nils-herrmann/clean_scirepeval huggingface dataset:
Feasibility and safety of virtual-reality-based early neurocognitive stimulation in critically ill patients

Growing evidence suggests that critical illness often results in significant long-term neurocognitive impairments in one-third of survivors. Although these neurocognitive impairments are long-lasting and devastating for survivors, rehabilitation rarely occurs during or after critical illness. Our aim is to describe an early neurocognitive stimulation intervention based on virtual reality for patients who are critically ill and to present the results of a proof-of-concept study testing the feasibility, safety, and suitability of this intervention.



Twenty critically ill adult patients undergoing or having undergone mechanical ventilation for ≥24 h received daily 20-min neurocognitive stimulation sessions when awake and alert during their ICU stay. The difficulty of the exercises included in the sessions progressively increased over successive sessions. Physiological data were recorded before, during, and after each session. Safety was assessed through heart rate, peripheral oxygen saturation, and respiratory rate. Heart rate variability analysis, an indirect measure of autonomic activity sensitive to cognitive demands, was used to assess the efficacy of the exercises in stimulating attention and working memory.



Patients successfully completed the sessions on most days. No sessions were stopped early for safety concerns, and no adverse events occurred. Heart rate variability analysis showed that the exercises stimulated attention and working memory. Critically ill patients considered the sessions enjoyable and relaxing without being overly fatiguing.



The results in this proof-of-concept study suggest that a virtual-reality-based neurocognitive intervention is feasible, safe, and tolerable, stimulating cognitive functions and satisfying critically ill patients. Future studies will evaluate the impact of interventions on neurocognitive outcomes.
The need to belong: desire for interpersonal attachments as a fundamental human motivation.

A hypothesized need to form and maintain strong, stable interpersonal relationships is evaluated in light of the empirical literature. The need is for frequent, nonaversive interactions within an ongoing relational bond. Consistent with the belongingness hypothesis, people form social attachments readily under most conditions and resist the dissolution of existing bonds. Belongingness appears to have multiple and strong effects on emotional patterns and on cognitive processes. Lack of attachments is linked to a variety of ill effects on health, adjustment, and well-being. Other evidence, such as that concerning satiation, substitution, and behavioral consequences, is likewise consistent with the hypothesized motivation. Several seeming counterexamples turned out not to disconfirm the hypothesis. Existing evidence supports the hypothesis that the need to belong is a powerful, fundamental, and extremely pervasive motivation.
Managing Bad News in Social Media: A Case Study on Domino's Pizza Crisis

Social media has become prominently popular. Tens of millions of users login to social media sites like Twitter to disseminate breaking news and share their opinions and thoughts. For businesses, social media is potentially useful for monitoring the public perception and the social reputation of companies and products. Despite great potential, how bad news about a company influences the public sentiments in social media has not been studied in depth. The aim of this study is to assess people’s sentiments in Twitter upon the spread of two types of information: corporate bad news and a CEO’s apology. We attempted to understand how sentiments on corporate bad news propagate in Twitter and whether any social network feature facilitates its spread. We investigated the Domino’s Pizza crisis in 2009, where bad news spread rapidly through social media followed by an official apology from the company. Our work shows that bad news spreads faster than other types of information, such as an apology, and sparks a great degree of negative sentiments in the network. However, when users converse about bad news repeatedly, their negative sentiments are softened. We discuss various reactions of users towards the bad news in social media such as negative purchase intent.
Google Driverless Car

This paper explores the impact that has been working towards the goal of vehicles that can shoulder the entire burden of driving. Google driverless cars are designed to operate safely and autonomously without requiring human intervention. They won’t have a steering wheel, accelerator or a brake pedal because they don’t need them, software and sensors do all the work. It takes you where you want to go at the push of a button. This Technology step towards improving road safety and transforming mobility for millions of people.
On risk, convenience, and Internet shopping behavior

The past century experienced a proliferation of retail formats in the marketplace. However, as a new century begins, these retail formats are being threatened by the emergence of a new kind of store, the online or Internet store. From being almost a novelty in 1995, online retailing sales were expected to reach $7 billion by 2000 . In this increasngly timeconstrained world, Internet stores allow consumers to shop from the convenience of remote locations. Yet most of these Internet stores are losing money . Why is such counterintuitive phenomena prevailing? The explanation may lie in the risks associated with Internet shopping. These risks may arise because consumers are concerned about the security of transmitting credit card information over the Internet. Consumers may also be apprehensive about buying something without touching or feeling it and being unable to return it if it fails to meet their approval. Having said this, however, we must point out that consumers are buying goods on the Internet. This is reflected in the fact that total sales on the Internet are on the increase [8, 11]. Who are the consumers that are patronizing the Internet? Evidently, for them the perception of the risk associated with shopping on the Internet is low or is overshadowed by its relative convenience. This article attempts to determine why certain consumers are drawn to the Internet and why others are not. Since the pioneering research done by Becker , it has been accepted that the consumer maximizes his utility subject to not only income constraints but also time constraints. A consumer seeks out his best decision given that he has a limited budget of time and money. While purchasing a product from a store, a consumer has to expend both money and time. Therefore, the consumer patronizes the retail store where his total costs or the money and time spent in the entire process are the least. Since the util-
Identifying Global Exceptional Patterns in Multi-database Mining

In multi-database mining, there can be many local patterns (frequent itemsets or association rules) in each database. At the end of multi-database mining, it is necessary to analyze these local patterns to gain global patterns, when putting all the data from the databases into a single dataset can destroy important information that reflect the distribution of global patterns. This paper develops an algorithm for synthesizing local patterns in multi-database is proposed. This approach is particularly fit to find potentially useful exceptions. The proposed method has been evaluated experimentally. The experimental results have shown that this method is efficient and appropriate to identifying exceptional patterns.
IoT based smart home: Security challenges, security requirements and solutions

Internet of Thing (IoT) is going to make such a world where physical things (smart home appliances, and smart watches etc.) revolutionized the information networks and services providing systems which provide innovative and smart services to human. With smart home technology, our living area is becoming more comfortable and convenient. Smart home technology provides automated, intelligent, smart, innovative and ubiquitous services to residential users through Information Communication Technology (ICT). Due to internet-connected, dynamic and heterogeneous nature of smart home environment creates new security, authentication and privacy challenges. In this paper, we investigate security attacks in smart home and evaluate their impact on the overall system security. We identified security requirements and solutions in the smart home environment. Based on several scenarios, we suggest to set security goals for the smart home environment. Based on historical data, we forecast security attacks (like malware, virus etc.) and estimated that how many attacks are expected to be launched in coming years.
Scalable Deep Learning Logo Detection

Existing logo detection methods usually consider a small number of logo classes and limited images per class with a strong assumption of requiring tedious object bounding box annotations, therefore not scalable to real-world dynamic applications. In this work, we tackle these challenges by exploring the webly data learning principle without the need for exhaustive manual labelling. Specifically, we propose a novel incremental learning approach, called Scalable Logo Self-co-Learning (SL), capable of automatically self-discovering informative training images from noisy web data for progressively improving model capability in a cross-model co-learning manner. Moreover, we introduce a very large (2,190,757 images of 194 logo classes) logo dataset “WebLogo-2M” by an automatic web data collection and processing method. Extensive comparative evaluations demonstrate the superiority of the proposed SL method over the state-of-the-art strongly and weakly supervised detection models and contemporary webly data learning approaches.
Information Security Challenges of Social Media for Companies

For companies and their employees, social media allows new ways to communicate with customers and colleagues. Vast amounts of information are being exchanged in social media. Information is a highly valuable asset, and therefore questions concerning information security become more and more important. Companies are becoming increasingly worried about information security in social media, but so far, this issue has not been studied. The present research closes this gap by studying the information security challenges social media represents for organizations. The research was conducted as a qualitative multiple case study for which information security managers from eleven public and private companies in one European country were interviewed. The study has three main findings. First, challenges arising from employees’ actions or unawareness in social media (especially reputation damage) seem to represent bigger threats to information security than threats caused by outside attacks. Second, the confusion of private and professional roles in social media represents an information security risk, and distinguishing between these roles becomes more difficult the higher an employee’s position in the company. Third, communication with employees and colleagues represents an information security challenge especially when communication is not steered by the company. Implications for research and practice are discussed.
Lexical Normalisation of Short Text Messages: Makn Sens a #twitter

Twitter provides access to large volumes of data in real time, but is notoriously noisy, hampering its utility for NLP. In this paper, we target out-of-vocabulary words in short text messages and propose a method for identifying and normalising ill-formed words. Our method uses a classifier to detect ill-formed words, and generates correction candidates based on morphophonemic similarity. Both word similarity and context are then exploited to select the most probable correction candidate for the word. The proposed method doesn’t require any annotations, and achieves state-of-the-art performance over an SMS corpus and a novel dataset based on Twitter.
Liposomal drug delivery systems: an update review.

The discovery of liposome or lipid vesicle emerged from self forming enclosed lipid bi-layer upon hydration; liposome drug delivery systems have played a significant role in formulation of potent drug to improve therapeutics. Recently the liposome formulations are targeted to reduce toxicity and increase accumulation at the target site. There are several new methods of liposome preparation based on lipid drug interaction and liposome disposition mechanism including the inhibition of rapid clearance of liposome by controlling particle size, charge and surface hydration. Most clinical applications of liposomal drug delivery are targeting to tissue with or without expression of target recognition molecules on lipid membrane. The liposomes are characterized with respect to physical, chemical and biological parameters. The sizing of liposome is also critical parameter which helps characterize the liposome which is usually performed by sequential extrusion at relatively low pressure through polycarbonate membrane (PCM). This mode of drug delivery lends more safety and efficacy to administration of several classes of drugs like antiviral, antifungal, antimicrobial, vaccines, anti-tubercular drugs and gene therapeutics. Present applications of the liposomes are in the immunology, dermatology, vaccine adjuvant, eye disorders, brain targeting, infective disease and in tumour therapy. The new developments in this field are the specific binding properties of a drug-carrying liposome to a target cell such as a tumor cell and specific molecules in the body (antibodies, proteins, peptides etc.); stealth liposomes which are especially being used as carriers for hydrophilic (water soluble) anticancer drugs like doxorubicin, mitoxantrone; and bisphosphonate-liposome mediated depletion of macrophages. This review would be a help to the researchers working in the area of liposomal drug delivery.
Stochastic Neural Networks for Hierarchical Reinforcement Learning

Deep reinforcement learning has achieved many impressive results in recent years. However, tasks with sparse rewards or long horizons continue to pose significant challenges. To tackle these important problems, we propose a general framework that first learns useful skills in a pre-training environment, and then leverages the acquired skills for learning faster in downstream tasks. Our approach brings together some of the strengths of intrinsic motivation and hierarchical methods: the learning of useful skill is guided by a single proxy reward, the design of which requires very minimal domain knowledge about the downstream tasks. Then a high-level policy is trained on top of these skills, providing a significant improvement of the exploration and allowing to tackle sparse rewards in the downstream tasks. To efficiently pre-train a large span of skills, we use Stochastic Neural Networks combined with an information-theoretic regularizer. Our experiments1 show2 that this combination is effective in learning a wide span of interpretable skills in a sample-efficient way, and can significantly boost the learning performance uniformly across a wide range of downstream tasks.
Maximizing versus satisficing: happiness is a matter of choice.

Can people feel worse off as the options they face increase? The present studies suggest that some people--maximizers--can. Study 1 reported a Maximization Scale, which measures individual differences in desire to maximize. Seven samples revealed negative correlations between maximization and happiness, optimism, self-esteem, and life satisfaction, and positive correlations between maximization and depression, perfectionism, and regret. Study 2 found maximizers less satisfied than nonmaximizers (satisficers) with consumer decisions, and more likely to engage in social comparison. Study 3 found maximizers more adversely affected by upward social comparison. Study 4 found maximizers more sensitive to regret and less satisfied in an ultimatum bargaining game. The interaction between maximizing and choice is discussed in terms of regret, adaptation, and self-blame.
An Ultra-wideband and Polarization-independent Metasurface for RCS Reduction

In this paper, an ultra-wideband and polarization-independent metasurface for radar cross section (RCS) reduction is proposed. The unit cell of the metasurface operates in a linear cross-polarization scheme in a broad band. The phase and amplitude of cross-polarized reflection can be separately controlled by its geometry and rotation angle. Based on the diffuse reflection theory, a 3-bit coding metasurface is designed to reduce the RCS in an ultra-wide band. The wideband property of the metasurface benefits from the wideband cross polarization conversion and flexible phase modulation. In addition, the polarization-independent feature of the metasurface is achieved by tailoring the rotation angle of each element. Both the simulated and measured results demonstrate that the proposed metasurface can reduce the RCS significantly in an ultra-wide frequency band for both normal and oblique incidences, which makes it promising in the applications such as electromagnetic cloaking.
The Pose Knows: Video Forecasting by Generating Pose Futures

Current approaches to video forecasting attempt to generate videos directly in pixel space using Generative Adversarial Networks (GANs) or Variational Autoencoders (VAEs). However, since these approaches try to model all the structure and scene dynamics at once, in unconstrained settings they often generate uninterpretable results. Our insight is that forecasting needs to be done first at a higher level of abstraction. Specifically, we exploit human pose detectors as a free source of supervision and break the video forecasting problem into two discrete steps. First we explicitly model the high level structure of active objects in the scene (humans) and use a VAE to model the possible future movements of humans in the pose space. We then use the future poses generated as conditional information to a GAN to predict the future frames of the video in pixel space. By using the structured space of pose as an intermediate representation, we sidestep the problems that GANs have in generating video pixels directly. We show through quantitative and qualitative evaluation that our method outperforms state-of-the-art methods for video prediction.
New trends in digital humanities

The field of research in digital humanities is undergoing a rapid transformation in recent years. A deep reflection on the current needs of the agents involved that takes into account key issues such as the inclusion of citizens in the creation and consumption of the cultural resources offered, the volume and complexity of datasets, available infrastructures, etcetera, is necessary. Present technologies make it possible to achieve projects that were impossible until recently, but the field is currently facing the challenge of proposing frameworks and systems to generalize and reproduce these proposals in other knowledge domains with similar but heterogeneous data sets. The track "New trends in digital humanities" of the Fourth International Conference on Technological Ecosystems for Enhancing Multiculturality (TEEM 2016), tries to set the basis of good practice in digital humanities by reflecting on models, technologies and methods to carry the transformation out.
A Similarity Measure for Text Classification and Clustering

Measuring the similarity between documents is an important operation in the text processing field. In this paper, a new similarity measure is proposed. To compute the similarity between two documents with respect to a feature, the proposed measure takes the following three cases into account: a) The feature appears in both documents, b) the feature appears in only one document, and c) the feature appears in none of the documents. For the first case, the similarity increases as the difference between the two involved feature values decreases. Furthermore, the contribution of the difference is normally scaled. For the second case, a fixed value is contributed to the similarity. For the last case, the feature has no contribution to the similarity. The proposed measure is extended to gauge the similarity between two sets of documents. The effectiveness of our measure is evaluated on several real-world data sets for text classification and clustering problems. The results show that the performance obtained by the proposed measure is better than that achieved by other measures.
Redundancy management software services for Seawolf ship control system

The ship control system for the U.S. Navy's newest attack submarine, Seawolf; incorporates hardware modular redundancy both in its core processing and its input/output system. This paper provides a practical experience report on the redundancy management software services developed for this system. Introductory material is presented to provide contextual information regarding the overall ship control system. An overview of the system's processing platform is presented in sufficient detail to define the problems associated with redundancy management and to describe hardware functionality which supports the software services. Policies and procedures for detection and isolation of faults are discussed as are reconfiguration responses to faults.
A model-based design methodology for cyber-physical systems

Model-based design is a powerful design technique for cyber-physical systems, but too often literature assumes knowledge of a methodology without reference to an explicit design process, instead focusing on isolated steps such as simulation, software synthesis, or verification. We combine these steps into an explicit and holistic methodology for model-based design of cyber-physical systems from abstraction to architecture, and from concept to realization. We decompose model-based design into ten fundamental steps, describe and evaluate an iterative design methodology, and evaluate this methodology in the development of a cyber-physical system.
DeepCredit: Exploiting User Cickstream for Loan Risk Prediction in P2P Lending

Peer-to-peer (P2P) lending or crowdlending, is a recent innovation allows a group of individual or institutional lenders to lend funds to individuals or businesses in return for interest payment on top of capital repayments. The rapid growth of P2P lending marketplaces has heightened the need to develop a support system to help lenders make sound lending decisions. But realizing such system is challenging in the absence of formal credit data used by the banking sector. In this paper, we attempt to explore the possible connections between user credit risk and how users behave in the lending sites. We present the first analysis of user detailed clickstream data from a large P2P lending provider. Our analysis reveals that the users’ sequences of repayment histories and financial activities in the lending site, have significant predictive value for their future loan repayments. In the light of this, we propose a deep architecture named DeepCredit, to automatically acquire the knowledge of credit risk from the sequences of activities that users conduct on the site. Experiments on our large-scale real-world dataset show that our model generates a high accuracy in predicting both loan delinquency and default, and significantly outperforms a number of baselines and competitive alternatives.