nils-herrmann/clean_scirepeval huggingface dataset:
Intelligent Vehicle Counting and Classification Sensor for Real-Time Traffic Surveillance

Real-time traffic surveillance is essential in today’s intelligent transportation systems and will surely play a vital role in tomorrow’s smart cities. The work detailed in this paper reports on the development and implementation of a novel smart wireless sensor for traffic monitoring. Computationally efficient and reliable algorithms for vehicle detection, speed and length estimation, classification, and time-synchronization were fully developed, integrated, and evaluated. Comprehensive system evaluation and extensive data analysis were performed to tune and validate the system for a reliable and robust operation. Several field studies conducted on highway and urban roads for different scenarios and under various traffic conditions resulted in 99.98% detection accuracy, 97.11% speed estimation accuracy, and 97% length-based vehicle classification accuracy. The developed system is portable, reliable, and cost-effective. The system can also be used for short-term or long-term installment on surface of highway, roadway, and roadside. Implementation cost of a single node including enclosure is US $50.
Machine learning-based QoE prediction for video streaming over LTE network

This paper presents a machine-learning software solution that performs a multi-dimensional prediction of QoE (Quality of Experience) based on network-related SIFs (System Influence Factors) as input data. The proposed solution is verified through experimental study based on video streaming emulation over LTE (Long Term Evolution) which allows the measurement of network-related SIF (i.e., delay, jitter, loss), and subjective assessment of MOS (Mean Opinion Score). Obtained results show good performance of proposed MOS predictor in terms of mean prediction error and thereby can serve as an encouragement to implement such solution in all-IP (Internet Protocol) real environment.
Socioeconomic inequalities in depression: a meta-analysis.

Low socioeconomic status (SES) is generally associated with high psychiatric morbidity, more disability, and poorer access to health care. Among psychiatric disorders, depression exhibits a more controversial association with SES. The authors carried out a meta-analysis to evaluate the magnitude, shape, and modifiers of such an association. The search found 51 prevalence studies, five incidence studies, and four persistence studies meeting the criteria. A random effects model was applied to the odds ratio of the lowest SES group compared with the highest, and meta-regression was used to assess the dose-response relation and the influence of covariates. Results indicated that low-SES individuals had higher odds of being depressed (odds ratio = 1.81, p < 0.001), but the odds of a new episode (odds ratio = 1.24, p = 0.004) were lower than the odds of persisting depression (odds ratio = 2.06, p < 0.001). A dose-response relation was observed for education and income. Socioeconomic inequality in depression is heterogeneous and varies according to the way psychiatric disorder is measured, to the definition and measurement of SES, and to contextual features such as region and time. Nonetheless, the authors found compelling evidence for socioeconomic inequality in depression. Strategies for tackling inequality in depression are needed, especially in relation to the course of the disorder.
Branding the brain : A critical review and outlook

The application of neuroscience to marketing, and in particular to the consumer psychology of brands, has gained popularity over the past decade in the academic and the corporate world. In this paper, we provide an overview of the current and previous research in this area and explainwhy researchers and practitioners alike are excited about applying neuroscience to the consumer psychology of brands. We identify critical issues of past research and discuss how to address these issues in future research. We conclude with our vision of the future potential of research at the intersection of neuroscience and consumer psychology.
Deep learning for stock prediction using numerical and textual information

This paper proposes a novel application of deep learning models, Paragraph Vector, and Long Short-Term Memory (LSTM), to financial time series forecasting. Investors make decisions according to various factors, including consumer price index, price-earnings ratio, and miscellaneous events reported in newspapers. In order to assist their decisions in a timely manner, many automatic ways to analyze those information have been proposed in the last decade. However, many of them used either numerical or textual information, but not both for a single company. In this paper, we propose an approach that converts newspaper articles into their distributed representations via Paragraph Vector and models the temporal effects of past events on opening prices about multiple companies with LSTM. The performance of the proposed approach is demonstrated on real-world data of fifty companies listed on Tokyo Stock Exchange.
Muscle injuries: biology and treatment.

Muscle injuries are one of the most common traumas occurring in sports. Despite their clinical importance, few clinical studies exist on the treatment of these traumas. Thus, the current treatment principles of muscle injuries have either been derived from experimental studies or been tested only empirically. Although nonoperative treatment results in good functional outcomes in the majority of athletes with muscle injuries, the consequences of failed treatment can be very dramatic, possibly postponing an athlete's return to sports for weeks or even months. Moreover, the recognition of some basic principles of skeletal muscle regeneration and healing processes can considerably help in both avoiding the imminent dangers and accelerating the return to competition. Accordingly, in this review, the authors have summarized the prevailing understanding on the biology of muscle regeneration. Furthermore, they have reviewed the existing data on the different treatment modalities (such as medication, therapeutic ultrasound, physical therapy) thought to influence the healing of injured skeletal muscle. In the end, they extend these findings to clinical practice in an attempt to propose an evidence-based approach for the diagnosis and optimal treatment of skeletal muscle injuries.
Not All Samples Are Created Equal: Deep Learning with Importance Sampling

Deep neural network training spends most of the computation on examples that are properly handled, and could be ignored. We propose to mitigate this phenomenon with a principled importance sampling scheme that focuses computation on “informative” examples, and reduces the variance of the stochastic gradients during training. Our contribution is twofold: first, we derive a tractable upper bound to the persample gradient norm, and second we derive an estimator of the variance reduction achieved with importance sampling, which enables us to switch it on when it will result in an actual speedup. The resulting scheme can be used by changing a few lines of code in a standard SGD procedure, and we demonstrate experimentally, on image classification, CNN fine-tuning, and RNN training, that for a fixed wall-clock time budget, it provides a reduction of the train losses of up to an order of magnitude and a relative improvement of test errors between 5% and 17%.
WarpGAN : Automatic Caircature Generation

We propose, WarpGAN, a fully automatic network that can generate caricatures given an input face photo. Besides transferring rich texture styles, WarpGAN learns to automatically predict a set of control points that can warp the photo into a caricature, while preserving identity. We introduce an identity-preserving adversarial loss that aids the discriminator to distinguish between different subjects. Moreover, WarpGAN allows customization of the generated caricatures by controlling the exaggeration extent and the visual styles. Experimental results on a public domain dataset, WebCaricature, show that WarpGAN is capable of generating a diverse set of caricatures while preserving the identities. Five caricature experts suggest that caricatures generated by WarpGAN are visually similar to hand-drawn ones and only prominent facial features are exaggerated. ∗ indicates equal contribution
Cannabidiol: pharmacology and potential therapeutic role in epilepsy and other neuropsychiatric disorders.

To present a summary of current scientific evidence about the cannabinoid, cannabidiol (CBD) with regard to its relevance to epilepsy and other selected neuropsychiatric disorders. We summarize the presentations from a conference in which invited participants reviewed relevant aspects of the physiology, mechanisms of action, pharmacology, and data from studies with animal models and human subjects. Cannabis has been used to treat disease since ancient times. Δ(9) -Tetrahydrocannabinol (Δ(9) -THC) is the major psychoactive ingredient and CBD is the major nonpsychoactive ingredient in cannabis. Cannabis and Δ(9) -THC are anticonvulsant in most animal models but can be proconvulsant in some healthy animals. The psychotropic effects of Δ(9) -THC limit tolerability. CBD is anticonvulsant in many acute animal models, but there are limited data in chronic models. The antiepileptic mechanisms of CBD are not known, but may include effects on the equilibrative nucleoside transporter; the orphan G-protein-coupled receptor GPR55; the transient receptor potential of vanilloid type-1 channel; the 5-HT1a receptor; and the α3 and α1 glycine receptors. CBD has neuroprotective and antiinflammatory effects, and it appears to be well tolerated in humans, but small and methodologically limited studies of CBD in human epilepsy have been inconclusive. More recent anecdotal reports of high-ratio CBD:Δ(9) -THC medical marijuana have claimed efficacy, but studies were not controlled. CBD bears investigation in epilepsy and other neuropsychiatric disorders, including anxiety, schizophrenia, addiction, and neonatal hypoxic-ischemic encephalopathy. However, we lack data from well-powered double-blind randomized, controlled studies on the efficacy of pure CBD for any disorder. Initial dose-tolerability and double-blind randomized, controlled studies focusing on target intractable epilepsy populations such as patients with Dravet and Lennox-Gastaut syndromes are being planned. Trials in other treatment-resistant epilepsies may also be warranted. A PowerPoint slide summarizing this article is available for download in the Supporting Information section here.
Using Image Fairness Representations in Diversity-Based Re-ranking for Recommendations

The trade-off between relevance and fairness in personalized recommendations has been explored in recent works, with the goal of minimizing learned discrimination towards certain demographics while still producing relevant results. We present a fairness-aware variation of the Maximal Marginal Relevance (MMR) re-ranking method which uses representations of demographic groups computed using a labeled dataset. This method is intended to incorporate fairness with respect to these demographic groups. We perform an experiment on a stock photo dataset and examine the trade-off between relevance and fairness against a well known baseline, MMR, by using human judgment to examine the results of the re-ranking when using different fractions of a labeled dataset, and by performing a quantitative analysis on the ranked results of a set of query images. We show that our proposed method can incorporate fairness in the ranked results while obtaining higher precision than the baseline, while our case study shows that even a limited amount of labeled data can be used to compute the representations to obtain fairness. This method can be used as a post-processing step for recommender systems and search.
EMPLOYEE MOTIVATION AND PERFORMANCE Ultimate Companion Limited Douala-Cameroon

The subject matter of this research; employee motivation and performance seeks to look at how best employees can be motivated in order to achieve high performance within a company or organization. Managers and entrepreneurs must ensure that companies or organizations have a competent personnel that is capable to handle this task. This takes us to the problem question of this research “why is not a sufficient motivation for high performance?” This therefore establishes the fact that money is for high performance but there is need to look at other aspects of motivation which is not necessarily money. Four theories were taken into consideration to give an explanation to the question raised in the problem formulation. These theories include: Maslow’s hierarchy of needs, Herzberg two factor theory, John Adair fifty-fifty theory and Vroom’s expectancy theory. Furthermore, the performance management process as a tool to measure employee performance and company performance. This research equally looked at the various reward systems which could be used by a company. In addition to the above, culture and organizational culture and it influence on employee behaviour within a company was also examined. An empirical study was done at Ultimate Companion Limited which represents the case study of this research work. Interviews and questionnaires were conducted to sample employee and management view on motivation and how it can increase performance at the company. Finally, a comparison of findings with theories, a discussion which raises critical issues on motivation/performance and conclusion constitute the last part of the research. Subject headings, (keywords)  Performance, Intrinsic, Extrinsic, Incentive, Tangible and Intangible, Reward
Mechanobiology of tendon.

Tendons are able to respond to mechanical forces by altering their structure, composition, and mechanical properties--a process called tissue mechanical adaptation. The fact that mechanical adaptation is effected by cells in tendons is clearly understood; however, how cells sense mechanical forces and convert them into biochemical signals that ultimately lead to tendon adaptive physiological or pathological changes is not well understood. Mechanobiology is an interdisciplinary study that can enhance our understanding of mechanotransduction mechanisms at the tissue, cellular, and molecular levels. The purpose of this article is to provide an overview of tendon mechanobiology. The discussion begins with the mechanical forces acting on tendons in vivo, tendon structure and composition, and its mechanical properties. Then the tendon's response to exercise, disuse, and overuse are presented, followed by a discussion of tendon healing and the role of mechanical loading and fibroblast contraction in tissue healing. Next, mechanobiological responses of tendon fibroblasts to repetitive mechanical loading conditions are presented, and major cellular mechanotransduction mechanisms are briefly reviewed. Finally, future research directions in tendon mechanobiology research are discussed.
A HIGH-RANK RNN LANGUAGE MODEL

We formulate language modeling as a matrix factorization problem, and show that the expressiveness of Softmax-based models (including the majority of neural language models) is limited by a Softmax bottleneck. Given that natural language is highly context-dependent, this further implies that in practice Softmax with distributed word embeddings does not have enough capacity to model natural language. We propose a simple and effective method to address this issue, and improve the state-of-the-art perplexities on Penn Treebank and WikiText-2 to 47.69 and 40.68
An Efficient Dynamic Scheduling Algorithm For Multiprocessor Real-Time Systems

Many time-critical applications require predictable performance and tasks in these applications have deadlines to be met. In this paper, we propose an efficient algorithm for nonpreemptive scheduling of dynamically arriving real-time tasks (aperiodic tasks) in multiprocessor systems. A real-time task is characterized by its deadline, resource requirements, and worst case computation time on p processors, where p is the degree of parallelization of the task. We use this parallelism in tasks to meet their deadlines and, thus, obtain better schedulability compared to nonparallelizable task scheduling algorithms. To study the effectiveness of the proposed scheduling algorithm, we have conducted extensive simulation studies and compared its performance with the myopic [8] scheduling algorithm. The simulation studies show that the schedulability of the proposed algorithm is always higher than that of the myopic algorithm for a wide variety of task parameters. Index Terms —Multiprocessor, real-time systems, dynamic scheduling, parallelizable tasks, resource constraints. —————————— ✦ ——————————
BCL-13: A 13-DOF Soft Robotic Hand for Dexterous Grasping and In-Hand Manipulation

This letter presents a dexterous soft robotic hand, BCL-13, with 4 fingers and 13 independently actuated joints capable of in-hand manipulation. The iconic dexterity is enabled by a novel soft robotic finger design with three degrees of freedom (DOFs), significantly improving over existing soft actuator dexterity and realizing human-finger-like workspace. The palm is also equipped with a dedicated rotational DOF to enable opposition of fingers. Investigations on human hand model reduction, in-hand manipulation principles, as well as the fabrication procedures of the soft robotic fingers and hand were presented in detail. Dedicated experiments using the fabricated prototypes were conducted to evaluate the effectiveness of the proposed robotic anthropomorphic system via a series of workspace, grasping, and in-hand manipulation tasks. The proposed BCL-13 hand offers a promising design solution to a lightweight, dexterous, affordable, and highly anthropomorphic robotic hand design.
The Gap of Semantic Parsing: A Survey on Automatic Math Word Problem Solvers

Automatically solving mathematical word problems (MWPs) is challenging, primarily due to the semantic gap between human-readable words and machine-understandable logics. Despite a long history dated back to the 1960s, MWPs has regained intensive attention in the past few years with the advancement of Artificial Intelligence (AI). To solve MWPs successfully is considered as a milestone towards general AI. Many systems have claimed promising results in self-crafted and small-scale datasets. However, when applied on large and diverse datasets, none of the proposed methods in the literatures achieves a high precision, revealing that current MWPs solvers are still far from intelligent. This motivated us to present a comprehensive survey to deliver a clear and complete picture of automatic math problem solvers. In this survey, we emphasize on algebraic word problems, summarize their extracted features and proposed techniques to bridge the semantic gap, and compare their performance in the publicly accessible datasets. We will also cover automatic solvers for other types of math problems such as geometric problems that require the understanding of diagrams. Finally, we will identify several emerging research directions for the readers with interests in MWPs.
Honey: A Potential Therapeutic Agent for Managing Diabetic Wounds

Diabetic wounds are unlike typical wounds in that they are slower to heal, making treatment with conventional topical medications an uphill process. Among several different alternative therapies, honey is an effective choice because it provides comparatively rapid wound healing. Although honey has been used as an alternative medicine for wound healing since ancient times, the application of honey to diabetic wounds has only recently been revived. Because honey has some unique natural features as a wound healer, it works even more effectively on diabetic wounds than on normal wounds. In addition, honey is known as an "all in one" remedy for diabetic wound healing because it can combat many microorganisms that are involved in the wound process and because it possesses antioxidant activity and controls inflammation. In this review, the potential role of honey's antibacterial activity on diabetic wound-related microorganisms and honey's clinical effectiveness in treating diabetic wounds based on the most recent studies is described. Additionally, ways in which honey can be used as a safer, faster, and effective healing agent for diabetic wounds in comparison with other synthetic medications in terms of microbial resistance and treatment costs are also described to support its traditional claims.
The Depression Anxiety Stress Scales (DASS): normative data and latent structure in a large non-clinical sample.

To provide UK normative data for the Depression Anxiety and Stress Scale (DASS) and test its convergent, discriminant and construct validity.



Cross-sectional, correlational and confirmatory factor analysis (CFA).



The DASS was administered to a non-clinical sample, broadly representative of the general adult UK population (N = 1,771) in terms of demographic variables. Competing models of the latent structure of the DASS were derived from theoretical and empirical sources and evaluated using confirmatory factor analysis. Correlational analysis was used to determine the influence of demographic variables on DASS scores. The convergent and discriminant validity of the measure was examined through correlating the measure with two other measures of depression and anxiety (the HADS and the sAD), and a measure of positive and negative affectivity (the PANAS).



The best fitting model (CFI =.93) of the latent structure of the DASS consisted of three correlated factors corresponding to the depression, anxiety and stress scales with correlated error permitted between items comprising the DASS subscales. Demographic variables had only very modest influences on DASS scores. The reliability of the DASS was excellent, and the measure possessed adequate convergent and discriminant validity  The DASS is a reliable and valid measure of the constructs it was intended to assess. The utility of this measure for UK clinicians is enhanced by the provision of large sample normative data.
Wireless Power for Mobile Battery Charger

This Wireless Mobile Battery Charger project is using technique of inductive coupling. The basic concept of his technique was applied in transformer construction. With this technique, the power from AC or DC can be transfer through the medium of magnetic field or air space. In this project, the method is divided into two major activities which is to purpose circuit construction and to fabricate the prototype. The result is to evaluate the distance of power that can be transferred using technique of inductive coupling.
Discovering Bitcoin ’ s Public Topology and Influential Nodes

The Bitcoin network relies on peer-to-peer broadcast to distribute pending transactions and confirmed blocks. The topology over which this broadcast is distributed affects which nodes have advantages and whether some attacks are feasible. As such, it is particularly important to understand not just which nodes participate in the Bitcoin network, but how they are connected. In this paper, we introduce AddressProbe, a technique that discovers peer-to-peer links in Bitcoin, and apply this to the live topology. To support AddressProbe and other tools, we develop CoinScope, an infrastructure to manage short, but large-scale experiments in Bitcoin. We analyze the measured topology to discover both highdegree nodes and a well connected giant component. Yet, efficient propagation over the Bitcoin backbone does not necessarily result in a transaction being accepted into the block chain. We introduce a “decloaking” method to find influential nodes in the topology that are well connected to a mining pool. Our results find that in contrast to Bitcoin’s idealized vision of spreading mining responsibility to each node, mining pools are prevalent and hidden: roughly 2% of the (influential) nodes represent threequarters of the mining power.