title: Dermatoscopy: Alternative uses in daily clinical practice

abstract: Dermatoscopy, also known as dermoscopy, epiluminescence microscopy, or surface microscopy, is a noninvasive technique allowing rapid and magnified (×10) in vivo observation of the skin with the visualization of morphologic features often imperceptible to the naked eye. Videodermatoscopy (VD) represents the evolution of dermatoscopy and is performed with a video camera equipped with lenses providing higher magnification (×10 to ×1000). Over the past few years, both dermatoscopy and VD have been demonstrated to be useful in a wide variety of cutaneous disorders, including ectoparasitic infestations, cutaneous/mucosal infections, hair and nail abnormalities, psoriasis, and other dermatologic as well as cosmetologic conditions. Depending on the skin disorder, both dermatoscopy and VD may be useful for differential diagnosis, prognostic evaluation, and monitoring response to treatment. Nowadays, it represents an important and relatively simple aid in daily clinical practice.


title: Analysis and design of a push-pull single-stage flyback power factor corrector

abstract: A novel mixed-mode controlled push-pull single-stage flyback power factor corrector (PFC) composed of two flyback PFCs with a coupled inductor is proposed in this paper. The studied single-stage PFC consists of two power modules which have common secondary winding, output diode, output capacitor, and input filtering capacitor. Besides possessing the capability of sharing the input current and output current equally, integrating two input inductors into one magnetic core makes the operating frequency of the core double the switching frequency. In addition, a cut-in-half duty cycle can reduce the conduction losses of the switches and both the turns and diameters of the inductor windings. Both the continuous conduction mode (CCM) and the transition mode (TM) controls are fulfilled in the proposed single-stage PFC during one line frequency cycle. Under CCM operations, the smaller inductor current ripple reduces both the core loss of the coupled inductor and the conduction losses of the switches, which not only promotes the heavy-load efficiencies, but also alleviates the current stresses of the switches and the output diode. Under TM operations, quasi-resonant (QR) valley witching on the switches and zero-current switching (ZCS) of the output diode can reduce the switching losses. The light-load conversion efficiencies are thus improved. Detailed analysis of the proposed topology is given. The experiments are conducted on a prototype with a universal line voltage, a 19-V output DC voltage, and a 190-W output power to verify its feasibility.


title: Data security issues in cloud-based Software-as-a-Service ERP

abstract: This paper discusses the data security issues and concerns that are prevalent when organizations are moving their Enterprise Resource Planning (ERP) systems to the cloud. Cloud computing has reinvented how organizations conduct business and has enabled them to innovate and compete in a dynamic environment through new and innovative business models. The growing popularity and success of the cloud has led to the emergence of cloud-based Software-as-a-Service (SaaS) ERP systems, a new alternative approach to traditional on-premise ERP systems. Cloud-based ERP has a myriad of benefits for organizations. However, infrastructure engineers need to address data security issues before moving their enterprise applications to the cloud. Cloud-based ERP raises specific concerns about the confidentiality and integrity of the data stored in the cloud. Such concerns that affect the adoption of cloud-based ERP are based on the size of the organization. Small to medium enterprises (SMEs) gain the maximum benefits from cloud-based ERP as many of the concerns around data security are not relevant to them. On the contrary, larger organizations are more cautious in moving their mission critical enterprise applications to the cloud. A hybrid solution where organizations can choose to keep their sensitive applications on-premise while leveraging the benefits of the cloud is proposed in this paper as an effective solution that is gaining momentum and popularity for large organizations.


title: Use of Botanicals in Food Supplements

abstract: Background/Aims: In the European Union, an elaborate legal framework regulates botanical products both under food and medicinal law. The decision as to which legal framework applies


title: TenniVis: Visualization for Tennis Match Analysis

abstract: Existing research efforts into tennis visualization have primarily focused on using ball and player tracking data to enhance professional tennis broadcasts and to aid coaches in helping their students. Gathering and analyzing this data typically requires the use of an array of synchronized cameras, which are expensive for non-professional tennis matches. In this paper, we propose TenniVis, a novel tennis match visualization system that relies entirely on data that can be easily collected, such as score, point outcomes, point lengths, service information, and match videos that can be captured by one consumer-level camera. It provides two new visualizations to allow tennis coaches and players to quickly gain insights into match performance. It also provides rich interactions to support ad hoc hypothesis development and testing. We first demonstrate the usefulness of the system by analyzing the 2007 Australian Open men's singles final. We then validate its usability by two pilot user studies where two college tennis coaches analyzed the matches of their own players. The results indicate that useful insights can quickly be discovered and ad hoc hypotheses based on these insights can conveniently be tested through linked match videos.


title: Graph-Based Shape Similarity of Petroglyphs

abstract: Petroglyphs can be found on rock panels all over the world. The possibilities of digital photography and more recently various 3D scanning methods opened a new stage for the documentation and analysis of petroglyphs. The existing work on petroglyph shape similarity has largely avoided the questions of articulation, merged petroglyphs and potentially missing parts of petroglyphs. We aim at contributing to close this gap by applying a novel petroglyph shape descriptor based on the skeletal graph. Our contribution is twofold: First, we provide a real-world dataset of petroglyph shapes. Second, we propose a graph-based shape descriptor for petroglyphs. Comprehensive evaluations show, that the combination of the proposed descriptor with existing ones improves the performance in petroglyph shape similarity modeling.


Automatic Annotation and Evaluation of Error Types for Grammatical Error Correction


title: Big data analytics in genomics: The point on Deep Learning solutions

abstract: Nowadays, Next Generation Sequeencing (NGS) is a catch-all term used to describe different modern DNA sequencing applications that produce big genomics data that can be analysed in a faster fashion than in the past. For this reason, NGS requires more and more sophisticated algorithms and high-performance parallel processing systems able to analyse and extract knowledge from a huge amount of genomics and molecular data. In this context, researchers are beginning to look at emerging deep learning algorithms able to perform efficient big data analytics. In this paper, we analyse and classify the major current deep learning solutions that allow biotechnology researchers to perform big genomics data analytics. Moreover, by means of a taxonomic analysis, we provide a clear picture of the current state of the art also discussing future challenges.


Perceiving emotion in non-social targets: The effect of trait empathy on emotional contagion through art


Optimizing Chemical Reactions with Deep Reinforcement Learning


title: A Multimodal Real-Time MRI Articulatory Corpus for Speech Research.

abstract: We present MRI-TIMIT: a large-scale database of synchronized audio and real-time magnetic resonance imaging (rtMRI) data for speech research. The database currently consists of speech data acquired from two male and two female speakers of American English. Subjects’ upper airways were imaged in the midsagittal plane while reading the same 460 sentence corpus used in the MOCHA-TIMIT corpus [1]. Accompanying acoustic recordings were phonemically transcribed using forced alignment. Vocal tract tissue boundaries were automatically identified in each video frame, allowing for dynamic quantification of each speaker’s midsagittal articulation. The database and companion toolset provide a unique resource with which to examine articulatory-acoustic relationships in speech production. Index Terms: speech production, speech corpora, real-time MRI, multi-modal database, large-scale phonetic tools


title: Ground-Free ECG Recording with Two Electrodes

abstract: ECG recordings normally use three electrodes?two for the differential inputs of the ECG amplifier and the third for ground. We analyze those situations where the ground electrode can be eliminated. We propose a model for the source of electrical interference and determine various parameter values. Making use of experimentally obtained data for the model parameters we suggest optimal design for a two-electrode amplifier. The two-electrode design is useful for biotelemetry, portable Holter monitors, and portable arrhythmia monitors. Under certain circumstances it may be useful for grounded monitoring equipment. The two-electrode technique has the advantage that it improves patient safety by eliminating the ground electrode. Fewer electrodes make patient attachment easier and lower electrode costs.


title: Paraphrase Generation from Latent-Variable PCFGs for Semantic Parsing

abstract: One of the limitations of semantic parsing approaches to open-domain question answering is the lexicosyntactic gap between natural language questions and knowledge base entries -- there are many ways to ask a question, all with the same answer. In this paper we propose to bridge this gap by generating paraphrases of the input question with the goal that at least one of them will be correctly mapped to a knowledge-base query. We introduce a novel grammar model for paraphrase generation that does not require any sentence-aligned paraphrase corpus. Our key idea is to leverage the flexibility and scalability of latent-variable probabilistic context-free grammars to sample paraphrases. We do an extrinsic evaluation of our paraphrases by plugging them into a semantic parser for Freebase. Our evaluation experiments on the WebQuestions benchmark dataset show that the performance of the semantic parser significantly improves over strong baselines.


title: Efficient Feature Selection via Analysis of Relevance and Redundancy

abstract: Feature selection is applied to reduce the number of features in many applications where data has hundreds or thousands of features. Existing feature selection methods mainly focus on finding relevant features. In this paper, we show that feature relevance alone is insufficient for efficient feature selection of high-dimensional data. We define feature redundancy and propose to perform explicit redundancy analysis in feature selection. A new framework is introduced that decouples relevance analysis and redundancy analysis. We develop a correlation-based method for relevance and redundancy analysis, and conduct an empirical study of its efficiency and effectiveness comparing with representative methods.


CS 2 : A Searchable Cryptographic Cloud Storage System


title: Analyzing the functional dynamics of technological innovation systems: A scheme of analysis

abstract: Various researchers and policy analysts have made empirical studies of innovation systems in order to understand their current structure and trace their dynamics. However, policy makers often experience difficulties in extracting practical guidelines from studies of this kind. In this paper, we operationalize our previous work on a functional approach to analyzing innovation system dynamics into a practical scheme of analysis for policy makers. The scheme is based on previous literature and our own experience in developing and applying functional thinking. It can be used by policy makers not only to identify the key policy issues but also to set policy goals.


title: Approximation Algorithms for Schema-Mapping Discovery from Data Examples

abstract: In recent years, data examples have been at the core of several different approaches to schema-mapping design. In particular, Gottlob and Senellart introduced a framework for schema-mapping discovery from a single data example, in which the derivation of a schema mapping is cast as an optimization problem. Our goal is to refine and study this framework in more depth. Among other results, we design a polynomial-time log(n)-approximation algorithm for computing optimal schema mappings from a given set of data examples (where n is the combined size of the given data examples) for a restricted class of schema mappings; moreover, we show that this approximation ratio cannot be improved. In addition to the complexity-theoretic results, we implemented the aforementioned log(n)-approximation algorithm and carried out an experimental evaluation in a real-world mapping scenario.


title: OpenPR: An Open-Source Partial-Reconfiguration Toolkit for Xilinx FPGAs

abstract: The Xilinx Partial Reconfiguration Early Access Software Tools for ISE 9.2i has been an instrumental package for performing a wide variety of research on Xilinx FPGAs, and is now superseded with the corresponding non-free add-on for ISE 12.3. The original package was developed and offered by Xilinx as a downloadable add-on package to the Xilinx ISE 9.2 tools. The 9.2i toolkit provided a methodology for creating rectangular partial reconfiguration modules that could be swapped in and out of a static baseline design with one or more PR slots. This paper presents a new PR toolkit called Open PR that, for starters, provides similar functionality as the Xilinx PR Toolkit, yet is extendable to explore other modes of partial reconfiguration. The distinguishing feature of this toolkit is that it is being released as open source, and is intended to extend to the needs of individual researchers.


title: Ocular biometrics databases: Standards and data interchange formats

abstract: The current challenges to developing commercial Ocular Biometric systems are to create applications that tolerate unconstrained environments. Designs for sensors, user interfaces, and algorithms must perform well under variable illumination, poses, and camera distances, with low-quality images, and within other real-world conditions. Practical deployment requires interoperability of data and processing algorithms from multiple systems. Facilitating meaningful progress ultimately demands Biometric Databases that contain real-world data. Accelerated standardization offers practical means to organize the tremendous volume of emerging biometric information. Standards enable data exchanges that are grounded on comparable and compatible formats. This paper describes the development of a research-oriented Ocular Biometric Database that contains images acquired in conditions relevant to unconstrained environments. The data was collected with different sensors and imaging systems under diverse conditions representing real-world applications. Images and related data were stored in a database adhering to extended biometric standards requirements and biometric data-interchange formats. Assessment and applicability reviews were performed on existing national and international standards pertaining to ocular biometrics and were used during the development. The paper describes the design approach with a goal to achieve interoperability and standardization of data and results. It also addresses recent developments in biometric data acquisition, pre- and post-processing, information storage, exchange, and access control.


title: Structure and dynamics of particle-accumulation in thermocapillary liquid bridges

abstract: The accumulation of small mono-disperse heavy particles in thermocapillary liquid bridges is investigated experimentally and numerically. We consider particle accumulation near the center of the toroidal vortex, the so-called toroidal core of particles (COP), and the particle-depletion zone near the axis of the liquid bridge. Based on the acceleration and deceleration of the tangential flow along the thermocapillary free surface it is argued that the interaction of the particles with the free surface is of key importance for the fast particle accumulation within a few characteristic momentum diffusion times. The experimentally determined particle-accumulation times are compared with time-scale estimates for accumulation due to either particle free-surface interaction or due to inertia of particles which are heavier than the liquid. We show that the experimental accumulation times are compatible with the accumulation times predicted by the particle–free-surface interaction (PSI) while the time-scale estimates based on the inertia of the particles are too large to explain the fast de-mixing observed in experiments. The shape of the COP resembles certain KAM tori of the incompressible flow of a hydrothermal wave. Two scenarios are proposed to explain the structure and the dynamics of the COP depending on the existence or non-existence of suitable KAM structures. The shape of the experimental particle-depletion zone agrees well with the release surface which is defined by the particle–free-surface interaction process. The favorable comparison of the dynamics and structure of experimental and numerical accumulation patterns provides strong evidence for the existence and relevance of the PSI as the most rapid physical accumulation mechanism.


title: Initiating sustainable development reporting : evidence from New Zealand

abstract: Purpose - The purpose of this paper is to document organizations' self descriptions of why they initiated sustainable development (SD) reporting and explore these explanations using an institutional theory framework. Design/methodology/approach - Constructs organizational narratives from semi-structured in-depth interviews with reporting champions who participated in an SD reporting workshop series. The narratives are analysed using institutional theory to explore how regulatory, normative and cognitive institutions combine with organizational dynamics to influence SD reporting activity. Findings - For these particular organizations, choosing to engage in reporting appears not to be a rational choice. Rather reporting is initiated because it has come to be an accepted part of pursuing a differentiation strategy, it offers some contribution to existing business challenges, and organizations value the rewards it offers. This rationale constitutes a cognitive mechanism within institutional theory. Originality/value - The paper provides useful information on initiating sustainable development reporting based on organizations' self descriptions.


title: A Novel System for Visual Navigation of Educational Videos Using Multimodal Cues

abstract: With recent developments and advances in distance learning and MOOCs, the amount of open educational videos on the Internet has grown dramatically in the past decade. However, most of these videos are lengthy and lack of high-quality indexing and annotations, which triggers an urgent demand for efficient and effective tools that facilitate video content navigation and exploration. In this paper, we propose a novel visual navigation system for exploring open educational videos. The system tightly integrates multimodal cues obtained from the visual, audio and textual channels of the video and presents them with a series of interactive visualization components. With the help of this system, users can explore the video content using multiple levels of details to identify content of interest with ease. Extensive experiments and comparisons against previous studies demonstrate the effectiveness of the proposed system.


title: Frustum-traced raster shadows: revisiting irregular z-buffers

abstract: We present a real-time system that renders antialiased hard shadows using irregular z-buffers (IZBs). For subpixel accuracy, we use 32 samples per pixel at roughly twice the cost of a single sample. Our system remains interactive on a variety of game assets and CAD models while running at 1080p and 2160p and imposes no constraints on light, camera or geometry, allowing fully dynamic scenes without precomputation. Unlike shadow maps we introduce no spatial or temporal aliasing, smoothly animating even subpixel shadows from grass or wires. Prior irregular z-buffer work relies heavily on GPU compute. Instead we leverage the graphics pipeline, including hardware conservative raster and early-z culling. We observe a duality between irregular z-buffer performance and shadow map quality; this allows common shadow map algorithms to reduce our cost. Compared to state-of-the-art ray tracers, we spawn similar numbers of triangle intersections per pixel yet completely rebuild our data structure in under 2 ms per frame.


title: Metabolomics Application in Maternal-Fetal Medicine

abstract: Metabolomics in maternal-fetal medicine is still an “embryonic” science. However, there is already an increasing interest in metabolome of normal and complicated pregnancies, and neonatal outcomes. Tissues used for metabolomics interrogations of pregnant women, fetuses and newborns are amniotic fluid, blood, plasma, cord blood, placenta, urine, and vaginal secretions. All published papers highlight the strong correlation between biomarkers found in these tissues and fetal malformations, preterm delivery, premature rupture of membranes, gestational diabetes mellitus, preeclampsia, neonatal asphyxia, and hypoxic-ischemic encephalopathy. The aim of this review is to summarize and comment on original data available in relevant published works in order to emphasize the clinical potential of metabolomics in obstetrics in the immediate future.


title: Distributed Constraint Satisfaction for Formalizing Distributed Problem Solving

abstract: We develop a formalism called a distributed constraint satisfaction problem (distributed CSP) and algorithms for solving distributed CSPs. A distributed CSP is a constraint satisfaction problem in which variables and constraints are distributed among multiple agents. Various application problems in distributed artificial intelligence can be formalized as distributed CSPs. We present our newly developed technique called asynchronous backtracking that allows agents to act asynchronously and concurrently without any global control, while guaranteeing the completeness of the algorithm. Furthermore, we describe how the asynchronous backtracking algorithm can be modified into a more efficient algorithm called an asynchronous weak-commitment search, which can revise a bad decision without exhaustive search by changing the priority order of agents dynamically. The experimental results on various example problems show that the asynchronous weak-commitment search algorithm is, by far more, efficient than the asynchronous backtracking algorithm and can solve fairly large-scale problems.


title: A Survey about Fuzzy Cognitive Maps Papers (Invited Paper)

abstract: A fountain type applicator for ink or the like having a cartridge handle for containing the ink, an applicator having a roller and a threaded extension received in a threaded recessed end forming a seal in one end of the cartridge, the applicator being passaged for flow of ink from the cartridge to the roller. The extension has a tubular piercing member truncated at its distal end to form a cutting edge for penetrating a central region of the bottom of the recess in the cartridge to establish communication from the cartridge through the tubular piercing member to the roller.


title: Analyzing soft-error vulnerability on GPGPU microarchitecture

abstract: The general-purpose computation on graphic processing units (GPGPU) becomes increasingly popular due to their high computational throughput for data parallel applications. Modern GPU architectures have limited capability for error detection and tolerance since they are originally designed for graphics processing. However, the rigorous execution correctness is required for general-purpose applications. This makes reliability a growing concern in GPGPU architecture design. With CMOS processing technologies continuously scaling down to the nano-scale, on-chip soft error rate (SER) has been predicted to increase exponentially. GPGPUs with hundreds of cores integrated in a single chip are prone to manifest high SER. This paper explores a first step to characterize GPGPU reliability in light of soft errors. We develop GPGPU-SODA (GPGPU Software Dependability Analysis), a framework to estimate the soft-error vulnerability of GPGPU microarchitecture. By using GPGPU-SODA, we observe that several microarchitecture structures in GPGPUs exhibit high soft-error susceptibility, and the structure vulnerability is sensitive to workload characteristics (e.g. branch divergences, memory coalescing). We further investigate several architectural optimizations. We find that both dynamic warp formation and increasing the number of threads supported by GPU largely affect the GPGPU soft-error robustness. However, changing the warp scheduling policy has minor impact on the structure vulnerability. The observations made in this study provide designers the useful guidance to build resilient GPGPUs: a comprehensive resiliency solution for GPGPUs should consider the entire GPGPU design instead of just focusing on a particular structure.


title: Classifying Gene Expression Data of Cancer Using Classifier Ensemble With Mutually Exclusive Features

abstract: The explosion of DNA and protein sequence data in public and private databases has been encouraging interdisciplinary research on biology and information technology. Gene expression profiles are just sequences of numbers, and the necessity of tools analyzing them to get useful information has risen significantly. In order to predict the cancer class of patients from the gene expression profile, this paper presents a classification framework that combines a pair of classifiers trained with mutually exclusive features. The idea behind feature selection with nonoverlapping correlation is to encourage classifier ensemble, which consists of multiple classifiers, to learn different aspects of training data, so that classifiers can search in a wide solution space. Experimental results show that the classifier ensemble produces higher recognition accuracy than conventional classifiers.


title: Adaptive tearing and cracking of thin sheets

abstract: This paper presents a method for adaptive fracture propagation in thin sheets. A high-quality triangle mesh is dynamically restructured to adaptively maintain detail wherever it is required by the simulation. These requirements include refining where cracks are likely to either start or advance. Refinement ensures that the stress distribution around the crack tip is well resolved, which is vital for creating highly detailed, realistic crack paths. The dynamic meshing framework allows subsequent coarsening once areas are no longer likely to produce cracking. This coarsening allows efficient simulation by reducing the total number of active nodes and by preventing the formation of thin slivers around the crack path. A local reprojection scheme and a substepping fracture process help to ensure stability and prevent a loss of plasticity during remeshing. By including bending and stretching plasticity models, the method is able to simulate a large range of materials with very different fracture behaviors.


title: Evidence based physical activity for school-age youth.

abstract: OBJECTIVES ::: To review the effects of physical activity on health and behavior outcomes and develop evidence-based recommendations for physical activity in youth. ::: ::: ::: STUDY DESIGN ::: A systematic literature review identified 850 articles; additional papers were identified by the expert panelists. Articles in the identified outcome areas were reviewed, evaluated and summarized by an expert panelist. The strength of the evidence, conclusions, key issues, and gaps in the evidence were abstracted in a standardized format and presented and discussed by panelists and organizational representatives. ::: ::: ::: RESULTS ::: Most intervention studies used supervised programs of moderate to vigorous physical activity of 30 to 45 minutes duration 3 to 5 days per week. The panel believed that a greater amount of physical activity would be necessary to achieve similar beneficial effects on health and behavioral outcomes in ordinary daily circumstances (typically intermittent and unsupervised activity). ::: ::: ::: CONCLUSION ::: School-age youth should participate daily in 60 minutes or more of moderate to vigorous physical activity that is developmentally appropriate, enjoyable, and involves a variety of activities.


title: Who's downloading pirated papers? Everyone

abstract: In increasing numbers, researchers around the world are turning to Sci-Hub, the controversial website that hosts 50 million pirated papers and counting. Now, with server log data from Alexandra Elbakyan, the neuroscientist who created Sci-Hub in 2011 as a 22-year-old graduate student in Kazakhstan, Science addresses some basic questions: Who are Sci-Hub's users, where are they, and what are they reading? The Sci-Hub data provide the first detailed view of what is becoming the world's de facto open-access research library. Among the revelations that may surprise both fans and foes alike: Sci-Hub users are not limited to the developing world. Some critics of Sci-Hub have complained that many users can access the same papers through their libraries but turn to Sci-Hub instead—for convenience rather than necessity. The data provide some support for that claim. Over the 6 months leading up to March, Sci-Hub served up 28 million documents, with Iran, China, India, Russia, and the United States the leading requestors.


Consumer Behavior Information Big Data Mining


title: A four channel 24-GHz FMCW radar sensor with two-dimensional target localization capabilities

abstract: The measurement of different target parameters using radar systems has been an active research area for the last decades. Particularly target angle measurement is a very demanding topic, because obtaining good measurement results often goes hand in hand with extensive hardware effort. Especially for sensors used in the mass market, e.g. in automotive applications like adaptive cruise control this may be prohibitive. Therefore we address target localization using a compact frequency-modulated continuous-wave (FMCW) radar sensor. The angular measurement results are improved compared to standard beamforming methods using an adaptive beamforming approach. This approach will be applied to the FMCW principle in a way that allows the use of well known methods for the determination of other target parameters like range or velocity. The applicability of the developed theory will be shown on different measurement scenarios using a 24-GHz prototype radar system.


title: Hierarchical quadratic programming: Fast online humanoid-robot motion generation

abstract: Hierarchical least-square optimization is often used in robotics to inverse a direct function when multiple incompatible objectives are involved. Typical examples are inverse kinematics or dynamics. The objectives can be given as equalities to be satisfied (e.g. point-to-point task) or as areas of satisfaction (e.g. the joint range). This paper proposes a complete solution to solve multiple least-square quadratic problems of both equality and inequality constraints ordered into a strict hierarchy. Our method is able to solve a hierarchy of only equalities 10 times faster than the iterative-projection hierarchical solvers and can consider inequalities at any level while running at the typical control frequency on whole-body size problems. This generic solver is used to resolve the redundancy of humanoid robots while generating complex movements in constrained environments.


title: A Secure Biometrics-Based Multi-Server Authentication Protocol Using Smart Cards

abstract: Recently, in 2014, He and Wang proposed a robust and efficient multi-server authentication scheme using biometrics-based smart card and elliptic curve cryptography (ECC). In this paper, we first analyze He–Wang’s scheme and show that their scheme is vulnerable to a known session-specific temporary information attack and impersonation attack. In addition, we show that their scheme does not provide strong user’s anonymity. Furthermore, He–Wang’s scheme cannot provide the user revocation facility when the smart card is lost/stolen or user’s authentication parameter is revealed. Apart from these, He–Wang’s scheme has some design flaws, such as wrong password login and its consequences, and wrong password update during password change phase. We then propose a new secure multi-server authentication protocol using biometric-based smart card and ECC with more security functionalities. Using the Burrows–Abadi–Needham logic, we show that our scheme provides secure authentication. In addition, we simulate our scheme for the formal security verification using the widely accepted and used automated validation of Internet security protocols and applications tool, and show that our scheme is secure against passive and active attacks. Our scheme provides high security along with low communication cost, computational cost, and variety of security features. As a result, our scheme is very suitable for battery-limited mobile devices as compared with He–Wang’s scheme.


title: Speaker recognition: A tutorial

abstract: A tutorial on the design and development of automatic speaker-recognition systems is presented. Automatic speaker recognition is the use of a machine to recognize a person from a spoken phrase. These systems can operate in two modes: to identify a particular person or to verify a person's claimed identity. Speech processing and the basic components of automatic speaker-recognition systems are shown and design tradeoffs are discussed. Then, a new automatic speaker-recognition system is given. This recognizer performs with 98.9% correct decalcification. Last, the performances of various systems are compared.


title: DERI&UPM: Pushing Corpus Based Relatedness to Similarity: Shared Task System Description

abstract: In this paper, we describe our system submitted for the semantic textual similarity (STS) task at SemEval 2012. We implemented two approaches to calculate the degree of similarity between two sentences. First approach combines corpus-based semantic relatedness measure over the whole sentence with the knowledge-based semantic similarity scores obtained for the words falling under the same syntactic roles in both the sentences. We fed all these scores as features to machine learning models to obtain a single score giving the degree of similarity of the sentences. Linear Regression and Bagging models were used for this purpose. We used Explicit Semantic Analysis (ESA) as the corpus-based semantic relatedness measure. For the knowledge-based semantic similarity between words, a modified WordNet based Lin measure was used. Second approach uses a bipartite based method over the WordNet based Lin measure, without any modification. This paper shows a significant improvement in calculating the semantic similarity between sentences by the fusion of the knowledge-based similarity measure and the corpus-based relatedness measure against corpus based measure taken alone.


title: Brands and brand equity: definition and management

abstract: This article assumes that brands should be managed as valuable, long‐term corporate assets. It is proposed that for a true brand asset mindset to be achieved, the relationship between brand loyalty and brand value needs to be recognised within the management accounting system. It is also suggested that strategic brand management is achieved by having a multi‐disciplinary focus, which is facilitated by a common vocabulary. This article seeks to establish the relationships between the constructs and concepts of branding, and to provide a framework and vocabulary that aids effective communication between the functions of accounting and marketing. Performance measures for brand management are also considered, and a model for the management of brand equity is provided.


title: DREAM: Diabetic Retinopathy Analysis Using Machine Learning

abstract: This paper presents a computer-aided screening system (DREAM) that analyzes fundus images with varying illumination and fields of view, and generates a severity grade for diabetic retinopathy (DR) using machine learning. Classifiers such as the Gaussian Mixture model (GMM), k-nearest neighbor (kNN), support vector machine (SVM), and AdaBoost are analyzed for classifying retinopathy lesions from nonlesions. GMM and kNN classifiers are found to be the best classifiers for bright and red lesion classification, respectively. A main contribution of this paper is the reduction in the number of features used for lesion classification by feature ranking using Adaboost where 30 top features are selected out of 78. A novel two-step hierarchical classification approach is proposed where the nonlesions or false positives are rejected in the first step. In the second step, the bright lesions are classified as hard exudates and cotton wool spots, and the red lesions are classified as hemorrhages and micro-aneurysms. This lesion classification problem deals with unbalanced datasets and SVM or combination classifiers derived from SVM using the Dempster-Shafer theory are found to incur more classification error than the GMM and kNN classifiers due to the data imbalance. The DR severity grading system is tested on 1200 images from the publicly available MESSIDOR dataset. The DREAM system achieves 100% sensitivity, 53.16% specificity, and 0.904 AUC, compared to the best reported 96% sensitivity, 51% specificity, and 0.875 AUC, for classifying images as with or without DR. The feature reduction further reduces the average computation time for DR severity per image from 59.54 to 3.46 s.


title: When IT Meets Telco: RAN as a Service

abstract: Network virtualization functions (NFV) and software-defined networking (SDN) are changing the landscape of the telecommunications industry, particularly infrastructure and network systems of Telco operators with the introduction of cloud computing, paradigms of virtualization and software approaches. In this paper, we describe a demonstrator which shows how IT technologies reduce the time of deployment of a wireless infrastructure. In less than 60s, a wireless LTE network is available for connecting Smartphone's. In this demo, the eNodeB, virtualized using docker containers, is orchestrated by OpenStack heat.


title: Practice Makes Perfect? When Does Massed Learning Improve Product Usage Proficiency?

abstract: Previous research has shown that spacing of information (over time) leads to better learning of product information. We develop a theoretical framework to describe how massed or spaced learning schedules interact with different learning styles to influence product usage proficiency. The core finding is that with experiential learning, proficiency in a product usage task is better under massed conditions, whereas with verbal learning, spacing works better. This effect is demonstrated for usage proficiency assessed via speed as well as quality of use. Further, massed learning also results in better usage proficiency on transfer tasks, for both experiential and verbal learning. We also find that massed learning in experiential learning conditions leads not only to better usage proficiency but also to positive perceptions of the product. Overall, the pattern of results is consistent with a conceptual mapping account, with massed experiences leading to a superior mental model of usage and thus to better usage proficiency.


title: Generic Digital Monopulse Tracking Receiver for advanced communication satellites

abstract: Monopulse tracking receiver also referred as Monopulse processor is a key subsystem of monopulse tracking system. The monopulse tracking system is commonly used in two configurations i.e. Single-Channel & Two-Channel to correct the pointing error of antenna. In order to meet the requirement of both the configurations using same hardware, a Generic Digital Monopulse Tracking Receiver (GDMTR) design is proposed to serve the tracking requirement of upcoming advance communication satellites. It can be configured to function as single or two-channel tracking receiver by changing the firmware. It is small in size, having less weight & power consumption and more design flexibility as compared to its analog counterpart. The GDMTR has been developed using FPGA based hardware to provide the re-configurability and its algorithms are successfully tested in laboratory with the help of stimulus generators. These algorithms have also been verified with operational tracking chain of satellite earth stations.


title: Incentive Mechanism Design in Mobile Opportunistic Data Collection With Time Sensitivity

abstract: Mobile crowdsensing systems aim at providing various novel sensing applications by recruiting pervasive users with mobile devices, which are now equipped with enriched built-in sensors (e.g., GPS, microphone, camera, gyroscope, accelerometer, etc.). A key factor to enable such systems is substantial participation of large amount of mobile users. In this paper, we focus on the data collection in mobile opportunistic crowdsensing, where the data can be transferred between mobile users via opportunistic device-to-device communications. The goal is to deliver the sensed data from the collector to the corresponding requester, which can maximize the collector’s rewards. Here, we assume that the data collection has time-sensitive characteristics, i.e., the reward is time-sensitive. We consider selfish mobile users with rational behaviors, and propose a credit-based incentive-aware mechanism to stimulate mobile users to participate in data collection for mobile opportunistic crowdsensing. Particularly, we propose an effective mechanism to define the expected rewards for the sensed data, and formulate the sensed data trading as a two-person cooperative game, whose solution is obtained through the Nash bargaining theory. Extensive simulations based on both synthetic and real-world mobility traces are conducted to validate the efficiency of our incentive-aware mechanisms.


title: A prediction-based reactive driving strategy for highly automated driving function on freeways

abstract: Highly automated driving on freeways requires a complex artificial intelligence that makes optimal decisions based on the current measurements and information. The architecture of the decision-making process, hereinafter referred to as driving strategy, should allow diversity in decision-making for various traffic situations and modular expandability of the overall intelligence. Besides a reactive response to changes in the dynamic environment, a deliberative component should also be considered to incorporate the future evolution of the environment. This paper presents a novel driving strategy that meets the above requirements. The complex driving task is discretized by organization into a finite set of “behavioral strategies” through the developed “decision network”. The decision-making process itself is realized by a nonlinear model predictive approach which is solved using combinatorial optimization formulation. Lastly, the capability of the proposed approach is demonstrated in two freeway situations.


title: Internet of Things gateways meet linux containers: Performance evaluation and discussion

abstract: Intenet of Things (IoT) applications often involve deployment of gateways at the network edge for integrating physical devices, pre-processing sensor data, and synchronizing it with the cloud. The deployment, configuration, and maintenance of the software running on the gateways in large-scale deployments is known to be challenging. In this work, we analyze the deployment requirements of IoT gateways and evaluate containerized deployment with linux containers as a potential approach addressing them. We perform several synthetic and application benchmarks providing an insight in the performance overhead introduced by linux containers and how they affect typical applications running on IoT gateways.


title: The theory of multidimensional persistence

abstract: Persistent homology captures the topology of a filtration - a one-parameter family of increasing spaces - in terms of a complete discrete invariant. This invariant is a multiset of intervals that denote the lifetimes of the topological entities within the filtration. In many applications of topology, we need to study a multifiltration: a family of spaces parameterized along multiple geometric dimensions. In this paper, we show that no similar complete discrete invariant exists for multidimensional persistence. Instead, we propose the rank invariant, a discrete invariant for the robust estimation of Betti numbers in a multifiltration, and prove its completeness in one dimension.


title: Intelligent track cleaning robot

abstract: From the times of the British raj to this date, scavenging rail tracks has been a puzzling social issue. The fact that humans have to clean human waste and other garbage thrown on rail tracks is a situation that needs immediate remedy. Manual scavenging is done now-a-days, owing to the peculiar nature of the job, and many who are engaged for this job suffer from related health problems. The prevailing condition can be rectified to some extent by the adequate use of robotics and control technology. The proposed idea of automatic railway track cleaning system comprises an automatic vehicle that goes on land and track. This proposed intelligent machine specially designed for the Indian railways can clean the railway tracks in a systematic manner. It consists of a four-wheel running robot with a suction unit, cleaning unit, automatic displacement unit, an intelligent control system, an intelligent train sensing unit, and power unit. This device is the first of its kind proposed to be developed exclusively for the Indian Railways. Also, the railways can save a lot of money on water and labour charges. The application of this project in the current railway cleaning scenario will ensure that there will not be any nauseating scenes at railway stations across the country. We have accomplished some functionality critical in the waste clean-up in railway tracks, and have also tried to find solution for connected problems.


title: After Sandy Hook Elementary: A Year in the Gun Control Debate on Twitter

abstract: The mass shooting at Sandy Hook elementary school on December 14, 2012 catalyzed a year of active debate and legislation on gun control in the United States. Social media hosted an active public discussion where people expressed their support and opposition to a variety of issues surrounding gun legislation. In this paper, we show how a content-based analysis of Twitter data can provide insights and understanding into this debate. We estimate the relative support and opposition to gun control measures, along with a topic analysis of each camp by analyzing over 70 million gun-related tweets from 2013. We focus on spikes in conversation surrounding major events related to guns throughout the year. Our general approach can be applied to other important public health and political issues to analyze the prevalence and nature of public opinion.


Employee participation and company performance A review of the literature


title: Privilege escalation attacks on Android

abstract: Android is a modern and popular software platform for smartphones. Among its predominant features is an advanced security model which is based on application-oriented mandatory access control and sandboxing. This allows developers and users to restrict the execution of an application to the privileges it has (mandatorily) assigned at installation time. The exploitation of vulnerabilities in program code is hence believed to be confined within the privilege boundaries of an application's sandbox. However, in this paper we show that a privilege escalation attack is possible. We show that a genuine application exploited at runtime or a malicious application can escalate granted permissions. Our results immediately imply that Android's security model cannot deal with a transitive permission usage attack and Android's sandbox model fails as a last resort against malware and sophisticated runtime attacks.


title: Sperm sexing in sheep and cattle: the exception and the rule.

abstract: Flow cytometric sorting for the preselection of sex has progressed considerably in the 20 years since its inception. This technique has allowed the production of pre-sexed offspring in a multitude of species and become a commercial success in cattle around the world. However, due to the stress inherent to the sex-sorting process, sex-sorted spermatozoa are widely recognized as functionally compromised in terms of their fertilizing lifespan within the female reproductive tract as a result of reduced motility and viability and changed functional state. These characteristics, when compared to non-sorted controls, are manifest in vivo as lower fertility. However, improvements to the technology and a greater understanding of its biological impact have facilitated recent developments in sheep, showing sex-sorting is capable of selecting a functionally superior population in terms of both in vitro and in vivo function. These results are reviewed in the context of recent developments in other species and the reasons for success after artificial insemination with sex-sorted ram spermatozoa are discussed.


title: Modeling non-progressive phenomena for influence propagation

abstract: Most previous work on modeling influence propagation has focused on progressive models, i.e., once a node is influenced (active) the node stays in that state and cannot become inactive. However, this assumption is unrealistic in many settings where nodes can transition between active and inactive states. For instance, a user of a social network may stop using an app and become inactive, but again activate when instigated by a friend, or when the app adds a new feature or releases a new version. In this work, we study such non-progressive phenomena and propose an efficient model of influence propagation. Specifically, we model influence propagation as a continuous-time Markov process with 2 states: active and inactive. Such a model is both highly scalable (we evaluated on graphs with over 2 million nodes), 17-20 times faster, and more accurate for estimating the spread of influence, as compared with state-of-the-art progressive models for several applications where nodes may switch states.


title: Applications of Path Compression on Balanced Trees

abstract: We devise a method for computing functions defined on paths in trees. The method is based on tree manipulation techniques first used for efficiently representing equivalence relations. It has an almost-linear running time. We apply the method to give O(m $\alpha$(m,n)) algorithms for two problems. A. Verifying a minimum spanning tree in an undirected graph (best previous bound: O(m log log n) ). B. Finding dominators in a directed graph (best previous bound: O(n log n + m) ). Here n is the number of vertices and m the number of edges in the problem graph, and $\alpha$(m,n) is a very slowly growing function which is related to a functional inverse of Ackermann''s function. The method is also useful for solving, in O(m $\alpha$(m,n)) time, certain kinds of pathfinding problems on reducible graphs. Such problems occur in global flow analysis of computer programs and in other contexts. A companion paper will discuss this application.


Steering in bicycles and motorcycles


title: Autonomous robot-mediated imitation learning for children with autism

abstract: Autism Spectrum Disorders (ASD) impact 1 in 88 children in the United States. The cost of ASD intervention is tremendous with huge individual and social consequences. In recent years, robotic systems have been introduced with considerable success for ASD intervention because of their potential to engage children with ASD. In this work, we present a novel closed-loop autonomous robotic system for imitation skill learning for ASD intervention. Children with ASD show powerful impairment in imitation, which has been associated with a host of neurodevelopmental and learning challenges over time. The presented robotic system offers dynamic, adaptive and autonomous interaction for learning of imitation skills with real-time performance evaluation and feedback. The system has been tested in a user study with young children with ASD and typically developing (TD) control sample. Further, the performance of the system was compared with that of a human therapist in the user study. The results demonstrate that the developed robotic system is well-tolerated by the target population, engaged the children with ASD more than a human therapist, and produced performances that were relatively better than that of a human therapist.


title: Analyzing the transitional region in low power wireless links

abstract: The wireless sensor networks community, has now an increased understanding of the need for realistic link layer models. Recent experimental studies have shown that real deployments have a "transitional region" with highly unreliable links, and that therefore the idealized perfect-reception-within-range models used in common network simulation tools can be very misleading. In this paper, we use mathematical techniques from communication theory to model and analyze the low power wireless links. The primary contribution of this work is the identification of the causes of the transitional region, and a quantification of their influence. Specifically, we derive expressions for the packet reception rate as a function of distance, and for the width of the transitional region. These expressions incorporate important channel and radio parameters such as the path loss exponent and shadowing variance of the channel; and the modulation and encoding of the radio. A key finding is that for radios using narrow-band modulation, the transitional region is not an artifact of the radio non-ideality, as it would exist even with perfect-threshold receivers because of multi-path fading. However, we hypothesize that radios with mechanisms to combat multi-path effects, such as spread-spectrum and diversity techniques, can reduce the transitional region.


title: Exploiting user similarity based on rated-item pools for improved user-based collaborative filtering

abstract: An approach to user-based collaborative filtering is proposed that refines prediction of item ratings that is based on global user similarity by incorporating information derived from a more detailed user comparison made on the basis of Rated Item Pools (RIPs). The preference spectrum defined by items that a user has rated, and ranging from best-liked to most disliked items, is divided into item sets, or RIPs, which supply the basis for a fine-grained calculation of similarity between users. The RIP-based approach makes it possible for the model to take advantage of user tastes that are matched at one end of the spectrum, e.g., two users agree on favorites, without requiring complete correspondence of item ratings between user profiles. The approach improves rating prediction, as compared to a baseline that uses the global user similarity alone. It does not unduly inflate computational complexity or rely on external resources, common shortcomings of competing rating prediction methods. Cases in which the nearest neighbors are relatively dissimilar, known to be challenging for user-based collaborative filtering, demonstrate particularly substantial improvement. Performance is shown to be stable across the choice of neighborhood size, number of pools and relative pool size.


The Visual Hull Concept for Silhouette-Based Image Understanding


title: Multiple deficits in ADHD: executive dysfunction, delay aversion, reaction time variability, and emotional deficits

abstract: BACKGROUND ::: The notion that ADHD constitutes a heterogeneous disorder is well accepted. However, this study contributes with new important knowledge by examining independent effects of a large range of neuropsychological deficits. In addition, the study investigated whether deficits in emotional functioning constitute a dissociable component of ADHD. ::: ::: ::: METHOD ::: The study included children with ADHD (n = 102; 7-13 years) and a control sample individually matched with regard to age and gender. The administered tasks were designed to tap into three different neuropsychological domains: executive functions (i.e., working memory, inhibition, and shifting), delay aversion, and reaction time variability. Parent ratings of emotion regulation and a test of emotion recognition were also included. ::: ::: ::: RESULTS ::: Children with ADHD differed significantly from controls on all measures, except for delay aversion and recognition of disgust. No main effects of gender or interaction effects of gender and group were found. More importantly, executive functioning, reaction time variability, and emotional functioning all contributed independently to distinguishing between children with ADHD and controls. ::: ::: ::: CONCLUSIONS ::: The current study supports the view of ADHD as a heterogeneous disorder related to multiple neuropsychological deficits. In addition, emotional functioning appears to be an area of importance for ADHD that needs to be incorporated into future theoretical models.


title: A Framework for Simplification of PDE-Based Lithium-Ion Battery Models

abstract: Simplified models are commonly used in battery management and control, despite their (often implicit) limitations in capturing the dynamic behavior of the battery across a wide range of operating conditions. This paper seeks to develop a framework for battery model simplification starting from an initial high-order physics-based model that will explicitly detail the assumptions underpinning the development of simplified battery models. Starting from the basis of a model capturing the electrochemical, thermal, electrical, and aging dynamics in a set of partial differential equations, a systematic approach based on singular perturbations and averaging is used to simplify the dynamics through identification of disparate timescales inherent in the problem. As a result, libraries of simplified models with interconnections based on the specified assumptions are obtained. A quantitative comparison of the simplified models relative to the original model is used to justify the model reductions. To demonstrate the utility of the framework, a set of battery charging strategies is evaluated at reduced computational effort on simplified models.


title: BeeParking: an ambient display to induce cooperative parking behavior

abstract: Interactive ambient systems offer a great potential for attracting user attention, raising awareness and supporting the acquisition of more desirable behaviors in the shared use of limited resources, like physical or digital spaces, energy, water and so on. In this paper we describe the iterative design of BeeParking, an ambient display and automatic notification system aimed to induce more cooperative use of a parking facility within a work environment. We also report main findings from a longitudinal in-situ evaluation showing how the system was adopted and how it affected users' parking behavior over time.


title: A comparative analysis of 6T, 7T, 8T and 9T SRAM cells in 90nm technology

abstract: Static Random Access Memory (SRAM) is a memory that is designed to provide high speed and low power applications. As the technology is shrinking down, the power supply is also scaled down which decreases the noise margin of the SRAM cells. The reduced noise margin further makes more leakage power in the SRAM cells. The main objective of this project is to deal with the power dissipation which occurs normally in the conventional Static Random Access Memory (SRAM) cells during the read and write operation. This problem can be solved by applying dual-threshold-voltage for 6T, 7T, 8T and 9T SRAM Cells. Their respective power dissipation and delay of these cells are calculated and compared. This is implemented in 90nm Generic Process Design Kit (GPDK) using Cadence Virtuoso Schematic Composer and the Spectre as the simulator.


title: Electron density distributions in substituted 2,3,4,5-tetraphenyl- 1-germacyclopenta-2,4-dienes studied by NMR spectroscopy

abstract: The signals in the13C NMR spectra of 2,3,4,5-tetraphenyl-1-germacyclopenta-2,4-dienes (R1=R2=H, Me,cyclo-C3H5, SiMe3, SnMe3, R1=Me, R2=H, Cl) were completely assigned using 2D NMR spectroscopy. The pattern of the variation of the chemical shifts in the13C NMR spectra indicates that the effects of substituents R1 and R2 on the heterocycle and on the phenyl groups are of inductive rather than mesomeric origin and include the direct through-space polarization of bonds (field effect).


title: Towards tags ranking for social images

abstract: Abstract Today social media websites have made the great success, which allow users to upload personal media data and encourage them to annotate media data with free tags. With the rich tags, users can more conveniently access the image content on these websites. However, the tags annotated by users are generally in a random order without recovering any important relevant information to the content of images. To target this issue, we propose a tags ranking scheme to automatically rank the tags with respect to given images by taking both their relevance to this image's visual content and their relationships into account. The proposed framework mainly comprises four stages. First, given a tag query, a set of web images is collected from multiple searching engines to cover the semantic space. Second, initial relevance scores of the tags with respect to a given image's visual content are estimated in Bayesian framework, in which a fused visual similarity is adopted through the linear combination of global and local visual similarities. Third, tags graph is constructed by mining the relationships among tags. At last, a random walk process over tags graph is performed to refine the tags ranking. Experimental results showed that the proposed method is both effective and efficient.


title: OFDM channel estimation and data detection with superimposed pilots

abstract: We propose three iterative superimposed-pilot based channel estimators for Orthogonal Frequency Division Multiplexing (OFDM) systems. Two are approximate maximum-likelihood, derived by using a Taylor expansion of the conditional probability density function of the received signal or by approximating the OFDM time signal as Gaussian, and one is minimum-mean square error. The complexity per iteration of these estimators is given by approximately O(NL^2), O(N^3) and O(NL), where N is the number of OFDM subcarriers and L is the channel length (time). Two direct (non-iterative) data detectors are also derived by averaging the log likelihood function over the channel statistics. These detectors require minimising the cost metric in an integer space, and we suggest the use of the sphere decoder for them. The Cramer–Rao bound for superimposed pilot based channel estimation is derived, and this bound is achieved by the proposed estimators. The optimal pilot placement is shown to be the equally spaced distribution of pilots. The bit error rate of the proposed estimators is simulated for N = 32 OFDM system. Our estimators perform fairly close to a separated training scheme, but without any loss of spectral efficiency.


title: Ultrasound registration: A review.

abstract: This article is a review of registration algorithms for use between ultrasound images (monomodal image-based ultrasound registration). Ultrasound is safe, inexpensive, and real-time, providing many advantages for clinical and scientific use on both humans and animals, but ultrasound images are also notoriously noisy and subject to several unique artifacts/distortions. This paper introduces the topic and unique aspects of ultrasound-to-ultrasound image registration, providing a broad introduction and summary of the literature and the field. Both theoretical and practical aspects are introduced. The first half of the paper is theoretical, organized according to the basic components of a registration framework, namely preprocessing, image-similarity metrics, optimizers, etc. It further subdivides these methods between those suitable for elastic (non-rigid) vs. inelastic (matrix) transforms. The second half of the paper is organized by anatomy and is practical in nature, presenting and discussing the complete published systems that have been validated for registration in specific anatomic regions.


title: Metal–Oxide RRAM

abstract: In this paper, recent progress of binary metal-oxide resistive switching random access memory (RRAM) is reviewed. The physical mechanism, material properties, and electrical characteristics of a variety of binary metal-oxide RRAM are discussed, with a focus on the use of RRAM for nonvolatile memory application. A review of recent development of large-scale RRAM arrays is given. Issues such as uniformity, endurance, retention, multibit operation, and scaling trends are discussed.


title: Acquisition of NIR-Green-Blue Digital Photographs from Unmanned Aircraft for Crop Monitoring

abstract: Payload size and weight are critical factors for small Unmanned Aerial Vehicles (UAVs). Digital color-infrared photographs were acquired from a single 12-megapixel camera that did not have an internal hot-mirror filter and had a red-light-blocking filter in front of the lens, resulting in near-infrared (NIR), green and blue images. We tested the UAV-camera system over two variably-fertilized fields of winter wheat and found a good correlation between leaf area index and the green normalized difference vegetation index (GNDVI). The low cost and very-high spatial resolution associated with the camera-UAV system may provide important information for site-specific agriculture.


title: A Theoretical Explanation for Perplexing Behaviors of Backpropagation-based Visualizations

abstract: Backpropagation-based visualizations have been proposed to interpret convolutional neural networks (CNNs), however a theory is missing to justify their behaviors: Guided backpropagation (GBP) and deconvolutional network (DeconvNet) generate more human-interpretable but less class-sensitive visualizations than saliency map. Motivated by this, we develop a theoretical explanation revealing that GBP and DeconvNet are essentially doing (partial) image recovery which is unrelated to the network decisions. Specifically, our analysis shows that the backward ReLU introduced by GBP and DeconvNet, and the local connections in CNNs are the two main causes of compelling visualizations. Extensive experiments are provided that support the theoretical analysis.


title: High Ringxiety: Attachment Anxiety Predicts Experiences of Phantom Cell Phone Ringing

abstract: Abstract Mobile cell phone users have reported experiencing ringing and/or vibrations associated with incoming calls and messages, only to find that no call or message had actually registered. We believe this phenomenon can be understood as a human signal detection issue, with potentially important influences from psychological attributes. We hypothesized that individuals higher in attachment anxiety would report more frequent phantom cell phone experiences, whereas individuals higher in attachment avoidance would report less frequent experiences. If these experiences are primarily psychologically related to attributes of interpersonal relationships, associations with attachment style should be stronger than for general sensation seeking. We also predicted that certain contexts would interact with attachment style to increase or decrease the likelihood of experiencing phantom cell phone calls and messages. Attachment anxiety directly predicted the frequency of phantom ringing and notification experiences,...


title: Optimal and Fair Energy Efficient Resource Allocation for Energy Harvesting-Enabled-PD-NOMA-Based HetNets

abstract: In this paper, the tradeoff among the energy efficiency, fairness, harvested energy, and system sum rate is studied. In this regard, various fairness methods, namely, max–min fairness, proportional fairness, and minimum delay potential fairness in power-domain non-orthogonal multiple access-based heterogeneous cellular networks are investigated. In order to perform successive interference cancellation (SIC), we use two ordering approaches and compare their performance. To this end, we propose joint subcarrier and power allocation algorithms to achieve fair energy efficient resource allocation for each fairness method and SIC ordering. Since the proposed optimization problems are non-convex and intractable, the existing methods to solve the convex problems could not be directly used. To overcome this difficulty, an iterative algorithm based on successive convex approximation is used. Moreover, to show the optimality gap of the proposed solution method, an optimal approach based on the monotonic optimization is applied in which we first transform each of the proposed optimization problems into a monotonic optimization problem of canonical form, and then, we obtain the optimal solution of each problem, which coincides with the optimal solution of the original non-convex problem. We finally study the performance of the proposed schemes using simulations for different values of the system parameters.


title: Palette-Based Image Recoloring Using Color Decomposition Optimization

abstract: Previous works on palette-based color manipulation typically fail to produce visually pleasing results with vivid color and natural appearance. In this paper, we present an approach to edit colors of an image by adjusting a compact color palette. Different from existing methods that fail to preserve inherent color characteristics residing in the source image, we propose a color decomposition optimization for flexible recoloring while retaining these characteristics. For an input image, we first employ a variant of the $k$ -means algorithm to create a palette consisting of a small set of most representative colors. Next, we propose a color decomposition optimization to decompose colors of the entire image into linear combinations of basis colors in the palette. The captured linear relationships then allow us to recolor the image by recombining the coding coefficients with a user-modified palette. Qualitative comparisons with existing methods show that our approach can more effectively recolor images. Further user study quantitatively demonstrates that our method is a good candidate for color manipulation tasks. In addition, we showcase some applications enabled by our method, including pattern colorings suggesting, color transfer, tissue staining analysis and color image segmentation.


title: Super-resolution of medical image using representation learning

abstract: Super-resolution (SR) of single image is a meaningful challenge in medical images based diagnosis, while the image resolution is limited. Also, numerous deep neural networks based models were proposed and achieve excellent performance which is superior to the previous handcrafted methods. In this paper, we employ a deep convolutional neural networks for the super-resolution (SR) of single medical image, which learns the nonlinear mapping from the low-resolution space to high-resolution space directly. In addition, we use three sets imaging data (Mammary gland, Prostate tissue and Human brain) training deep network respectively. Firstly, we use Randomized Rectified Linear Unit (RReLU), which incorporates a nonzero slope for negative part to solve the problem of over compression. Secondly, for the purpose of enhancing the quality of reconstructed result and reducing the noise of over-fitting, Nesterov's Accelerated Gradient (NAG) method on the SRCNN is used to accelerate the convergence of loss function and avoid the large oscillations. A comparative performance evaluation is carried out over a set of experiments using real imaging data to verify the validity of proposed algorithm.


title: A fundamental frequency estimator for the real-time processing of musical sounds for cochlear implants

abstract: A real-time fundamental frequency (F"0) estimator that operates in the frequency domain was developed for the processing of musical sounds in cochlear-implant (CI) sound processors. Its performance was evaluated with male and female sung-vowel stimuli in quiet, and in white noise and babble noise. The error rates of the developed F"0 estimator were much lower than those of a temporal F"0 estimator that was previously used in CI sound processors, and were comparable to the published error rates of F"0 estimators that were designed for other applications and evaluated with speech or musical instrument stimuli. It is envisaged that the experimental F"0 estimator will be used in advanced CI coding strategies to improve the perception of pitch by CI users, which may result in improved perception of musical sounds, as well as improved speech perception for tonal languages.


How the Brain Creates the Mind


title: 3D Face Recognition Using Shapes of Facial Curves

abstract: Recognition of human beings using shapes of their full facial surfaces is a difficult problem. Our approach is to approximate a facial surface using a collection of (closed) facial curves, and to compare surfaces by comparing their corresponding curves. The differences between shapes of curves are quantified using lengths of geodesic paths between them on a pre-defined curve shape space. The metric for comparing facial surfaces is a composition of the metric involving individual facial curves. These ideas are demonstrated in the context of face recognition using the nearest-neighbor classifier.


title: Lean UX: the next generation of user-centered agile development?

abstract: In this paper we discuss the opportunities and challenges of the recently introduced Lean UX software development philosophy. The point of view is product design and development in a software agency. Lean UX philosophy is identified by three ingredients: design thinking, Lean production and Agile development. The major challenge for an agency is the organizational readiness of the client organization to adopt a new way of working. Rather than any special tool or practice, we see that the renewal of user-centered design and development is hindered by existing purchase processes and slow decision making patterns.


title: Towards Internet-scale multi-view stereo

abstract: This paper introduces an approach for enabling existing multi-view stereo methods to operate on extremely large unstructured photo collections. The main idea is to decompose the collection into a set of overlapping sets of photos that can be processed in parallel, and to merge the resulting reconstructions. This overlapping clustering problem is formulated as a constrained optimization and solved iteratively. The merging algorithm, designed to be parallel and out-of-core, incorporates robust filtering steps to eliminate low-quality reconstructions and enforce global visibility constraints. The approach has been tested on several large datasets downloaded from Flickr.com, including one with over ten thousand images, yielding a 3D reconstruction with nearly thirty million points.


title: A framework for workload allocation in distributed transaction processing systems

abstract: Abstract Ever-increasing demands for high transaction rates, limitations of high-end processors, high availability, and modular growth considerations are all driving forces toward distributed architectures for transaction processing. However, a prerequisite to taking advantage of the capacity of a distributed transaction processing system is an effective strategy for workload allocation. The distribution of the workload should not only achieve load balancing, but also support an efficient transaction processing with a minimum of intersystem communication. To this end, adaptive schemes for transaction routing have to be employed that are highly responsive to workload fluctuations and configuration changes. Adaptive allocation schemes are also important for simplifying system administration, which is a major problem in distributed transaction processing systems. In this article we develop a taxonomic framework for workload allocation, in particular, transaction routing, in distributed transaction processing systems. This framework considers the influence of the underlying system architecture (e.g., shared nothing, shared disk) and transaction execution model as well as the major dependencies between workload, program, and data allocation. The main part of the framework covers structural (or architectural) and implementational alternatives for transaction routing to help identify key factors and basic tradeoffs in the design of appropriate allocation schemes. Finally, we show how existing schemes fit our taxonomy. The framework substantially facilitates a comparison of the different schemes and can guide the development of new, more effective protocols.


title: Simulation of Incremental Conductance MPPT based two phase interleaved boost converter using MATLAB/Simulink

abstract: The Photovoltaic power generation systems are connected to the load via DC-DC converters. The DC-DC converter circuits are controlled by Maximum Power Point Tracking (MPPT) controllers to extract maximum power from the source. Modeling and simulation of Interleaved Boost converter (IBC) with Incremental Conductance (InC) MPPT algorithm for PV application is discussed in this paper. The mathematical model of PV module, interleaved boost Converter and MPPT controller are simulated using MATLAB/Simulink. The PV system is simulated for various temperature and irradiation level and the results are obtained.


title: An Inductorless Self-Controlled Rectifier for Piezoelectric Energy Harvesting

abstract: This paper presents a high-efficiency inductorless self-controlled rectifier for piezoelectric energy harvesting. High efficiency is achieved by discharging the piezoelectric device (PD) capacitance each time the current produced by the PD changes polarity. This is achieved automatically without the use of delay lines, thereby making the proposed circuit compatible with any type of PD. In addition, the proposed rectifier alleviates the need for an inductor, making it suitable for on-chip integration. Reported experimental results show that the proposed rectifier can harvest up to 3.9 times more energy than a full wave bridge rectifier.


title: Combining Data Mining and Machine Learning for Effective Fraud Detection

abstract: This paper describes the automatic design of methods for detecting fraudulent behavior. Much of the design is accomplished using a series of machine learning methods. In particular, we combine data mining and constructive induction with more standard machine learning techniques to design methods for detecting fraudulent usage of cellular telephones based on profiling customer behavior. Specifically, we use a rulelearning program to uncover indicators of fraudulent behavior from a large database of cellular calls. These indicators are used to create profilers, which then serve as features to a system that combines evidence from multiple profilers to generate high-confidence alarms. Experiments indicate that this automatic approach performs nearly as well as the best hand-tuned methods for detecting fraud.


abstract: Information Selection in Noisy Environments with Large Action Spaces Pedro Tsividis (tsividis@mit.edu), Samuel J. Gershman (sjgershm@mit.edu), Joshua B. Tenenbaum (jbt@mit.edu), Laura Schulz (lshulz@mit.edu) Massachusetts Institute of Technology, Department of Brain and Cognitive Sciences, 77 Massachusetts Ave., Cambridge, MA 02139 USA Abstract A critical aspect of human cognition is the ability to effec- tively query the environment for information. The ‘real’ world is large and noisy, and therefore designing effec- tive queries involves prioritizing both scope – the range of hypotheses addressed by the query – and reliability – the likelihood of obtaining a correct answer. Here we de- signed a simple information-search game in which partic- ipants had to select an informative query from a large set of queries, trading off scope and reliability. We find that adults are effective information-searchers even in large, noisy environments, and that their information search is best explained by a model that balances scope and re- liability by selecting queries proportionately to their ex- pected information gain. Keywords: exploration; information search; active learn- ing; information gain. Introduction As scientists, we sometimes encounter (or conduct) experimental work that is stunning in its breadth but dis- appointing in its rigor, or work that is categorically deci- sive but disappointingly narrow in scope. As child or adult intuitive scientists searching for information, we often deal with these epistemic virtues – scope and rigor – as well; we can make general queries that drastically narrow the hypothesis space of answers or make nar- rower ones, and we can seek information from reliable sources that are more likely to give us correct answers, or from sources that are less so. Our success, whether as professional or intuitive scientists, hinges on our abil- ity to balance these two dimensions in order to produce queries whose answers will be informative. Early work on information search seemed to show that people fail to make rational decisions when it comes to information acquisition; in the Wason (1968) selection task, with a fairly small (12) action set, only 4% of sub- jects made the normative information-acquisition selec- tion. However, as Oaksford & Chater (1994) pointed out, the selection made most often by participants in the original Wason task was normative when environmental statistics were taken into account. Specifically, partici- pants’ decisions were best explained as maximizing ex- pected information gain in the service of helping them to decide between competing hypotheses. More recent work on information search has shown that children have strong intuitions about questions’ use- fulness and search adaptively (Nelson et al., 2013) and that adults can value information over explicit reward when the two are put in opposition (Markant & Gureckis, Our interest is in whether these trends persist when people are confronted with the large, noisy information spaces characteristic of the real world. As we explore the world and its affordances, we must select queries that have scope, in that they rule out large numbers of hy- potheses at once. And, inasmuch as we can help it, we should minimize noise by querying reliable sources. In the real world, these are often in opposition. So, we ask: how do people trade off scope and reliability when ex- ploring large, noisy information spaces? And when the potential questions are many, do they ask the right ones? Information-search task To begin to answer these questions, we designed a novel information-search task. Our goal was for it to be as simple as possible, while having as many features approaching natural exploration as possible. Thus we paired a very simple game – identifying a hidden number on a number line – with a relatively complex search pro- cedure. In playing the game, participants make queries that vary in the abstract features of scope and reliabil- ity. Additionally, at each point in the game, partici- pants are faced with a very large number of potential specific queries to choose from. Our task is similar to the Markant & Gureckis (2012) task in that it involves exploring a geometric space to test particular hypothe- ses, but we wanted to make explicit the abstract features of questions, rather than have these be implicit as a func- tion of the hypotheses at hand. Participants play by asking ‘questions’ about the hid- den number’s location, using ‘scanners’ that turn blue if the number is under the scanned region and red if the number is not. In each trial, a participant is given four scanners (Fig. 1). In some conditions, the scanners vary in size. Larger scanners can cover larger regions of the number line, ruling out (or in) a larger set of hypotheses than a smaller scanner. Thus, in the context of this study, the ‘scope’ is directly related to the length of the scan- ner. However, the scanners are not deterministic; they also vary in their reliability, which is the probability of providing an accurate signal about the presence of the hidden number (false positives and false negatives are equally likely). To efficiently find the hidden number, participants have to select scanners that provide a good trade-off be-


title: A Wearable Face Recognition System on Google Glass for Assisting Social Interactions

abstract: In this paper, we present a wearable face recognition (FR) system on Google Glass (GG) to assist users in social interactions. FR is the first step towards face-to-face social interactions. We propose a wearable system on GG, which acts as a social interaction assistant, the application includes face detection, eye localization, face recognition and a user interface for personal information display. To be useful in natural social interaction scenarios, the system should be robust to changes in face pose, scale and lighting conditions. OpenCV face detection is implemented in GG. We exploit both OpenCV and ISG (Integration of Sketch and Graph patterns) eye detectors to locate a pair of eyes on the face, between them the former is stable for frontal view faces and the latter performs better for oblique view faces. We extend the eigenfeature regularization and extraction (ERE) face recognition approach by introducing subclass discriminant analysis (SDA) to perform within-subclass discriminant analysis for face feature extraction. The new approach improves the accuracy of FR over varying face pose, expression and lighting conditions. A simple user interface (UI) is designed to present relevant personal information of the recognized person to assist in the social interaction. A standalone independent system on GG and a Client-Server (CS) system via Bluetooth to connect GG with a smart phone are implemented, for different levels of privacy protection. The performance on database created using GG is evaluated and comparisons with baseline approaches are performed. Numerous experimental studies show that our proposed system on GG can perform better real-time FR as compared to other methods.


A Theoretical Assessment of the Causes and Effects of Construction Project Delay


title: Sparse coding with memristor networks

abstract: Sparse representation of information provides a powerful means to perform feature extraction on high-dimensional data and is of broad interest for applications in signal processing, computer vision, object recognition and neurobiology. Sparse coding is also believed to be a key mechanism by which biological neural systems can efficiently process a large amount of complex sensory data while consuming very little power. Here, we report the experimental implementation of sparse coding algorithms in a bio-inspired approach using a 32 × 32 crossbar array of analog memristors. This network enables efficient implementation of pattern matching and lateral neuron inhibition and allows input data to be sparsely encoded using neuron activities and stored dictionary elements. Different dictionary sets can be trained and stored in the same system, depending on the nature of the input signals. Using the sparse coding algorithm, we also perform natural image processing based on a learned dictionary.


title: A universal model for mobility and migration patterns

abstract: Introduced in its contemporary form by George Kingsley Zipf in 1946, but with roots that go back to the work of Gaspard Monge in the 18th century, the gravity law is the prevailing framework to predict population movement, cargo shipping volume, inter-city phone calls, as well as bilateral trade flows between nations. Despite its widespread use, it relies on adjustable parameters that vary from region to region and suffers from known analytic inconsistencies. Here we introduce a stochastic process capturing local mobility decisions that helps us analytically derive commuting and mobility fluxes that require as input only information on the population distribution. The resulting radiation model predicts mobility patterns in good agreement with mobility and transport patterns observed in a wide range of phenomena, from long-term migration patterns to communication volume between different regions. Given its parameter-free nature, the model can be applied in areas where we lack previous mobility measurements, significantly improving the predictive accuracy of most of phenomena affected by mobility and transport processes.


title: Rickets: Part II

abstract: This is the continuation of a two-part review of rickets. This part emphasizes the specific pathophysiology, clinical features, pathoanatomy and radiographic findings of vitamin D deficiency rickets. Other forms of rickets, differential diagnostic considerations and the potential relationship between low levels of vitamin D metabolites and unexplained fractures in infants are also discussed.


title: O(√log n) approximation algorithms for min UnCut, min 2CNF deletion, and directed cut problems

abstract: We give O(√log n)-approximation algorithms for the MIN UNCUT, MIN 2CNF DELETION, DIRECTED BALANCED SEPERATOR, and DIRECTED SPARSEST CUT problems. The previously best known algorithms give an O(log n)-approximation for MIN UNCUT [9], DIRECTED BALANCED SEPERATOR [17], DIRECTED SPARSEST CUT [17], and an O(log n log log n)-approximation for MIN 2CNF DELETION [14].We also show that the integrality gap of an SDP relaxation of the MINIMUM MULTICUT problem is Ω(log n).


title: Solving dynamic vehicle routing problem via evolutionary search with learning capability

abstract: To date, dynamic vehicle routing problem (DVRP) has attracted great research attentions due to its wide range of real world applications. In contrast to traditional static vehicle routing problem, the whole routing information in DVRP is usually unknown and obtained dynamically during the routing execution process. To solve DVRP, many heuristic and metaheuristic methods have been proposed in the literature. In this paper, we present a novel evolutionary search paradigm with learning capability for solving DVRP. In particular, we propose to capture the structured knowledge from optimized routing solution in early time slot, which can be further reused to bias the customer-vehicle assignment when dynamic occurs. By extending our previous research work, the learning of useful knowledge, and the scheduling of dynamic customer requests are detailed here. Further, to evaluate the efficacy of the proposed search paradigm, comprehensive empirical studies on 21 commonly used DVRP instances with diverse properties are also reported.


User Activity Recognition in Smart Homes Using Pattern Clustering Applied to Temporal ANN Algorithm


Evaluation of the neurovascular bundle position at the palate with cone beam computed tomography: an observational study


title: Operations Management in Automated Semiconductor Manufacturing With Integrated Targeting, Near Real-Time Scheduling, and Dispatching

abstract: To help address the need for manufacturing cycle time reduction, Intel has adopted an integrated operations management approach that consists of three key components: targeting, near real-time scheduling, and dispatching. These components work in conjunction to maximize fab efficiency. Combined with fully automated execution, this approach allows Intel to implement a coordinated operational management philosophy at its fabrication facilities. OPSched was developed at Intel to fulfill the requirements of the integrated approach. We look at why this approach is suitable for automated semiconductor manufacturing based on the implementation of the OPSched system at Intel's high-volume manufacturing facilities and the results achieved.


title: Surface Edge Explorer (SEE): Planning Next Best Views Directly from 3D Observations

abstract: Surveying 3D scenes is a common task in robotics. Systems can do so autonomously by iteratively obtaining measurements. This process of planning observations to improve the model of a scene is called Next Best View (NBV) planning. NBV planning approaches often use either volumetric (e.g., voxel grids) or surface (e.g., triangulated meshes) representations. Volumetric approaches generalise well between scenes as they do not depend on surface geometry but do not scale to high-resolution models of large scenes. Surface representations can obtain high-resolution models at any scale but often require tuning of unintuitive parameters or multiple survey stages. This paper presents a scene-model-free NBV planning approach with a density representation. The Surface Edge Explorer (SEE) uses the density of current measurements to detect and explore observed surface boundaries. This approach is shown experimentally to provide better surface coverage in lower computation time than the evaluated state-of-the-art volumetric approaches while moving equivalent distances.


title: Comparative Analysis of Neural QA models on SQuAD

abstract: The task of Question Answering has gained prominence in the past few decades for testing the ability of machines to understand natural language. Large datasets for Machine Reading have led to the development of neural models that cater to deeper language understanding compared to information retrieval tasks. Different components in these neural architectures are intended to tackle different challenges. As a first step towards achieving generalization across multiple domains, we attempt to understand and compare the peculiarities of existing end-to-end neural models on the Stanford Question Answering Dataset (SQuAD) by performing quantitative as well as qualitative analysis of the results attained by each of them. We observed that prediction errors reflect certain model-specific biases, which we further discuss in this paper.


title: Design of Single-Switch Inverters for Variable Resistance/Load Modulation Operation

abstract: Single-Switch inverters such as the conventional Class-E inverter are often highly load sensitive, and maintain zero-voltage switching over only a narrow range of load resistances. This paper introduces a design methodology that enables rapid synthesis of Class E and related single-switch inverters that maintain ZVS operation over a wide range of resistive loads. We treat the design of Class-E inverters for variable resistance operation and show how the proposed methodology relates to circuit transformations on traditional Class-E designs. We also illustrate the use of this transformation approach to realize $\Phi _{2}$ inverters for variable-resistance operation. The proposed methodology is demonstrated and experimentally validated at 27.12 MHz in a Class E and $\Phi _{2}$ inverter designs that operate efficiently over 12:1 load resistance range for an 8:1 and 10:1 variation in output power, respectively, and a 25-W peak output power.


title: A Lightweight Privacy-Preserving Data Aggregation Scheme for Fog Computing-Enhanced IoT

abstract: Fog computing-enhanced Internet of Things (IoT) has recently received considerable attention, as the fog devices deployed at the network edge can not only provide low latency, location awareness but also improve real-time and quality of services in IoT application scenarios. Privacy-preserving data aggregation is one of typical fog computing applications in IoT, and many privacy-preserving data aggregation schemes have been proposed in the past years. However, most of them only support data aggregation for homogeneous IoT devices, and cannot aggregate hybrid IoT devices’ data into one in some real IoT applications. To address this challenge, in this paper, we present a lightweight privacy-preserving data aggregation scheme, called Lightweight Privacy-preserving Data Aggregation, for fog computing-enhanced IoT. The proposed LPDA is characterized by employing the homomorphic Paillier encryption, Chinese Remainder Theorem, and one-way hash chain techniques to not only aggregate hybrid IoT devices’ data into one, but also early filter injected false data at the network edge. Detailed security analysis shows LPDA is really secure and privacy-enhanced with differential privacy techniques. In addition, extensive performance evaluations are conducted, and the results indicate LPDA is really lightweight in fog computing-enhanced IoT.


Standardized measurements for garments / volumes Standardized measurements used to order compression garments can be used to calculate arm volumes in order to evaluate lymphedema treatment