nils-herrmann/clean_scirepeval huggingface dataset:
PRIVATE BLOCKCHAINS IN AUTOMOTIVE SAFETY

Blockchain has proven to be successful in decision making using the streaming live data in various applications, it is the latest form of Information Technology. There are two broad Blockchain categories, public and private. Public Blockchains are very transparent as the data is distributed and can be accessed by anyone within the distributed system. Private Blockchains are restricted and therefore data transfer can only take place in the constrained environment. Using private Blockchains in maintaining private records for managed history or governing regulations can be very effective due to the data and records, or logs being made with respect to particular user or application. The Blockchain system can also gather data records together and transfer them as secure data records to a third party who can then take further actions. In this paper, an automotive road safety case study is reviewed to demonstrate the feasibility of using private Blockchains in the automotive industry. Within this case study anomalies occur when a driver ignores the traffic rules. The Blockchain system itself monitors and logs the behavior of a driver using map layers, geo data, and external rules obtained from the local governing body. As the information is logged the driver’s privacy information is not shared and so it is both accurate and a secure system. Additionally private Blockchains are small systems therefore they are easy to maintain and faster when compared to distributed (public) Blockchains.
of Achievements in Materials and Manufacturing Engineering Quality Improvement Methodologies – PDCA Cycle , RADAR Matrix , DMAIC and DFSS

of this paper is to introduce the reader to the characteristics of PDCA tool and Six Sigma (DMAIC, DFSS) techniques and EFQM Excellence Model (RADAR matrix), which are possible to use for the continuous quality improvement of products, processes and services in organizations.  We compared the main characteristics of the presented methodologies aiming to show the main prerequisites, differences, strengths and limits in their application.  Depending on the purpose every organization will have to find a proper way and a combination of methodologies in its implementation process. The PDCA cycle is a well known fundamental concept of continuousimprovement processes, RADAR matrix provides a structured approach assessing the organizational performance, DMAIC is a systematic, and fact based approach providing framework of results-oriented project management, DFSS is a systematic approach to new products or processes design focusing on prevent activities.  This paper provides general information and observations on four presented methodologies. Further research could be done towards more detailed study of characteristics and positive effects of these methodologies.  The paper presents condensed presentation of main characteristics, strengths and limitations of presented methodologies. Our findings could be used as solid information for management decisions about the introduction of various quality programmes.  We compared four methodologies and showed their main characteristics and differences. We showed that some methodologies are more simple and therefore easily to understand and introduce (e.g. PDCA cycle). On the contrary Six Sigma and EFQM Excellence model are more complex and demanding methodologies and therefore need more time and resources for their proper implementation.
Deep residual learning for compressed sensing MRI

Compressed sensing (CS) enables significant reduction of MR acquisition time with performance guarantee. However, computational complexity of CS is usually expensive. To address this, here we propose a novel deep residual learning algorithm to reconstruct MR images from sparsely sampled k-space data. In particular, based on the observation that coherent aliasing artifacts from downsampled data has topologically simpler structure than the original image data, we formulate a CS problem as a residual regression problem and propose a deep convolutional neural network (CNN) to learn the aliasing artifacts. Experimental results using single channel and multi channel MR data demonstrate that the proposed deep residual learning outperforms the existing CS and parallel imaging algorithms. Moreover, the computational time is faster in several orders of magnitude.
Gamification in theory and action: A survey

Gamification has drawn the attention of academics, practitioners and business professionals in domains as diverse as education, information studies, human–computer interaction, and health. As yet, the term remains mired in diverse meanings and contradictory uses, while the concept faces division on its academic worth, underdeveloped theoretical foundations, and a dearth of standardized guidelines for application. Despite widespread commentary on its merits and shortcomings, little empirical work has sought to validate gamification as a meaningful concept and provide evidence of its effectiveness as a tool for motivating and engaging users in non-entertainment contexts. Moreover, no work to date has surveyed gamification as a field of study from a human–computer studies perspective. In this paper, we present a systematic survey on the use of gamification in published theoretical reviews and research papers involving interactive systems and human participants. We outline current theoretical understandings of gamification and draw comparisons to related approaches, including alternate reality games (ARGs), games with a purpose (GWAPs), and gameful design. We present a multidisciplinary review of gamification in action, focusing on empirical findings related to purpose and context, design of systems, approaches and techniques, and user impact. Findings from the survey show that a standard conceptualization of gamification is emerging against a growing backdrop of empirical participantsbased research. However, definitional subjectivity, diverse or unstated theoretical foundations, incongruities among empirical findings, and inadequate experimental design remain matters of concern. We discuss how gamification may to be more usefully presented as a subset of a larger effort to improve the user experience of interactive systems through gameful design. We end by suggesting points of departure for continued empirical investigations of gamified practice and its effects. & 2014 Elsevier Ltd. All rights reserved.
Factors Effecting Unemployment : A Cross Country Analysis

This paper investigates macroeconomic determinants of the unemployment for India, China and Pakistan for the period 1980 to 2009. The investigation was conducted through co integration, granger causality and regression analysis. The variables selected for the study are unemployment, inflation, gross domestic product, exchange rate and the increasing rate of population. The results of regression analysis showed significant impact of all the variables for all three countries. GDP of Pakistan showed positive relation with the unemployment rate and the reason of that is the poverty level and underutilization of foreign investment. The result of granger causality showed that bidirectional causality does not exist between any of the variable for all three countries. Co integration result explored that long term relationship do exist among the variables for all the models. It is recommended that distribution of income needs to be improved for Pakistan in order to have positive impact of growth on the employment rate.
Framework for female entrepreneurship in China

Entrepreneurial activity by women in the People’s Republic of China has become more active in recent years with much greater attention being paid within and outside of China. Academic research has sought to describe current conditions and future trends; however, there has been little systematic research done in this area. The aim of this article is to provide a clear picture of the general background and characteristics of Chinese female entrepreneurship based on Eastern cultural features. In addition, an entrepreneurial conceptual model about mainland Chinese women’s entrepreneurial activity is presented and a case study is used for illustration.
Contrastive Focus vs. Discourse-new: Evidence from Phonetic Prominence in English

This article presents new evidence from English that the theory of grammar makes a distinction between the contrastive focus and discourse-new status of constituents. The evidence comes from a phonetic investigation which compares the prosody of all-new sentences with the prosody of sentences combining contrastive focus and discourse-new constituents. We have found that while the sentences of these different types in our experimental materials are not distinguished in their patterns of distribution of pitch accents and phonological phrase organization, they do differ in patterns of phonetic prominence—duration, pitch and intensity, which vary according to the composition of the sentence in terms of contrastive and/or new constituents. The central new finding is that contrastive focus constituents are more phonetically prominent than discourse new constituents that are contained within the same sentence. These distinctions in phonetic prominence are plausibly the consequence of distinctions in the phonological representation of phrasal prosodic prominence (stress) for contrastive focus and discourse-new constituents in English.
Reinventing discovery learning : a field-wide research program

Whereas some educational designers believe that students should learn new concepts through explorative problem solving within dedicated environments that constrain key parameters of their search and then support their progressive appropriation of empowering disciplinary forms, others are critical of the ultimate efficacy of this discovery-based pedagogical philosophy, citing an inherent structural challenge of students constructing historically achieved conceptual structures from their ingenuous notions. This special issue presents six educational research projects that, while adhering to principles of discovery-based learning, are motivated by complementary philosophical stances and theoretical constructs. The editorial introduction frames the set of projects as collectively exemplifying the viability and breadth of discovery-based learning, even as these projects: (a) put to work a span of design heuristics, such as productive failure, surfacing implicit know-how, playing epistemic games, problem posing, or participatory simulation activities; (b) vary in their target content and skills, including building electric circuits, solving algebra problems, driving safely in traffic jams, and performing martial-arts maneuvers; and (c) employ different media, such as interactive computer-based modules for constructing models of scientific phenomena or mathematical problem situations, networked classroom collective ‘‘video games,’’ and intercorporeal master–student training practices. The authors of these papers consider the potential generativity of their design heuristics across domains and contexts.
Effective customer relationship management of health care : a study of hospitals in Thailand

This article investigates the effects and the relative importance of the four perceived service quality dimensions on corporate image, customer satisfaction, and customer loyalty. To obtain results, factor analysis and multiple regression techniques are applied to data collected from 500 Thai outpatients of the five largest private hospitals in Bangkok. The findings indicate that the four dimensions significantly affect corporate image, customer satisfaction, and customer loyalty. More specifically, the doctor concern dimension is the most important factor affecting customer satisfaction and customer loyalty. The tangibles dimension is the most important factor affecting corporate image.
Learning beyond datasets : Knowledge Graph Augmented Neural Networks for Natural language Processing

Machine Learning has been the quintessential solution for many AI problems, but learning is still heavily dependent on the specific training data. Some learning models can be incorporated with a prior knowledge in the Bayesian set up, but these learning models do not have the ability to access any organised world knowledge on demand. In this work, we propose to enhance learning models with world knowledge in the form of Knowledge Graph (KG) fact triples for Natural Language Processing (NLP) tasks. Our aim is to develop a deep learning model that can extract relevant prior support facts from knowledge graphs depending on the task using attention mechanism. We introduce a convolution-based model for learning representations of knowledge graph entity and relation clusters in order to reduce the attention space. We show that the proposed method is highly scalable to the amount of prior information that has to be processed and can be applied to any generic NLP task. Using this method we show significant improvement in performance for text classification with News20, DBPedia datasets and natural language inference with Stanford Natural Language Inference (SNLI) dataset. We also demonstrate that a deep learning model can be trained well with substantially less amount of labeled training data, when it has access to organised world knowledge in the form of knowledge graph.
Advanced supervised learning in multi-layer perceptrons — From backpropagation to adaptive learning algorithms

Computer Standards and Interfaces Special Issue on Neural Networks (5), 1994 Advanced Supervised Learning in Multi-layer Perceptrons From Backpropagation to Adaptive Learning Algorithms Martin Riedmiller Institut f ur Logik, Komplexit at und Deduktionssyteme University of Karlsruhe W-76128 Karlsruhe FRG riedml@ira.uka.de Abstract| Since the presentation of the backpropagation algorithm  a vast variety of improvements of the technique for training the weights in a feed-forward neural network have been proposed. The following article introduces the concept of supervised learning in multi-layer perceptrons based on the technique of gradient descent. Some problems and drawbacks of the original backpropagation learning procedure are discussed, eventually leading to the development of more sophisticated techniques. This article concentrates on adaptive learning strategies. Some of the most popular learning algorithms are described and discussed according to their classi cation in terms of global and local adaptation strategies. The behavior of several learning procedures on some popular benchmark problems is reported, thereby illuminating convergence, robustness, and scaling properties of the respective algorithms.
Changes in dispositional empathy in American college students over time: a meta-analysis.

The current study examines changes over time in a commonly used measure of dispositional empathy. A cross-temporal meta-analysis was conducted on 72 samples of American college students who completed at least one of the four subscales (Empathic Concern, Perspective Taking, Fantasy, and Personal Distress) of the Interpersonal Reactivity Index (IRI) between 1979 and 2009 (total N = 13,737). Overall, the authors found changes in the most prototypically empathic subscales of the IRI: Empathic Concern was most sharply dropping, followed by Perspective Taking. The IRI Fantasy and Personal Distress subscales exhibited no changes over time. Additional analyses found that the declines in Perspective Taking and Empathic Concern are relatively recent phenomena and are most pronounced in samples from after 2000.
Determinants of maximal whole-body fat oxidation in elite cross-country skiers: Role of skeletal muscle mitochondria.

Elite endurance athletes possess a high capacity for whole-body maximal fat oxidation (MFO). The aim was to investigate the determinants of a high MFO in endurance athletes. The hypotheses were that augmented MFO in endurance athletes is related to concomitantly increments of skeletal muscle mitochondrial volume density (MitoVD ) and mitochondrial fatty acid oxidation (FAOp ), that is, quantitative mitochondrial adaptations as well as intrinsic FAOp per mitochondria, that is, qualitative adaptations. Eight competitive male cross-country skiers and eight untrained controls were compared in the study. A graded exercise test was performed to determine MFO, the intensity where MFO occurs (FatMax ), and V ˙ O 2 Max . Skeletal muscle biopsies were obtained to determine MitoVD (electron microscopy), FAOp , and OXPHOSp (high-resolution respirometry). The following were higher (P < 0.05) in endurance athletes compared to controls: MFO (mean [95% confidence intervals]) (0.60 g/min [0.50-0.70] vs 0.32 [0.24-0.39]), FatMax (46% V ˙ O 2 Max [44-47] vs 35 [34-37]), V ˙ O 2 Max (71 mL/min/kg [69-72] vs 48 [47-49]), MitoVD (7.8% [7.2-8.5] vs 6.0 [5.3-6.8]), FAOp (34 pmol/s/mg muscle ww [27-40] vs 21 [17-25]), and OXPHOSp (108 pmol/s/mg muscle ww [104-112] vs 69 [68-71]). Intrinsic FAOp (4.0 pmol/s/mg muscle w.w/MitoVD [2.7-5.3] vs 3.3 [2.7-3.9]) and OXPHOSp (14 pmol/s/mg muscle ww/MitoVD [13-15] vs 11 [10-13]) were, however, similar in the endurance athletes and untrained controls. MFO and MitoVD correlated (r2  = 0.504, P < 0.05) in the endurance athletes. A strong correlation between MitoVD and MFO suggests that expansion of MitoVD might be rate-limiting for MFO in the endurance athletes. In contrast, intrinsic mitochondrial changes were not associated with augmented MFO.
Temporal TF-IDF: A High Performance Approach for Event Summarization in Twitter

In recent years, there has been increased interest in real-world event summarization using publicly accessible data made available through social networking services such as Twitter and Facebook. People use these outlets to communicate with others, express their opinion and commentate on a wide variety of real-world events. Due to the heterogeneity, the sheer volume of text and the fact that some messages are more informative than others, automatic summarization is a very challenging task. This paper presents three techniques for summarizing microblog documents by selecting the most representative posts for real-world events (clusters). In particular, we tackle the task of multilingual summarization in Twitter. We evaluate the generated summaries by comparing them to both human produced summaries and to the summarization results of similar leading summarization systems. Our results show that our proposed Temporal TF-IDF method outperforms all the other summarization systems for both the English and non-English corpora as they lead to informative summaries.
Impact of informational factors on online recommendation credibility: The moderating role of source credibility

a r t i c l e i n f o This study investigates the moderating effect of recommendation source credibility on the causal relationships between informational factors and recommendation credibility, as well as its moderating effect on the causal relationship between recommendation credibility and recommendation adoption. Using data from 199 responses from a leading online consumer discussion forum in China, we find that recommendation source credibility significantly moderates two informational factors' effects on readers' perception of recommendation credibility, each in a different direction. Further, we find that source credibility negatively moderates the effect of recommendation credibility on recommendation adoption. Traditional word-of-mouth (WOM) has been shown to play an important role on consumers' purchase decisions (e.g., ). With the popularization of the Internet, more and more consumers have shared their past consuming experiences (i.e., online consumer recommendation) online, and researchers often refer to this online WOM as electronic word-of-mouth (eWOM). Given the distinct characteristics of Internet communication (e.g., available to individuals without the limitation of time and location, directed to multiple individuals simultaneously), eWOM has conquered known limitations of traditional WOM. In general, eWOM has global reach and influence. In China, many online consumer discussion forums support eWOM, and much previous research [3,7,12,13,21] demonstrates that because eWOM provides indirect purchasing knowledge to readers, the recommendations on these forums can significantly affect their attitudes towards various kinds of consuming targets (e.g., stores, products and services). Various prior studies have postulated large numbers of antecedent factors which can affect information readers' cognition towards the recommendations, and many of them stem from elaboration likelihood model (ELM) (e. that there are two distinct routes that can affect information readers' attitude toward presented information: (1) the central route that considers the attitude formation (or change) as the result of the receivers' diligent consideration of the content of the information (informational factors); and (2) the peripheral route that requires less cognitive work attuned to simple cues in the information to influence attitude (information-irrelevant factors). ELM suggests two factors, named information readers' motivation and ability, can be the significant moderators to shift the effects of central and peripheral factors on readers' perception of information credibility. Other researchers [24,27] posit that the peripheral factor – source credibility – may also have a moderating rather than a direct effect on the causal relationship between the informational factors and the information credibility; this view is consistent with the attribution inference …
SPECIFIC AND CROSS-OVER EFFECTS OF FOAM ROLLING ON ANKLE DORSIFLEXION RANGE OF MOTION.

Flexibility is an important physical quality. Self-myofascial release (SMFR) methods such as foam rolling (FR) increase flexibility acutely but how long such increases in range of motion (ROM) last is unclear. Static stretching (SS) also increases flexibility acutely and produces a cross-over effect to contralateral limbs. FR may also produce a cross-over effect to contralateral limbs but this has not yet been identified.



To explore the potential cross-over effect of SMFR by investigating the effects of a FR treatment on the ipsilateral limb of 3 bouts of 30 seconds on changes in ipsilateral and contralateral ankle DF ROM and to assess the time-course of those effects up to 20 minutes post-treatment.



A within- and between-subject design was carried out in a convenience sample of 26 subjects, allocated into FR (n=13) and control (CON, n=13) groups. Ankle DF ROM was recorded at baseline with the in-line weight-bearing lunge test for both ipsilateral and contralateral legs and at 0, 5, 10, 15, 20 minutes following either a two-minute seated rest (CON) or 3 3 30 seconds of FR of the plantar flexors of the dominant leg (FR). Repeated measures ANOVA was used to examine differences in ankle DF ROM.



No significant between-group effect was seen following the intervention. However, a significant within-group effect (p<0.05) in the FR group was seen between baseline and all post-treatment time-points (0, 5, 10, 15 and 20 minutes). Significant within-group effects (p<0.05) were also seen in the ipsilateral leg between baseline and at all post-treatment time-points, and in the contralateral leg up to 10 minutes post-treatment, indicating the presence of a cross-over effect.



FR improves ankle DF ROM for at least 20 minutes in the ipsilateral limb and up to 10 minutes in the contralateral limb, indicating that FR produces a cross-over effect into the contralateral limb. The mechanism producing these cross-over effects is unclear but may involve increased stretch tolerance, as observed following SS.


LEVELS OF EVIDENCE
2c.