nils-herrmann/clean_scirepeval huggingface dataset:
Standalone OPC UA Wrapper for Industrial Monitoring and Control Systems

OPC unified architecture (UA), a communication standard for the manufacturing industry, enables exchanging control and management data among distributed entities in industrial automation systems. OPC UA wrapper is a migration strategy that provides UA clients with seamless access to legacy servers having OPC classic interfaces. This paper presents the design of a standalone OPC UA wrapper and discusses its performance through extensive experiments using a prototype implementation. The wrapper consists of two main components, i.e., UA server and classic client, which communicate with each other via shared memory and semaphore. One important feature of the design is that it employs a distributed component object model runtime library implemented in Java for platform independence. This makes it possible to build a cost-competitive wrapper system by using commercial off-the-shelf non-Windows solutions with low-cost microprocessors. Another key feature is the event-driven update interface between the UA and classic components, which we propose as an alternative to the sampling-based mechanism for the reduced delay. Through experiments using workloads from an industrial monitoring system, we present a systematic approach of identifying the system parameters having a direct impact on the wrapper performance and eventually tuning them such that the read and subscription services of OPC UA exhibit the best performance.
Similarity-Based Approach for Positive and Unlabeled Learning

Positive and unlabelled learning (PU learning) has been investigated to deal with the situation where only the positive examples and the unlabelled examples are available. Most of the previous works focus on identifying some negative examples from the unlabelled data, so that the supervised learning methods can be applied to build a classifier. However, for the remaining unlabelled data, which can not be explicitly identified as positive or negative (we call them ambiguous examples), they either exclude them from the training phase or simply enforce them to either class. Consequently, their performance may be constrained. This paper proposes a novel approach, called similarity-based PU learning (SPUL) method, by associating the ambiguous examples with two similarity weights, which indicate the similarity of an ambiguous example towards the positive class and the negative class, respectively. The local similarity-based and global similarity-based mechanisms are proposed to generate the similarity weights. The ambiguous examples and their similarity-weights are thereafter incorporated into an SVM-based learning phase to build a more accurate classifier. Extensive experiments on real-world datasets have shown that SPUL outperforms state-of-the-art PU learning methods.
On the multi-stage influence maximization problem

The influence maximization problem turns up in many online social networks (OSN) in which each participant can potentially influence the decisions made by others in the network. Relationships can be friendships, family relationships or even professional relationships. Such influences can be used to achieve some objective and the influence maximization problem attempts to make decisions so as to maximize the effect of these influences. Past work focused on a static problem whereby one tries to identify the participants who are the most influential. Recently, a multi-stage version of the problem was proposed in which outcomes of influence attempts are observed before additional participants are chosen. For example, in online advertising, one can provide an impression to a particular subject and if that subject clicks on the impression, then their friends are informed in the hope that this information will increase the chances that they also click on the impression and eventually purchase the product. This problem is computationally intensive; in this paper we investigate various optimization methods for finding its solution that yield close to optimal results while taking less computation time. These include greedy and particle swarm optimization algorithms.
Efficient HIK SVM Learning for Image Classification

Histograms are used in almost every aspect of image processing and computer vision, from visual descriptors to image representations. Histogram intersection kernel (HIK) and support vector machine (SVM) classifiers are shown to be very effective in dealing with histograms. This paper presents contributions concerning HIK SVM for image classification. First, we propose intersection coordinate descent (ICD), a deterministic and scalable HIK SVM solver. ICD is much faster than, and has similar accuracies to, general purpose SVM solvers and other fast HIK SVM training methods. We also extend ICD to the efficient training of a broader family of kernels. Second, we show an important empirical observation that ICD is not sensitive to the C parameter in SVM, and we provide some theoretical analyses to explain this observation. ICD achieves high accuracies in many problems, using its default parameters. This is an attractive property for practitioners, because many image processing tasks are too large to choose SVM parameters using cross-validation.
Handling multiple objectives with particle swarm optimization

This paper presents an approach in which Pareto dominance is incorporated into particle swarm optimization (PSO) in order to allow this heuristic to handle problems with several objective functions. Unlike other current proposals to extend PSO to solve multiobjective optimization problems, our algorithm uses a secondary (i.e., external) repository of particles that is later used by other particles to guide their own flight. We also incorporate a special mutation operator that enriches the exploratory capabilities of our algorithm. The proposed approach is validated using several test functions and metrics taken from the standard literature on evolutionary multiobjective optimization. Results indicate that the approach is highly competitive and that can be considered a viable alternative to solve multiobjective optimization problems.
A Data mining Model for predicting the Coronary Heart Disease using Random Forest Classifier

Coronary Heart Disease (CHD) is a common form of disease affecting the heart and an important cause for premature death. From the point of view of medical sciences, data mining is involved in discovering various sorts of metabolic syndromes. Classification techniques in data mining play a significant role in prediction and data exploration. Classification technique such as Decision Trees has been used in predicting the accuracy and events related to CHD. In this paper, a Data mining model has been developed using Random Forest classifier to improve the prediction accuracy and to investigate various events related to CHD. This model can help the medical practitioners for predicting CHD with its various events and how it might be related with different segments of the population. The events investigated are Angina, Acute Myocardial Infarction (AMI), Percutaneous Coronary Intervention (PCI), and Coronary Artery Bypass Graft surgery (CABG). Experimental results have shown that classification using
Low-loss ultra-wideband transition between conductor-backed coplanar waveguide and substrate integrated waveguide

A novel transition between conductor-backed coplanar waveguide (CBCPW) and substrate integrated waveguide (SIW) is presented for microwave and millimeter-wave integrated circuit design. The proposed integrated transition that can provide simultaneous field and impedance matching, exhibits outstanding low-loss performances over an ultra-wideband range (entire Ka-band in our case). In this work, a generalized impedance inverter whose parameters are accurately extracted by the use of a numerical Thru-Reflection-Line (TRL) calibration technique is utilized to design the transition. Measured results for the fabricated transition in back-to-back configuration show that the insertion loss is better than 0.4 dB while the return loss is better than 20 dB over the entire Ka-band.
Performance evaluation of Sentinel-1 data in SAR ship detection

This study addresses the performances of ship detection with data acquired by the newly launched Sentinel-1 SAR sensor. An automatic validation approach based on coastal AIS data is employed for measuring the detection efficiency. Results are compared with ship detection capabilities conducted on Radarsat-2 and CosmoSkymed datasets. The influence of different key parameters, such as SAR imaging characteristics (polarization, incidence angle) or meteorological conditions, is addressed. Such an analysis is useful for operational services to determine data specifications that assure optimum vessel detection for maritime surveillance applications.
Mitophagy: a balance regulator of NLRP3 inflammasome activation

The NLRP3 inflammasome is activated by a variety of external or host-derived stimuli and its activation initiates an inflammatory response through caspase-1 activation, resulting in inflammatory cytokine IL-1β maturation and secretion. The NLRP3 inflammasome activation is a kind of innate immune response, most likely mediated by myeloid cells acting as a host defense mechanism. However, if this activation is not properly regulated, excessive inflammation induced by overactivated NLRP3 inflammasome can be detrimental to the host, causing tissue damage and organ dysfunction, eventually causing several diseases. Previous studies have suggested that mitochondrial damage may be a cause of NLRP3 inflammasome activation and autophagy, which is a conserved self-degradation process that negatively regulates NLRP3 inflammasome activation. Recently, mitochondria-selective autophagy, termed mitophagy, has emerged as a central player for maintaining mitochondrial homeostasis through the elimination of damaged mitochondria, leading to the prevention of hyperinflammation triggered by NLRP3 inflammasome activation. In this review, we will first focus on the molecular mechanisms of NLRP3 inflammasome activation and NLRP3 inflammasome-related diseases. We will then discuss autophagy, especially mitophagy, as a negative regulator of NLPP3 inflammasome activation by examining recent advances in research.
An ultra-low power (ULP) zero-current-detector (ZCD) circuit for switching inductor converter applied in energy harvesting system

In this paper, we present an ultra-low power (ULP) zero-current-detector (ZCD) circuit by using sub-threshold design method and discontinued operating time to decrease power consumption, which can be widely used in switching inductor converters such as boost, buck or buck-boost converters. The designed ZCD circuit consumes only 40 nA quiescent current to realize nearly 70 dB DC gain and 650 kHz gain-bandwidth (GBW) when it operates. The main comparator of the ZCD circuit has high sensitivity and very low delay time to turn-off the synchronous switches of the DC-DC converter on time. The average quiescent current consumption is less than 1.6 nA because the operating time is only 40 μs under the minimum switching period of 1 ms. Finally, the designed ZCD circuit is used in a boost converter for vibrational energy harvesting applications.
Embodiment of abstract concepts: good and bad in right- and left-handers.

Do people with different kinds of bodies think differently? According to the body-specificity hypothesis, people who interact with their physical environments in systematically different ways should form correspondingly different mental representations. In a test of this hypothesis, 5 experiments investigated links between handedness and the mental representation of abstract concepts with positive or negative valence (e.g., honesty, sadness, intelligence). Mappings from spatial location to emotional valence differed between right- and left-handed participants. Right-handers tended to associate rightward space with positive ideas and leftward space with negative ideas, but left-handers showed the opposite pattern, associating rightward space with negative ideas and leftward with positive ideas. These contrasting mental metaphors for valence cannot be attributed to linguistic experience, because idioms in English associate good with right but not with left. Rather, right- and left-handers implicitly associated positive valence more strongly with the side of space on which they could act more fluently with their dominant hands. These results support the body-specificity hypothesis and provide evidence for the perceptuomotor basis of even the most abstract ideas.
Fast Inference in Sparse Coding Algorithms with Applications to Object Recognition

Adaptive sparse coding methods learn a possibly overcomplete set of basis functions, such that natural image patches can be reconstructed by linearly combining a small subset of these bases. The applicability of these methods to visual object recognition tasks has been limited because of the prohibitive cost of the optimization algorithms required to compute the sparse representation. In this work we propose a simple and efficient algorithm to learn basis functions. After training, this model also provides a fast and smooth approximator to the optimal representation, achieving even better accuracy than exact sparse coding algorithms on visual object recognition tasks.
Adolescent maturity and the brain: the promise and pitfalls of neuroscience research in adolescent health policy.

Longitudinal neuroimaging studies demonstrate that the adolescent brain continues to mature well into the 20s. This has prompted intense interest in linking neuromaturation to maturity of judgment. Public policy is struggling to keep up with burgeoning interest in cognitive neuroscience and neuroimaging. However, empirical evidence linking neurodevelopmental processes and adolescent real-world behavior remains sparse. Nonetheless, adolescent brain development research is already shaping public policy debates about when individuals should be considered mature for policy purposes. With this in mind, in this article we summarize what is known about adolescent brain development and what remains unknown, as well as what neuroscience can and cannot tell us about the adolescent brain and behavior. We suggest that a conceptual framework that situates brain science in the broader context of adolescent developmental research would help to facilitate research-to-policy translation. Furthermore, although contemporary discussions of adolescent maturity and the brain often use a deficit-based approach, there is enormous opportunity for brain science to illuminate the great strengths and potentialities of the adolescent brain. So, too, can this information inform policies that promote adolescent health and well-being.
Deep Spatial Regression Model for Image Crowd Counting

Computer vision techniques have been used to produce accurate and generic crowd count estimators in recent years. Due to severe occlusions, appearance variations, perspective distortions and illumination conditions, crowd counting is a very challenging task. To this end, we propose a deep spatial regression model(DSRM) for counting the number of individuals present in a still image with arbitrary perspective and arbitrary resolution. Our proposed model is based on Convolutional Neural Network (CNN) and long short term memory (LSTM). First, we put the images into a pretrained CNN to extract a set of high-level features. Then the features in adjacent regions are used to regress the local counts with a LSTM structure which takes the spatial information into consideration. The final global count is obtained by a sum of the local patches. We apply our framework on several challenging crowd counting datasets, and the experiment results illustrate that our method on the crowd counting and density estimation problem outperforms state-of-the-art methods in terms of reliability and effectiveness.
Primal-dual subgradient methods for convex problems

In this paper we present a new approach for constructing subgradient schemes for different types of nonsmooth problems with convex structure. Our methods are primaldual since they are always able to generate a feasible approximation to the optimum of an appropriately formulated dual problem. Besides other advantages, this useful feature provides the methods with a reliable stopping criterion. The proposed schemes differ from the classical approaches (divergent series methods, mirror descent methods) by presence of two control sequences. The first sequence is responsible for aggregating the support functions in the dual space, and the second one establishes a dynamically updated scale between the primal and dual spaces. This additional flexibility allows to guarantee a boundedness of the sequence of primal test points even in the case of unbounded feasible set. We present the variants of subgradient schemes for nonsmooth convex minimization, minimax problems, saddle point problems, variational inequalities, and stochastic optimization. In all situations our methods are proved to be optimal from the view point of worst-case black-box lower complexity bounds.
How are we searching the World Wide Web? A comparison of nine search engine transaction logs

The Web and especially major Web search engines are essential tools in the quest to locate online information for many people. This paper reports results from research that examines characteristics and changes in Web searching from nine studies of five Web search engines based in the U.S. and Europe. We compare interactions occurring between users and Web search engines from the perspectives of session length, query length, query complexity, and content viewed among the Web search engines. The results of our research shows (1) users are viewing fewer result pages, (2) searchers on U.S.-based Web search engines use more query operators tan searchers on European-based search engines, (3) there are statistically significant differences in the use of Boolean operators and result pages viewed, and (4) one can not necessary apply results from studies of one particular Web search engine to another Web search engine. The wide spread use of Web search engines, employment of simple queries, and decreased viewing of result pages may have resulted from algorithmic enhancements by Web search engine companies. We discuss the implications of the findings for the development of Web search engines and design of online content.
Multiphase Electric Machines for Variable-Speed Applications

Although the concept of variable-speed drives, based on utilization of multiphase machines, dates back to the late 1960s, it was not until the mid- to late 1990s that multiphase drives became serious contenders for various applications. These include electric ship propulsion, locomotive traction, electric and hybrid electric vehicles, ldquomore-electricrdquo aircraft, and high-power industrial applications. As a consequence, there has been a substantial increase in the interest for such drive systems worldwide, resulting in a huge volume of work published during the last ten years. An attempt is made in this paper to provide a brief review of the current state of the art in the area. After addressing the reasons for potential use of multiphase rather than three-phase drives and the available approaches to multiphase machine designs, various control schemes are surveyed. This is followed by a discussion of the multiphase voltage source inverter control. Various possibilities for the use of additional degrees of freedom that exist in multiphase machines are further elaborated. Finally, multiphase machine applications in electric energy generation are addressed.
Interactive Robots as Social Partners and Peer Tutors for Children: A Field Trial

Robots increasingly have the potential to interact with people in daily life. It is believed that, based on this ability, they will play an essential role in human society in the not-so-distant future. This article examined the proposition that robots could form relationships with children and that children might learn from robots as they learn from other children. In this article, this idea is studied in an 18-day field trial held at a Japanese elementary school. Two English-speaking “Robovie” robots interacted with firstand sixth-grade pupils at the perimeter of their respective classrooms. Using wireless identification tags and sensors, these robots identified and interacted with children who came near them. The robots gestured and spoke English with the children, using a vocabulary of about 300 sentences for speaking and 50 words for recognition. The children were given a brief picture–word matching English test at the start of the trial, af62 KANDA, HIRANO, EATON, ISHIGURO
A Comparison of Data Warehouse Development Methodologies Case Study of the Process Warehouse

Building a data warehouse is a very challenging issue because compared to software engineering it is quite a young discipline and does not yet offer well-established strategies and techniques for the development process. Current data warehouse development methods can fall within three basic groups: data-driven, goal-driven and user-driven. All three development approaches have been applied to the Process Warehouse that is used as the foundation of a process-oriented decision support system, which aims to analyse and improve business processes continuously. In this paper we evaluate all three development methodologies by various assessment criteria. The aim is to establish a link between the methodology and the requirement domain.
Recognition of Power-Quality Disturbances Using S-Transform-Based ANN Classifier and Rule-Based Decision Tree

This paper deals with a modified technique for the recognition of single stage and multiple power quality (PQ) disturbances. An algorithm based on Stockwell's transform and artificial neural network-based classifier and a rule-based decision tree is proposed in this paper. The analysis and classification of single stage PQ disturbances consisting of both events and variations such as sag, swell, interruption, harmonics, transients, notch, spike, and flicker are presented. Moreover, the proposed algorithm is also applied on multiple PQ disturbances such as harmonics with sag, swell, flicker, and interruption. A database of these PQ disturbances based on IEEE-1159 standard is generated in MATLAB for simulation studies. The proposed algorithm extracts significant features of various PQ disturbances using S-transform, which are used as input to this hybrid classifier for the classification of PQ disturbances. Satisfactory results of effective recognition and classification of PQ disturbances are obtained with the proposed algorithm. Finally, the proposed method is also implemented on real-time PQ events acquired in a laboratory to confirm the validity of this algorithm in practical conditions.