title: OCRSpell: an interactive spelling correction system for OCR errors in text

abstract: In this paper, we describe a spelling correction system designed specifically for OCR-generated text that selects candidate words through the use of information gathered from multiple knowledge sources. This system for text correction is based on static and dynamic device mappings, approximate string matching, and n-gram analysis. Our statistically based, Bayesian system incorporates a learning feature that collects confusion information at the collection and document levels. An evaluation of the new system is presented as well.


title: Seeing is believing: The effect of brain images on judgments of scientific reasoning

abstract: Abstract Brain images are believed to have a particularly persuasive influence on the public perception of research on cognition. Three experiments are reported showing that presenting brain images with articles summarizing cognitive neuroscience research resulted in higher ratings of scientific reasoning for arguments made in those articles, as compared to articles accompanied by bar graphs, a topographical map of brain activation, or no image. These data lend support to the notion that part of the fascination, and the credibility, of brain imaging research lies in the persuasive power of the actual brain images themselves. We argue that brain images are influential because they provide a physical basis for abstract cognitive processes, appealing to people’s affinity for reductionistic explanations of cognitive phenomena.


title: The Effects of Framing and Cause-Related Marketing on Crowdfunding Sponsors' Intentions: A model Development

abstract: In recent years, crowdfunding sprang up and became a new way to raise funds from the public instead of senior investors. Crowdfunding refers to enterprises and entrepreneurs raising funds from the crowd through social network by launching their projects on the crowdfunding platforms. However, more than half of the projects which were launched on the crowdfunding platforms did not raise enough funds in time. How to attract and persuade more people willing to sponsor turns on a big issue to project creators and platform managers. Therefore, it is essential to understand what factors influence on sponsors' intentions. Past studies found that people change their original choice if the question was demonstrated in different way. But the literature seldom focused on this situation in the domain of crowdfunding. Thus, this study develops a model to understand the effects of framing effect and cause-related marketing on crowdfunding sponsors' intentions. Follow up studies can do the empirical study of this model in the future.


Autonomous Waypoint Generation Strategy for On-Line Navigation in Unknown Environments


title: Dissecting Contextual Word Embeddings: Architecture and Representation

abstract: Contextual word representations derived from pre-trained bidirectional language models (biLMs) have recently been shown to provide significant improvements to the state of the art for a wide range of NLP tasks. However, many questions remain as to how and why these models are so effective. In this paper, we present a detailed empirical study of how the choice of neural architecture (e.g. LSTM, CNN, or self attention) influences both end task accuracy and qualitative properties of the representations that are learned. We show there is a tradeoff between speed and accuracy, but all architectures learn high quality contextual representations that outperform word embeddings for four challenging NLP tasks. Additionally, all architectures learn representations that vary with network depth, from exclusively morphological based at the word embedding layer through local syntax based in the lower contextual layers to longer range semantics such coreference at the upper layers. Together, these results suggest that unsupervised biLMs, independent of architecture, are learning much more about the structure of language than previously appreciated.


The mediator effectiveness hypothesis revisited


title: Morphosyntactic Learning and the Development of Tense

abstract: In this article, we propose that the Root Infinitive (RI) phenomenon in child language is best viewed and explained as the interaction between morphological learning and syntactic development. We make the following specific sugges tions: The optionality in RI reflects the presence of a grammar such as Chinese which does not manifest tense marking. The gradual elimination of the non tense-marking grammar is facilitated by the learning of the morphosyntactic system of the target language. Quantitative differences in the input data among morphosyntactic systems result in the cross-linguistic variation in the RI phe nomenon. More broadly, we aim to demonstrate that quantitative aspects of lan guage learning data and concrete mechanisms of the language learning process can play an important role in the generative approach to language acquisition. In Section 2, we give a brief overview of the RI literature along with some methodological remarks regarding the explanation of the phenomenon. In Sec tion 3, we lay out our theory of morphosyntactic learning and the broader vari ational approach to language acquisition. Our empirical work focuses on the development of tense in Spanish, French, and English. In Section 4 we show through corpus study of child-directed speech that differences in the morphosyn tactic systems of these three languages explain the brief RI stage in Spanish acquisition, the prolonged RI stage in English acquisition, as well the inter mediate status of the RI stage in French. In Section 5, we discuss how our


title: Review of Coupling Atmospheric and Hydrologic Models

abstract: Research on coupling atmospheric and hydrologic models has become an important issue in both hydrology and meteorology,and also been emphasized by many international scientific programs.Land surface scheme,which plays a crucial role of interface between atmospheric and hydrologic models,is introduced in the coupling process by many researchers.Based on a summary of land surface schemes,research on coupling atmospheric and hydrologic models is reviewed in detail from four aspects according to the methods and purposes of coupling:(1) forcing hydrologic model by atmospheric model outputs directly,(2) strengthening hydrological descriptions in land surface models,(3) setting up land surface model hydrologic model coupled system,and(4) some two-way coupling researches.The usual one-way coupling has its own drawback since the land-surface process is treated independently,which gives rise to inconsistent state variables between atmospheric and hydrologic models.This problem can be avoided if the two models share a same land surface scheme.Several key aspects in the study of such two-way coupling must be faced,including time-spatial scales difference,sub-grid heterogeneity,precipitation simulation accuracy,etc.Remote sensing,satellites,geological information system and four-dimensional variation data assimilation provide robust methods and techniques to resolve the main problems in coupling process.In the future,developing two-way coupling experiments and sensitivity analysis will be needed to investigate the interactions between atmosphere-vegetation-soil-hydrology processes through interdisciplinary researchers,to realize two-way coupled atmospheric-hydrological model system,and to improve the simulation and prediction both for atmospheric and hydrologic models.Such coupled model system can effectively decrease life and financial loss due to hydrological extreme events or climate abnormity,such as floods and draughts.


title: Vehicle detection for autonomous parking using a Soft-Cascade AdaBoost classifier

abstract: This paper presents a monocular algorithm for front and rear vehicle detection, developed as part of the FP7 V-Charge project's perception system. The system is made of an AdaBoost classifier with Haar Features Decision Stump. It processes several virtual perspective images, obtained by un- warping 4 monocular fish-eye cameras mounted all-around an autonomous electric car. The target scenario is the automated valet parking, but the presented technique fits well in any general urban and highway environment. A great attention has been given to optimize the computational performance. The accuracy in the detection and a low computation costs are provided by combining a multiscale detection scheme with a Soft-Cascade classifier design. The algorithm runs in real time on the project's hardware platform. The system has been tested on a validation set, compared with several AdaBoost schemes, and the corresponding results and statistics are also reported.


title: Learning to Learn Causal Models

abstract: Learning to understand a single causal system can be an achievement, but humans must learn about multiple causal systems over the course of a lifetime. We present a hierarchical Bayesian framework that helps to explain how learning about several causal systems can accelerate learning about systems that are subsequently encountered. Given experience with a set of objects, our framework learns a causal model for each object and a causal schema that captures commonalities among these causal models. The schema organizes the objects into categories and specifies the causal powers and characteristic features of these categories and the characteristic causal interactions between categories. A schema of this kind allows causal models for subsequent objects to be rapidly learned, and we explore this accelerated learning in four experiments. Our results confirm that humans learn rapidly about the causal powers of novel objects, and we show that our framework accounts better for our data than alternative models of causal learning.


title: Architectures for distributed and hierarchical Model Predictive Control - A review

abstract: The aim of this paper is to review and to propose a classification of a number of decentralized, distributed and hierarchical control architectures for large scale systems. Attention is focused on the design approaches based on Model Predictive Control. For the considered architectures, the underlying rationale, the fields of application, the merits and limitations are discussed, the main references to the literature are reported and some future developments are suggested. Finally, a number of open problems is listed.


title: Blockchain world - Do you need a blockchain? This chart will tell you if the technology can solve your problem

abstract: According to a study released this July by Juniper Research, more than half the world's largest companies are now researching blockchain technologies with the goal of integrating them into their products. Projects are already under way that will disrupt the management of health care records, property titles, supply chains, and even our online identities. But before we remount the entire digital ecosystem on blockchain technology, it would be wise to take stock of what makes the approach unique and what costs are associated with it. Blockchain technology is, in essence, a novel way to manage data. As such, it competes with the data-management systems we already have. Relational databases, which orient information in updatable tables of columns and rows, are the technical foundation of many services we use today. Decades of market exposure and well-funded research by companies like Oracle Corp. have expanded the functionality and hardened the security of relational databases. However, they suffer from one major constraint: They put the task of storing and updating entries in the hands of one or a few entities, whom you have to trust won't mess with the data or get hacked.


title: Standby-Power-Free Integrated Circuits Using MTJ-Based VLSI Computing

abstract: Nonvolatile spintronic devices have potential advantages, such as fast read/write and high endurance together with back-end-of-the-line compatibility, which offers the possibility of constructing not only stand-alone RAMs and embedded RAMs that can be used in conventional VLSI circuits and systems but also standby-power-free high-performance nonvolatile CMOS logic employing logic-in-memory architecture. The advantages of employing spintronic devices, especially magnetic tunnel junction (MTJ) devices with CMOS circuits, are discussed, and the current status of the MTJ-based VLSI computing paradigm is presented along with its prospects and remaining challenges.


title: Towards Better Availability and Accountability for IoT Updates by Means of a Blockchain

abstract: Building the Internet of Things requires deploying a huge number of objects with full or limited connectivity to the Internet. Given that these objects are exposed to attackers and generally not secured-by-design, it is essential to be able to update them, to patch their vulnerabilities and to prevent hackers from enrolling them into botnets. Ideally, the update infrastructure should implement the CIA triad properties, i.e., confidentiality, integrity and availability. In this work, we investigate how the use of a blockchain infrastructure can meet these requirements, with a focus on availability. In addition, we propose a peer-to-peer mechanism, to spread updates between objects that have limited access to the Internet. Finally, we give an overview of our ongoing prototype implementation.


title: Capacity Analysis of Cooperative Relaying Systems Using Non-Orthogonal Multiple Access

abstract: In this letter, we propose the cooperative relaying system using non-orthogonal multiple access (NOMA) to improve the spectral efficiency. The achievable average rate of the proposed system is analyzed for independent Rayleigh fading channels, and also its asymptotic expression is provided. In addition, a suboptimal power allocation scheme for NOMA used at the source is proposed.


title: Keyword Search Over Probabilistic RDF Graphs

abstract: In many real applications, RDF (Resource Description Framework) has been widely used as a W3C standard to describe data in the Semantic Web. In practice, RDF data may often suffer from the unreliability of their data sources, and exhibit errors or inconsistencies. In this paper, we model such unreliable RDF data by probabilistic RDF graphs, and study an important problem, keyword search query over probabilistic RDF graphs (namely, the pg-KWS query). To retrieve meaningful keyword search answers, we design the score rankings for subgraph answers specific for RDF data. Furthermore, we propose effective pruning methods (via offline pre-computed score bounds and probabilistic threshold) to quickly filter out false alarms. We construct an index over the pre-computed data for RDF, and present an efficient query answering approach through the index. Extensive experiments have been conducted to verify the effectiveness and efficiency of our proposed approaches.


title: Experiences in improving risk management processes using the concepts of the Riskit method

abstract: This paper describes experiences from two organizations that have used the Riskit method for risk management in their software projects. This paper presents the Riskit method, the organizations involved, case study designs, and findings from case studies. We focus on the experiences and insights gained through the application of the method in industrial context and propose some general conclusions based on the case studies.


title: Theoretical and Experimental Investigations into Crack Detection with BOTDR-Distributed Fiber Optic Sensors

abstract: AbstractDevelopment of a model for the analysis of strain transfer mechanism in Brillouin-based sensors with strain singularities is provided in this study. The main objective of the research pertained to the development of a method for accurate detection of cracks and their locations in sensing with Brillouin-based fiber optic distributed sensors. The work involved formulation of a shear lag–based model considering the elastic as well as elastoplastic stages of the fiber optic coating strains. Feasibility of the proposed approach is evaluated through an experimental program. The experimental program involved use of a Brillouin optical time domain reflectometer (BOTDR) for distributed measurement of strain and detection of simulated cracks in a 15-m-long beam. The results indicate that the discontinuities in the strain distribution based on the theoretical analysis provide the means to accurately pinpoint the location of simulated cracks. On the other hand, the distortion effect of the BOTDR system due to...


title: Off-road Path Following using Region Classification and Geometric Projection Constraints

abstract: We describe a realtime system for finding and tracking unstructured paths in off-road conditions. The system was designed as part of the recent Darpa Grand Challenge and was tested over hundreds of miles of off-road driving. The unique feature of our approach is to combine geometric projection used for recovering Pitch and Yaw with Learning approaches for identifying familiar "drivable" regions in the scene. The region-based component segments the image to "path" and "non-path" regions based on texture analysis borne out of a learning-by-examples principle. The boundary-based component looks for the path bounding lines assuming a geometric model of a planar pathway bounded by parallel edges taken by a perspective camera. The combined effect of both sub-systems forms a robust system capable of finding the path even in situations where the vehicle is positioned out of the path - a situation which is not common for human drivers but is relevant for autonomous driving where the vehicle may find itself occasionally veering out of the path.


title: An Intelligent Irrigation System Based on Wireless Sensor Network and Fuzzy Control

abstract: In order to resolve the problems which include loss of soil fertility and waste of water resource in agriculture production, we design an intelligent irrigation system based on wireless sensor networks and fuzzy control. The system mainly consists of wireless sensor networks and the monitoring center. All of the nodes in Monitoring area use solar power, collect the information of soil moisture, together with the growth information of different crops in different periods. Soil moisture content deviation and the rate of change of deviation are taken as input variables of fuzzy controller, and the fuzzy control regular database is established for the fuzzy irrigation control system. The monitoring center receives the data transmission from wireless sensor network node, and output information of irrigation water demands to the relay via a wireless sensor network to control opening and closing time of the valve in crop areas. The experimental results show that the system has a stable and reliable data transmission, which achieve real-time monitoring of soil on crop growth, give a right amount of irrigation based on crops growth information, which has broad application prospects.


title: gMission: A General Spatial Crowdsourcing Platform

abstract: As one of the successful forms of using Wisdom of Crowd, crowdsourcing, has been widely used for many human intrinsic tasks, such as image labeling, natural language understanding, market predication and opinion mining. Meanwhile, with advances in pervasive technology, mobile devices, such as mobile phones and tablets, have become extremely popular. These mobile devices can work as sensors to collect multimedia data(audios, images and videos) and location information. This power makes it possible to implement the new crowdsourcing mode: spatial crowdsourcing. In spatial crowdsourcing, a requester can ask for resources related a specific location, the mobile users who would like to take the task will travel to that place and get the data. Due to the rapid growth of mobile device uses, spatial crowdsourcing is likely to become more popular than general crowdsourcing, such as Amazon Turk and Crowdflower. However, to implement such a platform, effective and efficient solutions for worker incentives, task assignment, result aggregation and data quality control must be developed. ::: ::: In this demo, we will introduce gMission, a general spatial crowdsourcing platform, which features with a collection of novel techniques, including geographic sensing, worker detection, and task recommendation. We introduce the sketch of system architecture and illustrate scenarios via several case analysis.


title: An intelligent system for user behavior detection in Internet Banking

abstract: Security and making trust is the first step toward development in both real and virtual societies. Internet-based development is inevitable. Increasing penetration of technology in the internet banking and its effectiveness in contributing to banking profitability and prosperity requires that satisfied customers turn into loyal customers. Currently, a large number of cyber attacks have been focused on online banking systems, and these attacks are considered as a significant security threat. Banks or customers might become the victim of the most complicated financial crime, namely internet fraud. This study has developed an intelligent system that enables detecting the user's abnormal behavior in online banking. Since the user's behavior is associated with uncertainty, the system has been developed based on the fuzzy theory, This enables it to identify user behaviors and categorize suspicious behaviors with various levels of intensity. The performance of the fuzzy expert system has been evaluated using an receiver operating characteristic curve, which provides the accuracy of 94%. This expert system is optimistic to be used for improving e-banking services security and quality.


title: A Parallel-Hierarchical Model for Machine Comprehension on Sparse Data

abstract: Understanding unstructured text is a major goal within natural language processing. Comprehension tests pose questions based on short text passages to evaluate such understanding. In this work, we investigate machine comprehension on the challenging {\it MCTest} benchmark. Partly because of its limited size, prior work on {\it MCTest} has focused mainly on engineering better features. We tackle the dataset with a neural approach, harnessing simple neural networks arranged in a parallel hierarchy. The parallel hierarchy enables our model to compare the passage, question, and answer from a variety of trainable perspectives, as opposed to using a manually designed, rigid feature set. Perspectives range from the word level to sentence fragments to sequences of sentences; the networks operate only on word-embedding representations of text. When trained with a methodology designed to help cope with limited training data, our Parallel-Hierarchical model sets a new state of the art for {\it MCTest}, outperforming previous feature-engineered approaches slightly and previous neural approaches by a significant margin (over 15\% absolute).


Osteosclerotic lesions of the jaws.


title: Current Status of Reliability in Extended and Beyond CMOS Devices

abstract: In the history of electronics, solid-state materials replaced the vacuum parts to reduce power consumption and to obtain better reliability at a reduced cost. The size of solid-state transistors continued to reduce since its introduction, and currently, we have transistors with dimensions on the order of $\sim$ 10 nm. This downscaling of transistor dimension accompanied with proportionate changes in supply voltage so that the overall power consumption stays below a tolerable limit. The situation dramatically changed when requirements of proper electrostatic control over the entire length of the transistor and its reliable operation stopped supply voltage scaling, increased off -state leakage, and hence caused exponential increase in power consumption. The introduction of a high- $\kappa$ gate dielectric and then a multiple/surround gate within the CMOS architecture have improved the situation, but still have not provided the complete solution for the future. For future electronic devices, we need switches with extremely low off -state leakage, small supply voltage, and high-drive current without compromising the yield and reliability currently available in modern-day nanoscale transistors. The available options are based on extensions of current CMOS transistors by using nanowires, III–V compounds, carbon nanomaterials, transition metal dichalcogenides as channel, ferroelectrics as dielectric, and ferromagnets as source/drain. On the other hand, device architectures that are completely different from conventional CMOS (so-called beyond CMOS devices) such as tunnel transistors, nanoelectromechanical devices, and impact ionization transistors are also under consideration for future electronics. The performance of many of these beyond CMOS devices is yet to be comparable to that obtained in the current-day CMOS transistors. In addition, many of these proposed devices have a number of classical and some novel reliability concerns. This review article presents reliability concerns for a set of extended and beyond CMOS devices and shows that defects in different parts of these devices require detailed study to ensure their usability in electronics industry. Therefore, in addition to the current ongoing research to obtain better performance using the proposed devices, a parallel reliability analysis is also needed. This kind of early-stage reliability analysis may reveal whether any reliability concern is intrinsically related to the device design—hence suggests us to look for alternative device concepts for future CMOS.


title: Specificity of brain cathepsin D: cleavage of model peptides containing the susceptible Phe-Phe regions of myelin basic protein.

abstract: Brain cathepsin D, purified by affinity chromatography on Sepharose pepstatin columns, was incubated with synthetic peptides corresponding to the susceptible regions of the myelin basic protein encompassing the two Phe-Phe bonds. One peptide, Leu-Gly-Arg-Phe-Phe-Gly-Gly, was cleaved by cathepsin D at the Phe-Phe bond while another, Val-His-Phe-Phe-Lys-Asn-Gly, was resistant to cleavage. To determine if this was a result of His flanking the Phe-Phe bond, or chain length on the N-terminal side, two decapeptides were synthesized differing only in the presence or absence of His adjacent to Phe. The results show that both of the decapapetides were cleaved by cathepsin D at the Phe-Phe linkages. In addition, prolonged incubation led to release of N-terminal Lys, indicating an additional cleavage at the Phe-Lys bond. In contrast to the limited cleavage by cathepsin D, pepsin split all four peptides. These results support earlier work on the limited proteolysis of basic protein at the Phe-Phe bond and suggest additional sites upon prolonged exposure. Such peptides may have utility as alternative substrates for basic protein or as models for subsequent synthesis of possible inhibitors of the enzyme.


title: Embedding Uncertain Knowledge Graphs

abstract: Embedding models for deterministic Knowledge Graphs (KG) have been extensively studied, with the purpose of capturing latent semantic relations between entities and incorporating the structured knowledge they contain into machine learning. However, there are many KGs that model uncertain knowledge, which typically model the inherent uncertainty of relations facts with a confidence score, and embedding such uncertain knowledge represents an unresolved challenge. The capturing of uncertain knowledge will benefit many knowledge-driven applications such as question answering and semantic search by providing more natural characterization of the knowledge. In this paper, we propose a novel uncertain KG embedding model UKGE, which aims to preserve both structural and uncertainty information of relation facts in the embedding space. Unlike previous models that characterize relation facts with binary classification techniques, UKGE learns embeddings according to the confidence scores of uncertain relation facts. To further enhance the precision of UKGE, we also introduce probabilistic soft logic to infer confidence scores for unseen relation facts during training. We propose and evaluate two variants of UKGE based on different confidence score modeling strategies. Experiments are conducted on three real-world uncertain KGs via three tasks, i.e. confidence prediction, relation fact ranking, and relation fact classification. UKGE shows effectiveness in capturing uncertain knowledge by achieving promising results, and it consistently outperforms baselines on these tasks.


title: A 23 nW CMOS ultra-low power temperature sensor operational from 0.2 V

abstract: A proportional-to-absolute-temperature (PTAT) current element along with a current controlled oscillator (CCO) operational down to 0.2 V supply enables an ultra-low-power (ULP) temperature sensor. A bit-weighted current mirror (BWCM) architecture resists process-induced variation in the PTAT current. The power consumption is 23nW and the maximum inaccuracy is +1.5/-1.7°C from 0°C to 100°C with a 2-point calibration.


title: Malware Detection Using Dynamic Birthmarks

abstract: In this paper, we compare the effectiveness of Hidden Markov Models (HMMs) with that of Profile Hidden Markov Models (PHMMs), where both are trained on sequences of API calls. We compare our results to static analysis using HMMs trained on sequences of opcodes, and show that dynamic analysis achieves significantly stronger results in many cases. Furthermore, in comparing our two dynamic analysis approaches, we find that using PHMMs consistently outperforms our technique based on HMMs.


title: A multi-coil Magneto-Inductive transceiver for low-cost wireless sensor networks

abstract: A short-range low-cost Magneto-Inductive (MI) communication system is presented for wireless sensor network applications. The advantages of MI communications include the capability of communication in all conditions such as in air, underwater, and through solids. The hardware implementation and software design incorporate three coils for multi-channel receiving and transmission. Field tests have been conducted that demonstrate promising performance in terms of communication range and robustness against coil orientation mismatch.


title: Mondor’s Disease of the Penis Mistaken for Penile Fracture

abstract: Superficial thrombophlebitis of the dorsal vein of the penis, known as penile Mondor’s disease, is an uncommon genital disease. We report on a healthy 44-year-old man who presented with painful penile swelling, ecchymosis, and penile deviation after masturbation, which initially imitated a penile fracture. Thrombosis of the superficial dorsal vein of the penis without rupture of corpus cavernosum was found during surgical exploration. The patient recovered without erectile dysfunction.


title: Application of higher order statistics/spectra in biomedical signals—A review

abstract: For many decades correlation and power spectrum have been primary tools for digital signal processing applications in the biomedical area. The information contained in the power spectrum is essentially that of the autocorrelation sequence; which is sufficient for complete statistical descriptions of Gaussian signals of known means. However, there are practical situations where one needs to look beyond autocorrelation of a signal to extract information regarding deviation from Gaussianity and the presence of phase relations. Higher order spectra, also known as polyspectra, are spectral representations of higher order statistics, i.e. moments and cumulants of third order and beyond. HOS (higher order statistics or higher order spectra) can detect deviations from linearity, stationarity or Gaussianity in the signal. Most of the biomedical signals are non-linear, non-stationary and non-Gaussian in nature and therefore it can be more advantageous to analyze them with HOS compared to the use of second order correlations and power spectra. In this paper we have discussed the application of HOS for different bio-signals. HOS methods of analysis are explained using a typical heart rate variability (HRV) signal and applications to other signals are reviewed.


title: A Critique And Improvement Of An Evaluation Metric For Text Segmentation

abstract: The Pk evaluation metric, initially proposed by Beeferman, Berger, and Lafferty (1997), is becoming the standard measure for assessing text segmentation algorithms. However, a theoretical analysis of the metric finds several problems: the metric penalizes false negatives more heavily than false positives, overpenalizes near misses, and is affected by variation in segment size distribution. We propose a simple modification to the Pk metric that remedies these problems. This new metric-called WindowDiff-moves a fixed-sized window across the text and penalizes the algorithm whenever the number of boundaries within the window does not match the true number of boundaries for that window of text.


Eco-Compensation for Watershed Services in the People's Republic of China


title: Domain-sensitive opinion leader mining from online review communities

abstract: In this paper, we investigate how to identify domain-sensitive opinion leaders in online review communities, and present a model to rank domain-sensitive opinion leaders. To evaluate the effectiveness of the proposed model, we conduct preliminary experiments on a real-world dataset from Amazon.com. Experimental results indicate that the proposed model is effective in identifying domain-sensitive opinion leaders.


title: A Universal Crease Pattern for Folding Orthogonal Shapes

abstract: We present a universal crease pattern--known in geometry as the tetrakis tiling and in origami as box pleating--that can fold into any object made up of unit cubes joined face-to-face (polycubes). More precisely, there is one universal finite crease pattern for each number n of unit cubes that need to be folded. This result contrasts previous universality results for origami, which require a different crease pattern for each target object, and confirms intuition in the origami community that box pleating is a powerful design technique.


title: Doubly Robust Off-policy Value Evaluation for Reinforcement Learning

abstract: We study the problem of off-policy value evaluation in reinforcement learning (RL), where one aims to estimate the value of a new policy based on data collected by a different policy. This problem is often a critical step when applying RL in real-world problems. Despite its importance, existing general methods either have uncontrolled bias or suffer high variance. In this work, we extend the doubly robust estimator for bandits to sequential decision-making problems, which gets the best of both worlds: it is guaranteed to be unbiased and can have a much lower variance than the popular importance sampling estimators. We demonstrate the estimator's accuracy in several benchmark problems, and illustrate its use as a subroutine in safe policy improvement. We also provide theoretical results on the hardness of the problem, and show that our estimator can match the lower bound in certain scenarios.


title: Natural Products as Sources of New Drugs from 1981 to 2014.

abstract: This contribution is a completely updated and expanded version of the four prior analogous reviews that were published in this journal in 1997, 2003, 2007, and 2012. In the case of all approved therapeutic agents, the time frame has been extended to cover the 34 years from January 1, 1981, to December 31, 2014, for all diseases worldwide, and from 1950 (earliest so far identified) to December 2014 for all approved antitumor drugs worldwide. As mentioned in the 2012 review, we have continued to utilize our secondary subdivision of a "natural product mimic", or "NM", to join the original primary divisions and the designation "natural product botanical", or "NB", to cover those botanical "defined mixtures" now recognized as drug entities by the U.S. FDA (and similar organizations). From the data presented in this review, the utilization of natural products and/or their novel structures, in order to discover and develop the final drug entity, is still alive and well. For example, in the area of cancer, over the time frame from around the 1940s to the end of 2014, of the 175 small molecules approved, 131, or 75%, are other than "S" (synthetic), with 85, or 49%, actually being either natural products or directly derived therefrom. In other areas, the influence of natural product structures is quite marked, with, as expected from prior information, the anti-infective area being dependent on natural products and their structures. We wish to draw the attention of readers to the rapidly evolving recognition that a significant number of natural product drugs/leads are actually produced by microbes and/or microbial interactions with the "host from whence it was isolated", and therefore it is considered that this area of natural product research should be expanded significantly.


title: Video Games in the English as a Foreign Language Classroom

abstract: The purpose of this study was to investigate potentials of and to identify factors relevant to computer games at the center of a course curriculum. In order to improve the English listening ability of foreign language learners in Taiwan, two types of lessons were developed for Americapsilas Army, a free-for-download FPS military game. The current study compared different factors that contribute to the possible success of lessons that use video games as their core material. A deeper impression of the feasibility of using in-game dialogue for language learning was. Assessment of listening comprehension was conducted in pre- and post-tests. The study shows students feel they can learn English through a course that uses computer games and gives directions for further research.


title: Automatic identification of heart failure diagnostic criteria, using text analysis of clinical notes from electronic health records

abstract: Abstract Objective Early detection of Heart Failure (HF) could mitigate the enormous individual and societal burden from this disease. Clinical detection is based, in part, on recognition of the multiple signs and symptoms comprising the Framingham HF diagnostic criteria that are typically documented, but not necessarily synthesized, by primary care physicians well before more specific diagnostic studies are done. We developed a natural language processing (NLP) procedure to identify Framingham HF signs and symptoms among primary care patients, using electronic health record (EHR) clinical notes, as a prelude to pattern analysis and clinical decision support for early detection of HF. Design We developed a hybrid NLP pipeline that performs two levels of analysis: (1) At the criteria mention level, a rule-based NLP system is constructed to annotate all affirmative and negative mentions of Framingham criteria. (2) At the encounter level, we construct a system to label encounters according to whether any Framingham criterion is asserted, denied, or unknown. Measurements Precision, recall, and F-score are used as performance metrics for criteria mention extraction and for encounter labeling. Results Our criteria mention extractions achieve a precision of 0.925, a recall of 0.896, and an F-score of 0.910. Encounter labeling achieves an F-score of 0.932. Conclusion Our system accurately identifies and labels affirmations and denials of Framingham diagnostic criteria in primary care clinical notes and may help in the attempt to improve the early detection of HF. With adaptation and tooling, our development methodology can be repeated in new problem settings.


title: Naval structural antenna systems for broadband HF communications

abstract: This paper introduces the concept of multipurpose structural antenna system for shipborne installations. By combining properly shaped and loaded wire radiators together with an existing naval superstructure, such as the funnel or a big mast, it is possible to achieve a broadband high-frequency compact radiating system which permits handling both sea-wave and near vertical incidence sky-wave communications. When provided with a plurality of feeding points, the naval structural antenna may be used in multichannel mode, with a huge efficiency improvement over the conventional multichannel broadband systems requiring a combining network. Additionally it is shown that, when fed in monochannel mode, the structural antenna system exhibits focusing features with the possibility to achieve sectorial coverages. This paper demonstrates the advantage and limitation of this new system by carefully taking into account the interantenna coupling.


Fast Generalized Distillation for Semi-Supervised Domain Adaptation.


title: Promotion of plant growth of maize by plant growth promoting bacteria in different temperatures and soils.

abstract: Plant-growth-promoting bacteria isolated fr om the plants grown in differ ent climatic r egions of Germany and Uzbekistan wer e analyzed for plant-growth-promot ing effects and nutrient uptake in maize on dif ferent soils


title: Mesenchymal stem cells and extracellular matrix scaffold promote muscle regeneration by synergistically regulating macrophage polarization toward the M2 phenotype

abstract: BackgroundSkeletal muscle plays an important role in the body’s physiology but there are still no effective treatments for volumetric muscle loss (VML) resulting from severe traumatic injury or tumor excision. Recent studies show that a tissue engineering strategy using a compound containing mesenchymal stem cells (MSCs) and decellularized extracellular matrix (ECM) scaffold generates significant regenerative effects on VML injury, but the underlying mechanisms are not fully understood.MethodsThe characteristics of human umbilical cord MSCs, including multiplication capacity and multidifferentiation ability, were determined. We constructed a compound containing MSCs and decellularized ECM scaffold which was used for tissue regeneration in a VML model.ResultsWe found that MSCs and decellularized ECM scaffold generated synergistic effects on promoting skeletal muscle tissue regeneration. Interestingly, both MSCs and decellularized ECM scaffold could promote macrophage polarization toward the M2 phenotype and suppress macrophage polarization toward the M1 phenotype, which is widely regarded as an important promoting factor in tissue regeneration. More importantly, MSCs and decellularized ECM scaffold generate synergistic promoting effects on macrophage polarization toward the M2 phenotype, not just an additive effect.ConclusionsOur findings uncover a previously unknown mechanism that MSCs and decellularized ECM scaffold promote tissue regeneration via collaboratively regulating macrophage polarization.


title: Spintronic Nanodevices for Bioinspired Computing

abstract: Bioinspired hardware holds the promise of low-energy, intelligent, and highly adaptable computing systems. Applications span from automatic classification for big data management, through unmanned vehicle control, to control for biomedical prosthesis. However, one of the major challenges of fabricating bioinspired hardware is building ultra-high-density networks out of complex processing units interlinked by tunable connections. Nanometer-scale devices exploiting spin electronics (or spintronics) can be a key technology in this context. In particular, magnetic tunnel junctions (MTJs) are well suited for this purpose because of their multiple tunable functionalities. One such functionality, non-volatile memory, can provide massive embedded memory in unconventional circuits, thus escaping the von-Neumann bottleneck arising when memory and processors are located separately. Other features of spintronic devices that could be beneficial for bioinspired computing include tunable fast nonlinear dynamics, controlled stochasticity, and the ability of single devices to change functions in different operating conditions. Large networks of interacting spintronic nanodevices can have their interactions tuned to induce complex dynamics such as synchronization, chaos, soliton diffusion, phase transitions, criticality, and convergence to multiple metastable states. A number of groups have recently proposed bioinspired architectures that include one or several types of spintronic nanodevices. In this paper, we show how spintronics can be used for bioinspired computing. We review the different approaches that have been proposed, the recent advances in this direction, and the challenges toward fully integrated spintronics complementary metal-oxide-semiconductor (CMOS) bioinspired hardware.


title: Metrorrhagia in a child with an endodermal sinus tumor of the vagina: a case report.

abstract: In a female child presenting with vaginal bleeding or a mass protruding through the vagina, it is mandatory to perform an endoscopic and laparoscopic examination and biopsy to establish the correct diagnosis and precise extent of the lesion. An endodermal sinus tumor (EST) of the vagina is a rare, malignant germ cell tumor that occurs primarily in infants. Clearly, the vagina is a critical site for treatment, and the surgeon should seek to preserve the reproductive and sexual function, if possible. We report our experience of a case of EST of the vagina in a 9-month-old female with a history of metrorrhagia. We employed minimally invasive procedures for diagnosis and then, after the administration of platinum- based chemotherapy (PEB regimen), removed a residual lesion during follow-up. These procedures resulted in tumor regression. At follow-up, after two years, the patient is alive and disease-free.


title: Area-efficient transient power-rail electrostatic discharge clamp circuit with mis-triggering immunity in a 65-nm CMOS process

abstract: A novel, area-efficient transient power-rail electrostatic discharge (ESD) clamp circuit is proposed in this work. Current-mirror capacitors are used to reduce the layout area. Logic threshold voltages of inverters are modified to ensure a fully active on-state for the clamp device in ESD conditions. The proposed circuit reduces the layout area by about 56% compared with a circuit without current-mirror capacitors. Transmission line pulse (TLP) test results based on a 65-nm CMOS process demonstrate that the proposed circuit is an efficient on-chip ESD protection scheme for this process. In addition, the proposed circuit achieves a good immunity to mis-triggering with respect to fast power-up transitions.


title: Android digital forensics: data, extraction and analysis

abstract: Smartphones are exceedingly popular, with the Android platform being no exception. Also, the surge of applications available for such devices has revolutionized our lives, many of which process a significant amount of personal information. Instant Messaging applications are an excellent example of this. In addition to processing this information, there is a high likelihood that they store traces of it in local storage. Increasingly, smartphones are involved in law enforcement investigations. They may be found as evidence at the scene of a crime, and require forensic analysis. It has translated into strong demand for Android digital forensics. A critical stage in such an investigation is data acquisition. An investigator must extract the data (in a forensically sound way) before it can be analyzed. This paper provides a survey and analysis of many acquisition methods. In addition, we conduct our own experiment that showcases an excellent acquisition method in practice, and also shows our data analysis methodology as we analyze the private storage of two popular instant messaging applications.


title: Ask Me Anything: Free-Form Visual Question Answering Based on Knowledge from External Sources

abstract: We propose a method for visual question answering which combines an internal representation of the content of an image with information extracted from a general knowledge base to answer a broad range of image-based questions. This allows more complex questions to be answered using the predominant neural network-based approach than has previously been possible. It particularly allows questions to be asked about the contents of an image, even when the image itself does not contain the whole answer. The method constructs a textual representation of the semantic content of an image, and merges it with textual information sourced from a knowledge base, to develop a deeper understanding of the scene viewed. Priming a recurrent neural network with this combined information, and the submitted question, leads to a very flexible visual question answering approach. We are specifically able to answer questions posed in natural language, that refer to information not contained in the image. We demonstrate the effectiveness of our model on two publicly available datasets, Toronto COCO-QA and MS COCO-VQA and show that it produces the best reported results in both cases.


title: HapFACS 3.0: FACS-Based Facial Expression Generator for 3D Speaking Virtual Characters

abstract: With the growing number of researchers interested in modeling the inner workings of affective social intelligence, the need for tools to easily model its associated expressions has emerged. The goal of this article is two-fold: 1) we describe HapFACS, a free software and API that we developed to provide the affective computing community with a resource that produces static and dynamic facial expressions for three-dimensional speaking characters; and 2) we discuss results of multiple experiments that we conducted in order to scientifically validate our facial expressions and head animations in terms of the widely accepted Facial Action Coding System (FACS) standard, and its Action Units (AU). The result is that users, without any 3D-modeling nor computer graphics expertise, can animate speaking virtual characters with FACS-based realistic facial expression animations, and embed these expressive characters in their own application(s). The HapFACS software and API can also be used for generating repertoires of realistic FACS-validated facial expressions, useful for testing emotion expression generation theories.


title: Supporting all learners in school-wide computational thinking

abstract: The purpose of this study was to investigate how elementary school teachers with limited computer science experience in a high-need school integrated computational thinking into their instruction. The researchers conducted a cross-case analysis across different instructional contexts (e.g., general education classrooms, library, art) that included multiple observations and interviews over four months. Major themes included: (a) a wide range of implementation models emerged depending on teaching contexts, (b) ongoing professional development and embedded coaching resulted in increasing participation in computing education, (c) teachers and administrators viewed barriers to implementing computing from a problem solving framework, and (d) struggling learners, including students with disabilities and those living in poverty, benefitted from computing education that included scaffolding, modeling, and peer collaboration. After initial hesitation, teachers taught computing with support and coaching.Administrative support included freedom to take instructional risks.Teachers integrated computing into core instruction due to time constraints.Computing benefitted students at risk for academic failure.Student collaboration included teacher-promoted and student-initiated models.


title: Information warfare: Its Application in military and civilian contexts

abstract: The AA. consider four spheres of activity in which information warfare may very soon become relatively commonplace: military, corporate/economic, community/social, and personal. The intention is to provide an analytic framework for understanding key dimensions of information warfare and some of the myriad social ramifications arising from the co-option of internetworking technologies for the conduct of IW campaigns, be they military or civil, collective or indivisualistic, systematic or aleatory in character


title: A multiple beam management scheme on 5G mobile communication systems for supporting high mobility

abstract: Due to the consistently increasing data traffic, current fourth generation (4G) mobile communication systems have been rapidly congested. To accommodate the emerging data traffic, the fifth generation (5G) mobile communication systems have been developed based on the introduction of millimeter-wave bands and adoption of beamforming and massive antenna structure. However, due to the structural features of the 5G mobile communication systems operating a lot of narrow beams, it is difficult for high-speed mobile users to provide stable service environments. Therefore, In this paper, we propose a novel handover and radio resource management concept for overcoming the limitations. Our simulation results indicate that the proposed scheme can improve performances of the users with high mobility.


title: Understanding the Performance of Low Power Raspberry Pi Cloud for Big Data

abstract: Nowadays, Internet-of-Things (IoT) devices generate data at high speed and large volume. Often the data require real-time processing to support high system responsiveness which can be supported by localised Cloud and/or Fog computing paradigms. However, there are considerably large deployments of IoT such as sensor networks in remote areas where Internet connectivity is sparse, challenging the localised Cloud and/or Fog computing paradigms. With the advent of the Raspberry Pi, a credit card-sized single board computer, there is a great opportunity to construct low-cost, low-power portable cloud to support real-time data processing next to IoT deployments. In this paper, we extend our previous work on constructing Raspberry Pi Cloud to study its feasibility for real-time big data analytics under realistic application-level workload in both native and virtualised environments. We have extensively tested the performance of a single node Raspberry Pi 2 Model B with httperf and a cluster of 12 nodes with Apache Spark and HDFS (Hadoop Distributed File System). Our results have demonstrated that our portable cloud is useful for supporting real-time big data analytics. On the other hand, our results have also unveiled that overhead for CPU-bound workload in virtualised environment is surprisingly high, at 67.2%. We have found that, for big data applications, the virtualisation overhead is fractional for small jobs but becomes more significant for large jobs, up to 28.6%.


title: Examining micro-payments for participatory sensing data collections

abstract: The rapid adoption of mobile devices that are able to capture and transmit a wide variety of sensing modalities (media and location) has enabled a new data collection paradigm - participatory sensing. Participatory sensing initiatives organize individuals to gather sensed information using mobile devices through cooperative data collection. A major factor in the success of these data collection projects is sustained, high quality participation. However, since data capture requires a time and energy commitment from individuals, incentives are often introduced to motivate participants. In this work, we investigate the use of micro-payments as an incentive model. We define a set of metrics that can be used to evaluate the effectiveness of incentives and report on findings from a pilot study using various micro-payment schemes in a university campus sustainability initiative.


title: EEG Mind controlled Smart Prosthetic Arm

abstract: The field of prosthetics has seen many accomplishments over the last few years, especially with the incorporation of the latest technological advancements into these products. However, most of them have some problems, either with being incredibly expensive, hard to install and maintain, or may require surgical procedures. This paper introduces the initial design of a mind-controlled, smart, 3D printed prosthetic arm. The arm is controlled via brain commands, obtained from an electroencephalography (EEG) headset, and equipped with a network of smart sensors and actuators that give the patient intelligent feedback about the surrounding environment and the object in contact. This network provides the arm with normal hand functionality, smart reflexes and smooth movements. Various types of sensors are used including temperature, pressure, ultrasonic proximity sensors, accelerometers, potentiometers, strain gauges and gyroscopes. Preliminary experimental results show that the proposed EEG Mind-controlled Arm is a promising alternative for current solutions that require invasive and expensive surgical procedures.


title: Optimal Spline Trajectories by Modelling Kinematic Constraints in Robot Soccer.

abstract: We explore optimal motion planning and control for a soccer playing differential drive robot. Trajectories generated using end-slope cubic Bezier splines are optimized globally through Bayesian optimization and locally through Nelder-Mead downhill simplex method. A velocity profile for the trajectory is generated incorporating kinodynamic constraints of the robots. Control point optimization resulted in minimum time trajectories while respecting robot constraints. A novel trajectory tracking algorithm for differential drive robots is also developed which accounts for transmission delay. Gauss-Kronrod numerical integration, Newton Rhapson and other numerical methods have been employed for computational efficiency, keeping in mind the highly dynamic robot soccer environment. Extensive testing on developed simulator, as well as on our robot soccer team, has shown marked improvements in stability and speed over other techniques.


title: Abstraction Refinement via Inductive Learning

abstract: This paper concerns how to automatically create abstractions for program analysis. We show that inductive learning, the goal of which is to identify general rules from a set of observed instances, provides new leverage on the problem. An advantage of an approach based on inductive learning is that it does not require the use of a theorem prover.


Video stabilization and motion detection using Matlab video processing toolbox


title: A new planar marchand balun

abstract: In this paper, a novel planar Marchand balun using a patterned ground plane is presented. In the new design, with a slot under the coupled lines cut on the ground plane, the even-mode impedance can be increased substantially. Meanwhile, we propose that two additional separated rectangular conductors are placed under the coupled lines to act as two capacitors so that the odd-mode impedance is decreased. Design theory and procedure are presented to optimize the Marchand balun. As an example, one Marchand balun on a double-sided PCB is designed, simulated, fabricated and measured. The measured return loss is found to be better than - 10 dB over the frequency band from 1.2 GHz to 3.3 GHz, or around 100% bandwidth. The measured amplitude and phase imbalance between the two balanced output ports are within 1 dB and 4 0 , respectively, over the operating frequency band.


title: ORGANISATIONS AND VANILLA SOFTWARE: WHAT DO WE KNOW ABOUT ERP SYSTEMS AND COMPETITIVE ADVANTAGE?

abstract: Enterprise Resource Planning (ERP) systems have become a de facto standard for integrating business functions. But an obvious question arises: if every business is using the same socalled “Vanilla” software (e.g. an SAP ERP system) what happens to the competitive advantage from implementing IT systems? If we discard our custom-built legacy systems in favour of enterprise systems do we also jettison our valued competitive advantage from IT? While for some organisations ERPs have become just a necessity for conducting business, others want to exploit them to outperform their competitors. In the last few years, researchers have begun to study the link between ERP systems and competitive advantage. This link will be the focus of this paper. We outline a framework summarizing prior research and suggest two researchable questions. A future article will develop the framework with two empirical case studies from within part of the European food industry.


title: Two-Handed Gestures for Human-Computer Interaction

abstract: The present thesis is concerned with the development and evaluation (in terms of accuracy and utility) of systems using hand postures and hand gestures for enhanced Human-Computer Interaction (HCI). In our case, these systems are based on vision techniques, thus only requiring cameras, and no other specific sensors or devices. When dealing with hand movements, it is necessary to distinguish two aspects of these hand movements: the \textit{static} aspect and the \textit{dynamic} aspect. The static aspect is characterized by a pose or configuration of the hand in an image and is related to the Hand Posture Recognition (HPR) problem. The dynamic aspect is defined either by the trajectory of the hand, or by a series of hand postures in a sequence of images. This second aspect is related to the Hand Gesture Recognition (HGR) task. Given the recognized lack of common evaluation databases in the HGR field, a first contribution of this thesis was the collection and public distribution of two databases, containing both one- and two-handed gestures, which part of the results reported here will be based upon. On these databases, we compare two state-of-the-art models for the task of HGR. As a second contribution, we propose a HPR technique based on a new feature extraction. This method has the advantage of being faster than conventional methods while yielding good performances. In addition, we provide comparison results of this method with other state-of-the-art technique. Finally, the most important contribution of this thesis lies in the thorough study of the state-of-the-art not only in HGR and HPR but also more generally in the field of HCI. The first chapter of the thesis provides an extended study of the state-of-the-art. The second chapter of this thesis contributes to HPR. We propose to apply for HPR a technique employed with success for face detection. This method is based on the Modified Census Transform (MCT) to extract relevant features in images. We evaluate this technique on an existing benchmark database and provide comparison results with other state-of-the-art approaches. The third chapter is related to HGR. In this chapter we describe the first recorded database, containing both one- and two-handed gestures in the 3D space. We propose to compare two models used with success in HGR, namely Hidden Markov Models (HMM) and Input-Output Hidden Markov Model (IOHMM). The fourth chapter is also focused on HGR but more precisely on two-handed gesture recognition. For that purpose, a second database has been recorded using two cameras. The goal of these gestures is to manipulate virtual objects on a screen. We propose to investigate on this second database the state-of-the-art sequence processing techniques we used in the previous chapter. We then discuss the results obtained using different features, and using images of one or two cameras. In conclusion, we propose a method for HPR based on new feature extraction. For HGR, we provide two databases and comparison results of two major sequence processing techniques. Finally, we present a complete survey on recent state-of-the-art techniques for both HPR and HGR. We also present some possible applications of these techniques, applied to two-handed gesture interaction. We hope this research will open new directions in the field of hand posture and gesture recognition.


title: A cost-value approach for prioritizing requirements

abstract: Developing software systems that meet stakeholders' needs and expectations is the ultimate goal of any software provider seeking a competitive edge. To achieve this, you must effectively and accurately manage your stakeholders' system requirements: the features, functions, and attributes they need in their software system. Once you agree on these requirements, you can use them as a focal point for the development process and produce a software system that meets the expectations of both customers and users. However, in real world software development, there are usually more requirements than you can implement given stakeholders' time and resource constraints. Thus, project managers face a dilemma: how do you select a subset of the customers' requirements and still produce a system that meets their needs? The authors developed a cost-value approach for prioritizing requirements and applied it to two commercial projects.


title: Towards an OWL-based framework for extracting information from clinical texts

abstract: In this paper, we present our ongoing work towards an OWL-based framework for extracting a variety of information (including patient history) from clinical texts. Our framework integrates a well-known natural language processing (NLP) system by converting its ontology and output logical form interpretation into the Web Ontology Language (OWL). The OWL-based Semantic Query-Enhanced Web Rule Language (SQWRL) is then used as a platform for authoring Semantic Web-aware rules for extracting information of interest from the OWL knowledge based created from parsing a clinical report. We also describe our ongoing work on using this system for extracting a timeline-based patient medical record from the history of present illness section of clinical texts.


title: Organizational Citizenship Behavior (OCB) and Rewards

abstract: Organizational Citizenship Behavior (OCB) has attracted the attention of academicians and practitioners due to its proven significance towards organizational effectiveness. The current study aims to estimate empirically the degree of relationship between OCB and Organizational Rewards given to employees. The study will answer the question that “Does the display of OCB have any impact on the rewards given to employees by the organization?” Five most widely acknowledged dimensions of OCB - Altruism, Courtesy, Sportsmanship, Conscientiousness, and Civic Virtue -have been considered for analysis purposes. OCB is measured through self-evaluation as well as peer evaluations to avoid any social desirability effect and common method variance errors. The organizational rewards taken in the study are Salary Increment, Promotion and Performance Appraisal Grade. The current study includes all of these together for analyzing this relationship with rigor. This is the first ever study of OCB which incorporates all five major dimensions of OCB (measured through self-reporting as well as through peer reports) and three well acknowledged types of rewards together in a single study. The study thus draws various practical implications for organizational managers on basis of the empirical findings from banking sector of Pakistan.


title: Technical Section: Meshless quadrangulation by global parameterization

abstract: Point cloud is a basic description of discrete shape information. Parameterization of unorganized points is important for shape analysis and shape reconstruction of natural objects. In this paper we present a new algorithm for global parameterization of an unorganized point cloud and its application to the meshing of the cloud. Our method is guided by principal directions so as to preserve the intrinsic geometric properties. After initial estimation of principal directions, we develop a kNN(k-nearest neighbor) graph-based method to get a smooth direction field. Then the point cloud is cut to be topologically equivalent to a disk. The global parameterization is computed and its gradients align well with the guided direction field. A mixed integer solver is used to guarantee a seamless parameterization across the cut lines. The resultant parameterization can be used to triangulate and quadrangulate the point cloud simultaneously in a fully automatic manner, where the shape of the data is of any genus.


Pavements of Siderastrea radians on Cape Verde reefs


title: Missing data analyses: a hybrid multiple imputation algorithm using Gray System Theory and entropy based on clustering

abstract: Researchers and practitioners who use databases usually feel that it is cumbersome in knowledge discovery or application development due to the issue of missing data. Though some approaches can work with a certain rate of incomplete data, a large portion of them demands high data quality with completeness. Therefore, a great number of strategies have been designed to process missingness particularly in the way of imputation. Single imputation methods initially succeeded in predicting the missing values for specific types of distributions. Yet, the multiple imputation algorithms have maintained prevalent because of the further promotion of validity by minimizing the bias iteratively and less requirement on prior knowledge to the distributions. ::: ::: This article carefully reviews the state of the art and proposes a hybrid missing data completion method named Multiple Imputation using Gray-system-theory and Entropy based on Clustering (MIGEC). Firstly, the non-missing data instances are separated into several clusters. Then, the imputed value is obtained after multiple calculations by utilizing the information entropy of the proximal category for each incomplete instance in terms of the similarity metric based on Gray System Theory (GST). ::: ::: Experimental results on University of California Irvine (UCI) datasets illustrate the superiority of MIGEC to other current achievements on accuracy for either numeric or categorical attributes under different missing mechanisms. Further discussion on real aerospace datasets states MIGEC is also applicable for the specific area with both more precise inference and faster convergence than other multiple imputation methods in general.


title: Timing local streams: improving timeliness in data prefetching

abstract: Data prefetching technique is widely used to bridge the growing performance gap between processor and memory. Numerous prefetching techniques have been proposed to exploit data patterns and correlations in the miss address stream. In general, the miss addresses are grouped by some common characteristics, such as program counter or memory region they belong to, into localized streams to improve prefetch accuracy and coverage. However, the existing stream localization technique lacks the timing information of misses. This drawback can lead to a large fraction of untimely prefetches, which in turn limits the effectiveness of prefetching, wastes precious bandwidth and leads to high cache pollution potentially. This paper proposes a novel mechanism named stream timing technique that can largely reduce untimely prefetches and in turn increase the overall performance. Based on the proposed stream timing technique, we extend the conventional stride prefetcher and propose a new stride prefetcher called Time-Aware Stride (TAS) prefetcher. We have carried out extensive simulation experiments to verify the design of the stream timing technique and the TAS prefetcher. The simulation results show that the proposed stream timing technique is promising in reducing untimely prefetches and the IPC improvement of TAS prefetcher outperforms the existing stride prefetcher by 11%.


title: Nanoconnectomic upper bound on the variability of synaptic plasticity

abstract: What is the memory capacity of a human brain? The storage capacity in a computer memory is measured in bits, each of which can have a value of 0 or 1. In the brain, information is stored in the form of synaptic strength, a measure of how strongly activity in one neuron influences another neuron to which it is connected. The number of different strengths can be measured in bits. The total storage capacity of the brain therefore depends on both the number of synapses and the number of distinguishable synaptic strengths. Structurally, neurons consist of a cell body that influences other neurons through a cable-like axon. The cell body bears numerous short branches called dendrites, which are covered in tiny protrusions, or “spines”. Most excitatory synapses are formed between the axon of one neuron and a dendritic spine on another. When two neurons on either side of a synapse are active simultaneously, that synapse becomes stronger, a form of memory. The dendritic spine also becomes larger to accommodate the extra molecular machinery needed to support a stronger synapse. Some axons form two or more synapses with the same dendrite, but on different dendritic spines. These synapses should be the same strength because they will have experienced the same history of neural activity. Bartol et al. used a technique called serial section electron microscopy to create a 3D reconstruction of part of the brain that allowed the sizes of the dendritic spines these synapses form on to be compared. This revealed that the synaptic areas and volumes of the spine heads were nearly identical. This remarkable similarity can be used to estimate the number of bits of information that a single synapse can store, since the size of dendritic spines and their synapses can be used as proxies for synaptic strength. Measurements in a small cube of brain tissue revealed 26 different dendritic spine sizes, each associated with a distinct synaptic strength. This number translates into a storage capacity of roughly 4.7 bits of information per synapse. This estimate is markedly higher than previous suggestions. It implies that the total memory capacity of the brain – with its many trillions of synapses – may have been underestimated by an order of magnitude. Additional measurements in the same and other brain regions are needed to confirm this possibility.


title: Novel Wideband Transition Between Coplanar Waveguide and Microstrip Line

abstract: A novel wideband vertical transition for connecting the coplanar waveguide (CPW) to the microstrip line is proposed. This transition can be very useful for millimeter-wave packaging and vertical interconnects. It is multilayered, partly tapered, and consists of only one via interconnect. Two different transitions are designed. The first transition allows connectivity of a CPW with Zc=50 Ω to a microstrip line with Zc=16 Ω with a bandwidth of 10-60 GHz. The second transition has the same characteristic impedance, Zc=50 Ω, at the two ports. In this case, the operating frequency is from 40 MHz to 60 GHz. The return losses of both transitions are generally lower than -10 dB over their indicated frequency ranges, while the maximum measured insertion losses are 1.8 and 2.4 dB for the first and second transition, respectively. To extract the S-parameters of the transitions, a new thru-line technique, based on the standard thru-reflect-line two-tier calibration is introduced. Simulation and experimental results, showing good agreement, are presented and discussed.


title: Racing line optimization @ race optimal

abstract: Race Optimal (www.raceoptimal.com) is a project aimed at creating an effective and fully automated process for racing line optimization in order to produce a database of racing line for racing circuits around the world. The ideal racing line is defined as the trajectory around a track that allows a given vehicle to traverse the circuit in the minimum time. In practice, it is an abstraction that varies with track, environmental conditions, vehicle type and condition, competitive traffic, and other factors. A certain extent of the talent possessed by the elite driver is the ability to perceive this optimal path and its variations, as well as to navigate it as quickly as possible. Until now, automated methods have struggled to match the performance of human-generated or human-guided racing lines [1]. This article describes the optimization engine and representation scheme used at Race Optimal to compute realistic and high performing racing lines for a wide variety of vehicle types.


title: Axial Flux Segmented SRM With a Higher Number of Rotor Segments for Electric Vehicles

abstract: Motors for in-wheel electric vehicle application should have high specific torque. In addition, these motors should be rugged, insensitive to vibration, and temperature variation. Therefore, segmented rotor switched reluctance motor (SSRM) could be an attractive alternative to permanent magnet-based motors, which are being used for this application. A limitation of SSRM is that it requires full pitch winding for its operation. Since, in-wheel motors have high diameter to axial length (D/L) ratio, SSRM of these dimensions would be bulky and has high copper loss due to full pitch winding. Hence, in this paper, a novel SSRM with nonoverlapping winding is proposed. This motor is an axial flux SSRM (AFSSRM) with toroidal winding arrangement. It has single-stator, dual-rotor configuration with 12 stator poles and 16 rotor segments on each rotor disc. Design procedure for AFSSRM is developed and a flowchart describing the design algorithm is presented. A finite element method-based simulation is carried out to verify the performance of the proposed AFSSRM. In order to validate the performance of the motor, a prototype is fabricated and experimental results are presented.


title: Deep learning

abstract: Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.


title: A P300 event-related potential brain–computer interface (BCI): The effects of matrix size and inter stimulus interval on performance

abstract: We describe a study designed to assess properties of a P300 brain–computer interface (BCI). The BCI presents the user with a matrix containing letters and numbers. The user attends to a character to be communicated and the rows and columns of the matrix briefly intensify. Each time the attended character is intensified it serves as a rare event in an oddball sequence and it elicits a P300 response. The BCI works by detecting which character elicited a P300 response. We manipulated the size of the character matrix (either 3 � 3o r 6� 6) and the duration of the inter stimulus interval (ISI) between intensifications (either 175 or 350 ms). Online accuracy was highest for the 3 � 3 matrix 175-ms ISI condition, while bit rate was highest for the 6 � 6 matrix 175-ms ISI condition. Average accuracy in the best condition for each subject was 88%. P300 amplitude was significantly greater for the attended stimulus and for the 6 � 6 matrix. This work demonstrates that matrix size and ISI are important variables to consider when optimizing a BCI system for individual users and that a P300-BCI can be used for effective communication. # 2006 Elsevier B.V. All rights reserved.


MapReduce: A major step backwards


title: A Critique of the Balanced Scorecard as a Performance Measurement Tool

abstract: Given the dynamic nature of business markets, organizations have witnessed a rapid change in their performance measurement systems in the last three decades with most of the performance measurement tools being more sophisticated. One of the most widespread tools is the Balanced Scorecard (BSC). Within a decade since its emergence, a majority of the Fortune 1000 companies was implementing the BSC. Despite the widespread adoption, a growing body of scholarship finds limitations in the BSC, particularly in its concept, application, and practice. The same body of scholarship contends these limitations could either undermine the effectiveness of the BSC or cause firms to abandon the BSC altogether for better performance measurement alternatives. Hence, the main aim of this paper is to provide a review or rather a critique of the BSC as a performance measurement tool and debating whether the BSC is in fact a universal solution for corporate performance measurement.


title: Factors Influencing Strategic Decision-Making Processes

abstract: Decision-making is one of the most important functions of managers in any kind of organization. Among different manager's decisions strategic decision-making is a complex process that must be understood completely before it can be practiced effectively. Those responsible for strategic decision-making face a task of extreme complexity and ambiguity. For these reasons, over the past decades, numerous studies have been conducted to the construction of models to aid managers and executives in making better decisions concerning the complex and highly uncertain business environment. In spite of much work that has been conducted in the area of strategic decision-making especially during the 1990's, we still know little about strategic decision-making process and factors affecting it. This paper builds on previous theoretical and empirical studies to determine the extent to which contextual factors impact the strategic decision-making processes. Results showed that researches on contextual factors effecting strategic decision-making process are either limited or have produced contradictory results, especially studies relating decision’s familiarity, magnitude of impact, organizational size, firm’s performance, dynamism, hostility, heterogeneity, industry, cognitive diversity, cognitive conflict, and manager’s need for achievement to strategic decision-making processes. Thus, the study of strategic decision-making process remains very important and much more empirical research is required before any definitive conclusion can be reached.


title: A content-collaborative recommender that exploits WordNet-based user profiles for neighborhood formation

abstract: Collaborative and content-based filtering are the recommendation techniques most widely adopted to date. Traditional collaborative approaches compute a similarity value between the current user and each other user by taking into account their rating style, that is the set of ratings given on the same items. Based on the ratings of the most similar users, commonly referred to as neighbors, collaborative algorithms compute recommendations for the current user. The problem with this approach is that the similarity value is only computable if users have common rated items. The main contribution of this work is a possible solution to overcome this limitation. We propose a new content-collaborative hybrid recommender which computes similarities between users relying on their content-based profiles, in which user preferences are stored, instead of comparing their rating styles. In more detail, user profiles are clustered to discover current user neighbors. Content-based user profiles play a key role in the proposed hybrid recommender. Traditional keyword-based approaches to user profiling are unable to capture the semantics of user interests. A distinctive feature of our work is the integration of linguistic knowledge in the process of learning semantic user profiles representing user interests in a more effective way, compared to classical keyword-based profiles, due to a sense-based indexing. Semantic profiles are obtained by integrating machine learning algorithms for text categorization, namely a naive Bayes approach and a relevance feedback method, with a word sense disambiguation strategy based exclusively on the lexical knowledge stored in the WordNet lexical database. Experiments carried out on a content-based extension of the EachMovie dataset show an improvement of the accuracy of sense-based profiles with respect to keyword-based ones, when coping with the task of classifying movies as interesting (or not) for the current user. An experimental session has been also performed in order to evaluate the proposed hybrid recommender system. The results highlight the improvement in the predictive accuracy of collaborative recommendations obtained by selecting like-minded users according to user profiles.


title: Mitochondria-related male infertility

abstract: Approximately 15% of human couples are affected by infertility, and about half of these cases of infertility can be attributed to men, through low sperm motility (asthenozoospermia) or/and numbers (oligospermia). Because mitochondrial genome (mtDNA) mutations are identified in patients with fertility problems, there is a possibility that mitochondrial respiration defects contribute to male infertility. To address this possibility, we used a transmitochondrial mouse model (mito-mice) carrying wild-type mtDNA and mutant mtDNA with a pathogenic 4,696-bp deletion (DeltamtDNA). Here we show that mitochondrial respiration defects caused by the accumulation of DeltamtDNA induced oligospermia and asthenozoospermia in the mito-mice. Most sperm from the infertile mito-mice had abnormalities in the middle piece and nucleus. Testes of the infertile mito-mice showed meiotic arrest at the zygotene stage as well as enhanced apoptosis. Thus, our in vivo study using mito-mice directly demonstrates that normal mitochondrial respiration is required for mammalian spermatogenesis, and its defects resulting from accumulated mutant mtDNAs cause male infertility.


title: Software Engineering: Process, Principles, and Goals

abstract: This paper attempts to define the principles and goals that affect the practice of software engineering. Its intent is to organize these aspects of software engineering into a framework that rationalizes and encourages their proper use, while placing in perspective the diversity of techniques, methods, and tools that presently comprise the subject of software engineering.


title: Stock portfolio selection using learning-to-rank algorithms with news sentiment

abstract: Abstract In this study, we apply learning-to-rank algorithms to design trading strategies using relative performance of a group of stocks based on investors’ sentiment toward these stocks. We show that learning-to-rank algorithms are effective in producing reliable rankings of the best and the worst performing stocks based on investors’ sentiment. More specifically, we use the sentiment shock and trend indicators introduced in the previous studies, and we design stock selection rules of holding long positions of the top 25% stocks and short positions of the bottom 25% stocks according to rankings produced by learning-to-rank algorithms. We then apply two learning-to-rank algorithms, ListNet and RankNet, in stock selection processes and test long-only and long-short portfolio selection strategies using 10 years of market and news sentiment data. Through backtesting of these strategies from 2006 to 2014, we demonstrate that our portfolio strategies produce risk-adjusted returns superior to the S&P 500 index return, the hedge fund industry average performance - HFRIEMN, and some sentiment-based approaches without learning-to-rank algorithm during the same period.


title: A Survey and Study of Planar Antennas for Pico-Satellites

abstract: Works on pico-satellites have gained momentum recently, especially those that consider pico-satellites as part of a much larger constellation or swarm. This feature allows pico-satellites to provide high temporal resolution of observational data and redundancy. In particular, it reduces the need for satellite-to-ground communications and, hence, helps save energy and allows the execution of distributed processing algorithms on the satellites themselves. Consequently, satellite-to-satellite or cross-link communication is critical. To realize these advantages, the cross-link antenna employed on pico-satellites must meet many criteria, namely, small size, lightweight, low-power consumption, high gain, wide bandwidth, circular polarization, and beam steerability. To date, no works have examined the suitability of existing planar antenna designs for the use on pico-satellites. To this end, this paper contributes to the literature by focusing on microstrip patch and slot antennas that have the ability to achieve high gain, beam steering, and wide bandwidth. This paper reviews 66 planar antenna designs, which includes 38-patch and 28-slot antennas. In addition, we provide an extensive qualitative comparison of these antennas in terms of their mass, size, gain, beam steerability, type of polarization, operating frequency band, and return loss. In addition, we have evaluated three antenna designs that best address the pico-satellite challenges on a common platform. We find that the asymmetric E-shaped patch antenna design is the most suitable for the use on 2U CubeSats. This is because of its small size ( $34\times 13$ mm $^{2})$ and high gain (7.3 dB). In addition, the E-shaped patch antenna yields a wide −10-dB bandwidth of 2300 MHz and a small return loss of −15.2 dB.


title: CLIEL: context-based information extraction from commercial law documents

abstract: The effectiveness of document Information Extraction (IE) is greatly affected by the structure and layout of the documents being considered. In the case of legal documents relating to commercial law, an additional challenge is the many different and varied formats, structures and layouts used. In this paper, we present work on a flexible and scalable IE environment, the CLIEL (Commercial Law Information Extraction based on Layout) environment, for application to commercial law documentation that allows layout rules to be derived and then utilised to support IE. The proposed CLIEL environment operates using NLP (Natural Language Processing) techniques, JAPE (Java Annotation Patterns Engine) rules and some GATE (General Architecture for Text Engineering) modules. The system is fully described and evaluated using a commercial law document corpus. The results demonstrate that considering the layout is beneficial for extracting data point instances from legal document collections.


Learning of Word Boundaries in Continuous Speech Using Time Delay Neural Networks


title: Learning Categories From Few Examples With Multi Model Knowledge Transfer

abstract: Learning a visual object category from few samples is a compelling and challenging problem. In several real-world applications collecting many annotated data is costly and not always possible. However, a small training set does not allow to cover the high intraclass variability typical of visual objects. In this condition, machine learning methods provide very few guarantees. This paper presents a discriminative model adaptation algorithm able to proficiently learn a target object with few examples by relying on other previously learned source categories. The proposed method autonomously chooses from where and how much to transfer information by solving a convex optimization problem which ensures to have the minimal leave-one-out error on the available training set. We analyze several properties of the described approach and perform an extensive experimental comparison with other existing transfer solutions, consistently showing the value of our algorithm.


title: Cascaded Classification Models: Combining Models for Holistic Scene Understanding

abstract: One of the original goals of computer vision was to fully understand a natural scene. This requires solving several sub-problems simultaneously, including object detection, region labeling, and geometric reasoning. The last few decades have seen great progress in tackling each of these problems in isolation. Only recently have researchers returned to the difficult task of considering them jointly. In this work, we consider learning a set of related models in such that they both solve their own problem and help each other. We develop a framework called Cascaded Classification Models (CCM), where repeated instantiations of these classifiers are coupled by their input/output variables in a cascade that improves performance at each level. Our method requires only a limited "black box" interface with the models, allowing us to use very sophisticated, state-of-the-art classifiers without having to look under the hood. We demonstrate the effectiveness of our method on a large set of natural images by combining the subtasks of scene categorization, object detection, multiclass image segmentation, and 3d reconstruction.


title: Who should I cite: learning literature search models from citation behavior

abstract: Scientists depend on literature search to find prior work that is relevant to their research ideas. We introduce a retrieval model for literature search that incorporates a wide variety of factors important to researchers, and learns the weights of each of these factors by observing citation patterns. We introduce features like topical similarity and author behavioral patterns, and combine these with features from related work like citation count and recency of publication. We present an iterative process for learning weights for these features that alternates between retrieving articles with the current retrieval model, and updating model weights by training a supervised classifier on these articles. We propose a new task for evaluating the resulting retrieval models, where the retrieval system takes only an abstract as its input and must produce as output the list of references at the end of the abstract's article. We evaluate our model on a collection of journal, conference and workshop articles from the ACL Anthology Reference Corpus. Our model achieves a mean average precision of 28.7, a 12.8 point improvement over a term similarity baseline, and a significant improvement both over models using only features from related work and over models without our iterative learning.


title: Hype and Heavy Tails: A Closer Look at Data Breaches

abstract: Recent widely publicized data breaches have exposed thepersonal information of hundreds of millions of people. Somereports point to alarming increases in both the size and fre-quency of data breaches, spurring institutions around theworld to address what appears to be a worsening situation.But, is the problem actually growing worse? In this paper,we study a popular public dataset and develop BayesianGeneralized Linear Models to investigate trends in databreaches. Analysis of the model shows that neither sizenor frequency of data breaches has increased over the pastdecade. We nd that the increases that have attracted at-tention can be explained by the heavy-tailed statistical dis-tributions underlying the dataset. Speci cally, we nd thatdata breach size is log-normally distributed and that thedaily frequency of breaches is described by a negative bi-nomial distribution. These distributions may provide cluesto the generative mechanisms that are responsible for thebreaches. Additionally, our model predicts the likelihood ofbreaches of a particular size in the future. For example, we nd that in the next year there is only a 31% chance of abreach of 10 million records or more in the US. Regardlessof any trend, data breaches are costly, and we combine themodel with two di erent cost models to project that in thenext three years breaches could cost up to $55 billion.


1 The cognitive linguistics enterprise: an


title: Speed control of brushless DC motor using intelligent controllers

abstract: The brushless dc motors are widely used in the servo motor application because of their different advantages over conventional DC motors. However high performance motor drives require fast and accurate speed response of the system, quick recovery of speed from any disturbance and sensitivity to motor parameter variation. To operate the motor with proper reference speed different controllers are widely used in industrial application. In this present work, genetic algorithm based PID controller is used to tune the PID controller parameters for speed control of BLDC motor. Ziegler-Nicholas tuning formula is used for tuning and gain predication of GA-PI and GA-PID controller. Genetic algorithm is a robust optimization technique based on natural selection. The basic goal of GA to optimize the fitness function. In the present work we use integral square error (ISE) based fitness function to optimize the gain co-efficient of PID controller by considering the different parameters such as rise time settling time and overshoot of the response. Simulation results obtained from different conventional controllers as well fuzzy-PID controller are compared with the GA-PI and GA-PID controllers.


title: Improving Microaneurysm Detection Using an Optimally Selected Subset of Candidate Extractors and Preprocessing Methods

abstract: In this paper, we present an approach to improve microaneurysm detection in digital color fundus images. Instead of following the standard process which considers preprocessing, candidate extraction and classification, we propose a novel approach that combines several preprocessing methods and candidate extractors before the classification step. We ensure high flexibility by using a modular model and a simulated annealing-based search algorithm to find the optimal combination. Our experimental results show that the proposed method outperforms the current state-of-the-art individual microaneurysm candidate extractors.