query: Online Learning for Neural Machine Translation Post-editing

positive: Adaptive Subgradient Methods for Online Learning and Stochastic Optimization We present a new family of subgradient methods that dynamica lly incorporate knowledge of the geometry of the data observed in earlier iterations to perfo rm more informative gradient-based learning. Metaphorically, the adaptation allows us to find n eedles in haystacks in the form of very predictive but rarely seen features. Our paradigm stems fro m recent advances in stochastic optimization and online learning which employ proximal funct ions to control the gradient steps of the algorithm. We describe and analyze an apparatus for adap tively modifying the proximal function, which significantly simplifies setting a learning rate nd results in regret guarantees that are provably as good as the best proximal function that can be cho sen in hindsight. We give several efficient algorithms for empirical risk minimization probl ems with common and important regularization functions and domain constraints. We experimen tally study our theoretical analysis and show that adaptive subgradient methods outperform state-o f-the-art, yet non-adaptive, subgradient algorithms.

positive: The Meteor metric for automatic evaluation of machine translation The Meteor Automatic Metric for Machine Translation evaluation, originally developed and released in 2004, was designed with the explicit goal of producing sentence-level scores which correlate well with human judgments of translation quality. Several key design decisions were incorporated into Meteor in support of this goal. In contrast with IBM’s Bleu, which uses only precision-based features, Meteor uses and emphasizes recall in addition to precision, a property that has been confirmed by several metrics as being critical for high correlation with human judgments. Meteor also addresses the problem of reference translation variability by utilizing flexible word matching, allowing for morphological variants and synonyms to be taken into account as legitimate correspondences. Furthermore, the feature ingredients within Meteor are parameterized, allowing for the tuning of the metric’s free parameters in search of values that result in optimal correlation with human judgments. Optimal parameters can be separately tuned for different types of human judgments and for different languages. We discuss the initial design of the Meteor metric, subsequent improvements, and performance in several independent evaluations in recent years.

positive: A Stochastic Approximation Method Let M(x) denote the expected value at level x of the response to a certain experiment. M(x) is assumed to be a monotone function of x but is unknown tot he experiment, and it is desire to find the solution x=0 of the equation M(x) = a, where x is a given constant. we give a method for making successive experiments at levels x1, x2,... in such a way that x, will tend to 0 in probability.

positive: A Study of Translation Edit Rate with Targeted Human Annotation We examine a new, intuitive measure for evaluating machine-translation output that avoids the knowledge intensiveness of more meaning-based approaches, and the labor-intensiveness of human judgments. Translation Edit Rate (TER) measures the amount of editing that a human would have to perform to change a system output so it exactly matches a reference translation. We show that the single-reference variant of TER correlates as well with human judgments of MT quality as the four-reference variant of BLEU. We also define a human-targeted TER (or HTER) and show that it yields higher correlations with human judgments than BLEU—even when BLEU is given human-targeted references. Our results indicate that HTER correlates with human judgments better than HMETEOR and that the four-reference variants of TER and HTER correlate with human judgments as well as—or better than—a second human judgment does.

positive: A process study of computer-aided translation We investigate novel types of assistance for human translators, based on statistical machine translation methods. We developed the computer-aided tool Caitra that makes suggestions for sentence completion, shows word and phrase translation options, and allows postediting of machine translation output. We carried out a study of the translation process that involved non-professional translators that were native in either French or English and recorded their interaction with the tool. Users translated 192 sentences from French news stories into English. Most translators were faster and better when assisted by our tool. A detailed examination of the logs also provides insight into the human translation process, such as time spent on different activities and length of pauses.

negative: Miniaturized UWB Log-Periodic Square Fractal Antenna In this letter, the log-periodic square fractal geometry is presented for the design of a miniaturized patch antenna for the ultra-wideband (UWB) services (3.1-10.6 GHz). A miniaturization factor of 23% is achieved with a constant and stable gain in the desired band. The radiation pattern is broadside, which finds suitable applications in the UWB radars and medical imaging. Furthermore, the time-domain performance of the proposed antenna is investigated. A prototype model of the proposed antenna is fabricated and measured as a proof of concept.

negative: Analysis, design, and performance evaluation of asymmetrical half-bridge flyback converter for universal-line-voltage-range applications The asymmetrical half-bridge (AHB) flyback converter is an attractive topology for operation at higher switching frequencies because it can operate with zero-voltage switching of the primary-side switches and zero-current switching of the secondary-side rectifier. In this paper, a detailed analysis and design procedure of the AHB flyback converter for the universal-line-voltage-range applications is presented. The performance of the AHB flyback converter is evaluated by loss analysis based on the simulation waveforms obtained in Simplis and experimentally verified on a laboratory prototype of a 65-W (19.5-V, 3.33-A) universal-line-voltage-range adapter.

negative: Resistance to Change : The Moderating Effects of Leader-Member Exchange and Role Breadth Self-Efficacy The prevalence of resistance during change initiatives is well recognized in the change management literature. The implementation of the lean production system is no exception. It often requires substantial changes to processes and the way people work. As such, understanding how to manage this resistance is important. One view argues that the extent of resistance during change depends on the characteristics of the change process. This view posits that resistance can be reduced if organizations manage information flow, create room for participation and develop trust in management. In addition, this paper also proposes that is Leader-Member Exchange (LMX) and Role Breadth Self-Efficacy (RBSE) moderate the effect on the employees’ resistance to change. 

negative: Real-time wireless vibration monitoring system using LabVIEW Vibration analysis provides relevant information about abnormal working condition of machine parts. Vibration measurement is prerequisite for vibration analysis which is used for condition monitoring of machinery. Also, wireless vibration monitoring has many advantages over wired monitoring. This Paper presents, implementation of a reliable and low cost wireless vibration monitoring system. Vibration measurement has been done using 3-Axis digital output MEMS Accelerometer sensor. This sensor can sense vibrations in the range 0.0156g to 8g where, 1g is 9.81m/s2. Accelerometer Sensor is interfaced with Arduino-derived microcontroller board having Atmel's AT-mega328p microcontroller. The implemented system uses ZigBee communication protocol i.e. standard IEEE 802.15.4, for wireless communication between Sensor Unit and Vibration Monitoring Unit. The wireless communication has been done using XBee RF modules. National Instruments's LabVIEW software has been used for development of graphical user interface, data-logging and alarm indication on the PC. Experimental results show continuous real-time monitoring of machine's vibrations on charts. These results, along with data-log file have been used for vibration analysis. This analysis is used to ensure safe working condition of machinery and used in predictive maintenance.

negative: Big Data Cognitive Storage: An Optimum Way of handling the Educational Data in Indian Scenario The concept of big data has been incorporated in majority of areas. The educational sector has plethora of data especially in online education which plays a vital in modern education. Moreover digital learning which comprises of data and analytics contributes significantly to enhance teaching and learning. The key challenge for handling such data can be a costly affair. IBM has introduced the technology "Cognitive Storage" which ensures that the most relevant information is always on hand. This technology governs the incoming data, stores the data in definite media, application of levels of data protection, policies for the lifecycle and retention of different classes of data. This technology can be very beneficial for online learning in Indian scenario. This technology will be very beneficial in Indian society so as to store more information for the upliftment of the students’ knowledge.

negative: Development of an Assessment Model for Industry 4.0: Industry 4.0-MM The application of new technologies in the manufacturing environment is ushering a new era referred to as the 4th industrial revolution, and this digital transformation appeals to companies due to various competitive advantages it provides. Accordingly, there is a fundamental need for assisting companies in the transition to Industry 4.0 technologies/practices, and guiding them for improving their capabilities in a standardized, objective, and repeatable way. Maturity Models (MMs) aim to assist organizations by providing comprehensive guidance. Therefore, the literature is reviewed systematically with the aim of identifying existing studies related to MMs proposed in the context of Industry 4.0. Seven identified MMs are analyzed by comparing their characteristics of scope, purpose, completeness, clearness, and objectivity. It is concluded that none of them satisfies all expected criteria. In order to satisfy the need for a structured Industry 4.0 assessment/maturity model, SPICE-based Industry 4.0-MM is proposed in this study. Industry 4.0-MM has a holistic approach consisting of the assessment of process transformation, application management, data governance, asset management, and organizational alignment areas. The aim is to create a common base for performing an assessment of the establishment of Industry 4.0 technologies, and to guide companies towards achieving a higher maturity stage in order to maximize the economic benefits of Industry 4.0. Hence, Industry 4.0-MM provides standardization in continuous benchmarking and improvement of businesses in the manufacturing industry.

negative: Exosomes in tumor microenvironment: novel transporters and biomarkers Tumor microenvironment (TME) plays an integral part in the biology of cancer, participating in tumor initiation, progression, and response to therapy. Exosome is an important part of TME. Exosomes are small vesicles formed in vesicular bodies with a diameter of 30–100 nm and a classic “cup” or “dish” morphology. They can contain microRNAs, mRNAs, DNA fragments and proteins, which are shuttled from a donor cell to recipient cells. Exosomes secreted from tumor cells are called tumor-derived (TD) exosomes. There is emerging evidence that TD exosomes can construct a fertile environment to support tumor proliferation, angiogenesis, invasion and premetastatic niche preparation. TD exosomes also may facilitate tumor growth and metastasis by inhibiting immune surveillance and by increasing chemoresistance via removal of chemotherapeutic drugs. Therefore, TD-exosomes might be potential targets for therapeutic interventions via their modification or removal. For example, exosomes can serve as specific delivery vehicles to tumors of drugs, small molecules, or agents of prevention and gene therapy. Furthermore, the biomarkers detected in exosomes of biological fluids imply a potential for exosomes in the early detection and diagnosis, prediction of therapeutic efficacy, and determining prognosis of cancer. Although exosomes may serve as cancer biomarkers and aid in the treatment of cancer, we have a long way to go before we can further enhance the anti-tumor therapy of exosomes and develop exosome-based cancer diagnostic and therapeutic strategies.

negative: Weakly supervised named entity classification In this paper, we describe a new method for the problem of named entity classification for specialized or technical domains, using distant supervision. Our approach relies on a simple observation: in some specialized domains, named entities are almost unambiguous. Thus, given a seed list of names of entities, it is cheap and easy to obtain positive examples from unlabeled texts using a simple string match. Those positive examples can then be used to train a named entity classifier, by using the PU learning paradigm, which is learning from positive and unlabeled examples. We introduce a new convex formulation to solve this problem, and apply our technique in order to extract named entities from financial reports corresponding to healthcare companies.

negative: An Efficient Method of Number Plate Extraction from Indian Vehicles Image Automatic Number Plate Recognition (ANPR) is an image-processing technology that identifies vehicles by their number plates without direct human intervention. It is an application of computer vision and important area of research due to its many applications. The main process of ANPR is divided into four stages. This paper presents a simple and efficient method for the extraction of number plate from the vehicle image based on morphological operations, thresholding and sobel edge detection, and the connected component analysis.

negative: Information fusion for wireless sensor networks: Methods, models, and classifications Wireless sensor networks produce a large amount of data that needs to be processed, delivered, and assessed according to the application objectives. The way these data are manipulated by the sensor nodes is a fundamental issue. Information fusion arises as a response to process data gathered by sensor nodes and benefits from their processing capability. By exploiting the synergy among the available data, information fusion techniques can reduce the amount of data traffic, filter noisy measurements, and make predictions and inferences about a monitored entity. In this work, we survey the current state-of-the-art of information fusion by presenting the known methods, algorithms, architectures, and models of information fusion, and discuss their applicability in the context of wireless sensor networks.

negative:
Leadership style and patient safety: implications for nurse managers. OBJECTIVE
The purpose of this study was to explore the relationship between nurse manager (NM) leadership style and safety climate.

BACKGROUND
Nursing leaders are needed who will change the environment and increase patient safety. Hospital NMs are positioned to impact day-to-day operations. Therefore, it is essential to inform nurse executives regarding the impact of leadership style on patient safety.

METHODS
A descriptive correlational study was conducted in 41 nursing departments across 9 hospitals. The hospital unit safety climate survey and multifactorial leadership questionnaire were completed by 466 staff nurses. Bivariate and regression analyses were conducted to determine how well leadership style predicted safety climate.

RESULTS
Transformational leadership style was demonstrated as a positive contributor to safety climate, whereas laissez-faire leadership style was shown to negatively contribute to unit socialization and a culture of blame.

CONCLUSIONS
Nursing leaders must concentrate on developing transformational leadership skills while also diminishing negative leadership styles.

negative: Iterative Attention Mining for Weakly Supervised Thoracic Disease Pattern Localization in Chest X-Rays Given image labels as the only supervisory signal, we focus on harvesting, or mining, thoracic disease localizations from chest X-ray images. Harvesting such localizations from existing datasets allows for the creation of improved data sources for computer-aided diagnosis and retrospective analyses. We train a convolutional neural network (CNN) for image classification and propose an attention mining (AM) strategy to improve the model’s sensitivity or saliency to disease patterns. The intuition of AM is that once the most salient disease area is blocked or hidden from the CNN model, it will pay attention to alternative image regions, while still attempting to make correct predictions. However, the model requires to be properly constrained during AM, otherwise, it may overfit to uncorrelated image parts and forget the valuable knowledge that it has learned from the original image classification task. To alleviate such side effects, we then design a knowledge preservation (KP) loss, which minimizes the discrepancy between responses for X-ray images from the original and the updated networks. Furthermore, we modify the CNN model to include multi-scale aggregation (MSA), improving its localization ability on small-scale disease findings, e.g., lung nodules. We experimentally validate our method on the publicly-available ChestXray14 dataset, outperforming a class activation map (CAM)-based approach, and demonstrating the value of our novel framework for mining disease locations.

negative: Content-Based Image Retrieval Using Multiple Features Algorithms of Content-Based Image Retrieval (CBIR) have been well developed along with the explosion of information. These algorithms are mainly distinguished based on feature used to describe the image content. In this paper, the algorithms that are based on color feature and texture feature for image retrieval will be presented. Color Coherence Vector based image retrieval algorithm is also attempted during the implementation process, but the best result is generated from the algorithms that weights color and texture. 80% satisfying rate is achieved.

negative: DIFusion: Fast Skip-Scan with Zero Space Overhead Scan is a crucial operation in main-memory column-stores. It scans a column and returns a result bit vector indicating which records satisfy a filter predicate. ByteSlice is an in-memory data layout that chops data into multiple bytes and exploits early-stop capability by high-order bytes comparisons. As column widths are usually not multiples of byte, the last-byte of ByteSlice is padded with 0's, wasting memory bandwidth and computation power. To fully leverage the resources, we propose to weave a secondary index into the vacant bits (i.e., bits originally padded with 0's), forming our new layout coined DIFusion (Data Index Fusion). DIFusion enables skip-scan, a new fast scan that inherits the early-stopping capability from ByteSlice and at the same time possesses the data-skipping ability of index with zero space overhead. Empirical results show that skip-scan on DIFusion outperforms scan on ByteSlice.

negative: Monocyte emigration from bone marrow during bacterial infection requires signals mediated by chemokine receptor CCR2 Monocytes recruited to tissues mediate defense against microbes or contribute to inflammatory diseases. Regulation of the number of circulating monocytes thus has implications for disease pathogenesis. However, the mechanisms controlling monocyte emigration from the bone marrow niche where they are generated remain undefined. We demonstrate here that the chemokine receptor CCR2 was required for emigration of Ly6Chi monocytes from bone marrow. Ccr2−/− mice had fewer circulating Ly6Chi monocytes and, after infection with Listeria monocytogenes, accumulated activated monocytes in bone marrow. In blood, Ccr2−/− monocytes could traffic to sites of infection, demonstrating that CCR2 is not required for migration from the circulation into tissues. Thus, CCR2-mediated signals in bone marrow determine the frequency of Ly6Chi monocytes in the circulation.

negative: Incremental learning for ν-Support Vector Regression The ν-Support Vector Regression (ν-SVR) is an effective regression learning algorithm, which has the advantage of using a parameter ν on controlling the number of support vectors and adjusting the width of the tube automatically. However, compared to ν-Support Vector Classification (ν-SVC) (Schölkopf et al., 2000), ν-SVR introduces an additional linear term into its objective function. Thus, directly applying the accurate on-line ν-SVC algorithm (AONSVM) to ν-SVR will not generate an effective initial solution. It is the main challenge to design an incremental ν-SVR learning algorithm. To overcome this challenge, we propose a special procedure called initial adjustments in this paper. This procedure adjusts the weights of ν-SVC based on the Karush-Kuhn-Tucker (KKT) conditions to prepare an initial solution for the incremental learning. Combining the initial adjustments with the two steps of AONSVM produces an exact and effective incremental ν-SVR learning algorithm (INSVR). Theoretical analysis has proven the existence of the three key inverse matrices, which are the cornerstones of the three steps of INSVR (including the initial adjustments), respectively. The experiments on benchmark datasets demonstrate that INSVR can avoid the infeasible updating paths as far as possible, and successfully converges to the optimal solution. The results also show that INSVR is faster than batch ν-SVR algorithms with both cold and warm starts.

negative: Feature combination strategies for saliency-based visual attention systems acin ral eais a000 Abstract. Bottom-up or saliency-based visual attention allows primates to detect nonspecific conspicuous targets in cluttered scenes. A classical metaphor, derived from electrophysiological and psychophysical studies, describes attention as a rapidly shiftable ‘‘spotlight.’’ We use a model that reproduces the attentional scan paths of this spotlight. Simple multi-scale ‘‘feature maps’’ detect local spatial discontinuities in intensity, color, and orientation, and are combined into a unique ‘‘master’’ or ‘‘saliency’’ map. The saliency map is sequentially scanned, in order of decreasing saliency, by the focus of attention. We here study the problem of combining feature maps, from different visual modalities (such as color and orientation), into a unique saliency map. Four combination strategies are compared using three databases of natural color images: (1) Simple normalized summation, (2) linear combination with learned weights, (3) global nonlinear normalization followed by summation, and (4) local nonlinear competition between salient locations followed by summation. Performance was measured as the number of false detections before the most salient target was found. Strategy (1) always yielded poorest performance and (2) best performance, with a threefold to eightfold improvement in time to find a salient target. However, (2) yielded specialized systems with poor generalization. Interestingly, strategy (4) and its simplified, computationally efficient approximation (3) yielded significantly better performance than (1), with up to fourfold improvement, while preserving generality. © 2001 SPIE and IS&T. [DOI: 10.1117/1.1333677]

negative: Counter-Forensics: Attacking Image Forensics This chapter discusses counter-forensics, the art and science of impeding or misleading forensic analyses of digital images. Research on counter-forensics is motivated by the need to assess and improve the reliability of forensic methods in situations where intelligent adversaries make efforts to induce a certain outcome of forensic analyses. Counter-forensics is first defined in a formal decision-theoretic framework. This framework is then interpreted and extended to encompass the requirements to forensic analyses in practice, including a discussion of the notion of authenticity in the presence of legitimate processing, and the role of image models with regard to the epistemic underpinning of the forensic decision problem. A terminology is developed that distinguishes security from robustness properties, integrated from post-processing attacks, and targeted from universal attacks. This terminology is directly applied in a self-contained technical survey of counter-forensics against image forensics, notably techniques that suppress traces of image processing and techniques that synthesize traces of authenticity, including examples and brief evaluations. A discussion of relations to other domains of multimedia security and an overview of open research questions concludes the chapter. 1 Definition of Counter-Forensics This final chapter changes the perspective. It is devoted to digital image counterforensics, the art and science of impeding and misleading forensic analyses of digital images. Rainer Böhme Westfälische Wilhelms-Universität Münster, Dept. of Information Systems, Leonardo-Campus 3, 48149 Münster, Germany e-mail: rainer.boehme@wi.uni-muenster.de Matthias Kirchner International Computer Science Institute, 1947 Center Street, Berkeley, CA 94704 e-mail: kirchner@icsi.berkeley.edu

negative: Band-pass shielding enclosure for 2.4/5 GHz WLAN applications This paper presents a design of band-pass shielding enclosure for two frequency bands of WLAN. The proposed design employs a compact frequency selective surface to highlight the advantages of these miniaturized structures for the selective shielding of such enclosures that are electrically small. The results indicate that the proposed shielding enclosure has high transmittance in both the pass bands for horizontal and vertical polarizations, while offers a good amount of electromagnetic shielding outside these frequency bands.

negative: Big Data For Development: Applications and Techniques With the explosion of social media sites and proliferation of digital computing devices and Internet access, massive amounts of public data is being generated on a daily basis. Efficient techniques/ algorithms to analyze this massive amount of data can provide near real-time information about emerging trends and provide early warning in case of an imminent emergency (such as the outbreak of a viral disease). In addition, careful mining of these data can reveal many useful indicators of socioeconomic and political events, which can help in establishing effective public policies. The focus of this study is to review the application of big data analytics for the purpose of human development. The emerging ability to use big data techniques for development (BD4D) promises to revolutionalize healthcare, education, and agriculture; facilitate the alleviation of poverty; and help to deal with humanitarian crises and violent conflicts. Besides all the benefits, the large-scale deployment of BD4D is beset with several challenges due to the massive size, fast-changing and diverse nature of big data. The most pressing concerns relate to efficient data acquisition and sharing, establishing of context (e.g., geolocation and time) and veracity of a dataset, and ensuring appropriate privacy. In this study, we provide a review of existing BD4D work to study the impact of big data on the development of society. In addition to reviewing the important works, we also highlight important challenges and open issues.

negative: On-Chip High-Voltage Generation in Integrated Circuits Using an Improved Multiplier Technique An improved oped for generating +40 voltage multiplier technique has been develV internally in p-channel MNOS integrated circuits to enrtble them to be operated from standard +5and : 12-V supply rails. With this technique, the multiplication efficiency and current driving capabtilty are both independent of the number of multiplier stages. A mathematical model and simple equivalent circuit have been developed for the multiplier and the predicted performance agrees well with measured results. A multiplier has already been incorporated into a TTL compatible nonvolatile quad-latch, in which it occupies a chip area of 600 ~m X 240 pm. It is operated with a clock frequency of 1 MHz and can supManuscript received December 9, 1975; revised February 18, 1976. This paper is based on part of a presentation entitled “A non-volatile MNOS quad-latch,” which was presented at the First European SolidState Circuits Conference, Canterbury, England, September 2-5, 1975. The author is with the Allen Clark Research Centre, The Plessey Company Ltd., CasweJl, Towcester, Northants., England. MNOS Voltage ply a maximum load current of about 10 NA. The output impedance is 3.2 Mfl. INTRODUCTION ALTHOUGH MNOS technology is now well established for fabricating nonvolatile memory circuits, the relatively high potentials necessary to write or erase information, typically 30-40 V, are an obvious disadvantage. In many applications, the need to generate these voltages has prevented the use of MNOS devices being economically viable, especially when only a few bits of nonvolatile data are required. To overcome this problem, a method of on-chip high-voltage generation using a new voltage multiplier technique has been developed, enabling MNOS circuits to be operated with standard DICKSON: ON-CHIP HIGH-VOLTAGEGENERATION 375

negative: Using Fully Homomorphic Encryption for Statistical Analysis of Categorical, Ordinal and Numerical Data In recent years, there has been a growing trend towards outsourcing of computational tasks with the development of cloud services. The Gentry’s pioneering work of fully homomorphic encryption (FHE) and successive works have opened a new vista for secure and practical cloud computing. In this paper, we consider performing statistical analysis on encrypted data. To improve the efficiency of the computations, we take advantage of the batched computation based on the ChineseRemainder-Theorem. We propose two building blocks that work with FHE: a novel batch greater-than primitive, and matrix primitive for encrypted matrices. With these building blocks, we construct secure procedures and protocols for different types of statistics including the histogram (count), contingency table (with cell suppression) for categorical data; k-percentile for ordinal data; and principal component analysis and linear regression for numerical data. To demonstrate the effectiveness of our methods, we ran experiments in five real datasets. For instance, we can compute a contingency table with more than 50 cells from 4000 of data in just 5 minutes, and we can train a linear regression model with more than 40k of data and dimension as high as 6 within 15 minutes. We show that the FHE is not as slow as commonly believed and it becomes feasible to perform a broad range of statistical analysis on thousands of encrypted data.

negative: Bone-Conduction-Based Brain Computer Interface Paradigm -- EEG Signal Processing, Feature Extraction and Classification The paper presents a novel bone-conduction based brain-computer interface paradigm. Four sub-threshold acoustic frequency stimulus patterns are presented to the subjects in an oddball paradigm allowing for "aha-responses" generation to the attended targets. This allows for successful implementation of the bone-conduction based brain-computer interface (BCI) paradigm. The concept is confirmed with seven subjects in online bone-conducted auditory Morse-code patterns spelling BCI paradigm. We report also brain electrophysiological signal processing and classification steps taken to achieve the successful BCI paradigm. We also present a finding of the response latency variability in a function of stimulus difficulty.

negative: Anomalous Magnetic Field Activity During a Bioenergy Healing Experiment A few studies have reported magnetic fi eld changes during bioenergy healing. In a pilot experiment, we examined magnetic fi eld activity during hands-on healing and distant healing of mice with experimentally induced tumors. During healing sessions, we observed distinct magnetic fi eld oscillations adjacent to the mice cages, which were similar in appearance to those reported by Zimmerman (1985). The magnetic fi eld oscillations began as 20–30 Hz oscillations, slowing to 8–9 Hz, and then to less than 1 Hz, at which point the oscillations reversed and increased in frequency, with an overall symmetrical appearance resembling a “chirp wave.” The waves ranged from 1–8 milliGauss peak-to-peak in strength and 60–120 sec in duration. Evidence to date suggests that bioenergy healing may be detectable with DC gaussmeters, although we have not ruled out an artifactual basis for the oscillations reported here.

negative: Algorithms implementing distributed shared memory Four basic algorithms for implementing distributed shared memory are compared. Conceptually, these algorithms extend local virtual address spaces to span multiple hosts connected by a local area network, and some of them can easily be integrated with the hosts' virtual memory systems. The merits of distributed shared memory and the assumptions made with respect to the environment in which the shared memory algorithms are executed are described. The algorithms are then described, and a comparative analysis of their performance in relation to application-level access behavior is presented. It is shown that the correct choice of algorithm is determined largely by the memory access behavior of the applications. Two particularly interesting extensions of the basic algorithms are described, and some limitations of distributed shared memory are noted.<<ETX>>

negative: Tensor2Tensor for Neural Machine Translation Tensor2Tensor is a library for deep learning models that is very well-suited for neural machine translation and includes the reference implementation of the state-of-the-art Transformer model. 1 Neural Machine Translation Background Machine translation using deep neural networks achieved great success with sequence-tosequence models Sutskever et al. (2014); Bahdanau et al. (2014); Cho et al. (2014) that used recurrent neural networks (RNNs) with LSTM cells Hochreiter and Schmidhuber (1997). The basic sequence-to-sequence architecture is composed of an RNN encoder which reads the source sentence one token at a time and transforms it into a fixed-sized state vector. This is followed by an RNN decoder, which generates the target sentence, one token at a time, from the state vector. While a pure sequence-to-sequence recurrent neural network can already obtain good translation results Sutskever et al. (2014); Cho et al. (2014), it suffers from the fact that the whole input sentence needs to be encoded into a single fixed-size vector. This clearly manifests itself in the degradation of translation quality on longer sentences and was partially overcome in Bahdanau et al. (2014) by using a neural model of attention. Convolutional architectures have been used to obtain good results in word-level neural machine translation starting from Kalchbrenner and Blunsom (2013) and later in Meng et al. (2015). These early models used a standard RNN on top of the convolution to generate the output, which creates a bottleneck and hurts performance. Fully convolutional neural machine translation without this bottleneck was first achieved in Kaiser and Bengio (2016) and Kalchbrenner et al. (2016). The model in Kaiser and Bengio (2016) (Extended Neural GPU) used a recurrent stack of gated convolutional layers, while the model in Kalchbrenner et al. (2016) (ByteNet) did away with recursion and used left-padded convolutions in the decoder. This idea, introduced in WaveNet van den Oord et al. (2016), significantly improves efficiency of the model. The same technique was improved in a number of neural translation models recently, including Gehring et al. (2017) and Kaiser et al. (2017).

negative: Detecting Clickbait in Online Social Media: You Won't Believe How We Did It In this paper, we propose an approach for the detection of clickbait posts in online social media (OSM). Clickbait posts are short catchy phrases that attract a user’s attention to click to an article. The approach is based on a machine learning (ML) classifier capable of distinguishing between clickbait and legitimate posts published in OSM. The suggested classifier is based on a variety of features, including image related features, linguistic analysis, and methods for abuser detection. In order to evaluate our method, we used two datasets provided by Clickbait Challenge 2017. The best performance obtained by the ML classifier was an AUC of 0.8, accuracy of 0.812, precision of 0.819, and recall of 0.966. In addition, as opposed to previous studies, we found that clickbait post titles are statistically significant shorter than legitimate post titles. Finally, we found that counting the number of formal English words in the given contentis useful for clickbait detection.

negative: Application of Machine Learning Algorithms to an online Recruitment System In this work, we present a novel approach for evaluating job applicants in online recruitment systems, leveraging machine learning algorithms to solve the candidate ranking problem. An application of our approach is implemented in the form of a prototype system, whose functionality is showcased and evaluated in a real-world recruitment scenario. The proposed system extracts a set of objective criteria from the applicants’ LinkedIn profile, and infers their personality characteristics using linguistic analysis on their blog posts. Our system was found to perform consistently compared to human recruiters; thus, it can be trusted for the automation of applicant ranking and

negative: TopP-S: Persistent homology-based multi-task deep neural networks for simultaneous predictions of partition coefficient and aqueous solubility Aqueous solubility and partition coefficient are important physical properties of small molecules. Accurate theoretical prediction of aqueous solubility and partition coefficient plays an important role in drug design and discovery. The prediction accuracy depends crucially on molecular descriptors which are typically derived from a theoretical understanding of the chemistry and physics of small molecules. This work introduces an algebraic topology-based method, called element-specific persistent homology (ESPH), as a new representation of small molecules that is entirely different from conventional chemical and/or physical representations. ESPH describes molecular properties in terms of multiscale and multicomponent topological invariants. Such topological representation is systematical, comprehensive, and scalable with respect to molecular size and composition variations. However, it cannot be literally translated into a physical interpretation. Fortunately, it is readily suitable for machine learning methods, rendering topological learning algorithms. Due to the inherent correlation between solubility and partition coefficient, a uniform ESPH representation is developed for both properties, which facilitates multi-task deep neural networks for their simultaneous predictions. This strategy leads to a more accurate prediction of relatively small datasets. A total of six datasets is considered in this work to validate the proposed topological and multitask deep learning approaches. It is demonstrated that the proposed approaches achieve some of the most accurate predictions of aqueous solubility and partition coefficient. Our software is available online at http://weilab.math.msu.edu/TopP-S/. © 2018 Wiley Periodicals, Inc.

negative: Not All Neural Embeddings are Born Equal Neural language models learn word representations that capture rich linguistic and conceptual information. Here we investigate the embeddings learned by neural machine translation models. We show that translation-based embeddings outperform those learned by cutting-edge monolingual models at single-language tasks requiring knowledge of conceptual similarity and/or syntactic role. The findings suggest that, while monolingual models learn information about how concepts are related, neural-translation models better capture their true ontological status. It is well known that word representations can be learned from the distributional patterns in corpora. Originally, such representations were constructed by counting word co-occurrences, so that the features in one word’s representation corresponded to other words [11, 17]. Neural language models, an alternative means to learn word representations, use language data to optimise (latent) features with respect to a language modelling objective. The objective can be to predict either the next word given the initial words of a sentence [4, 14, 8], or simply a nearby word given a single cue word [13, 15]. The representations learned by neural models (sometimes called embeddings) generally outperform those acquired by co-occurrence counting models when applied to NLP tasks [3]. Despite these clear results, it is not well understood how the architecture of neural models affects the information encoded in their embeddings. Here, we explore this question by considering the embeddings learned by architectures with a very different objective function to monolingual language models: neural machine translation models. We show that translation-based embeddings outperform monolingual embeddings on two types of task: those that require knowledge of conceptual similarity (rather than simply association or relatedness), and those that require knowledge of syntactic role. We discuss what the findings indicate about the information content of different embeddings, and suggest how this content might emerge as a consequence of the translation objective. 1 Learning embeddings from language data Both neural language models and translation models learn real-valued embeddings (of specified dimension) for words in some pre-specified vocabulary, V , covering many or all words in their training corpus. At each training step, a ‘score’ for the current training example (or batch) is computed based on the embeddings in their current state. This score is compared to the model’s objective function, and the error is backpropagated to update both the model weights (affecting how the score is computed from the embeddings) and the embedding features. At the end of this process, the embeddings should encode information that enables the model to optimally satisfy its objective. 1.1 Monolingual models In the original neural language model [4] and subsequent variants [8], each training example consists of n subsequent words, of which the model is trained to predict the n-th word given the first n − 1 ar X iv :1 41 0. 07 18 v2 [ cs .C L ] 1 3 N ov 2 01 4 1 words. The model first represents the input as an ordered sequence of embeddings, which it transforms into a single fixed length ‘hidden’ representation by, e.g., concatenation and non-linear projection. Based on this representation, a probability distribution is computed over the vocabulary, from which the model can sample a guess at the next word. The model weights and embeddings are updated to maximise the probability of correct guesses for all sentences in the training corpus. More recent work has shown that high quality word embeddings can be learned via models with no nonlinear hidden layer [13, 15]. Given a single word in the corpus, these models simply predict which other words will occur nearby. For each word w in V , a list of training cases (w, c) : c ∈ V is extracted from the training corpus. For instance, in the skipgram approach [13], for each ‘cue word’ w the ‘context words’ c are sampled from windows either side of tokens of w in the corpus (with c more likely to be sampled if it occurs closer to w).1 For each w in V , the model initialises both a cue-embedding, representing the w when it occurs as a cue-word, and a context-embedding, used when w occurs as a context-word. For a cue word w, the model can use the corresponding cueembedding and all context-embeddings to compute a probability distribution over V that reflects the probability of a word occurring in the context of w. When a training example (w, c) is observed, the model updates both the cue-word embedding of w and the context-word embeddings in order to increase the conditional probability of c. 1.2 Translation-based embeddings Neural translation models generate an appropriate sentence in their target language St given a sentence Ss in their source language [see, e.g., 16, 6]. In doing so, they learn distinct sets of embeddings for the vocabularies Vs and Vt in the source and target languages respectively. Observing a training case (Ss, St), such a model represents Ss as an ordered sequence of embeddings of words from Vs. The sequence for Ss is then encoded into a single representation RS .2 Finally, by referencing the embeddings in Vt, RS and a representation of what has been generated thus far, the model decodes a sentence in the target language word by word. If at any stage the decoded word does not match the corresponding word in the training target St, the error is recorded. The weights and embeddings in the model, which together parameterise the encoding and decoding process, are updated based on the accumulated error once the sentence decoding is complete. Although neural translation models can differ in low-level architecture [7, 2], the translation objective exerts similar pressure on the embeddings in all cases. The source language embeddings must be such that the model can combine them to form single representations for ordered sequences of multiple words (which in turn must enable the decoding process). The target language embeddings must facilitate the process of decoding these representations into correct target-language sentences. 2 Comparing Mono-lingual and Translation-based Embeddings To learn translation-based embeddings, we trained both the RNN encoder-decoder [RNNenc, 7] and the RNN Search architectures [2] on a 300m word corpus of English-French sentence pairs. We conducted all experiments with the resulting (English) source embeddings from these models. For comparison, we trained a monolingual skipgram model [13] and its Glove variant [15] for the same number of epochs on the English half of the bilingual corpus. We also extracted embeddings from a full-sentence language model [CW, 8] trained for several months on a larger 1bn word corpus. As in previous studies [1, 5, 3], we evaluate embeddings by calculating pairwise (cosine) distances and correlating these distances with (gold-standard) human judgements. Table 1 shows the correlations of different model embeddings with three such gold-standard resources, WordSim-353 [1], MEN [5] and SimLex-999 [10]. Interestingly, translation embeddings perform best on SimLex-999, while the two sets of monolingual embeddings perform better on modelling the MEN and WordSim353. To interpret these results, it should be noted that SimLex-999 evaluation quantifies conceptual similarity (dog wolf ), whereas MEN and WordSim-353 (despite its name) quantify more general relatedness (dog collar) [10]. The results seem to indicate that translation-based embeddings better capture similarity, while monolingual embeddings better capture relatedness. 1 Subsequent variants use different algorithms for selecting the (w, c) from the training corpus [9, 12] Alternatively, subsequences (phrases) of Ss may be encoded at this stage in place of the whole sentence [2].

negative: Finding social roles in Wikipedia This paper investigates some of the social roles people play in the online community of Wikipedia. We start from qualitative comments posted on community oriented pages, wiki project memberships, and user talk pages in order to identify a sample of editors who represent four key roles: substantive experts, technical editors, vandal fighters, and social networkers. Patterns in edit histories and egocentric network visualizations suggest potential "structural signatures" that could be used as quantitative indicators of role adoption. Using simple metrics based on edit histories we compare two samples of Wikipedians: a collection of long term dedicated editors, and a cohort of editors from a one month window of new arrivals. According to these metrics, we find that the proportions of editor types in the new cohort are similar those observed in the sample of dedicated contributors. The number of new editors playing helpful roles in a single month's cohort nearly equal the number found in the dedicated sample. This suggests that informal socialization has the potential provide sufficient role related labor despite growth and change in Wikipedia. These results are preliminary, and we describe several ways that the method can be improved, including the expansion and refinement of role signatures and identification of other important social roles.

negative: Why does deep and cheap learning work so well? We show how the success of deep learning could depend not only on mathematics but also on physics: although well-known mathematical theorems guarantee that neural networks can approximate arbitrary functions well, the class of functions of practical interest can frequently be approximated through “cheap learning” with exponentially fewer parameters than generic ones. We explore how properties frequently encountered in physics such as symmetry, locality, compositionality, and polynomial log-probability translate into exceptionally simple neural networks. We further argue that when the statistical process generating the data is of a certain hierarchical form prevalent in physics and machine-learning, a deep neural network can be more efficient than a shallow one. We formalize these claims using information theory and discuss the relation to the renormalization group. We prove various “no-flattening theorems” showing when efficient linear deep networks cannot be accurately approximated by shallow ones without efficiency loss; for example, we show that n variables cannot be multiplied using fewer than 2 neurons in a single hidden layer.


query: An Intelligent Anti-phishing Strategy Model for Phishing Website Detection

positive:
CANTINA+: A Feature-Rich Machine Learning Framework for Detecting Phishing Web Sites Phishing is a plague in cyberspace. Typically, phish detection methods either use human-verified URL blacklists or exploit Web page features via machine learning techniques. However, the former is frail in terms of new phish, and the latter suffers from the scarcity of effective features and the high false positive rate (FP). To alleviate those problems, we propose a layered anti-phishing solution that aims at (1) exploiting the expressiveness of a rich set of features with machine learning to achieve a high true positive rate (TP) on novel phish, and (2) limiting the FP to a low level via filtering algorithms.
 Specifically, we proposed CANTINA+, the most comprehensive feature-based approach in the literature including eight novel features, which exploits the HTML Document Object Model (DOM), search engines and third party services with machine learning techniques to detect phish. Moreover, we designed two filters to help reduce FP and achieve runtime speedup. The first is a near-duplicate phish detector that uses hashing to catch highly similar phish. The second is a login form filter, which directly classifies Web pages with no identified login form as legitimate.
 We extensively evaluated CANTINA+ with two methods on a diverse spectrum of corpora with 8118 phish and 4883 legitimate Web pages. In the randomized evaluation, CANTINA+ achieved over 92% TP on unique testing phish and over 99% TP on near-duplicate testing phish, and about 0.4% FP with 10% training phish. In the time-based evaluation, CANTINA+ also achieved over 92% TP on unique testing phish, over 99% TP on near-duplicate testing phish, and about 1.4% FP under 20% training phish with a two-week sliding window. Capable of achieving 0.4% FP and over 92% TP, our CANTINA+ has been demonstrated to be a competitive anti-phishing solution.

positive: Survey of clustering algorithms Data analysis plays an indispensable role for understanding various phenomena. Cluster analysis, primitive exploration with little or no prior knowledge, consists of research developed across a wide variety of communities. The diversity, on one hand, equips us with many tools. On the other hand, the profusion of options causes confusion. We survey clustering algorithms for data sets appearing in statistics, computer science, and machine learning, and illustrate their applications in some benchmark data sets, the traveling salesman problem, and bioinformatics, a new field attracting intensive efforts. Several tightly related topics, proximity measure, and cluster validation, are also discussed.

positive: Term-Weighting Approaches in Automatic Text Retrieval The experimental evidence accumulated over the past 20 years indicates that text indexing systems based on the assignment of appropriately weighted single terms produce retrieval results that are superior to those obtainable with other more elaborate text representations. These results depend crucially on the choice of effective termweighting systems. This article summarizes the insights gained in automatic term weighting, and provides baseline single-term-indexing models with which other more elaborate content analysis procedures can be compared. 1. AUTOMATIC TEXT ANALYSIS In the late 195Os, Luhn [l] first suggested that automatic text retrieval systems could be designed based on a comparison of content identifiers attached both to the stored texts and to the users’ information queries. Typically, certain words extracted from the texts of documents and queries would be used for content identification; alternatively, the content representations could be chosen manually by trained indexers familiar with the subject areas under consideration and with the contents of the document collections. In either case, the documents would be represented by term vectors of the form D= (ti,tj,...ytp) (1) where each tk identifies a content term assigned to some sample document D. Analogously, the information requests, or queries, would be represented either in vector form, or in the form of Boolean statements. Thus, a typical query Q might be formulated as Q = (qa,qbr.. . ,4r) (2)

positive: A Statistical Approach to Mechanized Encoding and Searching of Literary Information Written communication of ideas is carried out on the basis of statistical probability in that a writer chooses that level of subject specificity and that combination of words which he feels will convey the most meaning. Since this process varies among individuals and since similar ideas are therefore relayed at different levels of specificity and by means of different words, the problem of literature searching by machines still presents major difficulties. A statistical approach to this problem will be outlined and the various steps of a system based on this approach will be described. Steps include the statistical analysis of a collection of documents in a field of interest, the establishment of a set of "notions" and the vocabulary by which they are expressed, the compilation of a thesaurus-type dictionary and index, the automatic encoding of documents by machine with the aid of such a dictionary, the encoding of topological notations (such as branched structures), the recording of the coded information, the establishment of a searching pattern for finding pertinent information, and the programming of appropriate machines to carry out a search.

positive: Client-Side Defense Against Web-Based Identity Theft Web spoofing is a significant problem involving fraudulent email and web sites that trick unsuspecting users into revealing private information. We discuss some aspects of common attacks and propose a framework for client-side defense: a browser plug-in that examines web pages and warns the user when requests for data may be part of a spoof attack. While the plugin, SpoofGuard, has been tested using actual sites obtained through government agencies concerned about the problem, we expect that web spoofing and other forms of identity theft will be continuing problems in

negative: Computational intelligence in sports: Challenges and opportunities within a new research domain Computational intelligence is a branch of artificial intelligence that comprises algorithms inspired by nature. The common characteristics of all these algorithms is their collective intelligence and adaptability to a changing environment. Due to their efficiency and simplicity, these algorithms have been employed for problem solving across social and natural sciences. The aim of this paper is to demonstrate that nature-inspired algorithms are also useful within the domain of sport, in particular for obtaining safe and effective training plans targeting various aspects of performance. We outline the benefits and opportunities of applying computational intelligence in sports, and we also comment on the pitfalls and challenges for the future development of this emerging research domain. © 2015 Elsevier Inc. All rights reserved.

negative: Vehicle-to-Vehicle Communication: Fair Transmit Power Control for Safety-Critical Information Direct radio-based vehicle-to-vehicle communication can help prevent accidents by providing accurate and up-to-date local status and hazard information to the driver. In this paper, we assume that two types of messages are used for traffic safety-related communication: 1) Periodic messages (ldquobeaconsrdquo) that are sent by all vehicles to inform their neighbors about their current status (i.e., position) and 2) event-driven messages that are sent whenever a hazard has been detected. In IEEE 802.11 distributed-coordination-function-based vehicular networks, interferences and packet collisions can lead to the failure of the reception of safety-critical information, in particular when the beaconing load leads to an almost-saturated channel, as it could easily happen in many critical vehicular traffic conditions. In this paper, we demonstrate the importance of transmit power control to avoid saturated channel conditions and ensure the best use of the channel for safety-related purposes. We propose a distributed transmit power control method based on a strict fairness criterion, i.e., distributed fair power adjustment for vehicular environments (D-FPAV), to control the load of periodic messages on the channel. The benefits are twofold: 1) The bandwidth is made available for higher priority data like dissemination of warnings, and 2) beacons from different vehicles are treated with ldquoequal rights,rdquo and therefore, the best possible reception under the available bandwidth constraints is ensured. We formally prove the fairness of the proposed approach. Then, we make use of the ns-2 simulator that was significantly enhanced by realistic highway mobility patterns, improved radio propagation, receiver models, and the IEEE 802.11p specifications to show the beneficial impact of D-FPAV for safety-related communications. We finally put forward a method, i.e., emergency message dissemination for vehicular environments (EMDV), for fast and effective multihop information dissemination of event-driven messages and show that EMDV benefits of the beaconing load control provided by D-FPAV with respect to both probability of reception and latency.

negative: Determining Gains Acquired from Word Embedding Quantitatively Using Discrete Distribution Clustering Word embeddings have become widelyused in document analysis. While a large number of models for mapping words to vector spaces have been developed, it remains undetermined how much net gain can be achieved over traditional approaches based on bag-of-words. In this paper, we propose a new document clustering approach by combining any word embedding with a state-of-the-art algorithm for clustering empirical distributions. By using the Wasserstein distance between distributions, the word-to-word semantic relationship is taken into account in a principled way. The new clustering method is easy to use and consistently outperforms other methods on a variety of data sets. More importantly, the method provides an effective framework for determining when and how much word embeddings contribute to document analysis. Experimental results with multiple embedding models are reported.

negative: Cyberbullying perpetration and victimization among adolescents in Hong Kong a r t i c l e i n f o Cyberbullying is a growing concern worldwide. Using a sample of 1917 secondary adolescents from seven schools, five psychometric measures (self-efficacy, empathy level, feelings regarding a harmonious school, sense of belonging to the school, and psychosocial wellbeing) and five scales regarding bullying experiences (cyber-and traditional bullying perpetration and victimization; reactions to cyberbullying victimization) were administered to explore the prevalence of cyberbullying in Hong Kong. Findings indicated that male adolescents were more likely than female adolescents to cyberbully others and to be cyber-victimized. Cyberbullying perpe-tration and victimization were found to be negatively associated with the adolescents' psychosocial health and sense of belonging to school. Cyber-and traditional bullying were positively correlated. Multivariate analyses indicated that being male, having a low sense of belonging to school, involvement in traditional bullying perpetra-tion, and experiencing cyber-victimization were associated with an increased propensity to cyberbully others. Technology is advancing rapidly, and peer harassment and aggression are no longer limited to traditional bullying through physical contact. Over the past decade, information and communication technology (ICT) has become increasingly important in the lives of adolescents. In a report by Lenhart, Madden, and Hitlin (2005), it is estimated that close to 90% of American adolescents aged 12 to 17 years surf the Inter-net, with 51% of them using it on a daily basis. Nearly half of the adolescents surveyed have personal mobile phones, and 33% have used a mobile phone to send a text message (Lenhart et al., 2005). Such heavy use of the Internet is not novel among adolescents in Hong Kong. Many empirical studies have been conducted on Hong Kong adolescents on their excessive and/or addictive use of the Internet. Findings of recent studies indicate that a substantially high prevalence rate of internet addiction is reported among Hong Kong adolescents (range In reality, the heavy usage of ICT such as instant mes-saging, e-mail, text messaging, blogs, and social networking sites not only allows adolescents to connect with friends and family, but at the same time also creates the potential to meet and interact with others in harmful ways (Ybarra, Diener-West, & Leaf, 2007). Cyberbullying or online bullying is one such growing concern. Traditional bullying is a widespread problem in both school and community settings, and has long been researched by scholars in general, involves an individual being exposed to negative actions by one or more individuals regularly …

negative: Dynamic Modeling and Performance Analysis of a Grid-Connected Current-Source Inverter-Based Photovoltaic System Voltage-source inverter (VSI) topology is widely used for grid interfacing of distributed generation (DG) systems. However, when employed as the power conditioning unit in photovoltaic (PV) systems, VSI normally requires another power electronic converter stage to step up the voltage, thus adding to the cost and complexity of the system. To make the proliferation of grid-connected PV systems a successful business option, the cost, performance, and life expectancy of the power electronic interface need to be improved. The current-source inverter (CSI) offers advantages over VSI in terms of inherent boosting and short-circuit protection capabilities, direct output current controllability, and ac-side simpler filter structure. Research on CSI-based DG is still in its infancy. This paper focuses on modeling, control, and steady-state and transient performances of a PV system based on CSI. It also performs a comparative performance evaluation of VSI-based and CSI-based PV systems under transient and fault conditions. Analytical expectations are verified using simulations in the Power System Computer Aided Design/Electromagnetic Transient Including DC (PSCAD/EMTDC) environment, based on a detailed system model.

negative: 3D deeply supervised network for automated segmentation of volumetric medical images While deep convolutional neural networks (CNNs) have achieved remarkable success in 2D medical image segmentation, it is still a difficult task for CNNs to segment important organs or structures from 3D medical images owing to several mutually affected challenges, including the complicated anatomical environments in volumetric images, optimization difficulties of 3D networks and inadequacy of training samples. In this paper, we present a novel and efficient 3D fully convolutional network equipped with a 3D deep supervision mechanism to comprehensively address these challenges; we call it 3D DSN. Our proposed 3D DSN is capable of conducting volume-to-volume learning and inference, which can eliminate redundant computations and alleviate the risk of over-fitting on limited training data. More importantly, the 3D deep supervision mechanism can effectively cope with the optimization problem of gradients vanishing or exploding when training a 3D deep model, accelerating the convergence speed and simultaneously improving the discrimination capability. Such a mechanism is developed by deriving an objective function that directly guides the training of both lower and upper layers in the network, so that the adverse effects of unstable gradient changes can be counteracted during the training procedure. We also employ a fully connected conditional random field model as a post-processing step to refine the segmentation results. We have extensively validated the proposed 3D DSN on two typical yet challenging volumetric medical image segmentation tasks: (i) liver segmentation from 3D CT scans and (ii) whole heart and great vessels segmentation from 3D MR images, by participating two grand challenges held in conjunction with MICCAI. We have achieved competitive segmentation results to state-of-the-art approaches in both challenges with a much faster speed, corroborating the effectiveness of our proposed 3D DSN.

negative: Targeted Employee Retention : Performance-Based and Job-Related Differences in Reported Reasons for Staying A content model of 12 retention factors is developed in the context of previous theory and research. Coding of open-ended responses from 24,829 employees in the leisure and hospitality industry lends support to the identified framework and reveals that job satisfaction, extrinsic rewards, constituent attachments, organizational commitment, and organizational prestige were the most frequently mentioned reasons for staying. Advancement opportunities and organizational prestige were more common reasons for staying among high performers and non-hourly workers, and extrinsic rewards was more common among low performers and hourly employees, providing support for ease/desirability of movement and psychological contract rationales. The findings highlight the importance of differentiating human resource management practices when the goal is to retain those employees valued most by the organization.

negative: An Alpha-wave-based binaural beat sound control system using fuzzy logic and autoregressive forecasting model We are developing a new real-time control system for customizing auditory stimulus (the binaural beat sound) by judging user alpha waves to entrain a userpsilas feeling in the most relaxed way. Since brainwave activity provides the necessary predictive information for arousal states, we use an autoregressive forecasting model to estimate the frequency response series of the alpha frequency bands and the inverted-U concept to determine the userpsilas arousal state. A fuzzy logic controller is also employed to regulate the binaural beat control signal on a forecasting error signal. Our system allows comfortable user self-relaxation. The results of experiments confirm the constructed systempsilas effectiveness and necessity.

negative: A Comparative Study of Stereovision Algorithms Stereo vision has been and continues to be one of the most researched domains of computer vision, having many applications, among them, allowing the depth extraction of a scene. This paper provides a comparative study of stereo vision and matching algorithms, used to solve the correspondence problem. The study of matching algorithms was followed by experiments on the Middlebury benchmarks. The tests focused on a comparison of 6 stereovision methods. In order to assess the performance, RMS and some statistics related were computed. In order to emphasize the advantages of each stereo algorithm considered, two-frame methods have been employed, both local and global. The experiments conducted have shown that the best results are obtained by Graph Cuts. Unfortunately, this has a higher computational cost. If high quality is not an issue in applications, local methods provide reasonable results within a much lower time-frame and offer the possibility of parallel

negative: Learning to rank academic experts in the DBLP dataset Expert finding is an information retrieval task that is concerned with the search for the most knowledgeable people with respect to a specific topic, and the search is based on documents that describe people’s activities. The task involves taking a user query as input and returning a list of people who are sorted by their level of expertise with respect to the user query. Despite recent interest in the area, the current state-of-the-art techniques lack in principled approaches for optimally combining different sources of evidence. This article proposes two frameworks for combining multiple estimators of expertise. These estimators are derived from textual contents, from graph-structure of the citation patterns for the community of experts, and from profile information about the experts. More specifically, this article explores the use of supervised learning to rank methods, as well as rank aggregation approaches, for combing all of the estimators of expertise. Several supervised learning algorithms, which are representative of the pointwise, pairwise and listwise approaches, were tested, and various state-of-the-art data fusion techniques were also explored for the rank aggregation framework. Experiments that were performed on a dataset of academic publications from the Computer Science domain attest the adequacy of the proposed approaches.

negative:
A novel end-to-end classifier using domain transferred deep convolutional neural networks for biomedical images BACKGROUND AND OBJECTIVES
Highly accurate classification of biomedical images is an essential task in the clinical diagnosis of numerous medical diseases identified from those images. Traditional image classification methods combined with hand-crafted image feature descriptors and various classifiers are not able to effectively improve the accuracy rate and meet the high requirements of classification of biomedical images. The same also holds true for artificial neural network models directly trained with limited biomedical images used as training data or directly used as a black box to extract the deep features based on another distant dataset. In this study, we propose a highly reliable and accurate end-to-end classifier for all kinds of biomedical images via deep learning and transfer learning.

METHODS
We first apply domain transferred deep convolutional neural network for building a deep model; and then develop an overall deep learning architecture based on the raw pixels of original biomedical images using supervised training. In our model, we do not need the manual design of the feature space, seek an effective feature vector classifier or segment specific detection object and image patches, which are the main technological difficulties in the adoption of traditional image classification methods. Moreover, we do not need to be concerned with whether there are large training sets of annotated biomedical images, affordable parallel computing resources featuring GPUs or long times to wait for training a perfect deep model, which are the main problems to train deep neural networks for biomedical image classification as observed in recent works.

RESULTS
With the utilization of a simple data augmentation method and fast convergence speed, our algorithm can achieve the best accuracy rate and outstanding classification ability for biomedical images. We have evaluated our classifier on several well-known public biomedical datasets and compared it with several state-of-the-art approaches.

CONCLUSIONS
We propose a robust automated end-to-end classifier for biomedical images based on a domain transferred deep convolutional neural network model that shows a highly reliable and accurate performance which has been confirmed on several public biomedical image datasets.

negative: Single-Fed Low-Profile High-Gain Circularly Polarized Slotted Cavity Antenna Using a High-Order Mode In this letter, a single-fed low-profile high-gain circularly polarized slotted cavity antenna using a high-order cavity mode, i.e., TE440 mode, is proposed. The proposed antenna has a simple structure that consists of a radiating linearly polarized slotted cavity antenna and a linear-to-circular polarization converter. An antenna prototype operating at WLAN 5.8-GHz band fabricated with low-cost standard printed circuit board (PCB) process is exemplified to validate the proposed concept. Measured results compared to their simulated counterparts are presented, and a good agreement between simulation and measurement is obtained.

negative: VAMS 2017: Workshop on Value-Aware and Multistakeholder Recommendation In this paper, we summarize VAMS 2017 - a workshop on value-aware and multistakeholder recommendation co-located with RecSys 2017. The workshop encouraged forward-thinking papers in this new area of recommender systems research and obtained a diverse set of responses ranging from application results to research overviews.

negative: Articulated pose estimation with flexible mixtures-of-parts We describe a method for human pose estimation in static images based on a novel representation of part models. Notably, we do not use articulated limb parts, but rather capture orientation with a mixture of templates for each part. We describe a general, flexible mixture model for capturing contextual co-occurrence relations between parts, augmenting standard spring models that encode spatial relations. We show that such relations can capture notions of local rigidity. When co-occurrence and spatial relations are tree-structured, our model can be efficiently optimized with dynamic programming. We present experimental results on standard benchmarks for pose estimation that indicate our approach is the state-of-the-art system for pose estimation, outperforming past work by 50% while being orders of magnitude faster.

negative: Learning from Explicit and Implicit Supervision Jointly For Algebra Word Problems Automatically solving algebra word problems has raised considerable interest recently. Existing state-of-the-art approaches mainly rely on learning from human annotated equations. In this paper, we demonstrate that it is possible to efficiently mine algebra problems and their numerical solutions with little to no manual effort. To leverage the mined dataset, we propose a novel structured-output learning algorithm that aims to learn from both explicit (e.g., equations) and implicit (e.g., solutions) supervision signals jointly. Enabled by this new algorithm, our model gains 4.6% absolute improvement in accuracy on the ALG514 benchmark compared to the one without using implicit supervision. The final model also outperforms the current state-of-the-art approach by 3%.

negative: Fault investigations on die-cast copper rotors It has been long known to the market that, the use of copper in the rotor in place of aluminium, taking advantage of copper's high conductivity, can be an economic solution for various applications, including electric vehicle, in the development of energy efficient motors. In comparison to aluminium, copper die-casting is extremely difficult to perform due to its high density, viscosity and melting point. Owing to these reasons, the copper die-cast rotors will evince several manufacturing problems. The objective of this paper is to identify the various defects in the manufacturing of copper die-cast rotor, and identify the lamination-coating-condition that sustains copper die-cast pressure and temperature. This paper evaluates the effectiveness of lamination coating on limiting the eddy current loss, and the recoating process to improve, in case the eddy current losses are in excess due to copper die-casting.

negative: The recognition of rice images by UAV based on capsule network It is important to recognize the rice image captured by unmanned aerial vehicle (UAV) for monitoring the growth of rice and preventing the diseases and pests. Aiming at the image recognition, we use rice images captured by UAV as our data source, the structure of capsule network (CapsNet) is built to recognize rice images in this paper. The images are preprocessed through histogram equalization method into grayscale images and through superpixel algorithm into the superpixel segmentation results. The both results are output into the CapsNet. The function of CapsNet is to perform the reverse analysis of rice images. The CapsNet consists of five layers: an input layer, a convolution layer, a primary capsules layer, a digital capsules layer and an output layer. The CapsNet trains classification and predicts the output vector based on routing-by-agreement protocol. Therefore, the features of rice image by UAV can be precisely and efficiently extracted. The method is more convenient than the traditional artificial recognition. It provides the scientific support and reference for decision-making process of precision agriculture.

negative: A functional taxonomy of wireless sensor network devices With the availability of cheap computing technology and the standardization of different wireless communication paradigms, wireless sensor networks (WSNs) are becoming a reality. This is visible in the increasing amount of research being done in the WSN area, and the growing number of companies offering commercial WSN solutions. In this regard, we realize that there is a need for an unambiguous classification and definition of WSN devices. Such a classification scheme would benefit the WSN research and industrial community. We consider the spectrum of wireless devices ranging from passive RF devices to gateway devices for the classification. The classification scheme is based on functionality of the devices. This scheme is analogous to an object-oriented classification, where an object is classified on the basis of its functionality and is described by its attributes. Attributes of the wireless devices are grouped into five broad categories: communication, sensing, power, memory, and "other features". Each of these are further classified to provide sufficient details that are required for a typical WSN application

negative: Permittivity and Loss Tangent Characterization for Garment Antennas Based on a New Matrix-Pencil Two-Line Method The emergence of wearable antennas to be integrated into garments has revealed the need for a careful electromagnetic characterization of textile materials. Therefore, we propose in this paper a new matrix-pencil two-line method that removes perturbations in the calculated effective permittivity and loss tangent which are caused by imperfect deembedding and inhomogeneities of the textile microstrip line structure. The approach has been rigorously validated for high-frequency laminates by comparing measured and simulated data for the resonance frequency of antennas designed using the calculated parameters. The method has been successfully applied to characterize the permittivity and loss tangent of a variety of textile materials and up to a frequency of 10 GHz. Furthermore it is shown that the use of electrotextiles in antenna design influences the effective substrate permittivity.

negative: Adapting Naive Bayes to Domain Adaptation for Sentiment Analysis In the community of sentiment analysis, supervised learning techniques have been shown to perform very well. When transferred to another domain, however, a supervised sentiment classifier often performs extremely bad. This is so-called domain-transfer problem. In this work, we attempt to attack this problem by making the maximum use of both the old-domain data and the unlabeled new-domain data. To leverage knowledge from the old-domain data, we proposed an effective measure, i.e., Frequently Co-occurring Entropy (FCE), to pick out generalizable features that occur frequently in both domains and have similar occurring probability. To gain knowledge from the newdomain data, we proposed Adapted Naïve Bayes (ANB), a weighted transfer version of Naive Bayes Classifier. The experimental results indicate that proposed approach could improve the performance of base classifier dramatically, and even provide much better performance than the transfer-learning baseline, i.e. the Naïve Bayes Transfer Classifier (NTBC).

negative: Mining geographic-temporal-semantic patterns in trajectories for location prediction In recent years, research on location predictions by mining trajectories of users has attracted a lot of attention. Existing studies on this topic mostly treat such predictions as just a type of location recommendation, that is, they predict the next location of a user using location recommenders. However, an user usually visits somewhere for reasons other than interestingness. In this article, we propose a novel mining-based location prediction approach called Geographic-Temporal-Semantic-based Location Prediction (GTS-LP), which takes into account a user's geographic-triggered intentions, temporal-triggered intentions, and semantic-triggered intentions, to estimate the probability of the user in visiting a location. The core idea underlying our proposal is the discovery of trajectory patterns of users, namely GTS patterns, to capture frequent movements triggered by the three kinds of intentions. To achieve this goal, we define a new trajectory pattern to capture the key properties of the behaviors that are motivated by the three kinds of intentions from trajectories of users. In our GTS-LP approach, we propose a series of novel matching strategies to calculate the similarity between the current movement of a user and discovered GTS patterns based on various moving intentions. On the basis of similitude, we make an online prediction as to the location the user intends to visit. To the best of our knowledge, this is the first work on location prediction based on trajectory pattern mining that explores the geographic, temporal, and semantic properties simultaneously. By means of a comprehensive evaluation using various real trajectory datasets, we show that our proposed GTS-LP approach delivers excellent performance and significantly outperforms existing state-of-the-art location prediction methods.

negative: Feature Selection and Kernel Learning for Local Learning-Based Clustering The performance of the most clustering algorithms highly relies on the representation of data in the input space or the Hilbert space of kernel methods. This paper is to obtain an appropriate data representation through feature selection or kernel learning within the framework of the Local Learning-Based Clustering (LLC) (Wu and Schölkopf 2006) method, which can outperform the global learning-based ones when dealing with the high-dimensional data lying on manifold. Specifically, we associate a weight to each feature or kernel and incorporate it into the built-in regularization of the LLC algorithm to take into account the relevance of each feature or kernel for the clustering. Accordingly, the weights are estimated iteratively in the clustering process. We show that the resulting weighted regularization with an additional constraint on the weights is equivalent to a known sparse-promoting penalty. Hence, the weights of those irrelevant features or kernels can be shrunk toward zero. Extensive experiments show the efficacy of the proposed methods on the benchmark data sets.

negative: Effects of Transformational Leadership Training on Attitudinal and Financial Outcomes : A Field Experiment A pretest-posttest control-group design (N = 20) was used to assess the effects of transformational leadership training, with 9 and 11 managers assigned randomly to training and control groups, respectively. Training consisted of a 1-day group session and 4 individual booster sessions thereafter on a monthly basis. Multivariate analyses of covariance, with pretest scores as the covariate, showed that the training resulted in significant effects on subordinates' perceptions of leaders' transformational leadership, subordinates' own organizational commitment, and 2 aspects of branch-level financial performance.

negative: Using Phase Instead of Optical Flow for Action Recognition Currently, the most common motion representation for action recognition is optical flow. Optical flow is based on particle tracking which adheres to a Lagrangian perspective on dynamics. In contrast to the Lagrangian perspective, the Eulerian model of dynamics does not track, but describes local changes. For video, an Eulerian phase-based motion representation, using complex steerable filters, has been successfully employed recently for motion magnification and video frame interpolation. Inspired by these previous works, here, we proposes learning Eulerian motion representations in a deep architecture for action recognition. We learn filters in the complex domain in an end-to-end manner. We design these complex filters to resemble complex Gabor filters, typically employed for phase-information extraction. We propose a phaseinformation extraction module, based on these complex filters, that can be used in any network architecture for extracting Eulerian representations. We experimentally analyze the added value of Eulerian motion representations, as extracted by our proposed phase extraction module, and compare with existing motion representations based on optical flow, on the UCF101 dataset.

negative: A generalized taxonomy of explanations styles for traditional and social recommender systems Recommender systems usually provide explanations of their recommendations to better help users to choose products, activities or even friends. Up until now, the type of an explanation style was considered in accordance to the recommender system that employed it. This relation was one-to-one, meaning that for each different recommender systems category, there was a different explanation style category. However, this kind of one-to-one correspondence can be considered as over-simplistic and non generalizable. In contrast, we consider three fundamental resources that can be used in an explanation: users, items and features and any combination of them. In this survey, we define (i) the Human style of explanation, which provides explanations based on similar users, (ii) the Item style of explanation, which is based on choices made by a user on similar items and (iii) the Feature style of explanation, which explains the recommendation based on item features rated by the user beforehand. By using any combination of the aforementioned styles we can also define the Hybrid style of explanation. We demonstrate how these styles are put into practice, by presenting recommender systems that employ them. Moreover, since there is inadequate research in the impact of social web in contemporary recommender systems and their explanation styles, we study new emerged social recommender systems i.e. Facebook Connect explanations (HuffPo, Netflix, etc.) and geo-social explanations that combine geographical with social data (Gowalla, Facebook Places, etc.). Finally, we summarize the results of three different user studies, to support that Hybrid is the most effective explanation style, since it incorporates all other styles.

negative: Who falls for phish?: a demographic analysis of phishing susceptibility and effectiveness of interventions In this paper we present the results of a roleplay survey instrument administered to 1001 online survey respondents to study both the relationship between demographics and phishing susceptibility and the effectiveness of several anti-phishing educational materials. Our results suggest that women are more susceptible than men to phishing and participants between the ages of 18 and 25 are more susceptible to phishing than other age groups. We explain these demographic factors through a mediation analysis. Educational materials reduced users' tendency to enter information into phishing webpages by 40% percent; however, some of the educational materials we tested also slightly decreased participants' tendency to click on legitimate links.

negative: Using Syntactic Features for Phishing Detection This paper reports on the comparison of the subject and object of verbs in their usage between phishing emails and legitimate emails. The purpose of this research is to explore whether the syntactic structures and subjects and objects of verbs can be distinguishable features for phishing detection. To achieve the objective, we have conducted two series of experiments: the syntactic similarity for sentences, and the subject and object of verb comparison. The results of the experiments indicated that both features can be used for some verbs, but more work has to be done for others. Keywords—phishing detection; syntactic similarity; parse tree path.

negative: User Traffic Profile for Traffic Reduction and Effective Bot C&C Detection Bots are malicious software components used for generating spams, launching denial of service attacks, phishing, identity theft and information exfiltration and such other illegal activities. Bot detection is an area of active research in recent times. Here we propose a bot detection mechanism for a single host. A user traffic profile is used to filter out normal traffic generated by the host. The remaining suspicious traffic is subject to detailed analysis. The nature of detailed analysis is derived from a characterization of bot traffic. The detection system is tested using two real world bots. Performance evaluation results show that the proposed system achieves a high detection rate (100%) and a low false positive rate (0–8%). The traffic filtering yielded an average reduction in traffic by 70%.

negative:
Malicious PDF detection using metadata and structural features Owed to their versatile functionality and widespread adoption, PDF documents have become a popular avenue for user exploitation ranging from large-scale phishing attacks to targeted attacks. In this paper, we present a framework for robust detection of malicious documents through machine learning. Our approach is based on features extracted from document metadata and structure. Using real-world datasets, we demonstrate the the adequacy of these document properties for malware detection and the durability of these features across new malware variants. Our analysis shows that the Random Forests classification method, an ensemble classifier that randomly selects features for each individual classification tree, yields the best detection rates, even on previously unseen malware.
 Indeed, using multiple datasets containing an aggregate of over 5,000 unique malicious documents and over 100,000 benign ones, our classification rates remain well above 99% while maintaining low false positives of 0.2% or less for different classification parameters and experimental scenarios. Moreover, the classifier has the ability to detect documents crafted for targeted attacks and separate them from broadly distributed malicious PDF documents. Remarkably, we also discovered that by artificially reducing the influence of the top features in the classifier, we can still achieve a high rate of detection in an adversarial setting where the attacker is aware of both the top features utilized in the classifier and our normality model. Thus, the classifier is resilient against mimicry attacks even with knowledge of the document features, classification method, and training set.

negative: Securing SMS Based One Time Password Technique from Man in the Middle Attack Security of financial transactions in E-Commerce is difficult to implement and there is a risk that user’s confidential data over the internet may be accessed by hackers. Unfortunately, interacting with an online service such as a banking web application often requires certain degree of technical sophistication that not all Internet users possess. For the last couple of year such naive users have been increasingly targeted by phishing attacks that are launched by miscreants who are aiming to make an easy profit by means of illegal financial transactions. In this paper, we have proposed an idea for securing e-commerce transaction from phishing attack. An approach already exists where phishing attack is prevented using one time password which is sent on user’s registered mobile via SMS for authentication. But this method can be counter attacked by “Man in the Middle”. In our paper, a new idea is proposed which is more secure compared to the existing online payment system using OTP. In this mechanism OTP is combined with the secure key and is then passed through RSA algorithm to generate the Transaction password. A Copy of this password is maintained at the server side and is being generated at the user side using a mobile application; so that it is not transferred over the insecure network leading to a fraudulent transaction. Keywords—Phishing, Replay attack, MITM attack, RSA, Random Generator.

negative: Interpretable and effective opinion spam detection via temporal patterns mining across websites Millions of ratings and reviews on online review websites are influential over business revenues and customer experiences. However, spammers are posting fake reviews in order to gain financial benefits, at the cost of harming honest businesses and customers. Such fake reviews can be illegal and it is important to detect spamming attacks to eliminate unjust ratings and reviews. However, most of the current approaches can be incompetent as they can only utilize data from individual websites independently, or fail to detect more subtle attacks even they can fuse data from multiple sources. Further, the revealed evidence fails to explain the more complicated real world spamming attacks, hindering the detection processes that usually have human experts in the loop. We close this gap by introducing a novel framework that can jointly detect and explain the potential attacks. The framework mines both macroscopic level temporal sentimental patterns and microscopic level features from multiple review websites. We construct multiple sentimental time series to detect atomic dynamics, based on which we mine various cross-site sentimental temporal patterns that can explain various attacking scenarios. To further identify individual spams within the attacks with more evidence, we study and identify effective microscopic textual and behavioral features that are indicative of spams. We demonstrate via human annotations, that the simple and effective framework can spot a sizable collection of spams that have bypassed one of the current commercial anti-spam systems.

negative: Definition and Multidimensionality of Security Awareness: Close Encounters of the Second Order This study proposes and examines a multidimensional definition of information security awareness. We also investigate its antecedents and analyze its effects on compliance with organizational information security policies. The above research goals are tested through the theoretical lens of technology threat avoidance theory and protection motivation theory. Information security awareness is defined as a second-order construct composed of the elements of threat and coping appraisals supplemented by the responsibilities construct to account for organizational environment. The study was executed in two stages. First, the participants (employees of a municipality) were exposed to a series of phishing messages. Second, the same individuals were asked to participate in a survey designed to examine their security awareness. The research model was tested using PLS-SEM approach. The results indicate that security awareness is in fact a second-order formative construct composed of six components. There are significant differences in security awareness levels between the victims of the phishing experiment and the employees who maintain compliance with security policies. Our study extends the theory by proposing and validating a general, yet practical definition of security awareness. It also bridges the gap between theory and practice - our contextualization of security awareness draws heavily on both fields.