Query: Joint Representation Learning of Cross-lingual Words and Entities via Attentive Distant Supervision

Relevant passages:
Multilingual Word Embeddings using Multigraphs <=SEP=> We present a family of neural-network– inspired models for computing continuous word representations, specifically designed to exploit both monolingual and multilingual text. This framework allows us to perform unsupervised training of embeddings that exhibit higher accuracy on syntactic and semantic compositionality, as well as multilingual semantic similarity, compared to previous models trained in an unsupervised fashion. We also show that such multilingual embeddings, optimized for semantic similarity, can improve the performance of statistical machine translation with respect to how it handles words not present in the parallel data.

Representing Text for Joint Embedding of Text and Knowledge Bases <=SEP=> Models that learn to represent textual and knowledge base relations in the same continuous latent space are able to perform joint inferences among the two kinds of relations and obtain high accuracy on knowledge base completion (Riedel et al., 2013). In this paper we propose a model that captures the compositional structure of textual relations, and jointly optimizes entity, knowledge base, and textual relation representations. The proposed model significantly improves performance over a model that does not share parameters among textual relations with common sub-structure.

Multilingual Models for Compositional Distributed Semantics <=SEP=> We present a novel technique for learning semantic representations, which extends the distributional hypothesis to multilingual data and joint-space embeddings. Our models leverage parallel data and learn to strongly align the embeddings of semantically equivalent sentences, while maintaining sufficient distance between those of dissimilar sentences. The models do not rely on word alignments or any syntactic information and are successfully applied to a number of diverse languages. We extend our approach to learn semantic representations at the document level, too. We evaluate these models on two cross-lingual document classification tasks, outperforming the prior state of the art. Through qualitative analysis and the study of pivoting effects we demonstrate that our representations are semantically plausible and can capture semantic relationships across languages without parallel data.

Word Translation Without Parallel Data <=SEP=> State-of-the-art methods for learning cross-lingual word embeddings have relied on bilingual dictionaries or parallel corpora. Recent studies showed that the need for parallel data supervision can be alleviated with character-level information. While these methods showed encouraging results, they are not on par with their supervised counterparts and are limited to pairs of languages sharing a common alphabet. In this work, we show that we can build a bilingual dictionary between two languages without using any parallel corpora, by aligning monolingual word embedding spaces in an unsupervised way. Without using any character information, our model even outperforms existing supervised methods on cross-lingual tasks for some language pairs. Our experiments demonstrate that our method works very well also for distant language pairs, like English-Russian or EnglishChinese. We finally describe experiments on the English-Esperanto low-resource language pair, on which there only exists a limited amount of parallel data, to show the potential impact of our method in fully unsupervised machine translation. Our code, embeddings and dictionaries are publicly available1.

Joint Representation Learning of Text and Knowledge for Knowledge Graph Completion <=SEP=> Joint representation learning of text and knowledge within a unified semantic space enables us to perform knowledge graph completion more accurately. In this work, we propose a novel framework to embed words, entities and relations into the same continuous vector space. In this model, both entity and relation embeddings are learned by taking knowledge graph and plain text into consideration. In experiments, we evaluate the joint learning model on three tasks including entity prediction, relation prediction and relation classification from text. The experiment results show that our model can significantly and consistently improve the performance on the three tasks as compared with other baselines.

Differential evolution algorithm with multiple mutation strategies based on roulette wheel selection <=SEP=> In this paper, we propose a differential evolution (DE) algorithm variant with a combination of multiple mutation strategies based on roulette wheel selection, which we call MMRDE. We first propose a new, reflection-based mutation operation inspired by the reflection operations in the Nelder–Mead method. We design an experiment to compare its performance with seven mutation strategies, and we prove its effectiveness at balancing exploration and exploitation of DE. Although our reflection-based mutation strategy can balance exploration and exploitation of DE, it is still prone to premature convergence or evolutionary stagnation when solving complex multimodal optimization problems. Therefore, we add two basic strategies to help maintain population diversity and increase the robustness. We use roulette wheel selection to arrange mutation strategies based on their success rates for each individual. MMRDE is tested with some improved DE variants based on 28 benchmark functions for real-parameter optimization that have been recommended by the Institute of Electrical and Electronics Engineers CEC2013 special session. Experimental results indicate that the proposed algorithm shows its effectiveness at cooperative work with multiple strategies. It can obtain a good balance between exploration and exploitation. The proposed algorithm can guide the search for a global optimal solution with quick convergence compared with other improved DE variants.

Using Clustering for Categorization of Support Tickets <=SEP=> Support tickets from customers contain much hidden information. Unsupervised machine learning methods are able to discover this hidden information. In this paper we propose the categorization of support tickets using clustering methods in combination with topic models. Furthermore label generation techniques are used to generate meaningful names for these categories. The results are compared with related

Smurf-based Distributed Denial of Service (DDoS) Attack Amplification in Internet <=SEP=> The Smurf-based distributed denial of service (DDoS) attack is an amplification attack where the attacker uses unprotected intermediate networks to amplify the attack traffic load and direct it to the victim computer. In this paper, we investigate the factors that contribute to the amplification of the smurf attack traffic and understand the relation among the original attack traffic, intermediate unprotected network and the final amplified attack traffic. We also define a new term called attack amplification factor which represents the degree of amplification that original attack traffic undergoes during its transmission towards the victim computer. It is also shown in this paper that utilizing this attack, it is possible for an attacker to just use a dialup modem and an unprotected intermediary network to exhaust even an ultra high speed optical line such as OC-192 of the victim network.

Kissing Nevus of the Penis <=SEP=> Kissing or divided nevi are similar in shape to congenital melanocytic nevi located on an adjacent part of the body that are separated during embryogenesis. Kissing nevi of the upper and lower eyelids have been reported infrequently since the first report in 1908. Kissing nevi of the penis are very rare, with only 12 cases being reported until now, and this is the first case report in the Korean dermatological literature. A previously healthy 27-year-old man presented with asymptomatic black colored patches, which were detected 10 years ago, on the glans penis and the prepuce with growth in size. We report here a case of kissing nevus of the penis, which showed an obvious mirror-image symmetry relative to the coronal sulcus.

構建一個中文國小數學文字問題語料庫(Building a Corpus for Developing the Chinese Elementary School Math Word Problem Solver)[In Chinese] <=SEP=> 

Course signals at Purdue: using learning analytics to increase student success <=SEP=> In this paper, an early intervention solution for collegiate faculty called Course Signals is discussed. Course Signals was developed to allow instructors the opportunity to employ the power of learner analytics to provide real-time feedback to a student. Course Signals relies not only on grades to predict students' performance, but also demographic characteristics, past academic history, and students' effort as measured by interaction with Blackboard Vista, Purdue's learning management system. The outcome is delivered to the students via a personalized email from the faculty member to each student, as well as a specific color on a stoplight -- traffic signal -- to indicate how each student is doing. The system itself is explained in detail, along with retention and performance outcomes realized since its implementation. In addition, faculty and student perceptions will be shared.

Customizing Computational Methods for Visual Analytics with Big Data <=SEP=> The volume of available data has been growing exponentially, increasing data problem's complexity and obscurity. In response, visual analytics (VA) has gained attention, yet its solutions haven't scaled well for big data. Computational methods can improve VA's scalability by giving users compact, meaningful information about the input data. However, the significant computation time these methods require hinders real-time interactive visualization of big data. By addressing crucial discrepancies between these methods and VA regarding precision and convergence, researchers have proposed ways to customize them for VA. These approaches, which include low-precision computation and iteration-level interactive visualization, ensure real-time interactive VA for big data.

Cooperative flood detection using GSMD via SMS <=SEP=> This paper proposes architecture for an early warning floods system to alert public against flood disasters. An effective early warning system must be developed with linkages between four elements, which are accurate data collection to undertake risk assessments, development of hazard monitoring services, communication on risk related information and existence of community response capabilities. This project focuses on monitoring water level remotely using wireless sensor network. The project also utilizes Global System for Mobile communication (GSM) and short message service (SMS) to relay data from sensors to computers or directly alert the respective victim's through their mobile phone. It is hope that the proposed architecture can be further develop into a functioning system, which would be beneficial to the community and act as a precautionary action to save lives in the case of flood disaster.

Propranolol treatment for hemangioma of infancy: risks and recommendations. <=SEP=> Hemangioma of infancy is a condition that may be associated with significant morbidity. While evidence most supports the use of corticosteroids, there is no well-defined or Federal Drug Administration (FDA)-approved systemic therapy for hemangioma of infancy. All currently used treatments have significant risks. Dramatic improvement of complicated hemangioma of infancy to propranolol was recently reported, but details for initiating therapy, monitoring, and potential risks were not included. We present two infants treated with propranolol, who suffered complications and propose a treatment protocol to minimize potential adverse events.

A Survey of the ATP-Binding Cassette (ABC) Gene Superfamily in the Salmon Louse (Lepeophtheirus salmonis) <=SEP=> Salmon lice, Lepeophtheirus salmonis (Krøyer, 1837), are fish ectoparasites causing significant economic damage in the mariculture of Atlantic salmon, Salmo salar Linnaeus, 1758. The control of L. salmonis at fish farms relies to a large extent on treatment with anti-parasitic drugs. A problem related to chemical control is the potential for development of resistance, which in L. salmonis is documented for a number of drug classes including organophosphates, pyrethroids and avermectins. The ATP-binding cassette (ABC) gene superfamily is found in all biota and includes a range of drug efflux transporters that can confer drug resistance to cancers and pathogens. Furthermore, some ABC transporters are recognised to be involved in conferral of insecticide resistance. While a number of studies have investigated ABC transporters in L. salmonis, no systematic analysis of the ABC gene family exists for this species. This study presents a genome-wide survey of ABC genes in L. salmonis for which, ABC superfamily members were identified through homology searching of the L. salmonis genome. In addition, ABC proteins were identified in a reference transcriptome of the parasite generated by high-throughput RNA sequencing (RNA-seq) of a multi-stage RNA library. Searches of both genome and transcriptome allowed the identification of a total of 33 genes / transcripts coding for ABC proteins, of which 3 were represented only in the genome and 4 only in the transcriptome. Eighteen sequences were assigned to ABC subfamilies known to contain drug transporters, i.e. subfamilies B (4 sequences), C (11) and G (2). The results suggest that the ABC gene family of L. salmonis possesses fewer members than recorded for other arthropods. The present survey of the L. salmonis ABC gene superfamily will provide the basis for further research into potential roles of ABC transporters in the toxicity of salmon delousing agents and as potential mechanisms of drug resistance.

Efficient Transformerless MOSFET Inverter for a Grid-Tied Photovoltaic System <=SEP=> The unipolar sinusoidal pulse width modulation full-bridge transformerless photovoltaic (PV) inverter can achieve high efficiency by using latest superjunction metal-oxide-semiconductor field-effect transistor (MOSFET) together with silicon carbide (SiC) diodes. However, the MOSFETs aSiCre limited to use in transformerless PV inverter due to the low reverse-recovery characteristics of the body diode. In this paper, a family of new transformerless PV inverter topology for a single-phase grid-tied operation is proposed using superjunction MOSFETs and SiC diodes as no reverse-recovery issues are required for the main power switches for unity power operation. The added clamping branch clamps the freewheeling voltage at the half of dc input voltage during the freewheeling period. As a result, the common-mode voltage kept constant during the whole grid period that reduces the leakage current significantly. In addition, dead time is not necessary for main power switches at both the high-frequency commutation and the grid zero crossing instant, results low-current distortion at output. Finally, a 1-kW prototype is built and tested to verify the theoretical analysis. The experimental results show 98.5% maximum efficiency and 98.32% European efficiency. Furthermore, to show the effectiveness, the proposed topology is compared with the other transformerless topologies.

Network-Based Intrusion Detection Using Unsupervised Adaptive Resonance Theory ( ART ) <=SEP=> This paper introduces the Unsupervised Neural Net based Intrusion Detector (UNNID) system, which detects network-based intrusions and attacks using unsupervised neural networks. The system has facilities for training, testing, and tunning of unsupervised nets to be used in intrusion detection. Using the system, we tested two types of unsupervised Adaptive Resonance Theory (ART) nets (ART-1 and ART-2). Based on the results, such nets can efficiently classify network traffic into normal and intrusive. The system uses a hybrid of misuse and anomaly detection approaches, so is capable of detecting known attack types as well as new attack types as anomalies.

AN ALGORITHM TO GROUP DEFECTS ON PRINTED CIRCUIT BOARD FOR AUTOMATED VISUAL INSPECTION <=SEP=> Due to disadvantages in manual inspection, an automated visual inspection system is needed to eliminate subjective aspects and provides fast and quantitative assessment of printed circuit board (PCB). Up to the present, there has been a lot of work and research concentrated on PCB defect detection. PCB defects detection is necessary for verification of the characteristics of PCB to make sure it is in conformity with the design specifications. However, besides the need to detect the defects, it is also essential to classify these defects so that the source of these defects can be identified. Unfortunately, this area has been neglected and not been given enough attention. Hence, this study proposes an algorithm to group the defects found on bare PCB. Using a synthetically generated PCB image, the algorithm is able to group 14 commonly known PCB defects into five groups. The proposed algorithm includes several image processing operations such as image subtraction, image adding, logical XOR and NOT, and flood fill operator.

Provably Secure Active IC Metering Techniques for Piracy Avoidance and Digital Rights Management <=SEP=> In the horizontal semiconductor business model where the designer's intellectual property (IP) is transparent to foundry and to other entities on the production chain, integrated circuits (ICs) overbuilding and IP piracy are prevalent problems. Active metering is a suite of methods enabling the designers to control their chips postfabrication. We provide a comprehensive description of the first known active hardware metering method and introduce new formal security proofs. The active metering method uniquely and automatically locks each IC upon manufacturing, such that the IP rights owner is the only entity that can provide the specific key to unlock or otherwise control each chip. The IC control mechanism exploits: 1) the functional description of the design, and 2) unique and unclonable IC identifiers. The locks are embedded by modifying the structure of the hardware computation model, in the form of a finite state machine (FSM). We show that for each IC hiding the locking states within the modified FSM structure can be constructed as an instance of a general output multipoint function that can be provably efficiently obfuscated. The hidden locks within the FSM may also be used for remote enabling and disabling of chips by the IP rights owner during the IC's normal operation. An automatic synthesis method for low overhead hardware implementation is devised. Attacks and countermeasures are addressed. Experimental evaluations demonstrate the low overhead of the method. Proof-of-concept implementation on the H.264 MPEG decoder automatically synthesized on a Xilinix Virtex-5 field-programmable gate array (FPGA) further shows the practicality, security, and the low overhead of the new method.

Situation assessment algorithm for a collision prevention assistant <=SEP=> In this paper, we present the concept of a collision prevention assistant, a system that we believe can significantly contribute to road safety. We propose a new situation assessment algorithm which is tailored to the action of braking and that further accounts for the nonlinearities that arise when vehicles cut out or come to a standstill. The effect of sensor uncertainty on the performance of the proposed algorithm is modelled using a Markov chain and analyzed by means of a Monte Carlo simulation on a typical traffic situation.

Latent dirichlet allocation based diversified retrieval for e-commerce search <=SEP=> Diversified retrieval is a very important problem on many e-commerce sites, e.g. eBay and Amazon. Using IR approaches without optimizing for diversity results in a clutter of redundant items that belong to the same products. Most existing product taxonomies are often too noisy, with overlapping structures and non-uniform granularity, to be used directly in diversified retrieval. To address this problem, we propose a Latent Dirichlet Allocation (LDA) based diversified retrieval approach that selects diverse items based on the hidden user intents. Our approach first discovers the hidden user intents of a query using the LDA model, and then ranks the user intents by making trade-offs between their relevance and information novelty. Finally, it chooses the most representative item for each user intent to display. To evaluate the diversity in the search results on e-commerce sites, we propose a new metric, average satisfaction, measuring user satisfaction with the search results. Through our empirical study on eBay, we show that the LDA model discovers meaningful user intents and the LDA-based approach provides significantly higher user satisfaction than the eBay production ranker and three other diversified retrieval approaches.

Service Personnel, Technology, and Their Interaction in Influencing Customer Satisfaction <=SEP=> Managing both the technologies and the personnel needed for providing high-quality, multichannel customer support creates a complex and persistent operational challenge. Adding to this difficulty, it is still unclear how service personnel and these new communication technologies interact to influence the customer’s perceptions of the service being provided. Motivated by both practical importance and inconsistent findings in the academic literature, this exploratory research examines the interaction of media richness, represented by three different technology contexts (telephone, e-mail, and online chat), with six customer service representative (CSR) characteristics and their influences on customer satisfaction. Using a large-sample customer survey data set, the article develops a multigroup structural equation model to analyze these interactions. Results suggest that CSR characteristics influence customer service satisfaction similarly across all three technology-mediated contexts. Of the characteristics studied, service representatives contribute to customer satisfaction more when they exhibit the characteristics of thoroughness, knowledgeableness, and preparedness, regardless of the richness of the medium used. Surprisingly, while three other CSR characteristics studied (courtesy, professionalism, and attentiveness) are traditionally believed to be important in face-to-face encounters, they had no significant impact on customer satisfaction in the technology-mediated contexts studied. Implications for both practitioners and researchers are drawn from the results and future research opportunities are

Hybrid Recommender Systems: Survey and Experiments <=SEP=> Recommender systems represent user preferences for the purpose of suggesting items to purchase or examine. They have become fundamental applications in electronic commerce and information access, providing suggestions that effectively prune large information spaces so that users are directed toward those items that best meet their needs and preferences. A variety of techniques have been proposed for performing recommendation, including content-based, collaborative, knowledge-based and other techniques. To improve performance, these methods have sometimes been combined in hybrid recommenders. This paper surveys the landscape of actual and possible hybrid recommenders, and introduces a novel hybrid, EntreeC, a system that combines knowledge-based recommendation and collaborative filtering to recommend restaurants. Further, we show that semantic ratings obtained from the knowledge-based part of the system enhance the effectiveness of collaborative filtering.

String Alignment with Substitution, Insertion, Deletion, Squashing and Expansion Operations <=SEP=> Let X and Y be any two strings of finite length. The problem of transforming X to Y using the edit operations of substitution, deletion, and insertion has been extensively studied in the literature. The problem can be solved in quadratic time if the edit operations are extended to include the operation of transposition of adjacent characters, and is NP-complete if the characters can be edited repeatedly. In this paper we consider the problem of transforming X to Y when the set of edit operations is extended to include the squashing and expansion operations. Whereas in the squashing operation two (or more) contiguous characters of X can be transformed into a single character of Y, in the expansion operation a single character in X may be expanded into two or more contiguous characters of Y. These operations are typically found in the recognition of cursive script. A quadratic time solution to the problem has been presented. This solution is optimal for the infinite-alphabet case. The strategy to compute the sequence of edit operations is also presented.

Predicting Outcome in Subarachnoid Hemorrhage (SAH) Utilizing the Full Outline of UnResponsiveness (FOUR) Score. <=SEP=> BACKGROUND
Existing scoring systems for aneurysmal subarachnoid hemorrhage (SAH) patients fail to accurately predict patient outcome. Our goal was to prospectively study the Full Outline of UnResponsiveness (FOUR) score as applied to newly admitted aneurysmal SAH patients.


METHODS
All adult patients presenting to Health Sciences Center in Winnipeg from January 2013 to July 2015 (2.5 year period) with aneurysmal SAH were prospectively enrolled in this study. All patients were followed up to 6 months. FOUR score was calculated upon admission, with repeat calculation at 7 and 14 days. The primary outcomes were: mortality, as well as dichotomized 1- and 6-month Glasgow Outcome Scale (GOS) and modified Rankin Scale (mRS) values.


RESULTS
Sixty-four patients were included, with a mean age of 54.2 years (range 26-85 years). The mean FOUR score upon admission pre- and post-external ventricular drain (EVD) was 10.3 (range 0-16) and 11.1 (range 3-16), respectively. There was a statistically significant association between pre-EVD FOUR score (total, eye, respiratory and motor sub-scores) with mortality, 1-month GOS, and 6-month GOS/mRS (p < 0.05 in all). The day 7 total, eye, respiratory, and motor FOUR scores were associated with mortality, 1-month GOS/mRS, and 6-month GOS/mRS (p < 0.05 in all). The day 14 total, eye, respiratory, and motor FOUR scores were associated with 6-month GOS (p < 0.05 in all). The day 7 cumulative FOUR score was associated with the development of clinical vasospasm (p < 0.05).


CONCLUSIONS
The FOUR score at admission and day 7 post-SAH is associated with mortality, 1-month GOS/mRS, and 6-month GOS/mRS. The FOUR score at day 14 post-SAH is associated with 6-month GOS. The brainstem sub-score was not associated with 1- or 6-month primary outcomes.

VERY HIGH RESOLUTION SAR TOMOGRAPHY VIA COMPRESSIVE SENSING <=SEP=> By using multi-pass SAR acquisitions, SAR tomography (TomoSAR) extends the synthetic aperture principle into the elevation direction for 3-D imaging. Since the orbits of modern space-borne SAR systems, like TerraSAR-X, are tightly controlled, the elevation resolution (depending on the elevation aperture size) is at least an order of magnitude lower than in range and azimuth. Hence, superresolution algorithms are desired. The high anisotropic 3D resolution element renders the signals sparse in elevation. This property suggests using compressive sensing (CS) methods. The paper presents the theory of 4D (i.e. space-time) CS TomoSAR and compares it with classical tomographic methods. Super-resolution properties and point localization accuracies are demonstrated using simulations and real data. A CS reconstruction of a building complex from TerraSAR-X spotlight data is presented. In addition, the model based time warp method for differential tomographic non-linear motion monitoring is proposed and validated by reconstructing seasonal motion (caused by thermal expansion) of a building complex.

Method of invariant manifold for chemical kinetics <=SEP=> In this paper, we review the construction of low-dimensional manifolds of reduced description for equations of chemical kinetics from the standpoint of the method of invariant manifold (MIM). The MIM is based on a formulation of the condition of invariance as an equation, and its solution by Newton iterations. A review of existing alternative methods is extended by a thermodynamically consistent version of the method of intrinsic low-dimensional manifolds. A grid-based version of the MIM is developed, and model extensions of low-dimensional dynamics are described. Generalizations to open systems are suggested. The set of methods covered makes it possible to e9ectively reduce description in chemical kinetics. ? 2003 Elsevier Ltd. All rights reserved.

A Robust Mid-Level Representation for Harmonic Content in Music Signals <=SEP=> When considering the problem of audio-to-audio matching, determining musical similarity using low-level features such as Fourier transforms and MFCCs is an extremely difficult task, as there is little semantic information available. Full semantic transcription of audio is an unreliable and imperfect task in the best case, an unsolved problem in the worst. To this end we propose a robust mid-level representation that incorporates both harmonic and rhythmic information, without attempting full transcription. We describe a process for creating this representation automatically, directly from multi-timbral and polyphonic music signals, with an emphasis on popular music. We also offer various evaluations of our techniques. Moreso than most approaches working from raw audio, we incorporate musical knowledge into our assumptions, our models, and our processes. Our hope is that by utilizing this notion of a musically-motivated mid-level representation we may help bridge the gap between symbolic and audio research.

Fast single image fog removal using the adaptive Wiener filter <=SEP=> We present in this paper a fast single image defogging method that uses a novel approach to refining the estimate of amount of fog in an image with the Locally Adaptive Wiener Filter. We provide a solution for estimating noise parameters for the filter when the observation and noise are correlated by decorrelating with a naively estimated defogged image. We demonstrate our method is 50 to 100 times faster than existing fast single image defogging methods and that our proposed method subjectively performs as well as the Spectral Matting smoothed Dark Channel Prior method.

Deploying SDP for machine learning <=SEP=> We discuss the use in machine learning of a general type of convex optimisation problem known as semi-definite programming (SDP) [1]. We intend to argue that SDP’s arise quite naturally in a variety of situations, accounting for their omnipresence in modern machine learning approaches, and we provide examples in support.