nils-herrmann/clean_scirepeval huggingface dataset:
Simulation of metal oxide surge arrester dynamic behavior under fast transients

Data on characteristics of metal-oxide surge arresters indicates that for fast front surges, those with rise times less than 8μs, the peak of the voltage wave occurs before the peak of the current wave and the residual voltage across the arrester increases as the time to crest of the arrester discharge current decreases. Several models have been proposed to simulate this frequency-dependent characteristic. These models differ in the calculation and adjustment of their parameters. In the present paper, a simulation of metal oxide surge arrester (MOSA) dynamic behavior during fast electromagnetic transients on power systems is done. Some models proposed in the literature are used. The simulations are performed with the Alternative Transients Program (ATP) version of Electromagnetic Transient Program (EMTP) to evaluate some metal oxide surge arrester models and verify their accuracy.
Transaction Security for E-commerce Application

E-commerce applications are becoming popular day b y day as they are working like a virtual shop. Writing good E-commerce application is tedious task and com plex also. The applications if made complex are ver y difficult to maintain. Usability is a very basic concept in the E-commerce application. User has to get the informa tion at one click and with proper feedback. As these are web based applic ations efficiency matters a lot for this application. As transaction in e-commerce faces the problems such as database expl oits, log data mining and sniffing attacks which ca n be resolved by using different security measure. Hence security is important in e-commerce application Sniffing Attack, Log Data Mining, DBMS exploit
Blockchain Disruption and Smart Contracts

Blockchain technology provides decentralized consensus and potentially enlarges the contracting space through smart contracts that offer tamper-proofness and algorithmic executions. Meanwhile, generating decentralized consensus entails distributing information that necessarily alters the informational environment. We analyze how decentralization affects consensus effectiveness and how the quintessential features of blockchain reshape industrial organization and the landscape of competition. Smart contracts can mitigate informational asymmetry and improve welfare and consumer surplus through enhanced entry and competition, yet the distribution of information during consensus generation may encourage greater collusion. In general, blockchains can sustain market equilibria with a wider range of economic outcomes. We further discuss the implications for antitrust policies targeted at blockchain applications, such as separating consensus record-keepers from users.
A Reference Model for Anti-Money Laundering in the Financial Sector

Anti-Money Laundering (AML) can be seen as a central problem for financial institutions because of the need to detect compliance violations in various customer contexts. Changing regulations and the strict supervision of financial authorities create an even higher pressure to establish an effective working compliance program. To support financial institutions in building a simple but efficient compliance program we develop a reference model that describes the process and data view for one key process of AML based on literature analysis and expert interviews. Therefore, this paper describes the customer identification process (CIP) as a part of an AML program using reference modeling techniques. The contribution of this work is (i) the application of multi-perspective reference modeling resulting in (ii) a reference model for AML customer identification. Overall, the results help to understand the complexity of AML processes and to establish a sustainable compliance program.
MetaGraph2Vec: Complex Semantic Path Augmented Heterogeneous Network Embedding

Network embedding in heterogeneous information networks (HINs) is a challenging task, due to complications of different node types and rich relationships between nodes. As a result, conventional network embedding techniques cannot work on such HINs. Recently, metapathbased approaches have been proposed to characterize relationships in HINs, but they are ineffective in capturing rich contexts and semantics between nodes for embedding learning, mainly because (1) metapath is a rather strict single path node-node relationship descriptor, which is unable to accommodate variance in relationships, and (2) only a small portion of paths can match the metapath, resulting in sparse context information for embedding learning. In this paper, we advocate a new metagraph concept to capture richer structural contexts and semantics between distant nodes. A metagraph contains multiple paths between nodes, each describing one type of relationships, so the augmentation of multiple metapaths provides an effective way to capture rich contexts and semantic relations between nodes. This greatly boosts the ability of metapath-based embedding techniques in handling very sparse HINs. We propose a new embedding learning algorithm, namely MetaGraph2Vec, which uses metagraph to guide the generation of random walks and to learn latent embeddings of multi-typed HIN nodes. Experimental results show that MetaGraph2Vec is able to outperform the state-of-theart baselines in various heterogeneous network mining tasks such as node classification, node clustering, and similarity search.
Data Imbalance Problem solving for SMOTE Based Oversampling: Study on Fault Detection Prediction Model in Semiconductor Manufacturing Process

Fault detection prediction of FAB (wafer fabrication) process in semiconductor manufacturing process is possible that improve product quality and reliability in accordance with the classification performance. However, FAB process is sometimes due to a fault occurs. And mostly it occurs “pass”. Hence, data imbalance occurs in the pass/fail class. If the data imbalance occurs, prediction models are difficult to predict “fail” class because increases the bias of majority class (pass class). In this paper, we propose the SMOTE (Synthetic Minority Oversampling Technique) based over sampling method for solving problem of data imbalance. The proposed method solve the imbalance of the between pass and fail by oversampling the minority class of fail. In addition, by applying the fault detection prediction model to measure the performance.
Cellulase Production by Bacillus subtilis isolated from Cow Dung

Cellulases are the enzymes hydrolyzing cellulosic biomass and are produced by the microorganisms that grown over cellulosic matters. Bacterial cellulases possess more advantages when compared to the cellulases from other sources. Cellulase producing bacteria was isolated from Cow dung. The organism was identified using 16 SrDNA sequencing and BLAST search. Cellulase was produced and the culture conditions like temperature, pH, and Incubation time and medium components like Carbon sources, nitrogen sources and role of natural substrates were optimized. The enzyme was further purified using ethanol precipitation and chromatography. Cellulase was then characterized using SDS-PAGE analysis and Zymographic Studies. The application of Cellulase in Biostoning was then analyzed.
SETTING THE STAGE FOR SELF-DRIVING CARS : Exploration of future autonomous driving experiences

Self-driving cars are under development and are predicted to reach the market within a few years. There is a need to understand how users will respond to the technology, and what possible benefits or difficulties they perceive. User involvement is a prerequisite for eliciting this information at the same time as several studies have demonstrated the problems associated with investigating “the future”. In this paper, two different approaches for exploring future automotive technology were applied in two studies of users’ future experiences with self-driving cars. Both studies used materials to mediate a shift in focus from today to tomorrow, but the outcomes varied with the different approaches. The results of the two studies provide insights into users’ expectations of autonomous cars and contribute to our knowledge on how different studies on the same topic can elicit different types of data.
A Hybrid Probabilistic Model for Camera Relocalization

We present a hybrid deep learning method for modelling the uncertainty of camera relocalization from a single RGB image. The proposed system leverages the discriminative deep image representation from a convolutional neural networks, and uses Gaussian Process regressors to generate the probability distribution of the six degree of freedom (6DoF) camera pose in an end-to-end fashion. This results in a network that can generate uncertainties over its inferences with no need to sample many times. Furthermore we show that our objective based on KL divergence reduces the dependence on the choice of hyperparameters. The results show that compared to the state-of-the-art Bayesian camera relocalization method, our model produces comparable localization uncertainty and improves the system efficiency significantly, without loss of accuracy.
Deep Multi-scale Convolutional Neural Network for Dynamic Scene Deblurring

Non-uniform blind deblurring for general dynamic scenes is a challenging computer vision problem as blurs arise not only from multiple object motions but also from camera shake, scene depth variation. To remove these complicated motion blurs, conventional energy optimization based methods rely on simple assumptions such that blur kernel is partially uniform or locally linear. Moreover, recent machine learning based methods also depend on synthetic blur datasets generated under these assumptions. This makes conventional deblurring methods fail to remove blurs where blur kernel is difficult to approximate or parameterize (e.g. object motion boundaries). In this work, we propose a multi-scale convolutional neural network that restores sharp images in an end-to-end manner where blur is caused by various sources. Together, we present multi-scale loss function that mimics conventional coarse-to-fine approaches. Furthermore, we propose a new large-scale dataset that provides pairs of realistic blurry image and the corresponding ground truth sharp image that are obtained by a high-speed camera. With the proposed model trained on this dataset, we demonstrate empirically that our method achieves the state-of-the-art performance in dynamic scene deblurring not only qualitatively, but also quantitatively.
SqueezeSegV2: Improved Model Structure and Unsupervised Domain Adaptation for Road-Object Segmentation from a LiDAR Point Cloud

Earlier work demonstrates the promise of deeplearning-based approaches for point cloud segmentation; however, these approaches need to be improved to be practically useful. To this end, we introduce a new model SqueezeSegV2 that is more robust to dropout noise in LiDAR point clouds. With improved model structure, training loss, batch normalization and additional input channel, SqueezeSegV2 achieves significant accuracy improvement when trained on real data. Training models for point cloud segmentation requires large amounts of labeled point-cloud data, which is expensive to obtain. To sidestep the cost of collection and annotation, simulators such as GTA-V can be used to create unlimited amounts of labeled, synthetic data. However, due to domain shift, models trained on synthetic data often do not generalize well to the real world. We address this problem with a domainadaptation training pipeline consisting of three major components: 1) learned intensity rendering, 2) geodesic correlation alignment, and 3) progressive domain calibration. When trained on real data, our new model exhibits segmentation accuracy improvements of 6.0-8.6% over the original SqueezeSeg. When training our new model on synthetic data using the proposed domain adaptation pipeline, we nearly double test accuracy on real-world data, from 29.0% to 57.4%. Our source code and synthetic dataset will be open-sourced.
Random Semantic Tensor Ensemble for Scalable Knowledge Graph Link Prediction

Link prediction on knowledge graphs is useful in numerous application areas such as semantic search, question answering, entity disambiguation, enterprise decision support, recommender systems and so on. While many of these applications require a reasonably quick response and may operate on data that is constantly changing, existing methods often lack speed and adaptability to cope with these requirements. This is aggravated by the fact that knowledge graphs are often extremely large and may easily contain millions of entities rendering many of these methods impractical. In this paper, we address the weaknesses of current methods by proposing Random Semantic Tensor Ensemble (RSTE), a scalable ensemble-enabled framework based on tensor factorization. Our proposed approach samples a knowledge graph tensor in its graph representation and performs link prediction via ensembles of tensor factorization. Our experiments on both publicly available datasets and real world enterprise/sales knowledge bases have shown that our approach is not only highly scalable, parallelizable and memory efficient, but also able to increase the prediction accuracy significantly across all datasets.
Isolate First, Then Share: a New OS Architecture for Datacenter Computing

Modern datacenter (DC) workloads are characterized by increasing diversity and differentiated QoS requirements in terms of the average or worst-case performance. The shift towards DC calls for the new OS architectures that not only gracefully achieve disparate performance goals, but also protect software investments. This paper presents the "isolate first, then share" OS architecture. We decompose the OS into the supervisor and several subOSes running side by side: a subOS directly manages physical resources without intervention from the supervisor (isolate resources first), while the supervisor can create, destroy, resize a subOS on-the-fly (then share). We confine state sharing among the supervisor and SubOSes (isolate states first), and provide fast inter-subOS communication mechanisms on demand (then share). We present the first implementation—RainForest, which supports unmodified Linux binaries. Our comprehensive evaluations show RainForest outperforms Linux with three different kernels, LXC, and Xen in terms of improving resource utilization, throughput, scalability, and worst-case performance. The RainForest source code is soon available.
Machine teaching: a machine learning approach to technology enhanced learning

Many applications of Technology Enhanced Learning are based on strong assumptions: Knowledge needs to be standardized, structured and most of all externalized into learning material that preferably is annotated with meta-data for efficient re-use. A vast body of valuable knowledge does not meet these assumptions, including informal knowledge such as experience and intuition that is key to many complex activities. We notice that knowledge, even if not standardized, structured and externalized, can still be observed through its application. We refer to this observable knowledge as PRACTICED KNOWLEDGE. We propose a novel approach to Technology Enhanced Learning named MACHINE TEACHING to convey this knowledge: Machine Learning techniques are used to extract machine models of Practiced Knowledge from observational data. These models are then applied in the learner’s context for his support. We identify two important subclasses of machine teaching, General and Detailed Feedback Machine Teaching. GENERAL FEEDBACK MACHINE TEACHING aims to provide the learner with a “grade-like” numerical rating of his work. This is a direct application of supervised machine learning approaches.  MACHINE TEACHING aims to provide the learner with in-depth support with respect to his activities. An analysis showed that a large subclass of Detailed Feedback Machine Teaching applications can be addressed through adapted recommender systems technology. The ability of the underlying machine learning techniques to capture structure and patterns in the observational data is crucial to the overall applicability of Machine Teaching. Therefore, we study the feasibility of Machine Teaching from a machine learning perspective. Following this goal, we evaluate the General Feedback Machine Teaching approach using state-of-the-art machine learning techniques: The exemplary Machine Teaching system is sought to provide the learner with quality estimations of his writing as judged by an online community. The results obtained in this evaluation are supportive of the applicability of Machine Teaching to this domain. To facilitate Detailed Feedback Machine Teaching, we present a novel matrix factorization model and algorithm. In addition to addressing the needs of Machine Teaching, it is also a contribution to the recommender systems field as it facilitates ranking estimation. An Evaluation in a Detailed Feedback Machine Teaching scenario for software engineers supports the feasibility of Machine Teaching in that domain. We therefore conclude that machine learning models capable of capturing important aspects of practiced knowledge can be found in both, General and Detailed Feedback Machine Teaching. Machine Teaching does not assume the knowledge to be externalized, but to be observable and therefore adds another body of knowledge to Technology Enhanced Learning not amenable to traditional Technology Enhanced Learning approaches.
Music Preferences , Personality Style , and Developmental Issues of Adolescents

The purpose of this study was to examine the personality characteristics and developmental issues of 3 groups of adolescent music listeners: those preferring light qualities of music, those preferring heavy qualities of music, and those who had eclectic preferences for music qualities. One hundred sixty-four adolescents completed an age-appropriate personality inventory and a systematic measure of music listening preference. The findings indicate that each of the 3 music preference groups is inclined to demonstrate a unique profile of personality dimensions and developmental issues. Those preferring heavy or light music qualities indicated at least moderate difficulty in negotiating several distinct domains of personality and/or developmental issues; those with more eclectic music preferences did not indicate similar difficulty. Thus, there was considerable support for the general hypothesis that adolescents prefer listening to music that reflects specific personalities and the developmental issues with which they are dealing.
The role of culture in health communication.

This paper examines the role of culture as a factor in enhancing the effectiveness of health communication. We describe culture and how it may be applied in audience segmentation and introduce a model of health communication planning--McGuire's communication/persuasion model--as a framework for considering the ways in which culture may influence health communication effectiveness. For three components of the model (source, message, and channel factors), the paper reviews how each affects communication and persuasion, and how each may be affected by culture. We conclude with recommendations for future research on culture and health communication.
Analysis of banking data using machine learning

In today's world large amount of data is generated in every field and banking industry is one of them. This data contains valuable information. Hence, it is very important to store, process, manage and analyze this data to extract knowledge from it. It helps to increase business profit. Banking industry plays very important role in economy of country. Customers are the main asset of the bank. Hence it is necessary to focus on problems faced by the banks. Here, we are working on customer retention and fraud detection. In this work, supervised artificial neural network algorithm is implemented for classification purpose.
RESTful Web service composition with BPEL for REST

Current Web service technology is evolving towards a simpler approach to define Web service APIs that challenges the assumptions made by existing languages for Web service composition. RESTful Web services introduce a new kind of abstraction, the resource, which does not fit well with the message-oriented paradigm of the Web service description language (WSDL). RESTful Web services are thus hard to compose using the Business Process Execution Language (WS-BPEL), due to its tight coupling to WSDL. The goal of the BPEL for REST extensions presented in this paper is twofold. First, we aim to enable the composition of both RESTful Web services and traditional Web services from within the same process-oriented service composition language. Second, we show how to publish a BPEL process as a RESTful Web service, by exposing selected parts of its execution state using the REST interaction primitives. We include a detailed example on how BPEL for REST can be applied to orchestrate a RESTful e-Commerce scenario and discuss how the proposed extensions affect the architecture of a process execution engine.
High Gain Monopole Antenna With Sleeve Ground Plane for WLAN Applications

In this letter, a compact monopole antenna is presented for wireless local area network (WLAN) applications. The proposed structure consists of a quarter-wavelength monopole, a ground plane, and an annular sleeve. The sleeve and the ground plane form a quarter-wavelength cavity. The open-ended cavity increases the radiation aperture and enhances the antenna gain. A small piece of substrate is used to support the monopole and match the input impedance. The simulated and measured results agree well. The measured impedance bandwidth is 160 MHz (2.38–2.54 GHz), which is sufficient for WLAN band. In the concerned band, the measured gain is above 3.6 dBi, which is 2.2 dB higher than that of a traditional monopole antenna.
cube 2 net : E icient ality Network Construction with Data Cube Organization

Networks are widely used to model structured data and enable various downstream applications. However, in the real world, most data are structureless, and the assumption of a given network for each particular task is oen invalid. In this work, given a set of objects, we propose to leverage data cube to organize its enormous ambient data. Upon that, we further provide a reinforcement learning algorithm to automatically explore the cube structure and eciently select appropriate data for the construction of a quality network, which can facilitate various tasks on the given set of objects. With extensive experiments of two classic networkmining tasks on dierent real-world large datasets, we show that our proposed cube2net pipeline is general, and much more eective and ecient in quality network construction, compared with other methods without the leverage of data cube or reinforcement learning.
Hateful Symbols or Hateful People? Predictive Features for Hate Speech Detection on Twitter

Hate speech in the form of racist and sexist remarks are a common occurrence on social media. For that reason, many social media services address the problem of identifying hate speech, but the definition of hate speech varies markedly and is largely a manual effort (BBC, 2015; Lomas, 2015). We provide a list of criteria founded in critical race theory, and use them to annotate a publicly available corpus of more than 16k tweets. We analyze the impact of various extra-linguistic features in conjunction with character n-grams for hatespeech detection. We also present a dictionary based the most indicative words in our data.