nils-herrmann/clean_scirepeval huggingface dataset:
An Embedded Non Contact Body Temperature Measurement System with Automatic Face Tracking and Neural Network Regression
Venue: 2016 International Automatic Control Conference (CACS)

In the last decade, many advances have been made in the field of automatic temperature estimation, including wearable sensor technologies (WST) infrared thermography (IRT) and non contact infrared thermometer (NCIT) In contrast with the WST and IRT, NCIT is inexpensive without the risk of potential skin irritation. Nevertheless, NCIT is limited in short valid estimation distance <12 cm) resulting in the non satisfaction of the surging application requirements nowadays. This paper proposed an algorithm based on Neural Network Regression not only to reduce the error from 0.6deg to 0.12deg, which is close to the medical instrument level, but as well to lengthen the valid distance to the range between 50 cm and 100 cm. Furthermore, this study developed an embedded automatic body temperature estimation system which could continuously and unconsciously measure the human temperature in real time. Integrated with face tracking and fuzzy control of Pan tilt unit, the system ensures that human face is focused while measuring. With wireless communication techniques, users can review their physiological Information via App and Web, which is beneficial to remote healthcare.
The relationship between self esteem and academic achievementmotivation in university students

self esteem is the strongest psychological factor in predicting learning performance and the most important factor in academic achievement of university students. Given the importance of self esteem and its impact on students' motivation to continue their education, the present study was conducted to examine the relationship between self esteem and academic achievement motivation in students at Zahedan University of Medical Sciences.  the present descriptive analytical study was conducted on 200 students at Zahedan University of Medical Sciences in 2015. Data collection was performed using a three part questionnaire (demographic characteristics, Coopersmith Self Esteem Inventory and Harter's Academic Motivation Questionnaire) Then, using the SPSS 19 software, the collected data were analyzed through descriptive statistics, independent t test, ANOVA, Pearson correlation and Chi square tests.  regarding self esteem, the results showed that 107 students were significantly below average, 73 students were average and 20 students were significantly above average. Significant relationships were also observed between selfesteem and intrinsic motivation (p<0.001) and between self esteem and extrinsic motivation (p<0.001)  the results of this study showed a significant relationship between self esteem and academic achievement motivation in university students.
Relationship between Self esteem and Academic Achievement of Secondary School Students

The study was conducted with the purpose to see the Relationship between Self Esteem and Academic Achievement of Secondary school students. A sample consisted of 400 secondary school students of 9 th class studying in government and private schools of Jammu District were taken for present study. Self Esteem Inventory prepared by M.S. Prasad and G.P. Thakur (1977) was used to collect data. Total marks obtained by particular student in the previous examination were taken as academic achievement of students. The collected data was analyzed with the help of Pearson product moment correlation (r).The results of the study showed that a positive and significant correlation between positive self males and their academic achievement. A positive and significant correlation was found between negative self females and academic achievement. A positive but not significant correlation was found between positive self females with their academic achievement. A positive but not significant correlation was found between negative self males and academic achievement of secondary school students.
La jurisprudence du Tribunal international du droit de la mer en matiere de delimitations maritimes

La premiere partie vise ainsi a analyser comment la decision du TIDM du 14 mars 2012 s'est inseree dans ce que le juge Wolfrum qualifiait d' acquis judiciaire voire plus largement dans la production normative existante alors la deuxieme partie cherche a prendre la mesure de la reception de cette decision par les autres juridictions internationales. En d'autres termes, il s'agira de s'interesser, dans un premier temps, a la facon dont le TIDM s'est nourri de la jurisprudence internationale puis dans un deuxieme temps a la facon dont il a, par sa decision, pris part a la production de la jurisprudence. Cette perspective positiviste, pour interessante qu'elle soit, peut toutefois sembler quelque peu insuffisante. Aussi est elle, dans un troisieme temps, completee d'une approche plus sociologique. Il s'agit alors moins de s'interesser au resultat qu'aux actions des individus producteurs de droit Par une etude centree sur les acteurs de la formation du droit (juges, arbitres, conseils, universitaires) il est en effet possible, dans une certaine mesure, de comprendre les mecanismes de circulation des savoirs judiciaires entre les differentes juridictions internationales dans le domaine des delimitations maritimes.
A really simple approximation of smallest grammar
Venue: Theor. Comput. Sci.

In this paper we present a really simple linear time algorithm constructing a context free grammar of size 4 g log 3 2 N g for the input string, where N is the size of the input string and g the size of the optimal grammar generating this string. The algorithm works for arbitrary size alphabets, but the running time is linear assuming that the alphabet S of the input string can be identified with numbers from 1 N c for some constant c. Algorithms with such an approximation guarantee and running time are known, however all of them were non trivial and their analyses were involved. The here presented algorithm computes the LZ77 factorisation and transforms it in phases to a grammar. In each phase it maintains an LZ77 like factorisation of the word with at most factors as well as additional O letters, where was the size of the original LZ77 factorisation. In one phase in a greedy way (by a left to right sweep and a help of the factorisation) we choose a set of pairs of consecutive letters to be replaced with new symbols, i.e. nonterminals of the constructed grammar. We choose at least 2/3 of the letters in the word and there are O many different pairs among them. Hence there are O log N phases, each of them introduces O nonterminals to a grammar. A more precise analysis yields a bound 4 log N As g this yields the desired bound g 4 g log N g
Global Economic Impacts Associated with Artificial Intelligence

Artificial intelligence "AI" a term first coined in 1956, is a branch of computer science that aims to create intelligent machines that work and react like humans.2 At the beginning of the study of AI, British mathematician Alan Turing proposed the true indication of computer intelligence to be when a question asker could not distinguish between answers from a human and those from a computer.3 In contrast, today, 60 years later, AI is characterized by a number of applications, including computers playing games against humans and understanding human languages, virtual personal assistants, and robotics which involve computers seeing, hearing, and reacting to sensory stimuli.4 Looking forward to the next decade, technologists have offered a wide array of predictions for AI, ranging from AI being used as a tool to aid relatively simple processes (which some refer to as weak AI) to robots with human like mental capabilities (which is sometimes referred to as strong AI).5 According to AI expert Sir Nigel Shadbolt: "What we really have in AI is a whole spectrum of abilities, from programs that are smart but they are not smart like us, to programs that are super clever in specific areas."6
A rank sum test for clustered data when the number of subjects in a group within a cluster is informative.
Venue: Biometrics

The Wilcoxon rank sum test is a popular nonparametric test for comparing two independent populations (groups) In recent years, there have been renewed attempts in extending the Wilcoxon rank sum test for clustered data, one of which (Datta and Satten, 2005,  addresses the issue of informative cluster size, i.e. when the outcomes and the cluster size are correlated. We are faced with a situation where the group specific marginal distribution in a cluster depends on the number of observations in that group (i.e. the intra cluster group size) We develop a novel extension of the rank sum test for handling this situation. We compare the performance of our test with the Datta Satten test, as well as the naive Wilcoxon rank sum test. Using a naturally occurring simulation model of informative intra cluster group size, we show that only our test maintains the correct size. We also compare our test with a classical signed rank test based on averages of the outcome values in each group paired by the cluster membership. While this test maintains the size, it has lower power than our test. Extensions to multiple group comparisons and the case of clusters not having samples from all groups are also discussed. We apply our test to determine whether there are differences in the attachment loss between the upper and lower teeth and between mesial and buccal sites of periodontal patients.
3D Deeply Supervised Network for Automatic Liver Segmentation from CT Volumes
Venue: MICCAI

Automatic liver segmentation from CT volumes is a crucial prerequisite yet challenging task for computer aided hepatic disease diagnosis and treatment. In this paper, we present a novel 3D deeply supervised network (3D DSN) to address this challenging task. The proposed 3D DSN takes advantage of a fully convolutional architecture which performs efficient end to end learning and inference. More importantly, we introduce a deep supervision mechanism during the learning process to combat potential optimization difficulties, and thus the model can acquire a much faster convergence rate and more powerful discrimination capability. On top of the high quality score map produced by the 3D DSN, a conditional random field model is further employed to obtain refined segmentation results. We evaluated our framework on the public MICCAI SLiver07 dataset. Extensive experiments demonstrated that our method achieves competitive segmentation results to state of the art approaches with a much faster processing speed.
A Fuzzy Based Integrated Framework for Assessing Time Contingency in Construction Projects

AbstractAssessing time contingency is an important part of construction project scheduling because it helps generate a more reliable schedule. Traditionally, the assessment is made using expert judgment and inherently inserted to the estimated activity duration and/or comprehensively placed at the project level. This practice has a major drawback in that project specific risks that require time contingency are not explicitly identified and treated. Risk management is performed in isolation to project scheduling. This paper presents a new framework for assessing the time contingency required for activities exposed to multiple risks. Fuzzy set theory is employed to model the imprecision and vagueness associated with the possibility of risk occurrence and the impact of those risks on activity durations. Alternative risk responses can be explored and their impacts on the project duration can be evaluated. A real life case example was used to demonstrate the application of the framework. The results indicate t.
Knowledge Engineering: Building Cognitive Assistants for Evidence based Reasoning

This book presents a significant advancement in the theory and practice of knowledge engineering, the discipline concerned with the development of intelligent agents that use knowledge and reasoning to perform problem solving and decision making tasks. It covers the main stages in the development of a knowledge based agent: understanding the application domain, modeling problem solving in that domain, developing the ontology, learning the reasoning rules, and testing the agent. The book focuses on a special class of agents: cognitive assistants for evidence based reasoning that learn complex problem solving expertise directly from human experts, support experts, and nonexperts in problem solving and decision making, and teach their problem solving expertise to students. A powerful learning agent shell, Disciple EBR, is included with the book, enabling students, practitioners, and researchers to develop cognitive assistants rapidly in a wide variety of domains that require evidence based reasoning, including intelligence analysis, cybersecurity, law, forensics, medicine, and education.
The global financial crisis: experiences of and implications for community based organizations providing health and social services in South Africa

The global financial crisis that began in 2008 with the collapse of the US real estate bubble is considered the worst economic turmoil since the Great Depression in the 1930s. While the crisis has negatively impacted the global economy and the flow of aid to Sub Saharan African countries, little is known about the implications of the crisis for community based organizations (CBOs) providing health related services in marginalized communities. We conducted qualitative interviews with managers of 14 CBOs providing health and social services to marginalized communities in South Africa about their experiences of the crisis. CBOs reported experiencing a marked decrease in funding received from both international and local donors as a result of the global financial crisis. At the same time, they experienced difficulties in securing new funding. Organizations addressed the funding problems by conducting organizational restructuring and implementing austerity measures that led to the retrenchment of staff, reduction in benefits and incentives for staff and volunteers, reduction in the number of communities served and rationing of services provided to these communities. These measures had negative psychological impacts on paid staff and volunteers and contributed to absenteeism and attrition among volunteers, and some of the organizations eventually closed down. Our findings show that the global financial crisis has far reaching implications for health, social and developmental services delivery and ravaging impacts on the economy of marginalized communities. Policy makers should explore mechanisms for protecting CBOs from the effects of economic shocks to guarantee the provision of critical services to marginalized communities.
A differential game approach to security investment and information sharing in a competitive environment

Information security economics, an emerging and thriving research topic, attempts to address the problems of distorted incentives for stakeholders in an Internet environment, including firms, hackers, the public sector, and other participants, using economic approaches. To alleviate consumer anxiety about the loss of sensitive information, and to further increase consumer demand, firms usually integrate their information security investment strategies to capture market share from competitors and their security information sharing strategies to increase consumer demand across all member firms in industry based information sharing centers. Using differential game theory, this article investigates dynamic strategies for security investment and information sharing for two competing firms under targeted attacks, in which both firms can influence the value of their information assets through the endogenous determination of pricing rates. We analytically and numerically examine how both security investment rates and information sharing rates are affected by several key parameters in a non cooperative scenario, including the efficiency of security investment rates, sensitivity parameters for pricing rates, coefficients of consumer demand losses, and the density of targeted attacks. Our results reveal that, confronted with a higher coefficient of consumer demand loss and a higher density of targeted attacks, both firms are reluctant to aggressively defend against hackers and would rather decrease the negative effect of hacker attacks by lowering their pricing rates. Also, we derive feedback equilibrium solutions for the situation where both firms cooperate in security investment, information sharing, or both. It is revealed that although a higher hacker attack density always decreases a firm's integral profits, both firms are not always willing to cooperate in security investment and information sharing. Specifically, the superior firm benefits most when both firms fully cooperate and benefits the least when they behave fully non cooperatively. However, the inferior firm enjoys the highest integral profit when both firms only cooperate in information sharing and the lowest integral profit in the completely cooperative situation.
Disentangling factors of variation in deep representation using adversarial training
Venue: NIPS

We introduce a conditional generative model for learning to disentangle the hidden factors of variation within a set of labeled observations, and separate them into complementary codes. One code summarizes the specified factors of variation associated with the labels. The other summarizes the remaining unspecified variability. During training, the only available source of supervision comes from our ability to distinguish among different observations belonging to the same class. Examples of such observations include images of a set of labeled objects captured at different viewpoints, or recordings of set of speakers dictating multiple phrases. In both instances, the intra class diversity is the source of the unspecified factors of variation: each object is observed at multiple viewpoints, and each speaker dictates multiple phrases. Learning to disentangle the specified factors from the unspecified ones becomes easier when strong supervision is possible. Suppose that during training, we have access to pairs of images, where each pair shows two different objects captured from the same viewpoint. This source of alignment allows us to solve our task using existing methods. However, labels for the unspecified factors are usually unavailable in realistic scenarios where data acquisition is not strictly controlled. We address the problem of disentaglement in this more general setting by combining deep convolutional autoencoders with a form of adversarial training. Both factors of variation are implicitly captured in the organization of the learned embedding space, and can be used for solving single image analogies. Experimental results on synthetic and real datasets show that the proposed method is capable of generalizing to unseen classes and intra class variabilities.
An Attribtue Based Statistic Model for Privacy Impact Assessment
Venue: 2016 International Conference on Collaboration Technologies and Systems (CTS)

Personally Identifiable Information (PII) includes any information that can be used to distinguish or trace an individual's identity such as name, social security number, date and place of birth, mother's maiden name, or biometric records. It also includes other information that is linked or linkable to an individual, such as medical, educational, financial, and employment information. PII is often the target of attacks, and loss of PII could result in identity theft. According to the U.S. Department of Justice, the average number of U.S. identity fraud victims annually is 11,571,900  The total financial loss attributed to identity theft in 2013 was $21 billion dollars, compared to $13.2 billion total loss in 2010.
Reduced graphene oxide wrapped core shell metal nanowires as promising flexible transparent conductive electrodes with enhanced stability.
Venue: Nanoscale

Transparent conductive electrodes (TCEs) are widely used in a wide range of optical electronic devices. Recently, metal nanowires (NWs) e.g. Ag and Cu, have drawn attention as promising flexible materials for TCEs. Although the study of core shell metal NWs, and the encapsulation/overcoating of the surface of single metal NWs have separately been an object of focus in the literature, herein for the first time we simultaneously applied both strategies in the fabrication of highly stable Ag Cu NW based TCEs by the utilization of Ag nanoparticles covered with reduced graphene oxide (rGO) The incorporation of Ag nanoparticles by galvanic displacement reaction was shown to significantly increase the long term stability of the electrode. Upon comparison with a CuNW reference, our novel rGO/Cu AgNW based TCEs unveiled remarkable opto electrical properties, with a 3 fold sheet resistance decrease (from 29.8 O sq 1 to 10.0 O sq 1) and an impressive FOM value (139.4) No detrimental effect was noticed in the relatively high transmittance value (T 77.6% at 550 nm) characteristic of CuNWs. In addition, our rGO/Cu AgNW based TCEs exhibited outstanding thermal stability up to 20 days at 80 degC in air, as well as improved mechanical flexibility. The superior performance herein reported compared with both CuNWs and AgNWs, and with a current conventional ITO reference, is believed to highlight the great potential of these novel materials as promising alternatives in optical electronic devices.
A Multi criteria Decision Making Approach for the Assessment of Information Credibility in Social Media
Venue: WILF

In Social Media, large amounts of User Generated Content (UGC) generally diffuse without any form of trusted external control. In this context, the risk of running into misinformation is not negligible. For this reason, assessing the credibility of both information and its sources in Social Media platforms constitutes nowadays a fundamental issue for users. In the last years, several approaches have been proposed to address this issue. Most of them employ machine learning techniques to classify information and misinformation. Other approaches exploit multiple kinds of relationships connecting entities in Social Media applications, focusing on credibility and trust propagation. Unlike previous approaches, in this paper we propose a model driven approach based on Multi Criteria Decision Making (MCDM) and quantifier guided aggregation. An overall credibility estimate for each piece of information is obtained based on multiple criteria connected to both UGC and users generating it. The proposed model is evaluated in the context of opinion spam detection in review sites, on a real world dataset crawled from Yelp, and it is compared with well known supervised machine learning techniques.
The influences of mathematics beliefs on mathematics achievement through mathematics self efficacy: A structural equation model

The purpose of present study was to examine the direct and indirect effects of mathematics beliefs on mathematics achievement. Specially, regarding to powerful predictor and mediator of mathematics self efficacy. The subjects of the study were middle school students of the academic year 2013. 400 of 8th graders were selected from four public schools of Shiraz in Iran to estimate and test the hypothesized effects of mathematics beliefs on mathematics self efficacy and mathematics achievement. A two stage Cluster Random Sampling Method was utilized. This study examined correlations between latent variables and their factors also determined coefficients between latent variables by using Measurement Model. Then the estimate of Structural Equation Model revealed that mathematics beliefs had a direct and an indirect effect on mathematics achievement. The model specified mathematics beliefs and mathematics achievement. The high goodness of fit indices, also acknowledged that postulated model has a good fit to the data.