title: Ecological vulnerability indicators

abstract: Abstract Indicators provide easy and quick information on the status or condition of an object of interest and are therefore widely used in policy-making. In recent years, policy interest in vulnerability research has increased and a growing number of studies have been aimed at developing vulnerability indicators. Some of these studies have been done within the social–ecological system (SES) framework, taking into account both social vulnerability and ecological vulnerability. A particular challenged faced, however, is with regard to the incorporation of indicators to capture the latter, especially as the concept of vulnerability as applied in ecology is relatively new and not yet well-explored. This paper expounds on this problem by answering the following questions: (1) How are the concept of vulnerability and thereby ecological vulnerability currently understood? (2) What are the challenges in the development and use of ecological vulnerability indicators? (3) What are the current efforts to overcome these challenges? One insight gathered is that due to the complexity, nonlinearity, and multiplicity of dynamics of natural systems, development of sufficiently general indicators of ecological vulnerability may not be realizable. Rather, ecological vulnerability assessment and the development of indicators thereof, whether done independent of the human system or within the SES, should be conducted at smaller scales and must be context-specific.


title: The Comorbid Psychiatric Symptoms of Internet Addiction: Attention Deficit and Hyperactivity Disorder (ADHD), Depression, Social Phobia, and Hostility

abstract: Abstract Purpose To: (1) determine the association between Internet addiction and depression, self-reported symptoms of attention deficit and hyperactivity disorder (ADHD), social phobia, and hostility for adolescents; and (2) evaluate the sex differences of association between Internet addiction and the above-mentioned psychiatric symptoms among adolescents. Methods A total of 2114 students (1204 male and 910 female) were recruited for the study. Internet addiction, symptoms of ADHD, depression, social phobia, and hostility were evaluated by the self-report questionnaire. Results The results demonstrated that adolescents with Internet addiction had higher ADHD symptoms, depression, social phobia, and hostility. Higher ADHD symptoms, depression, and hostility are associated with Internet addiction in male adolescents, and only higher ADHD symptoms and depression are associated with Internet addiction in female students. Conclusion These results suggest that Internet addiction is associated with symptoms of ADHD and depressive disorders. However, hostility was associated with Internet addiction only in males. Effective evaluation of, and treatment for ADHD and depressive disorders are required for adolescents with Internet addiction. More attention should be paid to male adolescents with high hostility in intervention of Internet addiction.


title: Manuka honey-impregnated dressings in the treatment of neuropathic diabetic foot ulcers

abstract: In this study, we investigate the effect of manuka honey-impregnated dressings (MHID) on the healing of neuropathic diabetic foot ulcers (NDFU). A total of 63 Caucasians, type 2 diabetic patients followed up in the diabetic foot outpatient clinic comprised the study population. Patients were randomised in two groups as follows: group I patients were treated with MHID and group II patients were treated with conventional dressings (CD). The patients were followed up on a weekly basis for 16 weeks. Mean healing time was 31 ± 4 days in group I versus 43 ± 3 days in group II (P < 0·05). In group I patients 78·13% of ulcers became sterile during the first week versus 35·5% in group II patients; the corresponding percentages for weeks 2, 4 and 6 were 15·62% versus 38·7%, 6·25% versus 12·9% and 0% versus 12·9% respectively. The percent of ulcers healed did not differ significantly between groups (97% for MHID and 90% for CD). MHID represent an effective treatment for NDFU leading to a significant reduction in the time of healing and rapid disinfection of ulcers.


title: Accidental degeneracy of double Dirac cones in a phononic crystal

abstract: Artificial honeycomb lattices with Dirac cone dispersion provide a macroscopic platform to study the massless Dirac quasiparticles and their novel geometric phases. In this paper, a quadruple-degenerate state is achieved at the center of Brillouin zone (BZ) in a two-dimensional honeycomb lattice phononic crystal, which is a result of accidental degeneracy of two double-degenerate states. In the vicinity of the quadruple-degenerate state, the dispersion relation is linear. Such quadruple degeneracy is analyzed by rigorous representation theory of groups. Using method, a reduced Hamiltonian is obtained to describe the linear Dirac dispersion relations of such quadruple-degenerate state, which is well consistent with the simulation results. Near such accidental degeneracy, we observe some unique wave propagating properties, such as defect insensitive propagating character and Talbot effect.


title: Looking at Intersections: A Survey of Intersection Monitoring, Behavior and Safety Analysis of Recent Studies

abstract: Intersections are known for their integral and complex nature due to a variety of the participants' behaviors and interactions. This paper presents a review of recent studies on the behavior at intersections and the safety analysis for three types of participants at intersections: vehicles, drivers, and pedestrians. This paper emphasizes on techniques which are strong candidates for automation with visual sensing technology. A new behavior and safety classification is presented based on key features used for intersection design, planning, and safety. In addition, performance metrics are introduced to evaluate different studies, and insights are provided regarding the state of the art, inputs, algorithms, challenges, and shortcomings.


title: Developing a retrieval based diagnostic aid for automated melanoma recognition of dermoscopic images

abstract: Skin cancer is one of the most frequent cancers among human beings. Whereas, malignant melanoma is the most aggressive and deadly type of skin cancer, and its incidence has been quickly increasing over the last years. The detection of the malignant melanoma in its early stages with dermoscopic images reduces the mortality considerably, hence this a crucial issue for the dermatologists. However, their interpretation is time consuming and subjective, even for trained dermatologists. The current computer-aided diagnosis (CAD) systems are mainly noninteractive in nature and their prediction represents just a cue for the dermatologist, as the final decision regarding the likelihood of the presence of a melanoma is left exclusively to him/her. Recently, developing CAD schemes that use image retrieval approach to search for the clinically relevant and visually similar lesions has been attracting research interest. Although preliminary studies have suggested that using retrieval might improve dermatologists' performance and/or increase their confidence in the decision making, this technology is still in the early development stage with lack of benchmark evaluation in ground-truth datasets to compare retrieval effectiveness. A CAD system based on both classification and retrieval would be more effective and robust. This work is focusing on by addressing the various issues related to the development of such an integrated and interactive CAD system by performing automatic lesion segmentation with an adaptive thresholding and region growing approach, extracting invariant features from lesions and classifying and retrieving those using Extreme Learning Machines (ELM) and a similarity fusion approach. Finally, various methods are evaluated with promising results in a benchmark dataset of International Skin Imaging Collaboration (ISIC).


title: Lane marking detection based on Convolution Neural Network from point clouds

abstract: Lane marking detection, as a key technique for the Highly Automated Driving (HAD) Map, has drawn much attention recently. Traditional methods mainly focused on processing color images, where the performance was affected by illumination variation, over-exposure and occlusion severely. Confronted with above problems, this paper proposes a lane detection algorithm based on Convolution Neural Network (CNN) from point clouds. Our contributions are twofold. On one hand, a CNN framework via gradual up-sampling is introduced, where robust and accurate detection results are achieved. Before applying the CNN model, we also design pre-processing steps, including point clouds registration, the road surface segmentation and orthogonal projection. On the other hand, we propose to analyze the layout of lanes by utilizing the global information and domain knowledge. Hence, false detections caused by ground arrows and texts could be eliminated. Visual and quantitative experiments demonstrate the effectiveness of our algorithm.


title: Distributionally robust chance constraints for non-linear uncertainties

abstract: This paper investigates the computational aspects of distributionally robust chance constrained optimization problems. In contrast to previous research that mainly focused on the linear case (with a few exceptions discussed in detail below), we consider the case where the constraints can be non-linear to the decision variable, and in particular to the uncertain parameters. This formulation is of great interest as it can model non-linear uncertainties that are ubiquitous in applications. Our main result shows that distributionally robust chance constrained optimization is tractable, provided that the uncertainty is characterized by its mean and variance, and the constraint function is concave in the decision variables, and quasi-convex in the uncertain parameters. En route, we establish an equivalence relationship between distributionally robust chance constraint and the robust optimization framework that models uncertainty in a deterministic manner. This links two broadly applied paradigms in decision making under uncertainty and extends previous results of the same spirit in the linear case to more general cases. We then consider probabilistic envelope constraints, a generalization of distributionally robust chance constraints first proposed in Xu et al. (Oper Res 60:682---700, 2012) for the linear case. We extend this framework to the non-linear case, and derive sufficient conditions that guarantee its tractability. Finally, we investigate tractable approximations of joint probabilistic envelope constraints, and provide the conditions when these approximation formulations are tractable.


title: Robust Crop and Weed Segmentation under Uncontrolled Outdoor Illumination

abstract: An image processing algorithm for detecting individual weeds was developed and evaluated. Weed detection processes included were normalized excessive green conversion, statistical threshold value estimation, adaptive image segmentation, median filter, morphological feature calculation and Artificial Neural Network (ANN). The developed algorithm was validated for its ability to identify and detect weeds and crop plants under uncontrolled outdoor illuminations. A machine vision implementing field robot captured field images under outdoor illuminations and the image processing algorithm automatically processed them without manual adjustment. The errors of the algorithm, when processing 666 field images, ranged from 2.1 to 2.9%. The ANN correctly detected 72.6% of crop plants from the identified plants, and considered the rest as weeds. However, the ANN identification rates for crop plants were improved up to 95.1% by addressing the error sources in the algorithm. The developed weed detection and image processing algorithm provides a novel method to identify plants against soil background under the uncontrolled outdoor illuminations, and to differentiate weeds from crop plants. Thus, the proposed new machine vision and processing algorithm may be useful for outdoor applications including plant specific direct applications (PSDA).


title: MSER-based text detection and communication algorithm for autonomous vehicles

abstract: Text detection and communication in the automotive context has attracted the attention of researchers only over the past few years. Detecting text in the automotive context, as opposed to the detection of text of printed pages, imposes additional challenges, such as the presence of obstacles, blurry frames, speedy vehicles, etc. In this paper, we present an in-vehicle real-time system able to localize texts and communicate them to the drivers. Our system begins by localizing regions of interest as a Maximally Stable Extremal Regions (MSERs). Afterwards, we apply a novel filtering stage which begins by dividing each ROI into 4 × 4 cells, counting the number of edges in each cell and comparing them to a well defined threshold. This is performed in order to filter out a considerable number of unwanted objects. The ROIs that contain text are fed into a recognition module based on the Optical Character Recognizer (OCR). Our proposed method achieves high ƒ-scores when tested against several videos containing a numerous panels.


title: Towards a theory of insider threat assessment

abstract: Insider attacks are a well-known problem acknowledged as a threat as early as 1980s. The threat is attributed to legitimate users who abuse their privileges, and given their familiarity and proximity to the computational environment, can easily cause significant damage or losses. Due to the lack of tools and techniques, security analysts do not correctly perceive the threat, and hence consider the attacks as unpreventable. In this paper, we present a theory of insider threat assessment. First, we describe a modeling methodology which captures several aspects of insider threat, and subsequently, show threat assessment methodologies to reveal possible attack strategies of an insider.


title: Determining the user intent of web search engine queries

abstract: Determining the user intent of Web searches is a difficult problem due to the sparse data available concerning the searcher. In this paper, we examine a method to determine the user intent underlying Web search engine queries. We qualitatively analyze samples of queries from seven transaction logs from three different Web search engines containing more than five million queries. From this analysis, we identified characteristics of user queries based on three broad classifications of user intent. The classifications of informational, navigational, and transactional represent the type of content destination the searcher desired as expressed by their query. We implemented our classification algorithm and automatically classified a separate Web search engine transaction log of over a million queries submitted by several hundred thousand users. Our findings show that more than 80% of Web queries are informational in nature, with about 10% each being navigational and transactional. In order to validate the accuracy of our algorithm, we manually coded 400 queries and compared the classification to the results from our algorithm. This comparison showed that our automatic classification has an accuracy of 74%. Of the remaining 25% of the queries, the user intent is generally vague or multi-faceted, pointing to the need to for probabilistic classification. We illustrate how knowledge of searcher intent might be used to enhance future Web search engines.


title: Recommendation over a Heterogeneous Social Network

abstract: With the Web content having been changed from homogeneity to heterogeneity, the recommendation becomes a more challenging issue. In this paper, we have investigated the recommendation problem on a general heterogeneous Web social network. We categorize the recommendation needs on it into two main scenarios: recommendation when a person is doing a search and recommendation when the person is browsing the information. We formalize the recommendation as a ranking problem over the heterogeneous network. Moreover, we propose using a random walk model to simultaneously ranking different types of objects and propose a pair-wise learning algorithm to learn the weight of each type of relationship in the model. Experimental results on two real-world data sets show that improvements can be obtained by comparing with the baseline methods.


title: Reference metadata extraction using a hierarchical knowledge representation framework

abstract: The integration of bibliographical information on scholarly publications available on the Internet is an important task in the academic community. Accurate reference metadata extraction from such publications is essential for the integration of metadata from heterogeneous reference sources. In this paper, we propose a hierarchical template-based reference metadata extraction method for scholarly publications. We adopt a hierarchical knowledge representation framework called INFOMAP, which automatically extracts metadata. The experimental results show that, by using INFOMAP, we can extract author, title, journal, volume, number (issue), year, and page information from different kinds of reference styles with a high degree of precision. The overall average accuracy is 92.39% for the six major reference styles compared in this study.


title: Confidence sets for persistence diagrams

abstract: Persistent homology is a method for probing topological properties of point clouds and functions. The method involves tracking the birth and death of topological features (2000) as one varies a tuning parameter. Features with short lifetimes are informally considered to be"topological noise,"and those with a long lifetime are considered to be"topological signal."In this paper, we bring some statistical ideas to persistent homology. In particular, we derive confidence sets that allow us to separate topological signal from topological noise.


title: Positron emission tomographic studies of the cortical anatomy of single-word processing

abstract: The use of positron emission tomography to measure regional changes in average blood flow during processing of individual auditory and visual words provides support for multiple, parallel routes between localized sensory-specific, phonological, articulatory and semantic-coding areas.


title: MT-VAE: Learning Motion Transformations to Generate Multimodal Human Dynamics

abstract: Long-term human motion can be represented as a series of motion modes---motion sequences that capture short-term temporal dynamics---with transitions between them. We leverage this structure and present a novel Motion Transformation Variational Auto-Encoders (MT-VAE) for learning motion sequence generation. Our model jointly learns a feature embedding for motion modes (that the motion sequence can be reconstructed from) and a feature transformation that represents the transition of one motion mode to the next motion mode. Our model is able to generate multiple diverse and plausible motion sequences in the future from the same input. We apply our approach to both facial and full body motion, and demonstrate applications like analogy-based motion transfer and video synthesis.


title: Risk-Sensitive Generative Adversarial Imitation Learning

abstract: We study risk-sensitive imitation learning where the agent's goal is to perform at least as well as the expert in terms of a risk profile. We first formulate our risk-sensitive imitation learning setting. We consider the generative adversarial approach to imitation learning (GAIL) and derive an optimization problem for our formulation, which we call risk-sensitive GAIL (RS-GAIL). We then derive two different versions of our RS-GAIL optimization problem that aim at matching the risk profiles of the agent and the expert w.r.t. Jensen-Shannon (JS) divergence and Wasserstein distance, and develop risk-sensitive generative adversarial imitation learning algorithms based on these optimization problems. We evaluate the performance of our JS-based algorithm and compare it with GAIL and the risk-averse imitation learning (RAIL) algorithm in two MuJoCo tasks.


Testing of Cognitive-Behavioral Model of Pathological Internet Use


title: Number development and developmental dyscalculia

abstract: There is a growing consensus that the neuropsychological underpinnings of developmental dyscalculia (DD) are a genetically determined disorder of ‘number sense’, a term denoting the ability to represent and manipulate numerical magnitude nonverbally on an internal number line. However, this spatially-oriented number line develops during elementary school and requires additional cognitive components including working memory and number symbolization (language). Thus, there may be children with familial-genetic DD with deficits limited to number sense and others with DD and comorbidities such as language delay, dyslexia, or attention-deficit-hyperactivity disorder. This duality is supported by epidemiological data indicating that two-thirds of children with DD have comorbid conditions while one-third have pure DD. Clinically, they differ according to their profile of arithmetic difficulties. fMRI studies indicate that parietal areas (important for number functions), and frontal regions (dominant for executive working memory and attention functions), are under-activated in children with DD. A four-step developmental model that allows prediction of different pathways for DD is presented. The core-system representation of numerical magnitude (cardinality; step 1) provides the meaning of ‘number’, a precondition to acquiring linguistic (step 2), and Arabic (step 3) number symbols, while a growing working memory enables neuroplastic development of an expanding mental number line during school years (step 4). Therapeutic and educational interventions can be drawn from this model.


Deep Learning for Semantic Similarity


title: Correction to: Guidelines for Adult Stroke Rehabilitation and Recovery: A Guideline for Healthcare Professionals From the American Heart Association/American Stroke Association

abstract: In the article by Winstein et al, “Guidelines for Adult Stroke Rehabilitation and Recovery: A Guideline for Healthcare Professionals …


title: An Automated Liquid Manipulation by Using a Ferrofluid-Based Robotic Sheet

abstract: This letter describes a method for automated liquid manipulation using a ferrofluid-based robotic sheet. The robotic sheet consists of a ferrofluid wrapped with thin polytetrafluoroethylene films. The robotic sheet automatically transports a target liquid using the deformation characteristics of ferrofluid subject to the influence of a magnetic field, and moves it to targeted locations via a visual feedback control with a camera. Based on previous findings regarding the flexibility of robotic sheets, the proposed system could manually move a target liquid, such as water. In this letter, the authors elucidate the feasibility of the automatic manipulation and behavior of the target liquid during manipulation. The authors also quantify liquid mixing and explain the effect of the oscillation characteristics on the result.


title: Self terminating lateral-vertical hybrid super-junction FET that breaks RDS.A — Charge balance trade-off window

abstract: In this paper, we present a novel lateral/vertical hybrid super-junction structure that breaks the fundamental trade-off between the Rds.A and Charge imbalance window for a Super-Junction MOSFET. This device structure can continue the scaling of Superjunction MOSFET well below 10mΩ.cm2 with good manufacturability window. Methods of scaling the Blocking Voltage and R ds.A are also discussed for this hybrid device structure. This device structure also offers an optional drain electrode on the top surface, if needed for co-packaging with other chips.


title: Tweeting 'When Online is Off'? Opportunistically Creating Mobile Ad-hoc Networks in Response to Disrupted Infrastructure

abstract: In this paper, we present a system that enables people to post and receive tweets despite disruptions of existing network infrastructure. Our system opportunistically deploys mobile ad hoc networks (MANETs) based on Wi-Fi in which people can communicate with each other in a peer-to-peer fashion. A MANET per se constitutes an isolated island, but as people carry devices around that can join other MANETs, eventually people can transport previously collected data to the online world. Compared to other systems that aim to enable communication in crisis, our system differs in two ways: it does not rely on existing network infrastructure, and it exploits established protocols and standards allowing it to run on off-the-shelf, commercially available smartphones. We evaluated our prototype with a group of students and practitioners. Overall, we received positive feedback on the potential of our technology, but also were pointed to limitations requiring future work.


title: ELASTIC: A Client-Side Controller for Dynamic Adaptive Streaming over HTTP (DASH)

abstract: Today, video distribution platforms use adaptive video streaming to deliver the maximum Quality of Experience to a wide range of devices connected to the Internet through different access networks. Among the techniques employed to implement video adaptivity, the stream-switching over HTTP is getting a wide acceptance due to its deployment and implementation simplicity. Recently it has been shown that the client-side algorithms proposed so far generate an on-off traffic pattern that may lead to unfairness and underutilization when many video flows share a bottleneck. In this paper we propose ELASTIC (fEedback Linearization Adaptive STreamIng Controller), a client-side controller designed using feedback control theory that does not generate an on-off traffic pattern. By employing a controlled testbed, allowing bandwidth capacity and delays to be set, we compare ELASTIC with other client-side controllers proposed in the literature. In particular, we have checked to what extent the considered algorithms are able to: 1) fully utilize the bottleneck, 2) fairly share the bottleneck, 3) obtain a fair share when TCP greedy flows share the bottleneck with video flows. The obtained results show that ELASTIC achieves a very high fairness and is able to get the fair share when coexisting with TCP greedy flows.


title: An Efficient DBSCAN using Genetic Algorithm based Clustering

abstract: Data mining is widely employed in business management and engineering. The major objective of data mining is to discover helpful and accurate information among a vast quantity of data, providing a orientation basis for decision makers. Data clustering is currently a very popular and frequently applied analytical method in data mining. DBSCAN is a traditional and widely-accepted density-based clustering method. It is used to find clusters of arbitrary shapes and sizes yet may have trouble with clusters of varying density. In this paper an efficient DBSCAN clustering using genetic algorithm is proposed. DBSCAN clustering provides some problem such as the algorithm is not efficient for noisy clusters; hence it is enhanced using genetic algorithm. The proposed technique is efficient in terms of accuracy and execution time. . ——————————  ——————————


title: TAO: System for Table Detection and Extraction from PDF Documents

abstract: Digital documents present knowledge in most areas of study, exchanging and communicating information in a portable way. To better use the knowledge embedded in an ever-growing information source, effective tools for automatic information extraction are needed. Tables are crucial information elements in documents of scientific nature. Most publications use tables to represent and report concrete findings of research. Current methods used to extract table data from PDF documents lack precision in detecting, extracting, and representing data from diverse layouts. We present the system TAble Organization (TAO) to automatically detect, extract and organize information from tables in PDF documents. TAO uses a processing, based on the k-nearest neighbor method and layout heuristics, to detect tables within a document and to extract table information. This system generates an enriched representation of the data extracted from tables in the PDF documents. TAO’s performance is comparable to other table extraction methods, but it overcomes some related work limitations and proves to be more robust in experiments with diverse document layouts.


title: The effectiveness of yoga in modifying risk factors for cardiovascular disease and metabolic syndrome: A systematic review and meta-analysis of randomized controlled trials

abstract: Background: Yoga, a popular mind-body practice, may produce changes in cardiovascular disease (CVD) and metabolic syndrome risk factors. Design: This was a systematic review and random-effects meta-analysis of randomized controlled trials (RCTs). Methods: Electronic searches of MEDLINE, EMBASE, CINAHL, PsycINFO, and The Cochrane Central Register of Controlled Trials were performed for systematic reviews and RCTs through December 2013. Studies were included if they were English, peer-reviewed, focused on asana-based yoga in adults, and reported relevant outcomes. Two reviewers independently selected articles and assessed quality using Cochrane's Risk of Bias tool. Results: Out of 1404 records, 37 RCTs were included in the systematic review and 32 in the meta-analysis. Compared to non-exercise controls, yoga showed significant improvement for body mass index (� 0.77kg/m 2 (95% confidence interval � 1.09 to � 0.44)), systolic blood pressure (� 5.21mmHg (� 8.01 to � 2.42)), low-density lipoprotein cholesterol (� 12.14mg/dl (� 21.80 to � 2.48)), and high-density lipoprotein cholesterol (3.20mg/dl (1.86 to 4.54)). Significant changes were seen in body weight (� 2.32kg (� 4.33 to � 0.37)), diastolic blood pressure (� 4.98mmHg (� 7.17 to � 2.80)), total cholesterol (� 18.48mg/dl (� 29.16 to � 7.80)), triglycerides (� 25.89mg/dl (� 36.19 to � 15.60), and heart rate (� 5.27 beats/min (� 9.55 to � 1.00)), but not fasting blood glucose (� 5.91mg/dl (� 16.32 to 4.50)) nor glycosylated hemoglobin (� 0.06% Hb (� 0.24 to 0.11)). No significant difference was found between yoga and exercise. One study found an impact on smoking abstinence. Conclusions: There is promising evidence of yoga on improving cardio-metabolic health. Findings are limited by small trial sample sizes, heterogeneity, and moderate quality of RCTs.


title: When choice is demotivating: Can one desire too much of a good thing?

abstract: Current psychological theory and research affirm the positive affective and motivational consequences of having personal choice. These findings have led to the popular notion that the more choice, the better--that the human ability to manage, and the human desire for, choice is unlimited. Findings from 3 experimental studies starkly challenge this implicit assumption that having more choices is necessarily more intrinsically motivating than having fewer. These experiments, which were conducted in both field and laboratory settings, show that people are more likely to purchase gourmet jams or chocolates or to undertake optional class essay assignments when offered a limited array of 6 choices rather than a more extensive array of 24 or 30 choices. Moreover, participants actually reported greater subsequent satisfaction with their selections and wrote better essays when their original set of options had been limited. Implications for future research are discussed.


title: LocED: Location-aware Energy Disaggregation Framework

abstract: Providing detailed appliance level energy consumption information may lead consumers to understand their usage behavior and encourage them to optimize the energy usage. Non-intrusive load monitoring (NILM) or energy disaggregation aims to estimate appliance level energy consumption from the aggregate consumption data of households. NILM algorithms, proposed hitherto, are either centralized or do require high performance systems to derive appliance level data, owing to the computational complexity associated. This approach raises several issues related to scalability and privacy of consumer's data. In this paper, we present the Location-aware Energy Disaggregation Framework (LocED) that utilizes occupancy of users to derive accurate appliance level usage information. LocED framework limits the appliances considered for disaggregation based on the current location of occupants. Thus, LocED can provide real-time feedback on appliance level energy consumption and run on an embedded system locally at the household. We propose several accuracy metrics to study the performance of LocED. To test the robustness of LocED, we empirically evaluated it across multiple publicly available datasets. LocED has significantly high energy disaggregation accuracy while exponentially reducing the computational complexity. We also release our comprehensive dataset DRED (Dutch Residential Energy Dataset) for public use, which measures electricity, occupancy and ambient parameters of the household.


title: Analysis and Simulation of Theme Park Queuing System

abstract: It has been an important issue to improve customers' satisfaction in theme parks for which become a major role of recreation in our daily life. Waiting for rides has been identified as a factor decreasing satisfaction. A previous study indicated that a virtual queuing system can reduce the total waiting time so the customer's satisfaction is improved. The results from a simulation tool Arena show that an index Satisfaction Value (SV) increases when the queuing system is introduced. In this study, a more complex scenario of theme park queuing system (TPQS) is first designed, followed by comparison of a number of combinations of the rides with various waiting time and distribution factors. Analysis is also carried out.


title: Emotion Detection in Code-switching Texts via Bilingual and Sentimental Information

abstract: Code-switching is commonly used in the free-form text environment, such as social media, and it is especially favored in emotion expressions. Emotions in codeswitching texts differ from monolingual texts in that they can be expressed in either monolingual or bilingual forms. In this paper, we first utilize two kinds of knowledge, i.e. bilingual and sentimental information to bridge the gap between different languages. Moreover, we use a term-document bipartite graph to incorporate both bilingual and sentimental information, and propose a label propagation based approach to learn and predict in the bipartite graph. Empirical studies demonstrate the effectiveness of our proposed approach in detecting emotion in code-switching texts.


title: Segmentation of polarimetric synthetic aperture radar data

abstract: A statistical image model is proposed for segmenting polarimetric synthetic aperture radar (SAR) data into regions of homogeneous and similar polarimetric backscatter characteristics. A model for the conditional distribution of the polarimetric complex data is combined with a Markov random field representation for the distribution of the region labels to obtain the posterior distribution. Optimal region labeling of the data is then defined as maximizing the posterior distribution of the region labels given the polarimetric SAR complex data (maximum a posteriori (MAP) estimate). Two procedures for selecting the characteristics of the regions are then discussed. Results using real multilook polarimetric SAR complex data are given to illustrate the potential of the two selection procedures and evaluate the performance of the MAP segmentation technique. It is also shown that dual polarization SAR data can yield segmentation resultS similar to those obtained with fully polarimetric SAR data.


title: Analysing Social Networks Via the Internet

abstract: THe purpose of this article is to introduce the reader to the history, concepts, measures and methods of social network analysis as applied to online information spaces. This is done through description as well as a sustained example using the online social news site Digg.com. Social network analysis is a rapidly expanding interdisciplinary paradigm , much of which is taking place with online data. As such, some concepts will only be addressed superficially, while others (such as positions, p* models and multilevel analysis) will be excluded entirely. The goal is to facilitate enough network literacy to begin a research project rather than provide a complete end-to-end solution. Social network analysis has emerged in the past half-century as a compelling complement to the standard toolkit of social science researchers. At its foundation is a belief that explanations for social organization are not to be found in innate drives or abstract forces. Instead we can look to the structure of relationships that constrain and enable interaction (Wellman, 1988) alongside the behaviors of agents that reproduce and alter these structures (Emirbayer & Mische, 1998). While this paradigm has been applied to fields as diverse as sexual contacts among adolescents (Bearman, Moody, & Stovel, 2004) and intravenous drug users (Koester, Glanz, & Baron, 2005), social network analysis is particularly well suited to understanding online interaction. There are two key facts about online interaction that make it particularly amenable to social network analysis the nature of online interaction and the nature of digital information. Online interaction is almost always social network-oriented. At its simplest, social networks refer to a series of nodes (such as people, organizations or web pages) and the specific links between two of these nodes. Hypertext (such as the World Wide Web) is an unstructured series of pages and links between pages. Communication online can be represented as a network of senders and recipients. Finally, relationships on social software sites constitute an obvious series of nodes (profiles) and links (friends). As Barry Wellman muses, “when computer networks link people as well as machines they become social networks” (1996, p. 214). While digital information does not have to be networkoriented, this certainly facilitates the capture of network data. Granted, communication patterns and relationships were studied as networks long before the internet. However, collecting in-person data is time consuming and difficult; people are sometimes unclear of who is in their personal network (or how strong the tie is), and it is important to gather high response rates. These problems can be minimized online because information is digital and encoded merely through the act of sending a message or adding a friend to one’s page. Also, there is virtually no marginal cost in making a perfect replica of the messages for analysis.


title: Unravelling the Myth of big data and artificial intelligence in sustainable natural resource development

abstract: Natural Resources businesses span one or more of the following verticals: Explore & Discover, Develop, Extract & Transport, Market & Trade. Natural Gas (NG) is a key component of the Natural Resources industry, which contributes both directly and indirectly to sustain and support the day to day energy needs of humans. Various forums drive key initiatives to maintain the sustainability of natural resources for decades to come. The recent evolution of advanced Information Technology (IT) fields, termed as ‘Big Data and Artificial Intelligence (BD&AI),’ fortify these initiatives by providing anytime, anywhere access of data (for example in remote offshore assets or facilities) from a vast corpus base, in addition to enabling natural human interaction. In this paper we intend to explore the key primitives of BD&AI and cite the method of real-world implementation in the Natural Gas industry to further enable sustainable development.


title: Development of personality in early and middle adulthood: Set like plaster or persistent change

abstract: Different theories make different predictions about how mean levels of personality traits change in adulthood. The biological view of the Five-factor theory proposes the plaster hypothesis: All personality traits stop changing by age 30. In contrast, contextualist perspectives propose that changes should be more varied and should persist throughout adulthood. This study compared these perspectives in a large (N 132,515) sample of adults aged 21– 60 who completed a Big Five personality measure on the Internet. Conscientiousness and Agreeableness increased throughout early and middle adulthood at varying rates; Neuroticism declined among women but did not change among men. The variety in patterns of change suggests that the Big Five traits are complex phenomena subject to a variety of developmental influences. How does personality change during adulthood? Psychologists since William James (1890/1950) have struggled with the question of whether various aspects of personality, including personality traits, change in meaningful ways during adulthood, and when those changes take place. Contemporary hypotheses about the development of personality traits stem from theories about what personality traits are. McCrae and Costa’s (1996) five-factor theory asserts that personality traits arise exclusively from biological causes (i.e., genes) and that they reach full maturity in early adulthood; thus, this theory predicts little or no change on any personality dimension after early adulthood. By contrast, contextualist perspectives argue that traits are multiply determined, and that one important influence on traits is the individual’s social environment (Haan, Millsap, & Hartka, 1986; Helson, Jones, & Kwan, 2002). Contextualist perspectives thus predict plasticity: Change is complex and ongoing, owing to the many factors that can affect personality traits. In this study, we set out to understand how personality traits change in early and middle adulthood by examining the Big Five personality trait dimensions (Goldberg, 1992; John & Srivastava, 1999; McCrae & Costa, 1999). We used a cross-sectional design to study how mean levels of personality traits differ by age and whether those age effects are moderated by gender. 1 We were particularly interested in examining whether change on all of the Big Five dimensions stops or slows in middle adulthood, as predicted by the five-factor theory, or whether change is ongoing and differentiated, as predicted by contextualist theories.


title: Personalization as a service: the architecture and a case study

abstract: Cloud computing has become a hot topic in the IT industry. Great efforts have been made to establish cloud computing platforms for enterprise users, mostly small businesses. However, there are few researches about the impact of cloud computing over individual users. In this paper we focus on how to provide personalized services for individual users in the cloud environment. We argue that a personalized cloud service shall compose of two parts. The client side program records user activities on personal de-vices such as PC. Besides that, the user model is also computed on the client side to avoid server overhead. The cloud side program fetches the user model periodically and adjusts its results accordingly. We build a personalized cloud data search engine prototype to prove our idea.


title: A Sequential Neural Information Diffusion Model with Structure Attention

abstract: In this paper, we propose a novel sequential neural network with structure attention to model information diffusion. The proposed model explores both sequential nature of an information diffusion process and structural characteristics of user connection graph. The recurrent neural network framework is employed to model the sequential information. The attention mechanism is incorporated to capture the structural dependency among users, which is defined as the diffusion context of a user. A gating mechanism is further developed to effectively integrate the sequential and structural information. The proposed model is evaluated on the diffusion prediction task. The performances on both synthetic and real datasets demonstrate its superiority over popular baselines and state-of-the-art sequence-based models.


title: Face-Voice Matching using Cross-modal Embeddings

abstract: Face-voice matching is a task to find correspondence between faces and voices. Many researches in cognitive science have confirmed human ability in the face-voice matching tasks. Such ability is useful for creating natural human machine interaction systems and in many other applications. In this paper, we propose a face-voice matching model that learns cross-modal embeddings between face images and voice characteristics. We constructed a novel FVCeleb dataset which consists of face images and utterances from 1,078 persons. These persons were selected from the MS-Celeb-1M face image dataset and the VoxCeleb audio dataset. In two-alternative forced-choice matching task with an audio input and two face-image candidates of the same gender, our model achieved 62.2% and 56.5% accuracy on the FVCeleb and the subset of the GRID corpus, respectively. These results are very similar to human performance reported in cognitive science studies.


title: Attention-based Multi-Patch Aggregation for Image Aesthetic Assessment

abstract: Aggregation structures with explicit information, such as image attributes and scene semantics, are effective and popular for intelligent systems for assessing aesthetics of visual data. However, useful information may not be available due to the high cost of manual annotation and expert design. In this paper, we present a novel multi-patch (MP) aggregation method for image aesthetic assessment. Different from state-of-the-art methods, which augment an MP aggregation network with various visual attributes, we train the model in an end-to-end manner with aesthetic labels only (i.e., aesthetically positive or negative). We achieve the goal by resorting to an attention-based mechanism that adaptively adjusts the weight of each patch during the training process to improve learning efficiency. In addition, we propose a set of objectives with three typical attention mechanisms (i.e., average, minimum, and adaptive) and evaluate their effectiveness on the Aesthetic Visual Analysis (AVA) benchmark. Numerical results show that our approach outperforms existing methods by a large margin. We further verify the effectiveness of the proposed attention-based objectives via ablation studies and shed light on the design of aesthetic assessment systems.


title: Concept based query expansion

abstract: Query expansion methods have been studied for a long time - with debatable success in many instances. In this paper we present a probabilistic query expansion model based on a similarity thesaurus which was constructed automatically. A similarity thesaurus reflects domain knowledge about the particular collection from which it is constructed. We address the two important issues with query expansion: the selection and the weighting of additional search terms. In contrast to earlier methods, our queries are expanded by adding those terms that are most similar to the concept of the query, rather than selecting terms that are similar to the query terms. Our experiments show that this kind of query expansion results in a notable improvement in the retrieval effectiveness when measured using both recall-precision and usefulness.


Bilingual Autoencoders with Global Descriptors for Modeling Parallel Sentences


Defect-Mediated Mechanics in Non-Stoichiometric Oxide Films


title: A Novel Framework for Database Security Based on Mixed Cryptography

abstract: Database security has become a vital issue in modern Web applications. Critical business data in databases is an evident target for attack. Therefore, ensuring the confidentiality, privacy and integrity of data is a major issue for the security of database systems. Recent high profile data thefts have shown that perimeter defenses are insufficient to secure sensitive data. This paper studies security of the databases shared between many parties from a cryptographic perspective. We propose Mixed Cryptography Database (MCDB), a novel framework to encrypt databases over untrusted networks in a mixed form using many keys owned by different parties. The encryption process is based on a new data classification according to the data owner. The proposed framework is very useful in strengthening the protection of sensitive data even if the database server is attacked at multiple points from the inside or outside.


title: Spatial zones for muscle coactivation and the control of postural stability

abstract: It is hypothesized that, depending on the motor task, the angular range of a joint may be subdivided into zones in which agonist and antagonist muscles are coactive, only one group of muscles is active or neither group is active. It is further hypothesized that central commands may change the size and location of these spatial zones. We investigated whether spatial zones are used by the nervous system and how they may be changed to provide postural stability of the elbow. We compared responses to sudden unloading of the elbow flexors in neurologically normal subjects with those in patients with postural control deficits due to unilateral hemispheric and/or subcortical lesions. By studying responses in patients, we sought to determine whether the specification of zones of agonist/antagonist muscle coactivation ("coactivation zones") may be essential for postural stability. At an initial elbow angle (130 degrees; full extension is 180 degrees), flexors were pre-activated by compensating an initial load which was equal to approximately 30% of the subject's maximal isometric voluntary contraction effort. Subjects were instructed not to correct the arm displacement elicited by a sudden decrease in the load. Data from 10 trials were collected at each of 4-6 final load levels (separated by 1.5-2 Nm) in order to map out the relationship between torque and angle in each subject. The procedure was repeated from a more flexed initial position of the elbow (100 degrees). EMG activity from two elbow flexors and two elbow extensors, as well as torque, velocity and joint position were recorded. Healthy control subjects and patients with mild clinical symptoms had coactivation zones or small silent zones around the final positions established after unloading. In these subjects, final positions of the limb were stable. Voluntary movement, i.e., transition of the limb from one initial position to another, was associated with a change in the location of the zone in articular space. The presence of large silent zones in patients with moderate or severe symptoms was correlated with postural instability and oscillations about the final position of the arm after unloading. The comparison of results from healthy and hemiparetic subjects implies that the central specification of the size and the location of a coactivation zone may be fundamental for the control of posture and movement.


title: Mobile Charging in Wireless-Powered Sensor Networks: Optimal Scheduling and Experimental Implementation

abstract: Wireless radio frequency (RF) energy transfer is a promising technology to provide a reliability-guaranteed power supply for wireless sensor networks. In this paper, we consider a special wireless-powered sensor network consisting of a mobile energy station that can travel through a preplanned path to charge wireless-powered sensors located in the considered area. We develop a hardware platform using off-the-shelf RF energy transfer hardware equipment to evaluate the practical performance of wireless sensor networks powered by RF energy transfer. We establish an empirical model based on our developed platform and use the empirical model to jointly optimize path planning and mobile charge scheduling for wireless-powered sensor networks. We derive the optimal policy for the mobile energy station to optimize its decisions about the path that it will travel and the subset of sensors to charge during each time period. Numerical results show that our derived policy significantly improves the performance of wireless sensor networks in different practical scenarios.


DATA REPLICATION STRATEGIES IN WIDE-AREA


title: A Simulation Environment for SCADA Security Analysis and Assessment

abstract: Simulation experiment is an important means of analyzing and assessing the security of SCADA (Supervisory Control and Data Acquisition) system, however, the existing simulation environments have some limitations in flexibility and extensibility. According to the type of industrial infrastructure and the structure of SCADA system, the abstract models of SCADA system are established, the reference architecture of SCADA system simulation environment is proposed, and the simulation environment for analyzing and assessing the security of SCADA system is designed and implemented. This simulation environment has the characteristics of extensibilities and adaptability, and integrated several components including the simulated enterprise network, OPC Client/HMI, industrial OPC server, SCADA protocol tester, SCADA RTUs, and the sensors and actuators, etc. Finally, experiment on the representative SCADA system attack scenario of has been conducted in this simulation environment to analyze and assess their security status, and the results demonstrate the effectiveness and practicability of the simulation environment.


title: Estimating the Scene Illumination Chromaticity by Using a Neural Network

abstract: A neural network can learn color constancy, defined here as the ability to estimate the chromaticity of a scene’s overall illumination. We describe a multilayer neural network that is able to recover the illumination chromaticity given only an image of the scene. The network is previously trained by being presented with a set of images of scenes and the chromaticities of the corresponding scene illuminants. Experiments with real images show that the network performs better than previous color constancy methods. In particular, the performance is better for images with a relatively small number of distinct colors. The method has application to machine vision problems such as object recognition, where illumination-independent color descriptors are required, and in digital photography, where uncontrolled scene illumination can create an unwanted color cast in a photograph.


title: Relevance of infection with human papillomavirus: The role of the p53 tumor suppressor protein and E6/E7 zinc finger proteins

abstract: Human papillomaviruses (HPV) are small circular, double-stranded DNA viruses infecting epithelial tissues. HPV types can be classified both as high-risk or low-risk. Of the more than 120 different identified types of HPV, the majority are involved in infections of the genital tract, cancer of the cervix, vulva, vagina and penis, and of non-anogenital localizations, such as the head and neck areas. From the point of view of the infection, human papillomaviruses have developed several molecular mechanisms to enable infected cells to suppress apoptosis. This review provides a comprehensive and critical summary of the current literature that focuses on cervical carcinoma and cancer of the head and neck caused by HPV. In particular, we discuss HPV virology, the molecular mechanisms of carcinogenesis, the role of the tumor suppressor protein p53 and the E6/E7 zinc finger proteins. Classification of HPV according to diagnosis is also described.


title: A Novel Method for Tracking and Counting Pedestrians in Real-Time Using a Single Camera

abstract: This paper presents a real-time system for pedestrian tracking in sequences of grayscale images acquired by a stationary camera. The objective is to integrate this system with a traffic control application such as a pedestrian control scheme at intersections. The proposed approach can also be used to detect and track humans in front of vehicles. Furthermore, the proposed schemes can be employed for the detection of several diverse traffic objects of interest (vehicles, bicycles, etc.) The system outputs the spatio-temporal coordinates of each pedestrian during the period the pedestrian is in the scene. Processing is done at three levels: raw images, blobs, and pedestrians. Blob tracking is modeled as a graph optimization problem. Pedestrians are modeled as rectangular patches with a certain dynamic behavior. Kalman filtering is used to estimate pedestrian parameters. The system was implemented on a Datacube MaxVideo 20 equipped with a Datacube Max860 and was able to achieve a peak performance of over 30 frames per second. Experimental results based on indoor and outdoor scenes demonstrated the system s robustness under many difficult situations such as partial or full occlusions of pedestrians.


title: Vehicles on RFID: Error-Cognitive Vehicle Localization in GPS-Less Environments

abstract: GPS has been commonly used for vehicle localization. However, since GPS signals are very sensitive to terrain and interference, they may become unavailable in places such as lower layers of multilayer bridges, streets besides high buildings, tunnels, etc. To address this issue, various techniques have been proposed. Nevertheless, none of them are applicable to safety-critical and highly mobile vehicular network applications, which require high localization accuracy anywhere and anytime. Meanwhile, with the advent of radio frequency identification (RFID) systems on roads (RSR), passive RFID tags with road-related information are expected to be deployed on road surfaces for driving safety and efficiency. In this paper, we introduce a novel RFID-based approach for vehicle localization in GPS-less road environments, where passive RFID tags are deployed on the roads and readers are installed on vehicles. To achieve accurate vehicle localization, we propose an error-cognitive localization system, which leverages both reader-to-tag and vehicle-to-vehicle communications to adaptively cognize and correct localization errors. Extensive simulation and experiment have verified the effectiveness of our proposed system. Particularly, our field tests show that, with sparsely deployed tags, which cost just a few dollars per kilometer, a vehicle can localize itself with submeter errors on average.


title: Fast decryption utilizing correlation calculation for BSS-based speech encryption system

abstract: Original BSS (blind source separation)-based speech encryption system utilizes BSS to perform decryption, but the complexity of BSS algorithms limits the decryption speed and its real-time application. Correlation calculation is an algebraic decryption method originally proposed for BSS-based image cryptosystem to overcome two limitations of BSS: permutation and scaling ambiguity and difficulty for separating strongly correlated images. Considering that there is similarity between encryption matrix for speech encryption and image encryption, this paper analyzes the correlation of speech signals with key signals, and then utilizes the correlation calculation to achieve speech decryption. The experiment results show that correlation calculation decryption nicely simplifies BSS-based speech encryption system, largely speeds up the speech decryption, and slightly improves the quality of decrypted speech signals.


title: A People Counting System Based on Face Detection and Tracking in a Video

abstract: Vision-based people counting systems have wide potential applications including video surveillance and public resources management. Most works in the literature rely on moving object detection and tracking, assuming that all moving objects are people. In this paper, we present our people counting approach based on face detection, tracking and trajectory classification. While we have used a standard face detector, we achieve face tracking combining a new scale invariant Kalman filter with kernel based tracking algorithm. From each potential face trajectory an angle histogram of neighboring points is then extracted. Finally, an Earth Mover's Distance-based K-NN classification discriminates true face trajectories from the false ones. Experimented on a video dataset of more than 160 potential people trajectories, our approach displays an accuracy rate up to 93%.


title: Brainwash: A Data System for Feature Engineering

abstract: A new generation of data processing systems, including web search, Google’s Knowledge Graph, IBM’s Watson, and several different recommendation systems, combine rich databases with software driven by machine learning. The spectacular successes of these trained systems have been among the most notable in all of computing and have generated excitement in health care, finance, energy, and general business. But building them can be challenging, even for computer scientists with PhD-level training. If these systems are to have a truly broad impact, building them must become easier. We explore one crucial pain point in the construction of trained systems: feature engineering. Given the sheer size of modern datasets, feature developers must (1) write code with few effective clues about how their code will interact with the data and (2) repeatedly endure long system waits even though their code typically changes little from run to run. We propose brainwash, a vision for a feature engineering data system that could dramatically ease the ExploreExtract-Evaluate interaction loop that characterizes many trained system projects.


title: Complexities of ethnography.

abstract: UNDERTAKING RESEARCH using an ethnographic approach can be one of the most challenging routes to generating new knowledge in nursing and health care. These challenges can include the time required to gain access to the research site, the duration and complexity of data collection, the role of the researcher and the impact the researcher might have on data collection and analysis. Ethnographic approaches are not new to nursing and health care but this approach to generating new knowledge has not been adopted as widely as other qualitative approaches. One reason for this could be that students undertaking undergraduate or master’s research projects much less frequently gain experience in using ethnography because of the practical and theoretical complexities outlined. As a result, these researchers are less likely to consider ethnography in their future research endeavours. This edition of Nurse Researcher includes two papers that describe the experiences of researchers who have undertaken different types of ethnographic research in very different research settings. In the first paper, Mahon and McPherson (2014) describe their experiences of undertaking a study exploring factors influencing nurses’ decisions to stay or leave bedside nursing using critical ethnography. In the second paper, Pereira de Melo et al (2014) describe the experiences of researchers undertaking three different ethnographic research projects in Brazil. Both offer a fascinating insight into how these researchers managed some of the complexities often encountered when conducting ethnographic research. All researchers will be familiar with the multiple barriers that need to be negotiated and managed before they can access a research setting. Pereira de Melo et al (2014) suggest that these can be particularly challenging in ethnographic research and might include hierarchies that exist between different professional groups and the need not to disrupt routines and protocols. Ethnography draws heavily on prolonged observation as the main means of data collection and this can sometimes be perceived as threatening by those who will be observed. It is for these reasons that researchers need to seek acceptance or trust in the research setting and why observation needs to be for a prolonged period of time. Pereira de Melo et al (2014) suggest that one important way to help overcome some of the access barriers is to discover and build an influential friend. This individual might be able to help negotiate access to the research site and to help the researcher become accepted once in the research site. It is clear that such strategies do not happen by accident and will need to be incorporated into the detailed planning of the research project. As noted, in ethnographic research it is required that researchers engage with participants in their lives for an extended period of time. Pereira de Melo et al (2014) argue that ethnographic researchers should spend at least six months in the research setting and describe this as one of the main limitations when planning ethnographic research. For student researchers, especially those below doctoral level, they describe time as ‘an enemy’ because the students have tight and immoveable deadlines. While students can adopt ethnographic approaches, time restrictions can have an impact on the theoretical maturity of a project. The result is that students are often advised to avoid ethnographic approaches. Offering such advice fails to recognise that the primary objective of much student research is to allow the students to learn more about how to plan and conduct research rather than on the outcome of the research. A prolonged period of engagement in the research setting requires that ethnographic researchers give some detailed consideration to their role in the research process. The researcher is the main data collection instrument in an ethnographic research project so it is inevitable that the individual will Complexities of ethnography


title: A transprecision floating-point platform for ultra-low power computing

abstract: In modern low-power embedded platforms, the execution of floating-point (FP) operations emerges as a major contributor to the energy consumption of compute-intensive applications with large dynamic range. Experimental evidence shows that 50% of the energy consumed by a core and its data memory is related to FP computations. The adoption of FP formats requiring a lower number of bits is an interesting opportunity to reduce energy consumption, since it allows to simplify the arithmetic circuitry and to reduce the memory bandwidth required to transfer data between memory and registers by enabling vectorization. From a theoretical point of view, the adoption of multiple FP types perfectly fits with the principle of transprecision computing, allowing fine-grained control of approximation while meeting specified constraints on the precision of final results. In this paper we propose an extended FP type system with complete hardware support to enable transprecision computing on low-power embedded processors, including two standard formats (binary32 and binary16) and two new formats (binary8 and binary16alt). First, we introduce a software library that enables exploration of FP types by tuning both precision and dynamic range of program variables. Then, we present a methodology to integrate our library with an external tool for precision tuning, and experimental results that highlight the clear benefits of introducing the new formats. Finally, we present the design of a transprecision FP unit capable of handling 8-bit and 16-bit operations in addition to standard 32-bit operations. Experimental results on FP-intensive benchmarks show that up to 90% of FP operations can be safely scaled down to 8-bit or 16-bit formats. Thanks to precision tuning and vectorization, execution time is decreased by 12% and memory accesses are reduced by 27% on average, leading to a reduction of energy consumption up to 30%.


title: The Ethics of Computing: A Survey of the Computing-Oriented Literature

abstract: Computing technologies and artifacts are increasingly integrated into most aspects of our professional, social, and private lives. One consequence of this growing ubiquity of computing is that it can have significant ethical implications that computing professionals need to be aware of. The relationship between ethics and computing has long been discussed. However, this is the first comprehensive survey of the mainstream academic literature of the topic. Based on a detailed qualitative analysis of the literature, the article discusses ethical issues, technologies that they are related to, and ethical theories, as well as the methodologies that the literature employs, its academic contribution, and resulting recommendations. The article discusses general trends and argues that the time has come for a transition to responsible research and innovation to ensure that ethical reflection of computing has practical and manifest consequences.


title: Industrial cyber physical systems: A survey for control-engineering tools

abstract: Cyber Physical Systems (CPS) play significant role in shaping smart manufacturing systems. CPS integrate computation with physical processes where behaviors are represented in both cyber and physical parts of the system. In order to understand CPS in the context of smart manufacturing, an overview of CPS technologies, components, and relevant standards is presented. A detailed technical review of the existing engineering tools and practices from major control vendors has been conducted. Furthermore, potential research areas have been identified in order to enhance the tools functionalities and capabilities in supporting CPS development process.


title: Knowledge management technologies and applications—literature review from 1995 to 2002

abstract: Abstract This paper surveys knowledge management (KM) development using a literature review and classification of articles from 1995 to 2002 with keyword index in order to explore how KM technologies and applications have developed in this period. Based on the scope of 234 articles of knowledge management applications, this paper surveys and classifies KM technologies using the seven categories as: KM framework, knowledge-based systems, data mining, information and communication technology, artificial intelligence/expert systems, database technology, and modeling, together with their applications for different research and problem domains. Some discussion is presented, indicating future development for knowledge management technologies and applications as the followings: (1) KM technologies tend to develop towards expert orientation, and KM applications development is a problem-oriented domain. (2) Different social studies methodologies, such as statistical method, are suggested to implement in KM as another kind of technology. (3) Integration of qualitative and quantitative methods, and integration of KM technologies studies may broaden our horizon on this subject. (4) The ability to continually change and obtain new understanding is the power of KM technologies and will be the application of future works.


title: A comprehensive cluster and classification mining procedure for daily stock market return forecasting

abstract: Abstract Data mining and big data analytic techniques are playing an important role in many application fields, including the financial markets. However, only few studies have focused on predicting daily stock market returns, and among these studies, the data mining procedures utilized are either incomplete or inefficient. This paper presents a comprehensive data mining process to forecast the daily direction of the S&P 500 Index ETF (SPY) return based on 60 financial and economical features. The fuzzy c-means method (FCM) is initially used to cluster the preprocessed data. A principal component analysis (PCA) is applied next to the entire data set and each of seven clusters. The dimension of the entire cleaned data set is then reduced according to the combining results from the entire data set and each cluster. Corresponding to different levels of the dimensionality reduction, twelve new data sets are generated from the entire cleaned data. Artificial neural networks (ANNs) and logistic regression models are then used with the twelve transformed data sets for classification in order to forecast the daily direction of future market returns and indicate the efficiency of dimensionality reduction with PCA. A group of hypothesis tests are performed over the classification and simulation results to show that the ANNs give significantly higher classification accuracy than logistic regression, and that the trading strategies guided by the comprehensive cluster and classification mining procedure based on PCA and ANNs gain higher risk-adjusted profits than the comparison benchmarks, as well as those strategies guided by the forecasts based on PCA and logistic regression models.


title: An individual with blepharophimosis-ptosis-epicanthus inversus syndrome (BPES) and additional features expands the phenotype associated with mutations in KAT6B.

abstract: Blepharophimosis-ptosis-epicanthus inversus syndrome (BPES) is an autosomal dominant disorder caused by mutations in FOXL2. We identified an individual with BPES and additional phenotypic features who did not have a FOXL2 mutation. We used whole exome sequencing to identify a de novo mutation in KAT6B (lysine acetyltransferase 6B) in this individual. The mutation was a 2-bp insertion leading to a frameshift which resulted in a premature stop codon. The resulting truncated protein does not have the C-terminal serine/methionine transcription activation domain necessary for interaction with other transcriptional and epigenetic regulators. This mutation likely has a dominant-negative or gain-of-function effect, similar to those observed in other genetic disorders resulting from KAT6B mutations, including Say-Barber-Biesecker-Young-Simpson (SBBYSS) and genitopatellar syndrome (GTPTS). Thus, our subject's phenotype broadens the spectrum of clinical findings associated with mutations in KAT6B. Furthermore, our results suggest that individuals with BPES without a FOXL2 mutation should be tested for KAT6B mutations. The transcriptional and epigenetic regulation mediated by KAT6B appears crucial to early developmental processes, which when perturbed can lead to a wide spectrum of phenotypic outcomes.


title: Augmented-reality scratch: a children's authoring environment for augmented-reality experiences

abstract: In this paper we introduce AR Scratch, the first augmented-reality (AR) authoring environment designed for children. By adding augmented-reality functionality to the Scratch programming platform, this environment allows pre-teens to create programs that mix real and virtual spaces. Children can display virtual objects on a real-world space seen through a camera, and they can control the virtual world through interactions between physical objects. This paper describes the system design process, which focused on appropriately presenting the AR technology to the typical Scratch population (children aged 8-12), as influenced by knowledge of child spatial cognition, programming expertise, and interaction metaphors. Evaluation of this environment is proposed, accompanied by results from an initial pilot study, as well as discussion of foreseeable impacts on the Scratch user community.


title: Automatic model-based tracing algorithm for vessel segmentation and diameter estimation

abstract: An automatic algorithm capable of segmenting the whole vessel tree and calculate vessel diameter and orientation in a digital ophthalmologic image is presented in this work. The algorithm is based on a parametric model of a vessel that can assume arbitrarily complex shape and a simple measure of match that quantifies how well the vessel model matches a given angiographic image. An automatic vessel tracing algorithm is described that exploits the geometric model and actively seeks vessel bifurcation, without user intervention. The proposed algorithm uses the geometric vessel model to determine the vessel diameter at each detected central axis pixel. For this reason, the algorithm is fine tuned using a subset of ophthalmologic images of the publically available DRIVE database, by maximizing vessel segmentation accuracy. The proposed algorithm is then applied to the remaining ophthalmological images of the DRIVE database. The segmentation results of the proposed algorithm compare favorably in terms of accuracy with six other well established vessel detection techniques, outperforming three of them in the majority of the available ophthalmologic images. The proposed algorithm achieves subpixel root mean square central axis positioning error that outperforms the non-expert based vessel segmentation, whereas the accuracy of vessel diameter estimation is comparable to that of the non-expert based vessel segmentation.


title: Functional MRI Assessment of Task-Induced Deactivation of the Default Mode Network in Alzheimer’s Disease and At-Risk Older Individuals

abstract: Alzheimer’s disease (AD) is the most common form of dementia in old age, and is characterized by prominent impairment of episodic memory. Recent functional imaging studies in AD have demonstrated alterations in a distributed network of brain regions supporting memory function, including regions of the default mode network. Previous positron emission tomography studies of older individuals at risk for AD have revealed hypometabolism of association cortical regions similar to the metabolic abnormalities seen in AD patients. In recent functional magnetic resonance imaging (fMRI) studies of AD, corresponding brain default mode regions have also been found to demonstrate an abnormal fMRI task-induced deactivation response pattern. That is, the relative decreases in fMRI signal normally observed in the default mode regions in healthy subjects performing a cognitive task are not seen in AD patients, or may even be reversed to a paradoxical activation response. Our recent studies have revealed alterations in the pattern of deactivation also in elderly individuals at risk for AD by virtue of their APOE e4 genotype, or evidence of mild cognitive impairment (MCI). In agreement with recent reports from other groups, these studies demonstrate that the pattern of fMRI task-induced deactivation is progressively disrupted along the continuum from normal aging to MCI and to clinical AD and more impaired in e4 carriers compared to non-carriers. These findings will be discussed in the context of current literature regarding functional imaging of the default network in AD and at-risk populations.


title: MimicGAN: Corruption-Mimicking for Blind Image Recovery&Adversarial Defense

abstract: Solving inverse problems continues to be a central challenge in computer vision. Existing techniques either explicitly construct an inverse mapping using prior knowledge about the corruption, or learn the inverse directly using a large collection of examples. However, in practice, the nature of corruption may be unknown, and thus it is challenging to regularize the problem of inferring a plausible solution. On the other hand, collecting task-specific training data is tedious for known corruptions and impossible for unknown ones. We present MimicGAN, an unsupervised technique to solve general inverse problems based on image priors in the form of generative adversarial networks (GANs). Using a GAN prior, we show that one can reliably recover solutions to underdetermined inverse problems through a surrogate network that learns to mimic the corruption at test time. Our system successively estimates the corruption and the clean image without the need for supervisory training, while outperforming existing baselines in blind image recovery. We also demonstrate that MimicGAN improves upon recent GAN-based defenses against adversarial attacks and represents one of the strongest test-time defenses available today.


title: The relationship between malnutrition parameters and pressure ulcers in hospitals and nursing homes

abstract: Objectives: Pressure ulcers (PU) remain a major health care problem throughout the world. Although malnutrition is considered to be one of the intrinsic risk factors for PU, more evidence is needed to identify the exact relation between PU and malnutrition. This study aims to identify whether there exists a relationship between PU and malnutrition in hospitals and nursing homes. Methods: A cross-sectional study was performed in April 2007 in hospitals and nursing homes in Germany. PU were assessed using the Braden scale. Malnutrition was assessed by low body mass index (BMI), undesired weight loss, and insufficient nutritional intake. Results: Two thousand three hundred ninety-three patients from 29 nursing homes and 4067 patients from 22 hospitals participated in the study. PU in both hospital and nursing home patients were significantly (P < 0.01) related to undesired weight loss (5%–10%). Moreover low nutritional intake and low BMI (<18.5) were also significantly related to PU in hospitals and nursing homes. Conclusion: There is a significant relationship between malnutrition parameters like undesired weight loss, BMI < 18.5, and low nutritional intake and PU.


title: Averaged-DQN: Variance Reduction and Stabilization for Deep Reinforcement Learning

abstract: Instability and variability of Deep Reinforcement Learning (DRL) algorithms tend to adversely affect their performance. Averaged-DQN is a simple extension to the DQN algorithm, based on averaging previously learned Q-values estimates, which leads to a more stable training procedure and improved performance by reducing approximation error variance in the target values. To understand the effect of the algorithm, we examine the source of value function estimation errors and provide an analytical comparison within a simplified model. We further present experiments on the Arcade Learning Environment benchmark that demonstrate significantly improved stability and performance due to the proposed extension.


title: Regularized Adversarial Sampling and Deep Time-aware Attention for Click-Through Rate Prediction

abstract: Improving the performance of click-through rate (CTR) prediction remains one of the core tasks in online advertising systems. With the rise of deep learning, CTR prediction models with deep networks remarkably enhance model capacities. In deep CTR models, exploiting users' historical data is essential for learning users' behaviors and interests. As existing CTR prediction works neglect the importance of the temporal signals when embed users' historical clicking records, we propose a time-aware attention model which explicitly uses absolute temporal signals for expressing the users' periodic behaviors and relative temporal signals for expressing the temporal relation between items. Besides, we propose a regularized adversarial sampling strategy for negative sampling which eases the classification imbalance of CTR data and can make use of the strong guidance provided by the observed negative CTR samples. The adversarial sampling strategy significantly improves the training efficiency, and can be co-trained with the time-aware attention model seamlessly. Experiments are conducted on real-world CTR datasets from both in-station and out-station advertising places.


title: An Analog-Assisted Tri-Loop Digital Low-Dropout Regulator

abstract: This paper presents an analog-assisted (AA) output-capacitor-free digital low-dropout (D-LDO) regulator with tri-loop control. For responding to instant load transients, the proposed high-pass AA loop momentarily adjusts the unit current of the power switch array, and significantly reduces the voltage spikes. In the proposed D-LDO, the overall 512 output current steps are divided into three sub-sections controlled by coarse/fine loops with carry-in/out operations. Therefore, the required shift register (SR) length is reduced, and a 9-bit output current resolution is realized by using only 28-SR bits. Besides, the coarse-tuning loop helps to reduce the recovery time, while the fine-tuning loop improves the output accuracy. To eliminate the limit cycle oscillation and reduce the quiescent current, a freeze mode is added after the fine-tuning operation. To reduce the output glitches and the recovery time, a nonlinear coarse word control is designed for the carry-in/out operations. The D-LDO is fabricated in a 65-nm general purpose CMOS process. A maximum voltage undershoot/overshoot of 105 mV is measured with a 10-mA/1-ns load step and a total capacitor of only 100 pF. Thus, the resulting figure-of-merit is 0.23 ps.


title: Generalized Dropout

abstract: Deep Neural Networks often require good regularizers to generalize well. Dropout is one such regularizer that is widely used among Deep Learning practitioners. Recent work has shown that Dropout can also be viewed as performing Approximate Bayesian Inference over the network parameters. In this work, we generalize this notion and introduce a rich family of regularizers which we call Generalized Dropout. One set of methods in this family, called Dropout++, is a version of Dropout with trainable parameters. Classical Dropout emerges as a special case of this method. Another member of this family selects the width of neural network layers. Experiments show that these methods help in improving generalization performance over Dropout.


title: Peers’ Influence on Exercise Enjoyment: A Self-Determination Theory Approach

abstract: The purpose of this research was to study the influence of motivational climate perceived in peers and basic psychological needs (competence, autonomy and relatedness) on self-determined motivation and enjoyment in exercise. A sample comprised of 394 non- competitive physical exercisers, 156 women and 238 men aged between 16 and 54 (M = 21.64, SD = 7. 18), completed the Motivational Climate Perceived in Peers Scale, Scale of Motivational Mediators in Physical Activity, Behavioural Regulation in Exercise Questionnaire-2 and Physical Activity Enjoyment Scale. A correlation analysis between the variables studied, a confirmatory factor analysis (CFA) and structural equation analysis were performed. The results showed that the task- involving peer motivational climate positively predicted the three basic psychological needs (competence, autonomy and relatedness). In turn, meeting the needs for competence and relatedness positively predicted self-determined motivation, which also positively predicted the enjoyment the exercisers had during the activity. There were no significant associations between the ego- involving peer motivational climate and psychological needs. A multisample analysis indicated that the model was invariant across age and degree of exercise involvement. This study reiterates the importance of increasing exercisers’ self-determined motivation in order to obtain more enjoyment and to be more committed to the exercise. Therefore, it is essential to foster perceptions of competence, autonomy and relatedness by means of a task-involving climate. The findings provide evidence for the importance of peer motivational climate in sports motivation. ::: ::: ::: ::: Key points ::: ::: ::: Task-involving peer motivational climate positively predicted the three basic psychological needs (competence, autonomy and relatedness). There were no significant associations between the ego-involving peer motivational climate and psychological needs. ::: ::: ::: Needs for competence and relatedness positively predicted self-determined motivation. ::: ::: ::: Self-determined motivation positively predicted enjoyment.


title: Decision making for leaders

abstract: Decision Making for Leaders is an introduction to Saaty's analytic hierarchy process (AHP) aimed at an audience of leaders in business, industry, and government. As such, the book can be viewed as a popularized version of Saaty's more technical works on AHP (for example, see [1]).


title: Benign simulators of melanoma on dermoscopy – black colour does not always indicate melanoma

abstract: Introduction: Simulators of malignant melanoma comprise a heterogeneous group of melanocytic and nonmelanocytic lesions of the skin. They are mainly characterized by the occurrence of black or blue-black colors - both in clinical diagnosis as on dermoscopy. Therefore the proper recognition of this cases is extremely needed in daily general practice of many specialties' doctors. Objective: The aim of the paper is the presentation of the most popular and possible simulators of malignant melanoma which have been accompanied by the occurrence of a black coloring on dermoscopy. This review presents a dierential diagnosis of melanoma simulators and malignant melanoma based on Pubmed databes and own clinical experience. State of knowledge: Paper reviews main potential benign melanoma-simulators such as melanocytic nevi (black nevus, blue nevus, and Spitz/Reed nevus), neoplasmatic proliferations (pigmented basal cell carcinoma - BCC and Kaposi sarcoma), vascular and hemorrhagic changes (labial venous lake, thrombosed hemangioma, pyogenic granuloma, subcorneal hematoma, and angiokeratoma), hyperkeratotic proliferations (seborrheic keratosis and melanoacanthoma) and miscellaneous (black comedo, cosmetic and posttraumatic tattoos). Authors described the main clinical and dermoscopic features of benign melanoma simulators. Conclusions: The knowledge about melanoma simulators can reduce the number of surgical procedures and will help not only dermatologists, but also the professionals of other specialties, such as general practitioners, oncologists and also the paediatricians.


title: 3D-PhysNet: Learning the Intuitive Physics of Non-Rigid Object Deformations

abstract: The ability to interact and understand the environment is a fundamental prerequisite for a wide range of applications from robotics to augmented reality. In particular, predicting how deformable objects will react to applied forces in real time is a significant challenge. This is further confounded by the fact that shape information about encountered objects in the real world is often impaired by occlusions, noise and missing regions e.g. a robot manipulating an object will only be able to observe a partial view of the entire solid. In this work we present a framework, 3D-PhysNet, which is able to predict how a three-dimensional solid will deform under an applied force using intuitive physics modelling. In particular, we propose a new method to encode the physical properties of the material and the applied force, enabling generalisation over materials. The key is to combine deep variational autoencoders with adversarial training, conditioned on the applied force and the material properties. We further propose a cascaded architecture that takes a single 2.5D depth view of the object and predicts its deformation. Training data is provided by a physics simulator. The network is fast enough to be used in real-time applications from partial views. Experimental results show the viability and the generalisation properties of the proposed architecture.


title: Mobile video popularity distributions and the potential of peer-assisted video delivery

abstract: The advances in wireless communications and mobile devices have resulted in a massive growth of the video services over mobile networks. In this article, we study the potential of peer-assisted video delivery in WiFi mobile networks aimed at reducing servers? load. Our study is based on a real measurement of mobile video viewing logs from a leading Internet video provider for 14 days. We analyze viewing behavior of users with respect to three main factors for the design of peer-assisted delivery network: viewing time, user population, and user locality. We then discuss the implications of the observations. Finally, we apply the findings on a BitTorrent-like VoD system and perform experiments with the collected viewing logs to demonstrate the benefits of peer-assisted video delivery. The peer assistance can reduce the servers? load by as much as 50 percent for popular videos on average.


title: Re-ranking for joint named-entity recognition and linking

abstract: Recognizing names and linking them to structured data is a fundamental task in text analysis. Existing approaches typically perform these two steps using a pipeline architecture: they use a Named-Entity Recognition (NER) system to find the boundaries of mentions in text, and an Entity Linking (EL) system to connect the mentions to entries in structured or semi-structured repositories like Wikipedia. However, the two tasks are tightly coupled, and each type of system can benefit significantly from the kind of information provided by the other. We present a joint model for NER and EL, called NEREL, that takes a large set of candidate mentions from typical NER systems and a large set of candidate entity links from EL systems, and ranks the candidate mention-entity pairs together to make joint predictions. In NER and EL experiments across three datasets, NEREL significantly outperforms or comes close to the performance of two state-of-the-art NER systems, and it outperforms 6 competing EL systems. On the benchmark MSNBC dataset, NEREL provides a 60% reduction in error over the next-best NER system and a 68% reduction in error over the next-best EL system.


title: Critical analysis of hopfield's neural network model for TSP and its comparison with heuristic algorithm for shortest path computation

abstract: For shortest path computation, Travelling-Salesman problem is NP-complete and is among the intensively studied optimization problems. Hopfield and Tank's proposed neural network based approach, for solving TSP, is discussed. Since original Hopfield's model suffers from some limitations as the number of cities increase, some modifications are discussed for better performance. With the increase in the number of cities, the best solutions provided by original Hopfield's neural network were considered to be far away from those provided by Lin and Kernighan using Heuristic algorithm. Results of both approaches are compared for different number of cities and are analyzed properly.


title: Surgery of secondary mitral insufficiency in patients with impaired left ventricular function

abstract: BackgroundSecondary mitral insufficiency (SMI) is an indicator of a poor prognosis in patients with ischemic and dilated cardiomyopathies. Numerous studies corroborated that mitral valve (MV) surgery improves survival and may be an alternative to heart transplantation in this group of patients.The aim of the study was to retrospectively analyze the early and mid-term clinical results after MV repair resp. replacement in patients with moderate-severe to severe SMI and left ventricular ejection fraction (LVEF) below 35%.MethodsWe investigated 40 patients with poor LVEF (mean, 28 ± 5%) and SMI who underwent MV repair (n = 26) resp. replacement (n = 14) at the University Hospital Muenster from January 1994 to December 2005. All patients were on maximized heart failure medication. 6 pts. had prior coronary artery bypass grafts (CABG). Twenty-seven patients were in New York Heart Association (NYHA) class III and 13 were in class IV. Eight patients were initially considered for transplantation. During the operation, 14 pts had CABG for incidental disease and 8 had tricuspid valve repair. Follow-up included echocardiography, ECG, and physician's examination and was completed in 90% among survivors. Additionally, the late results were compared with the survival after orthotope heart transplantation (oHTX) in adults with ischemic or dilated cardiomyopathies matched to the same age and time period (148 patients).ResultsThree operative deaths (7.5%) occurred as a result of left ventricular failure in one and multiorgan failure in two patients. There were 14 late deaths, 2 to 67 months after MV procedure. Progress of heart failure was the main cause of death. 18 patients who were still alive took part on the follow-up examination. At a mean follow-up of 50 ± 34 (2–112) months the NYHA class improved significantly from 3.2 ± 0.5 to 2.2 ± 0.4 (p < 0.001). The LVEF improved significantly from 29 ± 5% to 39 ± 16 (p < 0.05). There were no differences in survival after MV repair or replacement. The 1-, 3-, 5-year survival rates in the study group were 80%, 58% and 55% respectively. In the group of patients after oHTX the survival was accordingly 72%, 68%, 66% (p > 0.05).ConclusionHigh risk mitral valve surgery in patients with cardiomyopathy and SMI offers a real mid-term alternative method of treatment of patients in drug refractory heart failure with similar survival in comparison to heart transplantation.


title: Noname manuscript No. (will be inserted by the editor) Probabilistically Safe Motion Planning to Avoid Dynamic Obstacles with Uncertain Motion Patterns

abstract: This paper presents a real-time path planning algorithm that guarantees probabilistic feasibility for autonomous robots with uncertain dynamics operating amidst one or more dynamic obstacles with uncertain motion patterns. Planning safe trajectories under such conditions requires both accurate prediction and proper integration of future obstacle behavior within the planner. Given that available observation data is limited, the motion model must provide generalizable predictions that satisfy dynamic and environmental constraints, a limitation of existing approaches. This work presents a novel solution, named RR-GP, which builds a learned motion pattern model by combining the flexibility of Gaussian processes (GP) with the efficiency of RRT-Reach, a sampling-based reachability computation. Obstacle trajectory GP predictions are conditioned on dynamically feasible paths identified from the reachability analysis, yielding more accurate predictions of future behavior. RR-GP predictions are integrated with a robust path planner, using chance-constrained RRT, to identify probabilistically feasible paths. Theoretical guarantees of probabilistic feasibility are shown for linear systems under Gaussian uncertainty; approximations for nonlinear dynamics and/or non-Gaussian uncertainty are also presented. Simulations demonstrate that, with this planner, an autonomous vehicle can safely navigate a complex environment in real-time while significantly reducing the risk of collisions with dynamic obstacles.


title: Using ``Annotator Rationales'' to Improve Machine Learning for Text Categorization

abstract: We propose a new framework for supervised machine learning. Our goal is to learn from smaller amounts of supervised training data, by collecting a richer kind of training data: annotations with “rationales.” When annotating an example, the human teacher will also highlight evidence supporting this annotation—thereby teaching the machine learner why the example belongs to the category. We provide some rationale-annotated data and present a learning method that exploits the rationales during training to boost performance significantly on a sample task, namely sentiment classification of movie reviews. We hypothesize that in some situations, providing rationales is a more fruitful use of an annotator’s time than annotating more examples.


Research of Title Party News Identification Technology Based on Topic Sentence Similarity