nils-herrmann/clean_scirepeval huggingface dataset:
Social Comparison 2.0: Examining the Effects of Online Profiles on Social-Networking Sites

Through their features--such as profile photographs or the personal vita--online profiles on social-networking sites offer a perfect basis for social comparison processes. By looking at the profile photograph, the user gains an impression of a person's physical attractiveness, and the user's vita shows which career path the person is pursuing. Against the background of Festinger's Social Comparison Theory, the focus of this research is on the effects of online profiles on their recipients. Therefore, qualitative interviews (N = 12) and two online experiments were conducted in which virtual online profiles of either physically attractive or unattractive persons (N = 93) and profiles of users with either high or low occupational attainment (N = 103) were presented to the participants. Although qualitative interviews did not initially give reason to expect online profiles to constitute a basis for comparison processes, results of the experiments proved otherwise. The first study indicates that recipients have a more negative body image after looking at beautiful users than persons who were shown the less attractive profile pictures. Male participants of the second study, who were confronted with profiles of successful males, showed a higher perceived discrepancy between their current career status and an ideal vita than male participants who looked at profiles of less successful persons.
The role of positive and negative emotions in life satisfaction judgment across nations.

This study examined how the frequency of positive and negative emotions is related to life satisfaction across nations. Participants were 8,557 people from 46 countries who reported on their life satisfaction and frequency of positive and negative emotions. Multilevel analyses showed that across nations, the experience of positive emotions was more strongly related to life satisfaction than the absence of negative emotions. Yet, the cultural dimensions of individualism and survival/self-expression moderated these relationships. Negative emotional experiences were more negatively related to life satisfaction in individualistic than in collectivistic nations, and positive emotional experiences had a larger positive relationship with life satisfaction in nations that stress self-expression than in nations that value survival. These findings show how emotional aspects of the good life vary with national culture and how this depends on the values that characterize one's society. Although to some degree, positive and negative emotions might be universally viewed as desirable and undesirable, respectively, there appear to be clear cultural differences in how relevant such emotional experiences are to quality of life.
Bayesian network based extreme learning machine for subjectivity detection

Subjectivity detection is a task of natural language processing that aims to remove ‘factual’ or ‘neutral’ content, i.e., objective text that does not contain any opinion, from online product reviews. Such a pre-processing step is crucial to increase the accuracy of sentiment analysis systems, as these are usually optimized for the binary classification task of distinguishing between positive and negative content. In this paper, we extend the extreme learning machine (ELM) paradigm to a novel framework that exploits the features of both Bayesian networks and fuzzy recurrent neural networks to perform subjectivity detection. In particular, Bayesian networks are used to build a network of connections among the hidden neurons of the conventional ELM configuration in order to capture dependencies in high-dimensional data. Next, a fuzzy recurrent neural network inherits the overall structure generated by the Bayesian networks to model temporal features in the predictor. Experimental results confirmed the ability of the proposed framework to deal with standard subjectivity detection problems and also proved its capacity to address portability across languages in translation tasks.  2018
The Neurovascular Unit Coming of Age: A Journey through Neurovascular Coupling in Health and Disease

The concept of the neurovascular unit (NVU), formalized at the 2001 Stroke Progress Review Group meeting of the National Institute of Neurological Disorders and Stroke, emphasizes the intimate relationship between the brain and its vessels. Since then, the NVU has attracted the interest of the neuroscience community, resulting in considerable advances in the field. Here the current state of knowledge of the NVU will be assessed, focusing on one of its most vital roles: the coupling between neural activity and blood flow. The evidence supports a conceptual shift in the mechanisms of neurovascular coupling, from a unidimensional process involving neuronal-astrocytic signaling to local blood vessels to a multidimensional one in which mediators released from multiple cells engage distinct signaling pathways and effector systems across the entire cerebrovascular network in a highly orchestrated manner. The recently appreciated NVU dysfunction in neurodegenerative diseases, although still poorly understood, supports emerging concepts that maintaining neurovascular health promotes brain health.
Predicting Counterfactuals from Large Historical Data and Small Randomized Trials

When a new treatment is considered for use, whether a pharmaceutical drug or a search engine ranking algorithm, a typical question that arises is, will its performance exceed that of the current treatment? The conventional way to answer this counterfactual question is to estimate the effect of the new treatment in comparison to that of the conventional treatment by running a controlled, randomized experiment. While this approach theoretically ensures an unbiased estimator, it suffers from several drawbacks, including the difficulty in finding representative experimental populations as well as the cost of running randomized trials. Moreover, such trials neglect the huge quantities of available controlcondition data, which in principle can be utilized for the harder task of predicting individualized effects. In this paper we propose a discriminative framework for predicting the outcomes of a new treatment from a large dataset of the control condition and data from a small (and possibly unrepresentative) randomized trial comparing new and old treatments. Our learning objective, which requires minimal assumptions on the treatments, models the relation between the outcomes of the different conditions. This allows us to not only estimate mean effects but also to generate individual predictions for examples outside the small randomized sample. We demonstrate the utility of our approach through experiments in three areas: search engine operation, treatments to diabetes patients, and market value estimation of houses. Our results demonstrate that our approach can reduce the number and size of the currently performed randomized controlled experiments, thus saving significant time, money and effort on the part of practitioners.
Feature Engineering for Text Classification

Most research in text classification to date has used a “bag of words” representation in which each feature corresponds to a single word. This paper examines some alternative ways to represent text based on syntactic and semantic relationships between words (phrases, synonyms and hypernyms). We describe the new representations and try to justify our hypothesis that they could improve the performance of a rule-based learner. The representations are evaluated using the RIPPER learning algorithm on the Reuters-21578 and DigiTrad test corpora. On their own the new representations are not found to produce significant performance improvements. We also try combining classifiers based on different representations using a majority voting technique, and this improves performance on both test collections. In our opinion, more sophisticated Natural Language Processing techniques need to be developed before better text representations can be produced for classification.
A lattice-based approach to multi-robot motion planning for non-holonomic vehicles

Coordinating fleets of autonomous, non-holonomic vehicles is paramount to many industrial applications. While there exists solutions to efficiently calculate trajectories for individual vehicles, an effective methodology to coordinate their motions and to avoid deadlocks is still missing. Decoupled approaches, where motions are calculated independently for each vehicle and then centrally coordinated for execution, have the means to identify deadlocks, but not to solve all of them. We present a novel approach that overcomes this limitation and that can be used to complement the deficiencies of decoupled solutions with centralized coordination. Here, we formally define an extension of the framework of lattice-based motion planning to multi-robot systems and we validate it experimentally. Our approach can jointly plan for multiple vehicles and it generates kinematically feasible and deadlock-free motions.
CONSUMER SWITCHING BEHAVIOUR : A STUDY OF SHAMPOO BRANDS

Consumers or the Customers are valuable assets for any organisation as they are the ultimate destination of any products or services. Since, they are the ultimate end users of any product or services, thus, the success of any organisation depends upon the satisfaction of the consumers, if not they will switch to other brands. Due to this reason, the satisfaction of the consumers becomes priority for any organisations. For satisfying the consumers, one has to know about what consumer buy, why they buy it, when they buy it, how and how often they buy it and what made them to switch to other brands. The present paper is an attempt to study the shampoo buying patterns among the individuals. The study also examines the various factors which influence the consumers to buy a shampoo of particular brand and reasons for their switching to other brands.
A Distributed Ledger for Supply Chain Physical Distribution Visibility

Supply chains (SC) span many geographies, modes and industries and involve several phases where data flows in both directions from suppliers, manufacturers, distributors, retailers, to customers. This data flow is necessary to support critical business decisions that may impact product cost and market share. Current SC information systems are unable to provide validated, pseudo real-time shipment tracking during the distribution phase. This information is available from a single source, often the carrier, and is shared with other stakeholders on an as-needed basis. This paper introduces an independent, crowd-validated, online shipment tracking framework that complements current enterprise-based SC management solutions. The proposed framework consists of a set of private distributed ledgers and a single blockchain public ledger. Each private ledger allows the private sharing of custody events among the trading partners in a given shipment. Privacy is necessary, for example, when trading high-end products or chemical and pharmaceutical products. The second type of ledger is a blockchain public ledger. It consists of the hash code of each private event in addition to monitoring events. The latter provide an independently validated immutable record of the pseudo real-time geolocation status of the shipment from a large number of sources using commuters-sourcing.
SECMACE: Scalable and Robust Identity and Credential Management Infrastructure in Vehicular Communication Systems

Several years of academic and industrial research efforts have converged to a common understanding on fundamental security building blocks for the upcoming vehicular communication (VC) systems. There is a growing consensus toward deploying a special-purpose identity and credential management infrastructure, i.e., a vehicular public-key infrastructure (VPKI), enabling pseudonymous authentication, with standardization efforts toward that direction. In spite of the progress made by standardization bodies ( and harmonization efforts [Car2Car Communication Consortium (C2C-CC)], significant questions remain unanswered toward deploying a VPKI. Deep understanding of the VPKI, a central building block of secure and privacy-preserving VC systems, is still lacking. This paper contributes to the closing of this gap. We present SECMACE, a VPKI system, which is compatible with the IEEE 1609.2 and ETSI standards specifications. We provide a detailed description of our state-of-the-art VPKI that improves upon existing proposals in terms of security and privacy protection, and efficiency. SECMACE facilitates multi-domain operations in the VC systems and enhances user privacy, notably preventing linking pseudonyms based on timing information and offering increased protection even against honest-but-curious VPKI entities. We propose multiple policies for the vehicle–VPKI interactions and two large-scale mobility trace data sets, based on which we evaluate the full-blown implementation of SECMACE. With very little attention on the VPKI performance thus far, our results reveal that modest computing resources can support a large area of vehicles with very few delays and the most promising policy in terms of privacy protection can be supported with moderate overhead.
Job embeddedness: a theoretical foundation for developing a comprehensive nurse retention plan.

Using a new construct, job embeddedness, from the business management literature, this study first examines its value in predicting employee retention in a healthcare setting and second, assesses whether the factors that influence the retention of nurses are systematically different from those influencing other healthcare workers.



The shortage of skilled healthcare workers makes it imperative that healthcare providers develop effective recruitment and retention plans. With nursing turnover averaging more than 20% a year and competition to hire new nurses fierce, many administrators rightly question whether they should develop specialized plans to recruit and retain nurses.



A longitudinal research design was employed to assess the predictive validity of the job embeddedness concept. At time 1, surveys were mailed to a random sample of 500 employees of a community-based hospital in the Northwest region of the United States. The survey assessed personal characteristics, job satisfaction, organizational commitment, job embeddedness, job search, perceived alternatives, and intent to leave. One year later (time 2) the organization provided data regarding voluntary leavers from the hospital.



Hospital employees returned 232 surveys, yielding a response rate of 46.4 %. The results indicate that job embeddedness predicted turnover over and beyond a combination of perceived desirability of movement measures (job satisfaction, organizational commitment) and perceived ease of movement measures (job alternatives, job search). Thus, job embeddedness assesses new and meaningful variance in turnover in excess of that predicted by the major variables included in almost all the major models of turnover.



The findings suggest that job embeddedness is a valuable lens through which to evaluate employee retention in healthcare organizations. Further, the levers for influencing retention are substantially similar for nurses and other healthcare workers. Implications of these findings and recommendations for recruitment and retention policy development are presented.
Tumour antigens recognized by T lymphocytes: at the core of cancer immunotherapy

In this Timeline, we describe the characteristics of tumour antigens that are recognized by spontaneous T cell responses in cancer patients and the paths that led to their identification. We explain on what genetic basis most, but not all, of these antigens are tumour specific: that is, present on tumour cells but not on normal cells. We also discuss how strategies that target these tumour-specific antigens can lead either to tumour-specific or to crossreactive T cell responses, which is an issue that has important safety implications in immunotherapy. These safety issues are even more of a concern for strategies targeting antigens that are not known to induce spontaneous T cell responses in patients.
Only : Meaning and implicatures

The issue of how to account for the interpretation of ‘only’ has always been exciting and challenging. Over the years many sophisticated proposals have been brought forward, but ‘only’ always managed to strike back by exposing another new and strange property. In this paper we will argue that there is a way to approach the meaning of ‘only’ that can deal with some of its well-known challenges but still is faithful to classical ideas. In section 2 we will start our discussion by introducing the traditional and predominant view on the meaning of ‘only’ – we will call it the focus alternative approach. The main aim of the section will be to argue that this is not the right way to account for the meaning of ‘only’. In section 3 we will then introduce a different approach, proposed by von Stechow (1991) – the background alternatives approach. We will develop a formalization of the latter analysis making use of minimal models and show that there is a close relation between the two contrasting approaches. But even though both approaches share the same driving idea, the background alternatives approach is better capable to deal with the challenges of the meaning of ‘only’. The rest of the paper will support this claim by showing that the approach can account for well-known problems of focus alternative proposals. Of course, we cannot discuss all the puzzles of the meaning of ‘only’ in one paper. We have, therefore, decided to concentrate on two well-known problems that concern pragmatic properties of ‘only’. A closer discussion of the many semantic issues ‘only’ raises has to wait for another occasion. In section 4 we will deal with the question what part of the meaning of ‘only’ belongs to its semantics and what part has to be attributed to pragmatic considerations. The next section deals with the relevance dependence of ‘only’. Finally, in section 6 we will argue that we should account for the inference from ‘Only φ’ to φ as a conversational implicature. This part strongly builds on a proposal made in Schulz (to appear) and van Rooij & Schulz (2004). We will see that this Gricean
Rethinking floating point for deep learning

Reducing hardware overhead of neural networks for faster or lower power inference and training is an active area of research. Uniform quantization using integer multiply-add has been thoroughly investigated, which requires learning many quantization parameters, fine-tuning training or other prerequisites. Little effort is made to improve floating point relative to this baseline; it remains energy inefficient, and word size reduction yields drastic loss in needed dynamic range. We improve floating point to be more energy efficient than equivalent bit width integer hardware on a 28 nm ASIC process while retaining accuracy in 8 bits with a novel hybrid log multiply/linear add, Kulisch accumulation and tapered encodings from Gustafson’s posit format. With no network retraining, and drop-in replacement of all math and float32 parameters via round-to-nearest-even only, this open-sourced 8-bit log float is within 0.9% top-1 and 0.2% top-5 accuracy of the original float32 ResNet-50 CNN model on ImageNet. Unlike int8 quantization, it is still a general purpose floating point arithmetic, interpretable out-of-the-box. Our 8/38-bit log float multiply-add is synthesized and power profiled at 28 nm at 0.96× the power and 1.12× the area of 8/32-bit integer multiply-add. In 16 bits, our log float multiply-add is 0.59× the power and 0.68× the area of IEEE 754 float16 fused multiply-add, maintaining the same signficand precision and dynamic range, proving useful for training ASICs as well.
Specializing Joint Representations for the task of Product Recommendation

We propose a unified product embedded representation that is optimized for the task of retrieval-based product recommendation. To this end, we introduce a new way to fuse modality-specific product embeddings into a joint product embedding, in order to leverage both product content information, such as textual descriptions and images, and product collaborative filtering signal. By introducing the fusion step at the very end of our architecture, we are able to train each modality separately, allowing us to keep a modular architecture that is preferable in real-world recommendation deployments. We analyze our performance on normal and hard recommendation setups such as cold-start and cross-category recommendations and achieve good performance on a large product shopping dataset.
What Do Practitioners Vary in Using Scrum?

Agile software development has become a popular way of developing software. Scrum is the most frequently used agile framework, but it is often reported to be adapted in practice.  Thus, we aim to understand how Scrum is adapted in different contexts and what are the reasons for these changes.  Using a structured interview guideline, we interviewed ten German companies about their concrete usage of Scrum and analysed the results qualitatively.  All companies vary Scrum in some way. The least variations are in the Sprint length, events, team size and requirements engineering. Many users varied the roles, effort estimations and quality assurance.  Many variations constitute a substantial deviation from Scrum as initially proposed. For some of these variations, there are good reasons. Sometimes, however, the variations are a result of a previous non-agile, hierarchical organisation.
FoodNet: Recognizing Foods Using Ensemble of Deep Networks

In this letter, we propose a protocol for an automatic food recognition system that identifies the contents of the meal from the images of the food. We developed a multilayered convolutional neural network (CNN) pipeline that takes advantages of the features from other deep networks and improves the efficiency. Numerous traditional handcrafted features and methods are explored, among which CNNs are chosen as the best performing features. Networks are trained and fine-tuned using preprocessed images and the filter outputs are fused to achieve higher accuracy. Experimental results on the largest real-world food recognition database ETH Food-101 and newly contributed Indian food image database demonstrate the effectiveness of the proposed methodology as compared to many other benchmark deep learned CNN frameworks.
Psychopathy: assessment and forensic implications.

Psychopathy is commonly viewed as a personality disorder defined by a cluster of interpersonal, affective, lifestyle, and antisocial traits and behaviours, including grandiosity, egocentricity, deceptiveness, shallow emotions, lack of empathy or remorse, irresponsibility, impulsivity, and a tendency to violate social norms. In our article, we outline standard methods for the assessment of psychopathy, its association with antisocial personality disorder (ASPD), and its implications for clinical and forensic issues, including crime and violence, risk assessment, and treatment options.