nils-herrmann/clean_scirepeval huggingface dataset:
Individual privacy concern and organisational privacy practice: bridging the gap

Technology services offer users benefits, including travel assistance, reviews and recommendations, social contact, and access to useful information. Users try to evaluate the risks of disclosing personal information in light of the benefits they believe they will receive, by seeking a limited set of information cues about the technology service. Existing measures of privacy concern do not recognise this, and are disconnected from organisational privacy practice. For individuals, privacy research has not provided a model of privacy concern based on the trust signals generated by organisational privacy practice. For organisations, research has not unified its insights on privacy, information security and trust into a framework for effective privacy practice, and has not provided tools to analyse the root causes of privacy failures one of the many influences on people's privacy concern. This thesis approaches this problem by proposing a conceptual model based on the trust risk framework, which explicitly links organisational privacy practice with people's perceived risks of information disclosure; an antecedent of interpersonal privacy concern. For individuals, this thesis proposes a model of privacy concern encompassing in addition to interpersonal privacy concern environmental and dispositional privacy concern, the latter shown by this thesis to be influenced by an individual's personality (N 353) For organisations, this thesis proposes a Privacy Security Trust (PST) Framework as part of the conceptual model, which applies the two dimensions of trust ability and motivation to information privacy practice. A Q methodology study (N 58) which analysed the importance individuals attach to different information cues many describing a technology service's ability and motivation to safeguard and respect their privacy identified five salient privacy personas. Results from a validation study describing two innovative technology services (N 872 and N 740) found differences in dispositional privacy concern between the personas, and information cues specific to each persona influenced Perceived Information Risks and Intention to Use a technology service. Finally, from an organisational perspective, this thesis shows how the PST Framework may also be used as the basis of a privacy specific formal systems model to analyse and potentially predict privacy failures.
[2016 European Guidelines on cardiovascular disease prevention in clinical practice]
Venue: Kardiologia polska

Authors/Task Force Members: Massimo F. Piepoli* (Chairperson) (Italy) Arno W. Hoes* (Co Chairperson) (The Netherlands) Stefan Agewall (Norway)1, Christian Albus (Germany)9, Carlos Brotons (Spain)10, Alberico L. Catapano (Italy)3, Marie Therese Cooney (Ireland)1, Ugo Corra (Italy)1, Bernard Cosyns (Belgium)1, Christi Deaton (UK)1, Ian Graham (Ireland)1, Michael Stephen Hall (UK)7, F. D. Richard Hobbs (UK)10, Maja Lisa Lochen (Norway)1, Herbert Lollgen (Germany)8, Pedro Marques Vidal (Switzerland)1, Joep Perk (Sweden)1, Eva Prescott (Denmark)1, Josep Redon (Spain)5, Dimitrios J. Richter (Greece)1, Naveed Sattar (UK)2, Yvo Smulders (The Netherlands)1, Monica Tiberi (Italy)1, H. Bart van der Worp (The Netherlands)6, Ineke van Dis (The Netherlands)4, W. M. Monique Verschuren (The Netherlands)1
ECNU at SemEval 2016 Task 4: An Empirical Investigation of Traditional NLP Features and Word Embedding Features for Sentence level and Topic level Sentiment Analysis in Twitter
Venue: *SEMEVAL

This paper reports our submissions to Task 4, i.e. Sentiment Analysis in Twitter (SAT) in SemEval 2016, which consists of five subtasks grouped into two levels: (1) sentence level, i.e. message polarity classification (subtask A) and (2) topic level, i.e. tweet classification and quantification according to two point scale (subtask B and D) or five point scale (subtask C and E) We participated in all these five subtasks. To address these subtasks, we investigated several traditional Natural Language Processing (NLP) features including sentiment lexicon, linguistic and domain specific features, and word embedding features together with supervised machine learning methods. Officially released results showed that our systems rank above average.
Aspect Specific Sentimental Word Embedding for Sentiment Analysis of Online Reviews
Venue: WWW

Recently, Deep Convolutional Neural Networks (CNNs) have been widely applied to sentiment analysis of short texts. Naturally, word embedding techniques are used to learn continuous word representations for constructing sentence matrix as input to CNN. As for sentiment analysis of customer reviews, we argue that it is problematic to learn a single representation for a word while ignoring sentiment information and the discussed aspects. In this poster, we propose a novel word embedding model to learn sentimental word embedding given specific aspects by modeling both sentiment and syntactic context under the specific aspects. We apply our method as input to CNN for sentiment analysis in multiple domains. Experiments show that the CNN based on the proposed model can consistently achieve superior performance compared to CNN based on traditional word embedding method.
Chinese Sentiment Analysis Using Bidirectional LSTM with Word Embedding
Venue: ICCCS

Long Short Term Memory network have been successfully applied to sequence modeling task and obtained great achievements. However, Chinese text contains richer syntactic and semantic information and has strong intrinsic dependency between words and phrases. In this paper, we propose Bidirectional Long Short Term Memory (BLSTM) with word embedding for Chinese sentiment analysis. BLSTM can learn past and future information and capture stronger dependency relationship. Word embedding mainly extract words' feature from raw characters input and carry important syntactic and semantic information. Experimental results show that our model achieves 91.46 accuracy for sentiment analysis task.
Aspect Based Sentiment Analysis Using Word Embedding Restricted Boltzmann Machines
Venue: CSoNet

Recent years, many studies have addressed problems in sentiment analysis at different levels, and building aspect based methods has become a central issue for deep opinion mining. However, previous studies need to use two separated modules in order to extract aspect sentiment word pairs, then predict the sentiment polarity. In this paper, we use Restricted Boltzmann Machines in combination with Word Embedding model to build the joined model which not only extracts aspect terms appeared and classifies them into respective categories, but also completes the sentiment polarity prediction task. The experimental results show that the method we use in aspect based sentiment analysis tasks is better than other state of the art approaches.
Tweet Sentiment Analysis by Incorporating Sentiment Specific Word Embedding and Weighted Text Features
Venue: 2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI)

Previous studies have used many manually identified features and word embeddings for tweet sentiment classification. In this paper, we propose a new approach, which incorporates sentiment specific word embeddings (SSWE) and a weighted text feature model (WTFM) WTFM produces features based on text negation, tf.idf weighting scheme, and a Rocchio text classification method. Compared to other tweet sentiment feature generation approaches, WTFM is easy to build, simple, yet effective. Experiments show that the proposed approach outperforms the two state of the art tweet sentiment classification methods, SSWE and National Research Council Canada's (NRC) model.
Dimensional Sentiment Analysis for Chinese words Based on synonym lexicon and Word Embedding
Venue: 2016 International Conference on Asian Language Processing (IALP)

This paper is mainly about the BIT group submitted system to the IALP 2016 Shared Task. This system is to automatically acquire the valence arousal ratings of Chinese affective words. Two ways are designed to generate a given word's VA: one is based on Synonym Lexicons and the other is based on Word Embeddings. For the first way, we extend the annotated set based on synonym lexicon to improve coverage of unknown words, and then search test words or characters split from unknown words in extended annotated set. For the second way, we broaden the words coverage by building a local words segmentation lexicon in the vector space model. The cosine similarity is used to measure the distance between the test word and the annotated word. According to the experimental results, the strategy based on the synonym lexicons is better than the one based on the word embeddings, and makes our group in upper grades among 20 teams approximately.
18.1 A 20nm 9Gb/s/pin 8Gb GDDR5 DRAM with an NBTI monitor, jitter reduction techniques and improved power distribution
Venue: 2016 IEEE International Solid State Circuits Conference (ISSCC)

A 9Gb/s/pin 8Gb GDDR5 DRAM is implemented using a 20nm CMOS process. To cover operation up to 9Gb/s, which is the highest data rate among implemented GDDR5 DRAMs  this work includes an NBTI monitor, a WCK clock receiver with equalizing and duty cycle correction modes, CML to CMOS converters with wide range operation, active resonant loads at the end of WCK lane, and an on chip de emphasis circuit at a 4 to 1 multiplexer output as shown in Fig. 18.1.1. In addition, extra power pads improve the power distribution and release the frequency limitation at the memory core.
18.2 A 1.2V 20nm 307GB/s HBM DRAM with at speed wafer level I/O test scheme and adaptive refresh considering temperature distribution
Venue: ISSCC

Demand for higher bandwidth DRAM continues to increase, especially in high performance computing and graphics applications. However, conventional DRAM devices such as DDR4 DIMM and GDDR5 cannot satisfy these needs since they are bandwidth limited to less than 30GB/s. Also, if multiple GDDR DRAMs are used simultaneously for higher bandwidth, then high power consumption and routing congestion on PCBs become a big concern. In order to overcome these limitations, the high bandwidth memory (HBM) DRAM was recently introduced[1] HBM DRAM uses TSV and interposer technologies enabling multiple chip stacks and wide I/Os between the processor and memory: providing high capacity, low power and high bandwidth. This paper proposes the 2nd generation HBM to double the bandwidth from 128GB/s to more than 256GB/s and support pseudo channel mode and 8H stacks  In the pseudo channel mode, a legacy channel is divided into two pseudo channels and the two pseudo channels share the command address pins. Thus, one HBM has 16 pseudo channels instead of 8 legacy channels. To support various stack configurations including 8H stacks, a new architecture is adopted for flexible density ranging from 16Gb to 64Gb maintaining the same bandwidth. Finally, the bandwidth increase requires an active thermal solution to manage hotspots that develop from highly concentrated power consumption; we propose an adaptive refresh considering temperature distribution (ART) scheme as a solution.
Vietnam: Economic Strategy and Economic Reality

The paper examines the nature of the growing Vietnamese economy and its changing structural characteristics and contrasts this with two core elements of official thinking. The first element is the slogan of 'industrialisation and modernisation' (IM) and how this sits with the rather fast current growth in the services sectors, given the issues of transitioning through the recently reached 'middle income status' The second is that of "a socialist oriented market economy" (SOME) with reference to its origins, the nature of debates around it and its positive and negative implications for Vietnam's development. Our basic conclusions are that IM is misplaced, as it ignores services and is out of date, and the SOME has largely been a somewhat confused and transparent fig leaf for support for essentially private commercial interests associated with certain state conglomerates. Recent changes around the 2016 XIIth Congress suggest that lessons are being learned by some elements in the Party.
Placing fashion

This paper explores the spaces in which fashion is displayed and consumed. In order to 'place' fashion space within the contemporary city, the paper focuses on a set of alliances between art and fashion in the making of current consumption space. The collaboration between art and fashion opens up a means to critically explore how representational worlds are brought into being and offers new ways to understand how creative activity can be rooted in (and reflective of) broader social, economic and cultural concerns. Such collisions and collusions represent a key means of making and shaping commodity and brand value.
Evolution of a Heavy Metal Homeostasis/Resistance Island Reflects Increasing Copper Stress in Enterobacteria
Venue: Genome biology and evolution

Copper homeostasis in bacteria is challenged by periodic elevation of copper levels in the environment, arising from both natural sources and human inputs. Several mechanisms have evolved to efflux copper from bacterial cells, including the cus (copper sensing copper efflux system) and pco (plasmid borne copper resistance system) systems. The genes belonging to these two systems can be physically clustered in a Copper Homeostasis and Silver Resistance Island (CHASRI) on both plasmids and chromosomes in Enterobacteria. Increasing use of copper in agricultural and industrial applications raises questions about the role of human activity in the evolution of novel copper resistance mechanisms. Here we present evidence that CHASRI emerged and diversified in response to copper deposition across aerobic and anaerobic environments. An analysis of diversification rates and a molecular clock model suggest that CHASRI experienced repeated episodes of elevated diversification that could correspond to peaks in human copper production. Phylogenetic analyses suggest that CHASRI originated in a relative of Enterobacter cloacae as the ultimate product of sequential assembly of several pre existing two gene modules. Once assembled, CHASRI dispersed via horizontal gene transfer within Enterobacteriaceae and also to certain members of Shewanellaceae, where the original pco module was replaced by a divergent pco homolog. Analyses of copper stress mitigation suggest that CHASRI confers increased resistance aerobically, anaerobically, and during shifts between aerobic and anaerobic environments, which could explain its persistence in facultative anaerobes and emergent enteric pathogens.
Histidine biosynthesis plays a crucial role in metal homeostasis and virulence of Aspergillus fumigatus
Venue: Virulence

Aspergillus fumigatus is the most prevalent airborne fungal pathogen causing invasive fungal infections in immunosuppressed individuals. The histidine biosynthetic pathway is found in bacteria, archaebacteria, lower eukaryotes, and plants, but is absent in mammals. Here we demonstrate that deletion of the gene encoding imidazoleglycerol phosphate dehydratase (HisB) in A. fumigatus causes (i) histidine auxotrophy, (ii) decreased resistance to both starvation and excess of various heavy metals, including iron, copper and zinc, which play a pivotal role in antimicrobial host defense, (iii) attenuation of pathogenicity in 4 virulence models: murine pulmonary infection, murine systemic infection, murine corneal infection, and wax moth larvae. In agreement with the in vivo importance of histidine biosynthesis, the HisB inhibitor 3 amino 1,2,4 triazole reduced the virulence of the A. fumigatus wild type and histidine supplementation partially rescued virulence of the histidine auxotrophic mutant in the wax moth model. Taken together, this study reveals limited histidine availability in diverse A. fumigatus host niches, a crucial role for histidine in metal homeostasis, and the histidine biosynthetic pathway as being an attractive target for development of novel antifungal therapy approaches.
Groundwater depletion in Central Mexico: Use of GRACE and InSAR to support water resources management

Groundwater deficits occur in several areas of Central Mexico, where water resource assessment is limited by the availability and reliability of field data. In this context, GRACE and InSAR are used to remotely assess groundwater storage loss in one of Mexico's most important watersheds in terms of size and economic activity: the Lerma Santiago Pacifico (LSP) In situ data and Land Surface Models are used to subtract soil moisture and surface water storage changes from the total water storage change measured by GRACE satellites. As a result, groundwater mass change time series are obtained for a 12 years period. ALOS PALSAR images acquired from 2007 to 2011 were processed using the SBAS InSAR algorithm to reveal areas subject to ground motion related to groundwater over exploitation. In the perspective of providing guidance for groundwater management, GRACE and InSAR observations are compared with official water budgets and field observations. InSAR derived subsidence mapping generally agrees well with official water budgets, and shows that deficits occur mainly in cities and irrigated agricultural areas. GRACE does not entirely detect the significant groundwater losses largely reported by official water budgets, literature and InSAR observations. The difference is interpreted as returns of wastewater to the groundwater flow systems, which limits the watershed scale groundwater depletion but suggests major impacts on groundwater quality. This phenomenon is enhanced by ground fracturing as noticed in the field. Studying the fate of the extracted groundwater is essential when comparing GRACE data with higher resolution observations, and particularly in the perspective of further InSAR/GRACE combination in hydrogeology.
One year oral toxicity study on a genetically modified maize MON810 variety in Wistar Han RCC rats (EU 7th Framework Programme project GRACE)
Venue: Archives of Toxicology

The GRACE (GMO Risk Assessment and Communication of Evidence; www.grace fp7.eu) project was funded by the European Commission within the 7th Framework Programme. A key objective of GRACE was to conduct 90 day animal feeding trials, animal studies with an extended time frame as well as analytical, in vitro and in silico studies on genetically modified (GM) maize in order to comparatively evaluate their use in GM plant risk assessment. In the present study, the results of a 1 year feeding trial with a GM maize MON810 variety, its near isogenic non GM comparator and an additional conventional maize variety are presented. The feeding trials were performed by taking into account the guidance for such studies published by the EFSA Scientific Committee in 2011 and the OECD Test Guideline 452. The results obtained show that the MON810 maize at a level of up to 33 in the diet did not induce adverse effects in male and female Wistar Han RCC rats after a chronic exposure.
Effects of Probiotics and Synbiotics on Obesity, Insulin Resistance Syndrome, Type 2 Diabetes and Non Alcoholic Fatty Liver Disease: A Review of Human Clinical Trials
Venue: International journal of molecular sciences

The use of probiotics and synbiotics in the prevention and treatment of different disorders has dramatically increased over the last decade. Both probiotics and synbiotics are well known ingredients of functional foods and nutraceuticals and may provide beneficial health effects because they can influence the intestinal microbial ecology and immunity. The present study reviews the effects of probiotics and synbiotics on obesity, insulin resistance syndrome (IRS) type 2 diabetes (T2D) and non alcoholic fatty liver disease (NAFLD) in human randomized clinical trials. Select probiotics and synbiotics provided beneficial effects in patients with obesity, mainly affecting the body mass index and fat mass. Some probiotics had beneficial effects on IRS, decreasing the cell adhesion molecule 1 levels, and the synbiotics decreased the insulin resistance and plasma lipid levels. Moreover, select probiotics improved the carbohydrate metabolism, fasting blood glucose, insulin sensitivity and antioxidant status and also reduced metabolic stress in subjects with T2D. Some probiotics and synbiotics improved the liver and metabolic parameters in patients with NAFLD. The oral intake of probiotics and synbiotics as co adjuvants for the prevention and treatment of obesity, IRS, T2D and NAFLD is partially supported by the data shown in the present review. However, further studies are required to understand the precise mechanism of how probiotics and synbiotics affect these metabolic disorders.
JS Xtract: A Realtime Audio Feature Extraction Library for the Web

JS Xtract is an efficient modular JavaScript library for audio feature extraction, capable of operating on arbitrary time series data, or being bound to Web Audio objects. The library implements an extensive range of vector and scalar feature extractors, and allows both procedural and object oriented function calls. We show it performs well across a range of desktop and mobile browsers, and is capable of extracting audio features in realtime.
Supervised Aggregative Feature Extraction for Big Data Time Series Regression
Venue: IEEE Transactions on Industrial Informatics

In many applications, and especially those where batch processes are involved, a target scalar output of interest is often dependent on one or more time series of data. With the exponential growth in data logging in modern industries, such time series are increasingly available for statistical modeling in soft sensing applications. In order to exploit time series data for predictive modeling, it is necessary to summarize the information they contain as a set of features to use as model regressors. Typically this is done in an unsupervised fashion using simple techniques such as computing statistical moments, principal components or wavelet decompositions, often leading to significant information loss, and hence suboptimal predictive models. In this paper, a functional learning paradigm is exploited in a supervised fashion to derive continuous smooth estimates of time series data (yielding aggregated local information) while simultaneously estimating a continuous shape function yielding optimal predictions. The proposed supervised aggregative feature extraction (SAFE) methodology can be extended to support nonlinear predictive models by embedding the functional learning framework in a reproducing kernel Hilbert spaces (RKHSs) setting. SAFE has a number of attractive features including closed form solution and the ability to explicitly incorporate first and second order derivative information. Using simulation studies and a practical semiconductor manufacturing case study, we highlight the strengths of the new methodology with respect to standard unsupervised feature extraction approaches.