nils-herrmann/clean_scirepeval huggingface dataset:
Social capital in vulnerable urban settings : an analytical framework

Social capital has been identified as crucial to the fostering of resilience in rapidly expanding cities of the Global South. The purpose of this article is to better understand the complexities of urban social interaction and how such interaction can constitute ‘capital’ in achieving urban resilience. A concept analysis was conducted to establish what constitutes social capital, its relevance to vulnerable urban settings and how it can be measured. Social capital is considered to be constituted of three forms of interaction: bonds, bridges and linkages. The characteristics of these forms of interaction may vary according to the social, political, cultural and economic diversity to be found within vulnerable urban settings. A framework is outlined to explore the complex nature of social capital in urban settings. On the basis of an illustrative case study, indicators are established to indicate how culturally specific indicators are required to measure social capital that are sensitive to multiple levels of analysis and the development of a multidimensional framework. The framework outlined ought to be adapted to context and validated by future research.
Expect the Unexpected : Event-related Brain Response to Morphosyntactic Violations

Arguments about the existence of language-specific neural systems and particularly arguments about the independence of syntactic and semantic processing have recently focused on differences between the event-related potentials (ERPs) elicited by violations of syntactic structure (e.g. the P600) and those elicited by violations of semantic expectancy (e.g. the N400). However, the scalp distribution of the P600 component elicited by syntactic violations appears to resemble that elicited by rare categorical events ("odd-balls") in non-linguistic contexts, frequently termed the P3b. The relationship between the P600 and the P3b was explored by manipulating the grammaticality of sentences read for comprehension, as well as two factors known to influence P3b amplitude: odd-ball probability and event saliency. Oddball probability was manipulated by varying the frequency of morphosyntactic violations within blocks of sentences, and event saliency was manipulated by using two types of morphosyntactic violations, one of which was more striking than the other. The results indicate that the amplitude of the P600, like the P3b, was sensitive to both the probability and saliency manipulations, and that the scalp distributions for the effect of probability and grammaticality are essentially similar. An unexpected, but not wholly surprising, finding was the elicitation of an anterior negativity between 300 and 500 msec post-word onset, which may index working memory operations involved in sentence processing.
Consumer trust in B 2 C e-Commerce and the importance of social presence : experiments in e-Products and e-Services

Reducing social uncertainty—understanding, predicting, and controlling the behavior of other people—is a central motivating force of human behavior. When rules and customs are not su4cient, people rely on trust and familiarity as primary mechanisms to reduce social uncertainty. The relative paucity of regulations and customs on the Internet makes consumer familiarity and trust especially important in the case of e-Commerce. Yet the lack of an interpersonal exchange and the one-time nature of the typical business transaction on the Internet make this kind of consumer trust unique, because trust relates to other people and is nourished through interactions with them. This study validates a four-dimensional scale of trust in the context of e-Products and revalidates it in the context of e-Services. The study then shows the in:uence of social presence on these dimensions of this trust, especially benevolence, and its ultimate contribution to online purchase intentions. ? 2004
Automatic Differentiation of Algorithms for Machine Learning

Automatic differentiation—the mechanical transformation of numeric computer programs to calculate derivatives efficiently and accurately—dates to the origin of the computer age. Reverse mode automatic differentiation both antedates and generalizes the method of backwards propagation of errors used in machine learning. Despite this, practitioners in a variety of fields, including machine learning, have been little influenced by automatic differentiation, and make scant use of available tools. Here we review the technique of automatic differentiation, describe its two main modes, and explain how it can benefit machine learning practitioners. To reach the widest possible audience our treatment assumes only elementary differential calculus, and does not assume any knowledge of linear algebra.
Anabolic signaling deficits underlie amino acid resistance of wasting, aging muscle.

The nature of the deficit underlying age-related muscle wasting remains controversial. To test whether it could be due to a poor anabolic response to dietary amino acids, we measured the rates of myofibrillar and sarcoplasmic muscle protein synthesis (MPS) in 44 healthy young and old men, of similar body build, after ingesting different amounts of essential amino acids (EAA). Basal rates of MPS were indistinguishable, but the elderly showed less anabolic sensitivity and responsiveness of MPS to EAA, possibly due to decreased intramuscular expression, and activation (phosphorylation) after EAA, of amino acid sensing/signaling proteins (mammalian target of rapamycin, mTOR; p70 S6 kinase, or p70(S6k); eukaryotic initiation factor [eIF]4BP-1; and eIF2B). The effects were independent of insulin signaling since plasma insulin was clamped at basal values. Associated with the anabolic deficits were marked increases in NFkappaB, the inflammation-associated transcription factor. These results demonstrate first, EAA stimulate MPS independently of increased insulin availability; second, in the elderly, a deficit in MPS in the basal state is unlikely; and third, the decreased sensitivity and responsiveness of MPS to EAA, associated with decrements in the expression and activation of components of anabolic signaling pathways, are probably major contributors to the failure of muscle maintenance in the elderly. Countermeasures to maximize muscle maintenance should target these deficits.
An intelligent home networking system

This paper describes one solution of an intelligent home networking system for residential energy monitoring and controlling which consists of wirelessly controlled light dimmer switches, smart outlets, smart power extension cords, remote controllers, wireless PIR sensors and home energy controller. The system is based on ZigBee 2.4GHz wireless network protocol for easy configuration and management. Light dimmer switches are controlled by remote controller with capacitive proximity sensors instead of mechanical buttons for better user experience and improved functionality. Power consumption measured by smart outlets and power extension cords is monitored by home energy controller. It uses this information to perform user's defined operations. The system intelligence resides in the power saving recipes executed on energy controller triggered by sensors and events.
The Psychology of Esports: A Systematic Literature Review

Recently, the skill involved in playing and mastering video games has led to the professionalization of the activity in the form of ‘esports’ (electronic sports). The aim of the present paper was to review the main topics of psychological interest about esports and then to examine the similarities of esports to professional and problem gambling. As a result of a systematic literature search, eight studies were identified that had investigated three topics: (1) the process of becoming an esport player, (2) the characteristics of esport players such as mental skills and motivations, and (3) the motivations of esport spectators. These findings draw attention to the new research field of professional video game playing and provides some preliminary insight into the psychology of esports players. The paper also examines the similarities between esport players and professional gamblers (and more specifically poker players). It is suggested that future research should focus on esport players’ psychological vulnerability because some studies have begun to investigate the difference between problematic and professional gambling and this might provide insights into whether the playing of esports could also be potentially problematic for some players.
Survival of the unfittest : why the worst infrastructure gets built-- and what we can do about it

The article first describes characteristics of major infrastructure projects. Second, it documents a much neglected topic in economics: that ex ante estimates of costs and benefits are often very different from actual ex post costs and benefits. For large infrastructure projects the consequences are cost overruns, benefit shortfalls, and the systematic underestimation of risks. Third, implications for cost–benefit analysis are described, including that such analysis is not to be trusted for major infrastructure projects. Fourth, the article uncovers the causes of this state of affairs in terms of perverse incentives that encourage promoters to underestimate costs and overestimate benefits in the business cases for their projects. But the projects that are made to look best on paper are the projects that amass the highest cost overruns and benefit shortfalls in reality. The article depicts this situation as ‘survival of the unfittest’. Fifth, the article sets out to explain how the problem may be solved, with a view to arriving at more efficient and more democratic projects, and avoiding the scandals that often accompany major infrastructure investments. Finally, the article identifies current trends in major infrastructure development. It is argued that a rapid increase in stimulus spending, combined with more investments in emerging economies, combined with more spending on information technology is catapulting infrastructure investment from the frying pan into the fire.
A 128.7nW neural amplifier and Gm-C filter for EEG, using gm/ID methodology and a current reference without resistance

This paper presents a 128.7nW analog front-end amplifier and Gm-C filter for biomedical sensing applications, specifically for Electroencephalogram (EEG) use. The proposed neural amplifier has a supply voltage of 1.8V, consumes a total current of 71.59nA, for a total dissipated power of 128nW and has a gain of 40dB. Also, a 3th order Butterworth Low Pass Gm-C Filter with a 14.7nS transconductor is designed and presented. The filter has a pass band suitable for use in EEG (1-100Hz). The amplifier and filter utilize current sources without resistance which provide 56nA and (1.154nA ×5) respectively. The proposed amplifier occupies and area of 0.26mm2 in 0.3μm TSMC process.
LDO voltage regulator with foldback current limit and thermal protection ∗

This paper introduces the design of a l.8 V low dropout voltage regulator (LDO) and a foldback current limit circuit which limits the output current to 3 mA when load over-current occurs. The LDO was implemented in a 0.18 μm CMOS technology. The measured result reveals that the LDO′s power supply rejection (PSR) is about −58 dB and –54 dB at 20 Hz and 1 kHz respectively, the response time is 4 μs and the quiescent current is 20 μA. The designed LDO regulator can work with a supply voltage down to 2.0 V with a drop-out voltage of 200 mV at a maximum load current of 240 mA.
Insider Threat Program Best Practices

Based on experiences with different organizations having insider threat programs, the components needed for an insider threat auditing and mitigation program and methods of program validation that agencies can use when both initiating a program and reviewing an existing program has been described. This paper concludes with descriptions of each of the best practices derived from the model program. This final section is meant to be a standalone section that readers can detach and incorporate into their insider threat mitigation program guidance.
A Meta-Learning Perspective on Cold-Start Recommendations for Items

Matrix factorization (MF) is one of the most popular techniques for product recommendation, but is known to suffer from serious cold-start problems. Item cold-start problems are particularly acute in settings such as Tweet recommendation where new items arrive continuously. In this paper, we present a meta-learning strategy to address item cold-start when new items arrive continuously. We propose two deep neural network architectures that implement our meta-learning strategy. The first architecture learns a linear classifier whose weights are determined by the item history while the second architecture learns a neural network whose biases are instead adjusted. We evaluate our techniques on the real-world problem of Tweet recommendation. On production data at Twitter, we demonstrate that our proposed techniques significantly beat the MF baseline and also outperform production models for Tweet recommendation.
EVOLUTION OF PARAMETERS OF NONLINEAR POSITION CONTROL FOR DYNAMIC MODEL OF MOBILE ROBOT WITH FRICTION

In this paper, we propose two level control system for a mobile robot. The first level subsystem deals with the control of the linear and angular volocities using a multivariable PI controller described with a full matrix. The position control of the mobile robot represents the second level control, which is nonlinear. The nonlinear control design is implemented by a modified backstepping algorithm whose parameters are adjusted by a genetic algorithm, which is a robust nonlinear optimization method. The performance of the proposed system is investigated using a dynamic model of a nonholonomic mobile robot with friction. We present a new dynamic model in which the angular velocities of wheels are main variables. Simulation results show the good quality of position tracking capabilities a mobile robot with the various viscous friction torques.
Ontologies: State of the Art, Business Potential, and Grand Challenges

In this chapter, we give an overview of what ontologies are and how they can be used. We discuss the impact of the expressiveness, the number of domain elements, the community size, the conceptual dynamics, and other variables on the feasibility of an ontology project. Then, we break down the general promise of ontologies of facilitating the exchange and usage of knowledge to six distinct technical advancements that ontologies actually provide, and discuss how this should influence design choices in ontology projects. Finally, we summarize the main challenges of ontology management in real-world applications, and explain which expectations from practitioners can be met as
Twitter Paraphrase Identification with Simple Overlap Features and SVMs

We present an approach to identifying Twitter paraphrases using simple lexical overlap features. The work is part of ongoing research into the applicability of knowledgelean techniques to paraphrase identification. We utilize features based on overlap of word and character n-grams and train support vector machine (SVM). Our results demonstrate that character and word level overlap features in combination can give performance comparable to methods employing more sophisticated NLP processing tools and external resources. We achieve the highest F-score for identifying paraphrases on the Twitter Paraphrase Corpus as part of the SemEval-2015 Task1.
Adaptive End-to-End Dialog Systems

End-to-end learning framework is useful for building dialog systems for its simplicity in training and efficiency in model updating. However, current end-to-end approaches only consider user semantic inputs in learning and under-utilize other user information. Therefore, we propose to include user sentiment obtained through multimodal information (acoustic, dialogic and textual), in the end-to-end learning framework to make systems more user-adaptive and effective. We incorporated user sentiment information in both supervised and reinforcement learning settings. In both settings, adding sentiment information reduced the dialog length and improved the task success rate on a bus information search task. This work is the first attempt to incorporate multimodal user information in the adaptive end-toend dialog system training framework and attained state-of-the-art performance.
Dewey ' s Contribution to a Standard of Problem-Based Learning Practice

Although we already have many theory-based definitions and procedural descriptions of problem-based learning (PBL), we currently lack anything that could serve as a practical standard, that is an account of the critical practices that make an instructional activity recognizable as PBL. I argue here that the notion of inquiry developed in the writings of the American educational philosopher John Dewey could be useful in illuminating the features of observed interaction that would be relevant to a description of instructional practice. An example is provided based on a segment of recorded interaction in a tutorial group in a problem-based curriculum at a U.S. medical school. Within this segment, a conflict emerges among the students with respect to their planned handling of a case. Through their discussion, the students determine what they would need to know in order to resolve the conflict, or in Dewey’s words to make an "indeterminate situation determinate." The paper calls for additional work to produce a large corpus of fine-grained descriptions of instructional practice from a variety of PBL implementations. Such a collection would provide a basis for the eventual development of a PBL standard.
Alcohol, oxidative stress and free radical damage.

The involvement of free radical mechanisms in the pathogenesis of alcoholic liver disease (ALD) is demonstrated by the detection of lipid peroxidation markers in the liver and the serum of patients with alcoholism, as well as by experiments in alcohol-feed rodents that show a relationship between alcohol-induced oxidative stress and the development of liver pathology. Ethanol-induced oxidative stress is the result of the combined impairment of antioxidant defences and the production of reactive oxygen species by the mitochondrial electron transport chain, the alcohol-inducible cytochrome P450 (CYP) 2E1 and activated phagocytes. Furthermore, hydroxyethyl free radicals (HER) are also generated during ethanol metabolism by CYP2E1. The mechanisms by which oxidative stress contributes to alcohol toxicity are still not completely understood. The available evidence indicates that, by favouring mitochondrial permeability transition, oxidative stress promotes hepatocyte necrosis and/or apoptosis and is implicated in the alcohol-induced sensitization of hepatocytes to the pro-apoptotic action of TNF-alpha. Moreover, oxidative mechanisms can contribute to liver fibrosis, by triggering the release of pro-fibrotic cytokines and collagen gene expression in hepatic stellate cells. Finally, the reactions of HER and lipid peroxidation products with hepatic proteins stimulate both humoral and cellular immune reactions and favour the breaking of self-tolerance during ALD. Thus, immune responses might represent the mechanism by which alcohol-induced oxidative stress contributes to the perpetuation of chronic hepatic inflammation. Together these observations provide a rationale for the possible clinical application of antioxidants in the therapy for ALD.
MedChatBot: An UMLS based Chatbot for Medical Students

The use of natural dialog has great significance in the design of interactive tutoring systems. The nature of student queries can be confined to a small set of templates based on the task domain. This paper describes the development of a chatbot for medical students, that is based on the open source AIML based Chatterbean. We deploy the widely available Unified Medical Language System (UMLS) as the domain knowledge source for generating responses to queries. The AIML based chatbot is customized to convert natural language queries into relevant SQL queries. The SQL queries are run against the knowledge base and results returned to the user in natural dialog. Student survey was carried out to identify various queries posed by students. The chatbot was designed to address common template queries. Knowledge inference techniques were applied to generate responses for queries for which knowledge was not explicitly encoded. Query responses were rated by three experts on a 1-5 point likert scale, who agreed among themselves with Pearson Correlation Coefficient of 0. 54 and p References
Monte-Carlo Tree Search vs . Model-Predictive Controller : A Lane-Following Example

Monte-Carlo Tree Search (MCTS) has achieved remarkable success in the game 1 of Go. However, most success of MCTS is in games where actions are discrete. 2 For autonomous driving, the vehicle action such as throttle and steering angle is 3 continuous. To fill the gap, we propose an MCTS algorithm for continuous actions, 4 and used it specially for a lane-following scenario. We compared MCTS with a 5 standard Model Predictive Controller (MPC) on the Udacity simulator. Using the 6 same cost function and system model, this MCTS algorithm achieves a much lower 7 cost than MPC. MCTS drives with an adaptive speed, as well as exhibits a braking 8 behavior in sharp turns. MPC drives a nearly constant speed regardless of the curvy 9 track.
Seismic Waveform Classification and First-Break Picking Using Convolution Neural Networks

Regardless of successful applications of the convolutional neural networks (CNNs) in different fields, its application to seismic waveform classification and first-break (FB) picking has not been explored yet. This letter investigates the application of CNNs for classifying time-space waveforms from seismic shot gathers and picking FBs of both direct wave and refracted wave. We use representative subimage samples with two types of labeled waveform classification to supervise CNNs training. The goal is to obtain the optimal weights and biases in CNNs, which are solved by minimizing the error between predicted and target label classification. The trained CNNs can be utilized to automatically extract a set of time-space attributes or features from any subimage in shot gathers. These attributes are subsequently inputted to the trained fully connected layer of CNNs to output two values between 0 and 1. Based on the two-element outputs, a discriminant score function is defined to provide a single indication for classifying input waveforms. The FB is then located from the calculated score maps by sequentially using a threshold, the first local minimum rule of every trace and a median filter. Finally, we adopt synthetic and real shot data examples to demonstrate the effectiveness of CNNs-based waveform classification and FB picking. The results illustrate that CNN is an efficient automatic data-driven classifier and picker.