nils-herrmann/clean_scirepeval huggingface dataset:
Enhancing Process Control in Industry 4.0 Scenarios using Cyber-Physical Systems

One of the most interesting applications of Industry 4.0 paradigm is enhanced process control. Traditionally, process control solutions based on Cyber-Physical Systems (CPS) consider a top-down view where processes are represented as executable high-level descriptions. However, most times industrial processes follow a bottom-up model where processes are executed by low-level devices which are hard-programmed with the process to be executed. Thus, high-level components only may supervise the process execution as devices cannot modify dynamically their behavior. Therefore, in this paper we propose a vertical CPS-based solution (including a reference and a functional architecture) adequate to perform enhanced process control in Industry 4.0 scenarios with a bottom-up view. The proposed solution employs an event-driven service-based architecture where control is performed by means of finite state machines. Furthermore, an experimental validation is provided proving that in more than 97% of cases the proposed solution allows a stable and effective control.
Somatotype and body composition analysis of Korean youth soccer players
according to playing position for sports physiotherapy research

The purpose of this study was to investigate the somatotype and physical characteristic differences among elite youth soccer players.  In the present study, we evaluated twenty-two Korean youth soccer players in different playing positions. The playing positions were divided into forward (FW), midfielder (MF), defender (DF), and goalkeeper (GK). The participants' lean body mass (LBM), fat free mass (FFM), fat mass (FM), and basal metabolic rate (BMR) were measured and their somatotype determined according to the Heath-Carter method.  The youth soccer players had twelve ectomorphic, eight mesomorphic, and two central predominant types. The DFs were taller than, but otherwise similar in physical characteristics to the FWs and MFs. The GKs were taller and heavier than the other players; however, their somatotype components were not significantly different. LBM, FFM, and BMR were significantly higher in GKs than in FWs and MFs. Although LBM, FFM, and BMR values between GKs and DFs showed large differences, they were not statistically significant.  The present study may contribute to our understanding of the differences in somatotype and body composition of Korean youth soccer players involved in sports physiotherapy research.
Energy and Delay Tradeoff for Application Offloading in Mobile Cloud Computing

Recent work shows that offloading a mobile application from mobile devices to cloud servers can significantly reduce the energy consumption of mobile devices, thus extending the lifetime of mobile devices. However, previous work only considers the energy saving of mobile devices while ignoring the execution delay of mobile applications. To reduce the energy consumption of mobile devices, one may offload as many mobile applications as possible. However, offloading to cloud servers may incur a large execution delay because of the waiting time at the servers or the communication delay from the mobile devices to the servers. Thus, to balance the tradeoff between energy consumption and execution delay of mobile applications, it is necessary to determine whether the mobile application should be offloaded to the cloud server or run locally at the mobile devices. In this paper, we first formulate a joint optimization problem, which minimizes both the energy consumption at the mobile devices and the execution delay of mobile applications. We prove that the proposed problem is NP-hard. For a special case with unlimited residual energy at the mobile device and the same amount of resources required by each mobile application, we present a polynomial-time optimal solution. We also propose an efficient heuristic algorithm to solve the general case of the problem. Finally, simulation results demonstrate the effectiveness of the proposed scheme.
Very deep convolutional neural network based image classification using small training sample size

Since Krizhevsky won the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) 2012 competition with the brilliant deep convolutional neural networks (D-CNNs), researchers have designed lots of D-CNNs. However, almost all the existing very deep convolutional neural networks are trained on the giant ImageNet datasets. Small datasets like CIFAR-10 has rarely taken advantage of the power of depth since deep models are easy to overfit. In this paper, we proposed a modified VGG-16 network and used this model to fit CIFAR-10. By adding stronger regularizer and using Batch Normalization, we achieved 8.45% error rate on CIFAR-10 without severe overfitting. Our results show that the very deep CNN can be used to fit small datasets with simple and proper modifications and don't need to re-design specific small networks. We believe that if a model is strong enough to fit a large dataset, it can also fit a small one.
Modeling and Analysis of a Dual-Active-Bridge-Isolated Bidirectional DC/DC Converter to Minimize RMS Current With Whole Operating Range

The triple phase shift (TPS) modulation scheme, which provides three control freedoms, is of great importance for the optimized operation of a dual active bridge (DAB) isolated bidirectional dc/dc converter. First of all, this paper introduces an accurate, universal model to describe the analytic expressions of the DAB converter under TPS control. Based on this, six operating modes of the DAB converter are further discussed. Afterwards, the concept of global optimal condition (GOC) equations is proposed to derive the closed form of analytic expressions of an optimal modulation scheme that makes the DAB converter operate with minimized root-mean-square (rms) current during whole power range with different operating modes. According to the GOC equations, the physical explanation of the proposed modulation scheme is further given in details, and the complex interaction among the control variables, the transferred power, and rms current is revealed. The real-time optimization process of the proposed method is also specified. Finally, the proposed methods are applied to a laboratory prototype. The experimental results confirm the theoretical analysis and practical feasibility of the proposed strategies.
Comparison and Combination of Ear and Face Images in Appearance-Based Biometrics

Researchers have suggested that the ear may have advantages over the face for biometric recognition. Our previous experiments with ear and face recognition, using the standard principal component analysis approach, showed lower recognition performance using ear images. We report results of similar experiments on larger data sets that are more rigorously controlled for relative quality of face and ear images. We find that recognition performance is not significantly different between the face and the ear, for example, 70.5 percent versus 71.6 percent, respectively, in one experiment. We also find that multimodal recognition using both the ear and face results in statistically significant improvement over either individual biometric, for example, 90.9 percent in the analogous experiment.
Manuscript Matcher: A Content and Bibliometrics-based Scholarly Journal Recommendation System

While many web-based systems recommend relevant or interesting scientific papers and authors, few tools actually recommend journals as likely outlets for publication for a specific unpublished research manuscript. In this paper we discuss one such system, Manuscript Matcher, a commercial tool developed by the authors of this paper, that uses both content and bibliometric elements in its recommendations and interface to present suggestions on likely “best fit” publications based on a user’s draft title, abstract, and citations. In the current implementation, recommendations are well received with 64% positive user feedback. We briefly discuss system development and implementation, present an overview and contextualization against similar systems, and chart future directions for both product enhancements and user research. Our particular focus is on an analysis of current performance and user feedback especially as it could inform improvements to the system.
A Block Processing Approach to Fingerprint Ridge-Orientation Estimation

Fingerprint is one of the most universally accepted biometrics used in different and relevant fields of man’s endeavours including business transactions and human security. They have been used for the implementation of series of Automatic Fingerprint Identification Systems (AFIS) that have proved very adequate and efficient. Building an AFIS requires the implementation of different algorithms. One of such algorithms is the one concerned with fingerprint image enhancement which involves segmentation, normalization, ridge orientation estimation, ridge frequency estimation, filtering, binarization and thinning. In this research, the implementation of a modified approach to an existing ridge orientation estimation algorithm is presented with a view to increase speed and accuracy. The implementation was carried out in an environment characterized by Window Vista Home Basic operating system as platform and Matrix Laboratory (MatLab) as frontend engine. Synthetic images as well as real fingerprints obtained from selected staff and students of The Federal University of Technology, Akure (FUTA), Nigeria and the standard FVC2000 fingerprint database DB2 were used to test the adequacy of the resulting algorithm. The results show that the modified algorithm estimated the orientation with significant improvement over the original version.
An expert system for analyzing firewall rules

When deploying firewalls in an organization, it is essential to verify that the firewalls are configured properly. The problem of finding out what a given firewall configuration does occurs, for instance, when a new network administrator takes over, or a third party performs a technical security audit for the organization. While the problem can be approached via testing, non-intrusive techniques are often preferred. Existing tools for analyzing firewall configurations usually rely on hard-coded algorithms for analyzing access lists. In this paper we present a tool based on constraint logic programming (CLP) which allows the user to write higher level operations for, e.g., detecting common configuration mistakes. Our tool understands Cisco router access lists, and it is implemented using Eclipse, a constraint logic programming language. The problem of analyzing firewall configurations lends itself quite naturally to be solved by an expert system. We found it surprisingly easy to use logic statements to express knowledge on networking, firewalls, and common configuration mistakes, for instance. Using an existing generic inference engine allowed us to focus on defining the core concepts and relationships in the knowledge base.
Capability Driven Development: An Approach to Designing Digital Enterprises

The need for organizations to operate in changing environments is addressed by proposing an approach that integrates organizational development with information system (IS) development taking into account changes in the application context of the solution. This is referred to as Capability Driven Development (CDD). A meta-model representing business and IS designs consisting of goals, key performance indicators, capabilities, context and capability delivery patterns, is being proposed. The use of the meta-model is validated in three industrial case studies as part of an ongoing collaboration project, whereas one case is presented in the paper. Issues related to the use of the CDD approach, namely, CDD methodology and tool support are also discussed.
Complex Adaptations and the Evolution of Evolvability

1 One may wonder, ...] how complex organisms evolve at all. They seem to have so many genes, so many multiple or pleiotropic eeects of any one gene, so many possibilities for lethal mutations in early development, and all sorts of problems due to their long development.  The problem of complex adaptations is studied in two largely disconnected research traditions: evolutionary biology and evolutionary computer science. This paper summarizes the results from both areas and compares their implications. In evolutionary computer science it was found that the Darwinian process of mutation, recombination and selection is not universally eeective in improving complex systems like computer programs or chip designs. For adaptation to occur, these systems must possess "evolvability", i.e. the ability of random variations to sometimes produce improvement. It was found that evolvability critically depends on the way genetic variation maps onto phenotypic variation, an issue known as the representation problem. The genotype-phenotype map determines the variability of characters, which is the propensity to vary. Variability needs to be distinguished from variation, which are the actually realized diierences between individuals. The genotype-phenotype map is the common theme underlying such varied biological phenomena as genetic canalization, developmental constraints, biological versatility , developmental dissociability, morphological integration, and many more. For evolutionary biology the representation problem has important implications: how is it that extant species acquired a genotype-phenotype map which allows improvement by mutation and selection? Is the genotype-phenotype map able to change in evolution? What are the selective forces, if any, that shape the genotype-phenotype map? We propose that the genotype-phenotype map can evolve by two main routes: epistatic mutations, or the creation of new genes. A common result for organismic design is modularity. By modularity we mean a genotype-phenotype map in which there are few pleiotropic eeects among characters serving diierent functions, with pleiotropic eeects falling mainly among characters that are part of a single functional complex. Such a design is expected to improve evolvability by limiting the interference between the adaptation of diierent functions. Several population genetic models are reviewed that are intended to explain the evolutionary origin of a modular design. While our current knowledge is insuucient to assess the plausibil-ity of these models, they form the beginning of a framework for understanding the evolution of the genotype-phenotype map.
Retrieving Similar Styles to Parse Clothing

Clothing recognition is a societally and commercially important yet extremely challenging problem due to large variations in clothing appearance, layering, style, and body shape and pose. In this paper, we tackle the clothing parsing problem using a retrieval-based approach. For a query image, we find similar styles from a large database of tagged fashion images and use these examples to recognize clothing items in the query. Our approach combines parsing from: pre-trained global clothing models, local clothing models learned on the fly from retrieved examples, and transferred parse-masks (Paper Doll item transfer) from retrieved examples. We evaluate our approach extensively and show significant improvements over previous state-of-the-art for both localization (clothing parsing given weak supervision in the form of tags) and detection (general clothing parsing). Our experimental results also indicate that the general pose estimation problem can benefit from clothing parsing.
ISC: An Iterative Social Based Classifier for Adult Account Detection on Twitter

The widespread of adult content on online social networks (e.g., Twitter) is becoming an emerging yet critical problem. An automatic method to identify accounts spreading sexually explicit content (i.e., adult account) is of significant values in protecting children and improving user experiences. Traditional adult content detection techniques are ill-suited for detecting adult accounts on Twitter due to the diversity and dynamics in Twitter content. In this paper, we formulate the adult account detection as a graph based classification problem and demonstrate our detection method on Twitter by using social links between Twitter accounts and entities in tweets. As adult Twitter accounts are mostly connected with normal accounts and post many normal entities, which makes the graph full of noisy links, existing graph based classification techniques cannot work well on such a graph. To address this problem, we propose an iterative social based classifier (ISC), a novel graph based classification technique resistant to the noisy links. Evaluations using large-scale real-world Twitter data show that, by labeling a small number of popular Twitter accounts, ISC can achieve satisfactory performance in adult account detection, significantly outperforming existing techniques.
Gaiku : Generating Haiku with Word Associations Norms

creativeness / a pleasing field / of bloom Word associations are an important element of linguistic creativity. Traditional lexical knowledge bases such as WordNet formalize a limited set of systematic relations among words, such as synonymy, polysemy and hypernymy. Such relations maintain their systematicity when composed into lexical chains. We claim that such relations cannot explain the type of lexical associations common in poetic text. We explore in this paper the usage of Word Association Norms (WANs) as an alternative lexical knowledge source to analyze linguistic computational creativity. We specifically investigate the Haiku poetic genre, which is characterized by heavy reliance on lexical associations. We first compare the density of WAN-based word associations in a corpus of English Haiku poems to that of WordNet-based associations as well as in other non-poetic genres. These experiments confirm our hypothesis that the non-systematic lexical associations captured in WANs play an important role in poetic text. We then present Gaiku, a system to automatically generate Haikus from a seed word and using WAN-associations. Human evaluation indicate that generated Haikus are of lesser quality than human Haikus, but a high proportion of generated Haikus can confuse human readers, and a few of them trigger intriguing reactions. ∗ Supported by Deutsche Telekom Laboratories at BenGurion University of the Negev. † Supported by the Lynn and William Frankel Center for Computer Sciences.
A Neural Approach to Automated Essay Scoring

Traditional automated essay scoring systems rely on carefully designed features to evaluate and score essays. The performance of such systems is tightly bound to the quality of the underlying features. However, it is laborious to manually design the most informative features for such a system. In this paper, we develop an approach based on recurrent neural networks to learn the relation between an essay and its assigned score, without any feature engineering. We explore several neural network models for the task of automated essay scoring and perform some analysis to get some insights of the models. The results show that our best system, which is based on long short-term memory networks, outperforms a strong baseline by 5.6% in terms of quadratic weighted Kappa, without requiring any feature engineering.
Emotion recognition using a hierarchical binary decision tree approach

Automated emotion state tracking is a crucial element in the computational study of human communication behaviors. It is important to design robust and reliable emotion recognition systems that are suitable for real-world applications both to enhance analytical abilities to support human decision making and to design human–machine interfaces that facilitate efficient communication. We introduce a hierarchical computational structure to recognize emotions. The proposed structure maps an input speech utterance into one of the multiple emotion classes through subsequent layers of binary classifications. The key idea is that the levels in the tree are designed to solve the easiest classification tasks first, allowing us to mitigate error propagation. We evaluated the classification framework on two different emotional databases using acoustic features, the AIBO database and the USC IEMOCAP database. In the case of the AIBO database, we obtain a balanced recall on each of the individual emotion classes using this hierarchical structure. The performance measure of the average unweighted recall on the evaluation data set improves by 3.37% absolute (8.82% relative) over a Support Vector Machine baseline model. In the USC IEMOCAP database, we obtain an absolute improvement of 7.44% (14.58%) over a baseline Support Vector Machine modeling. The results demonstrate that the presented hierarchical approach is effective for classifying emotional utterances in multiple database contexts.
Gas exchange theory and the lactic acidosis (anaerobic) threshold.

The physiological requirements of performing exercise above the anaerobic threshold are considerably more demanding than for lower work rates. Lactic acidosis develops at a metabolic rate that is specific to the individual and the task being performed. Although numerous pyruvate-dependent mechanisms can lead to an elevated blood lactate, the increase in lactate during muscular exercise is accompanied by an increase in lactate/pyruvate ratio (i.e., increased NADH/NAD ratio). This is typically caused by an inadequate O2 supply to the mitochondria. Thus, the anaerobic threshold can be considered to be an important assessment of the ability of the cardiovascular system to supply O2 at a rate adequate to prevent muscle anaerobiosis during exercise testing. In this paper, we demonstrate, with statistical justification, that the pattern of arterial lactate and lactate/pyruvate ratio increase during exercise evidences threshold dynamics rather than the continuous exponential increase proposed by some investigators. The pattern of change in arterial bicarbonate (HCO3-) and pulmonary gas exchange supports this threshold concept. To estimate the anaerobic threshold by gas exchange methods, we measure CO2 output (VCO2) as a continuous function of O2 uptake (VO2) (V-slope analysis) as work rate is increased. The break-point in this plot reflects the obligate buffering of increasing lactic acid production by HCO3-. The anaerobic threshold measured by the V-slope analysis appears to be a sensitive index of the development of metabolic acidosis even in subjects in whom other gas exchange indexes are insensitive, owing to irregular breathing, reduced chemoreceptor sensitivity, impaired respiratory mechanics, or all of these occurrences.
Problem Based Learning: An Instructional Model and Its Constructivist Framework.

It is said that there’s nothing so practical as good theory. It may also be said that there’s nothing so theoretically interesting as good practice1. This is particularly true of efforts to relate constructivism as a theory of learning to the practice of instruction. Our goal in this paper is to provide a clear link between the theoretical principles of constructivism, the practice of instructional design, and the practice of teaching. We will begin with a basic characterization of constructivism identifying what we believe to be the central principles in learning and understanding. We will then identify and elaborate on eight instructional principles for the design of a constructivist learning environment. Finally, we will examine what we consider to be one of the best exemplars of a constructivist learning environment -Problem Based Learning as described by Barrows (1985, 1986, 1992).