nils-herrmann/clean_scirepeval huggingface dataset:
Survey of Important Issues in UAV Communication Networks

Unmanned aerial vehicles (UAVs) have enormous potential in the public and civil domains. These are particularly useful in applications, where human lives would otherwise be endangered. Multi-UAV systems can collaboratively complete missions more efficiently and economically as compared to single UAV systems. However, there are many issues to be resolved before effective use of UAVs can be made to provide stable and reliable context-specific networks. Much of the work carried out in the areas of mobile ad hoc networks (MANETs), and vehicular ad hoc networks (VANETs) does not address the unique characteristics of the UAV networks. UAV networks may vary from slow dynamic to dynamic and have intermittent links and fluid topology. While it is believed that ad hoc mesh network would be most suitable for UAV networks yet the architecture of multi-UAV networks has been an understudied area. Software defined networking (SDN) could facilitate flexible deployment and management of new services and help reduce cost, increase security and availability in networks. Routing demands of UAV networks go beyond the needs of MANETS and VANETS. Protocols are required that would adapt to high mobility, dynamic topology, intermittent links, power constraints, and changing link quality. UAVs may fail and the network may get partitioned making delay and disruption tolerance an important design consideration. Limited life of the node and dynamicity of the network lead to the requirement of seamless handovers, where researchers are looking at the work done in the areas of MANETs and VANETs, but the jury is still out. As energy supply on UAVs is limited, protocols in various layers should contribute toward greening of the network. This paper surveys the work done toward all of these outstanding issues, relating to this new class of networks, so as to spur further research in these areas.
Interplay Between the Gut-Brain Axis, Obesity and Cognitive Function

Obesity continues to be one of the major public health problems due to its high prevalence and co-morbidities. Common co-morbidities not only include cardiometabolic disorders but also mood and cognitive disorders. Obese subjects often show deficits in memory, learning and executive functions compared to normal weight subjects. Epidemiological studies also indicate that obesity is associated with a higher risk of developing depression and anxiety, and vice versa. These associations between pathologies that presumably have different etiologies suggest shared pathological mechanisms. Gut microbiota is a mediating factor between the environmental pressures (e.g., diet, lifestyle) and host physiology, and its alteration could partly explain the cross-link between those pathologies. Westernized dietary patterns are known to be a major cause of the obesity epidemic, which also promotes a dysbiotic drift in the gut microbiota; this, in turn, seems to contribute to obesity-related complications. Experimental studies in animal models and, to a lesser extent, in humans suggest that the obesity-associated microbiota may contribute to the endocrine, neurochemical and inflammatory alterations underlying obesity and its comorbidities. These include dysregulation of the HPA-axis with overproduction of glucocorticoids, alterations in levels of neuroactive metabolites (e.g., neurotransmitters, short-chain fatty acids) and activation of a pro-inflammatory milieu that can cause neuro-inflammation. This review updates current knowledge about the role and mode of action of the gut microbiota in the cross-link between energy metabolism, mood and cognitive function.
The outcome of children with selective mutism following cognitive behavioral intervention: a follow-up study

Selective mutism (SM) is a relatively rare childhood disorder and is underdiagnosed and undertreated. The purpose of the retrospective naturalistic study was to examine the long-term outcome of children with SM who were treated with specifically designed modular cognitive behavioral therapy (MCBT). Parents of 36 children who met diagnostic criteria of SM that received MCBT treatment were invited for a follow-up evaluation. Parents were interviewed using structured scales and completed questionnaires regarding the child, including the Selective Mutism Questionnaire (SMQ). Twenty-four subjects were identified and evaluated. Their mean age ± SD of onset of SM symptoms, beginning of treatment, and age at follow-up were 3.4 ± 1.4, 6.4 ± 3.1, and 9.3 ± 3.4 years, respectively. There was robust improvement from beginning of treatment to follow-up evaluation in SM, social anxiety disorder, and specific phobia symptoms. The recovery rate from SM was 84.2 %.  SM-focused MCBT is feasible in children and possibly effective in inducing long-term reduction of SM and comorbid anxiety symptoms.  • There are limited empirical data on selective mutism (SM) treatment outcome and specifically on cognitive-behavioral therapy, with the majority of studies being uncontrolled case reports of 1 to 2 cases each. • There is also limited data on the long-term outcome of children with SM following treatment.  • Modular cognitive behavioral treatment is a feasible and possibly effective treatment for SM. Intervention at a younger age is more effective comparing to an older age. • Treatment for SM also decreases the rate of psychiatric comorbidities, including separation anxiety disorder and specific phobia.
Effectuation in SPWM technique for 3-Φ VSI using STM32F4 discovery board interfaced with MATLAB

In this paper, Design and development of control circuit algorithm of 3-Φ Voltage Source Inverter (VSI) using Unipolar Sinusoidal Pulse Width Modulation (SPWM) techniques is proposed using STM32F407VGTx discovery board interfaced with MATLAB Simulink Environment. For implementing 3-Φ VSI, this method is most useful as it will eliminate complexity of control circuit algorithm occurred due to analog circuit, reduces cost, size of filter required, and improved overall inverter efficiency. Pulse width modulated voltage source inverter is preferred for high performance AC drives. The proposed control scheme and the result obtained by embedded simulations interfaced with STM32F4 microcontroller are presented and discussed. A simple hardware implementation is proposed and experimental results of SPWM based VSI are also shown. The Simulation results of the proposed scheme have excellent performance and compared with hardware results.
Creating state of the art, next-generation Virtual Reality exposure therapies for anxiety disorders using consumer hardware platforms: design considerations and future directions.

Decades of research and more than 20 randomized controlled trials show that Virtual Reality exposure therapy (VRET) is effective in reducing fear and anxiety. Unfortunately, few providers or patients have had access to the costly and technical equipment previously required. Recent technological advances in the form of consumer Virtual Reality (VR) systems (e.g. Oculus Rift and Samsung Gear), however, now make widespread use of VRET in clinical settings and as self-help applications possible. In this literature review, we detail the current state of VR technology and discuss important therapeutic considerations in designing self-help and clinician-led VRETs, such as platform choice, exposure progression design, inhibitory learning strategies, stimuli tailoring, gamification, virtual social learning and more. We illustrate how these therapeutic components can be incorporated and utilized in VRET applications, taking full advantage of the unique capabilities of virtual environments, and showcase some of these features by describing the development of a consumer-ready, gamified self-help VRET application for low-cost commercially available VR hardware. We also raise and discuss challenges in the planning, development, evaluation, and dissemination of VRET applications, including the need for more high-quality research. We conclude by discussing how new technology (e.g. eye-tracking) can be incorporated into future VRETs and how widespread use of VRET self-help applications will enable collection of naturalistic "Big Data" that promises to inform learning theory and behavioral therapy in general.
Influence of tobacco use in dental caries development.

This review article describes different forms of tobacco usage and its direct relationship with the prevalence of dental caries. Smoking along with co-existing factors like old age, bad oral hygiene habits, food habits, limited preventive dental visits and over all health standards, can be associated with high caries incidence. However, a direct etiological relationship is lacking. Environmental tobacco smoke (ETS) causes dental caries in children but no studies have been reported in adults. Existing findings are not sufficient and conclusive enough to confirm that ETS causes dental caries. Oral use of smokeless tobacco (ST), predominantly tobacco chewing, is presumably a positive contributing factor to higher incidence of dental caries. Unfortunately, published studies are not converging towards one single factor through which tobacco usage can have direct relationship to dental caries.
A Coarse-to-fine Deep Convolutional Neural Network Framework for Frame Duplication Detection and Localization in Video Forgery

Frame duplication is to duplicate a sequence of consecutive frames and insert or replace to conceal or imitate a specific event/content in the same source video. To automatically detect the duplicated frames in a manipulated video, we propose a coarse-to-fine deep convolutional neural network framework to detect and localize the frame duplications. We first run an I3D network [2] to obtain the most candidate duplicated frame sequences and selected frame sequences, and then run a Siamese network with ResNet network [6] to identify each pair of a duplicated frame and the corresponding selected frame. We also propose a heuristic strategy to formulate the video-level score. We then apply our inconsistency detector fine-tuned on the I3D network to distinguish duplicated frames from selected frames. With the experimental evaluation conducted on two video datasets, we strongly demonstrate that our proposed method outperforms the current state-of-the-art methods.
Gesture recognition using FPGA and OV7670 camera

Gesture recognition has lured everyone's attention as a new generation of HCI and visual input mode. FPGA presents a better overall performance and flexibility than DSP for parallel processing and pipelined operations in order to process high resolution and high frame rate video processing. Vision-based gesture recognition technique is the best way to recognize the gesture. In gesture recognition, the image acquisition and image segmentation is there. In this paper, the image acquisition is shown and also the image segmentation techniques are discussed. In this, to capture the gesture the OV7670 CMOS camera chip sensor is used that is attached to FPGA DE-1 board. By using this gesture recognition, we can control any application in a non-tangible way.
Systems thinking: Foundations for enhancing system of systems engineering

This paper explores Systems Thinking (ST) as an essential foundation for more effective System of Systems Engineering (SoSE). ST is explored as a particular worldview grounded in appreciation of system behavior (performance) stemming not from system elements, but rather from the interaction of the elements. Following a short introduction, three primary avenues are explored in this paper, including: (1) examination of the nature of ST with emphasis on the central tenets of the field, (2) identification of the contributions and role of ST for SoSE, and (3) exploration of thirteen points defining a ST worldview and their implications for SoSE. The paper closes with a set of implications for practitioners and challenges for advancing the SoSE field through further inclusion of ST.
Dismantling Composite Visualizations in the Scientific Literature

We are analyzing the visualizations in the scientific literature to enhance search services, detect plagiarism, and study bibliometrics. An immediate problem is the ubiquitous use of multi-part figures: single images with multiple embedded sub-visualizations. Such figures account for approximately 35% of the figures in the scientific literature. Conventional image segmentation techniques and other existing approaches have been shown to be ineffective for parsing visualizations. We propose an algorithm to automatically segment multi-chart visualizations into a set of single-chart visualizations, thereby enabling downstream analysis. Our approach first splits an image into fragments based on background color and layout patterns. An SVM-based binary classifier then distinguishes complete charts from auxiliary fragments such as labels, ticks, and legends, achieving an average 98.1% accuracy. Next, we recursively merge fragments to reconstruct complete visualizations, choosing between alternative merge trees using a novel scoring function. To evaluate our approach, we used 261 scientific multi-chart figures randomly selected from the Pubmed database. Our algorithm achieves 80% recall and 85% precision of perfect extractions for the common case of eight or fewer sub-figures per figure. Further, even imperfect extractions are shown to be sufficient for most chart classification and reasoning tasks associated with bibliometrics and academic search applications.
The usage of digital cameras as luminance meters

Many luminance measuring tasks require a luminance distribution of the total viewing field. The approach of imageresolving luminance measurement, which could benefit from the continual development of position-resolving radiation detectors, represents a simplification of such measuring tasks. Luminance measure cameras already exist which are specially manufactured for measuring tasks with very high requirements. Due to high-precision solutions these cameras are very expensive and are not commercially viable for many image-resolving measuring tasks. Therefore, it is desirable to measure luminance with digital still cameras which are freely available at reasonable prices. This paper presents a method for the usage of digital still cameras as luminance meters independent of the exposure settings. A calibration of the camera is performed with the help of an OECF (opto-electronic conversion function) measurement and the luminance is calculated with the camera’s digital RGB output values. The test method and computation of the luminance value irrespective of exposure variations is described. The error sources which influence the result of the luminance measurement are also discussed.
Ranking in Professional Sports: An Application of Linear Algebra for Computer Science Students

Linear algebra is one of the required mathematics courses for students majoring in computer science. With the small class sizes at our institution, we have the opportunity to use teaching strategies often reserved for senior level courses at larger universities. In this paper the authors discuss their experience with an innovative project schema that is designed for students in an elementary linear algebra course, and how it fullfills the requirements from the report to ACM [11].
Economic Growth and Business Cycles with Commitment Problem in Credit Market

Commitment problem in credit market and its eãects on economic growth are discussed. Completions of investment projects increase capital stock of the economy. These projects require credits which are ånanced by ånacial intermediaries. A simpliåed credit model of Dewatripont and Maskin is used to describe the ånancing process, in which the commitment problem or the \soft budget constraint" problem arises. However, in dynamic general equilibrium setup with endougenous determination of value and cost of projects, there arise multiple equilibria in the project ånancing model, namely reånancing equilirium and no-reånancing equilibrium. The former leads the economy to the stationary state with smaller capital stock level than the latter. Both the elimination of reånancing equilibrium and the possibility of \Animal Spirits Cycles" equilibrium are also discussed.
Spatial maps for the control of movement

Neurons in the ventral premotor cortex of the monkey encode the locations of visual, tactile, auditory and remembered stimuli. Some of these neurons encode the locations of stimuli with respect to the arm, and may be useful for guiding movements of the arm. Others encode the locations of stimuli with respect to the head, and may be useful for guiding movements of the head. We suggest that a general principle of sensory-motor integration is that the space surrounding the body is represented in body-part-centered coordinates. That is, there are multiple coordinate systems used to guide movement, each one attached to a different part of the body. This and other recent evidence from both monkeys and humans suggest that the formation of spatial maps in the brain and the guidance of limb and body movements do not proceed in separate stages but are closely integrated in both the parietal and frontal lobes.
Chest X-ray Inpainting with Deep Generative Models

Generative adversarial networks have been successfully applied to inpainting in natural images. However, the current state-of-the-art models have not yet been widely adopted in the medical imaging domain. In this paper, we investigate the performance of three recently published deep learning based inpainting models: context encoders, semantic image inpainting, and the contextual attention model, applied to chest x-rays, as the chest exam is the most commonly performed radiological procedure. We train these generative models on 1.2M 128 × 128 patches from 60K healthy x-rays, and learn to predict the center 64 × 64 region in each patch. We test the models on both the healthy and abnormal radiographs. We evaluate the results by visual inspection and comparing the PSNR scores. The outputs of the models are in most cases highly realistic. We show that the methods have potential to enhance and detect abnormalities. In addition, we perform a 2AFC observer study and show that an experienced human observer performs poorly in detecting inpainted regions, particularly those generated by the contextual attention model.
Applications of cytotoxicity assays and pre-lethal mechanistic assays for assessment of human hepatotoxicity potential.

While drug toxicity (especially hepatotoxicity) is the most frequent reason cited for withdrawal of an approved drug, no simple solution exists to adequately predict such adverse events. Simple cytotoxicity assays in HepG2 cells are relatively insensitive to human hepatotoxic drugs in a retrospective analysis of marketed pharmaceuticals. In comparison, a panel of pre-lethal mechanistic cellular assays hold the promise to deliver a more sensitive approach to detect endpoint-specific drug toxicities. The panel of assays covered by this review includes steatosis, cholestasis, phospholipidosis, reactive intermediates, mitochondria membrane function, oxidative stress, and drug interactions. In addition, the use of metabolically competent cells or the introduction of major human hepatocytes in these in vitro studies allow a more complete picture of potential drug side effect. Since inter-individual therapeutic index (TI) may differ from patient to patient, the rational use of one or more of these cellular assay and targeted in vivo exposure data may allow pharmaceutical scientists to select drug candidates with a higher TI potential in the drug discovery phase.
Using blockchain for enabling internet voting

Voting is an inherent process in a democratic society. Other methods for expressing the society participants’ will for example caucuses in US party elections or Landsgemeine in Switzerland can be inconvenient for the citizens and logistically difficult to organize. Furthermore, beyond inconvenience, there may be legitimate reasons for not being able to take part in the voting process, e.g. being deployed overseas in military or being on some other official assignment. Even more, filling in paper ballots and counting them is error-prone and time-consuming process. A well-known controversy took place during US presidental election in 2000 [Florida recount 2000], when a partial recount of the votes could have changed the outcome of the elections. As the recount was cancelled by the court, the actual result was not never known. Decline in elections’ participation rate has been observed in many old democracies [Summers 2016] and it should be the decision-makers goal to bring the electorate back to the polling booths. One way to do that would be to use internet voting. In this method, the ballots are cast using a personal computer or a smart phone and it sent over the internet to the election committee. However, there have been several critics against the internet voting methods [Springall et al. 2014]. In this report we consider, how to make internet voting protocols more secure by using blockchain.
CRITICAL SUCCESS FACTORS TO IMPROVE KNOWLEDGE SHARING IN SOUTH AFRICAN LOCAL GOVERNMENT

The demand for improved service delivery requires new approaches and attitudes from local government. Implementation of knowledge sharing practices in local government is one of the critical processes that can help to establish learning organisations. The main purpose of this paper is to investigate how knowledge management systems can be used to improve the knowledge sharing culture among local government employees. The study used an inductive research approach which included a thorough literature review and content analysis. The technology-organisation-environment theory was used as the theoretical foundation of the study. Making use of critical success factors, the study advises how existing knowledge sharing practices can be supported and how new initiatives can be developed, making use of a knowledge management system. The study recommends that local government must ensure that knowledge sharing practices and initiatives are fully supported and promoted by top management.
Home Energy Management Using Enhanced Differential Evolution and Chicken Swarm Optimization Techniques

Energy optimization is important aspect of smart gird (SG). SG integrates communication and information technology in traditional grid. In SG there is two-way communication between consumer and utility. It includes smart meter, Energy Management Controller (EMC) and smart appliances. Users can shift load from on peak hours to off peak hours by adapting Demand Side Management (DSM) strategies, which effectively reduce electricity cost. The objectives of this paper are the minimization of power consumption, electricity cost, reduction of Peak to Average Ratio (PAR) using Enhanced Differential Evolution (EDE) and Chicken Swarm Optimization (CSO) algorithms. For the calculation of cost Critical Peak Pricing (CPP) is used. The simulations result show that proposed schemes reduce electricity cost, reduce power consumption and PAR.
Cognitive dysfunction syndrome: a disease of canine and feline brain aging.

Brain aging is a degenerative process manifest by impairment of cognitive function; although not all pets are affected at the same level, once cognitive decline begins it is generally a progressive disorder. Diagnosis of cognitive dysfunction syndrome (CDS) is based on recognition of behavioral signs and exclusion of other medical causes that might mimic CDS or complicate its diagnosis. Drugs, diets, and supplements are now available that might slow CDS progression by various mechanisms including reducing oxidative stress and inflammation or improving mitochondrial and neuronal function. Moreover, available therapeutics may provide some level of improvement in cognitive and clinical signs of CDS.