nils-herrmann/clean_scirepeval huggingface dataset:
Emotion and the motivational brain

Psychophysiological and neuroscience studies of emotional processing undertaken by investigators at the University of Florida Laboratory of the Center for the Study of Emotion and Attention (CSEA) are reviewed, with a focus on reflex reactions, neural structures and functional circuits that mediate emotional expression. The theoretical view shared among the investigators is that expressed emotions are founded on motivational circuits in the brain that developed early in evolutionary history to ensure the survival of individuals and their progeny. These circuits react to appetitive and aversive environmental and memorial cues, mediating appetitive and defensive reflexes that tune sensory systems and mobilize the organism for action and underly negative and positive affects. The research reviewed here assesses the reflex physiology of emotion, both autonomic and somatic, studying affects evoked in picture perception, memory imagery, and in the context of tangible reward and punishment, and using the electroencephalograph (EEG) and functional magnetic resonance imaging (fMRI), explores the brain's motivational circuits that determine human emotion.
Auto-Calibration Around-View Monitoring System

This work is to establish an auto-calibration around-view monitoring system to avoid the inconsistent performance from deviations of camera installation. First, a vehicle is parked at a specific area which has a special pattern on the ground. Second, the captured images from four embedded cameras are compared with the reference pattern images to obtain the deviations in angle, X-axis and Y-axis. Finally, these deviations are used to compensate the coefficients of transformation matrices associated with view-point conversion and image stitching. Particularly, the adaptive edge recovery scheme is developed to help on precise alignments. The experimental results show that the proposed system implemented at the TI DaVinci DSP platform can accomplish auto-calibration of a vehicle within 60 seconds and have deviation rates smaller than 38% at stitched regions and 25% at the other regions. Additionally, ±2° deviation errors at angles, X-axis and Y-axis are allowed. Particularly, the proposed system can generate precise 2-meter around-view pictures at a vehicle speed of 30km/h. Therefore, the auto-calibration system proposed herein can be widely applied to vehicle manufacture, verification and repair.
MCloudDB: A Mobile Cloud Database Service Framework

The recent advance in mobile network technologies (3G/4G) provides an efficient mobile network infrastructure supporting mobile users to access large volumes of mobile data. Now, many mobile applications are developed based on mobile databases on devices and conventional databases. Due to the limitations of mobile devices, using mobile databases on mobile devices encounters certain scalability issues in mobile data accesses and storage. One alternative approach is to use cloud-based databases to support mobile users and applications on mobile devices. This also runs into the several challenges, such as mobility support, localization, energy consumption, and performance concerns. This paper presents a mobile enabled cloud database solution, called MCloudDB, to address the related issues and needs. MCloudDB provides a mobile enabled cloud database infrastructure with a framework, which can be used as a bridge to connect and integrate mobile applications with back-end cloud databases to support mobile cloud data management and access services. The paper presents the design of this framework and the related key solutions for essential features in mobile cloud databases, such as multi-tenancy, elasticity, seamless connectivity and disaster recovery. A simple application example and related experiment results are reported.
Preparing the next generation of IMA: A new technology for the scarlett program

This paper discusses requirements and strategies for the next generation of IMA modules in line with the goals of the European Research project SCARLETT. Even though the SCARLETT project starts from concepts and technology which is already available in modern aircrafts, the project's vision is to push the state of the art far beyond what is available today, with substantial consequences for the aerospace industry. SYSGO is one of the key members of the SCARLETT project and is responsible for the evaluation and specification of the platform software requirements, including multi-core support. With the operating system PikeOS, a field-proven implementation of the concept of SSV (Safe and Secure Virtualization) technology, SYSGO provides the foundation for the realization of various components within these projects, ranging from Core Processing Modules, Remote Data Concentrators and Remote Electronics as well as Simulation Environments. This paper provides a short overview of the project's framework, the key drivers, the goals and the anticipated innovations. It presents the new architecture for electronic device integration proposed by the SCARLETT project together with the resulting requirements for the platform software. In addition to SCARLETT project overview, this paper discusses specific software aspects resulting from the project and how they can be addressed with a safe and secure virtualization technology of a modern operating system.
A dynamic core loss model for soft ferromagnetic and power ferrite materials in transient finite element analysis

A dynamic core loss model is proposed to estimate core loss in both soft ferromagnetic and power ferrite materials with arbitrary flux waveforms. The required parameters are the standard core loss coefficients that are either directly provided by manufacturers or extracted from the loss curve associated with sinusoidal excitation. The model is applied to calculating core loss in both two-dimensional and three-dimensional transient finite element analysis, and the results are compared with measured data.
Direct type-specific conic fitting and eigenvalue bias correction

A new method to fit specific types of conics to scattered data points is introduced. Direct, specific fitting of ellipses and hyperbolae is achieved by imposing a quadratic constraint on the conic coefficients, whereby an improved partitioning of the design matrix is devised so as to improve computational efficiency and numerical stability by eliminating redundant aspects of the fitting procedure. Fitting of parabolas is achieved by determining an orthogonal basis vector set in the Grassmannian space of the quadratic terms’ coefficients. The linear combination of the basis vectors that fulfills the parabolic condition and has a minimum residual norm is determined using Lagrange multipliers. This is the first known direct solution for parabola specific fitting. Furthermore, the inherent bias of a linear conic fit is addressed. We propose a linear method of correcting this bias, producing better geometric fits which are still constrained to specific conic type.
A Cost-effective Smart Farming System with Knowledge Base

In this study, we research issues emerging in developing a cost-effective smart farming system. By proposing a system architecture and relevant solutions, we successfully integrate different modules related to sensing systems, communication, and data analytics into a whole system that not only monitors the farm environment but also performs remote automation and user control. Moreover, we make the smart farm system smarter with a knowledge base attached. Our smooth-running prototype for the oyster mushroom and hybrid maize culture verifies the unified integration and demonstrates that farmers are no longer worried about how to manage and control their farms at distance while enjoying their profits. Furthermore, our Smart Farm IoT Kit is designed with cheap but effective devices and lightweight communication. Last but not least, we further discuss other challenges inevitable for commercialization study.
The individual and social dynamics of knowledge sharing: an exploratory study

This paper aims to examine how knowledge sharing behavior is influenced by three sets of dynamics: a rational calculus that weighs the costs and benefits of sharing; a dispositional preference that favors certain patterns of sharing outcomes; and a relational effect based on working relationships.  Concepts from social exchange theory, social value orientation, and leader-member exchange theory are applied to analyze behavioral intentions to share knowledge. The study population consists of employees of a large pension fund in Canada. Participants answered a survey that used allocation games and situational vignettes to measure social value orientation, propensity to share knowledge, and perception of cost and benefit.  The results suggest that personal preferences about the distribution of sharing outcomes, individual perceptions about costs and benefits, and structural relationship with knowledge recipients, all affect knowledge sharing behavior significantly. Notably, it was found that propensity to share knowledge is positively related to perceived benefit to the recipient, thus suggesting that evaluation of cost and benefit in social exchange is not limited to self-interest, but is also influenced by perceived recipient benefit. Moreover, it was found that the relationship with the sharing target (superior or colleague) also influenced sharing.  Most studies emphasize the organizational benefits of knowledge sharing. This study examines knowledge sharing from the perspective of the individual who approaches knowledge sharing as a social exchange that involves perceptions of costs and benefits, preferences about sharing outcomes, and relationship with the sharing target. The study also introduces innovative methods to measure social value orientation and propensity to share knowledge.
Forgotten Siblings: Unifying Attacks on Machine Learning and Digital Watermarking

Machine learning is increasingly used in securitycritical applications, such as autonomous driving, face recognition, and malware detection. Most learning methods, however, have not been designed with security in mind and thus are vulnerable to different types of attacks. This problem has motivated the research field of adversarial machine learning that is concerned with attacking and defending learning methods. Concurrently, a separate line of research has tackled a very similar problem: In digital watermarking, a pattern is embedded in a signal in the presence of an adversary. As a consequence, this research field has also extensively studied techniques for attacking and defending watermarking methods. The two research communities have worked in parallel so far, unnoticeably developing similar attack and defense strategies. This paper is a first effort to bring these communities together. To this end, we present a unified notation of blackbox attacks against machine learning and watermarking. To demonstrate its efficacy, we apply concepts from watermarking to machine learning and vice versa. We show that countermeasures from watermarking can mitigate recent model-extraction attacks and, similarly, that techniques for hardening machine learning can fend off oracle attacks against watermarks. We further demonstrate a novel threat for watermarking schemes based on recent deep learning attacks from adversarial learning. Our work provides a conceptual link between two research fields and thereby opens novel directions for improving the security of both, machine learning and digital watermarking.
Measuring Negative and Positive Thoughts in Children: An Adaptation of the Children’s Automatic Thoughts Scale (CATS)

The aim of this study is to describe the factor structure and psychometric properties of an extended version of the Children’s Automatic Thoughts Scale (CATS), the CATS-Negative/Positive (CATS-N/P). The CATS was originally designed to assess negative self-statements in children and adolescents. However, positive thoughts also play a major role in childhood disorders such as anxiety and depression. Therefore, positive self-statements were added to the CATS. The CATS-N/P was administered to a community sample of 554 children aged 8–18 years. The results of a confirmatory factor analysis revealed that the positive self-statements formed a separate and psychometrically sound factor. Internal and short-term test–retest reliability was good. Boys reported more hostile and positive thoughts than girls; and younger children reported more negative thoughts concerning physical threat, social threat, and failure than older children. In conclusion, the results of the current study support the use of the CATS-N/P for the measurement of positive and negative thoughts in children. The application of the CATS-N/P can facilitate further research on cognitive factors in different childhood disorders.
Deep Learning based Large Scale Visual Recommendation and Search for E-Commerce

In this paper, we present a unied end-to-end approach to build a large scale Visual Search and Recommendation system for ecommerce. Previous works have targeted these problems in isolation. We believe a more eective and elegant solution could be obtained by tackling them together. We propose a unied Deep Convolutional Neural Network architecture, called VisNet 1, to learn embeddings to capture the notion of visual similarity, across several semantic granularities. We demonstrate the superiority of our approach for the task of image retrieval, by comparing against the state-of-the-art on the Exact Street2Shop  dataset. We then share the design decisions and trade-os made while deploying the model to power Visual Recommendations across a catalog of 50M products, supporting 2K queries a second at Flipkart, India’s largest e-commerce company. e deployment of our solution has yielded a signicant business impact, as measured by the conversion-rate.
The use of vacuum assisted closure (VAC™) in soft tissue injuries after high energy pelvic trauma

Application of vacuum-assisted closure (VAC™) in soft tissue defects after high-energy pelvic trauma is described as a retrospective study in a level one trauma center. Between 2002 and 2004, 13 patients were treated for severe soft tissue injuries in the pelvic region. All musculoskeletal injuries were treated with multiple irrigation and debridement procedures and broad-spectrum antibiotics. VAC™ was applied as a temporary coverage for defects and wound conditioning. The injuries included three patients with traumatic hemipelvectomies. Seven patients had pelvic ring fractures with five Morel–Lavallee lesions and two open pelviperineal trauma. One patient suffered from an open iliac crest fracture and a Morel–Lavallee lesion. Two patients sustained near complete pertrochanteric amputations of the lower limb. The average injury severity score was 34.1 ± 1.4. The application of VAC™ started in average 3.8 ± 0.4 days after trauma and was used for 15.5 ± 1.8 days. The dressing changes were performed in average every 3 days. One patient (8%) with a traumatic hemipelvectomy died in the course of treatment due to septic complications. High-energy trauma causing severe soft tissues injuries requires multiple operative debridements to prevent high morbidity and mortality rates. The application of VAC™ as temporary coverage of large tissue defects in pelvic regions supports wound conditioning and facilitates the definitive wound closure.
An Improved Variable On-Time Control Strategy for a CRM Flyback PFC Converter

The traditional critical conduction mode (CRM) flyback PFC converter with constant on-time control strategy usually suffers low power factor (PF) and high total harmonic distortion (THD) due to the nonsinusoidal input current waveform. In order to solve this problem, an improved variable on-time control strategy for the CRM flyback PFC converter is proposed in this letter. A simple analog divider circuit consisting of an operational amplifier, two signal switches, and an RC filter is proposed to modulate the turn-on time of the primary switch, and the PF and THD of the CRM flyback PFC converter can be evidently improved. The theoretical analysis is presented and the experiment results verify the advantages of the proposed control scheme.
Joint Dynamic Pricing of Multiple Perishable Products Under Consumer Choice

Yalçın Akçay College of Administrative Sciences and Economics, Koç University, Rumeli Feneri Yolu, Istanbul, Turkey, 34450, yakcay@ku.edu.tr Harihara Prasad Natarajan Department of Management, School of Business Administration, University of Miami, Coral Gables, FL 33124, hari@miami.edu Susan H. Xu Department of Supply Chain and Information Systems, Smeal College of Business, Penn State University University Park, PA 16802, shx@psu.edu
Occupational therapy effects on visual-motor skills in preschool children.

The purpose of this study was to evaluate the assumption that preschool children who receive occupational therapy will demonstrate significant improvement in their visual-motor skills as measured on the Developmental Test of Visual-Motor Integration (VMI) and the two supplemental Visual Perception and Motor Coordination tests.



Preschool children with developmental delays (n = 12) received occupational therapy a minimum of one individual 30-minute session, and one group 30-minute session per week for 1 school year. Their performance was compared to that of two control groups; preschool students without disabilities who received occupational therapy (n = 16) for one 30-minute group session per week and students without disabilities (n = 15) who received no occupational therapy. The VMI and two supplemental tests were administered three times to each student, at the beginning, middle, and end of school year.



Planned comparison tests showed that students with developmental delays demonstrated statistically significant improvement in visual-motor skills and developed skills at a rate faster than expected when compared to typically developing peers on the VMI. The effect size for preschool students without disabilities who received occupational therapy exceeded the effect size for the VMI and Visual Perception supplemental test for the preschool students without disabilities who received no therapy, although, the difference in the post-test performance of these two groups was not statistically significant.



The results of this study demonstrate that intervention, including occupational therapy, can effectively improve visual-motor skills in preschool-aged children.
Open Targets: a platform for therapeutic target identification and validation

We have designed and developed a data integration and visualization platform that provides evidence about the association of known and potential drug targets with diseases. The platform is designed to support identification and prioritization of biological targets for follow-up. Each drug target is linked to a disease using integrated genome-wide data from a broad range of data sources. The platform provides either a target-centric workflow to identify diseases that may be associated with a specific target, or a disease-centric workflow to identify targets that may be associated with a specific disease. Users can easily transition between these target- and disease-centric workflows. The Open Targets Validation Platform is accessible at https://www.targetvalidation.org.
Bilingual effects on cognitive and linguistic development: role of language, cultural background, and education.

A total of 104 six-year-old children belonging to 4 groups (English monolinguals, Chinese-English bilinguals, French-English bilinguals, Spanish-English bilinguals) were compared on 3 verbal tasks and 1 nonverbal executive control task to examine the generality of the bilingual effects on development. Bilingual groups differed in degree of similarity between languages, cultural background, and language of schooling. On the executive control task, all bilingual groups performed similarly and exceeded monolinguals; on the language tasks the best performance was achieved by bilingual children whose language of instruction was the same as the language of testing and whose languages had more overlap. Thus, executive control outcomes for bilingual children are general but performance on verbal tasks is specific to factors in the bilingual experience.
Scalable Metadata Management Techniques for Ultra-Large Distributed Storage Systems - A Systematic Review

The provisioning of an efficient ultra-large scalable distributed storage system for expanding cloud applications has been a challenging job for researchers in academia and industry. In such an ultra-large-scale storage system, data are distributed on multiple storage nodes for performance, scalability, and availability. The access to this distributed data is through its metadata, maintained by multiple metadata servers. The metadata carries information about the physical address of data and access privileges. The efficiency of a storage system highly depends on effective metadata management. This research presents an extensive systematic literature analysis of metadata management techniques in storage systems. This research work will help researchers to find the significance of metadata management and important parameters of metadata management techniques for storage systems. Methodical examination of metadata management techniques developed by various industry and research groups is described. The different metadata distribution techniques lead to various taxonomies. Furthermore, the article investigates techniques based on distribution structures and key parameters of metadata management. It also presents strengths and weaknesses of individual existing techniques that will help researchers to select the most appropriate technique for specific applications. Finally, it discusses existing challenges and significant research directions in metadata management for researchers.
Embedded Textual Content for Document Image Classification with Convolutional Neural Networks

In this paper we introduce a novel document image classification method based on combined visual and textual information. The proposed algorithm's pipeline is inspired to the ones of other recent state-of-the-art methods which perform document image classification using Convolutional Neural Networks. The main addition of our work is the introduction of a preprocessing step embedding additional textual information into the processed document images. To do so we combine Optical Character Recognition and Natural Language Processing algorithms to extract and manipulate relevant text concepts from document images. Such textual information is then visually embedded within each document image to improve the classification results of a Convolutional Neural Network. Our experiments prove that the overall document classification accuracy of a Convolutional Neural Network trained using these text-augmented document images is considerably higher than the one achieved by a similar model trained solely on classic document images, especially when different classes of documents share similar visual characteristics.
Mobile application testing — Challenges and solution approach through automation

By the time this paper has been presented, the mobile app landscape will have changed. New OS versions will have been released. A bunch of new devices will have hit the market. And mobile application testing will have become that much more complex and challenging for all of us. There is no doubt that mobile applications need specific testing approaches. This paper wants to investigate new directions in research on the type of testing and skills required on mobile app testing by answering the following three research questions: (RQ1) How mobile applications testing are so different from traditional web applications, that require specialized testing skills and techniques?, (RQ2) What are the new challenges and future trends in mobile application testing, and (RQ3) How far automation effective in testing mobile application?. We answer those questions by analyzing the current trends in mobile application development and testing, and by proposing my view on the topic.