title: A survey SCADA of and critical infrastructure incidents

abstract: In this paper, we analyze several cyber-security incidents involving critical infrastructure and SCADA systems. We classify these incidents based on Source Sector, Method of Operations, Impact, and Target Sector. Using this standardized taxonomy we can easily compare and contrast current and future SCADA incidents.


title: Robotic excavation in construction automation

abstract: This article presents some results of the autonomous excavation project conducted at the Australian Centre for Field Robotics (ACFR) with a focus on construction automation. The application of robotic technology and computer control is one key to construction industry automation. Excavation automation is a multidisciplinary task, encompassing a broad area of research and development. The ultimate goal of the ACFR excavation project is to demonstrate fully autonomous execution of excavation tasks in common construction, such as loading a truck or digging a trench. A number of difficult theoretical and practical problems must be solved to achieve this objective. The problems fall into three main groups: excavation planning, sensing and estimation, and control.


title: The King-Kong Effects: Improving sensation of walking in VR with visual and tactile vibrations at each step

abstract: In this paper we present novel sensory feedbacks named ”King-Kong Effects” to enhance the sensation of walking in virtual environments. King Kong Effects are inspired by special effects in movies in which the incoming of a gigantic creature is suggested by adding visual vibrations/pulses to the camera at each of its steps. In this paper, we propose to add artificial visual or tactile vibrations (King-Kong Effects or KKE) at each footstep detected (or simulated) during the virtual walk of the user. The user can be seated, and our system proposes to use vibrotactile tiles located under his/her feet for tactile rendering, in addition to the visual display. We have designed different kinds of KKE based on vertical or lateral oscillations, physical or metaphorical patterns, and one or two peaks for heal-toe contacts simulation. We have conducted different experiments to evaluate the preferences of users navigating with or without the various KKE. Taken together, our results identify the best choices for future uses of visual and tactile KKE, and they suggest a preference for multisensory combinations. Our King-Kong effects could be used in a variety of VR applications targeting the immersion of a user walking in a 3D virtual scene.


title: Statistical segmentation and structural recognition for floor plan interpretation

abstract: A generic method for floor plan analysis and interpretation is presented in this article. The method, which is mainly inspired by the way engineers draw and interpret floor plans, applies two recognition steps in a bottom-up manner. First, basic building blocks, i.e., walls, doors, and windows are detected using a statistical patch-based segmentation approach. Second, a graph is generated, and structural pattern recognition techniques are applied to further locate the main entities, i.e., rooms of the building. The proposed approach is able to analyze any type of floor plan regardless of the notation used. We have evaluated our method on different publicly available datasets of real architectural floor plans with different notations. The overall detection and recognition accuracy is about 95 %, which is significantly better than any other state-of-the-art method. Our approach is generic enough such that it could be easily adopted to the recognition and interpretation of any other printed machine-generated structured documents.


title: A proposed method for predicting US presidential election by analyzing sentiment in social media

abstract: US Presidential election is an event anticipated by US citizens and people around the world. By utilizing the big data provided by social media, this research aims to make a prediction of the party or candidate that will win the US presidential election 2016. This paper proposes two stages in research methodology which is data collection and implementation. Data used in this research are collected from Twitter. The implementation stage consists of preprocessing, sentiment analysis, aggregation, and implementation of Electoral College system to predict the winning party or candidate. The implementation of Electoral College will be limited only by using winner take all basis for all states. The implementations are referring from previous works with some addition of methods. The proposed method still unable to use real time data due to random user location value gathered from Twitter REST API, and researchers will be working on it for future works.


title: A Qualitative Approach to Examine Technology Acceptance

abstract: The research field of technology acceptance and software acceptance is a fertile field in the discipline of MIS. Acceptance research is mainly affected by the technology acceptance model (TAM). The TAM is counted as the major guideline for acceptance research. But recently more researchers discover the deficits of former acceptance research. The main cause of the criticism is the focus on quantitative research methods. We will show this with the help of former meta-studies and a literature review. Quantitative approaches are basically appropriate for the testing of theories. The development of new theories or constructs is followed to a lesser intent. In the article we will show how a qualitative approach can be used for theory-construction. We will introduce a qualitative research design and show how this approach can be used to develop new constructs of acceptance while some existing constructs taken from TAM and related theories cannot be confirmed.


title: Temporal Event Sequence Simplification

abstract: Electronic Health Records (EHRs) have emerged as a cost-effective data source for conducting medical research. The difficulty in using EHRs for research purposes, however, is that both patient selection and record analysis must be conducted across very large, and typically very noisy datasets. Our previous work introduced EventFlow, a visualization tool that transforms an entire dataset of temporal event records into an aggregated display, allowing researchers to analyze population-level patterns and trends. As datasets become larger and more varied, however, it becomes increasingly difficult to provide a succinct, summarizing display. This paper presents a series of user-driven data simplifications that allow researchers to pare event records down to their core elements. Furthermore, we present a novel metric for measuring visual complexity, and a language for codifying disjoint strategies into an overarching simplification framework. These simplifications were used by real-world researchers to gain new and valuable insights from initially overwhelming datasets.


title: A Practical and Secure Multi-keyword Search Method over Encrypted Cloud Data

abstract: Cloud computing technologies become more and more popular every year, as many organizations tend to outsource their data utilizing robust and fast services of clouds while lowering the cost of hardware ownership. Although its benefits are welcomed, privacy is still a remaining concern that needs to be addressed. We propose an efficient privacy-preserving search method over encrypted cloud data that utilizes minhash functions. Most of the work in literature can only support a single feature search in queries which reduces the effectiveness. One of the main advantages of our proposed method is the capability of multi-keyword search in a single query. The proposed method is proved to satisfy adaptive semantic security definition. We also combine an effective ranking capability that is based on term frequency-inverse document frequency (tf-idf) values of keyword document pairs. Our analysis demonstrates that the proposed scheme is proved to be privacy-preserving, efficient and effective.


title: Wrappers for feature subset selection

abstract: Abstract In the feature subset selection problem, a learning algorithm is faced with the problem of selecting a relevant subset of features upon which to focus its attention, while ignoring the rest. To achieve the best possible performance with a particular learning algorithm on a particular training set, a feature subset selection method should consider how the algorithm and the training set interact. We explore the relation between optimal feature subset selection and relevance. Our wrapper method searches for an optimal feature subset tailored to a particular algorithm and a domain. We study the strengths and weaknesses of the wrapper approach and show a series of improved designs. We compare the wrapper approach to induction without feature subset selection and to Relief, a filter approach to feature subset selection. Significant improvement in accuracy is achieved for some datasets for the two families of induction algorithms used: decision trees and Naive-Bayes.


title: A tactile proximity sensor

abstract: This paper introduces a novel tactile sensor with the ability to detect objects in the sensor's near proximity. For both tasks, the same capacitive sensing principle is used. The tactile part of the sensor provides a tactile sensor array enabling the sensor to gather pressure profiles of the mechanical contact area. Several tactile sensors have been developed in the past. These sensors lack the capability of detecting objects in their near proximity before a mechanical contact occurs. Therefore, we developed a tactile proximity sensor, which is able to measure the current flowing out of or even into the sensor. Measuring these currents and the exciting voltage makes a calculation of the capacitance coupled to the sensor's surface and, using more sensors of this type, the change of capacitance between the sensors possible. The sensor's mechanical design, the analog/digital signal processing and the hardware efficient demodulator structure, implemented on a FPGA, will be discussed in detail.


title: Implementation of a Memory Disclosure Attack on Memory Deduplication of Virtual Machines

abstract: SUMMARY Memory deduplication improves the utilization of physical memory by sharing identical blocks of data. Although memory deduplication is most effective when many virtual machines with same operating systems run on a CPU, cross-user memory deduplication is a covert channel and causes serious memory disclosure attack. It reveals the existence of an application or file on another virtual machine. The covert channel is a difference in write access time on deduplicated memory pages that are re-created by Copy-On-Write, but it has some interferences caused by execution environments. This paper indicates that the attack includes implementation issues caused by memory alignment, self-reflection between page cache and heap, and run-time modification (swap-out, anonymous pages, ASLR, preloading mechanism, and self-modification code). However, these problems are avoidable with some techniques. In our experience on KSM (kernel samepage merging) with the KVM virtual machine, the attack could detect the security level of attacked operating systems, find vulnerable applications, and confirm the status of attacked applications.


title: Nonlinear Dimensionality Reduction by Semidefinite Programming and Kernel Matrix Factorization

abstract: We describe an algorithm for nonlinear dimensionality reduction based on semidefinite programming and kernel matrix factorization. The algorithm learns a kernel matrix for high dimensional data that lies on or near a low dimensional manifold. In earlier work, the kernel matrix was learned by maximizing the variance in feature space while preserving the distances and angles between nearest neighbors. In this paper, adapting recent ideas from semi-supervised learning on graphs, we show that the full kernel matrix can be very well approximated by a product of smaller matrices. Representing the kernel matrix in this way, we can reformulate the semidefinite program in terms of a much smaller submatrix of inner products between randomly chosen landmarks. The new framework leads to order-of-magnitude reductions in computation time and makes it possible to study much larger problems in manifold learning.


Integrating bridge structural health monitoring and condition-based maintenance management


An Image Processing Approach for Detection, Quantification, and Identification of Plant Leaf Diseases -A Review


Compensation Management : Rewarding Performance


title: TopicRNN: A Recurrent Neural Network with Long-Range Semantic Dependency

abstract: In this paper, we propose TopicRNN, a recurrent neural network (RNN)-based language model designed to directly capture the global semantic meaning relating words in a document via latent topics. Because of their sequential nature, RNNs are good at capturing the local structure of a word sequence – both semantic and syntactic – but might face difficulty remembering long-range dependencies. Intuitively, these long-range dependencies are of semantic nature. In contrast, latent topic models are able to capture the global underlying semantic structure of a document but do not account for word ordering. The proposed TopicRNN model integrates the merits of RNNs and latent topic models: it captures local (syntactic) dependencies using an RNN and global (semantic) dependencies using latent topics. Unlike previous work on contextual RNN language modeling, our model is learned end-to-end. Empirical results on word prediction show that TopicRNN outperforms existing contextual RNN baselines. In addition, TopicRNN can be used as an unsupervised feature extractor for documents. We do this for sentiment analysis on the IMDB movie review dataset and report an error rate of 6.28%. This is comparable to the state-of-the-art 5.91% resulting from a semi-supervised approach. Finally, TopicRNN also yields sensible topics, making it a useful alternative to document models such as latent Dirichlet allocation.


title: Ultra-low-noise transimpedance amplifier for high-performance MEMS resonant gyroscopes

abstract: This paper describes a generic, ultra-low-noise transimpedance amplifier (TIA) for capacitive MEMS sensors. The TIA obtains both very low input referred current noise (5.12 fA/VHz) and wide dynamic range (123 dB) by employing a digitally-controlled floating resistor, a voltage-and-temperature tolerant current generator, and low-noise operational amplifier (op-amp). The floating resistor allows the amplifier to obtain excellent output linearity and obtain a wide dynamic range. An integrated digital controller of the current generator lowers the TIA noise by reducing the amount of fluctuation in the resistance of the floating resistor by eliminating the transmission of control voltages for the floating resistor through a bonding pad. A beta-multiplier current reference circuitry with a resistor with low temperature coefficient of resistance (TCR) generates bias current with very low sensitivity to variations in supply voltage and temperature. A two-stage Miller-compensated op-amp with optimized transistor dimensions is designed. The prototype chip was fabricated using a 0.18 μm standard CMOS process with 1.8 V supply voltage. The circuitry is evaluated with a high performance vibratory MEMS gyroscope and excellent bias stability (0.04 deg/h) is measured.


Decision Support Systems in the Process of Improving Patient Safety


A Novel Approach for hiding Sensitive Items in Data mining using correlation


title: 500+ times faster than deep learning: a case study exploring faster methods for text mining stackoverflow

abstract: Deep learning methods are useful for high-dimensional data and are becoming widely used in many areas of software engineering. Deep learners utilizes extensive computational power and can take a long time to train-- making it difficult to widely validate and repeat and improve their results. Further, they are not the best solution in all domains. For example, recent results show that for finding related Stack Overflow posts, a tuned SVM performs similarly to a deep learner, but is significantly faster to train. This paper extends that recent result by clustering the dataset, then tuning very learners within each cluster. This approach is over 500 times faster than deep learning (and over 900 times faster if we use all the cores on a standard laptop computer). Significantly, this faster approach generates classifiers nearly as good (within 2\% F1 Score) as the much slower deep learning method. Hence we recommend this faster methods since it is much easier to reproduce and utilizes far fewer CPU resources. More generally, we recommend that before researchers release research results, that they compare their supposedly sophisticated methods against simpler alternatives (e.g applying simpler learners to build local models).


title: Mesh Location in Open Ventral Hernia Repair: A Systematic Review and Network Meta-analysis

abstract: There is no consensus on the ideal location for mesh placement in open ventral hernia repair (OVHR). We aim to identify the mesh location associated with the lowest rate of recurrence following OVHR using a systematic review and meta-analysis. A search was performed for studies comparing at least two of four locations for mesh placement during OVHR (onlay, inlay, sublay, and underlay). Outcomes assessed were hernia recurrence and surgical site infection (SSI). Pairwise meta-analysis was performed to compare all direct treatment of mesh locations. A multiple treatment meta-analysis was performed to compare all mesh locations in the Bayesian framework. Sensitivity analyses were planned for the following: studies with a low risk of bias, incisional hernias, by hernia size, and by mesh type (synthetic or biologic). Twenty-one studies were identified (n = 5,891). Sublay placement of mesh was associated with the lowest risk for recurrence [OR 0.218 (95 % CI 0.06–0.47)] and was the best of the four treatment modalities assessed [Prob (best) = 94.2 %]. Sublay was also associated with the lowest risk for SSI [OR 0.449 (95 % CI 0.12–1.16)] and was the best of the 4 treatment modalities assessed [Prob (best) = 77.3 %]. When only assessing studies at low risk of bias, of incisional hernias, and using synthetic mesh, the probability that sublay had the lowest rate of recurrence and SSI was high. Sublay mesh location has lower complication rates than other mesh locations. While additional randomized controlled trials are needed to validate these findings, this network meta-analysis suggests the probability of sublay being the best location for mesh placement is high.


title: A Movie Recommender System: MOVREC

abstract: Now a day’s recommendation system has changed the style of searching the things of our interest. This is information filtering approach that is used to predict the preference of that user. The most popular areas where recommender system is applied are books, news, articles, music, videos, movies etc. In this paper we have proposed a movie recommendation system named MOVREC. It is based on collaborative filtering approach that makes use of the information provided by users, analyzes them and then recommends the movies that is best suited to the user at that time. The recommended movie list is sorted according to the ratings given to these movies by previous users and it uses K-means algorithm for this purpose. MOVREC also help users to find the movies of their choices based on the movie experience of other users in efficient and effective manner without wasting much time in useless browsing. This system has been developed in PHP using Dreamweaver 6.0 and Apache Server 2.0. The presented recommender system generates recommendations using various types of knowledge and data about users, the available items, and previous transactions stored in customized databases. The user can then browse the recommendations easily and find a movie of their choice.


title: Service Oriented Architecture based connectivity of automotive ECUs

abstract: Modern automotive industry uses embedded systems and corresponding Electronic Control Units (ECU) for a wide variety of purposes from entertainment to vehicle control. The use of these embedded systems became crucial to an extent that the performance of these embedded systems can severely affect the performance of the automobile. So the industry tries to develop new and efficient systems periodically, to improve the performance of the whole system. As the number of ECUs in an automobile increased, new technologies were developed to interconnect these ECUs. As a result of much efficient ECUs, the data produced by these ECUs and the data to be transmitted between these ECUs increased to a very large scale. The industry tried to select new connectivity solutions which provide higher bandwidth. The automotive embedded system industry thus ended up in using the well accepted and highly standardized Ethernet connectivity. This paved the way to develop new technologies for automotive embedded system. Service Oriented Architecture (SOA) has proved to be an efficient and flexible software design paradigm for high level systems. The main factors behind its success were the amount of scalability and reusability provided by this platform. The usage of SOA over the automotive Ethernet can provide the benefit of SOA to the automotive embedded world. Such an approach needs a deep feasibility study to identify whether such integration is possible, and to discover architectures and protocols which support it.


title: An Overview of Mobile Ad hoc Network: Application, Challenges and Comparison of Routing Protocols

abstract: In the past few years, we have seen a rapid expansion in the field of mobile computing due to the proliferation of inexpensive, widely available wireless devices A mobile ad hoc network (MANET) consists of mobile wireless nodes in which the communication between nodes is carried out without any centralized control. MANET is a self organized and self configurable network where the mobile nodes move arbitrarily. The mobile nodes can receive and forward packets as a router. In this paper provides insight into the potential applications of ad hoc networks and discusses the technological challenges that protocol designers and network developers are faced with. In this paper also present the impact of comparison of different routing protocol in term of different parameters.


title: Spatial Decision Support Systems: A coming of age by

abstract: Decision Support Systems (DDS) have developed to exploit Information Technology (IT) to assist decision-makers in a wide variety of fields. The need to use spatial data in many of these diverse fields has led to increasing interest in the development of Spatial Decision Support Systems (SDSS) based around the Geo- graphic Information System (GIS) technology. The paper examines the relationship between SDSS and GIS and suggests that SDSS is poised for further development owing to improvement in technology and the greater availability of spatial data.


title: Colorado: United States (2012)" PRONOSTIA: An Experimental Platform for Bearings Accelerated Degradation Tests

abstract: This paper deals with the presentation of an experimental platform called PRONOSTIA, which enables testing, verifying and validating methods related to bearing health assessment, diagnostic and prognostic. The choice of bearings is justified by the fact that most of failures of rotating machines are related to these components. Therefore, bearings can be considered as critical as their failure significantly decreases availability and security of machines. The main objective of PRONOSTIA is to provide real data related to accelerated degradation of bearings performed under constant and/or variable operating conditions, which are online controlled. The operating conditions are characterized by two sensors: a rotating speed sensor and a force sensor. In PRONOSTIA platform, the bearing's health monitoring is ensured by gathering online two types of signals: temperature and vibration (horizontal and vertical accelerometers). Furthermore, the data are recorded with a specific sampling frequency which allows catching all the frequency spectrum of the bearing during its whole degradation. Finally, the monitoring data provided by the sensors can be used for further processing in order to extract relevant features and continuously assess the health condition of the bearing. During the PHM conference, a "IEEE PHM 2012 Prognostic Challenge" is organized. For this purpose, a web link to the degradation data is provided to the competitors to allow them testing and verifying their prognostic methods. The results of each method can then be evaluated regarding its capability to accurately estimate the remaining useful life of the tested bearings.


title: The Conceptualisation and Measurement of DSM-5 Internet Gaming Disorder: The Development of the IGD-20 Test

abstract: Background ::: Over the last decade, there has been growing concern about ‘gaming addiction’ and its widely documented detrimental impacts on a minority of individuals that play excessively. The latest (fifth) edition of the American Psychiatric Association's Diagnostic and Statistical Manual of Mental Disorders (DSM-5) included nine criteria for the potential diagnosis of Internet Gaming Disorder (IGD) and noted that it was a condition that warranted further empirical study. Aim: The main aim of this study was to develop a valid and reliable standardised psychometrically robust tool in addition to providing empirically supported cut-off points. ::: ::: ::: Methods ::: A sample of 1003 gamers (85.2% males; mean age 26 years) from 57 different countries were recruited via online gaming forums. Validity was assessed by confirmatory factor analysis (CFA), criterion-related validity, and concurrent validity. Latent profile analysis was also carried to distinguish disordered gamers from non-disordered gamers. Sensitivity and specificity analyses were performed to determine an empirical cut-off for the test. ::: ::: ::: Results ::: The CFA confirmed the viability of IGD-20 Test with a six-factor structure (salience, mood modification, tolerance, withdrawal, conflict and relapse) for the assessment of IGD according to the nine criteria from DSM-5. The IGD-20 Test proved to be valid and reliable. According to the latent profile analysis, 5.3% of the total participants were classed as disordered gamers. Additionally, an optimal empirical cut-off of 71 points (out of 100) seemed to be adequate according to the sensitivity and specificity analyses carried. ::: ::: ::: Conclusions ::: The present findings support the viability of the IGD-20 Test as an adequate standardised psychometrically robust tool for assessing internet gaming disorder. Consequently, the new instrument represents the first step towards unification and consensus in the field of gaming studies.


title: WeARHand: Head-worn, RGB-D camera-based, bare-hand user interface with visually enhanced depth perception

abstract: We introduce WeARHand, which allows a user to manipulate virtual 3D objects with a bare hand in a wearable augmented reality (AR) environment. Our method uses no environmentally tethered tracking devices and localizes a pair of near-range and far-range RGB-D cameras mounted on a head-worn display and a moving bare hand in 3D space by exploiting depth input data. Depth perception is enhanced through egocentric visual feedback, including a semi-transparent proxy hand. We implement a virtual hand interaction technique and feedback approaches, and evaluate their performance and usability. The proposed method can apply to many 3D interaction scenarios using hands in a wearable AR environment, such as AR information browsing, maintenance, design, and games.


title: Visualization and cellular hierarchy inference of single-cell data using SPADE

abstract: High-throughput single-cell technologies provide an unprecedented view into cellular heterogeneity, yet they pose new challenges in data analysis and interpretation. In this protocol, we describe the use of Spanning-tree Progression Analysis of Density-normalized Events (SPADE), a density-based algorithm for visualizing single-cell data and enabling cellular hierarchy inference among subpopulations of similar cells. It was initially developed for flow and mass cytometry single-cell data. We describe SPADE's implementation and application using an open-source R package that runs on Mac OS X, Linux and Windows systems. A typical SPADE analysis on a 2.27-GHz processor laptop takes ∼5 min. We demonstrate the applicability of SPADE to single-cell RNA-seq data. We compare SPADE with recently developed single-cell visualization approaches based on the t-distribution stochastic neighborhood embedding (t-SNE) algorithm. We contrast the implementation and outputs of these methods for normal and malignant hematopoietic cells analyzed by mass cytometry and provide recommendations for appropriate use. Finally, we provide an integrative strategy that combines the strengths of t-SNE and SPADE to infer cellular hierarchy from high-dimensional single-cell data.


title: Estimating the class prior and posterior from noisy positives and unlabeled data

abstract: We develop a classification algorithm for estimating posterior distributions from positive-unlabeled data, that is robust to noise in the positive labels and effective for high-dimensional data. In recent years, several algorithms have been proposed to learn from positive-unlabeled data; however, many of these contributions remain theoretical, performing poorly on real high-dimensional data that is typically contaminated with noise. We build on this previous work to develop two practical classification algorithms that explicitly model the noise in the positive labels and utilize univariate transforms built on discriminative classifiers. We prove that these univariate transforms preserve the class prior, enabling estimation in the univariate space and avoiding kernel density estimation for high-dimensional data. The theoretical development and both parametric and nonparametric algorithms proposed here constitutes an important step towards wide-spread use of robust classification algorithms for positive-unlabeled data.


title: Computer simulation of sound field formed around transducer source used in underwater acoustic communication

abstract: Using transducers is the most common way for underwater wireless communication, and the sound field formed around its source is the most important index in this technique. In this paper, two types of transducer sources used in UTDCS (Underwater Target Detection Communication System) are present, and the computer simulations about their sound field are finished. The BEM (Boundary Element Method) is the theoretic foundation of the simulation. The corresponding tests finished in the anechoic tank show that the computer simulations based on BEM are intuitionistic and accurate, so it is suitable to analyze the underwater sound field. Its analysis provides a satisfying insight of the transducers' performance. More farther conclusion is that this simulation technique has the universal applicability for the sound field analysis of the diverse underwater transducers.


title: BANNER: An Executable Survey of Advances in Biomedical Named Entity Recognition

abstract: There has been an increasing amount of research on biomedical named entity recognition, t he most basic text extraction problem, resulting in significant progress by different research teams around the world. This has created a need for a freely-available, open source system implementing the advances described in the literature. In this paper we present BANNER, an open-source, executable survey of advances in biomedical named entity recognition, intended to serve as a benchmark for the field. BANNER is implemented in Java as a machine-learning system based on conditional random fields and includes a wide survey of the best techniques recently described in the literature. It is designed to maximize domain independence by not employing brittle semantic features or rule-based processing steps, and achieves significantly better performance than existing baseline systems. It is therefore useful to developers as an extensible NER implementation, to researchers as a standard for comparing innovative techniques, and to biologists requiring the ability to find novel entities in large amounts of text. BANNER is available for download at http://banner.sourceforge.net.


title: Webbed penis: a rare case.

abstract: Webbed penis belongs to a rare and little-known defect of the external genitalia. The term denotes the penis of normal size for age hidden in the adjacent scrotal and pubic tissues. Though rare, it can be treated easily by surgery. A case of webbed penis is presented with brief review of literature.


title: An investigation of the impacts of some external contextual factors on ERP systems success assessment: a case of firms in Baltic-Nordic region

abstract: Enterprise Resource Planning (ERP) systems are among the largest information systems (IS) investments made by firms, and the use of such systems is spreading globally. Many researchers have discussed their adoption and implementation, few have investigated the impact of external contextual factors on the success of such technologies in adopting firms. This study aims to fill this gap in research by examining the effects of three external contextual factors, i.e., industry type, industry climate, and national economic climate on ERP success assessment. We obtained data from Estonia and Finland and our analysis shows that industry and national economic climates have significant relationships with ERP success.


title: Survey of semi-regular multiresolution models for interactive terrain rendering

abstract: Rendering high quality digital terrains at interactive rates requires carefully crafted algorithms and data structures able to balance the competing requirements of realism and frame rates, while taking into account the memory and speed limitations of the underlying graphics platform. In this survey, we analyze multiresolution approaches that exploit a certain semi-regularity of the data. These approaches have produced some of the most efficient systems to date. After providing a short background and motivation for the methods, we focus on illustrating models based on tiled blocks and nested regular grids, quadtrees and triangle bin-trees triangulations, as well as cluster-based approaches. We then discuss LOD error metrics and system-level data management aspects of interactive terrain visualization, including dynamic scene management, out-of-core data organization and compression, as well as numerical accuracy.


title: Psychological Well-Being in Adult Life

abstract: in magnitude of research?studies of psychological problems dwarf the literature on positive psychological functioning?and in the meaning of basic terms (e.g., typical usage equates health with the absence of illness). A person is viewed as men tally sound if he or she does not suf fer from anxiety, depression, or other forms of psychological symp tomatology. This prevailing formula tion never gets to the heart of Well ness; to do so, we must define mental health as the presence of the positive. To explicate the positive is, how ever, to grapple with basic values and ideals of the human experience. These values are no less evident in definitions of human suffering, al though consensus in identification of the negative is somehow easier to achieve. Despite these challenges, much has been written, within the field of psychology and outside it, regarding the contours of positive psychological functioning. DEFINING FEATURES OF WELL-BEING


title: Global Diffusion via Cascading Invitations: Structure, Growth, and Homophily

abstract: Many of the world's most popular websites catalyze their growth through invitations from existing members. New members can then in turn issue invitations, and so on, creating cascades of member signups that can spread on a global scale. Although these diffusive invitation processes are critical to the popularity and growth of many websites, they have rarely been studied, and their properties remain elusive. For instance, it is not known how viral these cascades structures are, how cascades grow over time, or how diffusive growth affects the resulting distribution of member characteristics present on the site. In this paper, we study the diffusion of LinkedIn, an online professional network comprising over 332 million members, a large fraction of whom joined the site as part of a signup cascade. First we analyze the structural patterns of these signup cascades, and find them to be qualitatively different from previously studied information diffusion cascades. We also examine how signup cascades grow over time, and observe that diffusion via invitations on LinkedIn occurs over much longer timescales than are typically associated with other types of online diffusion. Finally, we connect the cascade structures with rich individual-level attribute data to investigate the interplay between the two. Using novel techniques to study the role of homophily in diffusion, we find striking differences between the local, edge-wise homophily and the global, cascade-level homophily we observe in our data, suggesting that signup cascades form surprisingly coherent groups of members.


title: SUN RGB-D: A RGB-D scene understanding benchmark suite

abstract: Although RGB-D sensors have enabled major break-throughs for several vision tasks, such as 3D reconstruction, we have not attained the same level of success in high-level scene understanding. Perhaps one of the main reasons is the lack of a large-scale benchmark with 3D annotations and 3D evaluation metrics. In this paper, we introduce an RGB-D benchmark suite for the goal of advancing the state-of-the-arts in all major scene understanding tasks. Our dataset is captured by four different sensors and contains 10,335 RGB-D images, at a similar scale as PASCAL VOC. The whole dataset is densely annotated and includes 146,617 2D polygons and 64,595 3D bounding boxes with accurate object orientations, as well as a 3D room layout and scene category for each image. This dataset enables us to train data-hungry algorithms for scene-understanding tasks, evaluate them using meaningful 3D metrics, avoid overfitting to a small testing set, and study cross-sensor bias.


Cepstral mean and variance normalization in the model domain


title: Spatio-temporal Object Detection Proposals

abstract: Spatio-temporal detection of actions and events in video is a challenging problem. Besides the difficulties related to recognition, a major challenge for detection in video is the size of the search space defined by spatio-temporal tubes formed by sequences of bounding boxes along the frames. Recently methods that generate unsupervised detection proposals have proven to be very effective for object detection in still images. These methods open the possibility to use strong but computationally expensive features since only a relatively small number of detection hypotheses need to be assessed. In this paper we make two contributions towards exploiting detection proposals for spatio-temporal detection problems. First, we extend a recent 2D object proposal method, to produce spatio-temporal proposals by a randomized supervoxel merging process. We introduce spatial, temporal, and spatio-temporal pairwise supervoxel features that are used to guide the merging process. Second, we propose a new efficient supervoxel method. We experimentally evaluate our detection proposals, in combination with our new supervoxel method as well as existing ones. This evaluation shows that our supervoxels lead to more accurate proposals when compared to using existing state-of-the-art supervoxel methods.


title: Content Evocation Using Web Scraping and Semantic Illustration

abstract: Web scraping is the process of automatically collecting information from the World Wide Web. It is a field with active developments, sharing a common goal with the semantic web vision, an ambitious initiative that still requires breakthroughs in text processing, artificial intelligence and human-computer interactions. It means extraction of content from different web pages using web scrapping and semantic illustration. Web Scrapping is a process of evocation of content from HTML pages and related to web indexing. A commonly used measure for tree similarity is the tree edit distance which easily can be extended to be a measure of how well a pattern can be matched in a tree. An obstacle for this approach is its time complexity, so we consider if faster algorithms for constrained tree edit distances are usable for web scraping, and to reduce the size of the tree representing the web page. Different applications of web scraping are used by current market to achieve best web scraping output, Like Web Data Extraction, Data Collection, Screen Scraping. Many different algorithms are used for web scraping like “tree pattern matching”, “tree mapping”, “approximate tree matching”. But in general “tree edit distance” algorithm is used. But with this algorithm many issues of incorrectness of data, low efficiency and higher time complexity have analyzed. In this research I am focus to improve the performance of tree edit distance problem. And I am also trying to focus on higher bound time complexity of this algorithm.


title: A systematic review of the scales used for the measurement of cancer-related fatigue (CRF)

abstract: Background: Fatigue in cancer is very common and can be experienced at all stages of disease and in survivors. There is no accepted definition of cancer-related fatigue (CRF) and no agreement on how it should be measured. A number of scales have been developed to quantify the phenomenon of CRF. These vary in the quality of psychometric properties, ease of administration, dimensions of CRF covered and extent of use in studies of cancer patients. This review seeks to identify the available tools for measuring CRF and to make recommendations for ongoing research into CRF. Methods: A systematic review methodology was used to identify scales that have been validated to measure CRF. The inclusion criteria required the scale to have been validated for use in cancer patients and/or widely used in this population. Scales also had to meet a minimum quality score for inclusion. Results: The reviewers identified 14 scales that met the inclusion criteria. The most commonly used scales and best validated were the Functional Assessment of Cancer Therapy Fatigue (FACT F), the European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire (EORTC QLQ C30) (fatigue subscale) and the Fatigue Questionnaire (FQ). Conclusions: Unidimensional scales are the easiest to administer and have been most widely used. The authors recommend the use of the EORTC QLQ C30 fatigue subscale or the FACT F. The FQ gives a multidimensional assessment and has also been widely used. A substantial minority of the scales identified have not been used extensively or sufficiently validated in cancer patients and cannot be recommended for routine use without further


title: Twitter as a Lifeline: Human-annotated Twitter Corpora for NLP of Crisis-related Messages

abstract: Microblogging platforms such as Twitter provide active communication channels during mass convergence and emergency events such as earthquakes, typhoons. During the sudden onset of a crisis situation, affected people post useful information on Twitter that can be used for situational awareness and other humanitarian disaster response efforts, if processed timely and effectively. Processing social media information pose multiple challenges such as parsing noisy, brief and informal messages, learning information categories from the incoming stream of messages and classifying them into different classes among others. One of the basic necessities of many of these tasks is the availability of data, in particular human-annotated data. In this paper, we present human-annotated Twitter corpora collected during 19 different crises that took place between 2013 and 2015. To demonstrate the utility of the annotations, we train machine learning classifiers. Moreover, we publish first largest word2vec word embeddings trained on 52 million crisis-related tweets. To deal with tweets language issues, we present human-annotated normalized lexical resources for different lexical variations.


title: High-functioning autism spectrum disorder as a basic disorder in adult psychiatry and psychotherapy: psychopathological presentation, clinical relevance and therapeutic concepts

abstract: Autism spectrum disorder (ASD) is characterized by deficits in social cognition and competence, communication, highly circumscribed interests and a strong desire for routines. Besides, there are specific abnormalities in perception and language. Typical symptoms are already present in early childhood. Traditionally autism has been regarded as a severe form of neurodevelopmental disorder which goes along with overtly abnormal language, learning difficulties and low IQ in the majority of cases. However, over the last decades, it has become clear that there are also many patients with high-functioning variants of ASD. These are patients with normal language at a superficial level of description and normal and sometimes above average intelligence. In high-functioning variants of the disease, they may run unrecognized until late in adult life. High-functioning ASD is associated with a very high prevalence of comorbid classical psychiatric disorders such as depression, anxiety, ADHD, tics, psychotic symptoms or emotionally unstable syndromes. In many such cases, there is a causal relationship between ASD and the comorbid psychiatric conditions in that the specific ASD symptoms result in chronic conflicts, misunderstandings and failure in private and vocational relationships. These problems in turn often lead to depression, anxiety and sometimes psychosis-like stress reactions. In this constellation, ASD has to be regarded as a basic disorder with causal relevance for secondary psychiatric syndromes. In this paper, we summarize the classical presentation of high-functioning ASD in adult psychiatry and psychotherapy and suggest a nosological model to classify different ASD conditions instead. To conclude, we outline first treatment concepts in out- and in-patient settings.


title: Global refinement of random forest

abstract: Random forest is well known as one of the best learning methods. In spite of its great success, it also has certain drawbacks: the heuristic learning rule does not effectively minimize the global training loss; the model size is usually too large for many real applications. To address the issues, we propose two techniques, global refinement and global pruning, to improve a pre-trained random forest. The proposed global refinement jointly relearns the leaf nodes of all trees under a global objective function so that the complementary information between multiple trees is well exploited. In this way, the fitting power of the forest is significantly enhanced. The global pruning is developed to reduce the model size as well as the over-fitting risk. The refined model has better performance and smaller storage cost, as verified in extensive experiments.


title: Segmenting cardiac MRI tagging lines using gabor filter banks

abstract: This paper describes a new method for the automated segmentation and extraction of cardiac MRI tagging lines. Our method is based on the novel use of a 2D Gabor filter bank. By convolving the tagged input image with our Gabor filters, the tagging lines are automatically enhanced and segmented out. We design the Gabor filter bank based on the input image's spatial and frequency characteristics. The final result is a combination of each filter's response in the Gabor filter bank. We demonstrate that compared to bandpass filter methods such as HARP, this method results in robust and accurate segmentation of the tagging lines.


title: Tutkimusraportti 95 Feature Representation and Discrimination Based on Gaussian Mixture Model Probability Densities – Practices and Algorithms

abstract: This study promotes the use of statistical methods in specific classification tasks since statistical methods have certain advantages which advocate their use in pattern recognition. One central problem in statistical methods is estimation of class conditional probability density functions based on examples in a training set. In this study maximum likelihood estimation methods for Gaussian mixture models are reviewed and discussed from a practical point of view. In addition, good practices for utilizing probability densities in feature classification and selection are discussed for Bayesian and, more importantly, for non-Bayesian tasks. As a result, the use of confidence information in the classification is proposed and a method for confidence estimation is presented. The propositions are tested experimentally.


title: LPC and LPCC method of feature extraction in Speech Recognition System

abstract: Automatic speech recognition (ASR) has been under the scrutiny of researchers for many years. Speech Recognition System is the ability to listen what we speak, interpreter and perform actions according to spoken information. After so many detailed study and optimization of ASR and various techniques of features extraction, accuracy of the system is still a big challenge. The selection of feature extraction techniques is completely based on the area of study. In this paper, a detailed theory about features extraction techniques like LPC and LPCC is examined. The goal of this paper is to study the comparative analysis of features extraction techniques like LPC and LPCC.


title: Participatory Simulations: Building Collaborative Understanding through Immersive Dynamic Modeling. Unpublished masters thesis

abstract: This article explores a new way to help people understand complex, dynamic systems. Participatory simulations plunge learners into life-sized, computer-supported simulations, creating new paths to scientific understanding. By wearing small, communicating computers called Thinking Tags, students are transformed into players in a large-scale microworld. Like classic microworlds, participatory simulations create a scenario, mediated by a set of underlying rules, that enables inquiry and experimentation. In addition, these new activities allow students to "dive into" a learning environment and directly engage with the complex system at hand. This article describes and analyzes a set of participatory simulations that were conducted with a group of high school biology students. The students' experiences are tracked from their initial encounter with an immersive simulation through their exploration of the system and final description of its underlying rules. The article explores the educational potential of part...


title: Dictionary Learning for Sparse Coding: Algorithms and Convergence Analysis

abstract: In recent years, sparse coding has been widely used in many applications ranging from image processing to pattern recognition. Most existing sparse coding based applications require solving a class of challenging non-smooth and non-convex optimization problems. Despite the fact that many numerical methods have been developed for solving these problems, it remains an open problem to find a numerical method which is not only empirically fast, but also has mathematically guaranteed strong convergence. In this paper, we propose an alternating iteration scheme for solving such problems. A rigorous convergence analysis shows that the proposed method satisfies the global convergence property: the whole sequence of iterates is convergent and converges to a critical point. Besides the theoretical soundness, the practical benefit of the proposed method is validated in applications including image restoration and recognition. Experiments show that the proposed method achieves similar results with less computation when compared to widely used methods such as K-SVD.


Employing Two Question Answering Systems in TREC 2005


title: Heparin-induced thrombocytopenia: further evidence for a unique immune response

abstract: Cardiopulmonary bypass surgery (CPB) is associated with a high incidence of IgG Abs against platelet factor 4/heparin (PF4/H) complexes by day 6 after surgery. These Abs are associated with an immune-mediated adverse drug reaction, heparin-induced thrombocytopenia. Although the early onset of the anti-PF4/H IgG response is compatible with a secondary immune response, the rapid decline of Ab titers thereafter is not. To shed light on the origin of these Abs, in the present study, we prospectively compared the kinetics of these Abs with that of Abs against 2 recall Ags and to that of autoantibodies in 166 CPB patients over 4 months. Surgery induced strong inflammation, as shown by an increase in mean C-reactive protein levels. Consistent with previous studies, anti-PF4/H IgG optical density transiently increased between baseline and day 10 ( P < .001; not associated with C-reactive protein levels), followed by a decrease over the next months. In contrast, concentrations of antidiphtheria toxin IgG and antitetanus toxin IgG increased constantly over the 4 months after surgery by 25%-30%. IgG autoantibodies did not change. Therefore, the transient kinetics of the anti-PF4/H IgG response resembled neither that of recall Abs nor that of IgG autoantibodies, but rather showed a unique profile.


title: Load Signature Study—Part I: Basic Concept, Structure, and Methodology

abstract: Load signature is the unique consumption pattern intrinsic to each individual electrical appliance/piece of equipment. This paper focus on building a universal platform to better understand and explore the nature of electricity consumption patterns using load signatures and advanced technology, such as feature extraction and intelligent computing. Through this knowledge, we can explore and develop innovative applications to achieve better utilization of resources and develop more intelligent ways of operation. This paper depicts the basic concept, features of load signatures, structure and methodology of applying mathematical programming techniques, pattern recognition tools, and committee decision mechanism to perform load disaggregation. New indices are also introduced to aid our understanding of the nature of load signatures and different disaggregation algorithms.


title: Design of Bus-Side RCD Snubber Circuit for Three-Phase High-Power Inverters Conception d ’ un circuit d ’ amortissement RCD côté bus pour onduleurs triphasés haute puissance

abstract: To protect the transistors operating in high-power converters, the simple and low-cost RCD snubber circuits are widely used. This paper presents operation and design principles of RCD snubbers that can be effectively used in many industrial applications. The typical RCD snubbers can be classified as: 1) the switch-side snubber (if the snubbers are connected at the collector–emitter terminals of the transistor switch) and 2) the bus-side snubber (if the snubbers are connected at the dc bus). These two classes of snubbers are compared in terms of energy losses and operation under variable duty cycle theoretically. The latter snubber topology is chosen in this work as a more advantageous approach. The paper further proposes the design methodology for the bus-side snubbers. The proposed snubber methodology and its functionality are verified using the detailed simulation as well as the experimental prototype of a three-phase converter feeding 60-hp induction motor.


title: Young Adult Outcome of Hyperactive Children: Adaptive Functioning in Major Life Activities.

abstract: ABSTRACT Objective The authors report the adaptive functioning of hyperactive and control children in southeastern Wisconsin (Milwaukee) followed to young adulthood. Method Interviews with participants concerning major life activities were collected between 1992 and 1996 and used along with employer ratings and high school records at the young adult follow-up (mean = 20 years, range 19-25) for this large sample of hyperactive (H; n = 149) and community control (CC; n = 72) children initially seen in 1978-1980 and studied for at least 13 years. Age, duration of follow-up, and IQ were statistically controlled as needed. Results The H group had significantly lower educational performance and attainment, with 32% failing to complete high school. H group members had been fired from more jobs and manifested greater employer-rated attention-deficit/hyperactivity disorder and oppositional defiant disorder symptoms and lower job performance than the CC group. Socially, the H group had fewer close friends, more trouble keeping friends, and more social problems as rated by parents. Far more H than CC group members had become parents (38% versus 4%) and had been treated for sexually transmitted disease (16% versus 4%). Severity of lifetime conduct disorder was predictive of several of the most salient outcomes (failure to graduate, earlier sexual intercourse, early parenthood) whereas attention-deficit/hyperactivity disorder and oppositional defiant disorder at work were predictive of job performance and risk of being fired. Conclusions These findings corroborate prior research and go further in identifying sexual activity and early parenthood as additional problematic domains of adaptive functioning at adulthood.


title: Temporal Relational Ranking for Stock Prediction

abstract: Stock prediction aims to predict the future trends of a stock in order to help investors to make good investment decisions. Traditional solutions for stock prediction are based on time-series models. With the recent success of deep neural networks in modeling sequential data, deep learning has become a promising choice for stock prediction. ::: However, most existing deep learning solutions are not optimized towards the target of investment, i.e., selecting the best stock with the highest expected revenue. Specifically, they typically formulate stock prediction as a classification (to predict stock trend) or a regression problem (to predict stock price). More importantly, they largely treat the stocks as independent of each other. The valuable signal in the rich relations between stocks (or companies), such as two stocks are in the same sector and two companies have a supplier-customer relation, is not considered. ::: In this work, we contribute a new deep learning solution, named Relational Stock Ranking (RSR), for stock prediction. Our RSR method advances existing solutions in two major aspects: 1) tailoring the deep learning models for stock ranking, and 2) capturing the stock relations in a time-sensitive manner. The key novelty of our work is the proposal of a new component in neural network modeling, named Temporal Graph Convolution, which jointly models the temporal evolution and relation network of stocks. To validate our method, we perform back-testing on the historical data of two stock markets, NYSE and NASDAQ. Extensive experiments demonstrate the superiority of our RSR method. It outperforms state-of-the-art stock prediction solutions achieving an average return ratio of 98% and 71% on NYSE and NASDAQ, respectively.


title: No Multiplication? No Floating Point? No Problem! Training Networks for Efficient Inference

abstract: For successful deployment of deep neural networks on highly--resource-constrained devices (hearing aids, earbuds, wearables), we must simplify the types of operations and the memory/power resources used during inference. Completely avoiding inference-time floating-point operations is one of the simplest ways to design networks for these highly-constrained environments. By discretizing both our in-network non-linearities and our network weights, we can move to simple, compact networks without floating point operations, without multiplications, and avoid all non-linear function computations. Our approach allows us to explore the spectrum of possible networks, ranging from fully continuous versions down to networks with bi-level weights and activations. Our results show that discretization can be done without loss of performance and that we can train a network that will successfully operate without floating-point, without multiplication, and with less RAM on both regression tasks (auto encoding) and multi-class classification tasks (ImageNet). The memory needed to deploy our discretized networks is less than one third of the equivalent architecture that does use floating-point operations.


title: SWITCHED CAPACITOR LOCATION IDENTIFICATION USING TLS-ESPRIT APPROACH

abstract: This paper presents a new method in identifying the switched capacitor location from the measured transient voltage waveform. The measured transient voltage waveform due to capacitor switching is composed of a summation of damped sinusoidal components. Total Least Square-Estimation of Signal Parameters Via Rotational Invariance Technique (TLS-ESPRIT) is used to estimate the transient signal parameters of different modes contained in the signals. These parameters are functions of the switched capacitor location. Feed Forward Artificial Neural Network (FF-ANN) is employed to identify the location of the switched capacitor based on the frequencies and amplitudes of various modes, contained in the transient voltage signal, extracted using TLS-ESPRIT. The simulation results prove the competence of the proposed methodology.


title: Evaluating Text GANs as Language Models

abstract: Generative Adversarial Networks (GANs) are a promising approach for text generation that, unlike traditional language models (LM), does not suffer from the problem of "exposure bias". However, A major hurdle for understanding the potential of GANs for text generation is the lack of a clear evaluation metric. In this work, we propose to approximate the distribution of text generated by a GAN, which permits evaluating them with traditional probability-based LM metrics. We apply our approximation procedure on several GAN-based models and show that they currently perform substantially worse than state-of-the-art LMs. Our evaluation procedure promotes better understanding of the relation between GANs and LMs, and can accelerate progress in GAN-based text generation.


title: Predicting Armed Conflict, 2010–2050

abstract: The article predicts changes in global and regional incidences of armed conflict for the 2010–2050 period. The predictions are based on a dynamic multinomial logit model estimation on a 1970–2009 cross-sectional data set of changes between no armed conflict, minor conflict, and major conflict. Core exogenous predictors are population size, infant mortality rates, demographic composition, education levels, oil dependence, ethnic cleavages, and neighborhood characteristics. Predictions are obtained through simulating the behavior of the conflict variable implied by the estimates from this model. We use projections for the 2011–2050 period for the predictors from the UN World Population Prospects and the International Institute for Applied Systems Analysis. We treat conflicts, recent conflict history, and neighboring conflicts as endogenous variables. Out-of-sample validation of predictions for 2007–2009 (based on estimates for the 1970–2000 period) indicates that the model predicts well, with an area under the receiver operator curve of 0.937. Using a p > .30 threshold for positive prediction, the true positive rate 7–9 years into the future is 0.79 and the False Positive Rate 0.085. We predict a continued decline in the proportion of the world's countries that have internal armed conflict, from about 15% in 2009 to 7% in 2050. The decline is particularly strong in the Western Asia and North Africa region and less clear in Africa south of Sahara. The remaining conflict countries will increasingly be concentrated in East, Central, and Southern Africa and in East and South Asia.


title: Out of order floating point coprocessor for RISC V ISA

abstract: Floating point operations are important and essential part of many scientific and engineering applications. Floating point coprocessor (FPU) performs operations like addition, subtraction, division, square root, multiplication, fused multiply and accumulate and compare. Floating point operations are part of ARM, MIPS, and RISC-V etc. instruction sets. The FPU can be a part of hardware or be implemented in software. This paper details an architectural exploration for floating point coprocessor enabled with RISC-V [1] floating point instructions. The Floating unit that has been designed with RISC-V floating point instructions is fully compatible with IEEE 754-2008 standard as well. The floating point coprocessor is capable of handling both single and double precision floating point data operands in out-of-order for execution and in-order commit. The front end of the floating point processor accepts three data operands, rounding mode and associated Opcode fields for decoding. Each floating point operation is tagged with an instruction token for in-order completion and commit. The output of the floating point unit is tagged with either single or double precision results along with floating point exceptions, if any, based on the RISC-V instruction set. The coprocessor for floating point integrates with integer pipeline. The proposed architecture for floating point coprocessor with out-of-order execution, in-order commit and completion/retire has been synthesized, tested and verified on Xilinx Virtex 6 xc6vlx550t-2ff1759 FPGA. A system frequency of 240MHz for single precision floating point and 180 MHz double precision floating point operations has been observed on FPGA.


Denial in Other Forms


title: Learning to Draw Dynamic Agent Goals with Generative Adversarial Networks

abstract: We address the problem of designing artificial agents capable of reproducing human behavior in a competitive game involving dynamic control. Given data consisting of multiple realizations of inputs generated by pairs of interacting players, we model each agent's actions as governed by a time-varying latent goal state coupled to a control model. These goals, in turn, are described as stochastic processes evolving according to player-specific value functions depending on the current state of the game. We model these value functions using generative adversarial networks (GANs) and show that our GAN-based approach succeeds in producing sample gameplay that captures the rich dynamics of human agents. The latent goal dynamics inferred and generated by our model has applications to fields like neuroscience and animal behavior, where the underlying value functions themselves are of theoretical interest.


title: Increasing trend of scientists to switch between topics

abstract: Despite persistent efforts in understanding the creativity of scientists over different career stages, little is known about the underlying dynamics of research topic switching that drives innovation. Here, we analyze the publication records of individual scientists, aiming to quantify their topic switching dynamics and its influence. We find that the co-citing network of papers of a scientist exhibits a clear community structure where each major community represents a research topic. Our analysis suggests that scientists have a narrow distribution of number of topics. However, researchers nowadays switch more frequently between topics than those in the early days. We also find that high switching probability in early career is associated with low overall productivity, yet with high overall productivity in latter career. Interestingly, the average citation per paper, however, is in all career stages negatively correlated with the switching probability. We propose a model that can explain the main observed features. How does a scientist’s tendency to explore a variety of topics affect their career? Here, the authors analyze scientific publication data to understand how often scientists switch topics, how topic switching has changed over time, and how it relates to research productivity.


title: The future of the internet of things

abstract: The IoT can become ubiquitous worldwide---if the pursuit of systemic trustworthiness can overcome the potential risks.


title: Optimal decisions on group buying option with a posted retail price and heterogeneous demand

abstract: Assuming the demand is heterogeneous, we make optimal decisions on group buying facing a retail option.Customers are classified by their sensitivity on participating group buying.Only in the individualistic dominant market, there probably is a group buying option improving the total revenue.We also extend two types customer classification to multiple types in this paper.Sometimes, discrimination may increase revenue. Group buying is now a popular business mechanism whereby customers are encouraged to bargain together. Today's group buying usually adopts a fixed group price rather than using a dynamic pricing mechanism and many retailers sell products using both a group buying and a posted price. Therefore, there are usually different shopping channels as the market can be divided into two segments, a group buying segment and an individual segment, according to consumers' preference. However, sometimes the group buying option can decrease total revenue by influencing the individual market segment. Consequently, the seller has to decide: (1) whether to offer a group buying option and (2) if offering such an option, which combination is the best? In this paper, we classify customers into collectivist customers and individualistic customers depending on their different valuations of the time and energy spent in group buying communication and study the optimal pricing strategy according to actual market information. Although collectivist customers prefer to participate in group buying, our results show that this is true only in certain conditions in an individualistic dominant market where the potential demand of the individualistic customers is greater than that of the collectivist customers or the price coefficient of the individualistic customers is less than that of the collectivist customers. In these circumstances, the seller should provide a posted retail price together with a group buying option which can assist in customer discrimination, but in other conditions, the seller should only provide a posted retail price. Additionally, we have extended the two customer classifications to multiple classifications in this paper.


title: A 94 GHz 3D Image Radar Engine With 4TX/4RX Beamforming Scan Technique in 65 nm CMOS Technology

abstract: This paper presents a fully-integrated 3D image radar engine utilizing beamforming for electrical scanning and precise ranging technique for distance measurement. Four transmitters and four receivers form a sensor frontend with phase shifters and power combiners adjusting the beam direction. A built-in 31.3 GHz clock source and a frequency tripler provide both RF carrier and counting clocks for the distance measurement. Flip-chip technique with low-temperature co-fired ceramic (LTCC) antenna design creates a miniature module as small as 6.5 × 4.4 × 0.8 cm ::: 3 ::: . Designed and fabricated in 65 nm CMOS technology, the transceiver array chip dissipates 960 mW from a 1.2-V supply and occupies chip area of 3.6 × 2.1 mm 2. This prototype achieves ±28° scanning range, 2-m maximum distance, and 1 mm depth resolution.


title: New City Logistics Paradigm: From the “Last Mile” to the “Last 50 Miles” Sustainable Distribution

abstract: The distribution of goods in urban areas, together with private traffic flows, are among the main sources of energy consumption, air pollution and noise. As a consequence, in the 2000s, several EU cities started to implement logistical solutions for the sustainable city. In this context, this study addresses the implementation of a new eco-logistic system, which serves multiple adjacent cities by using electric vehicles to deliver goods of any type within their urban areas. This paper describes the results of a project developed in the province of Vicenza (northern Italy) and covering the main cities (Bassano del Grappa, Thiene, Schio and Valdagno) in the foothills (the so-called Pedemontana Veneta zone). The eco-logistic system aims to cover the last 50 miles of distribution (typical area of an Italian province) with a centralised platform that performs green deliveries with electric vehicles from/to the downtown areas. A preliminary and extensive “on field” analysis by means of door-to-door questionnaires was conducted to identify the performance required by the eco-logistic system. Therefore, the design of such a distribution system is performed to assess and define the conditions that make this solution profitable from both the economic and the environmental perspective.


title: An Output-Capacitorless Low-Dropout Regulator With Direct Voltage-Spike Detection

abstract: An output-capacitorless low-dropout regulator (LDO) with a direct voltage-spike detection circuit is presented in this paper. The proposed voltage-spike detection is based on capacitive coupling. The detection circuit makes use of the rapid transient voltage at the LDO output to increase the bias current momentarily. Hence, the transient response of the LDO is significantly enhanced due to the improvement of the slew rate at the gate of the power transistor. The proposed voltage-spike detection circuit is applied to an output-capacitorless LDO implemented in a standard 0.35-?m CMOS technology (where VTHN ? 0.5 V and VTHP ? -0.65 V). Experimental results show that the LDO consumes 19 ?A only. It regulates the output at 0.8 V from a 1-V supply, with dropout voltage of 200 mV at the maximum output current of 66.7 mA. The voltage spike and the recovery time of the LDO with the proposed voltage-spike detection circuit are reduced to about 70 mV and 3 ?s, respectively, whereas they are more than 420 mV and 30 ?s for the LDO without the proposed detection circuit.


Size Recommendation System for Fashion E-commerce


Leveraging Context Information for Natural Question Generation


title: FPGA-Based Test-Bench for Resonant Inverter Load Characterization

abstract: Resonant converters often require accurate load characterization in order to ensure appropriate and safe control. Besides, for systems with a highly variable load, as the induction heating systems, a real-time load estimation is mandatory. This paper presents the development of an FPGA-based test-bench aimed to extract the electrical equivalent of the induction heating loads. The proposed test-bench comprises a resonant power converter, sigma-delta ADCs, and an embedded system implemented in an FPGA. The characterization algorithm is based on the discrete-time Fourier series computed directly from the ΔΣ ADC bit-streams, and the FPGA implementation has been partitioned into hardware and software platforms to optimize the performance and resources utilization. Analytical and simulation results are verified through experimental measurements with the proposed test-bench. As a result, the proposed platform can be used as a load identification tool either for stand-alone or PC-hosted operation.


title: automan: a simple, Python-based, automation framework for numerical computing

abstract: We present an easy-to-use, Python-based framework that allows a researcher to automate their computational simulations. In particular the framework facilitates assembling several long-running computations and producing various plots from the data produced by these computations. The framework makes it possible to reproduce every figure made for a publication with a single command. It also allows one to distribute the computations across a network of computers. The framework has been used to write research papers in numerical computing. This paper discusses the design of the framework, and the benefits of using it. The ideas presented are general and should help researchers organize their computations for better reproducibility.


Mechanisms of immunosuppression by FK506 : preservation of T cell transmembrane signal transduction


title: SDN-SPS: Semi-Physical Simulation for Software-Defined Networks

abstract: Nowadays, software-defined networking (SDN) is one of the key technologies promoting the development of the next generation network. In addition, simulating and building the SDN real network is one of the most important approaches to do SDN research. However, there are many aspects that limit the test platform-based experiment and the traditional software-only simulation, such as the various demands of network, complex applications, and flexible network architecture. Combining test platform with the software-only simulation, this paper proposes a new hardware-in-the-loop emulation: to emulate a SDN. We use the system-in-the-loop technology of OPNET to build the SDN hardware-in-the-loop emulation platform. The emulation test platform includes the sensor network part, all of which will use the software simulation, together with the transmission network and the cloud data center both of which will use the real network. We run the simulation to generate a few applications in the cloud data center, and then collect the data through the transmission network and the sensor network, which makes these applications share the sensing data. This paper proves that the simulation test platform of hardware-in-the-loop can achieve energy efficiency under the demand of QoS.


title: Microcontroller based Noise Detection and Controlling Electronic Appliances

abstract: Noise pollution ranked second among the environmental stressors for public health impact. In silence zones such as hospitals and school area adults and children are in significant number. In many silence zones sound level is not maintained as per the specified rules which causes bad impact on people’s health. The main objective of this paper is to propose a prototype that will detect the silence zone and automatically turn off the DJ system and also warn the respective authority if sound level is above the specified level in areas other than silence zones. It will help in providing an automated solution to reduce the sound pollution using a microcontroller and RF module, GSM. RF module is used to detect the silence zones and GSM used for sending the message to concerned authority when sound is beyond permissible level. Basically, the system will detect the silence zone with the help of RF transmitter and receiver pair and turn off the DJ system. If sound goes above the limit then with the help of GSM message is sent. This prototype was successfully implemented and tested with different conditions of sound and can be used to help the society to overcome the sound pollution to some extent.


title: A Trilateral Weighted Sparse Coding Scheme for Real-World Image Denoising

abstract: Most of existing image denoising methods assume the corrupted noise to be additive white Gaussian noise (AWGN). However, the realistic noise in real-world noisy images is much more complex than AWGN, and is hard to be modelled by simple analytical distributions. As a result, many state-of-the-art denoising methods in literature become much less effective when applied to real-world noisy images captured by CCD or CMOS cameras. In this paper, we develop a trilateral weighted sparse coding (TWSC) scheme for robust real-world image denoising. Specifically, we introduce three weight matrices into the data and regularisation terms of the sparse coding framework to characterise the statistics of realistic noise and image priors. TWSC can be reformulated as a linear equality-constrained problem and can be solved by the alternating direction method of multipliers. The existence and uniqueness of the solution and convergence of the proposed algorithm are analysed. Extensive experiments demonstrate that the proposed TWSC scheme outperforms state-of-the-art denoising methods on removing realistic noise.


title: Social connectedness : concept and measurement

abstract: Social connectedness, i.e. the experience of belonging and relatedness between people, is a central concept in understanding and evaluating communication media, in particular awareness systems. A generic measure based on this construct can support the design of such systems. The current paper describes the construction of two questionnaires for the measurement of this concept. These questionnaires were subsequently applied in survey studies in order to establish the structure of the concept and to identify the items that are suited for the measurement of its dimensions. One questionnaire was subjected to an initial validation. We conclude with some preliminary suggestions regarding (design) approaches to foster social connectedness.


title: On Effective Causal Learning

abstract: We have developed a framework for identifying causal relationships between events which are effective in the sense that they can be put to practical use, without regard to what the “true” causes really are. A rapid causal learning process is devised for temporally correlated events that can be observed proximally which is sufficient for the learning of many causalities involving basic physical and social phenomena. The system relies on a diachronic aspect of causes which is a characterization of consistent temporally correlated events and a synchronic aspect of causes which is a characterization of the contextual factors that enable the diachronic causal relations. The causal learning method is applied to some problem solving situations that allows some basic knowledge to be learned rapidly and that results in drastic reductions of the search space and amount of computation involved. This method is necessary to jump start the chain of causal learning processes that allow more complex and intricate causal relationships to be learned based on earlier knowledge.


Mathematics Course-Taking and Gains in Mathematics Achievement. Statistics in Brief.


title: A survey of RDF data management systems

abstract: RDF is increasingly being used to encode data for the semantic web and data exchange. There have been a large number of works that address RDF data management following different approaches. In this paper we provide an overview of these works. This review considers centralized solutions (what are referred to as warehousing approaches), distributed solutions, and the techniques that have been developed for querying linked data. In each category, further classifications are provided that would assist readers in understanding the identifying characteristics of different approaches.


title: Combined optical-infrared single-ion frequency standard.

abstract: A combined infrared–optical frequency-standard scheme has been demonstrated with a single ion of ytterbium. The optical S21/2–D25/2 reference transition at 411 nm and the infrared F27/2–D25/23.43?µm transition were driven sequentially. The structure of the infrared F27/2–D25/2 reference transition at 3.43 µm has also been studied. Infrared transition linewidths of 300 kHz were observed. The output of a Nd:YAG laser and an extended-cavity diode laser were difference-frequency mixed in a crystal of AgGaS2 to produce narrow-linewidth light at 3.43 µm. Both lasers were frequency stabilized to high-finesse reference cavities.


title: PGP2X: Principal Geometric Primitives Parameters Extraction

abstract: In reverse engineering, it is important to extract the 3D geometric primitives that compose an object. It is also important to find the values of the parameters describing each primitive. This paper presents an approach for the estimation of the parameters of geometric primitives once their type is known using 3D information. The primitives of interest are planes, spheres, cylinders, cones, tori and partial instances of the latter four types. The proposed approach extends methods found in the literature for planes, spheres, cylinders and cones and proposes a new method for dealing with tori. The results of the proposed method are compared to approaches found in the literature as well as with ground truth values. The proposed method can be applied to the estimation of parameters of geometric primitives of synthetic CAD models as well as for models of real objects acquired with 3D scanners.


title: MittOS: Supporting Millisecond Tail Tolerance with Fast Rejecting SLO-Aware OS Interface

abstract: MittOS provides operating system support to cut millisecond-level tail latencies for data-parallel applications. In MittOS, we advocate a new principle that operating system should quickly reject IOs that cannot be promptly served. To achieve this, MittOS exposes a fast rejecting SLO-aware interface wherein applications can provide their SLOs (e.g., IO deadlines). If MittOS predicts that the IO SLOs cannot be met, MittOS will promptly return EBUSY signal, allowing the application to failover (retry) to another less-busy node without waiting. We build MittOS within the storage stack (disk, SSD, and OS cache managements), but the principle is extensible to CPU and runtime memory managements as well. MittOS' no-wait approach helps reduce IO completion time up to 35% compared to wait-then-speculate approaches.


title: Nonnegative Matrix Factorization with the Itakura-Saito Divergence: With Application to Music Analysis

abstract: This letter presents theoretical, algorithmic, and experimental results about nonnegative matrix factorization (NMF) with the Itakura-Saito (IS) divergence. We describe how IS-NMF is underlaid by a well-defined statistical model of superimposed gaussian components and is equivalent to maximum likelihood estimation of variance parameters. This setting can accommodate regularization constraints on the factors through Bayesian priors. In particular, inverse-gamma and gamma Markov chain priors are considered in this work. Estimation can be carried out using a space-alternating generalized expectation-maximization (SAGE) algorithm; this leads to a novel type of NMF algorithm, whose convergence to a stationary point of the IS cost function is guaranteed. ::: ::: We also discuss the links between the IS divergence and other cost functions used in NMF, in particular, the Euclidean distance and the generalized Kullback-Leibler (KL) divergence. As such, we describe how IS-NMF can also be performed using a gradient multiplicative algorithm (a standard algorithm structure in NMF) whose convergence is observed in practice, though not proven. ::: ::: Finally, we report a furnished experimental comparative study of Euclidean-NMF, KL-NMF, and IS-NMF algorithms applied to the power spectrogram of a short piano sequence recorded in real conditions, with various initializations and model orders. Then we show how IS-NMF can successfully be employed for denoising and upmix (mono to stereo conversion) of an original piece of early jazz music. These experiments indicate that IS-NMF correctly captures the semantics of audio and is better suited to the representation of music signals than NMF with the usual Euclidean and KL costs.


title: Exploring miniaturized EEG electrodes for brain-computer interfaces. An EEG you do not see?

abstract: Electroencephalography (EEG) allows the study of the brain–behavior relationship in humans. Most of what we have learned with EEG was through observing the brain–behavior relationship under well-controlled laboratory conditions. However, by reducing “normal” behavior to a minimum the ecological validity of the results can be limited. Recent developments toward mobile EEG solutions allow to study the brain–behavior relationship outside the laboratory in more natural situations. Besides mobility and robustness with respect to motion, mobile EEG systems should also interfere as little as possible with the participant's behavior. For example, natural interaction with other people could be hindered when it is obvious that a participant wears an EEG cap. This study evaluates the signal quality obtained with an unobtrusive solution for EEG monitoring through the integration of miniaturized EEG ton-electrodes into both a discreet baseball cap and an individualized ear piece. We show that such mini electrodes located at scalp and ear locations can reliably record event related potentials in a P300 brain–computer–interface application.


Informed Representations for Linear-Time Discourse Parsing


title: Approved for External Publication Counting Positives Accurately Despite Inaccurate Classification

abstract: Most supervised machine learning research assumes the training set is a random sample from the target population, thus the class distribution is invariant. In real world situations, however, the class distribution changes, and is known to erode the effectiveness of classifiers and calibrated probability estimators. This paper focuses on the problem of accurately estimating the number of positives in the test set—quantification—as opposed to classifying individual cases accuratel y. It compares three methods: classify & count, an adjusted variant, and a mixture model. An empirical evaluation on a text classification benchmark reveals that the simple method is consistently biased, and that the mixture model is surprisingly effective even when positives are very scarce in the training set—a common case in information retrieval.


title: Model-Based Collaborative Filtering Analysis of Student Response Data: Machine-Learning Item Response Theory

abstract: We apply collaborative filtering (CF) to dichotomously scored student response data (right, wrong, or no interaction), finding optimal parameters for each student and item based on cross-validated prediction accuracy. The approach is naturally suited to comparing different models, both unidimensional and multidimensional in ability, including a widely used subset of Item Response Theory (IRT) models which obtain as specific instances of the CF: the one-parameter logistic (Rasch) model, Birnbaum’s 2PL model, and Reckase’s multidimensional generalization M2PL. We find that IRT models perform well relative to generalized alternatives, and thus this method offers a fast and stable alternate approach to IRT parameter estimation. Using both real and simulated data we examine cases where oneor two-dimensional IRT models prevail and are not improved by increasing the number of features. Model selection is based on prediction accuracy of the CF, though it is shown to be consistent with factor analysis. In multidimensional cases the item parameterizations can be used in conjunction with cluster analysis to identify groups of items which measure different ability dimensions.


title: Single-Ended Speech Quality Measurement Using Machine Learning Methods

abstract: We describe a novel single-ended algorithm constructed from models of speech signals, including clean and degraded speech, and speech corrupted by multiplicative noise and temporal discontinuities. Machine learning methods are used to design the models, including Gaussian mixture models, support vector machines, and random forest classifiers. Estimates of the subjective mean opinion score (MOS) generated by the models are combined using hard or soft decisions generated by a classifier which has learned to match the input signal with the models. Test results show the algorithm outperforming ITU-T P.563, the current "state-of-art" standard single-ended algorithm. Employed in a distributed double-ended measurement configuration, the proposed algorithm is found to be more effective than P.563 in assessing the quality of noise reduction systems and can provide a functionality not available with P.862 PESQ, the current double-ended standard algorithm


title: An autonomous excavator with vision-based track-slippage control

abstract: This paper describes a vision-based control system for a tracked mobile robot (an excavator). The system includes several controllers that collaborate to move the mobile vehicle from a starting position to a goal position. First, the path planner designs an optimum path using a predefined elevation map of the work space. Second, a fuzzy logic path-tracking controller estimates the rotational and translational velocities for the vehicle to move along the predesigned path. Third, a cross coupling controller corrects the possible orientation error that may occur when moving along the path. A motor controller then converts the track velocities to the corresponding rotational wheel velocities. Fourth, a vision-based motion tracking system is implemented to find the three-dimensional (3-D) motion of the vehicle as it moves in the work space. Finally, a specially-designed slippage controller detects slippage by comparing the motion through reading of flowmeters and the vision system. If slippage has occurred, the remaining path is corrected within the path tracking controller to stop at the goal position. Experiments are conducted to test and verify the presented control system. An analysis of the results shows that improvement is achieved in both path-tracking accuracy and slippage control problems.


On the use of X-vectors for Robust Speaker Recognition.


title: Predicting football match results with logistic regression

abstract: Many efforts has been made in order to predict football matches result and selecting significant variables in football. Prediction is very useful in helping managers and clubs make the right decision to win leagues and tournaments. In this paper a logistic regression model is built to predict matches results of Barclays' Premier League season 2015/2016 for home win or away win and to determine what are the significant variable to win matches. Our work is different from others as we use only significant variables gathered from researches in the same field rather than making our own guess on what are the significant variables. We also used data gathered from video game FIFA, as Shin and Gasparyan [8] showed us that including data from the video game could improve prediction quality. The model was built using variations of training data from 2010/2011 season until 2015/2016. Logistic regression is a classification method which can be used to predict sports results and it can gives additional knowledge through regression coefficients. The variables used are “Home Offense”, “Home Defense”, “Away Offense”, and “Away Defense”. We conducted experiments by altering seasons of training data used. Prediction accuracy of built model is 69.5%. We found our work improved significantly compared to the one Snyder [9] did. We concluded that the significant variables are “Home Defense”, and “Away Defense”.


title: State, market and civil organizations: new theories, new practices and their implications for rural development.

abstract: Preface - Acknowledgements - Notes on the Contributors - State, Market, and Civil Organizations: New Theories, New Practices, and Their Implication for Rural Development A.Janvry, E.Sadoulet & E.Thorbecke - Markets and State: Against Minimalism P.Streeten - Political Constraints on the Development State: Alternative Theoretical Explanations E.Sadoulet - The Rocky Road to Reform: Trade, Industrial, Financial, and Agricultural Strategies L.Taylor - Political Economy of Structural Adjustment: A General Equilibrium-Interest Group Perspective T.Roe - Impact of State and Civil Institutions on the Operation of Rural Market and Non-Market Configurations E.Thorbecke - Grassroot Organizations and NGOs in Rural Development: Opportunities with Diminishing States and Expanding Markets N.Uphoff - Between State, Markets and Households: A Neo-Institutional Analysis of Local Organizations and Institutions J.B.Nugent - Analytics of the Institution of Informal Cooperation in Rural Development P.Bardhan - Civil Institutions and Evolution K.Basu - The Rural Community, Mutual Assistance, and Structural Adjustment M.Fafchamps - Role of Collateral in Credit Contracts C.Bell - Land Reform as Commenced Business: The Evidence Against Stopping M.Lipton - Institutions and Economic Linkages at the Village Level in West Java, Indonesia I.Adelman & K.Ralston - Political Economy of Economic Decline & Reform in Africa: The Role of the State, Markets & Civil Institutions D.Sahn & A.Sarris - Market, State, and Civil Organizations in Latin America Beyond the Debt Crisis: The Context for Rural Development A.de Janvry & E.Sadoulet - State-Market-Civil Institutions: The Case of Eastern Europe and the Soviet Republics G.Rausser & S.Johnson - Index


title: Generating a concept hierarchy for sentiment analysis

abstract: In this paper, we propose an unsupervised machine learning method to automatically construct a product hierarchical concept model based on the online reviews of this product. Our method starts by representing each candidate noun using a feature context vector, which is simply a vector of all its co-occurring neighbors excluding itself. We then applied bisection clustering to hierarchically cluster the context vectors to obtain a cluster hierarchy. Lastly, we proposed and evaluated two methods to label each intermediate clustering node with the most representative member context feature vector. Experiments conducted on 3 sets of on-line reviews (in both Chinese and English) benchmarked qualitatively and quantitatively against a well known existing approach demonstrated the effectiveness and robustness of our approach.


title: The State-of-the-Art in Twitter Sentiment Analysis: A Review and Benchmark Evaluation

abstract: Twitter has emerged as a major social media platform and generated great interest from sentiment analysis researchers. Despite this attention, state-of-the-art Twitter sentiment analysis approaches perform relatively poorly with reported classification accuracies often below 70%, adversely impacting applications of the derived sentiment information. In this research, we investigate the unique challenges presented by Twitter sentiment analysis and review the literature to determine how the devised approaches have addressed these challenges. To assess the state-of-the-art in Twitter sentiment analysis, we conduct a benchmark evaluation of 28 top academic and commercial systems in tweet sentiment classification across five distinctive data sets. We perform an error analysis to uncover the causes of commonly occurring classification errors. To further the evaluation, we apply select systems in an event detection case study. Finally, we summarize the key trends and takeaways from the review and benchmark evaluation and provide suggestions to guide the design of the next generation of approaches.