nils-herrmann/clean_scirepeval huggingface dataset:
Does democracy preempt civil wars ?

This paper proposes that the appropriate measure for capturing the political aspects that matter for social conflict is the level of inclusiveness of the political system. I analyze, theoretically and empirically, the relationship between inclusiveness of the political system and its stability. According to the model, high inclusive systems, such as the proportional representation system, are more stable than low inclusive systems that favor political exclusion, such as the majoritarian system. Empirically, it seems that democracy is not enough to deter social conflicts. The level of inclusiveness of the political system is important in explaining the probability of civil wars. D 2004 Elsevier B.V. All rights reserved.
Self-Interest , Altruism , Incentives , and Agency Theory

Many people are suspicious of self-interest and incentives and oppose motivating humans through incentives. I analyze the meaning of incentives in the logic of choice and argue that it is inconceivable that purposeful actions are anything other than responses to incentives. Money is not always the best way to motivate people. But where money incentives are required, they are required precisely because people are motivated by things other than money. Self-interest is consistent with altruistic motives. Agency problems, however, cannot be solved by instilling greater altruism in people because altruism, the concern for the well-being of others, does not make a person into a perfect agent who does the bidding of others. I discuss the universal tendency of people to behave in non-rational ways. Though they are Resourceful, Evaluative Maximizers (REMMs) humans are imperfect because their brains are biologically structured so as to blind them from perceiving and correcting errors that cause psychic pain. The result is systematic, non-functional behavior. I discuss a Pain Avoidance Model (PAM) that complements REMM by capturing the non-rational component of human behavior (the crux of human self-control problems). Recognizing these selfcontrol problems leads to an expansion of agency theory since they are a second source of agency costs in addition to those generated by conflicts of interest between people.
Toward a Social Psychology of Race and Race Relations for the Twenty-First Century.

The United States, like many nations, continues to experience rapid growth in its racial minority population and is projected to attain so-called majority-minority status by 2050. Along with these demographic changes, staggering racial disparities persist in health, wealth, and overall well-being. In this article, we review the social psychological literature on race and race relations, beginning with the seemingly simple question: What is race? Drawing on research from different fields, we forward a model of race as dynamic, malleable, and socially constructed, shifting across time, place, perceiver, and target. We then use classic theoretical perspectives on intergroup relations to frame and then consider new questions regarding contemporary racial dynamics. We next consider research on racial diversity, focusing on its effects during interpersonal encounters and for groups. We close by highlighting emerging topics that should top the research agenda for the social psychology of race and race relations in the twenty-first century.
RFID Security and Privacy : Threats and Countermeasures

The vulnerability of Radio Frequency Identification (RFID) and the objection of consumers to buy products that include non-protected RFID are holding organizations back from investing in this promising technology. Information security and privacy are therefore important academic research areas. This research presents the RFID Threat Countermeasure Framework (RTCF) to better understand the wide range of RFID threats and their corresponding protection countermeasures. We conclude that RFID security and privacy developments are very promising but do require more development iterations to become practically useful for organizations.
Rotational Machine Health Monitoring and Fault Detection Using EMD-Based Acoustic Emission Feature Quantification

Acoustic emission (AE)-signal-based techniques have recently been attracting researchers' attention to rotational machine health monitoring and diagnostics due to the advantages of the AE signals over the extensively used vibration signals. Unlike vibration-based methods, the AE-based techniques are in their infant stage of development. From the perspective of machine health monitoring and fault detection, developing an AE-based methodology is important. In this paper, a methodology for rotational machine health monitoring and fault detection using empirical mode decomposition (EMD)-based AE feature quantification is presented. The methodology incorporates a threshold-based denoising technique into EMD to increase the signal-to-noise ratio of the AE bursts. Multiple features are extracted from the denoised signals and then fused into a single compressed AE feature. The compressed AE features are then used for fault detection based on a statistical method. A gear fault detection case study is conducted on a notional split-torque gearbox using AE signals to demonstrate the effectiveness of the methodology. A fault detection performance comparison using the compressed AE features with the existing EMD-based AE features reported in the literature is also conducted.
Coin detection and recognition using neural networks

Coin identification and recognition and is important to enhance the extended operation of Vending machines, Pay phone system and coin counting machines. Coin recognition is a difficult task in machine intelligence and computer vision problems because of its various rotations and widely changed patterns. Therefore, an efficient algorithm is designed to be robust and invariant to rotation, translation and scaling. The objective of this work is to find whether the object is coin or not if so denomination of the coin is found. The Fourier approximation of the coin image is used to reduce the variations on surface of coin such as light reflection effect. Then coins can be distinguished by feeding those features into a multi-layered BP neural network.
MoCoGAN: Decomposing Motion and Content for Video Generation

Visual signals in a video can be divided into content and motion. While content specifies which objects are in the video, motion describes their dynamics. Based on this prior, we propose the Motion and Content decomposed Generative Adversarial Network (MoCoGAN) framework for video generation. The proposed framework generates a video by mapping a sequence of random vectors to a sequence of video frames. Each random vector consists of a content part and a motion part. While the content part is kept fixed, the motion part is realized as a stochastic process. To learn motion and content decomposition in an unsupervised manner, we introduce a novel adversarial learning scheme utilizing both image and video discriminators. Extensive experimental results on several challenging datasets with qualitative and quantitative comparison to the state-of-the-art approaches, verify effectiveness of the proposed framework. In addition, we show that MoCoGAN allows one to generate videos with same content but different motion as well as videos with different content and same motion. Our code is available at https://github.com/sergeytulyakov/mocogan.
The carbon balance of terrestrial ecosystems in China

Global terrestrial ecosystems absorbed carbon at a rate of 1–4 Pg yr-1 during the 1980s and 1990s, offsetting 10–60 per cent of the fossil-fuel emissions. The regional patterns and causes of terrestrial carbon sources and sinks, however, remain uncertain. With increasing scientific and political interest in regional aspects of the global carbon cycle, there is a strong impetus to better understand the carbon balance of China. This is not only because China is the world’s most populous country and the largest emitter of fossil-fuel CO2 into the atmosphere, but also because it has experienced regionally distinct land-use histories and climate trends, which together control the carbon budget of its ecosystems. Here we analyse the current terrestrial carbon balance of China and its driving mechanisms during the 1980s and 1990s using three different methods: biomass and soil carbon inventories extrapolated by satellite greenness measurements, ecosystem models and atmospheric inversions. The three methods produce similar estimates of a net carbon sink in the range of 0.19–0.26 Pg carbon (PgC) per year, which is smaller than that in the conterminous United States but comparable to that in geographic Europe. We find that northeast China is a net source of CO2 to the atmosphere owing to overharvesting and degradation of forests. By contrast, southern China accounts for more than 65 per cent of the carbon sink, which can be attributed to regional climate change, large-scale plantation programmes active since the 1980s and shrub recovery. Shrub recovery is identified as the most uncertain factor contributing to the carbon sink. Our data and model results together indicate that China’s terrestrial ecosystems absorbed 28–37 per cent of its cumulated fossil carbon emissions during the 1980s and 1990s.
Semi-Supervised Multimodal Learning with Deep Generative Models

In recent years, deep neural networks are used mainly as discriminators of multimodal learning. We should have large amounts of labeled data for training them, but obtaining such data is difficult because it requires much labor to label inputs. Therefore, semi-supervised learning, which improves the discriminator performance using unlabeled data, is important. Among semi-supervised learning, methods based on deep generative models such as variational autoencoders (VAEs) are known to be trained end-to-end with high accuracy. In this paper, we propose a novel model of semi-supervised multimodal learning based on multimodal VAEs: SS-HMVAE. Furthermore, to cope with unimodal inputs in test data, we propose an extended model based on existing studies of complementation of missing values, which we call SS-HMVAE-kl. From experimentation, we confirm that the proposed model has higher performance than either conventional unimodal or multimodal semi-supervised learning.
Risk assessment of hand washing efficacy using literature and experimental data.

This study simulated factors that influence the levels of bacteria on foodservice workers' hands. Relevant data were collected from the scientific literature and from laboratory experiments. Literature information collected included: initial bacterial counts on hands and water faucet spigots, bacterial population changes during hand washing as effected by soap type, sanitizing agent, drying method, and the presence of rings. Experimental data were also collected using Enterobacter aerogenes as a surrogate for transient bacteria. Both literature and experimental data were translated into appropriate discrete or probability distribution functions. The appropriate statistical distribution for each phase of the hand washing process was determined. These distributions were: initial count on hands, beta (2.82, 2.32, 7.5); washing reduction using regular soap, beta (3.01, 1.91, -3.00, 0.60); washing reduction using antimicrobial soap, beta (4.19, 2.99, -4.50, 1.50); washing reduction using chlorhexidine gluconate (CHG), triangular (-4.75, -1.00, 0); reductions from hot air drying, beta (3.52, 1.92, -0.20, 1.00); reduction from paper towel drying, triangular (-2.25, -0.75, 0); reduction due to alcohol sanitizer, gamma (-1.23, 4.42) -5.8; reduction due to alcohol-free sanitizer, gamma (2.22, 5.38) -5.00; and the effect of rings, beta (8.55, 23.35, 0.10, 0.45). Experimental data were fit to normal distributions (expressed as log percentage transfer rate): hand-to-spigot transfer, normal (-0.80, 1.09); spigot to hand, normal (0.36, 0.90). Soap with an antimicrobial agent (in particular, CHG) was observed to be more effective than regular soap. Hot air drying had the capacity to increase the amount of bacterial contamination on hands, while paper towel drying caused a slight decrease in contamination. There was little difference in the efficacy of alcohol and alcohol-free sanitizers. Ring wearing caused a slight decrease in the efficacy of hand washing. The experimental data validated the simulated combined effect of certain hand washing procedures based on distributions derived from reported studies. The conventional hand washing system caused a small increase in contamination on hands vs. the touch-free system. Sensitivity analysis revealed that the primary factors influencing final bacteria counts on the hand were sanitizer, soap, and drying method. This research represents an initial framework from which sound policy can be promulgated to control bacterial transmission via hand contacts.
Analysis of Social Networks Using the Techniques of Web Mining

Web 2.0 technologies have brought new ways of connecting people in social networks for collaboration in various on-line communities. Social network analysis deals with the interactions between individuals by considering them as nodes of a network (graph) whereas their relations are mapped as network edges. Study of such structures lies on the intersection of different areas of research: sociology, graph theory and data mining. This paper studies issues around analysis of social networks using web mining techniques. Techniques and concepts of web mining and social networks analysis will be introduced and reviewed along with a discussion about how to use web mining techniques for social networks analysis. This paper also sets out a process for social network analysis using web mining. Comparison of social networks with other networks is also studied. Discussions of the challenges and future research are also included.  Web Mining, Social Networking, Social Network Analysis, Association Rule
Intrinsic Motivation and Engagement as “ Active Ingredients ” in Garden-Based Education : Examining Models and Measures Derived From Self-Determination Theory

This article may be used for research, teaching, and private study purposes. Any substantial or systematic reproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any form to anyone is expressly forbidden. The publisher does not give any warranty express or implied or make any representation that the contents will be complete or accurate or up to date. The accuracy of any instructions, formulae, and drug doses should be independently verified with primary sources. The publisher shall not be liable for any loss, actions, claims, proceedings, demand, or costs or damages whatsoever or howsoever caused arising directly or indirectly in connection with or arising out of the use of this material. Building on self-determination theory, this study presents a model of intrinsic motivation and engagement as " active ingredients " in garden-based education. The model was used to create reliable and valid measures of key constructs, and to guide the empirical exploration of motivational processes in garden-based learning. Teacher-and student-reports of garden engagement, administered to 310 middle school students, demonstrated multidimensional structures, good measurement properties, convergent validity, and the expected correlations with self-perceptions in the garden, garden learning , achievement, and engagement in science and school. Exploratory path analyses, calculated using multiple regression, provided initial support for the self-determination model of motivation: students' perceived autonomy, competence, and intrinsic motivation uniquely predicted their engagement in the garden, which in turn, predicted learning in the gardens and achievement in school. School gardens are flourishing. In the United States, they number in the tens of thousands, with 4,000 in California alone (California Department of Education, 2010). Goals for school gardens are as unique as the schools themselves, but in general they target four student outcomes: a) science learning and school achievement; b) ecological and environmental awareness and responsible behaviors; such as recycling and composting; d) knowledge about food systems 1. The Learning-Gardens Educational Assessment Group (or LEAG) is an interdisciplinary group of faculty and students from the Department of Psychology and the Graduate School of Education at Portland State University and the leadership of Lane Middle School of Portland Public Schools organized around a garden-based education program, the 17 and nutrition, and healthy eating, especially consumption of fresh fruits and vegetables; and d) positive youth development (Ratcliffe, Goldberg, Rogers, & Merrigan, 2010). Evidence about the beneficial effects of garden programs comes from qualitative and quantitative research and case studies from multiple disciplines …
On the design of security protocols for mobile communications

Use of mobile personal computers in open networked environment is revolutionalising the way we use computers. Mobile networked computing is raising important information security and privacy issues. This paper is concerned with the design of authentication protocols for a mobile computing environment. The paper rst analyses the authenti-cation initiator protocols proposed by Beller,Chang and Yacobi (BCY) and the modiications considered by Carlsen and points out some weaknesses. The paper then suggests improvements to these protocols. The paper proposes secure end-to-end protocols between mobile users using both symmetric and public key based systems. These protocols enable mutual authentication and establish a shared secret key between mobile users. Furthermore, these protocols provide a certain degree of anonymity of the communicating users to be achieved visa -vis other system users.
Beyond Training and Awareness: From Security Culture to Security Risk Management

DXC Technology were asked to participate in a Cyber Vulnerability Investigation into organizations in the Defense sector in the UK. Part of this work was to examine the influence of socio-technical and/or human factors on cyber security – where possible linking factors to specific technical risks. Initial research into the area showed that (commercially, at least) most approaches to developing security culture in organisations focus on end users and deal solely with training and awareness regarding identifying and avoiding social engineering attacks and following security procedures. The only question asked and answered is how to ensure individuals conform to security policy and avoid such attacks. But experience of recent attacks (e.g., Wannacry, Sony hacks) show that responses to cyber security requirements are not just determined by the end users’ level of training and awareness, but grow out of the wider organizational culture – with failures at different levels of the organization. This is a known feature of socio-technical research. As a result, we have sought to develop and apply a different approach to measuring security culture, based on discovering the distribution of beliefs and values (and resulting patterns of behavior) throughout the organization. Based on our experience, we show a way we can investigate these patterns of behavior and use them to identify socio-technical vulnerabilities by comparing current and ‘ideal’ behaviors. In doing so, we also discuss how this approach can be further developed and successfully incorporated into commercial practice, while retaining scientific validity.
Crime Pattern Detection Using Data Mining

Data mining can be used to model crime detection problems. Crimes are a social nuisance and cost our society dearly in several ways. Any research that can help in solving crimes faster will pay for itself. About 10% of the criminals commit about 50% of the crimes. Here we look at use of clustering algorithm for a data mining approach to help detect the crimes patterns and speed up the process of solving crime. We will look at k-means clustering with some enhancements to aid in the process of identification of crime patterns. We applied these techniques to real crime data from a sheriff's office and validated our results. We also use semi-supervised learning technique here for knowledge discovery from the crime records and to help increase the predictive accuracy. We also developed a weighting scheme for attributes here to deal with limitations of various out of the box clustering tools and techniques. This easy to implement data mining framework works with the geo-spatial plot of crime and helps to improve the productivity of the detectives and other law enforcement officers. It can also be applied for counter terrorism for homeland security.
NetGAN: Generating Graphs via Random Walks

B. Baselines • Configuration model. In addition to randomly rewiring all edges in the input graph, we also generate random graphs with similar overlap as graphs generated by NetGAN using the configuration model. For this, we randomly select a share of edges (e.g. 39%) and keep them fixed, and shuffle the remaining edges. This leads to a graph with the specified edge overlap; in Table 2 we show that with the same edge overlap, NetGAN’s generated graphs in general match the input graph better w.r.t the statistics we measure.
Recognizing academic performance, sleep quality, stress level, and mental health using personality traits, wearable sensors and mobile phones

What can wearable sensors and usage of smart phones tell us about academic performance, self-reported sleep quality, stress and mental health condition? To answer this question, we collected extensive subjective and objective data using mobile phones, surveys, and wearable sensors worn day and night from 66 participants, for 30 days each, totaling 1,980 days of data. We analyzed daily and monthly behavioral and physiological patterns and identified factors that affect academic performance (GPA), Pittsburg Sleep Quality Index (PSQI) score, perceived stress scale (PSS), and mental health composite score (MCS) from SF-12, using these month-long data. We also examined how accurately the collected data classified the participants into groups of high/low GPA, good/poor sleep quality, high/low self-reported stress, high/low MCS using feature selection and machine learning techniques. We found associations among PSQI, PSS, MCS, and GPA and personality types. Classification accuracies using the objective data from wearable sensors and mobile phones ranged from 67-92%.