nils-herrmann/clean_scirepeval huggingface dataset:
Learning When to Concentrate or Divert Attention: Self-Adaptive Attention Temperature for Neural Machine Translation

A new model with the self-adaptive attention temperature for the softness of attention distribution; Improved results on the datasets and showed that attention temperature differs for decoding diverse words; Try to figure out better demonstration for the effects of temperature.  A new NMT model with self-adaptive attention temperature; Attention varies at each time step based on the temperature; Improved results on the benchmark datasets; Analysis shows that temperatures vary when translating words of different types.
Plant polyphenols as dietary antioxidants in human health and disease

Polyphenols are secondary metabolites of plants and are generally involved in defense against ultraviolet radiation or aggression by pathogens. In the last decade, there has been much interest in the potential health benefits of dietary plant polyphenols as antioxidant. Epidemiological studies and associated meta-analyses strongly suggest that long term consumption of diets rich in plant polyphenols offer protection against development of cancers, cardiovascular diseases, diabetes, osteoporosis and neurodegenerative diseases. Here we present knowledge about the biological effects of plant polyphenols in the context of relevance to human health.
Deep learning with stock indicators and two-dimensional principal component analysis for closing price prediction system

The stock market is an important component in the current economic market. And stock price prediction has recently garnered significant interest among investment brokers, individual investors and researchers. In general, stock market is very complex nonlinear dynamic system. Accordingly, accurate prediction of stock market is a very challenging task, owing to the inherent noisy environment and high volatility related to outside factors. In this paper, we focus on deep learning method to achieve high precision in stock market forecast. And a deep belief networks(DBNs), which is a kind of deep learning algorithm model, coupled with stock technical indicators(STIs) and two-dimensional principal component analysis((2D)2PCA) is introduced as a novel approach to predict the closing price of stock market. A comparison experiment is also performed to evaluate this model.
Electric field calculation and insulation analysis of high voltage insulating bushing

High voltage insulating bushing is an important component of GIS of the high potential and ground potential insulation. The even electric field distribution and reasonable structure of bushing will guarantee the safe operation of GIS. In order to solve the problem of structural design and electric field distribution of 126 (kV) GIS bushing, a mathematical model to calculate the electric field distribution of high voltage insulating bushing was established in this study, and numerical simulation and visualization processing for electric field distribution of bushing was made by ANSYS. Furthermore, the insulation size was determined and verified. Consequently, the numerical foundation of insulation structural design and the development of 126 (kV) GIS bushing are provided.
Ghrelin-induced sleep responses in ad libitum fed and food-restricted rats

Ghrelin is an endogenous ligand for the growth hormone secretagogue receptor and a well-characterized food intake regulatory peptide. Hypothalamic ghrelin-, neuropeptide Y (NPY)-, and orexin-containing neurons form a feeding regulatory circuit. Orexins and NPY are also implicated in sleep-wake regulation. Sleep responses and motor activity after central administration of 0.2, 1, or 5 microg ghrelin in free-feeding rats as well as in feeding-restricted rats (1 microg dose) were determined. Food and water intake and behavioral responses after the light onset injection of saline or 1 microg ghrelin were also recorded. Light onset injection of ghrelin suppressed non-rapid-eye-movement sleep (NREMS) and rapid-eye-movement sleep (REMS) for 2 h. In the first hour, ghrelin induced increases in behavioral activity including feeding, exploring, and grooming and stimulated food and water intake. Ghrelin administration at dark onset also elicited NREMS and REMS suppression in hours 1 and 2, but the effect was not as marked as that, which occurred in the light period. In hours 3-12, a secondary NREMS increase was observed after some doses of ghrelin. In the feeding-restricted rats, ghrelin suppressed NREMS in hours 1 and 2 and REMS in hours 3-12. Data are consistent with the notion that ghrelin has a role in the integration of feeding, metabolism, and sleep regulation.
Reading as Critical Thinking

Reading involves an interactive process in which the reader actively produces meaning through a set of mental processes. There is obviously an ongoing interaction between the reader and the text. Critical reading is related to thinking and that is why we cannot read without thinking. Critical reading involves the following skills: predicting, acknowledging, comparing, evaluating and decision-making. Schemata can be seen as the organized background knowledge, which leads the reader to expect and predict aspects in their interpretation of discourse.
IT-governance practices: COBIT

With the increasing dependence of enterprises on IT, and with the widely spreading use of e-business, IT governance is attracting increasing worldwide attention. A proper IT governance would promote enterprise performance through intelligent and efficient utilization of IT. In addition, standard IT governance practices would provide a suitable open environment for e-business that provides compatibility for inter-enterprise interaction. This review is concerned with introducing the current state of IT governance, in four main steps. First, the review identifies what is meant by IT governance, and presents the main organizations concerned with its development, namely: ISACA (Information Systems Audit and Control Association) and ITGI (Information Technology Governance Institute). Secondly, the review highlights COBIT (Control Objectives for Information and related Technologies) the widely acknowledged IT governance framework, produced by ITGI. Thirdly, the current state of COBIT use is addressed using a recent global survey. Finally, comments and recommendations on the future development of IT governance are concluded. Understanding IT governance The word governance brings attention to the more familiar word government. To Webster's dictionary , both are of the same meaning. The dictionary defines the word government as "the individual or body that exercises administrative power". The word is known to be of Greek origin, and means "to steer" . Currently, the two words are usually used to mean two related, but different, meanings. While the word government is defined as
Information Links and Electronic Marketplaces: The Role of Interorganizational Information Systems in Vertical Markets

Interorganizational information systems, i.e., systems spanning more than a single organization, are proliferating as companies become aware of the potential of these systems to affect interorganizational interactions in terms of economic efficiency and strategic conduct. This new technology can have far-reaching impacts on the structure of entire industries. This article identifies two types of interorganizational information systems, information links and electronic markets. It then explores how economic models can be employed to study the implications of information links for the coordination of individual organizations with their customers and their suppliers, and the implications of electronic market systems for efficiency and competition in vertical markets. Finally, the strategic significance of interorganizational systems is addressed, and certain potential long-term impacts on the structure of markets, industries and organizations are discussed. This research was supported in part with funding from an Irvine Faculty Research Fellowship and from the National Science Foundation (Grant Number IRI-9015497). The author is grateful to the three anonymous referees for their valuable comments during the review process.
A state of the art survey of data mining-based fraud detection and credit scoring

Credit risk has been a widespread and deep penetrating problem for centuries, but not until various credit derivatives and products were developed and novel technologies began radically changing the human society, have fraud detection, credit scoring and other risk management systems become so important not only to some specific firms, but to industries and governments worldwide. Frauds and unpredictable defaults cost billions of dollars each year, thus, forcing financial institutions to continuously improve their systems for loss reduction. In the past twenty years, amounts of studies have proposed the use of data mining techniques to detect frauds, score credits and manage risks, but issues such as data selection, algorithm design, and hyperparameter optimization affect the perceived ability of the proposed solutions and it is difficult for auditors and researchers to explore and figure out the highest level of general development in this area. In this survey we focus on a state of the art survey of recently developed data mining techniques for fraud detection and credit scoring. Several outstanding experiments are recorded and highlighted, and the corresponding techniques, which are mostly based on supervised learning algorithms, unsupervised learning algorithms, semisupervised algorithms, ensemble learning, transfer learning, or some hybrid ideas are explained and analysed. The goal of this paper is to provide a dense review of up-to-date techniques for fraud detection and credit scoring, a general analysis on the results achieved and upcoming challenges for further researches.
Exploring EFL Teachers ’ Perceptions of Task-Based Language Teaching : A Case Study of Korean Secondary School Classroom Practice

The purpose of this study is to explore EFL teachers’ perceptions of task-based language teaching (TBLT) in a Korean secondary school context. The data for this study were collected through questionnaires from a total of 228 teachers at 38 different middle and high schools in Korea. The survey was conducted from August through October of the 2005 academic year, and the data were analyzed quantitatively and qualitatively. The overall findings of the survey show that the majority of respondents have a higher level of understanding about TBLT concepts, regardless of teaching levels, but that there exist some negative views on implementing TBLT with regard to its classroom practice. Additionally, some useful implications are proposed based on research findings in order to help teachers and teacher trainers to construct and implement TBLT more effectively.
Modeling User Intrinsic Characteristic on Social Media for Identity Linkage

Most users on social media have intrinsic characteristics, such as interests and political views, that can be exploited to identify and track them. It raises privacy and identity issues in online communities. In this paper we investigate the problem of user identity linkage on two behavior datasets collected from different experiments. Specifically, we focus on user linkage based on users' interaction behaviors with respect to content topics. We propose an embedding method to model a topic as a vector in a latent space so as to interpret its deep semantics. Then a user is modeled as a vector based on his or her interactions with topics. The embedding representations of topics are learned by optimizing the joint-objective: the compatibility between topics with similar semantics, the discriminative abilities of topics to distinguish identities, and the consistency of the same user's characteristics fromtwo datasets. The effectiveness of our method is verified on real-life datasets and the results show that it outperforms related methods.
Group work in occupational therapy: a survey of practice.

This paper documents the extent to which occupational therapists use groups in practice. A questionnaire was mailed to 300 occupational therapists nationwide. Questions included the types of groups occupational therapists lead, the facilities in which the groups take place, the patients included, the activities presented, and individual and groups goals. Results were tabulated based on the responses of 120 therapists. We established that 60% of occupational therapists in all areas of practice lead groups in treatment. Of the 209 groups described by the respondents, there was a significantly greater number of activity groups than verbal groups. Also, there were significantly more groups with ten or less members than groups of more than ten. This paper describes the ten categories of groups that were identified in this study.
Reinforcement Learning For Automated Trading

The impact of Automated Trading Systems (ATS) on financial markets is growing every year and the trades generated by an algorithm now account for the majority of orders that arrive at stock exchanges. In this paper we explore how to find a trading strategy via Reinforcement Learning (RL), a branch of Machine Learning (ML) that allows to find an optimal strategy for a sequential decision problem by directly interacting with the environment. We show that the the long-short strategy learned for a synthetic asset, whose price follows a stochastic process with some exploitable patterns, consistently outperforms the market. RL thus shows the potential to deal with many financial problems, that can be often formulated as sequential decision problems.
The Subthreshold Autism Trait Questionnaire (SATQ): development of a brief self-report measure of subthreshold autism traits.

The current study was motivated by a need for a self-report questionnaire that assesses a broad range of subthreshold autism traits, is brief and easily administered, and is relevant to the general population. An initial item pool was administered to 1,709 students. Structural validity analysis resulted in a 24-item questionnaire termed the Subthreshold Autism Trait Questionnaire (SATQ; Cronbach's alpha coefficient = .73, test-retest reliability = .79). An exploratory factor analysis suggested 5 factors. Confirmatory factor analysis indicated the 5 factor solution was an adequate fit and outperformed two other models. The SATQ successfully differentiated between an ASD and student group and demonstrated convergent validity with other ASD measures. Thus, the current study introduces and provides initial psychometric support for the SATQ.
Differentiable plasticity: training plastic neural networks with backpropagation

How can we build agents that keep learning from experience, quickly and efficiently, after their initial training? Here we take inspiration from the main mechanism of learning in biological brains: synaptic plasticity, carefully tuned by evolution to produce efficient lifelong learning. We show that plasticity, just like connection weights, can be optimized by gradient descent in large (millions of parameters) recurrent networks with Hebbian plastic connections. First, recurrent plastic networks with more than two million parameters can be trained to memorize and reconstruct sets of novel, high-dimensional (1,000+ pixels) natural images not seen during training. Crucially, traditional non-plastic recurrent networks fail to solve this task. Furthermore, trained plastic networks can also solve generic meta-learning tasks such as the Omniglot task, with competitive results and little parameter overhead. Finally, in reinforcement learning settings, plastic networks outperform a non-plastic equivalent in a maze exploration task. We conclude that differentiable plasticity may provide a powerful novel approach to the learning-to-learn problem.
Learning Non-maximum Suppression

Object detectors have hugely profited from moving towards an end-to-end learning paradigm: proposals, fea tures, and the classifier becoming one neural network improved results two-fold on general object detection. One indispensable component is non-maximum suppression (NMS), a post-processing algorithm responsible for merging all detections that belong to the same object. The de facto standard NMS algorithm is still fully hand-crafted, suspiciously simple, and &#x2014; being based on greedy clustering with a fixed distance threshold &#x2014; forces a trade-off between recall and precision. We propose a new network architecture designed to perform NMS, using only boxes and their score. We report experiments for person detection on PETS and for general object categories on the COCO dataset. Our approach shows promise providing improved localization and occlusion handling.
Real-Time Semantic Segmentation of Crop and Weed for Precision Agriculture Robots Leveraging Background Knowledge in CNNs

Precision farming robots, which target to reduce the amount of herbicides that need to be brought out in the fields, must have the ability to identify crops and weeds in real time to trigger weeding actions. In this paper, we address the problem of CNN-based semantic segmentation of crop fields separating sugar beet plants, weeds, and background solely based on RGB data. We propose a CNN that exploits existing vegetation indexes and provides a classification in real time. Furthermore, it can be effectively re-trained to so far unseen fields with a comparably small amount of training data. We implemented and thoroughly evaluated our system on a real agricultural robot operating in different fields in Germany and Switzerland. The results show that our system generalizes well, can operate at around 20 Hz, and is suitable for online operation in the fields.
Optimal trajectory generation for dynamic street scenarios in a Frenét Frame

Safe handling of dynamic highway and inner city scenarios with autonomous vehicles involves the problem of generating traffic-adapted trajectories. In order to account for the practical requirements of the holistic autonomous system, we propose a semi-reactive trajectory generation method, which can be tightly integrated into the behavioral layer. The method realizes long-term objectives such as velocity keeping, merging, following, stopping, in combination with a reactive collision avoidance by means of optimal-control strategies within the Frenét-Frame  of the street. The capabilities of this approach are demonstrated in the simulation of a typical high-speed highway scenario.
Interactive Language Acquisition with One-shot Visual Concept Learning through a Conversational Game

Building intelligent agents that can communicate with and learn from humans in natural language is of great value. Supervised language learning is limited by the ability of capturing mainly the statistics of training data, and is hardly adaptive to new scenarios or flexible for acquiring new knowledge without inefficient retraining or catastrophic forgetting. We highlight the perspective that conversational interaction serves as a natural interface both for language learning and for novel knowledge acquisition and propose a joint imitation and reinforcement approach for grounded language learning through an interactive conversational game. The agent trained with this approach is able to actively acquire information by asking questions about novel objects and use the justlearned knowledge in subsequent conversations in a one-shot fashion. Results compared with other methods verified the effectiveness of the proposed approach.
Impact of Dictionaries on Automated Content Analysis - The Use of Compound Concepts in Analysing Knowledge Management Research

Within the knowledge management (KM) domain, we perceive an increasing number of publications. Considering this increase, content analysis (CA) is a popular and empirically established method to handle context-sensitive information and to achieve an improved understanding. While CA can be applied in an automated way by using software solutions, a problem concerns the analysis of compound concepts (e.g. “intellectual capital”). Whereas dictionaries (i.e. lists of compound concepts) have been suggested to solve this problem, lack of research exists concerning the impact of using such a dictionary. By focusing on the KM domain and using the example of 614 publications within the Journal of Knowledge Management (JoKM), this paper aims to evaluate the impact of dictionaries for automated CA. We perform CA applying the automated approach with and without using a self-developed KM dictionary. The results were compared in terms of result similarity as well as result relevance to the KM discipline. Our findings reveal that using a dictionary for automated CA can lead to an improved context understanding and time savings. However, these benefits are opposed by subjectivity that results from the manual extraction of compound concepts to be used in the dictionary.
Deep Direct Reinforcement Learning for Financial Signal Representation and Trading

Can we train the computer to beat experienced traders for financial assert trading? In this paper, we try to address this challenge by introducing a recurrent deep neural network (NN) for real-time financial signal representation and trading. Our model is inspired by two biological-related learning concepts of deep learning (DL) and reinforcement learning (RL). In the framework, the DL part automatically senses the dynamic market condition for informative feature learning. Then, the RL module interacts with deep representations and makes trading decisions to accumulate the ultimate rewards in an unknown environment. The learning system is implemented in a complex NN that exhibits both the deep and recurrent structures. Hence, we propose a task-aware backpropagation through time method to cope with the gradient vanishing issue in deep training. The robustness of the neural system is verified on both the stock and the commodity future markets under broad testing conditions.
Emerging Imaging and Genomic Tools for Developmental Systems Biology.

Animal development is a complex and dynamic process orchestrated by exquisitely timed cell lineage commitment, divisions, migration, and morphological changes at the single-cell level. In the past decade, extensive genetic, stem cell, and genomic studies provided crucial insights into molecular underpinnings and the functional importance of genetic pathways governing various cellular differentiation processes. However, it is still largely unknown how the precise coordination of these pathways is achieved at the whole-organism level and how the highly regulated spatiotemporal choreography of development is established in turn. Here, we discuss the latest technological advances in imaging and single-cell genomics that hold great promise for advancing our understanding of this intricate process. We propose an integrated approach that combines such methods to quantitatively decipher in vivo cellular dynamic behaviors and their underlying molecular mechanisms at the systems level with single-cell, single-molecule resolution.