nils-herrmann/clean_scirepeval huggingface dataset:
Reverse Attention for Salient Object Detection

Benefit from the quick development of deep learning techniques, salient object detection has achieved remarkable progresses recently. However, there still exists following two major challenges that hinder its application in embedded devices, low resolution output and heavy model weight. To this end, this paper presents an accurate yet compact deep network for efficient salient object detection. More specifically, given a coarse saliency prediction in the deepest layer, we first employ residual learning to learn side-output residual features for saliency refinement, which can be achieved with very limited convolutional parameters while keep accuracy. Secondly, we further propose reverse attention to guide such side-output residual learning in a top-down manner. By erasing the current predicted salient regions from side-output features, the network can eventually explore the missing object parts and details which results in high resolution and accuracy. Experiments on six benchmark datasets demonstrate that the proposed approach compares favorably against state-of-the-art methods, and with advantages in terms of simplicity, efficiency (45 FPS) and model size (81 MB).
Mobile Learning : Taking Education to a New Level

Technological advances are taking a major role in every field of our life. Today, younger generation is more attached to technology, immerging it mostly for social purposes. Therefore, the importance of its existence cannot be ignored. For that, it is the time for every mentor to apply technology to education. Instructors from different majors need to realize that integrating technology into education is a powerful tool that helps them moderate their course, but never a replacement to their existence. This paper’s interest is to deliver a personal experience to other instructors on how to correctly use technology for educational purposes. One way of clarifying this point is to shed light on a very common social application that is WhatsApp. It is a social application available on every smartphone that is usually used as a social medium among users from different generation. This paper used WhatsApp as an application that can associate technology with learning and teachers’ moderation and collaboration under one roof, and that is by applying Mobile learning. One main question to rise at this point is whether students are going to be collaborative or not with their teacher in applying technology into education. There will be an anticipated approach from this paper on both Mobile learning and WhatsApp, that is to reach an agreement that Mobile learning is essential and adds value to the educational material we have in hand. Great examples from my own data are going to be presented to encourage others to predict new ways that can be added to my effort and others as well. The result hoped for after this paper is to be able to answer any digital immigrants’ questions and help them to be more confident with technology.
Generalized B-spline surfaces of arbitrary topology

B-spline surfaces, although widely used, are incapable of describing surfaces of arbitrary topology. It is not possible to model a general closed surface or a surface with handles as a single non-degenerate B-spline. In practice such surfaces are often needed. In this paper, we present generalizations of biquadratic and bicubic B-spline surfaces that are capable of capturing surfaces of arbitrary topology (although restrictions are placed on the connectivity of the control mesh). These results are obtained by relaxing the sufficient but not necessary smoothness constraints imposed by B-splines and through the use of an n-sided generalization of B&eacute;zier surfaces called S-patches.
A convex model of humanoid momentum dynamics for multi-contact motion generation

Linear models for control and motion generation of humanoid robots have received significant attention in the past years, not only due to their well known theoretical guarantees, but also because of practical computational advantages. However, to tackle more challenging tasks and scenarios such as locomotion on uneven terrain, a more expressive model is required. In this paper, we are interested in contact interaction-centered motion optimization based on the momentum dynamics model. This model is non-linear and non-convex; however, we find a relaxation of the problem that allows us to formulate it as a single convex quadratically-constrained quadratic program (QCQP) that can be very efficiently optimized and is useful for multi-contact planning. This convex model is then coupled to the optimization of end-effector contact locations using a mixed integer program, which can also be efficiently solved. This becomes relevant e.g. to recover from external pushes, where a predefined stepping plan is likely to fail and an online adaptation of the contact location is needed. The performance of our algorithm is demonstrated in several multi-contact scenarios for a humanoid robot.
Inferring friendship network structure by using mobile phone data.

Data collected from mobile phones have the potential to provide insight into the relational dynamics of individuals. This paper compares observational data from mobile phones with standard self-report survey data. We find that the information from these two data sources is overlapping but distinct. For example, self-reports of physical proximity deviate from mobile phone records depending on the recency and salience of the interactions. We also demonstrate that it is possible to accurately infer 95% of friendships based on the observational data alone, where friend dyads demonstrate distinctive temporal and spatial patterns in their physical proximity and calling patterns. These behavioral patterns, in turn, allow the prediction of individual-level outcomes such as job satisfaction.
Tool wear condition monitoring using a sensor fusion model based on fuzzy inference system

One of the biggest problems in manufacturing is the failure of machine tools due to loss of surface material in cutting operations like drilling and milling. Carrying on the process with a dull tool may damage the workpiece material fabricated. On the other hand, it is unnecessary to change the cutting tool if it is still able to continue cutting operation. Therefore, an effective diagnosis mechanism is necessary for the automation of machining processes so that production loss and downtime can be avoided. This study concerns with the development of a tool wear condition-monitoring technique based on a two-stage fuzzy logic scheme. For this, signals acquired from various sensors were processed to make a decision about the status of the tool. In the first stage of the proposed scheme, statistical parameters derived from thrust force, machine sound (acquired via a very sensitive microphone) and vibration signals were used as inputs to fuzzy process; and the crisp output values of this process were then taken as the input parameters of the second stage. Conclusively, outputs of this stage were taken into a threshold function, the output of which is used to assess the condition of the tool.
Privacy-friendly spatial crowdsourcing in vehicular networks

With the evolution of conventional VANETs (Vehicle Ad-hoc Networks) into the IoV (Internet of Vehicles), vehicle-based spatial crowdsourcing has become a potential solution for crowdsourcing applications. In vehicular networks, a spatial-temporal task/question can be outsourced (i.e., task/question relating to a particular location and in a specific time period) to some suitable smart vehicles (also known as workers) and then these workers can help solve the task/question. However, an inevitable barrier to the widespread deployment of spatial crowdsourcing applications in vehicular networks is the concern of privacy. Hence, We propose a novel privacy-friendly spatial crowdsourcing scheme. Unlike the existing schemes, the proposed scheme considers the privacy issue from a new perspective according that the spatial-temporal tasks can be linked and analyzed to break the location privacy of workers. Specifically, to address the challenge, three privacy requirements (i.e. anonymity, untraceability, and unlinkability) are defined and the proposed scheme combines an efficient anonymous technique with a new composite privacy metric to protect against attackers. Detailed privacy analyses show that the proposed scheme is privacy-friendly. In addition, performance evaluations via extensive simulations are also conducted, and the results demonstrate the efficiency and effectiveness of the proposed scheme.
Low noise amplifier circuit design for 5 GHz to 6 GHz

The development of wireless portable electronics is moving towards smaller and lighter devices. Although low noise amplifier (LNA) performance is extremely good nowadays, the design engineer still has to make some complex system trades. Many LNA are large, heavy and consume a lot of power. The design of an LNA in radio frequency (RF) circuits requires the trade-off of many important characteristics, such as gain, noise figure (NF), stability, power consumption and complexity. This situation forces designers to make choices in the design of RF circuits. The designed simulation process is done using the Advance Design System (ADS), while FR4 strip board is used for fabrication purposes. A single stage LNA has successfully designed with 7.78 dB forward gain and 1.53 dB noise figure; it is stable along the UNII frequency band.
SiLK: A Tool Suite for Unsampled Network Flow Analysis at Scale

A large organization can generate over ten billion network flow records per day, a high-velocity data source. Finding useful, security-related anomalies in this volume of data is challenging. Most large network flow tools sample the data to make the problem manageable, but sampling unacceptably reduces the fidelity of analytic conclusions. In this paper we discuss SiLK, a tool suite created to analyze this high-volume data source without sampling. SiLK implementation and architectural design are optimized to manage this Big Data problem. SiLK provides not just network flow capture and analysis, but also includes tools to analyze large sets and dictionaries that frequently relate to network flow data, incorporating higher-variety data sources. These tools integrate disparate data sources with SiLK analysis.
ChemTok: A New Rule Based Tokenizer for Chemical Named Entity Recognition

Named Entity Recognition (NER) from text constitutes the first step in many text mining applications. The most important preliminary step for NER systems using machine learning approaches is tokenization where raw text is segmented into tokens. This study proposes an enhanced rule based tokenizer, ChemTok, which utilizes rules extracted mainly from the train data set. The main novelty of ChemTok is the use of the extracted rules in order to merge the tokens split in the previous steps, thus producing longer and more discriminative tokens. ChemTok is compared to the tokenization methods utilized by ChemSpot and tmChem. Support Vector Machines and Conditional Random Fields are employed as the learning algorithms. The experimental results show that the classifiers trained on the output of ChemTok outperforms all classifiers trained on the output of the other two tokenizers in terms of classification performance, and the number of incorrectly segmented entities.
StreamWiki: Enabling Viewers of Knowledge Sharing Live Streams to Collaboratively Generate Archival Documentation for Effective In-Stream and Post Hoc Learning

Knowledge-sharing live streams are distinct from traditional educational videos, at least because of the large concurrently-viewing audience and the real-time discussions between viewers and the streamer. Though this creates unique opportunities for interactive learning, it also brings a challenge for creating a useful archive for post hoc learning. This paper presents the results of interviews with knowledge sharing streamers, their moderators, and viewers to understand current experiences and needs for sharing and learning knowledge through live streaming. Based on those findings, we built StreamWiki, a tool which leverages the viewers during live streams to produce useful archives of the interactive learning experience. On StreamWiki, moderators initiate tasks that viewers complete by conducting microtasks, such as writing a summary, commenting, and voting for informative comments. As a result, a summary document is built in real time. Through the tests of our prototype with streamers and viewers, we found that StreamWiki could help understanding the content and the context of the stream, during the stream and for post hoc learning.
Self-Explonations: How Students Study and Use Examples in Learning to Solve Problems

The present paper analyzes the self-generated explanations (from talk-aloud protocols) that “Good” ond “Poor” students produce while studying worked-out exomples of mechanics problems, and their subsequent reliance on examples during problem solving. We find that “Good” students learn with understanding: They generate many explanations which refine and expand the conditions for the action ports of the exomple solutions, ond relate these actions to principles in the text. These self-explanations are guided by accurate monitoring of their own understanding and misunderstanding. Such learning results in example-independent knowledge and in a better understanding of the principles presented in the text. “Poor” students do not generate sufficient self-explonations, monitor their learning inaccurately, and subsequently rely heovily an examples. We then discuss the role of self-explanations in facilitating problem solving, as well OS the adequacy of current Al models of explanation-based learning to account for these psychological findings.
Astaxanthin, a carotenoid with potential in human health and nutrition.

Astaxanthin (1), a red-orange carotenoid pigment, is a powerful biological antioxidant that occurs naturally in a wide variety of living organisms. The potent antioxidant property of 1 has been implicated in its various biological activities demonstrated in both experimental animals and clinical studies. Compound 1 has considerable potential and promising applications in human health and nutrition. In this review, the recent scientific literature (from 2002 to 2005) is covered on the most significant activities of 1, including its antioxidative and anti-inflammatory properties, its effects on cancer, diabetes, the immune system, and ocular health, and other related aspects. We also discuss the green microalga Haematococcus pluvialis, the richest source of natural 1, and its utilization in the promotion of human health, including the antihypertensive and neuroprotective potentials of 1, emphasizing our experimental data on the effects of dietary astaxanthin on blood pressure, stroke, and vascular dementia in animal models, is described.
Current Trends in Athletic Shoe Design

The characteristics of athletic shoes have been described with terms like cushioning, stability, and guidance.1,2 Despite many years of effort to optimize athletic shoe construction, the prevalence of running-related lower extremity injuries has not significantly declined; however, athletic performance has reached new heights.3-5 Criteria for optimal athletic shoe construction have been proposed, but no clear consensus has emerged.6-8 Given the unique demands of various sports, sportspecific shoe designs may simultaneously increase performance and decrease injury incidence.9-11 The purpose of this report is to provide an overview of current concepts in athletic shoe design, with emphasis on running shoes, so that athletic trainers and therapists (ATs) can assist their patients in selection of an appropriate shoe design.
Modulating Gene Expression through Psychotherapy : The Contribution of Non-Invasive Somatic Interventions

Mapping the relationship between gene expression and psychopathology is proving to be among the most promising new frontiers for advancing the understanding, treatment, and prevention of mental disorders. Each cell in the human body contains some 23,688 genes, yet only a tiny fraction of a cell’s genes are active or “expressed” at any given moment. The interactions of biochemical, psychological, and environmental factors influencing gene expression are complex, yet relatively accessible technologies for assessing gene expression have allowed the identification of specific genes implicated in a range of psychiatric disorders, including depression, anxiety, and schizophrenia. Moreover, successful psychotherapeutic interventions have been shown to shift patterns of gene expression. Five areas of biological change in successful psychotherapy that are dependent upon precise shifts in gene expression are identified in this paper. Psychotherapy ameliorates (a) exaggerated limbic system responses to innocuous stimuli, (b) distortions in learning and memory, (c) imbalances between sympathetic and parasympathetic nervous system activity, (d) elevated levels of cortisol and other stress hormones, and (e) impaired immune functioning. The thesis of this paper is that psychotherapies which utilize non-invasive somatic interventions may yield greater precision and power in bringing about therapeutically beneficial shifts in gene expression that control these biological markers. The paper examines the manual stimulation of acupuncture points during psychological exposure as an example of such a somatic intervention. For each of the five areas, a testable proposition is presented to encourage research that compares acupoint protocols with conventional therapies in catalyzing advantageous shifts in gene expression.
Incorporating GAN for Negative Sampling in Knowledge Representation Learning

Knowledge representation learning aims at modeling knowledge graph by encoding entities and relations into a low dimensional space. Most of the traditional works for knowledge embedding need negative sampling to minimize a marginbased ranking loss. However, those works construct negative samples through a random mode, by which the samples are often too trivial to fit the model efficiently. In this paper, we propose a novel knowledge representation learning framework based on Generative Adversarial Networks (GAN). In this GAN-based framework, we take advantage of a generator to obtain high-quality negative samples. Meanwhile, the discriminator in GAN learns the embeddings of the entities and relations in knowledge graph. Thus, we can incorporate the proposed GAN-based framework into various traditional models to improve the ability of knowledge representation learning. Experimental results show that our proposed GANbased framework outperforms baselines on triplets classification and link prediction tasks.
WHY WOULD CORPORATIONS BEHAVE IN SOCIALLY RESPONSIBLE WAYS ? AN INSTITUTIONAL THEORY OF CORPORATE SOCIAL RESPONSIBILITY

I offer an institutional theory of corporate social responsibility consisting of a series of propositions specifying the conditions under which corporations are likely to behave in socially responsible ways. I argue that the relationship between basic economic conditions and corporate behavior is mediated by several institutional conditions: public and private regulation, the presence of nongovernmental and other independent organizations that monitor corporate behavior, institutionalized norms regarding appropriate corporate behavior, associative behavior among corporations themselves, and organized dialogues among corporations and their stakeholders.
A method of low-cost IMU calibration and alignment

This paper presents a straightforward way to fully calibrate low-cost IMU sensor in field. In this approach, a plastic cube that is made by 3D printer is introduced as the body carrier of IMU. The accelerometer is firstly calibrated by using famous multi-position method and then followed with the correction of misalignment between sensor frame and body frame. A normal swivel chair is reformed as an easy turntable to perform smooth rotation. The gyroscope is calibrated by using angle domain method with the angular displacement reference that is estimated by using laser scan matching process. Experiment is executed and the comparison between raw readings and calibrated measurements is conducted, which verifies the validation of the proposed method.
The importance of a match in a tournament

A quantitative measure of "match importance" is useful in a number of decision problems, for example: to inform policy-making regarding income distribution in sport; as a metric in tournament design; for selecting matches for broadcasting; for scheduling matches in a tournament; for assigning referees; and for determining when competitors should make the greatest effort. To date measures of match importance used in such analyses have been relatively naïve. We discuss a general measure that considers the effect of a particular match on the end of tournament position, given the results of all other matches, some played, some predicted. We use logistic regression to predict matches and Monte Carlo simulation to compute the match importance measure, and apply these to soccer matches in the English Football Association Premier League.
On Product Uncertainty in Online Markets: Theory and Evidence

Online markets pose a difficulty for evaluating products, particularly experience goods, such as used cars, that cannot be easily described online. This exacerbates product uncertainty, the buyer’s difficulty in evaluating product characteristics, and predicting how a product will perform in the future. However, the IS literature has focused on seller uncertainty and ignored product uncertainty. To address this void, this study conceptualizes product uncertainty and examines its effects and antecedents in online markets for used cars (eBay Motors).
Deep Learning: Computational Aspects

In this article we review computational aspects of Deep Learning (DL). Deep learning uses network architectures consisting of hierarchical layers of latent variables to construct predictors for high-dimensional input-output models. Training a deep learning architecture is computationally intensive, and efficient linear algebra libraries is the key for training and inference. Stochastic gradient descent (SGD) optimization and batch sampling are used to learn from massive data sets. We demonstrate data analytic applications in engineering, finance, spatio-temporal, and traffic flow modeling.