nils-herrmann/clean_scirepeval huggingface dataset:
smogd: software for the measurement of genetic diversity.

Software for the measurement of genetic diversity (SMOGD) is a web-based application for the calculation of the recently proposed genetic diversity indices G'(ST) and D(est) . SMOGD includes bootstrapping functionality for estimating the variance, standard error and confidence intervals of estimated parameters, and SMOGD also generates genetic distance matrices from pairwise comparisons between populations. SMOGD accepts standard, multilocus Genepop and Arlequin formatted input files and produces HTML and tab-delimited output. This allows easy data submission, quick visualization, and rapid import of results into spreadsheet or database programs.
The relationship among peak power output, lactate threshold, and short-distance cycling performance: effects of incremental exercise test design.

The purpose of this study was to compare the physiological results of 2 incremental graded exercise tests (GXTs) and correlate these results with a short-distance laboratory cycle time trial (TT). Eleven men (age 25 +/- 5 years, Vo(2)max 62 +/- 8 ml.kg(-1).min(-1)) randomly underwent 3 laboratory tests performed on a cycle ergometer. The first 2 tests consisted of a GXT consisting of either 3-minute (GXT(3-min)) or 5-minute (GXT(5-min)) workload increments. The third test involved 1 laboratory 30-minute TT. The peak power output, lactate threshold, onset of blood lactate accumulation, and maximum displacement threshold (Dmax) determined from each GXT was not significantly different and in agreement when measured from the GXT(3-min) or GXT(5-min). Furthermore, similar correlation coefficients were found among the results of each GXT and average power output in the 30-minute cycling TT. Hence, the results of either GXT can be used to predict performance or for training prescription.
Intrinsic Social Motivation via Causal Influence in Multi-Agent RL

We derive a new intrinsic social motivation for multi-agent reinforcement learning (MARL), in which agents are rewarded for having causal influence over another agent’s actions. Causal influence is assessed using counterfactual reasoning. The reward does not depend on observing another agent’s reward function, and is thus a more realistic approach to MARL than taken in previous work. We show that the causal influence reward is related to maximizing the mutual information between agents’ actions. We test the approach in challenging social dilemma environments, where it consistently leads to enhanced cooperation between agents and higher collective reward. Moreover, we find that rewarding influence can lead agents to develop emergent communication protocols. We therefore employ influence to train agents to use an explicit communication channel, and find that it leads to more effective communication and higher collective reward. Finally, we show that influence can be computed by equipping each agent with an internal model that predicts the actions of other agents. This allows the social influence reward to be computed without the use of a centralised controller, and as such represents a significantly more general and scalable inductive bias for MARL with independent agents.
Soft Proposal Networks for Weakly Supervised Object Localization

Weakly supervised object localization remains challenging, where only image labels instead of bounding boxes are available during training. Object proposal is an effective component in localization, but often computationally expensive and incapable of joint optimization with some of the remaining modules. In this paper, to the best of our knowledge, we for the first time integrate weakly supervised object proposal into convolutional neural networks (CNNs) in an end-to-end learning manner. We design a network component, Soft Proposal (SP), to be plugged into any standard convolutional architecture to introduce the nearly cost-free object proposal, orders of magnitude faster than state-of-the-art methods. In the SP-augmented CNNs, referred to as Soft Proposal Networks (SPNs), iteratively evolved object proposals are generated based on the deep feature maps then projected back, and further jointly optimized with network parameters, with image-level supervision only. Through the unified learning process, SPNs learn better object-centric filters, discover more discriminative visual evidence, and suppress background interference, significantly boosting both weakly supervised object localization and classification performance. We report the best results on popular benchmarks, including PASCAL VOC, MS COCO, and ImageNet.
Recent developments in human gait research: parameters, approaches, applications, machine learning techniques, datasets and challenges

Human gait provides a way of locomotion by combined efforts of the brain, nerves, and muscles. Conventionally, the human gait has been considered subjectively through visual observations but now with advanced technology, human gait analysis can be done objectively and empirically for the better quality of life. In this paper, the literature of the past survey on gait analysis has been discussed. This is followed by discussion on gait analysis methods. Vision-based human motion analysis has the potential to provide an inexpensive, non-obtrusive solution for the estimation of body poses. Data parameters for gait analysis have been discussed followed by preprocessing steps. Then the implemented machine learning techniques have been discussed in detail. The objective of this survey paper is to present a comprehensive analysis of contemporary gait analysis. This paper presents a framework (parameters, techniques, available database, machine learning techniques, etc.) for researchers in identifying the infertile areas of gait analysis. The authors expect that the overview presented in this paper will help advance the research in the field of gait analysis. Introduction to basic taxonomies of human gait is presented. Applications in clinical diagnosis, geriatric care, sports, biometrics, rehabilitation, and industrial area are summarized separately. Available machine learning techniques are also presented with available datasets for gait analysis. Future prospective in gait analysis are discussed in the end.
Checking the realizability of BPMN 2.0 choreographies

Choreographies allow business and service architects to specify with a global perspective the requirements of applications built over distributed and interacting software entities. While being a standard for the abstract specification of business workflows and collaboration between services, the Business Process Modeling Notation (BPMN) has only been recently extended into BPMN 2.0 to support an interaction model of choreography, which, as opposed to interconnected interface models, is better suited to top-down development processes. An important issue with choreographies is real-izability, i.e., whether peers obtained via projection from a choreography interact as prescribed in the choreography requirements. In this work, we propose a realizability checking approach for BPMN 2.0 choreographies. Our approach is formally grounded on a model transformation into the LOTOS NT process algebra and the use of equivalence checking. It is also completely tool-supported through interaction with the Eclipse BPMN 2.0 editor and the CADP process algebraic toolbox.
Organizations Are Users Too: Characterizing and Detecting the Presence of Organizations on Twitter

Much work on the demographics of social media platforms such as Twitter has focused on the properties of individuals, such as gender or age. However, because credible detectors for organization accounts do not exist, these and future largescale studies of human behavior on social media can be contaminated by the presence of accounts belonging to organizations. We analyze organizations on Twitter to assess their distinct behavioral characteristics and determine what types of organizations are active. We first create a dataset of manually classified accounts from a representative sample of Twitter and then introduce a classifier to distinguish between organizational and personal accounts. In addition, we find that although organizations make up less than 10% of the accounts, they are significantly more connected, with an order of magnitude more friends and followers.
Comparative study of load balancing routing algorithms in hybrid networks

In this paper, a comparative study of routing protocols is performed in a hybrid network to recommend the best routing protocol to perform load balancing for Internet traffic. Open Shortest Path First (OSPF), Interior Gateway Routing Protocol (IGRP) and Intermediate System to Intermediate System (IS-IS) routing protocols are compared in OPNET modeller 14 to investigate their capability of ensuring fair distribution of traffic in a hybrid network. The network simulated is scaled to a campus. The network loads are varied in size and performance study is made by running simulations with all the protocols. The only considered performance factors for observation are packet drop, network delay, throughput and network load. IGRP presented better performance as compared to other protocols. The benefit of using IGRP is reduced packet drop, reduced network delay, increased throughput while offering relative better distribution of traffic in a hybrid network.
Mobility-aware caching for content-centric wireless networks: modeling and methodology

As mobile services are shifting from connection- centric communications to content-centric communications, content-centric wireless networking emerges as a promising paradigm to evolve the current network architecture. Caching popular content at the wireless edge, including base stations and user terminals, provides an effective approach to alleviate the heavy burden on backhaul links, as well as lower delays and deployment costs. In contrast to wired networks, a unique characteristic of content-centric wireless networks (CCWNs) is the mobility of mobile users. While it has rarely been considered by existing works on caching design, user mobility contains various helpful side information that can be exploited to improve caching efficiency at both BSs and user terminals. In this article, we present a general framework for mobility-aware caching in CCWNs. Key properties of user mobility patterns that are useful for content caching are first identified, and then different design methodologies for mobility-aware caching are proposed. Moreover, two design examples are provided to illustrate the proposed framework in detail, and interesting future research directions are identified.
Biomechanical Simulation Human Head Helmet for Reducing Traumatic Brain Injury

In human beings, head is one of the most important parts. Injuries in this part can cause serious damages to overall health. In some cases, they can be fatal. The present paper analyses the deformations of a helmet mounted on a human head, using finite element method. It studies the amount of von Mises pressure and stress caused by a vertical blow from above on the skull. The extant paper aims at developing new methods for improving the design and achieving more energy absorption by applying more appropriate models. In this study, a thermoplastic damper is applied and modelled in order to reduce the amount of energy transferred to the skull and to minimize the damages inflicted on human head.
Neuroprotective Effect of Natural Products Against Alzheimer’s Disease

Nature has gifted mankind with a plethora of flora-bearing fruits, vegetables and nuts. The diverse array of bioactive nutrients present in these natural products plays a pivotal role in prevention and cure of various neurodegenerative diseases, such as Alzheimer’s disease (AD), Parkinson’s disease and other neuronal dysfunctions. Accumulated evidence suggests that naturally occurring phyto-compounds, such as polyphenolic antioxidants found in fruits, vegetables, herbs and nuts, may potentially hinder neurodegeneration, and improve memory and cognitive function. Nuts such as walnut have also demonstrated neuroprotective effect against AD. The molecular mechanisms behind the curative effects rely mainly on the action of phytonutrients on distinct signalling pathways associated with protein folding and neuroinflammation. The neuroprotective effects of various naturally occurring compounds in AD is evaluating in this review.
A survey on location estimation techniques for events detected in Twitter

Detection of events using voluntarily generated content in microblogs has been the objective of numerous recent studies. One essential challenge tackled in these studies is estimating the locations of events. In this paper, we review the state-of-the-art location estimation techniques used in the localization of events detected in microblogs, particularly in Twitter, which is one of the most popular microblogging platforms worldwide. We analyze these techniques with respect to the targeted event type, granularity of estimated locations, location-related features selected as sources of spatial evidence, and the method used to make aggregate decisions based on the extracted evidence. We discuss the strengths and advantages of alternative solutions to various problems related to location estimation, as well as their preconditions and limitations. We examine the most widely used evaluation methods to analyze the accuracy of estimations and present the results reported in the literature. We also discuss our findings and highlight important research challenges that may need further attention.
BLITHE: Behavior Rule-Based Insider Threat Detection for Smart Grid

In this paper, we propose a behavior rule-based methodology for insider threat (BLITHE) detection of data monitor devices in smart grid, where the continuity and accuracy of operations are of vital importance. Based on the dc power flow model and state estimation model, three behavior rules are extracted to depict the behavior norms of each device, such that a device (trustee) that is being monitored on its behavior can be easily checked on the deviation from the behavior specification. Specifically, a rule-weight and compliance-distance-based grading strategy is designed, which greatly improves the effectiveness of the traditional grading strategy for evaluation of trustees. The statistical property, i.e., the mathematical expectation of compliance degree of each trustee, is particularly analyzed from both theoretical and practical perspectives, which achieves satisfactory tradeoff between detection accuracy and false alarms to detect more sophisticated and hidden attackers. In addition, based on real data run in POWER WORLD for IEEE benchmark power systems, and through comparative analysis, we demonstrate that BLITHE outperforms the state of arts for detecting abnormal behaviors in pervasive smart grid applications.
Roles for chemokines in liver disease.

Sustained hepatic inflammation is an important factor in progression of chronic liver diseases, including hepatitis C or non-alcoholic steatohepatitis. Liver inflammation is regulated by chemokines, which regulate the migration and activities of hepatocytes, Kupffer cells, hepatic stellate cells, endothelial cells, and circulating immune cells. However, the effects of the different chemokines and their receptors vary during pathogenesis of different liver diseases. During development of chronic viral hepatitis, CCL5 and CXCL10 regulate the cytopathic versus antiviral immune responses of T cells and natural killer cells. During development of nonalcoholic steatohepatitis, CCL2 and its receptor are up-regulated in the liver, where they promote macrophage accumulation, inflammation, fibrosis, and steatosis, as well as in adipose tissue. CCL2 signaling thereby links hepatic and systemic inflammation related to metabolic disorders and insulin resistance. Several chemokine signaling pathways also promote hepatic fibrosis. Recent studies have shown that other chemokines and immune cells have anti-inflammatory and antifibrotic activities. Chemokines and their receptors can also contribute to the pathogenesis of hepatocellular carcinoma, promoting proliferation of cancer cells, the inflammatory microenvironment of the tumor, evasion of the immune response, and angiogenesis. We review the roles of different chemokines in the pathogenesis of liver diseases and their potential use as biomarkers or therapeutic targets.
Comparing SVM and convolutional networks for epileptic seizure prediction from intracranial EEG

Recent research suggests that electrophysiological changes develop minutes to hours before the actual clinical onset in focal epileptic seizures. Seizure prediction is a major field of neurological research, enabled by statistical analysis methods applied to features derived from intracranial Electroencephalographic (EEG) recordings of brain activity. However, no reliable seizure prediction method is ready for clinical applications. In this study, we use modern machine learning techniques to predict seizures from a number of features proposed in the literature. We concentrate on aggregated features that encode the relationship between pairs of EEG channels, such as cross-correlation, nonlinear interdependence, difference of Lyapunov exponents and wavelet analysis-based synchrony such as phase locking. We compare L1-regularized logistic regression, convolutional networks, and support vector machines. Results are reported on the standard Freiburg EEG dataset which contains data from 21 patients suffering from medically intractable focal epilepsy. For each patient, at least one method predicts 100% of the seizures on average 60 minutes before the onset, with no false alarm. Possible future applications include implantable devices capable of warning the patient of an upcoming seizure as well as implanted drug-delivery devices.
A 25 to 45 GHz SiGe receiver MMIC

The frequency range 25-45 GHz contains point-to-point bands at 28, 32, 38 and 42 GHz, potential implementation of 5G at 28 and 39 GHz and various military applications. Traditionally, short gate length GaAs pHEMT technology has been used to develop products for these frequencies. Alternatively, SiGe HBT technology may offer lower cost solutions for systems that can tolerate lower performance. This paper presents the design and measurements of a broadband SiGe receiver with a noise figure of 6 ± 1 dB over the entire 25 to 45 GHz bandwidth. The measured gain exceeds 20 dB from 26 to 43 GHz and, with gain control, is sufficiently linear to support 1024 QAM in the 35 to 45 GHz range. To the authors' knowledge, this the broadest bandwidth achieved to date with a SiGe receiver at these Ka or low millimetre-wave frequencies.
Greek Teachers ’ Attitudes toward the Inclusion of Students with Special Educational Needs

The aim of this study is to examine the teachers’ attitudes toward the inclusion of students with special educational needs, in public schools and how these attitudes are influenced by their self-efficacy perceptions. The sample is comprised of 416 preschool, primary and secondary education teachers. The results show that, in general, teachers develop positive attitude toward the inclusive education. Higher self-efficacy was associated rather with their capacity to come up against negative experiences at school, than with their attitude toward disabled learners in the classroom and their ability to meet successfully the special educational needs students. The results are consistent with similar studies and reveal the need of establishing collaborative support networks in school districts and the development of teacher education programs, in order to achieve the enrichment of their knowledge and skills to address diverse needs appropriately.
Structured Sparse Convolutional Autoencoder

This paper aims to improve the feature learning in Convolutional Networks (Convnet) by capturing the structure of objects. A new sparsity function is imposed on the extracted featuremap to capture the structure and shape of the learned object, extracting interpretable features to improve the prediction performance. The proposed algorithm is based on organizing the activation within and across featuremap by constraining the node activities through `2 and `1 normalization in a structured form.
Assessing the implementability of telehealth interventions for self-management support: a realist review

There is a substantial and continually growing literature on the effectiveness and implementation of discrete telehealth interventions for health condition management. However, it is difficult to predict which technologies are likely to work and be used in practice. In this context, identifying the core mechanisms associated with successful telehealth implementation is relevant to consolidating the likely elements for ensuring a priori optimal design and deployment of telehealth interventions for supporting patients with long-term conditions (LTCs).



We adopted a two-stage realist synthesis approach to identify the core mechanisms underpinning telehealth interventions. In the second stage of the review, we tested inductively and refined our understanding of the mechanisms. We reviewed qualitative papers focused on COPD, heart failure, diabetes, and behaviours and complications associated with these conditions. The review included 15 papers published 2009 to 2014.



Three concepts were identified, which suggested how telehealth worked to engage and support health-related work. Whether or not and how a telehealth intervention enables or limits the possibility for relationships with professionals and/or peers. Telehealth has the potential to reshape and extend existing relationships, acting as a partial substitute for the role of health professionals. The second concept is fit: successful telehealth interventions are those that can be well integrated into everyday life and health care routines and the need to be easy to use, compatible with patients' existing environment, skills, and capacity, and that do not significantly disrupt patients' lives and routines. The third concept is visibility: visualisation of symptoms and feedback has the capacity to improve knowledge, motivation, and a sense of empowerment; engage network members; and reinforce positive behaviour change, prompts for action and surveillance.



Upfront consideration should be given to the mechanisms that are most likely to ensure the successful development and implementation of telehealth interventions. These include considerations about whether and how the telehealth intervention enables or limits the possibility for relationships with professionals and peers, how it fits with existing environment and capacities to self-manage, and visibility-enabling-enhanced awareness to self and others.