nils-herrmann/clean_scirepeval huggingface dataset:
BUSINESS-TO-BUSINESS E-BUSINESS MODELS : CLASSIFICATION AND TEXTILE INDUSTRY IMPLICATIONS

Since the introduction of the Internet and e-commerce in the mid-1990s, there has been a lot of hype surrounding e-business, the impact that it will have on the way that companies do business, and how it will change the global economy as a whole. Since the crash of the dotcom companies in 2001, there has been much less hype surrounding the use of the Internet for business. There seems to have been a realization that e-business may not be the answer to all of a company’s problems, but can be a great asset in the struggle to increase efficiencies in daily business dealings, and that the Web is primarily a new way of relating to customers and suppliers. This paper categorizes and discusses the different types of business-to-business electronic business models currently being used by businesses and discussed in the academic literature, and shows how these business models are being implemented within the textile industry. This paper is divided into three parts. Part I gives an overview and some important definitions associated with businessto-business e-business, and discusses some characteristics that are unique to doing business on the Internet. Risks and benefits associated with doing business online are also discussed. Part II analyzes the different types of e-business models seen in the academic literature. Based on the analysis of the literature, a taxonomy of e-business models was developed. This new classification system organized e-business models into the following categories: sourcing models, ownership models, service-based models, customer relationship management models, supply chain models, interaction Models and revenue models. Part III reviews how these e-business models are currently being used within the textile industry. A preliminary analysis of 79 textile manufacturing companies was conducted to identify the applications of e-business.
SkinWatch: skin gesture interaction for smart watch

We propose SkinWatch, a new interaction modality for wearable devices. SkinWatch provides gesture input by sensing deformation of the skin under a wearable wrist device, also known as a smart watch. A gesture command that is matched by learning data and two-dimensional linear input recognizes the gestures. The sensing part is small, thin, and stable, to accept accurate input via a user's skin. We also implement an anti-error mechanism to prevent unexpected input when the user moves or rotates his or her forearm. The whole sensor costs less than $1.50 and the sensor layer does not exceed a height of more than 3 mm in this prototype. We demonstrate sample applications with a practical task; using two-finger skin gesture input.
Cachexia: a new definition.

On December 13th and 14th a group of scientists and clinicians met in Washington, DC, for the cachexia consensus conference. At the present time, there is no widely agreed upon operational definition of cachexia. The lack of a definition accepted by clinician and researchers has limited identification and treatment of cachectic patient as well as the development and approval of potential therapeutic agents. The definition that emerged is: "cachexia, is a complex metabolic syndrome associated with underlying illness and characterized by loss of muscle with or without loss of fat mass. The prominent clinical feature of cachexia is weight loss in adults (corrected for fluid retention) or growth failure in children (excluding endocrine disorders). Anorexia, inflammation, insulin resistance and increased muscle protein breakdown are frequently associated with cachexia. Cachexia is distinct from starvation, age-related loss of muscle mass, primary depression, malabsorption and hyperthyroidism and is associated with increased morbidity. While this definition has not been tested in epidemiological or intervention studies, a consensus operational definition provides an opportunity for increased research.
Feature engineering in Context-Dependent Deep Neural Networks for conversational speech transcription

We investigate the potential of Context-Dependent Deep-Neural-Network HMMs, or CD-DNN-HMMs, from a feature-engineering perspective. Recently, we had shown that for speaker-independent transcription of phone calls (NIST RT03S Fisher data), CD-DNN-HMMs reduced the word error rate by as much as one third—from 27.4%, obtained by discriminatively trained Gaussian-mixture HMMs with HLDA features, to 18.5%—using 300+ hours of training data (Switchboard), 9000+ tied triphone states, and up to 9 hidden network layers.
Gay and bisexual identity development among female-to-male transsexuals in North America: emergence of a transgender sexuality.

We studied a North American sample of female-to-male (FtM) transsexuals sexually attracted to men, aiming to understand their identity and sexuality in the context of a culture of transgender empowerment. Sex-reassigned FtM transsexuals, 18 years or older and attracted to men, were recruited via an FtM community conference and listserv. Participants (N = 25) responded to open-ended questions about identity development, sexual behavior, and social support. Data were analyzed by content analysis. Scores for sexual identity, self esteem, sexual functioning, and psychological adjustment were compared to those of a comparison group (N = 76 nontransgender gay and bisexual men). Of the 25 FtMs, 15 (60%) identified as gay, 8 (32%) as bisexual, and 2 (8%) as queer. All were comfortable with their gender identity and sexual orientation. The FtM group was more bisexual than the nontransgender gay and bisexual controls. No significant group differences were found in self esteem, sexual satisfaction, or psychological adjustment. For some FtMs, sexual attractions and experiences with men affirmed their gender identity; for others, self-acceptance of a transgender identity facilitated actualization of their attractions toward men. Most were "out" as transgender among friends and family, but not on the job or within the gay community. Disclosure and acceptance of their homosexuality was limited. The sexual identity of gay and bisexual FtMs appears to mirror the developmental process for nontransgender homosexual men and women in several ways; however, participants also had experiences unique to being both transgender and gay/bisexual. This signals the emergence of a transgender sexuality.
Do individual investors drive post-earnings announcement drift ?

This study examines how individual investor trade in response to quarterly earnings surprises and the relation of trades to subsequent returns. Individuals are highly significant net buyers after negative earnings surprises; net buying is weaker after positive surprises. There is no indication that trading by any of our investor subcategories explains the concentration of drift at subsequent earnings announcement dates. Post-announcement individual net buying is a significant negative predictor of stock returns over the next three quarters. However, individual investor trading fails to subsume any of the power of earnings surprises to predict future abnormal returns. Overall, the evidence does not support the hypothesis that individual investors drive postearnings announcement drift.
Neural Shift-Reduce CCG Semantic Parsing

We present a shift-reduce CCG semantic parser. Our parser uses a neural network architecture that balances model capacity and computational cost. We train by transferring a model from a computationally expensive loglinear CKY parser. Our learner addresses two challenges: selecting the best parse for learning when the CKY parser generates multiple correct trees, and learning from partial derivations when the CKY parser fails to parse. We evaluate on AMR parsing. Our parser performs comparably to the CKY parser, while doing significantly fewer operations. We also present results for greedy semantic parsing with a relatively small drop in performance.
Characterizing affective instability in borderline personality disorder.

This study sought to understand affective instability among patients with borderline personality disorder by examining the degree of instability in six affective domains. The authors also examined the subjective intensity with which moods are experienced and the association between instability and intensity of affect.



In a group of 152 patients with personality disorders, subjective affective intensity and six dimensions of affective instability were measured. The mean scores for lability and intensity for each affective domain for patients with borderline personality disorder were compared with those of patients with other personality disorders through analyses that controlled for other axis I affective disorders, age, and sex.



Greater lability in terms of anger and anxiety and oscillation between depression and anxiety, but not in terms of oscillation between depression and elation, was associated with borderline personality disorder. Contrary to expectation, the experience of an increase in subjective affective intensity was not more prominent in patients with borderline personality disorder than in those with other personality disorders.



By applying a finer-grained perspective on affective instability than those of previous personality disorder studies, this study points to patterns of affective experience characteristic of patients with borderline personality disorder.
Deep learning in pharmacy: The prediction of aqueous solubility based on deep belief network

The aqueous solubility of a drug is a significant factor for its bioavailability. Since many drugs on the market are the oral drugs, their absorption and metabolism in organisms are closely related to its aqueous solubility. As one of the most important properties of drug, the molecule aqueous solubility has received increasing attentions in drug discovery field. The methods of shallow machine learning have been applied to the field of pharmacy, with some success. In this paper, we established a multilayer deep belief network based on semi-supervised learning model to predict the aqueous solubility of compounds. This method can be used for recognizing whether compounds are soluble or not. Firstly, we discussed the influence of feature dimension to predict accuracy. Secondly, we analyzed the parameters of model in predicting aqueous solubility of drugs and contrasted the shallow machine learning with the similar deep architecture. The results showed that the model we proposed can predict aqueous solubility accurately, the accuracy of DBN reached 85.9%. The stable performance on the evaluation metrics confirms the practicability of our model. Moreover, the DBN model could be applied to reduce the cost and time of drug discovery by predicting aqueous solubility of drugs.
Sleep after learning aids memory recall.

In recent years, the effect of sleep on memory consolidation has received considerable attention. In humans, these studies concentrated mainly on procedural types of memory, which are considered to be hippocampus-independent. Here, we show that sleep also has a persisting effect on hippocampus-dependent declarative memory. In two experiments, we examined high school students' ability to remember vocabulary. We show that declarative memory is enhanced when sleep follows within a few hours of learning, independent of time of day, and with equal amounts of interference during retention intervals. Sleep deprivation has a detrimental effect on memory, which was significant after a night of recovery sleep. Thus, fatigue accumulating during wake intervals could be ruled out as a confound.
Targeting the right students using data mining

The education domain offers a fertile ground for many interesting and challenging data mining applications. These applications can help both educators and students, and improve the quality of education. In this paper, we present a real-life application for the Gifted Education Programme (GEP) of the Ministry of Education (MOE) in Singapore. The application involves many data mining tasks. This paper focuses only on one task, namely, selecting students for remedial classes. Traditionally, a cut-off mark for each subject is used to select the weak students. That is, those students whose scores in a subject fall below the cut-off mark for the subject are advised to take further classes in the subject. In this paper, we show that this traditional method requires too many students to take part in the remedial classes. This not only increases the teaching load of the teachers, but also gives unnecessary burdens to students, which is particularly undesirable in our case because the GEP students are generally taking more subjects than non-GEP students, and the GEP students are encouraged to have more time to explore advanced topics. With the help of data mining, we are able to select the targeted students much more precisely.
A single-layer folded substrate integrated waveguide (SIW) filter

A novel single-layer folded filter using the substrate integrated waveguide (SIW) technique is presented in this paper. A prototype at X-band for the proposed filter has been fabricated using standard printed-circuit-board (PCB) process, and is measured with a vector network analyzer (VNA). The measured results show the proposed filter has a narrow band-pass bandwidth, small insert loss and good frequency selective performances. The proposed filter has a compact size and it is suitable for designing microwave and millimeter-wave circuits.
Towards an Ontology-based Security Management

The paramount complexity of enterprise in formation leads to hard-to-deal security management issues and system configurations. We present a security management framework of an arbitrary information system (IS) which builds upon knowledge-based resources, such as security ontology (SO) providing reusable security knowledge interoperability, aggregation and reasoning exploiting security knowledge from diverse sources; in addition, the separation of security requirements from their technical implementations facilitates the security management. We provide a feasible framework which links the high-level policy statements and deployable security controls and facilitates the security expert's work
The Adressa dataset for news recommendation

Datasets for recommender systems are few and often inadequate for the contextualized nature of news recommendation. News recommender systems are both time- and location-dependent, make use of implicit signals, and often include both collaborative and content-based components. In this paper we introduce the Adressa compact news dataset, which supports all these aspects of news recommendation. The dataset comes in two versions, the large 20M dataset of 10 weeks' traffic on Adresseavisen's news portal, and the small 2M dataset of only one week's traffic. We explain the structure of the dataset and discuss how it can be used in advanced news recommender systems.
Link prediction in bipartite graphs using internal links and weighted projection

Many real-world complex networks, like client-product or file-provider relations, have a bipartite nature and evolve during time. Predicting links that will appear in them is one of the main approach to understand their dynamics. Only few works address the bipartite case, though, despite its high practical interest and the specific challenges it raises. We define in this paper the notion of internal links in bipartite graphs and propose a link prediction method based on them. We describe the method and experimentally compare it to a basic collaborative filtering approach. We present results obtained for two typical practical cases. We reach the conclusion that our method performs very well, and that internal links play an important role in bipartite graphs and their dynamics.
A Bayesian Approach to Causal Discovery

We examine the Bayesian approach to the discovery of directed acyclic causal models and compare it to the constraint-based approach. Both approaches rely on the Causal Markov assumption, but the two di er signi cantly in theory and practice. An important di erence between the approaches is that the constraint-based approach uses categorical information about conditional-independence constraints in the domain, whereas the Bayesian approach weighs the degree to which such constraints hold. As a result, the Bayesian approach has three distinct advantages over its constraint-based counterpart. One, conclusions derived from the Bayesian approach are not susceptible to incorrect categorical decisions about independence facts that can occur with data sets of nite size. Two, using the Bayesian approach, ner distinctions among model structures|both quantitative and qualitative|can be made. Three, information from several models can be combined to make better inferences and to better account for modeling uncertainty. In addition to describing the general Bayesian approach to causal discovery, we review approximationmethods for missing data and hidden variables, and illustrate di erences between the Bayesian and constraint-based methods using arti cial and real examples.
Best practices for eye tracking of television and video user experiences

Eye tracking is a usability tool that employs a device that measures on-screen eye fixations and movements to determine how users visually interact with an interface. In this paper, we present best practices for planning and moderating eye tracking sessions as well as offer details regarding the technical configuration used during our studies. We found that eye tracking is pivotal in determining complex usability issues that would be difficult to uncover with traditional observational and think-aloud techniques.
IT Adoption: A Persuasive Perspective

Based on Fogg’s Functional Triad, we develop a model to explain how a feature of persuasive technologies can influence usage intention by shaping an individual’s beliefs in regard to the Functional Triad. Specifically, we operationalize the Functional Triad by adopting the constructs of perceived usefulness, perceived medium function, and perceived social presence, respectively. We investigate the trade-off transparency of an online decision aid as a persuasive feature of a computing technology. Through an experimental study, the results show that 1) perceived tool function, perceived medium function, and perceived social actor function all significantly influence intention to use a computing technology; 2) trade-off transparency influences the perceptions of these three functions; and 3) the three functions fully mediate the effect of trade-off transparency on usage intention. Implications for researchers and technology designers are also discussed.
Multimodal speech recognition: increasing accuracy using high speed video data

To date, multimodal speech recognition systems based on the processing of audio and video signals show significantly better results than their unimodal counterparts. In general, researchers divide the solution of the audio–visual speech recognition problem into two parts. First, in extracting the most informative features from each modality and second, in the most successful way of fusion both modalities. Ultimately, this leads to an improvement in the accuracy of speech recognition. Almost all modern studies use this approach with video data of a standard recording speed of 25 frames per second. The choice of such a recording speed is easily explained, since the vast majority of existing audio–visual databases are recorded with this rate. However, it should be noticed that the number of 25 frames per second is a world standard for many areas and has never been specifically calculated for speech recognition tasks. The main purpose of this study is to investigate the effect brought by the high-speed video data (up to 200 frames per second) on the speech recognition accuracy. And also to find out whether the use of a high-speed video camera makes the speech recognition systems more robust to acoustical noise. To this end, we recorded a database of audio–visual Russian speech with high-speed video recordings, which consists of records of 20 speakers, each of them pronouncing 200 phrases of continuous Russian speech. Experiments performed on this database showed an improvement in the absolute speech recognition rate up to 3.10%. We also proved that the use of the high-speed camera with 200 fps allows achieving better recognition results under different acoustically noisy conditions (signal-to-noise ratio varied between 40 and 0 dB) with different types of noise (e.g. white noise, babble noise).
Avatar reshaping and automatic rigging using a deformable model

3D scans of human figures have become widely available through online marketplaces and have become relatively easy to acquire using commodity scanning hardware. In addition to static uses of such 3D models, such as 3D printed figurines or rendered 3D still imagery, there are numerous uses for an animated 3D character that uses such 3D scan data. In order to effectively use such models as dynamic 3D characters, the models must be properly rigged before they are animated. In this work, we demonstrate a method to automatically rig a 3D mesh by matching a set of morphable models against the 3D scan. Once the morphable model has been matched against the 3D scan, the skeleton position and skinning attributes are then copied, resulting in a skinning and rigging that is similar in quality to the original hand-rigged model. In addition, the use of a morphable model allows us to reshape and resize the 3D scan according to approximate human proportions. Thus, a human 3D scan can be modified to be taller, shorter, fatter or skinnier. Such manipulations of the 3D scan are useful both for social science research, as well as for visualization for applications such as fitness, body image, plastic surgery and the like.
Large-Scale Hierarchical Text Classification with Recursively Regularized Deep Graph-CNN

Text classification to a hierarchical taxonomy of topics is a common and practical problem. Traditional approaches simply use bag-of-words and have achieved good results. However, when there are a lot of labels with different topical granularities, bagof-words representation may not be enough. Deep learning models have been proven to be effective to automatically learn different levels of representations for image data. It is interesting to study what is the best way to represent texts. In this paper, we propose a graph-CNN based deep learning model to first convert texts to graph-of-words, and then use graph convolution operations to convolve the word graph. Graph-ofwords representation of texts has the advantage of capturing non-consecutive and long-distance semantics. CNN models have the advantage of learning different level of semantics. To further leverage the hierarchy of labels, we regularize the deep architecture with the dependency among labels. Our results on both RCV1 and NYTimes datasets show that we can significantly improve large-scale hierarchical text classification over traditional hierarchical text classification and existing deep models.