title: Efficient and self-tuning incremental query expansion for top-k query processing

abstract: We present a novel approach for efficient and self-tuning query expansion that is embedded into a top-k query processor with candidate pruning. Traditional query expansion methods select expansion terms whose thematic similarity to the original query terms is above some specified threshold, thus generating a disjunctive query with much higher dimensionality. This poses three major problems: 1) the need for hand-tuning the expansion threshold, 2) the potential topic dilution with overly aggressive expansion, and 3) the drastically increased execution cost of a high-dimensional query. The method developed in this paper addresses all three problems by dynamically and incrementally merging the inverted lists for the potential expansion terms with the lists for the original query terms. A priority queue is used for maintaining result candidates, the pruning of candidates is based on Fagin's family of top-k algorithms, and optionally probabilistic estimators of candidate scores can be used for additional pruning. Experiments on the TREC collections for the 2004 Robust and Terabyte tracks demonstrate the increased efficiency, effectiveness, and scalability of our approach.


title: Differentially Private Model Publishing for Deep Learning

abstract: Deep learning techniques based on neural networks have shown significant success in a wide range of AI tasks. Large-scale training datasets are one of the critical factors for their success. However, when the training datasets are crowdsourced from individuals and contain sensitive information, the model parameters may encode private information and bear the risks of privacy leakage. The recent growing trend of the sharing and publishing of pre-trained models further aggravates such privacy risks. To tackle this problem, we propose a differentially private approach for training neural networks. Our approach includes several new techniques for optimizing both privacy loss and model accuracy. We employ a generalization of differential privacy called concentrated differential privacy(CDP), with both a formal and refined privacy loss analysis on two different data batching methods. We implement a dynamic privacy budget allocator over the course of training to improve model accuracy. Extensive experiments demonstrate that our approach effectively improves privacy loss accounting, training efficiency and model quality under a given privacy budget.


title: Human brain activation during sexual stimulation of the penis

abstract: Penile sensory information is essential for reproduction, but almost nothing is known about how sexually salient inputs from the penis are processed in the brain. We used positron emission tomography to measure regional cerebral blood flow (rCBF) during various stages of male sexual performance. Compared to a passive resting condition (without penile erection), sexual stimulation of the penis increased rCBF in an area of the right hemisphere encompassing the posterior insula and adjacent posterior part of the secondary somatosensory cortex (SII) and decreased rCBF in the right amygdala. No activation was observed in either the thalamus, genital part of primary somatosensory cortex (SI), or hypothalamus. Based on these results we put forward the concept that during sexual performance the salience of the stimulus, represented by activation of the insula and SII, is of greater significance than the exact location of the stimulus, encoded in SI. The absence of activation in the hypothalamus indicates that this region is more important for the onset of sexual arousal than for the resulting sexual performance. Deactivation of the amygdala during sexual stimulation of the penis corresponds with a decrease of vigilance during sexual performance. J. Comp. Neurol. 493:33–38, 2005. © 2005 Wiley-Liss, Inc. Indexing terms: positron emission tomography; human; penile stimulation; insula; secondary somatosensory cortex; amygdala


Exploring Relationships: A Systematic Review on Intimate Partner Violence and Attachment


title: Neural Networks for Text Correction and Completion in Keyboard Decoding

abstract: Despite the ubiquity of mobile and wearable text messaging applications, the problem of keyboard text decoding is not tackled sufficiently in the light of the enormous success of the deep learning Recurrent Neural Network (RNN) and Convolutional Neural Networks (CNN) for natural language understanding. In particular, considering that the keyboard decoders should operate on devices with memory and processor resource constraints, makes it challenging to deploy industrial scale deep neural network (DNN) models. This paper proposes a sequence-to-sequence neural attention network system for automatic text correction and completion. Given an erroneous sequence, our model encodes character level hidden representations and then decodes the revised sequence thus enabling auto-correction and completion. We achieve this by a combination of character level CNN and gated recurrent unit (GRU) encoder along with and a word level gated recurrent unit (GRU) attention decoder. Unlike traditional language models that learn from billions of words, our corpus size is only 12 million words; an order of magnitude smaller. The memory footprint of our learnt model for inference and prediction is also an order of magnitude smaller than the conventional language model based text decoders. We report baseline performance for neural keyboard decoders in such limited domain. Our models achieve a word level accuracy of $90\%$ and a character error rate CER of $2.4\%$ over the Twitter typo dataset. We present a novel dataset of noisy to corrected mappings by inducing the noise distribution from the Twitter data over the OpenSubtitles 2009 dataset; on which our model predicts with a word level accuracy of $98\%$ and sequence accuracy of $68.9\%$. In our user study, our model achieved an average CER of $2.6\%$ with the state-of-the-art non-neural touch-screen keyboard decoder at CER of $1.6\%$.


Effect of Compensation on Motivation , Organizational Commitment and Employee Performance ( Studies at Local Revenue Management in Kendari City )


title: UWB/Omni-Directional Microstrip Monopole Antenna for Microwave Imaging Applications

abstract: In this paper, a new design of multi-resonance monopole antenna for use in microwave imaging systems is presented. The antenna conflguration consists of an ordinary square radiating patch, a feed-line, and a modifled ground plane with pairs of L-shaped slits and parasitic structures which provides a wide usable fractional bandwidth of more than 130% (2.95{14.27GHz). In the presented design, by cutting a pair of L-shaped slits and also by embedding a pair of inverted L-shaped parasitic structures in the ground plane additional resonances at 9.5GHz and 13.7GHz can be achieved. By using these structures, the usable upper frequency of the antenna is extended from 10.3GHz to 14.27GHz. The proposed antenna has a symmetrical structure with ordinary square radiating patch, therefore displays good omni-directional radiation patterns even at higher frequencies. The antenna has su-cient and acceptable gain level and also its radiation e-ciency is greater than 86% across the entire radiating band. The designed antenna has a small dimension.


title: Multilingual Question Answering over Linked Data QALD-3: Lab Overview

abstract: The third edition of the open challenge on Question Answering over Linked Data QALD-3 has been conducted as a half-day lab at CLEF 2013. Differently from previous editions of the challenge, has put a strong emphasis on multilinguality, offering two tasks: one on multilingual question answering and one on ontology lexicalization. While no submissions were received for the latter, the former attracted six teams who submitted their systems' results on the provided datasets. This paper provides an overview of QALD-3, discussing the approaches proposed by the participating systems as well as the obtained results.


title: Stop-words in keyphrase extraction problem

abstract: Keyword extraction problem is one of the most significant tasks in information retrieval. High-quality keyword extraction sufficiently influences the progress in the following subtasks of information retrieval: classification and clustering, data mining, knowledge extraction and representation, etc. The research environment has specified a layout for keyphrase extraction. However, some of the possible decisions remain uninvolved in the paradigm. In the paper the authors observe the scope of interdisciplinary methods applicable to automatic stop list feeding. The chosen method belongs to the class of experiential models. The research procedure based on this method allows to improve the quality of keyphrase extraction on the stage of candidate keyphrase building. Several ways to automatic feeding of the stop lists are proposed in the paper as well. One of them is based on provisions of lexical statistics and the results of its application to the discussed task point out the non-gaussian nature of text corpora. The second way based on usage of the Inspec train collection to the feeding of stop lists improves the quality considerably.


title: Chameleon: Versatile and practical near-DRAM acceleration architecture for large memory systems

abstract: The performance of computer systems is often limited by the bandwidth of their memory channels, but further increasing the bandwidth is challenging under the stringent pin and power constraints of packages. To further increase performance under these constraints, various near-DRAM acceleration (NDA) architectures, which tightly integrate accelerators with DRAM devices using 3D/2.5D-stacking technology, have been proposed. However, they have not prevailed yet because they often rely on expensive HBM/HMC-like DRAM devices which also suffer from limited capacity, whereas the scalability of memory capacity is critical for some computing segments such as servers. In this paper, we first demonstrate that data buffers in a load-reduced DIMM (LRDIMM), which is originally developed to support large memory systems for servers, are supreme places to integrate near-DRAM accelerators. Second, we propose Chameleon, an NDA architecture that can be realized without relying on 3D/2.5D-stacking technology and seamlessly integrated with large memory systems for servers. Third, we explore three microarchitectures that abate constraints imposed by taking LRDIMM architecture for NDA. Our experiment demonstrates that a Chameleon-based system can offer 2.13 χ higher geo-mean performance while consuming 34% lower geo-mean data transfer energy than a system that integrates the same accelerator logic within the processor.


A Fast Fractal-Curvelet Image Coder


title: Pretreatment for low pressure membranes in water treatment: a review.

abstract: The application of low pressure membranes (LPMs) to drinking water treatment and wastewater reuse has undergone accelerated development in the past decade. Integration of pretreatment with LPM filtration has been widely employed at full scale to reduce membrane fouling and/or increase the removal of certain aquatic contaminants. In principle, pretreatment of source water can impact membrane filtration in three ways: altering contaminant size distributions, changing mutual affinities of contaminants or their affinities to membrane surfaces, and suppressing undesirable microbial growth or removing biodegradable contaminants. The literature shows that, compared to the well-demonstrated enhancement of contaminant removal, impact of pretreatment to membrane fouling is often small or even negative, which is further complicated by variations in source water quality and membrane properties. Coagulation has been the most successful pretreatment for fouling reduction. Novel technologies are in immediate need for fo...


title: Multi-level Attention-Based Neural Networks for Distant Supervised Relation Extraction

abstract: 25th Irish Conference on Artificial Intelligence and Cognitive Science, Dublin, Ireland, 7-8 December 2017


title: Efficient Deformable Shape Correspondence via Kernel Matching

abstract: We present a method to match three dimensional shapes under non-isometric deformations, topology changes and partiality. We formulate the problem as matching between a set of pair-wise and point-wise descriptors, imposing a continuity prior on the mapping, and propose a projected descent optimization procedure inspired by difference of convex functions (DC) programming. Surprisingly, in spite of the highly non-convex nature of the resulting quadratic assignment problem, our method converges to a semantically meaningful and continuous mapping in most of our experiments, and scales well. We provide preliminary theoretical analysis and several interpretations of the method.


title: A Novel Image Steganographic Approach for Hiding Text in Color Images using HSI Color Model

abstract: 2 Abstract: Image Steganography is the process of embedding text in images such that its existence cannot be detected by Human Visual System (HVS) and is known only to sender and receiver. This paper presents a novel approach for image steganography using Hue-Saturation-Intensity (HSI) color space based on Least Significant Bit (LSB). The proposed method transforms the image from RGB color space to Hue-Saturation-Intensity (HSI) color space and then embeds secret data inside the Intensity Plane (I-Plane) and transforms it back to RGB color model after embedding. The said technique is evaluated by both subjective and Objective Analysis. Experimentally it is found that the proposed method have larger Peak Signal-to Noise Ratio (PSNR) values, good imperceptibility and multiple security levels which shows its superiority as compared to several existing methods.


title: CTrigger: exposing atomicity violation bugs from their hiding places

abstract: Multicore hardware is making concurrent programs pervasive. Unfortunately, concurrent programs are prone to bugs. Among different types of concurrency bugs, atomicity violation bugs are common and important. Existing techniques to detect atomicity violation bugs suffer from one limitation: requiring bugs to manifest during monitored runs, which is an open problem in concurrent program testing. This paper makes two contributions. First, it studies the interleaving characteristics of the common practice in concurrent program testing (i.e., running a program over and over) to understand why atomicity violation bugs are hard to expose. Second, it proposes CTrigger to effectively and efficiently expose atomicity violation bugs in large programs. CTrigger focuses on a special type of interleavings (i.e., unserializable interleavings) that are inherently correlated to atomicity violation bugs, and uses trace analysis to systematically identify (likely) feasible unserializable interleavings with low occurrence-probability. CTrigger then uses minimum execution perturbation to exercise low-probability interleavings and expose difficult-to-catch atomicity violation. We evaluate CTrigger with real-world atomicity violation bugs from four sever/desktop applications (Apache, MySQL, Mozilla, and PBZIP2) and three SPLASH2 applications on 8-core machines. CTrigger efficiently exposes the tested bugs within 1--235 seconds, two to four orders of magnitude faster than stress testing. Without CTrigger, some of these bugs do not manifest even after 7 full days of stress testing. In addition, without deterministic replay support, once a bug is exposed, CTrigger can help programmers reliably reproduce it for diagnosis. Our tested bugs are reproduced by CTrigger mostly within 5 seconds, 300 to over 60000 times faster than stress testing.


title: Network Structure and Knowledge Transfer: The Effects of Cohesion and Range

abstract: This research considers how different features of informal networks affect knowledge transfer. As a complement to previous research that has emphasized the dyadic tie strength component of informal networks, we focus on how network structure influences the knowledge transfer process. We propose that social cohesion around a relationship affects the willingness and motivation of individuals to invest time, energy, and effort in sharing knowledge with others. We further argue that the network range, ties to different knowledge pools, increases a person's ability to convey complex ideas to heterogeneous audiences. We also examine explanations for knowledge transfer based on absorptive capacity, which emphasizes the role of common knowledge, and relational embeddedness, which stresses the importance of tie strength. We investigate the network effect on knowledge transfer using data from a contract R&D firm. The results indicate that both social cohesion and network range ease knowledge transfer, over and abov...


title: Information security incident management process

abstract: The modern requirements and the best practices in the field of Information Security (IS) Incident Management Process (ISIMP) are analyzed. "IS event" and "IS incident" terms, being used for ISIMP, have been defined. An approach to ISIMP development has been created. According to this approach ISIMP processes are described. As an example the «Vulnerabilities, IS events and incidents detection and notification» joint process is examined in detail.


title: Database architecture optimized for the new bottleneck: Memory access

abstract: In the past decade, advances in speed of commodity CPUs have far out-paced advances in memory latency. Main-memory access is therefore increasingly a performance bottleneck for many computer applications, including database systems. In this article, we use a simple scan test to show the severe impact of this bottleneck. The insights gained are translated into guidelines for database architecture; in terms of both data structures and algorithms. We discuss how vertically fragmented data structures optimize cache performance on sequential data access. We then focus on equi-join, typically a random-access operation, and introduce radix algorithms for partitioned hash-join. The performance of these algorithms is quantied using a detailed analytical model that incorporates memory access cost. Experiments that validate this model were performed on the Monet database system. We obtained exact statistics on events like TLB misses, L1 and L2 cache misses, by using hardware performance counters found in modern CPUs. Using our cost model, we show how the carefully tuned memory access pattern of our radix algorithms make them perform well, which is conrmed by experimental results.


title: The Digital Twin: Demonstrating the Potential of Real Time Data Acquisition in Production Systems

abstract: Abstract The acquisition of data and the development of different options in production system and factory planning requires up to 2/3rds of the total needed time resources. The digitization of production systems offers the possibility of automated data acquisition. Nevertheless, approaches concerning fully automated data acquisition systems are not widely spread among SME (small and medium sized enterprises). On the one hand, this is caused by the heterogeneous databases, on the other hand by insufficient data processing systems. Furthermore, the advantages of The Digital Twin are not sufficiently known due to the lack of competence in SME concerning matters of Industry 4.0. In order to transfer knowledge about the benefits of digitalization, the development of demonstrating platforms is crucial. This paper introduces a learning factory based concept to demonstrate the potentials and advantages of real time data acquisition and subsequent simulation based data processing. Therefore, an existing learning factory will be upgraded regarding both, multi-modal data acquisition technologies as well as a locally independent optimization environment. Thereby the requirements of SME concerning flexible, easy to use, scalable and service oriented digitization applications are met. The approach is part of a concept for the realization of a Cyber Physical Production System (CPPS) in SME that ensures the development of an image of the production with the aid of a multi-modal data acquisition.


title: Oral syphilis: report of three cases and characterization of the inflammatory cells.

abstract: Syphilis is a sexually transmitted infectious disease caused by Treponema pallidum. This study reports 3 cases of syphilis and highlights the importance of identifying oral lesions for its final diagnosis. Case 1: a 48-year-old male patient presented with a bleeding ulcer in the lower lip. Overall clinical examination revealed patchy alopecia and skin target lesions. Case 2: a 61-year-old male patient presented with white spots on the lateral tongue and nodules on the dorsum of the tongue. Overall clinical examination showed erythematous target lesions on the abdomen, forearm, palms of the hand, and soles of the feet. Case 3: a 17-year-old male patient presented with an ulcerated lesion on the tongue and lymph node involvement. The following serologic tests were requested: Venereal Disease Research Laboratory, fluorescent treponemal antibody-absorption, anti-HIV-1 and anti-HIV-2, and anti-hepatitis C virus. An incisional biopsy revealed epithelial hyperplasia associated with intense and diffuse mononuclear inflammatory cell infiltration consisting mainly of plasma cells, in a perivascular and perineural distribution. The final diagnosis in the 3 patients was syphilis. Treatment consisted of 1 weekly dose of penicillin (2.4 million units, intramuscular) for 2 or 3 weeks. Immunohistochemical reactions for XIIIa, CD3, CD20, CD68, CD163, S100, CD1a, CD11c, CD83, CD138, and CD208 were performed. Clinicians should be familiarized with oral syphilis lesions in order to be able to diagnose this emerging infectious disease of variable clinical presentation.


title: Unbalanced Three-Phase Optimal Power Flow for Smart Grids

abstract: Advanced distribution management system (DMS), an evolution of supervisory control and data acquisition obtained by extending its working principles from transmission to distribution, is the brain of a smart grid. Advanced DMS assesses smart functions in the distribution system and is also responsible for assessing control functions such as reactive dispatch, voltage regulation, contingency analysis, capability maximization, or line switching. Optimal power flow (OPF)-based tools can be suitably adapted to the requirements of smart distribution network and be employed in an advanced DMS framework. In this paper, the authors present a methodology for unbalanced three-phase OPF (TOPF) for DMS in a smart grid. In the formulation of the TOPF, control variables of the optimization problem are actual active load demand and reactive power outputs of microgenerators. The TOPF is based on a quasi-Newton method and makes use of an open-source three-phase unbalanced distribution load flow. Test results are presented on the IEEE 123-bus Radial Distribution Feeder test case.


title: Design of an all-GaN bidirectional DC-DC converter for medium voltage DC ship power systems using series-stacked GaN modules

abstract: In this paper a high-voltage enhancement-mode Gallium Nitride (GaN) High Electron Mobility Transistor (HEMT)-based module is presented for medium voltage shipboard power systems applications. Series-stacked lateral enhancement-mode GaN HEMTs with integrated quasi-active gate driver are the base for the proposed GaN module. This structure consists of passive components and just one gate driver to control all of the devices. Test results showed an acceptable synchronization of gate voltage and consequently small delay between the drain-source voltage of the two devices during switching transitions using the proposed gate controller. An application of a bi-directional boost converter, that can be used as an interface between the medium voltage and low voltage buses onboard the ship using the proposed GaN module, was demonstrated. Simulation and test results verified the idea.


title: The Adoption of Spreadsheet Software: Testing Innovation Diffusion Theory in the Context of End-User Computing

abstract: Judging by the wealth of problems reported in the literature, information systems (IS) and general managers are not sure how to manage the introduction of new information technology. One step toward providing sound management guidelines is to improve understanding of the social forces which affect the introduction and diffusion process within organizations. This research takes a step toward that goal by examining the validity of innovation diffusion theory within the context of end-user computing. The research involved a field study and historical analysis of the diffusion of spreadsheet software in organizations. To assist in controlling exogenous factors, only finance and accounting departments were studied. Over 500 professionals in 24 business units from 18 large businesses in manufacturing and services participated in the research. Findings supported hypotheses that earlier adopters of spreadsheet software were younger, more highly educated, more attuned to mass media, more involved in interpersonal communication, and more likely to be opinion leaders. Also supported was the hypothesized sigmoidal distribution of adoption over time. Application of the theory was not supported in all areas, however, suggesting that information technology diffusion is different from other diffusion phenomena. Contrary to theory, interpersonal channels of communication were dominant in all phases of adoption decision making. And contrary to their hypothesized role as change agent, IS departments played a minor role in the diffusion process. This was consistent with the observed user-led nature of the phenomenon. Implications for research and practice are discussed.


title: An improved two-diode photovoltaic (PV) model for PV system

abstract: This paper proposes a MATLAB Simulink simulator for photovoltaic (PV) system. The main contribution of this work is the utilization of the two-diode model to represent the PV cell. This model is known to have better accuracy at low irradiance level which allows for a more accurate prediction of PV system performance. To reduce computational time, the input parameters are reduced to four and the values of Rp and Rs are estimated by an efficient iteration method. Furthermore, all the inputs to the simulator are information available on standard PV module datasheet. The simulator supports large array simulation that can be interfaced with MPPT algorithms and power electronic converters. The accurateness of the simulator is verified by applying the model to two PV modules. It is envisaged that the proposed work can be very useful for PV professionals who require simple, fast and accurate PV simulator to design their systems.


title: A structured experiment of test-driven development

abstract: Abstract Test Driven Development (TDD) is a software development practice in which unit test cases are incrementally written prior to code implementation. We ran a set of structured experiments with 24 professional pair programmers. One group developed a small Java program using TDD while the other (control group), used a waterfall-like approach. Experimental results, subject to external validity concerns, tend to indicate that TDD programmers produce higher quality code because they passed 18% more functional black-box test cases. However, the TDD programmers took 16% more time. Statistical analysis of the results showed that a moderate statistical correlation existed between time spent and the resulting quality. Lastly, the programmers in the control group often did not write the required automated test cases after completing their code. Hence it could be perceived that waterfall-like approaches do not encourage adequate testing. This intuitive observation supports the perception that TDD has the potential for increasing the level of unit testing in the software industry.


title: The Economics of Career Concerns, Part II: Application to Missions and Accountability of Government Agencies,” Review of Economic Studies

abstract: The paper uses a simple multitask career concern model in order to analyse the incentives of government agencies' officials. Incentives are impaired by the agency pursuing multiple missions. A lack of focus is even more problematic in the case of fuzzy missions, that is when outsiders are uncertain about the exact nature of the missions actually pursued by the agency. Consequently agencies pursuing multiple missions receive less autonomy. The paper further shows that professionalization creates a sense of mission for the agency, and that the specialization of officials raises their incentives. Last, the paper compares its predictions with the stylized facts on Government bureaucracies.


title: Distinct Class-Specific Saliency Maps for Weakly Supervised Semantic Segmentation

abstract: In this paper, we deal with a weakly supervised semantic segmentation problem where only training images with image-level labels are available. We propose a weakly supervised semantic segmentation method which is based on CNN-based class-specific saliency maps and fully-connected CRF. To obtain distinct class-specific saliency maps which can be used as unary potentials of CRF, we propose a novel method to estimate class saliency maps which improves the method proposed by Simonyan et al. (2014) significantly by the following improvements: (1) using CNN derivatives with respect to feature maps of the intermediate convolutional layers with up-sampling instead of an input image; (2) subtracting the saliency maps of the other classes from the saliency maps of the target class to differentiate target objects from other objects; (3) aggregating multiple-scale class saliency maps to compensate lower resolution of the feature maps. After obtaining distinct class saliency maps, we apply fully-connected CRF by using the class maps as unary potentials. By the experiments, we show that the proposed method has outperformed state-of-the-art results with the PASCAL VOC 2012 dataset under the weakly-supervised setting.


title: 5G fronthaul-latency and jitter studies of CPRI over ethernet

abstract: Common Public Radio Interface (CPRI) is a successful industry cooperation defining the publicly available specification for the key internal interface of radio base stations between the radio equipment control (REC) and the radio equipment (RE) in the fronthaul of mobile networks. However, CPRI is expensive to deploy, consumes large bandwidth, and currently is statically configured. On the other hand, an Ethernet-based mobile fronthaul will be cost-efficient and more easily reconfigurable. Encapsulating CPRI over Ethernet (CoE) is an attractive solution, but stringent CPRI requirements such as delay and jitter are major challenges that need to be met to make CoE a reality. This study investigates whether CoE can meet delay and jitter requirements by performing FPGA-based Verilog experiments and simulations. Verilog experiments show that CoE encapsulation with fixed Ethernet frame size requires about tens of microseconds. Numerical experiments show that the proposed scheduling policy of CoE flows on Ethernet can reduce jitter when redundant Ethernet capacity is provided. The reduction in jitter can be as large as 1 {\mu}s, hence making Ethernet-based mobile fronthaul a credible technology.


title: Submodular meets Spectral: Greedy Algorithms for Subset Selection, Sparse Approximation and Dictionary Selection

abstract: We study the problem of selecting a subset of k random variables from a large set, in order to obtain the best linear prediction of another variable of interest. This problem can be viewed in the context of both feature selection and sparse approximation. We analyze the performance of widely used greedy heuristics, using insights from the maximization of submodular functions and spectral analysis. We introduce the submodularity ratio as a key quantity to help understand why greedy algorithms perform well even when the variables are highly correlated. Using our techniques, we obtain the strongest known approximation guarantees for this problem, both in terms of the submodularity ratio and the smallest k-sparse eigenvalue of the covariance matrix. We further demonstrate the wide applicability of our techniques by analyzing greedy algorithms for the dictionary selection problem, and significantly improve the previously known guarantees. Our theoretical analysis is complemented by experiments on real-world and synthetic data sets; the experiments show that the submodularity ratio is a stronger predictor of the performance of greedy algorithms than other spectral parameters.


title: Chinese sign language recognition based on trajectory and hand shape features

abstract: Sign language recognition(SLR) is a challenging task due to the diversity of the signs. To tackle the problem, this paper utilize both trajectory features and hand shape features. Since the trajectory features and hand shape features are not in the same domain, it is unreasonable to concatenate them naively or model them with a unified model. To deal with the issue, we adopt Support Vector Machine(SVM) and validation Hidden Markov Models(VHMM), respectively. To depict the direction of the trajectory, we first employ histogram of oriented displacement(HOD) with SVM to SLR. We propose the relative distance features(RDF) by using VHMM to consider the relationship between hands and the other body parts. As for hand shape feature, we explore histogram of oriented gradient(HOG) in local hand regions with VHMM, too. To facilitate late fusion, we normalize the probabilities of different features to the same range and fuse them for the final classification. To demonstrate the effectiveness of our proposed method, we conduct the experiments both in ChaLearn dataset and our self-build Kinect-based Chinese sign language dataset. The results show that our method outperforms the classical methods and some state-of-the-art methods.


A Model of Heteroassociative Memory: Deciphering Surprising Features and Locations


title: Some new indexes of cluster validity

abstract: We review two clustering algorithms (hard c-means and single linkage) and three indexes of crisp cluster validity (Hubert's statistics, the Davies-Bouldin index, and Dunn's index). We illustrate two deficiencies of Dunn's index which make it overly sensitive to noisy clusters and propose several generalizations of it that are not as brittle to outliers in the clusters. Our numerical examples show that the standard measure of interset distance (the minimum distance between points in a pair of sets) is the worst (least reliable) measure upon which to base cluster validation indexes when the clusters are expected to form volumetric clouds. Experimental results also suggest that intercluster separation plays a more important role in cluster validation than cluster diameter. Our simulations show that while Dunn's original index has operational flaws, the concept it embodies provides a rich paradigm for validation of partitions that have cloud-like clusters. Five of our generalized Dunn's indexes provide the best validation results for the simulations presented.


Prevalence of individual and bulk tank milk antibodies of bovine herpesvirus type 1 and its relation to milk quality parameters on dairy farms in Catalonia (north-east Spain)


title: Dynamic Routing Protocol Implementation Decision between EIGRP, OSPF and RIP Based on Technical Background Using OPNET Modeler

abstract: This paper presents th implementation decisions to be made when th choice is between protocols that involve distance vector or link state or the combination of both. Here a comparison is made between different parameters and a detailed simulation study is performed on the network with different routing protocols and it has been shown that EIGRP provides a better network convergence time, less bandwidth requirements and better CPU and memory utilization compared to OSPF and RIP. EIGRP, OSPF and RIP are the dynamic routing protocols being used in the practical networks to propagat network topology information to the neighboring routers. There have been a large number of static and dynamic routing protocols available but choice of the right protocol for routing is dependent on many parameters critical being network convergence time, scalability, memory and CPU requirements, security and bandwidth requirement etc.


Augmented Reality Enabled IoT Services for Environmental Monitoring Utilising Serious Gaming Concept


title: Low-Complexity Iterative Algorithms for (Discrete) Compressed Sensing

abstract: We consider iterative (`turbo') algorithms for compressed sensing. First, a unified exposition of the different approaches available in the literature is given, thereby enlightening the general principles and main differences. In particular we discuss i) the estimation step (matched filter vs. optimum MMSE estimator), ii) the unbiasing operation (implicitly or explicitly done and equivalent to the calculation of extrinsic information), and iii) thresholding vs. the calculation of soft values. Based on these insights we propose a low-complexity but well-performing variant utilizing a Krylov space approximation of the optimum linear MMSE estimator. The derivations are valid for any probability density of the signal vector. However, numerical results are shown for the discrete case. The novel algorithms shows very good performance and even slightly faster convergence compared to approximative message passing.


title: The epistemology of a rule-based expert system: a framework for explanation

abstract: Production rules are a popular representation for encoding heuristic knowledge in programs for scientific and medical problem solving. However, experience with one of these programs, MYCIN, indicates that the representation has serious limitations: people other than the original rule authors find it difficult to modify the rule set, and the rules are unsuitable for use in other settings, such as for application to teaching. These paroblems are rooted in fundamental limitations in MYCIN''s original rule representation: the view that expert knowledge can be encoded as a uniform, weakly-structured set of if/then associations is found to be wanting. To illustrate these problems, this paper examines MYCIN''s rujles from the perspective of a teacher trying to justify them and to convey a problem-solving approach. We discover that individual rules play different roles, have different kinds of justifications, and are constructed using different rationales for the ordering and choice of premise clauses. This design knowledge, consisting of structural and strategic concepts which lie outside the representation, is shown to be procedurally embedded in the rules. Moreover, because the data/hypothesis associations are themselves a proceduralized form of underlying disease models, they can only be supported by appealing to this deeper level of knowledge. Making explicit this structural, strategic and support knowledge enhances the ability to understand and modify the system.


title: Detecting topic evolution in scientific literature: how can citations help?

abstract: Understanding how topics in scientific literature evolve is an interesting and important problem. Previous work simply models each paper as a bag of words and also considers the impact of authors. However, the impact of one document on another as captured by citations, one important inherent element in scientific literature, has not been considered. In this paper, we address the problem of understanding topic evolution by leveraging citations, and develop citation-aware approaches. We propose an iterative topic evolution learning framework by adapting the Latent Dirichlet Allocation model to the citation network and develop a novel inheritance topic model. We evaluate the effectiveness and efficiency of our approaches and compare with the state of the art approaches on a large collection of more than 650,000 research papers in the last 16 years and the citation network enabled by CiteSeerX. The results clearly show that citations can help to understand topic evolution better.


DUTH at SemEval-2018 Task 2: Emoji Prediction in Tweets


title: A methodology for fire detection using colour pixel classification

abstract: Lately, fire outbreaks are common issues and its occurrence could cause severe damage toward nature and human properties. Thus, fire detection has been an important issue to protect human life and property and has increases in recent years. This paper focusing on the algorithm of fire detection using image processing techniques i.e. colour pixel classification. This Fire detection system does not require any special type of sensors and it has the ability to monitor large area and depending on the quality of camera used. The objective of this research is to design a methodology for fire detection using image as input. The propose algorithm is using colour pixel classification. This system used image enhancement technique, RGB and YCbCr colour models with given conditions to separate fire pixel from background and isolates luminance from chrominance contrasted from original image to detect fire. The propose system achieved 90% fire detection rate on average.


SemEval-2018 Task 11: Machine Comprehension Using Commonsense Knowledge


Higher-Order Syntactic Attention Network for Longer Sentence Compression


title: Hypothesis generation guided by co-word clustering

abstract: Co-word analysis was applied to keywords assigned to MEDLINE documents contained in sets of complementary but disjoint literatures. In strategical diagrams of disjoint literatures, based on internal density and external centrality of keyword-containing clusters, intermediate terms (linking the disjoint partners) were found in regions of below-median centrality and density. Terms representing the disjoint literature themes were found in close vicinity in strategical diagrams of intermediate literatures. Based on centrality-density ratios, characteristic values were found which allow a rapid identification of clusters containing possible intermediate and disjoint partner terms. Applied to the already investigated disjoint pairs Raynaud"s Disease - Fish Oil, Migraine - Magnesium, the method readily detected known and unknown (but relevant) intermediate and disjoint partner terms. Application of the method to the literature on Prions led to Manganese as possible disjoint partner term. It is concluded that co-word clustering is a powerful method for literature-based hypothesis generation and knowledge discovery.


title: How Does a Sperm Activate a Sea Urchin Egg

abstract: Unfertilized sea urchin eggs are metabolically quiescent and arrested in interphase of the cell division cycle. Immediately after fertilization, metabolism and protein synthesis rates increase 20—fold and the cell division cycle resumes. The intracellular signal that triggers cell cycle progression at fertilization is a transient increase in intracellular free calcium concentration (Cai).


title: A Multitask Objective to Inject Lexical Contrast into Distributional Semantics

abstract: Distributional semantic models have trouble distinguishing strongly contrasting words (such as antonyms) from highly compatible ones (such as synonyms), because both kinds tend to occur in similar contexts in corpora. We introduce the multitask Lexical Contrast Model (mLCM), an extension of the effective Skip-gram method that optimizes semantic vectors on the joint tasks of predicting corpus contexts and making the representations of WordNet synonyms closer than that of matching WordNet antonyms. mLCM outperforms Skip-gram both on general semantic tasks and on synonym/antonym discrimination, even when no direct lexical contrast information about the test words is provided during training. mLCM also shows promising results on the task of learning a compositional negation operator mapping adjectives to their antonyms.


title: ECG analysis using nonlinear PCA neural networks for ischemia detection

abstract: The detection of ischemic cardiac beats from a patient's electrocardiogram (EGG) signal is based on the characteristics of a specific part of the beat called the ST segment. The correct classification of the beats relies heavily on the efficient and accurate extraction of the ST segment features. An algorithm is developed for this feature extraction based on nonlinear principal component analysis (NLPCA). NLPCA is a method for nonlinear feature extraction that is usually implemented by a multilayer neural network. It has been observed to have better performance, compared with linear principal component analysis (PCA), in complex problems where the relationships between the variables are not linear. In this paper, the NLPCA techniques are used to classify each segment into one of two classes: normal and abnormal (ST+, ST-, or artifact). During the algorithm training phase, only normal patterns are used, and for classification purposes, we use only two nonlinear features for each ST segment. The distribution of these features is modeled using a radial basis function network (RBFN). Test results using the European ST-T database show that using only two nonlinear components and a training set of 1000 normal samples from each file produce a correct classification rate of approximately 80% for the normal beats and higher than 90% for the ischemic beats.


title: Performance Analysis of Online Anticipatory Algorithms for Large Multistage Stochastic Integer Programs

abstract: Despite significant algorithmic advances in recent years, finding optimal policies for large-scale, multistage stochastic combinatorial optimization problems remains far beyond the reach of existing methods. This paper studies a complementary approach, online anticipatory algorithms, that make decisions at each step by solving the anticipatory relaxation for a polynomial number of scenarios. Online anticipatory algorithms have exhibited surprisingly good results on a variety of applications and this paper aims at understanding their success. In particular, the paper derives sufficient conditions under which online anticipatory algorithms achieve good expected utility and studies the various types of errors arising in the algorithms including the anticipativity and sampling errors. The sampling error is shown to be negligible with a logarithmic number of scenarios. The anticipativity error is harder to bound and is shown to be low, both theoretically and experimentally, for the existing applications.


title: Infinite Edge Partition Models for Overlapping Community Detection and Link Prediction

abstract: A hierarchical gamma process infinite edge partition model is proposed to factorize the binary adjacency matrix of an unweighted undirected relational network under a Bernoulli-Poisson link. The model describes both homophily and stochastic equivalence, and is scalable to big sparse networks by focusing its computation on pairs of linked nodes. It can not only discover overlapping communities and inter-community interactions, but also predict missing edges. A simplified version omitting inter-community interactions is also provided and we reveal its interesting connections to existing models. The number of communities is automatically inferred in a nonparametric Bayesian manner, and efficient inference via Gibbs sampling is derived using novel data augmentation techniques. Experimental results on four real networks demonstrate the models' scalability and state-of-the-art performance.


title: Virtual machine allocation policies against co-resident attacks in cloud computing

abstract: While the services-based model of cloud computing makes more and more IT resources available to a wider range of customers, the massive amount of data in cloud platforms is becoming a target for malicious users. Previous studies show that attackers can co-locate their virtual machines (VMs) with target VMs on the same server, and obtain sensitive information from the victims using side channels. This paper investigates VM allocation policies and practical countermeasures against this novel kind of co-resident attack by developing a set of security metrics and a quantitative model. A security analysis of three VM allocation policies commonly used in existing cloud computing platforms reveals that the server's configuration, oversubscription and background traffic have a large impact on the ability to prevent attackers from co-locating with the targets. If the servers are properly configured, and oversubscription is enabled, the best policy is to allocate new VMs to the server with the most VMs. Based on these results, a new strategy is introduced that effectively decreases the probability of attackers achieving co-residence. The proposed solution only requires minor changes to current allocation policies, and hence can be easily integrated into existing cloud platforms to mitigate the threat of co-resident attacks.


title: 5G in a Convergent Internet of Things Era: An Overview

abstract: The future of services is expected to heavily rely on "Internet of Things" (IoT) and the ability to create smart spaces such as, smart cities, smart houses, smart transportation, and smart outdoor monitoring (SOM) in the coming few decades. Such smart spaces will require the deployment of significantly large numbers of devices connected to the internet such as sensors, actuators, and wearable computing. It is expected that the number of the connected devices to the internet will exceed 50 billion devices by the year of 2020, therefore, there is a need for a super network to handle and control the potentially huge amounts of data gathered and delivered to these devices. 5G technology is a new complete wireless technology which has great potential to aid the IoT phenomenon as the main enabling technology. It has exposed many significant features such as higher bandwidth, high integrity, low latency, higher spectral efficiency, and higher network capacity than any other generation. In this study, an overview is presented for the 5G as the best candidate to be the underlying technology required to implement IoT applications.


title: Are we on the Right Track?: An Examination of Information Retrieval Methodologies

abstract: The unpredictability of user behavior and the need for effectiveness make it difficult to define a suitable research methodology for Information Retrieval (IR). In order to tackle this challenge, we categorize existing IR methodologies along two dimensions: (1) empirical vs. theoretical, and (2) top-down vs. bottom-up. The strengths and drawbacks of the resulting categories are characterized according to 6 desirable aspects. The analysis suggests that different methodologies are complementary and therefore, equally necessary. The categorization of the 167 full papers published in the last SIGIR (2016 and 2017) and ICTIR (2017) conferences suggest that most of existing work is empirical bottom-up, suggesting lack of some desirable aspects. With the hope of improving IR research practice, we propose a general methodology for IR that integrates the strengths of existing research methods.


title: A hybrid feature selection scheme for unsupervised learning and its application in bearing fault diagnosis

abstract: With the development of the condition-based maintenance techniques and the consequent requirement for good machine learning methods, new challenges arise in unsupervised learning. In the real-world situations, due to the relevant features that could exhibit the real machine condition are often unknown as priori, condition monitoring systems based on unimportant features, e.g. noise, might suffer high false-alarm rates, especially when the characteristics of failures are costly or difficult to learn. Therefore, it is important to select the most representative features for unsupervised learning in fault diagnostics. In this paper, a hybrid feature selection scheme (HFS) for unsupervised learning is proposed to improve the robustness and the accuracy of fault diagnostics. It provides a general framework of the feature selection based on significance evaluation and similarity measurement with respect to the multiple clustering solutions. The effectiveness of the proposed HFS method is demonstrated by a bearing fault diagnostics application and comparison with other features selection methods.


title: Online Reconstruction of Structural Information from Datacenter Logs

abstract: Well-run datacenter application architectures are heavily instrumented to provide detailed traces of messages and remote invocations. Reconstructing user sessions, call graphs, transaction trees, and other structural information from these messages, a process known as sessionization, is the foundation for a variety of diagnostic, profiling, and monitoring tasks essential to the operation of the datacenter. We present the design and implementation of a system which processes log streams at gigabits per second and reconstructs user sessions comprising millions of transactions per second in real time with modest compute resources, while dealing with clock skew, message loss, and other real-world phenomena that make such a task challenging. Our system is based on the Timely Dataflow framework for low latency, data-parallel computation, and we demonstrate its utility with a number of use-cases and traces from a large, operational, mission-critical enterprise data center.


title: On Kelly Betting: Some Limitations

abstract: The focal point of this paper is the so-called Kelly Criterion, a prescription for optimal resource allocation among a set of gambles which are repeated over time. The criterion calls for maximization of the expected value of the logarithmic growth of wealth. While significant literature exists providing the rationale for such an optimization, this paper concentrates on the limitations of the Kelly-based theory. To this end, we fill a void in published results by providing specific examples quantifying what difficulties are encountered when Taylor-style approximations are used and when wealth drawdowns are considered. For the case of drawdown, we describe some research directions which we feel are promising for improvement of the theory.


title: Visual abnormal behavior detection based on trajectory sparse reconstruction analysis

abstract: Abnormal behavior detection has been one of the most important research branches in intelligent video content analysis. In this paper, we propose a novel abnormal behavior detection approach by introducing trajectory sparse reconstruction analysis (SRA). Given a video scenario, we collect trajectories of normal behaviors and extract the control point features of cubic B-spline curves to construct a normal dictionary set, which is further divided into Route sets. On the dictionary set, sparse reconstruction coefficients and residuals of a test trajectory to the Route sets can be calculated with SRA. The minimal residual is used to classify the test behavior into a normal behavior or an abnormal one. SRA is solved by L1-norm minimization, leading to that a few of dictionary samples are used when reconstructing a behavior trajectory, which guarantees that the proposed approach is valid even when the dictionary set is very small. Experimental results with comparisons show that the proposed approach improves the state-of-the-art.


title: Fault diagnosis and augmented reality-based troubleshooting of HVAC systems

abstract: Maintaining Heating, ventilating and air conditioning (HVAC) systems in buildings and vehicles in superior condition is essential to energy waste minimization, increased equipment availability and improved thermal comfort of occupants. HVAC systems are complex interconnected systems. Consequently, early detection and diagnosis of incipient faults in such systems using robust methodologies and tools is salient for achieving thermal comfort. The increased complexity, cross-subsystem fault propagation, and the associated information propagation delays in networked HVAC systems makes fault diagnosis and maintenance a challenging task. This motivates us to incorporate the emerging technologies, such as real-time monitoring, remote diagnosis and augmented reality (AR) technologies, for efficient fault diagnosis and troubleshooting in such systems. This paper presents an integrated AR-based framework to monitor an HVAC system (or any cyber-physical system) in real-time, access system heath information remotely and act on that information proactively to prevent or minimize the system down time.


Person Re-identification by Unsupervised ` 1 Graph Learning


title: A High Performance Uncooled a-Si TFT Infrared Sensor

abstract: This paper reports the theory, fabrication and testing of a novel uncooled infrared (IR) sensor using amorphous silicon thin film transistors (a-Si TFTs) as the active elements. The measured temperature coefficient of the channel current (TCC) of the a-Si TFT is about 4.18%/K at room temperature. What's more, finger-cross TFT is designed, which can greatly improve the detectivity of uncooled infrared sensor by increasing the ratio of channel width to length (W/L). Nano-TiO2 /polyimide (PI) hybrid membrane is applied in infrared absorption layer, which increases infrared absorptance by about 15%. A new thermal-isolation materials-polyimide is applied in uncooled infrared sensor, which not only has good thermal-isolation, but also simplifies the fabrication process and improves the yield. The whole process of a-Si TFT uncooled infrared sensor is compatible with the standard IC process. Preliminary experimental results show that a detectivity of 1.0 times 109cmHzfrac12/W is achieved at a chopping frequency of 30 Hz


A 3-Month, Randomized, Double-Blind, Placebo-Controlled Study Evaluating the Ability of an Extra-Strength Marine Protein Supplement to Promote Hair Growth and Decrease Shedding in Women with Self-Perceived Thinning Hair


title: Power electronics traction transformer

abstract: With the new generation of full low floor passenger trains, the constraints of weight and size on the traction transformer are becoming stronger. The ultimate target weight for the transformer is 1 kg/kVA. The reliability and the efficiency are also becoming more important. To address these issues, a multilevel topology using medium frequency transformers has been developed. It permits to reduce the weight and the size of the system and improves the global life cycle cost of the vehicle. The proposed multilevel converter consists of sixteen bidirectional direct current converters (cycloconverters) connected in series to the catenary 15 kV, 16.7 Hz through a choke inductor. The cycloconverters are connected to sixteen medium frequency transformers (400 Hz) that are fed by sixteen four-quadrant converters connected in parallel to a 1.8 kV DC link with a 2f filter. The control, the command and the hardware of the prototype are described in detail.


title: Low-Power Self Reconfigurable Multiplexer Based Decoder for Adaptive Resolution Flash ADCs

abstract: This paper presents a new improved multiplexer based decoder for flash analog-to-digital converters. The proposed decoder is based on 2:1 multiplexers. It calculates the binary code for low operand length thermometer code at initial stages and groups the output of initial stages to generate the final result. The proposed decoder can be configured to operate on thermometer code with reduced length without any extra overhead. This 'self-reconfigurable' property is particularly useful in adaptive resolution analog-to-digital converters. Simulation results indicate that the proposed decoder results in reduced delay, power and power delay product when compared to existing digital decoders for flash analog-digital converters.


title: Assessing food and nutrition security in Nepal: evidence from diet diversity and food expenditure patterns

abstract: The widespread prevalence of nutrient deficiency and poor nutritional outcomes has been a significant concern in developing and emerging countries like Nepal. The level of diversity in household diets is an indirect measure of diet quality and the extent to which nutritional needs of households are being met. However, there is a limited understanding on the trends, patterns and determinants of the dietary diversity in Nepal. This study is an attempt to enrich the literature on this issue. Based on unit level data from three rounds (1995, 2004, and 2011) of the Nepal Living Standard Survey (NLSS), we used multilevel modeling, quantile regression, and the Blinder-Oaxaca decomposition method to decipher the trends, determinants and drivers of dietary diversity in Nepal. Our study finds that the changes in household socio-demographic and agricultural characteristics are very important in explaining the improvement in dietary quality. Changes in household characteristics account for at least 37% of the observed improvement and agriculture-related changes explain at least 16% of the observed improvement. Our findings are highly robust across the different model specifications and concludes by bringing a focus on multi-sectoral approach to tackle nutrition issues in Nepal.


title: Virtual synchronous machines — Classification of implementations and analysis of equivalence to droop controllers for microgrids

abstract: The concept of Virtual Synchronous Machines (VSM) is emerging as an alternative approach for control of power electronic converters operating in the power system. One main motivation for applying VSM-based control is to achieve a simple approach for emulating the inertia effect of traditional synchronous machines. This paper provides a comprehensive literature review on VSM and a possible classification of the different schemes. In addition, the small-signal response of the inertia emulation characteristics of VSM-based control is proved to be equivalent to conventional droop-based control for standalone and microgrid operation of converters. Thus, the droop gain and the filter time constant of the power feedback in a droop controller can be directly related to the damping factor and the inertia constant of a Virtual Synchronous Machine. The derived results are providing additional physics-based insight into the operation and tuning of both types of controllers.


title: Screening for recently selected alleles by analysis of human haplotype similarity.

abstract: There is growing interest in the use of haplotype-based methods for detecting recent selection. Here, we describe a method that uses a sliding window to estimate similarity among the haplotypes associated with any given single-nucleotide polymorphism (SNP) allele. We used simulations of natural selection to provide estimates of the empirical power of the method to detect recently selected alleles and found it to be comparable in power to the popular long-range haplotype test and more powerful than methods based on nucleotide diversity. We then applied the method to a recently selected allele--the sickle mutation at the HBB locus--and found it to have a signal of selection that was significantly stronger than that of simulated models both with and without strong selection. Using this method, we also evaluated >4,000 SNPs on chromosome 20, indicating the applicability of the method to regional data sets.


title: Users Guide to LOWTRAN 7

abstract: Abstract : LOWTRAN 7 is a low-resolution propagation model and computer code for predicting atmospheric transmittance and background radiance from 0 to 50,000 cm -1 at a resolution of 20 cm -1. The code is based on the LOWTRAN 6 (1983) model. Multiple scattered radiation has been added to the model as well as new molecular band model parameters and new ozone and molecular oxygen absorption parameters for the UV. Other modifications include a wind dependent desert model, new cirrus cloud models, and new cloud and rain models. The code also includes new representative (geographical and seasonal) atmospheric models and updated aerosol models with options to replace them with user-derived values. An improved extra-terrestrial solar source function is also included. Six modes of program execution are allowed with the new model and computer code for a given slant path geometry. This report contains a description to users for operating the LOWTRAN 7 computer code. It summarizes the capabilities of the new code, provides complete operating instructions as well as input and output from test cases for user validation. Also included are operating instructions for three programs that utilize LOWTRAN 7 output (plot, filter and scanning function programs). Keywords: Computer program documentation, Subroutines, Radiative transfer, Aerosols, Clouds, Attenuation, Lowtran, Computer code, Ultraviolet, Visible, Infrared.


title: Analysis and understanding of multi-class invoices

abstract: In this paper a system for processing documents that can be grouped into classes is illustrated. We have considered invoices as a case-study. The system is divided into three phases: document analysis, classification, and understanding. We illustrate the analysis and understanding phases. The system is based on knowledge constructed by means of a learning procedure. The experimental results demonstrate the reliability of our document analysis and understanding procedures. They also present evidence that it is possible to use a small learning set of invoices to obtain reliable knowledge for the understanding phase.


title: Target discrimination in synthetic aperture radar using artificial neural networks

abstract: This paper addresses target discrimination in synthetic aperture radar (SAR) imagery using linear and nonlinear adaptive networks. Neural networks are extensively used for pattern classification but here the goal is discrimination. We show that the two applications require different cost functions. We start by analyzing with a pattern recognition perspective the two-parameter constant false alarm rate (CFAR) detector which is widely utilized as a target detector in SAR. Then we generalize its principle to construct the quadratic gamma discriminator (QGD), a nonparametrically trained classifier based on local image intensity. The linear processing element of the QCD is further extended with nonlinearities yielding a multilayer perceptron (MLP) which we call the NL-QGD (nonlinear QGD). MLPs are normally trained based on the L(2) norm. We experimentally show that the L(2) norm is not recommended to train MLPs for discriminating targets in SAR. Inspired by the Neyman-Pearson criterion, we create a cost function based on a mixed norm to weight the false alarms and the missed detections differently. Mixed norms can easily be incorporated into the backpropagation algorithm, and lead to better performance. Several other norms (L(8), cross-entropy) are applied to train the NL-QGD and all outperformed the L(2) norm when validated by receiver operating characteristics (ROC) curves. The data sets are constructed from TABILS 24 ISAR targets embedded in 7 km(2) of SAR imagery (MIT/LL mission 90).


Dense Visual SLAM


title: Poisson Hole Process: Theory and Applications to Wireless Networks

abstract: Interference field in wireless networks is often modeled by a homogeneous Poisson Point Process (PPP). While it is realistic in modeling the inherent node irregularity and provides meaningful first-order results, it falls short in modeling the effect of interference management techniques, which typically introduce some form of spatial interaction among active transmitters. In some applications, such as cognitive radio and device-to-device networks, this interaction may result in the formation of holes in an otherwise homogeneous interference field. The resulting interference field can be accurately modeled as a Poisson Hole Process (PHP). Despite the importance of PHP in many applications, the exact characterization of interference experienced by a typical node in a PHP is not known. In this paper, we derive several tight upper and lower bounds on the Laplace transform of this interference. Numerical comparisons reveal that the new bounds outperform all known bounds and approximations, and are remarkably tight in all operational regimes of interest. The key in deriving these tight and yet simple bounds is to capture the local neighborhood around the typical point accurately while simplifying the far field to attain tractability. Ideas for tightening these bounds further by incorporating the effect of overlaps in the holes are also discussed. These results immediately lead to an accurate characterization of the coverage probability of the typical node in a PHP under Rayleigh fading.


Deanonymisation in Ethereum Using Existing Methods for Bitcoin


title: Cost benefits of ergonomic intervention in a hospital: a preliminary study using Oxenburgh's productivity model.

abstract: This case study of absenteeism amongst nurses was carried out using the productivity model of Oxenburgh (1991). Data on absenteeism amongst nurses were collected from one private hospital. Areas of high risk of injury were identified and the presence of ergonomic risk factors determined. The productivity model was used to calculate the costs of absenteeism in terms of actual rates of pay and loss of productivity. Potential benefits resulting from ergonomic improvements to the work environment were estimated using the productivity model. The model predicted that even modest reductions in injury would justify the additional expenditure in a relatively short period of time. Further investigations of injuries to nurses in both State and Private Sector Hospitals seem to be justified.


title: Ontological Multidimensional Data Models and Contextual Data Quality

abstract: Data quality assessment and data cleaning are context-dependent activities. Motivated by this observation, we propose the Ontological Multidimensional Data Model (OMD model), which can be used to model and represent contexts as logic-based ontologies. The data under assessment is mapped into the context, for additional analysis, processing, and quality data extraction. The resulting contexts allow for the representation of dimensions, and multidimensional data quality assessment becomes possible. At the core of a multidimensional context we include a generalized multidimensional data model and a Datalog+/- ontology with provably good properties in terms of query answering. These main components are used to represent dimension hierarchies, dimensional constraints, dimensional rules, and define predicates for quality data specification. Query answering relies upon and triggers navigation through dimension hierarchies, and becomes the basic tool for the extraction of quality data. The OMD model is interesting per se, beyond applications to data quality. It allows for a logic-based, and computationally tractable representation of multidimensional data, extending previous multidimensional data models with additional expressive power and functionalities.


title: Population genomics of Bronze Age Eurasia

abstract: The Bronze Age of Eurasia (around 3000–1000 BC) was a period of major cultural changes. However, there is debate about whether these changes resulted from the circulation of ideas or from human migrations, potentially also facilitating the spread of languages and certain phenotypic traits. We investigated this by using new, improved methods to sequence low-coverage genomes from 101 ancient humans from across Eurasia. We show that the Bronze Age was a highly dynamic period involving large-scale population migrations and replacements, responsible for shaping major parts of present-day demographic structure in both Europe and Asia. Our findings are consistent with the hypothesized spread of Indo-European languages during the Early Bronze Age. We also demonstrate that light skin pigmentation in Europeans was already present at high frequency in the Bronze Age, but not lactose tolerance, indicating a more recent onset of positive selection on lactose tolerance than previously thought.


title: Classification of access network types: Ethernet wireless LAN, ADSL, cable modem or dialup?

abstract: Ethernet, wireless LAN, ADSL, cable modem and dialup are common access networks, but have dramatically different characteristics. Fast and accurate classification of access network type can improve protocol or application performance significantly. In this paper, we propose a simple and efficient end-end scheme to classify the type of an access network into three categories: Ethernet, wireless LAN and low-bandwidth connection. Our scheme is based on the intrinsic characteristics of the various access networks and utilizes the median and entropy of packet pair inter-arrival times. Extensive experiments show that our scheme obtains accurate classification results in a very short time (10 to 100 seconds).


title: Zeta potential of protoplasts from gravireacting maize roots

abstract: Protoplasts were isolated from cortical cells of the elongating zone of maize (Zea mays L. cv. LG 11) roots and submitted to microelectrophoresis. Significant and transient differences in zeta potential between protoplasts from upper and lower root sides were compared with the gravireaction and the differential elongation of these roots. The maximum difference in the zeta potential was obtained between protoplasts from the upper and lower cortical cells after 90 min, exactly the time of gravipresentation for which the maximum rate of gravireaction was observed. In addition, this almost corresponded to the time for which the difference between the elongation rates of upper and lower sides of the extending zone began to increase. Consequently, the changes in the charges of the plasmalemma of the cortical cells from the growing part of roots could be more or less directly related to the root graviresponse.


title: On the complexity of the Extended String-to-String Correction Problem

abstract: The Extended String-to-String Correction Problem [ESSCP] is defined as the problem of determining, for given strings A and B over alphabet V, a minimum-cost sequence S of edit operations such that S(A) e B. The sequence S may make use of the operations: Change, Insert, Delete and Swaps , each of constant cost W C , W I , W D , and W S respectively. Swap permits any pair of adjacent characters to be interchanged. The principal results of this paper are: (1) a brief presentation of an algorithm (the CELLAR algorithm) which solves ESSCP in time O(¦A¦* ¦B¦* ¦V¦ s *s), where s e min(4W C , W I +W D )/W S + 1; (2) presentation of polynomial time algorithms for the cases (a) W S e 0, (b) W S > 0, W C e W I e W D e


Black elite: The new market for highly educated Black Americans : a report prepared for the Carnegie Commission on Higher Education


title: ADMM for harmonic retrieval from one-bit sampling with time-varying thresholds

abstract: Parameter estimation of sinusoids is a problem of significance in many practical applications. This problem is revisited through a new alternating direction method of multipliers (ADMM) based approach, where the unknown parameters are estimated from one-bit quantized noisy measurements with time varying thresholds. The proposed method is computationally efficient and easy to implement, since each ADMM update has simple closed-form formula. Moreover, it provides accurate estimates by exploiting group sparsity hidden in the signal model. Simulations demonstrate the effectiveness of our algorithm.


title: Look into Person: Self-Supervised Structure-Sensitive Learning and a New Benchmark for Human Parsing

abstract: Human parsing has recently attracted a lot of research interests due to its huge application potentials. However existing datasets have limited number of images and annotations, and lack the variety of human appearances and the coverage of challenging cases in unconstrained environment. In this paper, we introduce a new benchmark Look into Person (LIP) that makes a significant advance in terms of scalability, diversity and difficulty, a contribution that we feel is crucial for future developments in human-centric analysis. This comprehensive dataset contains over 50,000 elaborately annotated images with 19 semantic part labels, which are captured from a wider range of viewpoints, occlusions and background complexity. Given these rich annotations we perform detailed analysis of the leading human parsing approaches, gaining insights into the success and failures of these methods. Furthermore, in contrast to the existing efforts on improving the feature discriminative capability, we solve human parsing by exploring a novel self-supervised structure-sensitive learning approach, which imposes human pose structures into parsing results without resorting to extra supervision (i.e., no need for specifically labeling human joints in model training). Our self-supervised learning framework can be injected into any advanced neural networks to help incorporate rich high-level knowledge regarding human joints from a global perspective and improve the parsing results. Extensive evaluations on our LIP and the public PASCAL-Person-Part dataset demonstrate the superiority of our method.


title: Great Question! Question Quality in Community Q&A

abstract: Asking the right question in the right way is an art (and a science). In a community question-answering setting, a good question is not just one that is found to be useful by other people: a question is good if it is also presented clearly and shows prior research. Using a community question-answering site that allows voting over the questions, we show that there is a notion of question quality that goes beyond mere popularity. We present techniques using latent topic models to automatically predict the quality of questions based on their content. Our best system achieves a prediction accuracy of 72%, beating out strong baselines by a significant amount. We also examine the effect of question quality on the dynamics of user behavior and the longevity of questions.


title: Design evolution metrics for defect prediction in object oriented systems

abstract: Testing is the most widely adopted practice to ensure software quality. However, this activity is often a compromise between the available resources and software quality. In object-oriented development, testing effort should be focused on defective classes. Unfortunately, identifying those classes is a challenging and difficult activity on which many metrics, techniques, and models have been tried. In this paper, we investigate the usefulness of elementary design evolution metrics to identify defective classes. The metrics include the numbers of added, deleted, and modified attributes, methods, and relations. The metrics are used to recommend a ranked list of classes likely to contain defects for a system. They are compared to Chidamber and Kemerer's metrics on several versions of Rhino and of ArgoUML. Further comparison is conducted with the complexity metrics computed by Zimmermann et al. on several releases of Eclipse. The comparisons are made according to three criteria: presence of defects, number of defects, and defect density in the top-ranked classes. They show that the design evolution metrics, when used in conjunction with known metrics, improve the identification of defective classes. In addition, they show that the design evolution metrics make significantly better predictions of defect density than other metrics and, thus, can help in reducing the testing effort by focusing test activity on a reduced volume of code.


title: Kernel PCA for novelty detection

abstract: Kernel principal component analysis (kernel PCA) is a non-linear extension of PCA. This study introduces and investigates the use of kernel PCA for novelty detection. Training data are mapped into an infinite-dimensional feature space. In this space, kernel PCA extracts the principal components of the data distribution. The squared distance to the corresponding principal subspace is the measure for novelty. This new method demonstrated a competitive performance on two-dimensional synthetic distributions and on two real-world data sets: handwritten digits and breast-cancer cytology.


title: Modifying a sparse Cholesky factorization

abstract: Given a sparse symmetric positive definite matrix ${\bf AA}^{\sf T}$ and an associated sparse Cholesky factorization ${\bf LDL}^{\sf T}$ or ${\bf LL}^{\sf T}$, we develop sparse techniques for obtaining the new factorization associated with either adding a column to ${\bf A}$ or deleting a column from ${\bf A}$. Our techniques are based on an analysis and manipulation of the underlying graph structure and on ideas of Gill et al.\ [ Math. Comp., 28 (1974), pp. 505--535] for modifying a dense Cholesky factorization. We show that our methods extend to the general case where an arbitrary sparse symmetric positive definite matrix is modified. Our methods are optimal in the sense that they take time proportional to the number of nonzero entries in ${\bf L}$ and ${\bf D}$ that change.


title: Maybe Poor Johnny Really Cannot Encrypt: The Case for a Complexity Theory for Usable Security

abstract: Psychology and neuroscience literature shows the existance of upper bounds on the human capacity for executing cognitive tasks and for information processing. These bounds are where, demonstrably, people start experiencing cognitive strain and consequently committing errors in the tasks execution. We argue that the usable security discipline should scientifically understand such bounds in order to have realistic expectations about what people can or cannot attain when coping with security tasks. This may shed light on whether Johnny will be ever be able to encrypt. We propose a conceptual framework for evaluation of human capacities in security that also assigns systems to complexity categories according to their security and usability. From what we have initiated in this paper, we ultimately aim at providing designers of security mechanisms and policies with the ability to say: "This feature of the security mechanism X or this security policy element Y is inappropriate, because this evidence shows that it is beyond the capacity of its target community".


title: Challenge: RFID Hacking for Fun and Profit

abstract: Passive radio frequency identification (RFID) tags are ubiquitous today due to their low cost (a few cents), relatively long communication range ($\sim$7-11~m), ease of deployment, lack of battery, and small form factor. Hence, they are an attractive foundation for environmental sensing. Although RFID-based sensors have been studied in the research literature and are also available commercially, manufacturing them has been a technically-challenging task that is typically undertaken only by experienced researchers. In this paper, we show how even hobbyists can transform commodity RFID tags into sensors by physically altering (`hacking') them using COTS sensors, a pair of scissors, and clear adhesive tape. Importantly, this requires no change to commercial RFID readers. We also propose a new legacy-compatible tag reading protocol called Differential Minimum Response Threshold (DMRT) that is robust to the changes in an RF environment. To validate our vision, we develop RFID-based sensors for illuminance, temperature, touch, and gestures. We believe that our approach has the potential to open up the field of batteryless backscatter-based RFID sensing to the research community, making it an exciting area for future work.


title: Few-shot Classifier GAN

abstract: Fine-grained image classification with a few-shot classifier is a highly challenging open problem at the core of a numerous data labeling applications. In this paper, we present Few-shot Classifier Generative Adversarial Network as an approach for few-shot classification. We address the problem of few-shot classification by designing a GAN model in which the discriminator and the generator compete to output labeled data in any case. In contrast to previous methods, our techniques generate then classify images into multiple fake or real classes. A key innovation of our adversarial approach is to allow fine- grained classification using multiple fake classes with semi- supervised deep learning. A major strength of our techniques lies in its label-agnostic characteristic, in the sense that the system handles both labeled and unlabeled data during training. We validate quantitatively our few-shot classifier on the MNIST and SVHN datasets by varying the ratio of labeled data over unlabeled data in the training set. Our quantitative analysis demonstrates that our techniques produce better classification performance when using multiple fake classes and larger amount of unlabelled data.


title: Freebal: dedicated gravity compensation for the upper extremities

abstract: In most upper-extremity rehabilitation robotics, several components affect the therapy outcome. A common component is gravity compensation which alleviates upper-extremity movements. Gravity compensation by itself could improve motor control further or faster, separate from other effects of robotic therapy. To investigate the rehabilitation value of gravity compensation separately, we created the dedicated gravity compensation system, Freebal. The sling systems with ideal spring mechanisms in the Freebal are well suited for providing compensation forces. The device has steplessly scalable forces, a large range of motion with constant compensation forces, independent control of the compensation of the lower and upper arm, and low movement impedance. It also does not need external power, force sensors or active controllers. Finally, the Freebal can be easily moved, serviced and used in arm rehabilitation with either sitting or standing subjects.


title: INVESTIGATING CRIME-TO-TWITTER RELATIONSHIPS IN URBAN ENVIRONMENTS - FACILITATING A VIRTUAL NEIGHBORHOOD WATCH

abstract: Social networks offer vast potential for marketing agencies, as members freely provide private information, for instance on their current situation, opinions, tastes, and feelings. The use of social networks to feed into crime platforms has been acknowledged to build a kind of a virtual neighborhood watch. Current attempts that tried to automatically connect news from social networks with crime platforms have concentrated on documentation of past events, but neglected the opportunity to use Twitter data as a decision support system to detect future crimes. In this work, we attempt to unleash the wisdom of crowds materialized in tweets from Twitter. This requires to look at Tweets that have been sent within a vicinity of each other. Based on the aggregated Tweets traffic we correlate them with crime types. Apparently, crimes such as disturbing the peace or homicide exhibit different Tweet patterns before the crime has been committed. We show that these tweet patterns can strengthen the explanation of criminal activity in urban areas. On top of that, we go beyond pure explanatory approaches and use predictive analytics to provide evidence that Twitter data can improve the prediction of crimes.


Inheritance of a Factor Causing Sudden Death in Childhood and Early Adult Life


title: Learning to Generate Corrective Patches using Neural Machine Translation

abstract: Bug fixing is generally a manually-intensive task. However, recent work has proposed the idea of automated program repair, which aims to repair (at least a subset of) bugs in different ways such as code mutation, etc. Following in the same line of work as automated bug repair, in this paper we aim to leverage past fixes to propose fixes of current/future bugs. Specifically, we propose Ratchet, a corrective patch generation system using neural machine translation. By learning corresponding pre-correction and post-correction code in past fixes with a neural sequence-to-sequence model, Ratchet is able to generate a fix code for a given bug-prone code query. We perform an empirical study with five open source projects, namely Ambari, Camel, Hadoop, Jetty and Wicket, to evaluate the effectiveness of Ratchet. Our findings show that Ratchet can generate syntactically valid statements 98.7% of the time, and achieve an F1-measure between 0.41-0.83 with respect to the actual fixes adopted in the code base. In addition, we perform a qualitative validation using 20 participants to see whether the generated statements can be helpful in correcting bugs. Our survey showed that Ratchet's output was considered to be helpful in fixing the bugs on many occasions, even if fix was not 100% correct.


title: Multi-hop Greedy Gossip with Eavesdropping

abstract: Greedy Gossip with Eavesdropping (GGE) is a randomized gossip algorithm that computes the average consensus by exploiting the broadcast nature of wireless communications. Each node eavesdrops on its immediate neighbors to track their values so that when it comes time to gossip, a node can myopically exchange information with the neighbor that will give the greatest immediate improvement in local squared error. In previous work, we showed that the improvement achieved using GGE over standard randomized gossip (i.e., exchanging information equally often with all neighbors) is proportional to the maximum node degree. Thus, for network topologies such as random geometric graphs, where node degree grows with the network size, the improvements of GGE scale with network size, but for grid-like topologies, where the node degree remains constant, GGE yields limited improvement. This paper presents an extension to GGE, which we call “multi-hop GGE”, that improves the rate of convergence for grid-like topologies. Multi-hop GGE relies on artificially increasing neighborhood size by performing greedy updates with nodes beyond one hop neighborhoods. We show that multi-hop GGE converges to the average consensus and illustrate via simulation that multi-hop GGE improves the performance of GGE for different network topologies.


title: A Force-Isotropic Underactuated Finger

abstract: This paper presents the design of a new underactuated finger. The main characteristic of this two-degree-of-freedom / one-actuator finger is to be force-isotropic. This means that the intensity of the forces exerted at the center of each phalanx, on the grasped object, are identical. Until now, this characteristic was observed on some underactuated fingers for some very specific configurations only (mainly when the phalanxes were lined up). With the proposed finger, this characteristic remains true whatever the configuration of the phalanxes is.


title: INFORMATION SECURITY AWARENESS : ITS ANTECEDENTS AND MEDIATING EFFECTS ON SECURITY COMPLIANT BEHAVIOR

abstract: Abstract Information security awareness (ISA) is referred to as a state of consciousness and knowledge about security issues and is frequently found to impact security compliant behavior. However, to date we know little about the factors influencing ISA and its mediating effect on behavior. Our study addresses these gaps. We propose a research model that studies ISA’s institutional, individual, and environmental antecedents and investigates the mediating role of ISA. The model was empirically tested with survey data from 475 employees. The model explains a substantial proportion of the variance of ISA (.50) and intention to comply (.41). The results imply that the provision of security policies and employees’ knowledge on information systems are the most influential antecedents of ISA. The study shows that ISA mediates the relationship between ISA’s antecedents and behavioral intention. The findings will be useful for stakeholders interested in encouraging employees’ information security policy compliant behavior.


title: SAR Target Recognition via Supervised Discriminative Dictionary Learning and Sparse Representation of the SAR-HOG Feature

abstract: Automatic target recognition (ATR) in synthetic aperture radar (SAR) images plays an important role in both national defense and civil applications. Although many methods have been proposed, SAR ATR is still very challenging due to the complex application environment. Feature extraction and classification are key points in SAR ATR. In this paper, we first design a novel feature, which is a histogram of oriented gradients (HOG)-like feature for SAR ATR (called SAR-HOG). Then, we propose a supervised discriminative dictionary learning (SDDL) method to learn a discriminative dictionary for SAR ATR and propose a strategy to simplify the optimization problem. Finally, we propose a SAR ATR classifier based on SDDL and sparse representation (called SDDLSR), in which both the reconstruction error and the classification error are considered. Extensive experiments are performed on the MSTAR database under standard operating conditions and extended operating conditions. The experimental results show that SAR-HOG can reliably capture the structures of targets in SAR images, and SDDL can further capture subtle differences among the different classes. By virtue of the SAR-HOG feature and SDDLSR, the proposed method achieves the state-of-the-art performance on MSTAR database. Especially for the extended operating conditions (EOC) scenario “Training 17 ∘ —Testing 45 ∘ ”, the proposed method improves remarkably with respect to the previous works.


title: Sustainable development and the water–energy–food nexus: A perspective on livelihoods

abstract: The water–energy–food nexus is being promoted as a conceptual tool for achieving sustainable development. Frameworks for implementing nexus thinking, however, have failed to explicitly or adequately incorporate sustainable livelihoods perspectives. This is counterintuitive given that livelihoods are key to achieving sustainable development. In this paper we present a critical review of nexus approaches and identify potential linkages with sustainable livelihoods theory and practice, to deepen our understanding of the interrelated dynamics between human populations and the natural environment. Building upon this review, we explore the concept of ‘environmental livelihood security’ – which encompasses a balance between natural resource supply and human demand on the environment to promote sustainability – and develop an integrated nexus-livelihoods framework for examining the environmental livelihood security of a system. The outcome is an integrated framework with the capacity to measure and monitor environmental livelihood security of whole systems by accounting for the water, energy and food requisites for livelihoods at multiple spatial scales and institutional levels. We anticipate this holistic approach will not only provide a significant contribution to achieving national and regional sustainable development targets, but will also be effective for promoting equity amongst individuals and communities in local and global development agendas.


title: Know your personalization: learning topic level personalization in online services

abstract: Online service platforms (OSPs), such as search engines, news-websites, ad-providers, etc., serve highly personalized content to the user, based on the profile extracted from her history with the OSP. In this paper, we capture OSP's personalization for an user in a new data structure called the personalization vector (?), which is a weighted vector over a set of topics, and present efficient algorithms to learn it. Our approach treats OSPs as black-boxes, and extracts η by mining only their output, specifically, the personalized (for an user) and vanilla (without any user information) contents served, and the differences in these content. We believe that such treatment of OSPs is a unique aspect of our work, not just enabling access to (so far hidden) profiles in OSPs, but also providing a novel and practical approach for retrieving information from OSPs by mining differences in their outputs. We formulate a new model called Latent Topic Personalization (LTP) that captures the personalization vector in a learning framework and present efficient inference algorithms for determining it. We perform extensive experiments targeting search engine personalization, using data from both real Google users and synthetic setup. Our results indicate that LTP achieves high accuracy (R-pre = 84%) in discovering personalized topics.For Google data, our qualitative results demonstrate that the topics determined by LTP for a user correspond well to his ad-categories determined by Google.


title: Cyclical Learning Rates for Training Neural Networks

abstract: It is known that the learning rate is the most important hyper-parameter to tune for training deep neural networks. This paper describes a new method for setting the learning rate, named cyclical learning rates, which practically eliminates the need to experimentally find the best values and schedule for the global learning rates. Instead of monotonically decreasing the learning rate, this method lets the learning rate cyclically vary between reasonable boundary values. Training with cyclical learning rates instead of fixed values achieves improved classification accuracy without a need to tune and often in fewer iterations. This paper also describes a simple way to estimate "reasonable bounds" -- linearly increasing the learning rate of the network for a few epochs. In addition, cyclical learning rates are demonstrated on the CIFAR-10 and CIFAR-100 datasets with ResNets, Stochastic Depth networks, and DenseNets, and the ImageNet dataset with the AlexNet and GoogLeNet architectures. These are practical tools for everyone who trains neural networks.