. This technique improves the light output rise time by a factor of over 4.


title: An underwater laser vision system for relative 3-D posture estimation to mesh-like targets

abstract: In this paper we described the development of both the hardware and the algorithms for a novel laser vision system suitable for measuring distances from both solid and mesh-like targets in underwater environments. The system was developed as a part of the AQUABOT project that developed an underwater robotic system for autonomous inspection of offshore aquaculture installation. The system takes into account the hemispherical optics typical in underwater vehicle designs and implements an array of line-lasers to ensure that mesh-like targets provide reflections in a consistent manner. The developed algorithms for the laser vision system are capable of providing either raw pointcloud data sets from each laser or with additional processing high level information like distance and relative orientation of the target with respect to the ROV can be recovered. An automatic calibration procedure along with the accompanying hardware that was developed, is described in this paper, to reduce the calibration overhead required by regular maintenance operations as is typical for underwater vehicles operating in sea-water. A set of experimental results in controlled laboratory environment as well as at offshore aquaculture installations demonstrate the performance of the system.


title: Horn-Schunck Optical Flow with a Multi-Scale Strategy

abstract: The seminal work of Horn and Schunck is the first variational method for optical flow estimation. It introduced a novel framework where the optical flow is computed as the solution of a minimization problem. From the assumption that pixel intensities do not change over time, the optical flow constraint equation is derived. This equation relates the optical flow with the derivatives of the image. There are infinitely many vector fields that satisfy the optical flow constraint, thus the problem is ill-posed. To overcome this problem, Horn and Schunck introduced an additional regularity condition that restricts the possible solutions. Their method minimizes both the optical flow constraint and the magnitude of the variations of the flow field, producing smooth vector fields. One of the limitations of this method is that, typically, it can only estimate small motions. In the presence of large displacements, this method fails when the gradient of the image is not smooth enough. In this work, we describe an implementation of the original Horn and Schunck method and also introduce a multi-scale strategy in order to deal with larger displacements. For this multi-scale strategy, we create a pyramidal structure of downsampled images and change the optical flow constraint equation with a nonlinear formulation. In order to tackle this nonlinear formula, we linearize it and solve the method iteratively in each scale. In this sense, there are two common approaches: one that computes the motion increment in the iterations; or the one we follow, that computes the full flow during the iterations. The solutions are incrementally refined over the scales. This pyramidal structure is a standard tool in many optical flow methods.


Delayed Rejection in Reversible Jump Metropolis-Hastings


title: Device Exploration of NanoSheet Transistors for Sub-7-nm Technology Node

abstract: In this paper, lateral gate-all-around nano-sheet transistors (NSH-FETs) are explored from intrinsic performance to dc and ring oscillator (RO) benchmark compared with FinFETs and nanowire transistors (NW-FETs) for sub-7-nm node. The band structure calculated technology computer aided design results show comparable intrinsic performance to FinFETs at same channel cross section. On top of that, dc and RO are evaluated by taking into account electrostatics, parasitic components, and layout configurations. The NSH-FETs show an advantage in drive current with the NSH width but their RO performance is limited by the device capacitance. The multiple narrow NSH-FET shows ~5% higher drive current compared to the NW-FET at similar subthreshold swing, allowing heavier capacitive loaded circuit. In addition, NSH-FETs can provide the device design freedom from aggressive fin pitch scaling.


title: Practical SVBRDF capture in the frequency domain

abstract: Spatially-varying reflectance and small geometric variations play a vital role in the appearance of real-world surfaces. Consequently, robust, automatic capture of such models is highly desirable; however, current systems require either specialized hardware, long capture times, user intervention, or rely heavily on heuristics. We describe an acquisition setup that utilizes only portable commodity hardware (an LCD display, an SLR camera) and contains no moving parts. In particular, a laptop screen can be used for illumination. Our setup, aided by a carefully constructed image formation model, automatically produces realistic spatially-varying reflectance parameters over a wide range of materials from diffuse to almost mirror-like specular surfaces, while requiring relatively few photographs. We believe our system is the first to offer such generality, while requiring only standard office equipment and no user intervention or parameter tuning. Our results exhibit a good qualitative match to photographs taken under novel viewing and lighting conditions for a range of materials.


title: IEEE 802.11s Wireless Mesh Networks: Challenges and Perspectives

abstract: A promising solution for wireless environments is the wireless mesh technology that envisages supplementing wired infrastructure with a wireless backbone for providing Internet connectivity to mobile nodes (MNs) or users in residential areas and offices. The IEEE 802.11 TGs has started to work in developing a mesh standard for local area wireless networks. Although a lot of progress has been made and a few new drafts have been released recently, there exist many issues that demand enhanced or even new solutions to 802.11s mesh networking. This paper aims to overview the latest version of the IEEE 802.11s protocol (Draft 2.02), especially the MAC and routing layers, and to point out the challenges that these networks have to overcome in these layers.


title: ESCDL-1, a new cell line derived from chicken embryonic stem cells, supports efficient replication of Mardiviruses

abstract: Marek's disease virus is the etiological agent of a major lymphoproliferative disorder in poultry and the prototype of the Mardivirus genus. Primary avian somatic cells are currently used for virus replication and vaccine production, but they are largely refractory to any genetic modification compatible with the preservation of intact viral susceptibility. We explored the concept of induction of viral replication permissiveness in an established pluripotent chicken embryonic stem cell-line (cES) in order to derive a new fully susceptible cell-line. Chicken ES cells were not permissive for Mardivirus infection, but as soon as differentiation was triggered, replication of Marek's disease virus was detected. From a panel of cyto-differentiating agents, hexamethylene bis (acetamide) (HMBA) was found to be the most efficient regarding the induction of permissiveness. These initial findings prompted us to analyse the effect of HMBA on gene expression, to derive a new mesenchymal cell line, the so-called ESCDL-1, and monitor its susceptibility for Mardivirus replication. All Mardiviruses tested so far replicated equally well on primary embryonic skin cells and on ESCDL-1, and the latter showed no variation related to its passage number in its permissiveness for virus infection. Viral morphogenesis studies confirmed efficient multiplication with, as in other in vitro models, no extra-cellular virus production. We could show that ESCDL-1 can be transfected to express a transgene and subsequently cloned without any loss in permissiveness. Consequently, ESCDL-1 was genetically modified to complement viral gene deletions thus yielding stable trans-complementing cell lines. We herein claim that derivation of stable differentiated cell-lines from cES cell lines might be an alternative solution to the cultivation of primary cells for virology studies.


title: Reliability and validity study on the Hungarian versions of the Oswestry Disability Index and the Quebec Back Pain Disability Scale

abstract: Purpose ::: Patient-reported outcome measurements (PROMs) are widely used in spine care. The development of reliable and valid National versions of spine-related disability questionnaires is strongly recommended from both the clinical and scientific points-of-view. The aims of this study were to adapt and validate the Oswestry Disability Index (ODI) and the Quebec Back Pain Disability Scale (QDS) for use with the Hungarian language.


title: Emotion hacking VR (EH-VR): amplifying scary VR experience by accelerating real heart rate using false vibrotactile biofeedback

abstract: Emotion hacking virtual reality (EH-VR) system is an interactive system that hacks one's heartbeat and controls it to accelerate scary VR experience. The EH-VR system provides vibrotactile biofeedback, which resembles a heartbeat, from the floor. The system determines false heartbeat frequency by detecting user's heart rate in real time and calculates false heart rate, which is faster than the one observed according to the quadric equation model. With the system, we demonstrate "Pressure of unknown" which is a CG VR space originally created to express the metaphor of scare. A user experiences this space by using a wheel chair as a controller to walk through a VR world displayed via HMD while receiving vibrotac-tile feedback of false heartbeat calculated from its own heart rate from the floor.


title: An Optimization Model for Outlier Detection in Categorical Data

abstract: In this paper, we formally define the problem of outlier detection in categorical data as an optimization problem from a global viewpoint. Moreover, we present a local-search heuristic based algorithm for efficiently finding feasible solutions. Experimental results on real datasets and large synthetic datasets demonstrate the superiority of our model and algorithm.


A Grounded Theory Approach to Information Technology Adoption


title: A Simple Subthreshold CMOS Voltage Reference Circuit With Channel- Length Modulation Compensation

abstract: This brief presents a simple reference circuit with channel-length modulation compensation to generate a reference voltage of 221 mV using subthreshold of MOSFETs at supply voltage of 0.85 V with power consumption of 3.3 muW at room temperature using TSMC 0.18-mum technology. The proposed circuit occupied in less than 0.0238 mm 2 achieves the reference voltage variation of 2 mV/V for supply voltage from 0.9 to 2.5V and about 6 mV of temperature variation in the range from -20degC to 120 degC. The agreement of simulation and measurement data is demonstrated


A Comparison of Two Java Component Extraction Approaches


title: A Framework for Medical Image Retrieval Using Machine Learning and Statistical Similarity Matching Techniques With Relevance Feedback

abstract: A content-based image retrieval (CBIR) framework for diverse collection of medical images of different imaging modalities, anatomic regions with different orientations and biological systems is proposed. Organization of images in such a database (DB) is well defined with predefined semantic categories; hence, it can be useful for category-specific searching. The proposed framework consists of machine learning methods for image prefiltering, similarity matching using statistical distance measures, and a relevance feedback (RF) scheme. To narrow down the semantic gap and increase the retrieval efficiency, we investigate both supervised and unsupervised learning techniques to associate low-level global image features (e.g., color, texture, and edge) in the projected PCA-based eigenspace with their high-level semantic and visual categories. Specially, we explore the use of a probabilistic multiclass support vector machine (SVM) and fuzzy c-mean (FCM) clustering for categorization and prefiltering of images to reduce the search space. A category-specific statistical similarity matching is proposed in a finer level on the prefiltered images. To incorporate a better perception subjectivity, an RF mechanism is also added to update the query parameters dynamically and adjust the proposed matching functions. Experiments are based on a ground-truth DB consisting of 5000 diverse medical images of 20 predefined categories. Analysis of results based on cross-validation (CV) accuracy and precision-recall for image categorization and retrieval is reported. It demonstrates the improvement, effectiveness, and efficiency achieved by the proposed framework


title: BootJacker: compromising computers using forced restarts

abstract: BootJacker is a proof-of-concept attack tool which demonstrates that authentication mechanisms employed by an operating system can be bypassed by obtaining physical access and simply forcing a restart. The key insight that enables this attack is that the contents of memory on some machines are fully preserved across a warm boot. Upon a reboot, BootJacker uses this residual memory state to revive the original host operating system environment and run malicious payloads. Using BootJacker, an attacker can break into a locked user session and gain access to open encrypted disks, web browser sessions or other secure network connections. BootJacker's non-persistent design makes it possible for an attacker to leave no traces on the victim machine.


title: Impact Evaluation of a Policy Intervention for HIV Prevention in Washington, DC

abstract: Syringe exchange programs (SEPs) lower HIV risk. From 1998 to 2007, Congress prohibited Washington, DC, from using municipal revenue for SEPs. We examined the impact of policy change on IDU-associated HIV cases. We used surveillance data for new IDU-associated HIV cases between September 1996 and December 2011 to build an ARIMA model and forecasted the expected number of IDU-associated cases in the 24 months following policy change. Interrupted time series analyses (ITSA) were used to assess epidemic impact of policy change. There were 176 IDU-associated HIV cases in the 2 years post-policy change; our model predicted 296 IDU-associated HIV cases had the policy remained in place, yielding a difference of 120 averted HIV cases. ITSA identified significant immediate (B = −6.0355, p = .0005) and slope changes (B = −.1241, p = .0427) attributed to policy change. Policy change is an effective structural intervention for HIV prevention when it facilitates the implementation of services needed by vulnerable populations.


title: An Introduction to Quantum Programming in Quipper

abstract: Quipper is a recently developed programming language for expressing quantum computations. This paper gives a brief tutorial introduction to the language, through a demonstration of how to make use of some of its key features. We illustrate many of Quipper's language features by developing a few well known examples of Quantum computation, including quantum teleportation, the quantum Fourier transform, and a quantum circuit for addition.


title: Short-term meditation training improves attention and self-regulation

abstract: Recent studies suggest that months to years of intensive and systematic meditation training can improve attention. However, the lengthy training required has made it difficult to use random assignment of participants to conditions to confirm these findings. This article shows that a group randomly assigned to 5 days of meditation practice with the integrative body-mind training method shows significantly better attention and control of stress than a similarly chosen control group given relaxation training. The training method comes from traditional Chinese medicine and incorporates aspects of other meditation and mindfulness training. Compared with the control group, the experimental group of 40 undergraduate Chinese students given 5 days of 20-min integrative training showed greater improvement in conflict scores on the Attention Network Test, lower anxiety, depression, anger, and fatigue, and higher vigor on the Profile of Mood States scale, a significant decrease in stress-related cortisol, and an increase in immunoreactivity. These results provide a convenient method for studying the influence of meditation training by using experimental and control methods similar to those used to test drugs or other interventions.


title: Health Fog: a novel framework for health and wellness applications

abstract: In the past few years the role of e-health applications has taken a remarkable lead in terms of services and features inviting millions of people with higher motivation and confidence to achieve a healthier lifestyle. Induction of smart gadgetries, people lifestyle equipped with wearables, and development of IoT has revitalized the feature scale of these applications. The landscape of health applications encountering big data need to be replotted on cloud instead of solely relying on limited storage and computational resources of handheld devices. With this transformation, the outcome from certain health applications is significant where precise, user-centric, and personalized recommendations mimic like a personal care-giver round the clock. To maximize the services spectrum from these applications over cloud, certain challenges like data privacy and communication cost need serious attention. Following the existing trend together with an ambition to promote and assist users with healthy lifestyle we propose a framework of Health Fog where Fog computing is used as an intermediary layer between the cloud and end users. The design feature of Health Fog successfully reduces the extra communication cost that is usually found high in similar systems. For enhanced and flexible control of data privacy and security, we also introduce the cloud access security broker (CASB) as an integral component of Health Fog where certain polices can be implemented accordingly. The modular framework design of Health Fog is capable of engaging data from multiple resources together with adequate level of security and privacy using existing cryptographic primitives.


title: Botcoin: Monetizing Stolen Cycles

abstract: At the current stratospheric value of Bitcoin, miners with access to significant computational horsepower are literally printing money. For example, the first operator of a USD $1,500 custom ASIC mining platform claims to have recouped his investment in less than three weeks in early February 2013, and the value of a bitcoin has more than tripled since then. Not surprisingly, cybercriminals have also been drawn to this potentially lucrative endeavor, but instead are leveraging the resources available to them: stolen CPU hours in the form of botnets. We conduct the first comprehensive study of Bitcoin mining malware, and describe the infrastructure and mechanism deployed by several major players. By carefully reconstructing the Bitcoin transaction records, we are able to deduce the amount of money a number of mining botnets have made.


title: Cross Domain Recommender Systems: A Systematic Literature Review

abstract: Cross domain recommender systems (CDRS) can assist recommendations in a target domain based on knowledge learned from a source domain. CDRS consists of three building blocks: domain, user-item overlap scenarios, and recommendation tasks. The objective of this research is to identify the most widely used CDRS building-block definitions, identify common features between them, classify current research in the frame of identified definitions, group together research with respect to algorithm types, present existing problems, and recommend future directions for CDRS research. To achieve this objective, we have conducted a systematic literature review of 94 shortlisted studies. We classified the selected studies using the tag-based approach and designed classification grids. Using classification grids, it was found that the category-domain contributed a maximum of 62%, whereas the time domain contributed at least 3%. User-item overlaps were found to have equal contribution. Single target domain recommendation task was found at a maximum of 78%, whereas cross-domain recommendation task had a minor influence at only 10%. MovieLens contributed the most at 22%, whereas Yahoo-music provided 1% between 29 datasets. Factorization-based algorithms contributed a total of 37%, whereas semantics-based algorithms contributed 6% among seven types of identified algorithm groups. Finally, future directions were grouped into five categories.


title: Effects of endogenous opioid peptides and their analogs on the activities of hypothalamic arcuate neurons in brain slices from diestrous and ovariectomized rats

abstract: Abstract Various endogenous opioid peptides and some of their analogs were used in this study to test their effects on the membrane activities of hypothalamic arcuate neurons in brain slices. Both ovariectomized and diestrous rats were used in the study, and freshly prepared brain slices from these animals were used for extracellular single-unit recording studies. All of the opioids exhibited potent inhibitory effects on the firing of arcuate neurons, viz., β-endorphin inhibited 55% ( n = 33), DAGO 62% ( n = 21), dynorphin A 55% ( n = 11), U50,488 36% ( n = 39), Met-enkephalin 35% ( n = 54), and DPDPE 50% ( n = 8) of tested arcuate neurons from ovariectomized rats. Significantly higher percentage of inhibition was observed in slice preparations from diestrous rats for DAGO 86% ( n = 22), and slightly higher for dynorphin A 59% ( n = 22) and U50,488 53% ( n = 15). Pretreatment with naloxone prevented most of the actions by β-endorphin and DAGO, and nor-binaltorphimine prevented those by dynorphin A and U50,488. Most of the effects of Met-enkephalin could also be blocked by nor-binaltorphimine (67%, n = 6), but less by naltrindole (25%, n = 8). Naltrindole, however, seemed to be more effective in blocking the action of [D-Pen 2,5 ]-enkephalin (100%, n = 2). In summary, all opioids tested exerted potent inhibitory effects upon the firing of arcuate neurons possibly through multiple opioid receptors, and the presence of ovarian hormones may have an effect on the neuron's responsiveness to opioid acting on μ type receptors.


title: On the Complexity of Policy Iteration

abstract: Decision-making problems in uncertain or stochastic domains are often formulated as Markov decision processes (MDPs). Policy iteration (PI) is a popular algorithm for searching over policy-space, the size of which is exponential in the number of states. We are interested in bounds on the complexity of PI that do not depend on the value of the discount factor. In this paper we prove the first such non-trivial, worst-case, upper bounds on the number of iterations required by PI to converge to the optimal policy. Our analysis also sheds new light on the manner in which PI progresses through the space of policies.