nils-herrmann/clean_scirepeval huggingface dataset:
Distributed Breakout Algorithm for Solving Distributed Constraint Satisfaction Problems

This paper presents a new algorithm for solving distributed constraint satisfaction problems (distributed CSPs) called the distributed breakout algorithm, which is inspired by the breakout algorithm for solving centraiized CSPs. In this algorithm, each agent tries to optimize its evaluation value (the number of constraint violations) by exchanging its current value and the possible amount of its improvement among neighboring agents. Instead of detecting the fact that agents as a whole are trapped in a local-minimum, each agent detects whether it is in a quasi-local-minimum, which is a weaker condition than a local-minimum, and changes the weights of constraint violations to escape from the quasi-local-minimum. Experimental evaluations show this algorithm to be much more efficient than existing algorithms for critically difficult problem instances of distributed graph-coloring problems.
Flexible and Stretchable Electronics Paving the Way for Soft Robotics

Planar and rigid wafer-based electronics are intrinsically incompatible with curvilinear and deformable organisms. Recent development of organic and inorganic flexible and stretchable electronics enabled sensing, stimulation, and actuation of/for soft biological and artificial entities. This review summarizes the enabling technologies of soft sensors and actuators, as well as power sources based on flexible and stretchable electronics. Examples include artificial electronic skins, wearable biosensors and stimulators, electronics-enabled programmable soft actuators, and mechanically compliant power sources. Their potential applications in soft robotics are illustrated in the framework of a five-step human–robot interaction loop. Outlooks of future directions and challenges are provided at the end.
The Ontogenesis of Social Dominance : A Strategy-Based Evolutionary Perspective

Social dominance results when members of a social group vary in their ability to acquire resources in the presence of others (i.e., compete). Traditional approaches to social dominance often emphasize coercive behavior, but nonetheless suggest that dominant individuals are socially central (e.g., watched, attractive social partners). These patterns, however, apply to humans only up to a certain age. This apparent discontinuity may give the false impression that social dominance is less relevant to human social organization than it is to animal social organization. This paper reintroduces the ethological concept of social dominance, but reinterprets it from a strategy-based perspective. That is, if social dominance is defined as differential ability to control resources—without reference to how this is done—then children evidently employ different strategies to compete with peers (e.g., coercive and prosocial). Furthermore, the type of strategy children employ and peers’ responses to it depend largely on the ages of the children. By adopting a strategy-based approach to social dominance and explicitly incorporating developmental processes and uniquely human capacities, human social dominance patterns appear to be more similar to primate patterns than commonly believed. Implications for social competence, peer relationships, and the development of the self are discussed.  1999
On Extracting Structured Knowledge from Unstructured Business Documents

Efficient management of text data is a major concern of business organizations. In this direction, we propose a novel approach to extract structured knowledge from large corpora of unstructured business documents. This knowledge is represented in the form of object instances, which are common ways of organizing the available information about entities, and are modeled here using document templates. The approach itself is based on the observation that a significant fraction of these documents are created using the cut-copy-pastemethod, and thus, it is important to factor this observation into business document analysis projects. Correspondingly, our approach solves the problem of object instance extraction in two steps, namely similarity search and then extraction of object instances from the selected documents. Early qualitative results on a couple of carefully selected document corpora indicate the effective applicability of the approach for solving an important component of the efficient text management problem.
A forensic evidence recovery from mobile device applications

In recent past, there are a lot of research advancements in mobile forensics tools. This is so due to increase usage of mobile phones in storage of information, law enforcement, mobile online transactions, and also negatively by criminals due to increased computational capabilities. Mobile forensics devices continue to remain a very challenging task due to poor user data retrieval techniques for evidence retrieval. Recently, third party applications assume a veritable feet because it is supported by majority of mobile devices platforms, thereby making it easy to extract information of its users’ for future criminal audit. This paper proposes an evidence data retrieval method from InstagramApp using two networks-based platforms [that is, pure peer-to-peer (PPP) and special cluster peer (SCP)-based], whose concept is to manage mobile device communication and generate multiple copies of users data/information to be dumped across three servers. The forensic test results were obtained from PPP and SCP developed to securely extract data from mobile devices. This shows that, SCP outperformed PPP in terms of the time taken to fulfil forensic auditor’s request, throughput and broadband utilisation which are 42.82% to 57.18%, 56.81% to 43.19% and 35.41% to 64.53% respectively.
Depth Estimation from Single Image Using CNN-Residual Network

In this project, we tackle the problem of depth estimation from single image. The mapping between a single image and the depth map is inherently ambiguous, and requires both global and local information. We employ a fully convolutional architecture, which first extracts image feature by pretrained ResNet-50 network. We do transfer learning by replacing the fully connected layer of ResNet-50 with upsampling blocks to recover the size of depth map. The upsampling block combines residual learning concept. This CNN-Residual network can be trained en-to-end, and runs real time on images with enough computing power. We demonstrate that our method of doing up-sampling by CNN-Residual network yields better result than fully connected layer, because it avoids overfitting. We also compare our model with pure CNN network and illustrates the effectiveness of tansfer learning. We also show the influence of different loss functions during training. The results are shown by comparing qualitative visualization and quantitative metrics.
Overweight among primary school-age children in Malaysia.

This study is a secondary data analysis from the National Health Morbidity Survey III, a population-based study conducted in 2006. A total of 7,749 children between 7 and 12 years old were recruited into the study. This study seeks to report the prevalence of overweight (including obesity) children in Malaysia using international cut-off point and identify its associated key social determinants. The results show that the overall prevalence of overweight children in Malaysia was 19.9%. The urban residents, males, Chinese, those who are wealthy, have overweight or educated guardians showed higher prevalence of overweight. In multivariable analysis, higher likelihood of being overweight was observed among those with advancing age (OR=1.15), urban residents (OR=1.16, 95% CI: 1.01-1.36), the Chinese (OR=1.45, 95% CI: 1.19-1.77), boys (OR=1.23, 95% CI: 1.08-1.41), and those who came from higher income family. In conclusion, one out of five of 7-12 year-old-children in Malaysia were overweight. Locality of residence, ethnicity, gender, guardian education, and overweight guardian were likely to be the predictors of this alarming issue. Societal and public health efforts are needed in order to reduce the burden of disease associated with obesity.
The challenge of decentralized marketplaces

Online trust systems are playing an important role in to-days world and face various challenges in building them. Billions of dollars of products and services are traded through electronic commerce, files are shared among large peer-to-peer networks and smart contracts can potentially replace paper contracts with digital contracts. These systems rely on trust mechanisms in peer-to-peer networks like reputation systems or a trustless public ledger. In most cases, reputation systems are build to determine the trustworthiness of users and to provide incentives for users to make a fair contribution to the peer-to-peer network. The main challenges are how to set up a good trust system, how to deal with security issues and how to deal with strategic users trying to cheat on the system. The Sybil attack, the most important attack on reputation systems is discussed. At last match making in two sided markets and the strategy proofness of these markets are discussed.
Artificial intelligence in medicine and cardiac imaging: harnessing big data and advanced computing to provide personalized medical diagnosis and treatment.

Although advances in information technology in the past decade have come in quantum leaps in nearly every aspect of our lives, they seem to be coming at a slower pace in the field of medicine. However, the implementation of electronic health records (EHR) in hospitals is increasing rapidly, accelerated by the meaningful use initiatives associated with the Center for Medicare & Medicaid Services EHR Incentive Programs. The transition to electronic medical records and availability of patient data has been associated with increases in the volume and complexity of patient information, as well as an increase in medical alerts, with resulting "alert fatigue" and increased expectations for rapid and accurate diagnosis and treatment. Unfortunately, these increased demands on health care providers create greater risk for diagnostic and therapeutic errors. In the near future, artificial intelligence (AI)/machine learning will likely assist physicians with differential diagnosis of disease, treatment options suggestions, and recommendations, and, in the case of medical imaging, with cues in image interpretation. Mining and advanced analysis of "big data" in health care provide the potential not only to perform "in silico" research but also to provide "real time" diagnostic and (potentially) therapeutic recommendations based on empirical data. "On demand" access to high-performance computing and large health care databases will support and sustain our ability to achieve personalized medicine. The IBM Jeopardy! Challenge, which pitted the best all-time human players against the Watson computer, captured the imagination of millions of people across the world and demonstrated the potential to apply AI approaches to a wide variety of subject matter, including medicine. The combination of AI, big data, and massively parallel computing offers the potential to create a revolutionary way of practicing evidence-based, personalized medicine.
Supporting integrated STEM in the elementary classroom: a professional development approach centered on an engineering design challenge

Science, technology, engineering, and mathematics (STEM) education is becoming more prevalent at the elementary level, and there has been a push to focus on the integration between the STEM disciplines. Researchers within this study sought to understand the extent to which triads composed of a classroom teacher, student teacher, and an engineering fellow were able to use the context of an engineering design challenge to integrate and incorporate STEM concepts into the elementary classroom. Using a content analysis approach, researchers analyzed STEM integration across four phases of learning: professional development workshop, lesson plan, classroom enactment, and post-lesson reflection.  Results highlight the ability for triads to conceptualize the integration of STEM concepts but also the challenge to sustain the integration of STEM concepts across phases of enactment.  The need to support teacher learning of STEM content and pedagogical practices for integration are discussed.
Combination of Structural and Factual Descriptors for Document Stream Segmentation

This paper extends a previous work being done by . Having no information about the document separation in the flow, the system operates progressively by examining successive pairs of pages looking for continuity or rupture descriptors. Four document levels have been introduced to better extract those descriptors and reduce the ambiguity in their extraction: records, technical documents, fundamental documents and cases. At each level, structural and factual descriptors are first extracted and then compared between pairs of pages or documents. To reinforce the descriptor interest and focus the system on equivalent descriptors in the pairs, the descriptors are accompanied by their context. The extraction of the context is facilitated by the determination of the physical and logical structure in the pages. Contextual rules based on these descriptors are employed for the determination of either a continuity, a rupture or an uncertainty between the pairs. To overcome the problem of information emptiness in the current page, a logbook is used to gather the descriptors in all the previous pages of the record and a buffer allows to delay the comparison. These latter points were added to the previous work that widely reinforce the current system increasing its precision of more than 6%.
Road Detection From Remote Sensing Images by Generative Adversarial Networks

Road detection with high-precision from very high resolution remote sensing imagery is very important in a huge variety of applications. However, most existing approaches do not automatically extract the road with a smooth appearance and accurate boundaries. To address this problem, we proposed a novel end-to-end generative adversarial network. In particular, we construct a convolutional network based on adversarial training that could discriminate between segmentation maps coming either from the ground truth or generated by the segmentation model. The proposed method could improve the segmentation result by finding and correcting the difference between ground truth and result output by the segmentation model. Extensive experiments demonstrate that the proposed method outperforms the state-of-the-art methods greatly on the performance of segmentation map.
Native-NMOS-triggered SCR (NANSCR) for ESD protection in 0.13-/spl mu/m CMOS integrated circuits

A novel native-NMOS-triggered SCR (NANSCR) is proposed for efficient ESD protection design in a 0.13-/spl mu/m CMOS process. As compared with the traditional LVTSCR, the trigger voltage, turn-on resistance, turn-on speed, and CDM ESD level of NAN SCR have been greatly improved to protect the ultra-thin gate oxide against ESD stresses. The proposed NANSCR can be designed for the input, output, and power-rail ESD protection circuits without latchup danger in a 0.13-/spl mu/m CMOS process with VDD of 1.2 V. A new whole-chip ESD protection scheme realized with the NANSCR devices has been also demonstrated with the consideration of pin-to-pin ESD zapping.
A practical traffic management system for integrated LTE-WiFi networks

Mobile operators are leveraging WiFi to relieve the pressure posed on their networks by the surging bandwidth demand of applications. However, operators often lack intelligent mechanisms to control the way users access their WiFi networks. This lack of sophisticated control creates poor network utilization, which in turn degrades the quality of experience (QoE). To meet user traffic demands, it is evident that operators need solutions that optimally balance user traffic across cellular and WiFi networks. Motivated by the lack of practical solutions in this space, we design and implement ATOM - an end-to-end system for adaptive traffic offloading for WiFi-LTE deployments. ATOM has two novel components: (i) A network interface selection algorithm that maps user traffic across WiFi and LTE to optimize user QoE and (ii) an interface switching service that seamlessly re-directs ongoing user sessions in a cost-effective and standards-compatible manner. Our evaluations on a real LTE-WiFi testbed using YouTube traffic reveals that ATOM reduces video stalls by 3-4 times compared to naive solutions.
The economic costs of pain in the United States.

In 2008, according to the Medical Expenditure Panel Survey (MEPS), about 100 million adults in the United States were affected by chronic pain, including joint pain or arthritis. Pain is costly to the nation because it requires medical treatment and complicates treatment for other ailments. Also, pain lowers worker productivity. Using the 2008 MEPS, we estimated 1) the portion of total U.S. health care costs attributable to pain; and 2) the annual costs of pain associated with lower worker productivity. We found that the total costs ranged from $560 to $635 billion in 2010 dollars. The additional health care costs due to pain ranged from $261 to $300 billion. This represents an increase in annual per person health care costs ranging from $261 to $300 compared to a base of about $4,250 for persons without pain. The value of lost productivity due to pain ranged from $299 to $335 billion. We found that the annual cost of pain was greater than the annual costs of heart disease ($309 billion), cancer ($243 billion), and diabetes ($188 billion). Our estimates are conservative because they do not include costs associated with pain for nursing home residents, children, military personnel, and persons who are incarcerated.



This study estimates that the national cost of pain ranges from $560 to $635 billion, larger than the cost of the nation's priority health conditions. Because of its economic toll on society, the nation should invest in research, education, and training to advocate the successful treatment, management, and prevention of pain.
Development and evaluation of collision warning / collision avoidance algorithms using an errable driver model

Collision warning/collision avoidance (CW/CA) systems must be designed to work seamlessly with a human driver, providing warning or control actions when the driver’s response (or lack of) is deemed inappropriate. The effectiveness of CW/CA systems working with a human driver needs to be evaluated thoroughly because of legal/liability and other (e.g. traffic flow) concerns. CW/CA systems tuned only under open-loop manoeuvres were frequently found to work unsatisfactorily with human-in-theloop. However, tuning CW/CA systems with human drivers co-existing is slow and non-repeatable. Driver models, if constructed and used properly, can capture human/control interactions and accelerate the CW/CA development process. Design and evaluation methods for CW/CA algorithms can be categorised into three approaches, scenario-based, performance-based and human-centred. The strength and weakness of these approaches were discussed in this paper and a humanised errable driver model was introduced to improve the developing process. The errable driver model used in this paper is a model that emulates human driver’s functions and can generate both nominal (error-free) and devious (with error) behaviours. The car-following data used for developing and validating the model were obtained from a large-scale naturalistic driving database. Three error-inducing behaviours were introduced: human perceptual limitation, time delay and distraction. By including these error-inducing behaviours, rear-end collisions with a lead vehicle were found to occur at a probability similar to traffic accident statistics in the USA. This driver model is then used to evaluate the performance of several existing CW/CA algorithms. Finally, a new CW/CA algorithm was developed based on this errable driver model.
Electrical stimulation of a small brain area reversibly disrupts consciousness

The neural mechanisms that underlie consciousness are not fully understood. We describe a region in the human brain where electrical stimulation reproducibly disrupted consciousness. A 54-year-old woman with intractable epilepsy underwent depth electrode implantation and electrical stimulation mapping. The electrode whose stimulation disrupted consciousness was between the left claustrum and anterior-dorsal insula. Stimulation of electrodes within 5mm did not affect consciousness. We studied the interdependencies among depth recording signals as a function of time by nonlinear regression analysis (h(2) coefficient) during stimulations that altered consciousness and stimulations of the same electrode at lower current intensities that were asymptomatic. Stimulation of the claustral electrode reproducibly resulted in a complete arrest of volitional behavior, unresponsiveness, and amnesia without negative motor symptoms or mere aphasia. The disruption of consciousness did not outlast the stimulation and occurred without any epileptiform discharges. We found a significant increase in correlation for interactions affecting medial parietal and posterior frontal channels during stimulations that disrupted consciousness compared with those that did not. Our findings suggest that the left claustrum/anterior insula is an important part of a network that subserves consciousness and that disruption of consciousness is related to increased EEG signal synchrony within frontal-parietal networks.
The impact of agile practices on communication in software development

Agile software development practices such as eXtreme Programming (XP) and SCRUM have increasingly been adopted to respond to the challenges of volatile business environments, where the markets and technologies evolve rapidly and present the unexpected. In spite of the encouraging results so far, little is known about how agile practices affect communication. This article presents the results from a study which examined the impact of XP and SCRUM practices on communication within software development teams and within the focal organization. The research was carried out as a case study in F-Secure where two agile software development projects were compared from the communication perspective. The goal of the study is to increase the understanding of communication in the context of agile software development: internally among the developers and project leaders and in the interface between the development team and stakeholders (i.e. customers, testers, other development teams). The study shows that agile practices improve both informal and formal communication. However, it further indicates that, in larger development situations involving multiple external stakeholders, a mismatch of adequate communication mechanisms can sometimes even hinder the communication. The study highlights the fact that hurdles and improvements in the communication process can both affect the feature requirements and task subtask dependencies as described in coordination theory. While the use of SCRUM and some XP practices facilitate team and organizational communication of the dependencies between product features and working tasks, the use of agile practices requires that the team and organization use also additional plan-driven practices to ensure the efficiency of external communication between all the actors of software development.