nils-herrmann/clean_scirepeval huggingface dataset:
Learning, Attentional Control, and Action Video Games

While humans have an incredible capacity to acquire new skills and alter their behavior as a result of experience, enhancements in performance are typically narrowly restricted to the parameters of the training environment, with little evidence of generalization to different, even seemingly highly related, tasks. Such specificity is a major obstacle for the development of many real-world training or rehabilitation paradigms, which necessarily seek to promote more general learning. In contrast to these typical findings, research over the past decade has shown that training on 'action video games' produces learning that transfers well beyond the training task. This has led to substantial interest among those interested in rehabilitation, for instance, after stroke or to treat amblyopia, or training for various precision-demanding jobs, for instance, endoscopic surgery or piloting unmanned aerial drones. Although the predominant focus of the field has been on outlining the breadth of possible action-game-related enhancements, recent work has concentrated on uncovering the mechanisms that underlie these changes, an important first step towards the goal of designing and using video games for more definite purposes. Game playing may not convey an immediate advantage on new tasks (increased performance from the very first trial), but rather the true effect of action video game playing may be to enhance the ability to learn new tasks. Such a mechanism may serve as a signature of training regimens that are likely to produce transfer of learning.
Financing Major Investments : Information about Capital Structure Decisions

We evaluate U.S. firms’ leverage determinants by studying how 1,801 firms paid for 2,073 very large investments during the period 1989-2006. This approach complements existing empirical work on capital structure, which typically estimates regression models for a broad set of CRSP/Compustat firms. If firms making large investments generally raise new external funds, their securities issuances should provide information about managers’ attitudes toward leverage. Our data indicate that large investments are mostly externally financed and that firms issue securities that tend to move them quite substantially toward target debt ratios. Firms also tend to issue more equity following a share price runup or when the market-to-book ratio is high. We find little support for the standard pecking order hypothesis.
The theory of planned behavior: a review of its applications to health-related behaviors.

To review applications of Ajzen's theory of planned behavior in the domain of health and to verify the efficiency of the theory to explain and predict health-related behaviors.



Most material has been drawn from Current Contents (Social and Behavioral Sciences and Clinical Medicine) from 1985 to date, together with all peer-reviewed articles cited in the publications thus identified.



The results indicated that the theory performs very well for the explanation of intention; an averaged R2 of .41 was observed. Attitude toward the action and perceived behavioral control were most often the significant variables responsible for this explained variation in intention. The prediction of behavior yielded an averaged R2 of .34. Intention remained the most important predictor, but in half of the studies reviewed perceived behavioral control significantly added to the prediction.



The efficiency of the model seems to be quite good for explaining intention, perceived behavioral control being as important as attitude across health-related behavior categories. The efficiency of the theory, however, varies between health-related behavior categories.
MatchZoo: A Toolkit for Deep Text Matching

In recent years, deep neural models have been widely adopted for text matching tasks, such as question answering and information retrieval, showing improved performance as compared with previous methods. In this paper, we introduce the MatchZoo toolkit that aims to facilitate the designing, comparing and sharing of deep text matching models. Specically, the toolkit provides a unied data preparation module for dierent text matching problems, a exible layer-based model construction process, and a variety of training objectives and evaluation metrics. In addition, the toolkit has implemented two schools of representative deep text matching models, namely representation-focused models and interactionfocused models. Finally, users can easily modify existing models, create and share their own models for text matching in MatchZoo.
Wearable sweat sensors

Patients contact doctors after they have developed ailments with noticeable symptoms, and are thereafter passive recipients of care and monitoring by specialists. This approach largely fails in preventing the onset of health conditions, prioritizing diagnostics and treatment over proactive healthcare. It further occludes individuals from being active agents in monitoring their own health. The growing field of wearable sensors (or wearables) aims to tackle the limitations of centralized, reactive healthcare by giving individuals insight into the dynamics of their own physiology. The long-term vision is to develop sensors that can be integrated into wearable formats like clothing, wristbands, patches, or tattoos to continuously probe a range of body indicators. By relaying physiological information as the body evolves over healthy and sick states, these sensors will enable individuals to monitor themselves without expensive equipment or trained professionals . Various physical and chemical sensors will need to be integrated to obtain a complete picture of dynamic health. These sensors will generate vast time series of data that will need to be parsed with big-data techniques to generate personalized baselines indicative of the user’s health1–4. Sensor readings that cohere with the established baseline can then indicate that the body is in a healthy, equilibrium state, while deviations from the baseline can provide early warnings about developing health conditions. Eventually, deviations caused by different pathologies can be ‘fingerprinted’ to make diagnosis more immediate and autonomous. Together, the integration of wearables with big-data analytics can enable individualized fitness monitoring, early detection of developing health conditions, and better management of chronic diseases. This envisioned medical landscape built around continuous, point-of-care sensing spearheads personalized, predictive, and ultimately preventive healthcare.
A Semantic web: Intelligence in information retrieval

The rapid growth of web resources lead to a need of enhanced Search scheme for information retrieval. Every single user contributes a part of new information to be added to the web every day. This huge data supplied are of diverse area in origin being added, without a mere relation. Hence, a novel search scheme must be applied for bringing out the relevant results on querying web for data. The current web search scheme could bring out only relevant pages to be as results. But, a Semantic web is a solution to this issue through providing a suitable result on understanding the appropriate need of information. It can be acquired through extending the support for databases in machine readable form. It leads to redefinition of current web into semantic web by adding semantic annotations. This paper gives an overview of Semantic mapping approaches. The main goal of this paper is to propose the steps for bringing out a new Semantic web discovery algorithm with an efficient Semantic mapping and a novel Classification Scheme for categorization of concepts.
Bedtime procrastination: introducing a new area of procrastination

Procrastination is a prevalent and problematic phenomenon that has mostly been studied in the domain of academic behavior. The current study shows that procrastination may also lead to harmful outcomes in the area of health behavior, introducing bedtime procrastination as an important factor related to getting insufficient sleep and consequently affecting individual well-being. Bedtime procrastination is defined as failing to go to bed at the intended time, while no external circumstances prevent a person from doing so.



To empirically support the conceptual introduction of bedtime procrastination, an online survey study was conducted among a community sample (N = 177). The relationship between bedtime procrastination and individual difference variables related to self-regulation and general procrastination was assessed. Moreover, it was investigated whether bedtime procrastination was a predictor of self-reported sleep outcomes (experienced insufficient sleep, hours of sleep, fatigue during the day).



Bedtime procrastination was negatively associated with self-regulation: people who scored lower on self-regulation variables reported more bedtime procrastination. Moreover, self-reported bedtime procrastination was related to general reports of insufficient sleep above and beyond demographics and self-regulation.



Introducing a novel domain in which procrastinators experience problems, bedtime procrastination appears to be a prevalent and relevant issue that is associated with getting insufficient sleep.
Number Sense and Dyscalculia 1 To appear in : Human Behavior , Learning , and the Developing Brain : Atypical Development

In this chapter we review the possible biological bases for developmental dyscalculia, which is a disorder in mathematical abilities presumed to be due to impaired brain function. By reviewing what is known about the localization of numerical cognition functions in the adult brain, the causes of acquired dyscalculia, and the normal development of numerical cognition, we propose several hypotheses for causes of developmental dyscalculia, including that of a core deficit of “number sense” related to an impairment in the horizontal intraparietal sulcus (HIPS) area. We then discuss research on dyscalculia, including the contribution of recent imaging results in special populations, and evaluate to what extent this research supports our hypotheses. We conclude that there is promising preliminary evidence for a core deficit of number sense in dyscalculia, but we also emphasize that more research is needed to test the hypothesis of multiple types of dyscalculia, particularly in the area of dyscalculia subtyping. We complete the chapter with a discussion of future directions to be taken, the implications for education, and the construction of number sense remediation software in our laboratory. Number Sense and Dyscalculia 3
Explanation in artificial intelligence: Insights from the social sciences

There has been a recent resurgence in the area of explainable artificial intelligence as researchers and practitioners seek to make their algorithms more understandable. Much of this research is focused on explicitly explaining decisions or actions to a human observer, and it should not be controversial to say that looking at how humans explain to each other can serve as a useful starting point for explanation in artificial intelligence. However, it is fair to say that most work in explainable artificial intelligence uses only the researchers’ intuition of what constitutes a ‘good’ explanation. There exists vast and valuable bodies of research in philosophy, psychology, and cognitive science of how people define, generate, select, evaluate, and present explanations, which argues that people employ certain cognitive biases and social expectations towards the explanation process. This paper argues that the field of explainable artificial intelligence should build on this existing research, and reviews relevant papers from philosophy, cognitive psychology/science, and social psychology, which study these topics. It draws out some important findings, and discusses ways that these can be infused with work on explainable artificial intelligence.
Not all bytes are equal: Neural byte sieve for fuzzing

Fuzzing is a popular dynamic program analysis technique used to find vulnerabilities in complex software. Fuzzing involves presenting a target program with crafted malicious input designed to cause crashes, buffer overflows, memory errors, and exceptions. Crafting malicious inputs in an efficient manner is a difficult open problem and often the best approach to generating such inputs is through applying uniform random mutations to pre-existing valid inputs (seed files). We present a learning technique that uses neural networks to learn patterns in the input files from past fuzzing explorations to guide future fuzzing explorations. In particular, the neural models learn a function to predict good (and bad) locations in input files to perform fuzzing mutations based on the past mutations and corresponding code coverage information. We implement several neural models including LSTMs and sequence-to-sequence models that can encode variable length input files. We incorporate our models in the state-of-the-art AFL (American Fuzzy Lop) fuzzer and show significant improvements in terms of code coverage, unique code paths, and crashes for various input formats including ELF, PNG, PDF, and XML.
PredRNN++: Towards A Resolution of the Deep-in-Time Dilemma in Spatiotemporal Predictive Learning

We present PredRNN++, a recurrent network for spatiotemporal predictive learning. In pursuit of a great modeling capability for short-term video dynamics, we make our network deeper in time by leveraging a new recurrent structure named Causal LSTM with cascaded dual memories. To alleviate the gradient propagation difficulties in deep predictive models, we propose a Gradient Highway Unit, which provides alternative quick routes for the gradient flows from outputs back to long-range previous inputs. The gradient highway units work seamlessly with the causal LSTMs, enabling our model to capture the short-term and the long-term video dependencies adaptively. Our model achieves state-of-the-art prediction results on both synthetic and real video datasets, showing its power in modeling entangled motions.
Nature based solution for improving mental health and well-being in urban areas.

The general disproportion of urban development and the socio-economical crisis in Serbia, followed by a number of acute and chronic stressors, as well as years of accumulated trauma, prevented the parallel physical, mental and social adaptation of society as a whole. These trends certainly affected the quality of mental health and well-being, particularly on the vulnerable urban population, increasing the absolute number of people with depression, stress and psychosomatic disorders. This study was pioneering in Serbia and was conducted in collaboration with the Faculty of Forestry, the Institute of Mental Health and the Botanical Garden in Belgrade, in order to understand how spending time and performing horticulture therapy in specially designed urban green environments can improve mental health. The participants were psychiatric patients (n=30), users of the day hospital of the Institute who were randomly selected for the study, and the control group, assessed for depression, anxiety and stress before and after the intervention, using a DASS21 scale. During the intervention period the study group stayed in the Botanical garden and participated in a special programme of horticulture therapy. In order to exclude any possible "special treatment'' or ''placebo effect", the control group was included in occupational art therapy while it continued to receive conventional therapy. The test results indicated that nature based therapy had a positive influence on the mental health and well-being of the participants. Furthermore, the difference in the test results of the subscale stress before and after the intervention for the study group was F1.28 = 5.442 and p<;.05. According to socio demographic and clinical variables, the interesting trend was recorded on the subscale of anxiety showing that the male participants in the study group were more anxious, with the most pronounced inflection noted on this scale after treatment. The results of this study have shown that recuperation from stress, depression and anxiety was possible and much more complete when participants were involved in horticulture therapy as a nature-based solution for improving mental health.
History of Cloud Application Architectures From Deployment Monoliths via Microservices to Serverless Architectures and Possible Roads Ahead-A Review from the Frontline ( invited paper )

This paper presents a review of cloud application architectures and its evolution. It reports 1 observations being made during the course of a research project that tackled the problem to transfer 2 cloud applications between different cloud infrastructures. As a side effect we learned a lot about 3 commonalities and differences from plenty of different cloud applications which might be of value for 4 cloud software engineers and architects. Throughout the course of the research project we analyzed 5 industrial cloud standards, performed systematic mapping studies of cloud-native application related 6 research papers, performed action research activities in cloud engineering projects, modeled a cloud 7 application reference model, and performed software and domain specific language engineering 8 activities. Two major (and sometimes overlooked) trends can be identified. First, cloud computing 9 and its related application architecture evolution can be seen as a steady process to optimize 10 resource utilization in cloud computing. Second, this resource utilization improvements resulted 11 over time in an architectural evolution how cloud applications are being build and deployed. A shift 12 from monolithic servce-oriented architectures (SOA), via independently deployable microservices 13 towards so called serverless architectures is observable. Especially serverless architectures are more 14 decentralized and distributed, and make more intentional use of independently provided services. In 15 other words, a decentralizing trend in cloud application architectures is observable that emphasizes 16 decentralized architectures known from former peer-to-peer based approaches. That is astonishing 17 because with the rise of cloud computing (and its centralized service provisioning concept) the 18 research interest in peer-to-peer based approaches (and its decentralizing philosophy) decreased. 19 But this seems to change. Cloud computing could head into future of more decentralized and more 20 meshed services.
TRIZ for software architecture

A key element to designing software architectures of good quality is the systematic handling of contradicting quality requirements and the structuring principles that support them. The theory of inventive problem solving (TRIZ) by Altshuller offers tools that can be used to define such a systematic way. This paper describes the idea and preliminary results of using inventive principles and the contradiction matrix for the resolution of contradictions in the design of software architectures. By rearchitecting a flight simulation system these tools are analysed and their further development is proposed.
B cells and their role in the teleost gut.

Mucosal surfaces are the main route of entry for pathogens in all living organisms. In the case of teleost fish, mucosal surfaces cover the vast majority of the animal. As these surfaces are in constant contact with the environment, fish are perpetually exposed to a vast number of pathogens. Despite the potential prevalence and variety of pathogens, mucosal surfaces are primarily populated by commensal non-pathogenic bacteria. Indeed, a fine balance between these two populations of microorganisms is crucial for animal survival. This equilibrium, controlled by the mucosal immune system, maintains homeostasis at mucosal tissues. Teleost fish possess a diffuse mucosa-associated immune system in the intestine, with B cells being one of the main responders. Immunoglobulins produced by these lymphocytes are a critical line of defense against pathogens and also prevent the entrance of commensal bacteria into the epithelium. In this review we will summarize recent literature regarding the role of B-lymphocytes and immunoglobulins in gut immunity in teleost fish, with specific focus on immunoglobulin isotypes and the microorganisms, pathogenic and non-pathogenic that interact with the immune system.
Probabilistic Natural Language Generation with Wasserstein Autoencoders

Probabilistic generation of natural language sentences is an important task in NLP. Existing models such as variational autoencoders (VAE) for sequence generation are extremely difficult to train due to the issues associated with the KullbackLeibler (KL) loss collapsing to zero. One has to implement various heuristics such as KL weight annealing and word dropout in a carefully engineered manner to successfully train a text VAE. In this paper, we propose the use of Wasserstein autoencoders (WAE) for probabilistic natural language sentence generation. We show that sequence-to-sequence WAEs are more robust towards hyperparameters and can be trained in a straightforward manner without the need for any weight annealing. Empirical evidence shows that the latent space learned by WAEs exhibits properties of continuity and smoothness as in VAEs, while simultaneously achieving much higher BLEU scores for sentence reconstruction.
Capability and Digital Transformation : A Firm Performance Perspective Completed Research Paper

As emerging digital technologies and capabilities continue to dominate our economic landscape, organizations are facing increased scrutiny on how digital transformation can provide the mechanism for innovation and firm performance. Using resource-based view (RBV) framework, this research examines the mediating effects of digital transformation in the relationship between IT capability and firm performance. Empirical data collected from CIOs from US firms reveal that although IT capability positively influences firm performance, it is mediated by digital transformation. Furthermore, our findings show that digital transformation positively influences innovation and firm performance while innovation is reaffirmed as having a positive implication on firm performance.
New media design for cultural institutions

Community co-creation programs are increasingly used by cultural institutions in an attempt to draw new audiences to their collections. By providing engaging interactive experiences in partnership with the community, institutions may well increase their audience numbers in the short term; but to optimize the viability and longevity of such programs, institutions and designers should consider the integration of strategic design methods with curatorial processes in order to reconsider the capture, display and promotion of collections and/or exhibitions. This case study uses a project from the State Library of Queensland, Australia to showcase a human computer interaction-derived design method developed by the authors to ensure a strategic response to community co-creation initiatives. Using a variety of media, the new Multi-Platform Communication Design method has enabled the design of web-based distribution; a community and a facilitator's training program; and the development of a mobile multimedia laboratory. This paper details the design method by which these multiple communication platforms were developed and implemented to achieve successful project delivery.
An Artificial Language Evaluation of Distributional Semantic Models

Recent studies of distributional semantic models have set up a competition between word embeddings obtained from predictive neural networks and word vectors obtained from count-based models. This paper is an attempt to reveal the underlying contribution of additional training data and post-processing steps on each type of model in word similarity and relatedness inference tasks. We do so by designing an artificial language, training a predictive and a count-based model on data sampled from this grammar, and evaluating the resulting word vectors in paradigmatic and syntagmatic tasks defined with respect to the grammar.