nils-herrmann/clean_scirepeval huggingface dataset:
Development of a Compact Switched-Reluctance Motor Drive for EV Propulsion With Voltage-Boosting and PFC Charging Capabilities

This paper presents a compact battery-powered switched-reluctance motor (SRM) drive for an electric vehicle with voltage-boosting and on-board power-factor-corrected-charging capabilities. Although the boost-type front-end DC/DC converter is externally equipped, the on-board charger is formed by the embedded components of SRM windings and converter. In the driving mode, the DC/DC boost converter is controlled to establish boostable well-regulated DC-link voltage for the SRM drive from the battery. During demagnetization of each stroke, the winding stored energy is automatically recovered back to the battery. Moreover, the regenerative braking is achieved by properly setting the SRM in the regenerating mode. The controls of two power stages are properly designed and digitally realized using a common digital signal processor. Good winding current and speed dynamic responses of the SRM drive are obtained. In addition, the static operation characteristics are also improved. In the charging mode, the power devices that were embedded in the SRM converter and the three motor-phase windings are used to form a buck-boost switch-mode rectifier to charge the battery from utility with good power quality. All constituted circuit components of the charger are placed on board, and only the insertion of power cords is needed.
Discrete Wigner Function Derivation of the Aaronson-Gottesman Tableau Algorithm

The Gottesman–Knill theorem established that stabilizer states and Clifford operations can be efficiently simulated classically. For qudits with odd dimension three and greater, stabilizer states and Clifford operations have been found to correspond to positive discrete Wigner functions and dynamics. We present a discrete Wigner function-based simulation algorithm for odd-d qudits that has the same time and space complexity as the Aaronson–Gottesman algorithm for qubits. We show that the efficiency of both algorithms is due to harmonic evolution in the symplectic structure of discrete phase space. The differences between the Wigner function algorithm for odd-d and the Aaronson–Gottesman algorithm for qubits are likely due only to the fact that the Weyl–Heisenberg group is not in SU(d) for d = 2 and that qubits exhibit state-independent contextuality. This may provide a guide for extending the discrete Wigner function approach to qubits.
A Variational Principle for Graphical Models

Graphical models bring together graph theory and probability theory in a powerful formalism for multivariate statistical modeling. In statistical signal processing— as well as in related fields such as communication theory, control theory and bioinformatics—statistical models have long been formulated in terms of graphs, and algorithms for computing basic statistical quantities such as likelihoods and marginal probabilities have often been expressed in terms of recursions operating on these graphs. Examples include hidden Markov models, Markov random fields, the forward-backward algorithm and Kalman filtering [ Rabiner and Juang (1993); Pearl (1988); Kailath et al. (2000)]. These ideas can be understood, unified and generalized within the formalism of graphical models. Indeed, graphical models provide a natural framework for formulating variations on these classical architectures, and for exploring entirely new families of statistical models. The recursive algorithms cited above are all instances of a general recursive algorithm known as the junction tree algorithm [ Lauritzen and Spiegelhalter, 1988]. The junction tree algorithm takes advantage of factorization properties of the joint probability distribution that are encoded by the pattern of missing edges in a graphical model. For suitably sparse graphs, the junction tree algorithm provides a systematic and practical solution to the general problem of computing likelihoods and other statistical quantities associated with a graphical model. Unfortunately, many graphical models of practical interest are not " suitably sparse, " so that the junction tree algorithm no longer provides a viable computational solution to the problem of computing marginal probabilities and other expectations. One popular source of methods for attempting to cope with such cases is the Markov chain Monte Carlo (MCMC) framework, and indeed there is a significant literature on
Recognition of Acute Lymphoblastic Leukemia Cells in Microscopic Images Using K-Means Clustering and Support Vector Machine Classifier

Acute lymphoblastic leukemia is the most common form of pediatric cancer which is categorized into three L1, L2, and L3 and could be detected through screening of blood and bone marrow smears by pathologists. Due to being time-consuming and tediousness of the procedure, a computer-based system is acquired for convenient detection of Acute lymphoblastic leukemia. Microscopic images are acquired from blood and bone marrow smears of patients with Acute lymphoblastic leukemia and normal cases. After applying image preprocessing, cells nuclei are segmented by k-means algorithm. Then geometric and statistical features are extracted from nuclei and finally these cells are classified to cancerous and noncancerous cells by means of support vector machine classifier with 10-fold cross validation. These cells are also classified into their sub-types by multi-Support vector machine classifier. Classifier is evaluated by these parameters: Sensitivity, specificity, and accuracy which values for cancerous and noncancerous cells 98%, 95%, and 97%, respectively. These parameters are also used for evaluation of cell sub-types which values in mean 84.3%, 97.3%, and 95.6%, respectively. The results show that proposed algorithm could achieve an acceptable performance for the diagnosis of Acute lymphoblastic leukemia and its sub-types and can be used as an assistant diagnostic tool for pathologists.
The Measurement of Dielectric Properties of Liquids at Microwave Frequencies Using Open-ended Coaxial Probes

Significant progress has been made in recent years in the development of microwave tomographic imaging systems for medical applications. In order to design an appropriate microwave imaging system for industrial applications, and to interpret the images produced, the materials under imaging need to be characterised. In this paper, we describe the use of open-ended coaxial probes for the measurement of dielectric properties of liquids at frequencies between 400MHz and 20GHz. The results obtained using the Misra-Blackham model for a number of liquids including water of different salinity are compared with those published in the literature showing a good agreement. For saline water, in particular, the frequency of the minimum loss depends on the salinity. It may change from 1.5GHz for the inclusion of 0.2% NaCl to 7GHz for the inclusion of 3.5% NaCl. The real part of the permittivity may also change by approximately 50% from 400MHz to 20GHz.
Autophagy as a regulated pathway of cellular degradation.

Macroautophagy is a dynamic process involving the rearrangement of subcellular membranes to sequester cytoplasm and organelles for delivery to the lysosome or vacuole where the sequestered cargo is degraded and recycled. This process takes place in all eukaryotic cells. It is highly regulated through the action of various kinases, phosphatases, and guanosine triphosphatases (GTPases). The core protein machinery that is necessary to drive formation and consumption of intermediates in the macroautophagy pathway includes a ubiquitin-like protein conjugation system and a protein complex that directs membrane docking and fusion at the lysosome or vacuole. Macroautophagy plays an important role in developmental processes, human disease, and cellular response to nutrient deprivation.
Feature Selection for Maximizing the Area Under the ROC Curve

Feature selection is an important pre-processing step for solving classification problems. A good feature selection method may not only improve the performance of the final classifier, but also reduce the computational complexity of it. Traditionally, feature selection methods were developed to maximize the classification accuracy of a classifier. Recently, both theoretical and experimental studies revealed that a classifier with the highest accuracy might not be ideal in real-world problems. Instead, the Area Under the ROC Curve (AUC) has been suggested as the alternative metric, and many existing learning algorithms have been modified in order to seek the classifier with maximum AUC. However, little work was done to develop new feature selection methods to suit the requirement of AUC maximization. To fill this gap in the literature, we propose in this paper a novel algorithm, called AUC and Rank Correlation coefficient Optimization (ARCO) algorithm. ARCO adopts the general framework of a well-known method, namely minimal redundancy- maximal-relevance (mRMR) criterion, but defines the terms ”relevance” and ”redundancy” in totally different ways. Such a modification looks trivial from the perspective of algorithmic design. Nevertheless, experimental study on four gene expression data sets showed that feature subsets obtained by ARCO resulted in classifiers with significantly larger AUC than the feature subsets obtained by mRMR. Moreover, ARCO also outperformed the Feature Assessment by Sliding Thresholds algorithm, which was recently proposed for AUC maximization, and thus the efficacy of ARCO was validated.
Modelling consumer credit risk via survival analysis

Credit risk models are used by financial companies to evaluate in advance the insolvency risk caused by credits that enter into default. Many models for credit risk have been developed over the past few decades. In this paper, we focus on those models that can be formulated in terms of the probability of default by using survival analysis techniques. With this objective three different mechanisms are proposed based on the key idea of writing the default probability in terms of the conditional distribution function of the time to default. The first method is based on a Cox’s regression model, the second approach uses generalized linear models under censoring and the third one is based on nonparametric kernel estimation, using the product-limit conditional distribution function estimator by Beran. The resulting nonparametric estimator of the default probability is proved to be consistent and asymptotically normal. An empirical study, based on modified real data, illustrates the three methods.
Style Augmentation: Data Augmentation via Style Randomization

We introduce style augmentation, a new form of data augmentation based on random style transfer, for improving the robustness of convolutional neural networks (CNN) over both classification and regression based tasks. During training, our style augmentation randomizes texture, contrast and color, while preserving shape and semantic content. This is accomplished by adapting an arbitrary style transfer network to perform style randomization, by sampling input style embeddings from a multivariate normal distribution instead of inferring them from a style image. In addition to standard classification experiments, we investigate the effect of style augmentation (and data augmentation generally) on domain transfer tasks. We find that data augmentation significantly improves robustness to domain shift, and can be used as a simple, domain agnostic alternative to domain adaptation. Comparing style augmentation against a mix of seven traditional augmentation techniques, we find that it can be readily combined with them to improve network performance. We validate the efficacy of our technique with domain transfer experiments in classification and monocular depth estimation, illustrating consistent improvements in generalization.
Computerized Analysis of Blood Cell Images for the Accurate Diagnosis of Malaria

In this paper, we are presenting an approach for the analysis of blood cell images which is used for the detection of malaria making use of computer techniques. We have used S and V component images of HSV color model. Sequential edge linking algorithm, Euclidian distance based clustering. Zack’s thresholding technique is used to segment these images, The proposed approach is implemented with Pathology and blood cell images.
Monocyte and macrophage plasticity in tissue repair and regeneration.

Heterogeneity and high versatility are the characteristic features of the cells of monocyte-macrophage lineage. The mononuclear phagocyte system, derived from the bone marrow progenitor cells, is primarily composed of monocytes, macrophages, and dendritic cells. In regenerative tissues, a central role of monocyte-derived macrophages and paracrine factors secreted by these cells is indisputable. Macrophages are highly plastic cells. On the basis of environmental cues and molecular mediators, these cells differentiate to proinflammatory type I macrophage (M1) or anti-inflammatory or proreparative type II macrophage (M2) phenotypes and transdifferentiate into other cell types. Given a central role in tissue repair and regeneration, the review focuses on the heterogeneity of monocytes and macrophages with current known mechanisms of differentiation and plasticity, including microenvironmental cues and molecular mediators, such as noncoding RNAs.
Eye gaze tracking with google cardboard using purkinje images

Mobile phone-based Virtual Reality (VR) is rapidly growing as a platform for stereoscopic 3D and non-3D digital content and applications. The ability to track eye gaze in these devices would be a tremendous opportunity on two fronts: firstly, as an interaction technique, where interaction is currently awkward and limited, and secondly, for studying human visual behavior. We propose a method to add eye gaze tracking to these existing devices using their on-board display and camera hardware, with a minor modification to the headset enclosure. We present a proof-of-concept implementation of the technique and show results demonstrating its feasibility. The software we have developed will be made available as open source to benefit the research community.
Improving Multi-label Emotion Classification via Sentiment Classification with Dual Attention Transfer Network

In this paper, we target at improving the performance of multi-label emotion classification with the help of sentiment classification. Specifically, we propose a new transfer learning architecture to divide the sentence representation into two different feature spaces, which are expected to respectively capture the general sentiment words and the other important emotion-specific words via a dual attention mechanism. Extensive experimental results demonstrate that our transfer learning approach can outperform several strong baselines and achieve the state-of-the-art performance on two benchmark datasets.
Bilateral Choanal Atresia with Tessier Type 3 Facial Cleft: A Rare Association

Five-day-old male neonate born to non-consanguineous parents by normal vaginal delivery was presented with deformity of lip, face and eyelid on the right side, and cyanosis. The cyanosis had improved soon after endotracheal intubation. Examination revealed deformity of upper lip, upper jaw and lower eyelid of right eye; this type of craniofacial cleft is called as Tessier type 3 . There was associated bilateral choanal atresia. The baby accidently self-extubated himself after 2 days, but could maintain thereafter with oxygen administration through hood. We plan to manage choanal atresia endoscopically at least on one side first before undertaking the reconstruction of the facial cleft part.
SecMANO: Towards Network Functions Virtualization (NFV) Based Security MANagement and Orchestration

Network Functions Virtualization (NFV) has recently emerged as one of the major technological driving forces that significantly accelerate the evolution of today's computer and communication networks. Despite the advantages of NFV, e.g., saving investment cost, optimizing resource consumption, improving operational efficiency, simplifying network service lifecycle management, lots of novel security threats and vulnerabilities will be introduced, thereby impeding its further development and deployment in practice. In this paper, we briefly report our threat analysis in the context of NFV, and identify the corresponding security requirements. The purpose is to establish a comprehensive threat taxonomy and provide a guideline to develop effective security countermeasures. Furthermore, a conceptual design framework for NFV based security management and service orchestration is presented, with an objective to dynamically and adaptively deploy and manage security functions on the demands of users and customers. A use case about NFV based access control is also developed, illustrating the feasibility and advantages of implementing NFV based security management and orchestration.
Constructivism in Education : An overview of contributions to the literature and to the

Constructivism in education has evolved over the past century due to the contributions of many individuals in the U. S. and abroad. This article provides an overview of the contributions of theorists, researchers, and educators most closely associated with its rise in the field of education. The article also provides an outline and guide to the annotated bibliography on constructivism in education on the JPACTe website.  This article provides an overview of the development of constructivist theory in education. It traces the roots of constructivism in the areas of educational philosophy, cognitive theory, research on teaching, the “social curriculum,” professional development and brain research. The article also provides an annotated bibliography aligned with these topics. Defining Constructivism in Education Constructivism allows us as, as educators, the conceptual tools with which to view our students and how they learn in a way that is congruent with best practice. Until recently, “best practice” has been defined by traditional behaviorist definitions focused on student academic outcomes; constructivist “best” practice is a relatively new focus of research. “Constructivist best practice” in the past has been defined by practitioners and those observing them: by teacher anecdotal evidence, clinical observation, the success of affective and social teaching, and, increasingly, the positive relationship of constructivist teaching and academic success (Zins, Weissberg, Wang, & Walberg, 2004). Constructivism requires that we understand that “(M)eaning is not given to us in our encounters, but it is given by us, constructed by us, each in our own way, according to how our understanding is currently organized.” (Duckworth, 1987, p. 112) (emphasis
Lightweight security algorithm for low power IoT devices

In today's technology, ever increasing number of electronics applications require secure communication, for example the Internet of things devices. Elliptic Curve Diffie Hellman (EC-DH) Algorithm has emerged as an attractive and effective public-key cryptosystem. Elliptic curves are widely used in various key exchange techniques that include the Diffie-Hellman Key agreement scheme. When contrasted with conventional cryptosystems like RSA, ECC offers equivalent security with smaller key sizes, which results in lower power consumption, speedier calculations, and also lower memory and transmission capacity (bandwidth) reserve. This is particularly valid and helpful for applications like IoT gadgets, which are regularly constrained regarding their CPU processing speed, power, and area. This work includes the software and hardware implementation of Diffie-Hellman, Elliptic Curve Diffie-Hellman (ECDH) Key agreement algorithm, and RSA algorithm. The proposed work also involves analysis of power, performance, area, and their comparisons thereof. The comparison is based on metrics obtained, after implementing the algorithms in synopsys using 90 nm UMC Faraday library. The ECDH algorithm is found to be better than others as far as power and area are concerned.
Powering MEMS portable devices — a review of non-regenerative and regenerative power supply systems with special emphasis on piezoelectric energy harvesting systems

Power consumption is forecast by the International Technology Roadmap of Semiconductors (ITRS) to pose long-term technical challenges for the semiconductor industry. The purpose of this paper is threefold: (1) to provide an overview of strategies for powering MEMS via non-regenerative and regenerative power supplies; (2) to review the fundamentals of piezoelectric energy harvesting, along with recent advancements, and (3) to discuss future trends and applications for piezoelectric energy harvesting technology. The paper concludes with a discussion of research needs that are critical for the enhancement of piezoelectric energy harvesting devices. (Some figures in this article are in colour only in the electronic version)
Identifying ECUs Using Inimitable Characteristics of Signals in Controller Area Networks

As the functions of vehicles are more computerized for the safety and convenience of drivers, attack surfaces of vehicle are accordingly increasing. Many attack results have shown that an attacker could intentionally control vehicles. Most of them exploit the vulnerability that controller area network (CAN) protocol, a de-facto standard for the in-vehicle networks, does not support message origin authentication. Although a number of methods to resolve this security vulnerability have been suggested, they have their each limitation to be applied into the current system. They have required either the modification of the CAN standard or dramatical communication load increase, which is infeasible in practice. In this paper, we propose a novel identification method, which works in the physical layer of the in-vehicle CAN network. Our method identifies electronic control units (ECUs) using inimitable characteristics of electrical CAN signals enabling detection of a malicious ECU. Unlike previous attempts to address the security problem in the in-vehicle CAN network, our method works by simply adding a monitoring unit to the existing network, making it deployable in current systems and compliant with required CAN standards. Our experimental results show that our method is able to correctly identify ECUs. In case of misclassfication rate for ECU idnetification, our method yields 0.36% in average which is approximate four times lower than the method proposed by P.-S. Murvay  et al. This paper is also the first to identify potential attack models that systems should be able to detect.
A Recommender System Combining Social Networks for Tourist Attractions

The fast development of Web technologies has introduced a world of big data. How efficiently and effectively to retrieve the information from the ocean of data that the users really want is an important topic. Recommendation systems have become a popular approach to personalized information retrieval. On the other hand, social media have quickly entered into your life. The information from social networks can be an effective indicator for recommender systems. In this paper we present a recommendation mechanism which calculates similarity among users and users' trustability and analyzes information collected from social networks. To validate our method an information system for tourist attractions built on this recommender system has been presented. We further evaluate our system by experiments. The results show our method is feasible and effective.