title: A superblock-based flash translation layer for NAND flash memory

abstract: In NAND flash-based storage systems, an intermediate software layer called a flash translation layer (FTL)is usually employed to hide the erase-before-write characteristics of NAND flash memory. This paper proposes a novel superblockbased FTL scheme, which combines a set of adjacent logical blocks into a superblock. In the proposed FTL scheme, superblocks are mapped at coarse granularity,while pages inside the superblock are mapped freely at fine granularity to any location in several physical blocks. To reduce extra storage and flash memory operations, the fine-grain mapping information is stored in the spare area of NAND flash memory. This hybrid mapping technique has the flexibility provided by fine-grain address translation, while reducing the memory overhead to the level of coarse-grain address translation. Our experimental results show that the proposed FTL scheme decreases the garbage collection overhead up to 40% compared to previous FTL schemes.


title: Photo tourism: exploring photo collections in 3D

abstract: We present a system for interactively browsing and exploring large unstructured collections of photographs of a scene using a novel 3D interface. Our system consists of an image-based modeling front end that automatically computes the viewpoint of each photograph as well as a sparse 3D model of the scene and image to model correspondences. Our photo explorer uses image-based rendering techniques to smoothly transition between photographs, while also enabling full 3D navigation and exploration of the set of images and world geometry, along with auxiliary information such as overhead maps. Our system also makes it easy to construct photo tours of scenic or historic locations, and to annotate image details, which are automatically transferred to other relevant images. We demonstrate our system on several large personal photo collections as well as images gathered from Internet photo sharing sites.


title: Area-level disadvantage and alcohol use disorder in northern Mexico

abstract: Abstract Background In Mexico, the Northern States are highly impacted by alcohol consumption and associated problems. Little is known about the association between contextual social disadvantage and alcohol use disorder in this region. Methods Information from 1265 current drinkers surveyed in the U.S.-Mexico Study on Alcohol and Related Conditions (UMSARC) was combined with official data on neighborhood disadvantage (index of urban marginalization, a composite of ten indicators of area-level social disadvantage) for 302 neighborhoods. Using statistical marginal models, we estimated the association of neighborhood disadvantage with alcohol use disorder (AUD; based on DSM-5 criteria), alone and with adjustment for individual and contextual covariates. We also tested for moderation of neighborhood disadvantage effects by sex, education, internal migration and border area. Results There was a statistically significant increase in the odds of AUD of 59% (AOR = 1.59; 95%CI = 1.03, 2.46) for every one-point increase on the neighborhood disadvantage scale, after adjustment for covariates. A significant interaction between sex and neighborhood disadvantage was indicated by two measures of additive interaction (AP = 0.55; p Conclusions Neighborhood disadvantage is a risk factor for AUD independent of other variables, specifically in men. Studies of contextual variables offer the possibility for understanding the role of collective circumstances on individuals in society. Future studies of alcohol use in this geographic area should consider effects of contextual determinants such as disadvantage.


title: B-trees, shadowing, and clones

abstract: B-trees are used by many file systems to represent files and directories. They provide guaranteed logarithmic time key-search, insert, and remove. File systems like WAFL and ZFS use shadowing, or copy-on-write, to implement snapshots, crash recovery, write-batching, and RAID. Serious difficulties arise when trying to use b-trees and shadowing in a single system. This article is about a set of b-tree algorithms that respects shadowing, achieves good concurrency, and implements cloning (writeable snapshots). Our cloning algorithm is efficient and allows the creation of a large number of clones. We believe that using our b-trees would allow shadowing file systems to better scale their on-disk data structures.


title: Challenges and improvements in distributed software development: a systematic review

abstract: Distributed Software Development (DSD) has recently evolved, resulting in an increase in the available literature. Organizations now have a tendency to make greater development efforts in more attractive zones. The main advantage of this lies in a greater availability of human resources in decentralized zones at less cost. There are, however, some disadvantages which are caused by the distance that separates the development teams. Coordination and communication become more difficult as the software components are sourced from different places, thus affecting project organization, project control, and product quality. New processes and tools are consequently necessary. This work presents the findings of a systematic review of the literature related to the challenges concerning Distributed Software Development, whose purpose is to identify the solutions and improvements proposed up to the present day.


title: Statistically Significant Detection of Linguistic Change

abstract: We propose a new computational approach for tracking and detecting statistically significant linguistic shifts in the meaning and usage of words. Such linguistic shifts are especially prevalent on the Internet, where the rapid exchange of ideas can quickly change a word's meaning. Our meta-analysis approach constructs property time series of word usage, and then uses statistically sound change point detection algorithms to identify significant linguistic shifts. We consider and analyze three approaches of increasing complexity to generate such linguistic property time series, the culmination of which uses distributional characteristics inferred from word co-occurrences. Using recently proposed deep neural language models, we first train vector representations of words for each time period. Second, we warp the vector spaces into one unified coordinate system. Finally, we construct a distance-based distributional time series for each word to track its linguistic displacement over time. We demonstrate that our approach is scalable by tracking linguistic change across years of micro-blogging using Twitter, a decade of product reviews using a corpus of movie reviews from Amazon, and a century of written books using the Google Book Ngrams. Our analysis reveals interesting patterns of language usage change commensurate with each medium.


title: Investigation of the Failure of Advanced High Strength Steels Heterogeneous Spot Welds

abstract: Nowadays, environmental regulation encourages carmakers to reduce the global vehicle weight. Steelmakers develop grades with high performance (Advanced High Strength Steels, AHSS) and fine steel sheet assemblies are used in car body structures, with an optimized thickness in each part. However, unusual fracture modes are sometimes observed during the mechanical tests of heterogeneous AHSS welds, made of dissimilar steel grades and sheet thicknesses. Weld fractures can occur with a strength lower than expected. This study aims at understanding these fracture mechanisms and focuses on two common steel grades joined by Resistance Spot Welding (RSW): DP600 (a dual phase steel) and Usibor®1500 (a martensitic steel). The parameters affecting the failure modes and load bearing capacity are investigated during two common types of tests: the Cross Tension and Tensile Shear tests. The positive effects of heterogeneous welding with respect to the corresponding homogeneous configurations are discussed, as well as the consequences of a so-called Dome failure occurring at the weld nugget boundary.


title: SAFE: A Simple Approach for Feature Extraction from App Descriptions and App Reviews

abstract: A main advantage of app stores is that they aggregate important information created by both developers and users. In the app store product pages, developers usually describe and maintain the features of their apps. In the app reviews, users comment these features. Recent studies focused on mining app features either as described by developers or as reviewed by users. However, extracting and matching the features from the app descriptions and the reviews is essential to bear the app store advantages, e.g. allowing analysts to identify which app features are actually being reviewed and which are not. In this paper, we propose SAFE, a novel uniform approach to extract app features from the single app pages, the single reviews and to match them. We manually build 18 part-of-speech patterns and 5 sentence patterns that are frequently used in text referring to app features. We then apply these patterns with several text pre-and post-processing steps. A major advantage of our approach is that it does not require large training and configuration data. To evaluate its accuracy, we manually extracted the features mentioned in the pages and reviews of 10 apps. The extraction precision and recall outperformed two state-of-the-art approaches. For well-maintained app pages such as for Google Drive our approach has a precision of 87% and on average 56% for 10 evaluated apps. SAFE also matches 87% of the features extracted from user reviews to those extracted from the app descriptions.


title: Preparedness for Emergency Response: Guidelines for the Emergency Planning Process

abstract: Especially since the terrorist attacks of 11 September 2001, governments worldwide have invested considerable resources in the writing of terrorism emergency response plans. Particularly in the United States, the federal government has created new homeland security organisations and urged state and local governments to draw up plans. This emphasis on the written plan tends to draw attention away from the process of planning itself and the original objective of achieving community emergency preparedness. This paper reviews the concepts of community preparedness and emergency planning, and their relationships with training, exercises and the written plan. A series of 10 planning process guidelines are presented that draw upon the preparedness literature for natural and technological disasters, and can be applied to any environmental threat.


title: Eigenvalue Spectra of Random Matrices for Neural Networks

abstract: Center for Neur obiology and Behavior , Columbia University ,,Colle ge of Physicians and Sur geons, New York, Ne w York 10032, USA(Receive d 18 July 2006; published 2 Nov ember 2006)The dynamics of neural networks is insuenced strongly by the spectrum of eigen values of the matrixdescribing their synaptic connectivity .In large networks, elements of the synaptic connecti vity matrix canbe chosen randomly from appropriate distributions, making results from random matrix theory highlyrele vant. Unfortunately , classic results on the eigen value spectra of random matrices do not apply tosynaptic connectivity matrices because of the constraint that indi vidual neurons are either ex citatory orinhibitory . Therefore, we compute eigen value spectra of large random matrices with ex citatory andinhibitory columns drawn from distributions with different means and equal or different variances.


title: Describing Videos using Multi-modal Fusion

abstract: Describing videos with natural language is one of the ultimate goals of video understanding. Video records multi-modal information including image, motion, aural, speech and so on. MSR Video to Language Challenge provides a good chance to study multi-modality fusion in caption task. In this paper, we propose the multi-modal fusion encoder and integrate it with text sequence decoder into an end-to-end video caption framework. Features from visual, aural, speech and meta modalities are fused together to represent the video contents. Long Short-Term Memory Recurrent Neural Networks (LSTM-RNNs) are then used as the decoder to generate natural language sentences. Experimental results show the effectiveness of multi-modal fusion encoder trained in the end-to-end framework, which achieved top performance in both common metrics evaluation and human evaluation.


title: A comparison of sentence retrieval techniques

abstract: Identifying redundant information in sentences is useful for several applications such as summarization, document provenance, detecting text reuse and novelty detection. The task of identifying redundant information in sentences is defined as follows: Given a query sentence the task is to retrieve sentences from a given collection that express all or some subset of the information present in the query sentence. Sentence retrieval techniques rank sentences based on some measure of their similarity to a query. The effectiveness of such techniques depends on the similarity measure used to rank sentences. An effective retrieval model should be able to handle low word overlap between query and candidate sentences and go beyond just word overlap. Simple language modeling techniques like query likelihood retrieval have outperformed TF-IDF and word overlap based methods for ranking sentences. In this paper, we compare the performance of sentence retrieval using different language modeling techniques for the problem of identifying redundant information.


title: The Omniglot Challenge: A 3-Year Progress Report

abstract: Three years ago, we released the Omniglot dataset for one-shot learning, along with five challenge tasks and a computational model that addresses these tasks. The model was not meant to be the final word on Omniglot; we hoped that the community would build on our work and develop new approaches. In the time since, we have been pleased to see wide adoption of the dataset. There has been notable progress on one-shot classification, but researchers have adopted new splits and procedures that make the task easier. There has been less progress on the other four tasks. We conclude that recent approaches are still far from human-like concept learning on Omniglot, a challenge that requires performing many tasks with a single model.


title: Building a practical and reliable classifier for malware detection

abstract: Having a machine learning algorithm that can correctly classify malicious software has become a necessity as old methods of detection based on hashes and hand written heuristics tend to fail when dealing with the intensive flow of new malware. However, in order to be practical, the machine learning classifiers must also have a reasonable training time and a very small amount, preferably zero, of false positives. There were a few authors who addressed both these issues in their papers but creating such a model is more difficult when more than 3 million files are involved/needed in the training. We mapped a zero false positive perceptron in a new space, applied a feature selection algorithm and used the resulted model in an ensemble, voting or a rule based clustering system we’ve managed to achieve a detection rate around 99 % and 0.07 % false positives while keeping the training time suitable for large data sets.


The Impact of Social Media on Academic Performance of Selected College Students


title: A Domain-Specific Language for Low-Level Secure Multiparty Computation Protocols

abstract: Sharemind is an efficient framework for secure multiparty computations (SMC). Its efficiency is in part achieved through a large set of primitive, optimized SMC protocols that it makes available to applications built on its top. The size of this set has brought with it an issue not present in frameworks with a small number of supported operations: the set of protocols must be maintained, as new protocols are still added to it and possible optimizations for a particular sub-protocol should be propagated into larger protocols working with data of different types. To ease the maintenance of existing and implementation of new protocols, we have devised a domain-specific language (DSL) and its optimizing compiler for specifying protocols for secure computation. In this paper, we give the rationale of the design, describe the translation steps, the location of the compiler in the whole Sharemind protocol stack, and the results obtained with this system.


title: Learning Type-Aware Embeddings for Fashion Compatibility

abstract: Outfits in online fashion data are composed of items of many different types (e.g. top, bottom, shoes) that share some stylistic relationship with one another. A representation for building outfits requires a method that can learn both notions of similarity (for example, when two tops are interchangeable) and compatibility (items of possibly different type that can go together in an outfit). This paper presents an approach to learning an image embedding that respects item type, and jointly learns notions of item similarity and compatibility in an end-to-end model. To evaluate the learned representation, we crawled 68,306 outfits created by users on the Polyvore website. Our approach obtains 3-5% improvement over the state-of-the-art on outfit compatibility prediction and fill-in-the-blank tasks using our dataset, as well as an established smaller dataset, while supporting a variety of useful queries.


title: Findings of the E2E NLG Challenge

abstract: This paper summarises the experimental setup and results of the first shared task on end-to-end (E2E) natural language generation (NLG) in spoken dialogue systems. Recent end-to-end generation systems are promising since they reduce the need for data annotation. However, they are currently limited to small, delexicalised datasets. The E2E NLG shared task aims to assess whether these novel approaches can generate better-quality output by learning from a dataset containing higher lexical richness, syntactic complexity and diverse discourse phenomena. We compare 62 systems submitted by 17 institutions, covering a wide range of approaches, including machine learning architectures -- with the majority implementing sequence-to-sequence models (seq2seq) -- as well as systems based on grammatical rules and templates.


title: Soft pneumatic actuator with adjustable stiffness layers for Multi-DoF Actuation

abstract: The soft pneumatic actuators (SPAs) are a solution toward the highly customizable and light actuators with the versatility of actuation modes, and an inherent compliance. Such flexibility allows SPAs to be considered as alternative actuators for wearable rehabilitative devices and search and rescue robots. The actuator material and air-chamber design dictate the actuator's mechanical performance. Therefore, each actuator design with a single pressure source produces a highly customized motion but only a single degree of freedom (DoF). We present a novel design and fabrication method for a SPA with different modes of actuation using integrated adjustable stiffness layers (ASLs). Unlike the most SPA designs where one independent chamber is needed for each mode of actuation, here we have a single chamber that drives three different modes of actuation by activating different combinations of ASLs. Adapting customized micro heaters and thermistors for modulating the temperature and stiffness of ASLs, we considerably broaden the work space of the SPA actuator. Here, a thorough characterization of the materials and the modeling of the actuator are presented. We propose a design methodology for developing application specific actuators with multi-DoFs that are light and compact.


title: Android App Store (Google Play) Mining and Analysis

abstract: The aim of mining and analysis of Apps in Google Play, the largest Android app store, is to provide in-depth insight on the hidden properties of the repository to app developers or app market contr ...


An empirical study of how third-party websites influence the feedback mechanism between online Word-of-Mouth and retail sales


Crosslingual Distributed Representations of Words


title: Automating the design of graphical presentations of relational information

abstract: The goal of the research described in this paper is to develop an application-independent presentation tool that automatically designs effective graphical presentations (such as bar charts, scatter plots, and connected graphs) of relational information. Two problems are raised by this goal: The codification of graphic design criteria in a form that can be used by the presentation tool, and the generation of a wide variety of designs so that the presentation tool can accommodate a wide variety of information. The approach described in this paper is based on the view that graphical presentations are sentences of graphical languages. The graphic design issues are codified as expressiveness and effectiveness criteria for graphical languages. Expressiveness criteria determine whether a graphical language can express the desired information. Effectiveness criteria determine whether a graphical language exploits the capabilities of the output medium and the human visual system. A wide variety of designs can be systematically generated by using a composition algebra that composes a small set of primitive graphical languages. Artificial intelligence techniques are used to implement a prototype presentation tool called APT (A Presentation Tool), which is based on the composition algebra and the graphic design criteria.


title: Calibration, Registration, and Synchronization for High Precision Augmented Reality Haptics

abstract: In our current research we examine the application of visuo-haptic augmented reality setups in medical training. To this end, highly accurate calibration, system stability, and low latency are indispensable prerequisites. These are necessary to maintain user immersion and avoid breaks in presence which potentially diminish the training outcome. In this paper we describe the developed calibration methods for visuo-haptic integration, the hybrid tracking technique for stable alignment of the augmentation, and the distributed framework ensuring low latency and component synchronization. Finally, we outline an early prototype system based on the multimodal augmented reality framework. The latter allows colocated visuo-haptic interaction with real and virtual scene components in a simplified open surgery setting.


title: Intra-abdominal Pressure and Trunk Muscular Activities during Abdominal Bracing and Hollowing

abstract: This study examined the difference in intra-abdominal pressure (IAP) between abdominal bracing and hollowing in relation to trunk muscular activities. IAP with a pressure transducer placed in the rectum and surface electromyograms for rectus abdominis, external oblique, internal oblique, and erector spinae during the 2 tasks were obtained in 7 young adult men. The difference between IAP at rest and its peak value (ΔIAPmax) showed high intra- and inter-day repeatability, and was significantly greater in abdominal bracing (116.4±15.0 mmHg) than in abdominal hollowing (9.9±4.5 mmHg). The trunk muscular activities at ΔIAPmax were significantly higher in abdominal bracing than in abdominal hollowing, and in the internal oblique than in the other 3 muscles. In both abdominal bracing and hollowing, the changes in IAP during the tasks were linearly correlated with those in trunk muscular activities, but the slope of the regression line for the relationship differed between the 2 tasks. The current results indicate that 1) abdominal bracing is an effective maneuver to elevate IAP compared with abdominal hollowing, and 2) in the 2 tasks, the changes in IAP are linked with those in trunk muscular activities, but the association is task-specific.


title: Current Issues in Sampling-Based Motion Planning

abstract: In this paper, we discuss the field of sampling-based motion planning. In contrast to methods that construct boundary representations of configuration space obstacles, samplingbased methods use only information from a collision detector as they search the configuration space. The simplicity of this approach, along with increases in computation power and the development of efficient collision detection algorithms, has resulted in the introduction of a number of powerful motion planning algorithms, capable of solving challenging problems with many degrees of freedom. First, we trace how sampling-based motion planning has developed. We then discuss a variety of important issues for sampling-based motion planning, including uniform and regular sampling, topological issues, and search philosophies. Finally, we address important issues regarding the role of randomization in sampling-based motion planning.


title: The temperature dependence of dielectric dispersion in polycrystalline chromites

abstract: The variation of resistivity and dielectric constant with temperature at high and low frequencies has been investigated for the polycrystalline systems CeCrO3 and Ca0·1Ce0·9CrO3. At high frequencies the activation energy of resistivity was found to be 0·22±0·01 eV for both materials. This activation energy is probably the energy required for p-type controlled valence conduction between Cr3+ and Cr4+ ions. Using Koops' theory for dispersion in inhomogeneous materials, expressions were obtained for the temperature dependence of resistivity and dielectric constant at high and low frequencies. The experimental results are shown to be consistent with this theory.


title: The capacitated facility location-allocation problem under uncertain environment

abstract: Facility location-allocation (FLA) problem has been widely studied by operational researchers due to its many practical applications. In real life, it is usually very hard to present the customers' demands in a precise way and thus they are regarded to be uncertain. Since the uncertain demands can be estimated from historical data, researchers tried to describe FLA problem under stochastic environment. Although stochastic models can cater for a variety of cases, they are not sufficient to describe many other situations, where the probability distribution of customers' demands may be unknown or partially known. Instead we have to invite some domain experts to evaluate their belief degree that each event will occur. This paper will consider the capacitated FLA problem under small sample or no-sample cases and establish an uncertain expected value model based on uncertain measure. In order to solve this model, the simplex algorithm, Monte Carlo simulation and a genetic algorithm are integrated to produce a hybrid intelligent algorithm. Finally, a numerical example is presented to illustrate the uncertain model and the algorithm.


Expert systems: advanced artificial intelligence concepts for integrated crop management


title: Speaker-independent isolated word recognition using dynamic features of speech spectrum

abstract: This paper proposes a new isolated word recognition technique based on a combination of instantaneous and dynamic features of the speech spectrum. This technique is shown to be highly effective in speaker-independent speech recognition. Spoken utterances are represented by time sequences of cepstrum coefficients and energy. Regression coefficients for these time functions are extracted for every frame over an approximately 50 ms period. Time functions of regression coefficients extracted for cepstrum and energy are combined with time functions of the original cepstrum coefficients, and used with a staggered array DP matching algorithm to compare multiple templates and input speech. Speaker-independent isolated word recognition experiments using a vocabulary of 100 Japanese city names indicate that a recognition error rate of 2.4 percent can be obtained with this method. Using only the original cepstrum coefficients the error rate is 6.2 percent.


title: Dynamic time warping for speech recognition with training part to reduce the computation

abstract: Dynamic time warping (DTW) is a popular automatic speech recognition (ASR) method based on template matching[1] [2]. DTW algorithm compares the parameters of an unknown word with the parameters of one reference template. But the recognition rate is limited. To increase the number of reference templates for the same word will improve the recognition rate, but it will lead to spend a lot of computing time and memory resource. In this paper we proposed a method to reduce the number of reference templates, thus reduces the computing time and memory resource and also keep the high recognition rate.


title: A parallel implementation of K-means clustering on GPUs

abstract: Graphics Processing Units (GPU) have recently been the subject of attention in research as an efficient coprocessor for implementing many classes of highly parallel applications. The GPUs design is engineered for graphics applications, where many independent SIMD workloads are simultaneously dispatched to processing elements. While parallelism has been explored in the context of traditional CPU threads and SIMD processing elements, the principles involved in dividing the steps of a parallel algorithm for execution on GPU architectures remains a significant challenge. In this paper, we introduce a first step towards building an efficient GPU-based parallel implementation of a commonly used clustering algorithm called K-Means on an NVIDIA G80 PCI express graphics board using the CUDA processing extensions. Clustering algorithms are important for search, data mining, spam and intrusion detection applications. Modern desktop machines commonly include desktop search software that can be greatly enhanced by these advances, while low-power machines such as laptops can reduce power consumption by utilizing the video chip for these clustering and indexing operations. Our preliminary results show over a 13x performance improvement compared to a baseline 3 GHz Intel Pentium(R) based PC running the same algorithm with an average spec G80 graphics card, the NVIDIA 8600GT. The low cost of these video cards (less than $100 market price as of 2008), and the high performance gains suggest that our approach is both practical and economical for common applications.


title: A telco's perspectives: Open and flexible transport SDN

abstract: This Paper presents a telco's perspectives and experiences to design and implement open and flexible architecture for Transport SDN (T-SDN). We believe that this could be realized by adopting open source based SDN controllers because they already have widely accepted and referenced architecture with a rich ecosystem. Moreover, we could have further benefits from open source such as reducing time to deploy and saving cost to develop. Regarding transport domain, however, there are still some challenges for us to adopt open source controllers. That is, which open source is appropriate, how to seamlessly integrate with legacy system, and which features of open source controller are adequate in current environment are things like that. In this paper, we discuss these challenges in depth and give some directions from our Proof-of-Concept (PoC) experiences for developing T-SDN based on OpenDayLight (ODL).


title: Detection of acute hepatopancreatic necrosis disease (AHPND) in Mexico

abstract: Acute hepatopancreatic necrosis disease (AHPND), which has also been referred to as early mortality syndrome (EMS), initially emerged as a destructive disease of cultured shrimp species in Asia in 2009. The pathogen associated with the disease, Vibrio parahaemolyticus, sub- sequently spread to the Western Hemisphere and emerged in Mexico in early 2013. The spread to the Western Hemisphere is a major concern to shrimp producers in the region. To date, the only peer-reviewed published method for determining whether mortalities are due to AHPND is through histological examination. A novel PCR detection method was employed to assess samples from Mexico in order to confirm the presence of the pathogen in this country. This manuscript details the detection methods used to confirm the presence of AHPND in Mexico. Both immersion and per os challenge studies were used to expose the Penaeus vannamei to the bacteria in order to induce the disease. Histological analysis confirmed AHPND status following the challenge studies. Also provided are the details of the molecular test by PCR that was used for screening candidate V. parahaemolyticus isolates. A rapid PCR assay for detection of AHPND may help with early detection and help prevent the spread of AHPND to other countries.


title: Multi-order transmission line-radial stub networks for broadband impedance matching and power combining in a watt-level silicon power amplifier

abstract: In this paper, multi-order, on-chip transmission line-radial stub ladder networks are designed to perform fundamental and harmonic impedance matching and power combining in a broadband silicon power amplifier. A 16–40GHz, 1W PA with integrated 50Ω output matching and power combining is designed in a 130nm SiGe BiCMOS process. The PA achieves a peak gain of 23.8dB and a maximum single ended output power of 30.6dBm with 21.9% power-added efficiency (PAE). On chip transmission line-radial stub-based matching and combing networks are designed and optimized for maximum efficiency to provide optimum load conditions at fundamental and harmonic frequencies and to combine the power of 16 separate amplifiers with minimized passive loss. Load pull simulations are performed in the frequency range considering load conditions up to the 5th harmonic. The simulated total loss from the output matching and the 16-to-1 combining network is less than 2.9dB in the entire frequency band.


Improving Neural Text Normalization with Data Augmentation at Character- and Morphological Levels


title: Epidemiology overview of gastrointestinal and renal toxicity of NSAIDs

abstract: Abstract This review updates previous systematic reviews to explore the overall levels of risk of serious upper gastrointestinal complications of treatment with non-steroidal anti-inflammatory drugs (NSAIDs) and to investigate the importance of dose in explaining variations in risk. Thirty-six eligible case control studies were published between 1985 and 2000 and involved 19,648 cases and 105,373 controls. Eight eligible cohort studies included around 400,000 exposed subjects and 1 million non-exposed controls. The pooled unadjusted odds ratio from case control studies (compared with non-use of NSAIDs) for serious gastrointestinal complications is 4.06 (CI95% 3.47, 4.75); the pooled odds ratio from cohort studies is 2.29 (CI95% 1.50, 3.51). Unadjusted odds ratios range from 1.81 for ibuprofen to 7.46 for piroxicam. These data require careful interpretation because the statistical power of analyses of individual NSAIDs is lower than that for the class as a whole. There is a rank order of risk for different NSAIDs at low doses but at higher doses the odds ratios tend to converge. Ibuprofen is associated with a lower risk of serious gastrointestinal complications than other NSAIDs; this advantage is probably lost at higher doses (>1800 mg/day).


title: Classification of Histopathology Images of Breast into Benign and Malignant using a Single-layer Convolutional Neural Network

abstract: Breast cancer is known as the second most prevalent cancer among women worldwide, and an accurate and fast diagnosis of it requires a pathologist to go through a time-consuming process examining different captured images under varying magnifications. Computer vision and machine learning techniques are used by many scholars to automate this process and provide a faster and more accurate diagnosis of such cancers, but most of them have utilized handengineered feature descriptors to classify the type of images (whether benign or malignant) based upon. Deep learning techniques have made a significant progress in the world of pattern recognition, image classification, object detection, etc. Convolutional Neural Networks (CNN) -- a special kind of deep learning methods -- are best known for identifying patterns in the images; they try to represent an abstract form of images containing the most salient information needed for distinguishing them from different similar-looking images. The main aim of this paper is to employ CNN for the task of breast cancer classification given an unknown image of the patient for an accurate diagnosis. A new network design is proposed to extract the most informative features from a collection of histopathology images provided by BreakHis database of microscopic breast tumor images. The experimental results carried on 1,995 histopathological images (with a 40× magnifying factor), demonstrated an improved accuracy compared to some prior works, and a comparable performance regarding one of the previous works.


title: Multidimensional poverty measures for analysing educational poverty in European countries

abstract: This paper studies the degree of educational poverty in European countries by focusing on data from two editions of the OECD Programme for International Student Assessment (PISA): 2006 and 2015. We focus on students’ proficiency levels in various literacy domains and propose a multidimensional approach that enables the measurement of not only the incidence of educational poverty but also its depth and severity. Subsequently, we perform a microeconometric analysis of school factors that are associated with the probability of educational poverty using a Partial Proportional Odds Model. The main results demonstrate that in recent years, the incidence of educational poverty became more relevant in many countries, while most of them experienced a reduction of poverty depth and severity. Several school factors can be manipulated to avoid the trap of educational poverty, such as improving the disciplinary climate, adopting an adaptive style of teaching, and increasing the amount of instructional time.


title: Study and prototyping of practically large-scale mmWave antenna systems for 5G cellular devices

abstract: This article discusses the challenges, benefits and approaches associated with realizing largescale antenna arrays at mmWave frequency bands for future 5G cellular devices. Key design considerations are investigated to deduce a novel and practical phased array antenna solution operating at 28 GHz with near spherical coverage. The approach is further evolved into a first-of- a-kind cellular phone prototype equipped with mmWave 5G antenna arrays consisting of a total of 32 low-profile antenna elements. Indoor measurements are carried out using the presented prototype to characterize the proposed mmWave antenna system using 16-QAM modulated signals with 27.925 GHz carrier frequency. The biological implications due to the absorbed electromagnetic waves when using mmWave cellular devices are studied and compared in detail with those of 3/4G cellular devices.


title: A risk management framework for software engineering practice

abstract: Formal risk analysis and management in software engineering is still an emerging part of project management. We provide a brief introduction to the concepts of risk management for software development projects, and then an overview of a new risk management framework. Risk management for software projects is intended to minimize the chances of unexpected events, or more specifically to keep all possible outcomes under tight management control. Risk management is also concerned with making judgments about how risk events are to be treated, valued, compared and combined. The ProRisk management framework is intended to account for a number of the key risk management principles required for managing the process of software development. It also provides a support environment to operationalize these management tasks.


title: Comparing Policy Gradient and Value Function Based Reinforcement Learning Methods in Simulated Electrical Power Trade

abstract: In electrical power engineering, reinforcement learning algorithms can be used to model the strategies of electricity market participants. However, traditional value function based reinforcement learning algorithms suffer from convergence issues when used with value function approximators. Function approximation is required in this domain to capture the characteristics of the complex and continuous multivariate problem space. The contribution of this paper is the comparison of policy gradient reinforcement learning methods, using artificial neural networks for policy function approximation, with traditional value function based methods in simulations of electricity trade. The methods are compared using an AC optimal power flow based power exchange auction market model and a reference electric power system model.


title: Bayesian Symbol-Refined Tree Substitution Grammars for Syntactic Parsing

abstract: We propose Symbol-Refined Tree Substitution Grammars (SR-TSGs) for syntactic parsing. An SR-TSG is an extension of the conventional TSG model where each nonterminal symbol can be refined (subcategorized) to fit the training data. We aim to provide a unified model where TSG rules and symbol refinement are learned from training data in a fully automatic and consistent fashion. We present a novel probabilistic SR-TSG model based on the hierarchical Pitman-Yor Process to encode backoff smoothing from a fine-grained SR-TSG to simpler CFG rules, and develop an efficient training method based on Markov Chain Monte Carlo (MCMC) sampling. Our SR-TSG parser achieves an F1 score of 92.4% in the Wall Street Journal (WSJ) English Penn Treebank parsing task, which is a 7.7 point improvement over a conventional Bayesian TSG parser, and better than state-of-the-art discriminative reranking parsers.


title: Compressive demosaicing for periodic color filter arrays

abstract: The utility of Compressed Sensing (CS) for demosaicing of images captured using random panchromatic color filter arrays (CFA) has been investigated in [1]. Meanwhile, most camera manufacturers employ periodic CFAs such as the popular Bayer CFA. In this paper, we derive a CS-based solution to demosaicing images captured using the general class of periodic CFAs. It is well known that periodic CFAs can be designed to effectively separate luminance and chrominance frequency bands [2, 3]. We employ this ability to reduce artifacts associated with luminance-chrominance overlap at the solver side. We show that the modified compressive demo-saicing method coupled with the additional constraint that chrominance channels have smooth surfaces achieves further improved results for most periodic CFAs.


title: Approximation algorithms for multi-dimensional assignment problems with decomposable costs

abstract: The k-dimensional assignment problem with decomposable costs is formulated as follows. Given is a complete k-partite graph G = (X0 ∪ ⋯ ∪ Xk − 1, E), with |Xi| = p for each i, and a nonnegative length function defined on the edges of G. A clique of G is a subset of vertices meeting each Xi in exactly one vertex. The cost of a clique is a function of the lengths of the edges induced by the clique. Four specific cost functions are considered in this paper; namely, the cost of a clique is either the sum of the lengths of the edges induced by the clique (sum costs), or the minimum length of a spanning star (star costs) or of a traveling salesman tour (tour costs) or of a spanning tree (tree costs) of the induced subgraph. The problem is to find a minimum-cost partition of the vertex set of G into cliques. We propose several simple heuristics for this problem, and we derive worst-case bounds on the ratio between the cost of the solutions produced by these heuristics and the cost of an optimal solution. The worst-case bounds are stated in terms of two parameters, viz. k and τ, where the parameter τ indicates how close the edge length function comes to satisfying the triangle inequality.


title: The chemokine system and its contribution to leukemogenesis and treatment responsiveness in patients with acute myeloid leukemia.

abstract: The chemokine family consists of approximately 50 small (8-14 kDa), basic proteins that are expressed and released by a wide range of normal and malignant cells. Most chemokines act through heptahelical transmembrane G protein- coupled receptors. Based on their molecular structure these cytokines are divided into the two major subgroups CCL and CXCL chemokines that bind to CCR or CXCR receptors respectively. Primary human acute myelogenous leukemia (AML) cells show constitutive release of a wide range of chemokines, but the chemokine release profile differs between patients. Among the commonly expressed chemokines are proangiogenic CXCL8, antiangiogenic CXCL4/9-11 and several leukocyte-chemotactic chemokines. Systemic serum levels of leukocyte-chemotactic chemokines depend both on patient age, disease status, the chemotherapy regimen and development of complicating infections. The local chemokine network in human AML is probably further modulated by the hypoxic bone marrow microenvironment and the local release of chemokines by nonleukemic bone marrow stromal cells. Usually primary AML cells also express several chemokine receptors. Specific chemokine inhibitors are now being developed, including chemokine-neutralizing or receptor-blocking antibodies, antisense strategies, receptor-blocking small molecules or inhibitors of downstream signaling. The use of CXCR4-antagonists for mobilization of peripheral blood stem cells has been documented in several clinical studies. Although animal studies suggest that chemokine inhibition also may become useful in the treatment of graft versus host disease, the possible use of chemokine-targeting therapy for other indications than stem cell mobilization requires further studies.


title: Exploring Mediation Effect of Mental Alertness for Expressive Lights: Preliminary Results of LED Light Animations on Intention to Buy Hedonic Products and Choose between Healthy and Unhealthy Food

abstract: Expressive light has been explored in a handful of previous studies as a means for robots, especially appearance- constrained robots that are not able to employ human-like expressions, to convey internal states and interact with people. However, it is still unknown how different light expressions can affect a person's perception and behavior. In this poster, we explore this research question by studying the effects of different expressive light animations on people's intention to buy hedonic products and how they choose between healthy and unhealthy food. Our preliminary results show that participants assigned to a positive and low arousal light animation condition had a higher intention of purchasing hedonic products and were inclined to choose unhealthy over healthy food. Such findings are in line with previous literature in marketing research, suggesting that mental alertness mediates the effect of external stimuli on a person's behavioral intentions. Future work is thus required to evaluate such findings in a human-robot interaction context.


title: Discovering the Internet of Things (IoT) within the business process management: a literature review on technological revitalization

abstract: Purpose – We still know very little about how the Internet of Things (IoT) is changing the way of interpreting the business process management inside and outside firms and this topic is progressively becoming increasingly hot in the leading managerial literature (Al-Mashari and Zairi, 2000). Furthermore, management scholars are aiming at investigating the impact and the role of the IoT on the business process management in terms of promotion of knowledge flow, innovation and competitiveness. Likewise, management researchers aim at understanding how the IoT fosters innovation within organizations and which implications this phenomenon may have on the business process management and competitiveness of firms (Al-Mashari and Zairi, 1999). The purpose of this paper is to offer a literature review on the topic, by considering the technological revitalization through the IoT as a key driver for the business process management of the industrial firm. Design/methodology/approach – The present paper offers a litera...


title: Real-time prediction of inpatient length of stay for discharge prioritization

abstract: Objective Hospitals are challenged to provide timely patient care while maintaining high resource utilization. This has prompted hospital initiatives to increase patient flow and minimize nonvalue added care time. Real-time demand capacity management (RTDC) is one such initiative whereby clinicians convene each morning to predict patients able to leave the same day and prioritize their remaining tasks for early discharge. Our objective is to automate and improve these discharge predictions by applying supervised machine learning methods to readily available health information. ::: ::: Materials and Methods The authors use supervised machine learning methods to predict patients’ likelihood of discharge by 2 p.m. and by midnight each day for an inpatient medical unit. Using data collected over 8000 patient stays and 20 000 patient days, the predictive performance of the model is compared to clinicians using sensitivity, specificity, Youden’s Index (i.e., sensitivity + specificity – 1), and aggregate accuracy measures. ::: ::: Results The model compared to clinician predictions demonstrated significantly higher sensitivity ( P .10). Early discharges were less predictable than midnight discharges. The model was more accurate than clinicians in predicting the total number of daily discharges and capable of ranking patients closest to future discharge. ::: ::: Conclusions There is potential to use readily available health information to predict daily patient discharges with accuracies comparable to clinician predictions. This approach may be used to automate and support daily RTDC predictions aimed at improving patient flow.


title: On the Construction of Lightweight Circulant Involutory MDS Matrices

abstract: In the present paper, we investigate the problem of constructing MDS matrices with as few bit XOR operations as possible. The key contribution of the present paper is constructing MDS matrices with entries in the set of $$m\times m$$ non-singular matrices over $$\mathbb {F}_2$$ directly, and the linear transformations we used to construct MDS matrices are not assumed pairwise commutative. With this method, it is shown that circulant involutory MDS matrices, which have been proved do not exist over the finite field $$\mathbb {F}_{2^m}$$, can be constructed by using non-commutative entries. Some constructions of $$4\times 4$$ and $$5\times 5$$ circulant involutory MDS matrices are given when $$m=4,8$$. To the best of our knowledge, it is the first time that circulant involutory MDS matrices have been constructed. Furthermore, some lower bounds on XORs that required to evaluate one row of circulant and Hadamard MDS matrices of order 4 are given when $$m=4,8$$. Some constructions achieving the bound are also given, which have fewer XORs than previous constructions.


Which is the Effective Way for Gaokao: Information Retrieval or Neural Networks?


Gravitational Search Algorithm and Recent Trends


title: A Suggestion of Modified Classification of Trigeminal Schwannomas According to Location, Shape, and Extension

abstract: Background ::: Comprehensive knowledge of the anatomical features of trigeminal schwannomas (TSs) is essential in planning surgery to achieve complete tumor resection. In the current report, we propose a modified classification of TSs according to their location of origin, shape, and extension into the adjacent compartment, and discuss appropriate surgical strategies with this classification.


title: Analysis of a concentric coplanar capacitive sensor for nondestructive evaluation of multi-layered dielectric structures

abstract: A concentric coplanar capacitive sensor is analyzed for the quantitative characterization of material properties for multi-layered dielectrics. The sensor output signal, transcapacitance CT, is related to the thickness and dielectric constant of each layer of the material under test. Electrostatic Green's functions due to point charges over different dielectric structures are derived utilizing the Hankel transform given the cylindrical symmetry of the proposed sensor. Numerical implementations based on the Green's functions are presented. The sensor electrodes are divided into a number of circular filaments, and the sensor surface charge distribution is then calculated using the method of moments (MoM). From the sensor surface charge, CT is calculated. Numerical calculations on sensor optimization are conducted and show that normalized CT as a function of sensor configuration is determined solely by its own relative dimensions, regardless of the overall dimensions of the sensor. In addition, calculations indicate how the sensor can be optimized for sensitivity to changes in core permittivity of a three-layer test-piece such as an aircraft radome. Benchmark experiment results are provided for one, two-, and three-layer test-pieces and very good agreement with calculated CT is observed. The sensor is also applied to water ingression measurements in a sandwich structure resembling the aircraft radome, in which the water-injected area can be successfully detected from the sensor output signal.


title: Scheherazade: crowd-powered interactive narrative generation

abstract: Interactive narrative is a form of storytelling in which users affect a dramatic storyline through actions by assuming the role of characters in a virtual world. This extended abstract outlines the SCHEHERAZADE-IF system, which uses crowdsourcing and artificial intelligence to automatically construct text-based interactive narrative experiences.


title: Online slip parameter estimation for tracked vehicle odometry on loose slope

abstract: In case of volcanic eruption, a robotic volcano exploration for observing restricted areas is expected to judge the evacuation call for inhabitants. An unmanned ground vehicle (UGV) is one possibility to apply to such exploration missions. When a UGV traverses on volcanic fields, a slippage between the vehicle and the terrain occurs. This is because the volcanic environment is covered with loose soil and rocks, and there are many slopes. The slippage causes several problems for UGVs, particularly localization and terrainability. Therefore, in this research, we propose a slip estimation method based on a slip model to apply to slip-compensated odometry for tracked vehicles. First, we propose a slip model for tracked vehicles based on the force acting on a robot on a slope. The proposed slip model has two parameters: a pitch angle dependence and a constant component, and these parameters were identified by indoor slope-traveling experiments. Next, we propose a slip parameter estimation method using a particle filter technique with a velocity measurement sensor, and report on the effectiveness of our method by slope-traveling experiments. The experimental result shows that the accuracy of our position estimation method based on the slip-compensated odometry is improved in comparison with conventional methods by using the slip parameters.


title: The Design and Evolution of Modular Neural Network Architectures

abstract: To investigate the relations between structure and function in both artificial and natural neural networks, we present a series of simulations and analyses with modular neural networks. We suggest a number of design principles in the form of explicit ways in which neural modules can cooperate in recognition tasks. These results may supplement recent accounts of the relation between structure and function in the brain. The networks used consist of several modules, standard subnetworks that serve as higher order units with a distinct structure and function. The simulations rely on a particular network module called the categorizing and learning module. This module, developed mainly for unsupervised categorization and learning, is able to adjust its local learning dynamics. The way in which modules are interconnected is an important determinant of the learning and categorization behaviour of the network as a whole. Based on arguments derived from neuroscience, psychology, computational learning theory, and hardware implementation, a framework for the design of such modular networks is presented. A number of small-scale simulation studies shows how intermodule connectivity patterns implement ''neural assemblies'' that induce a particular category structure in the network. Learning and categorization improves because the induced categories are more compatible with the structure of the task domain. In addition to structural compatibility, two other principles of design are proposed that underlie information processing in interactive activation networks: replication and recurrence. Because a general theory for relating network architectures to specific neural functions does not exist, we extend the biological metaphor of neural networks, by applying genetic algorithms (a biocomputing method for search and optimization based on natural selection and evolution) to search for optimal modular network architectures for learning a visual categorization task. The best performing network architectures seemed to have reproduced some of the overall characteristics of the natural visual system, such as the organization of coarse and fine processing of stimuli in separate pathways. A potentially important result is that a genetically defined initial architecture cannot only enhance learning and recognition performance, but it can also induce a system to better generalize its learned behaviour to instances never encountered before. This may explain why for many vital learning tasks in organisms only a minimal exposure to relevant stimuli is necessary.


title: Adaptation impact and environment models for architecture-based self-adaptive systems

abstract: Self-adaptive systems have the ability to adapt their behavior to dynamic operating conditions. In reaction to changes in the environment, these systems determine the appropriate corrective actions based in part on information about which action will have the best impact on the system. Existing models used to describe the impact of adaptations are either unable to capture the underlying uncertainty and variability of such dynamic environments, or are not compositional and described at a level of abstraction too low to scale in terms of specification effort required for non-trivial systems. In this paper, we address these shortcomings by describing an approach to the specification of impact models based on architectural system descriptions, which at the same time allows us to represent both variability and uncertainty in the outcome of adaptations, hence improving the selection of the best corrective action. The core of our approach is a language equipped with a formal semantics defined in terms of Discrete Time Markov Chains that enables us to describe both the impact of adaptation tactics, as well as the assumptions about the environment. To validate our approach, we show how employing our language can improve the accuracy of predictions used for decision-making in the Rainbow framework for architecture-based self-adaptation.


title: Adapting predominant and novel sense discovery algorithms for identifying corpus-specific sense differences

abstract: Word senses are not static and may have temporal, spatial or corpus-specific scopes. Identifying such scopes might benefit the existing WSD systems largely. In this paper, while studying corpus specific word senses, we adapt three existing predominant and novel-sense discovery algorithms to identify these corpus-specific senses. We make use of text data available in the form of millions of digitized books and newspaper archives as two different sources of corpora and propose automated methods to identify corpus-specific word senses at various time points. We conduct an extensive and thorough human judgment experiment to rigorously evaluate and compare the performance of these approaches. Post adaptation, the output of the three algorithms are in the same format and the accuracy results are also comparable, with roughly 45-60% of the reported corpus-specific senses being judged as genuine.


title: Human milk oligosaccharides: Every baby needs a sugar mama

abstract: Human milk oligosaccharides (HMOs) are a family of structurally diverse unconjugated glycans that are highly abundant in and unique to human milk. Originally, HMOs were discovered as a prebiotic “bifidus factor” that serves as a metabolic substrate for desired bacteria and shapes an intestinal microbiota composition with health benefits for the breast-fed neonate. Today, HMOs are known to be more than just “food for bugs”. An accumulating body of evidence suggests that HMOs are antiadhesive antimicrobials that serve as soluble decoy receptors, prevent pathogen attachment to infant mucosal surfaces and lower the risk for viral, bacterial and protozoan parasite infections. In addition, HMOs may modulate epithelial and immune cell responses, reduce excessive mucosal leukocyte infiltration and activation, lower the risk for necrotizing enterocolitis and provide the infant with sialic acid as a potentially essential nutrient for brain development and cognition. Most data, however, stem from in vitro, ex vivo or animal studies and occasionally from association studies in mother–infant cohorts. Powered, randomized and controlled intervention studies will be needed to confirm relevance for human neonates. The first part of this review introduces the pioneers in HMO research, outlines HMO structural diversity and describes what is known about HMO biosynthesis in the mother's mammary gland and their metabolism in the breast-fed infant. The second part highlights the postulated beneficial effects of HMO for the breast-fed neonate, compares HMOs with oligosaccharides in the milk of other mammals and in infant formula and summarizes the current roadblocks and future opportunities for HMO research.


title: Exploiting Explicit Paths for Multi-hop Reading Comprehension

abstract: We focus on the task of multi-hop reading comprehension where a system is required to reason over a chain of multiple facts, distributed across multiple passages, to answer a question. Inspired by graph-based reasoning, we present a path-based reasoning approach for textual reading comprehension. It operates by generating potential paths across multiple passages, extracting implicit relations along this path, and composing them to encode each path. The proposed model achieves a 2.3% gain on the WikiHop Dev set as compared to previous state-of-the-art and, as a side-effect, is also able to explain its reasoning through explicit paths of sentences.


title: Cycle-consistency Training for End-to-end Speech Recognition

abstract: This paper presents a method to train end-to-end automatic speech recognition (ASR) models using unpaired data. Although the end-to-end approach can eliminate the need for expert knowledge such as pronunciation dictionaries to build ASR systems, it still requires a large amount of paired data, i.e., speech utterances and their transcriptions. Cycle-consistency losses have been recently proposed as a way to mitigate the problem of limited paired data. These approaches compose a reverse operation with a given transformation, e.g., text-to-speech (TTS) with ASR, to build a loss that only requires unsupervised data, speech in this example. Applying cycle consistency to ASR models is not trivial since fundamental information, such as speaker traits, are lost in the intermediate text bottleneck. To solve this problem, this work presents a loss that is based on the speech encoder state sequence instead of the raw speech signal. This is achieved by training a Text-To-Encoder model and defining a loss based on the encoder reconstruction error. Experimental results on the LibriSpeech corpus show that the proposed cycle-consistency training reduced the word error rate by 14.7% from an initial model trained with 100-hour paired data, using an additional 360 hours of audio data without transcriptions. We also investigate the use of text-only data mainly for language modeling to further improve the performance in the unpaired data training scenario.


title: The State of the Art of Feature Extraction Techniques in Speech Recognition

abstract: This paper surveys feature extraction techniques applied in automatic speech recognition. After so many researches and improvement, the accuracy is a key issue in speech recognition systems. Speech recognition process converts the speech signal into its corresponding written text by the computer system. In this paper, we brief few well-known techniques of feature extraction like LPC, MFCC, RASTA, PCA, LDA, PLP.


title: Supervised Representation Learning for Audio Scene Classification

abstract: This paper investigates the use of supervised feature learning approaches for extracting relevant and discriminative features from acoustic scene recordings. Owing to the recent release of open datasets for acoustic scene classification problems, representation learning techniques can now be envisioned for solving the problem of feature extraction. This paper makes a step toward this goal by first introducing a supervised nonnegative matrix factorization SNMF. Our goal through this SNMF is to induce the matrix decomposition to carry out discriminative information in addition to the usual generative ones. We achieve this objective by augmenting the nonnegative matrix factorization optimization problem with a novel loss function related to class labels of each column of the matrix to decompose. While the scale of the datasets available is still small compared to those available in computer vision, we have studied models based on convolutional neural networks. We have analyzed the performances of these models on the DCASE-16 dataset and a corrected version of the LITIS Rouen one. Our experiments show that despite the small-scale setting, supervised feature learning is favorably competitive compared to the current state-of-the-art features. We also point out that for smaller scale dataset, SNMF is indeed slightly less prone to overfitting than convolutional neural networks. While the performances of these learned features are interesting per se, a deeper analysis of their behavior in the acoustic scene problem context raises open and difficult questions that we believe, need to be addressed for further performance breakthroughs.


title: Effect of a histidine-excess diet on a tetrahydrofolylpolyglutamate pattern in rat liver.

abstract: The effect of a histidine-excess diet on the hepatic folylpol-yglutamate pattern in rat was studied. Rats were fed ad libitum 9.7% casein basal diets with 0.6% methionine (controls) or the basal diets with 3.5% histidine. The average daily weight gain and the food intake in histidine-supplemented rats (His-rats) did not significantly differ from controls. The liver weight in His-rats, however, was 50% higher than controls. Hepatic methyltetrahydropteroylpentaglutamate (CH3-H4PteGlu5), and tetrahydropteroylmonoglutamate concentrations in His-rats was 5.7- and 2-fold higher than controls, respectively. The tetrahyd-ropteroylpentaglutamate (H4PteGlu5) concentration in the His-rats was 74% lower than controls. Considering the homeostasis of folate cofactors in tissues, these results suggest that the hepatic regeneration systems for H4PteGlu5 in His-rats might be repressed and an apparent methylfolate trap might be attained rather on a pteroylpentaglutamate level than a monoglutamate level, and that the activity for catabolizing the excess histidine might exceed the regenerating activity for folate cofactors.


title: Multi-View K-Means Clustering on Big Data

abstract: In past decade, more and more data are collected from multiple sources or represented by multiple views, where different views describe distinct perspectives of the data. Although each view could be individually used for finding patterns by clustering, the clustering performance could be more accurate by exploring the rich information among multiple views. Several multi-view clustering methods have been proposed to unsupervised integrate different views of data. However, they are graph based approaches, e.g. based on spectral clustering, such that they cannot handle the large-scale data. How to combine these heterogeneous features for unsupervised large-scale data clustering has become a challenging problem. In this paper, we propose a new robust large-scale multi-view clustering method to integrate heterogeneous representations of largescale data. We evaluate the proposed new methods by six benchmark data sets and compared the performance with several commonly used clustering approaches as well as the baseline multi-view clustering methods. In all experimental results, our proposed methods consistently achieve superiors clustering performances.


title: Elevator Acceleration Sensing: Design and Estimation Recognition Algorithm Using Crowdsourcing

abstract: In human activity sensing areas, we focus on elevator recognition using accelerometer. In this paper, our main challenge is to create an elevator acceleration recognition algorithm. In order to put this algorithm into real world use, This algorithm should be robust enough that can ignore the differences of the elevators, devices and subjects. To realize this aim, we build a crowdsourcing system which consists of an android application and a server. Android users can use this application to calculate elevator's moving distance. They can also upload elevators' accelerometer signals to the server, so we can get elevator acceleration data from all over the world. after publishing the system, we use the data we collected to build an elevator acceleration database. By using this database, we build a robust elevator recognition algorithm. we also use the features of elevators to distinguish them.


title: A Core Ontology of Know-How and Knowing-That for improving knowledge sharing and decision making in the digital age

abstract: AbstractThis paper proposes a Core Ontology of Know-How and Knowing-That (COOK). Know-How and Knowing-That are two types of knowledge according to the epistemological point of view. COOK is founded on work analysis in epistemology, philosophy of capacity/disposition and philosophy of action. This work allows to establish a conceptual analysis of Know-How and Knowing-That. Know-How is defined as the disposition to perform a type of action, whereas Knowing-That is defined as a belief state and concerns a description which can be factual or prescriptive. Taking into account the whole amount of data for an organisation, COOK enhances knowledge identification, decision-making and knowledge sharing among organisation or between different organisations geographically dispersed. Furthermore, COOK is used for the semantic refinement of knowledge evaluation criteria family. These criteria can be related to (i) functional point of view which aims to calculate the contribution degree of Know-How/Knowing-That to the o...


title: A Game Theoretical Model for Adversarial Learning

abstract: It is now widely accepted that in many situations where classifiers are deployed, adversaries deliberately manipulate data in order to reduce the classifier’s accuracy. The most prominent example is email spam, where spammers routinely modify emails to get past classifier-based spam filters. In this paper we model the interaction between the adversary and the data miner as a two-person sequential noncooperative Stackelberg game and analyze the outcomes when there is a natural leader and a follower. We then proceed to model the interaction (both discrete and continuous) as an optimization problem and note that even solving linear Stackelberg game is NP-Hard. Finally we use a real spam email data set and evaluate the performance of local search algorithm under different strategy spaces.


title: Effect of extracts from bean (phaseolus vulgaris) and field bean (vicia faba) varieties on intestinal D-glucose transport in rat in vivo

abstract: Saline extract solutions from seeds of both Phaseolus vulgaris and Vicia faba varieties were prepared and tested for their total polyphenolic content and effect on intestinal D-Glucose transport across rat ileum in vivo. Intestinal D-glucose transport was significantly reduced (P Vicia faba, var. caballar > Vicia faba, var. minor > Phaseolus vulgaris, var. albus) is closely related to the polyphenolic content. Clear differences in the mode of action of the extracts were also observed. Extracts with appreciable polyphenolic content caused both a reversible and a lasting inhibitory effect on glucose transport, whilst extracts with negligible polyphenolic content only induced a reversible inhibition. Polyamide treatment of the extracts decreased their ability to inhibit glucose transport, but their inhibitory effect was not completely abolished by the treatment, further suggesting the presence of some other anti-absorptive factor(s) in the seeds.


title: Incremental Factorization Machines for Persistently Cold-starting Online Item Recommendation

abstract: Real-world item recommenders commonly suffer from a persistent cold-start problem which is caused by dynamically changing users and items. In order to overcome the problem, several context-aware recommendation techniques have been recently proposed. In terms of both feasibility and performance, factorization machine (FM) is one of the most promising methods as generalization of the conventional matrix factorization techniques. However, since online algorithms are suitable for dynamic data, the static FMs are still inadequate. Thus, this paper proposes incremental FMs (iFMs), a general online factorization framework, and specially extends iFMs into an online item recommender. The proposed framework can be a promising baseline for further development of the production recommender systems. Evaluation is done empirically both on synthetic and real-world unstable datasets.


title: A Survey on Energy Conserving Mechanisms for the Internet of Things: Wireless Networking Aspects

abstract: The Internet of Things (IoT) is an emerging key technology for future industries and everyday lives of people, where a myriad of battery operated sensors, actuators, and smart objects are connected to the Internet to provide services such as mobile healthcare, intelligent transport system, environmental monitoring, etc. Since energy efficiency is of utmost importance to these battery constrained IoT devices, IoT-related standards and research works have focused on the device energy conserving issues. This paper presents a comprehensive survey on energy conserving issues and solutions in using diverse wireless radio access technologies for IoT connectivity, e.g., the 3rd Generation Partnership Project (3GPP) machine type communications, IEEE 802.11ah, Bluetooth Low Energy (BLE), and Z-Wave. We look into the literature in broad areas of standardization, academic research, and industry development, and structurally summarize the energy conserving solutions based on several technical criteria. We also propose future research directions regarding energy conserving issues in wireless networking-based IoT.


title: BOLD Features to Detect Texture-less Objects

abstract: Object detection in images withstanding significant clutter and occlusion is still a challenging task whenever the object surface is characterized by poor informative content. We propose to tackle this problem by a compact and distinctive representation of groups of neighboring line segments aggregated over limited spatial supports and invariant to rotation, translation and scale changes. Peculiarly, our proposal allows for leveraging on the inherent strengths of descriptor-based approaches, i.e. robustness to occlusion and clutter and scalability with respect to the size of the model library, also when dealing with scarcely textured objects.


Differences in epidemiology, liver disease and treatment response among HCV genotypes


title: Maximum Resilience of Artificial Neural Networks

abstract: The deployment of Artificial Neural Networks (ANNs) in safety-critical applications poses a number of new verification and certification challenges. In particular, for ANN-enabled self-driving vehicles it is important to establish properties about the resilience of ANNs to noisy or even maliciously manipulated sensory input. We are addressing these challenges by defining resilience properties of ANN-based classifiers as the maximal amount of input or sensor perturbation which is still tolerated. This problem of computing maximal perturbation bounds for ANNs is then reduced to solving mixed integer optimization problems (MIP). A number of MIP encoding heuristics are developed for drastically reducing MIP-solver runtimes, and using parallelization of MIP-solvers results in an almost linear speed-up in the number (up to a certain limit) of computing cores in our experiments. We demonstrate the effectiveness and scalability of our approach by means of computing maximal resilience bounds for a number of ANN benchmark sets ranging from typical image recognition scenarios to the autonomous maneuvering of robots.


title: Face Recognition Techniques: A Survey

abstract: Face is the index of mind. It is a complex multidimensional structure and needs a good computing technique for recognition. While using automatic system for face recognition, computers are easily confused by changes in illumination, variation in poses and change in angles of faces. A numerous techniques are being used for security and authentication purposes which includes areas in detective agencies and military purpose. These surveys give the existing methods in automatic face recognition and formulate the way to still increase the performance.


title: Poisson image editing

abstract: Using generic interpolation machinery based on solving Poisson equations, a variety of novel tools are introduced for seamless editing of image regions. The first set of tools permits the seamless importation of both opaque and transparent source image regions into a destination region. The second set is based on similar mathematical ideas and allows the user to modify the appearance of the image seamlessly, within a selected region. These changes can be arranged to affect the texture, the illumination, and the color of objects lying in the region, or to make tileable a rectangular selection.


title: An interior-point method for large-scale l1-regularized logistic regression

abstract: Logistic regression with ‘1 regularization has been proposed as a promising method for feature selection in classification problems. In this paper we describe an efficient interior-point method for solving large-scale ‘1-regularized logistic regression problems. Small problems with up to a thousand or so features and examples can be solved in seconds on a PC; medium sized problems, with tens of thousands of features and examples, can be solved in tens of seconds (assuming some sparsity in the data). A variation on the basic method, that uses a preconditioned conjugate gradient method to compute the search step, can solve very large problems, with a million features and examples (e.g., the 20 Newsgroups data set), in a few minutes, on a PC. Using warm-start techniques, a good approximation of the entire regularization path can be computed much more efficiently than by solving a family of problems independently.


title: Relationships among ERP post-implementation success constructs: An analysis at the organizational level

abstract: Enterprise resource planning (ERP) systems implementation success factors have been widely researched; however, few have investigated ERP post-implementation success in organizational contexts. The paucity of research into ERP system success evaluations partly motivates this research. To that end, the objective of this study is twofold. First, it primarily investigates the relationships among six constructs or dimensions in a respecified ERP system success measurement model, which was developed from prior relevant frameworks. Second, this research adds to the body of knowledge in the information system (IS) success evaluation domain, especially with its focus on ERP packages. The extended ERP system success model was tested using data collected in a cross-sectional field survey of 109 firms in two European countries. Structural equation modeling (SEM) was used to test six relevant hypotheses. The SEM results showed that five out of the six hypotheses have significant, positive associations. Namely, the constructs of System Quality, Service Quality, Individual Impact, Workgroup Impact, and Organizational Impact have strong relevance in ERP success conceptualization, whereas Information Quality does not, at least, in the context of our data. The pertinence of the study's findings for IS success evaluation as well as its implications for practice and research are discussed.


title: A Multi-Modal Neuro-Physiological Study of Phishing Detection and Malware Warnings

abstract: Detecting phishing attacks (identifying fake vs. real websites) and heeding security warnings represent classical user-centered security tasks subjected to a series of prior investigations. However, our understanding of user behavior underlying these tasks is still not fully mature, motivating further work concentrating at the neuro-physiological level governing the human processing of such tasks. We pursue a comprehensive three-dimensional study of phishing detection and malware warnings, focusing not only on what users' task performance is but also on how users process these tasks based on: (1) neural activity captured using Electroencephalogram (EEG) cognitive metrics, and (2) eye gaze patterns captured using an eye-tracker. Our primary novelty lies in employing multi-modal neuro-physiological measures in a single study and providing a near realistic set-up (in contrast to a recent neuro-study conducted inside an fMRI scanner). Our work serves to advance, extend and support prior knowledge in several significant ways. Specifically, in the context of phishing detection, we show that users do not spend enough time analyzing key phishing indicators and often fail at detecting these attacks, although they may be mentally engaged in the task and subconsciously processing real sites differently from fake sites. In the malware warning tasks, in contrast, we show that users are frequently reading, possibly comprehending, and eventually heeding the message embedded in the warning. Our study provides an initial foundation for building future mechanisms based on the studied real-time neural and eye gaze features, that can automatically infer a user's "alertness" state, and determine whether or not the user's response should be relied upon.


Language Based Mapping of Science Assessment Items to Skills


title: Model-driven Scheduling for Distributed Stream Processing Systems

abstract: Abstract Distributed Stream Processing Systems (DSPS) are “Fast Data” platforms that allow streaming applications to be composed and executed with low latency on commodity clusters and Clouds. Such applications are composed as a Directed Acyclic Graph (DAG) of tasks, with data parallel execution using concurrent task threads on distributed resource slots. Scheduling such DAGs for DSPS has two parts—allocation of threads and resources for a DAG, and mapping threads to resources. Existing schedulers often address just one of these, make the assumption that performance linearly scales, or use ad hoc empirical tuning at runtime. Instead, we propose model-driven techniques for both mapping and allocation that rely on low-overhead a priori performance modeling of tasks. Our scheduling algorithms are able to offer predictable and low resource needs that is suitable for elastic pay-as-you-go Cloud resources, support a high input rate through high VM utilization, and can be combined with other mapping approaches as well. These are validated for micro and application benchmarks, and compared with contemporary schedulers, for the Apache Storm DSPS.


title: Using Link Features for Entity Clustering in Knowledge Graphs

abstract: Knowledge graphs holistically integrate information about entities from multiple sources. A key step in the construction and maintenance of knowledge graphs is the clustering of equivalent entities from different sources. Previous approaches for such an entity clustering suffer from several problems, e.g., the creation of overlapping clusters or the inclusion of several entities from the same source within clusters. We therefore propose a new entity clustering algorithm CLIP that can be applied both to create entity clusters and to repair entity clusters determined with another clustering scheme. In contrast to previous approaches, CLIP not only uses the similarity between entities for clustering but also further features of entity links such as the so-called link strength. To achieve a good scalability we provide a parallel implementation of CLIP based on Apache Flink. Our evaluation for different datasets shows that the new approach can achieve substantially higher cluster quality than previous approaches.


title: Tomato necrotic ringspot virus, a new tospovirus isolated in Thailand

abstract: A new tospovirus isolated from naturally infected tomato plants grown in Nakhon Pathom province (Thailand) was characterized. Infected plants showed symptoms consisting of necrotic spots, necrotic ringspots and stem necrosis. This virus was detected using general antibodies that could recognize watermelon silver mottle virus (WSMoV), capsicum chlorosis virus (CaCV) and melon yellow spot virus (MYSV). However, it did not react with specific monoclonal antibodies (MAbs) to WSMoV and CaCV or a specific MAb to MYSV. The complete nucleotide sequences of S and M RNAs of the virus were determined. They were 3,023 and 4,716 nucleotides in length, respectively, and contained two ORFs in an ambisense arrangement. Sequence analysis indicated that amino acid sequence of the N protein shared 58.2%, 56.0% and 51.8% identity with those of CaCV, WSMoV and MYSV, respectively. The virus was experimentally transmitted by Thrips palmi and Ceratothripoides claratris. Based on our results, we conclude that this tospovirus isolate should be considered a member of a new species. The name tomato necrotic ringspot virus (TNRV) is proposed for this tospovirus.


title: Multi-Task Zero-Shot Action Recognition with Prioritised Data Augmentation

abstract: Zero-Shot Learning (ZSL) promises to scale visual recognition by bypassing the conventional model training requirement of annotated examples for every category. This is achieved by establishing a mapping connecting low-level features and a semantic description of the label space, referred as visual-semantic mapping, on auxiliary data. Re-using the learned mapping to project target videos into an embedding space thus allows novel-classes to be recognised by nearest neighbour inference. However, existing ZSL methods suffer from auxiliary-target domain shift intrinsically induced by assuming the same mapping for the disjoint auxiliary and target classes. This compromises the generalisation accuracy of ZSL recognition on the target data. In this work, we improve the ability of ZSL to generalise across this domain shift in both model- and data-centric ways by formulating a visual-semantic mapping with better generalisation properties and a dynamic data re-weighting method to prioritise auxiliary data that are relevant to the target classes. Specifically: (1) We introduce a multi-task visual-semantic mapping to improve generalisation by constraining the semantic mapping parameters to lie on a low-dimensional manifold, (2) We explore prioritised data augmentation by expanding the pool of auxiliary data with additional instances weighted by relevance to the target domain. The proposed new model is applied to the challenging zero-shot action recognition problem to demonstrate its advantages over existing ZSL models.


title: FA-RPN: Floating Region Proposals for Face Detection

abstract: We propose a novel approach for generating region proposals for performing face detection. Instead of classifying anchor boxes using features from a pixel in the convolutional feature map, we adopt a pooling-based approach for generating region proposals. However, pooling hundreds of thousands of anchors which are evaluated for generating proposals becomes a computational bottleneck during inference. To this end, an efficient anchor placement strategy for reducing the number of anchor-boxes is proposed. We then show that proposals generated by our network (Floating Anchor Region Proposal Network, FA-RPN) are better than RPN for generating region proposals for face detection. We discuss several beneficial features of FA-RPN proposals (which can be enabled without re-training) like iterative refinement, placement of fractional anchors and changing size/shape of anchors. Our face detector based on FA-RPN obtains 89.4% mAP with a ResNet-50 backbone on the WIDER dataset.


title: Multi-view low-rank dictionary learning for image classification

abstract: Recently, a multi-view dictionary learning (DL) technique has received much attention. Although some multi-view DL methods have been presented, they suffer from the problem of performance degeneration when large noise exists in multiple views. In this paper, we propose a novel multi-view DL approach named multi-view low-rank DL (MLDL) for image classification. Specifically, inspired by the low-rank matrix recovery theory, we provide a multi-view dictionary low-rank regularization term to solve the noise problem. We further design a structural incoherence constraint for multi-view DL, such that redundancy among dictionaries of different views can be reduced. In addition, to enhance efficiency of the classification procedure, we design a classification scheme for MLDL, which is based on the idea of collaborative representation based classification. We apply MLDL for face recognition, object classification and digit classification tasks. Experimental results demonstrate the effectiveness and efficiency of the proposed approach. We offer a multi-view low-rank dictionary learning method for image classification.Multi-view dictionary low-rank regularization term is designed to handle noise.Structural incoherence constraint is given to reduce redundancy in dictionaries.Multi-view collaborative representation based classification scheme is provided.Effectiveness and efficiency of our method are demonstrated on four datasets.


title: Semantic Object Segmentation in Tagged Videos via Detection

abstract: Semantic object segmentation (SOS) is a challenging task in computer vision that aims to detect and segment all pixels of the objects within predefined semantic categories. In image-based SOS, many supervised models have been proposed and achieved impressive performances due to the rapid advances of well-annotated training images and machine learning theories. However, in video-based SOS it is often difficult to directly train a supervised model since most videos are weakly annotated by tags. To handle such tagged videos, this paper proposes a novel approach that adopts a segmentation-by-detection framework. In this framework, object detection and segment proposals are first generated using the models pre-trained on still images, which provide useful cues to roughly localize the semantic objects. Based on these proposals, we propose an efficient algorithm to initialize object tracks by solving a joint assignment problem. As such tracks provide rough spatiotemporal configurations of the semantic objects, a voting-based refinement algorithm is further proposed to improve their spatiotemporal consistency. Extensive experiments demonstrate that the proposed framework can robustly and effectively segment semantic objects in tagged videos, even when the image-based object detectors provide inaccurate proposals. On various public benchmarks, the proposed approach obtains substantial improvements over the state-of-the-arts.


title: PSO-Based Tsallis Thresholding Selection Procedure for Image Segmentation

abstract: Multilevel thresholding is a method that is widely used in image segmentation. The thresholding problem is treated as an optimization problem with an objective function. In this article, a simple and histogram based approach is presented for multilevel thresholding in image segmentation. The proposed method combines Tsallis objective function and Particle Swarm Optimization (PSO). The PSO algorithm is used to find the optimal threshold values which maximize the Tsallis objective function. Simulations are performed over various standard test images with different number of thresholds and comparisons are performed with Genetic Algorithm (GA). The experimental results show that the proposed PSO based thresholding method performs better than the GA method.


title: OptiMathSAT: A Tool for Optimization Modulo Theories

abstract: Many SMT problems of interest may require the capability of finding models that are optimal wrt. some objective functions. These problems are grouped under the umbrella term of Optimization Modulo Theories – OMT. In this paper we present OptiMathSAT, an OMT tool extending the MathSAT5 SMT solver. OptiMathSAT allows for solving a list of optimization problems on SMT formulas with linear objective functions –on the Boolean, the rational and the integer domains, and on their combination thereof– including MaxSMT. Multiple objective functions can be combined together and handled either independently, or lexicographically, or in a min-max/max-min fashion.


The Complexity of Yamnitsky and Levin's Simplices Algorithm


title: Why Do Incremental Encoders Do a Reasonably Good Job in Electrical Drives with Digital Control?

abstract: The technical requirements of encoders to be used in electrical drives with digital control are not yet completely understood. A main statement of (Kennel, 2005) is that neither high resolution resolvers nor high resolution optical encoders do really provide features that could make them competitive to analogue precision tacho generators. As a result the behaviour of digital speed control can still not be as good as of former analogue speed control. The question is, however, why servo drives with digital speed control in fact are successful - nobody does really worry about any impact of optical encoders, because the performance of servo drives with digital control has indeed improved in comparison to former drives with analogue control. This paper explains the technical reasons for that and hereby contributes to better understanding of speed and position encoders, which still are the technical bottle neck for further improvements in digital drive control


title: Dynamic Statistical Parametric Mapping Combining fMRI and MEG for High-Resolution Imaging of Cortical Activity

abstract: Abstract Functional magnetic resonance imaging (fMRI) can provide maps of brain activation with millimeter spatial resolution but is limited in its temporal resolution to the order of seconds. Here, we describe a technique that combines structural and functional MRI with magnetoencephalography (MEG) to obtain spatiotemporal maps of human brain activity with millisecond temporal resolution. This new technique was used to obtain dynamic statistical parametric maps of cortical activity during semantic processing of visually presented words. An initial wave of activity was found to spread rapidly from occipital visual cortex to temporal, parietal, and frontal areas within 185 ms, with a high degree of temporal overlap between different areas. Repetition effects were observed in many of the same areas following this initial wave of activation, providing evidence for the involvement of feedback mechanisms in repetition priming.


title: A Customizable Framework for Prioritizing Systems Security Engineering Processes, Activities, and Tasks

abstract: As modern systems become increasingly complex, current security practices lack effective methodologies to adequately address the system security. This paper proposes a repeatable and tailorable framework to assist in the application of systems security engineering (SSE) processes, activities, and tasks as defined in the recently released National Institute of Standards and Technology (NIST) Special Publication 800–160. First, a brief survey of systems-oriented security methodologies is provided. Next, an examination of the relationships between the NIST-defined SSE processes is conducted to provide context for the engineering problem space. These findings inform a mapping of the NIST SSE processes to seven system-agnostic security domains which enable prioritization for three types of systems (conventional IT, cyber-physical, and defense). These concrete examples provide further understanding for applying and prioritizing the SSE effort. The goal of this paper is assist practitioners by informing the efficient application of the 30 processes, 111 activities, and 428 tasks defined in NIST SP 800–160. The customizable framework tool is available online for developers to employ, modify, and tailor to meet their needs.


The Nested Indian Buffet Process :for Flexible Topic Modeling


title: Making web applications more energy efficient for OLED smartphones

abstract: A smartphone’s display is one of its most energy consuming components. Modern smartphones use OLED displays that consume more energy when displaying light colors as op- posed to dark colors. This is problematic as many popular mobile web applications use large light colored backgrounds. To address this problem we developed an approach for auto- matically rewriting web applications so that they generate more energy efficient web pages. Our approach is based on program analysis of the structure of the web application im- plementation. In the evaluation of our approach we show that it can achieve a 40% reduction in display power con- sumption. A user study indicates that the transformed web pages are acceptable to users with over 60% choosing to use the transformed pages for normal usage.