nils-herrmann/clean_scirepeval huggingface dataset:
Performance of coconut oil as an alternative transformer liquid insulation

Transformer mineral oil has been replaced by alternative oils such as synthetic oil and natural esters due to their biodegradability and environmentally friendly nature. This paper presents performance of coconut oil as such an alternative. Generally, as the other alternative oils, coconut oil has high conductivity due to the presence of free fatty acids. In this study, three different types of coconut oil samples consisting of virgin, copra and RBD (refined, bleached and deodorized) were initially tested by frequency dielectric spectroscopy (FDS) measurements to see how the conductivity was improved by dehydration and neutralization. The FDS results were fitted by inverse power dependence and Cole-Cole models to estimate the conductivity and response functions. Afterwards, a set of new coconut oil samples extracted from copra were thermally aged at 120°C under sealed conditions and compared with that of mineral oil. The performances of oil samples were evaluated using the test results of breakdown voltage, acidity, interfacial tension and FDS measurements under different aging periods such as 2, 5 and 7 weeks. Another 4 sets of new coconut and mineral oil samples were subjected to simulated thermal faults and electrical faults which include aging for 12 hours at 160°C, one hour at 200°C, exposing to partial discharges for four hours and subjecting to 20 low energy breakdowns. The performance comparisons were done by FDS measurements and dissolved gas analysis. In parallel, field-aged coconut oil samples collected from a sealed distribution transformer with 11 years of service were also tested by FDS measurements. In general, coconut oil shows its suitability as an alternative to the mineral oils for transformers, despite limitations found in some of their physical properties. It was found that the FDS results were in good agreement with chemical test results and with the estimated conductivity values.
A CONCEPTUAL FRAMEWORK FOR DETECTING FINANCIAL CRIME IN MOBILE MONEY TRANSACTIONS

Mobile money has made it possible for the unbanked to access financial service to areas previous not accessibly to traditional banking systems. Africa in particular, has indeed seen a growth in use of such services owing to the high penetration of mobile phones. While traditional banking services have been well regulated and secured, mobile money services are still new and vulnerable. Also, attacks and crimes targeting the internet, new technologies and new methods of payments have become sophisticated. This scenario requires novel proactive, real time techniques and solutions to detect financial crimes in mobile money transactions (MMT). The Financial Action Task Force (FATF) 2012 requires mobile money to be subject for monitoring and for compliance. Payment systems have evolved from hard cash, to credit cards, debit cards and now to the M-money, there are several approaches that have been used to detect financial crime in platforms such as credit cards and in the traditional banking system. However, most of these approaches are not suitable for m-money methods. A conceptual framework for detection of mobile money financial crime is proposed. The framework incorporates data mining techniques, big data analytics, Know Your Customers, historical databases and a knowledge base among other things.
Software Cost Estimation Meets Software Diversity

The previous goal of having a one-size-fits-all software cost (and schedule) estimation model is no longer achievable. Sources of wide variation in the nature of software development and evolution processes, products, properties, and personnel (PPPPs) require a variety of estimation models and methods best fitting their situations. This Technical Briefing will provide a short history of pattern-breaking changes in software estimation methods, a summary of the sources of variation in software PPPPs and their estimation implications, a summary of the types of estimation methods being widely used or emerging, a summary of the best estimation-types for the various PPPP-types, and a process for guiding an organization's choices of estimation methods as their PPPP-types evolve.
Continental Airlines Flies High with Real-Time Business Intelligence

Real-time business intelligence (BI) is taking Continental Airlines to new heights. Powered by a real-time data warehouse, the company has dramatically changed all aspects of its business. Continental’s president and COO, Larry Kellner, describes the impact of real-time BI in the following way: “Real-time BI is critical to the accomplishment of our business strategy and has created significant business benefits." In fact, Continental has realized more than $500 million in cost savings and revenue generation over the past six years from its BI initiatives, producing an ROI of more than 1,000 percent.
A Soft Robotic Orthosis for Wrist Rehabilitation 1

In the United States about 795,000 people suffer from a stroke each year . Of those who survive the stroke, the majority experience paralysis on one side of their body, a condition known as hemiparesis. Physical therapy has been shown to restore functionality of the paretic limbs, especially when done early and often . However, the effectiveness of therapy is limited by the availability of therapists and the amount of practice that patients do on their own. Robot-assisted therapy has been explored as a means of guiding patients through the time-intensive exercise regimes that most therapy techniques prescribe. Several wearable, robotic orthoses for the hand and wrist have been developed and are still being developed today . However, few of these existing solutions allow for any significant range of motion, and those that do only offer one degree of freedom. The assisted degree of freedom is almost always flexion/extension, despite the fact that supination/ pronation is crucial in nearly all activities of daily living. In addition, current devices are often large, heavy, and uncomfortable for the wearer, presenting significant deterrents to practice. This paper presents a soft wearable device for the wrist that provides motion-specific assistance with rehabilitation for hemiparetic stroke patients at home. Unlike conventional robotassisted rehabilitation, this pneumatically actuated orthosis is portable, soft, and lightweight, making it more suitable for use outside of the clinic. In addition, this device supports all the degrees of freedom of the wrist, including supination and pronation, which are critical to many tasks.
10 Watts UHF broadband GaN based power amplifier for multi-band applications

The demand of broadband amplifier has been boosted to meet the requirements of multi-mode and multi-band wireless applications. GaN HEMT is the next generation of RF power transistor technology. In this paper, we have designed a 10W UHF broadband class-AB Power Amplifier (PA) based on GaN HEMT. The proposed amplifier has been designed and developed in the frequency range from 200-500 MHz. A maximum drain efficiency of 71% is achieved.
Stake-Bleeding Attacks on Proof-of-Stake Blockchains

We describe a general attack on proof-of-stake (PoS) blockchains without checkpointing. Our attack leverages transaction fees, the ability to treat transactions "out of context," and the standard longest chain rule to completely dominate a blockchain. The attack grows in power with the number of honest transactions and the stake held by the adversary, and can be launched by an adversary controlling any constant fraction of the stake. With the present statistical profile of blockchain protocols, the attack can be launched given a few years of prior blockchain operation; hence it is within the realm of feasibility for PoS protocols. Most importantly, it demonstrates how closely transaction fees and rewards are coupled with the security properties of PoS protocols. More broadly, our attack must be reflected and countered in any future PoS design that avoids checkpointing, as well as any effort to remove checkpointing from existing protocols. We describe several mechanisms for protecting against the attack that include context-sensitivity of transactions and chain density statistics.
From Data Quality to Big Data Quality

This article investigates the evolution of data quality issues from traditional structured data managed in relational databases to Big Data. In particular, The paper examines the nature of the relationship between Data Quality and several research coordinates that are relevant in Big Data, such as the variety of data types, data sources and application domains, focusing on maps, semistructured texts, linked open data, sensor & sensor networks and official statistics. Consequently a set of structural characteristics is identified and a systematization of the a posteriori correlation between them and quality dimensions is provided. Finally, Big Data quality issues are considered in a conceptual framework suitable to map the evolution of the quality paradigm according to three core coordinates that are significant in the context of the Big Data phenomenon: the data type considered, the source of data, and the application domain. Thus, the framework allows ascertaining the relevant changes in data quality emerging with the Big Data phenomenon, through an integrative and theoretical literature review.
The CN2 Induction Algorithm

Systems for inducing concept descriptions from examples are valuable tools for assisting in the task of knowledge acquisition for expert systems. This paper presents a description and empirical evaluation of a new induction system, CN2, designed for the efficient induction of simple, comprehensible production rules in domains where problems of poor description language and/or noise may be present. Implementations of the CN2, ID3, and AQ algorithms are compared on three medical classification tasks.
Design and Simulation of Bridgeless PFC Boost Rectifiers

This work presents new three-level unidirectional single-phase PFC rectifier topologies well-suited for applications targeting high efficiency and/or high power density. The characteristics of a selected novel rectifier topology, including its principles of operation, modulation strategy, PID control scheme, and a power circuit design related analysis are presented. Finally, a 220-V/3-kW laboratory prototype is constructed and used in order to verify the characteristics of the new converter, which include remarkably low switching losses and single ac-side boost inductor, that allow for a 98.6% peak efficiency with a switching frequency of 140 kHz.
Social media analytics: tracking, modeling and predicting the flow of information through networks

Online social media represent a fundamental shift of how information is being produced, transferred and consumed. User generated content in the form of blog posts, comments, and tweets establishes a connection between the producers and the consumers of information. Tracking the pulse of the social media outlets, enables companies to gain feedback and insight in how to improve and market products better. For consumers, the abundance of information and opinions from diverse sources helps them tap into the wisdom of crowds, to aid in making more informed decisions.
 The present tutorial investigates techniques for social media modeling, analytics and optimization. First we present methods for collecting large scale social media data and then discuss techniques for coping with and correcting for the effects arising from missing and incomplete data. We proceed by discussing methods for extracting and tracking information as it spreads among the users. Then we examine methods for extracting temporal patterns by which information popularity grows and fades over time. We show how to quantify and maximize the influence of media outlets on the popularity and attention given to particular piece of content, and how to build predictive models of information diffusion and adoption. As the information often spreads through implicit social and information networks we present methods for inferring networks of influence and diffusion. Last, we discuss methods for tracking the flow of sentiment through networks and emergence of polarization.
Convolutive blind separation of non-stationary sources

| Acoustic signals recorded simultaneously in a reverberant environment can be described as sums of di erently convolved sources. The task of source separation is to identify the multiple channels and possibly to invert those in order to obtain estimates of the underlying sources. We tackle the problem by explicitly exploiting the non-stationarity of the acoustic sources. Changing cross-correlations at multiple times give a su cient set of constraints for the unknown channels. A least squares optimization allows us to estimate a forward model, identifying thus the multi-path channel. In the same manner we can nd an FIR backward model, which generates well separated model sources. Furthermore, for more than three channels we have su cient conditions to estimate underlying additive sensor noise powers. We show good performance in real room environments and demonstrate the algorithm's utility for automatic speech recognition. Keywords| blind source separation, non-stationary signals, multi-path channel, multiple decorrelation, frequency
S-CNN-BASED SHIP DETECTION FROM HIGH-RESOLUTION REMOTE SENSING IMAGES

Reliable ship detection plays an important role in both military and civil fields. However, it makes the task difficult with high-resolution remote sensing images with complex background and various types of ships with different poses, shapes and scales. Related works mostly used gray and shape features to detect ships, which obtain results with poor robustness and efficiency. To detect ships more automatically and robustly, we propose a novel ship detection method based on the convolutional neural networks (CNNs), called SCNN, fed with specifically designed proposals extracted from the ship model combined with an improved saliency detection method. Firstly we creatively propose two ship models, the “V” ship head model and the “ ||” ship body one, to localize the ship proposals from the line segments extracted from a test image. Next, for offshore ships with relatively small sizes, which cannot be efficiently picked out by the ship models due to the lack of reliable line segments, we propose an improved saliency detection method to find these proposals. Therefore, these two kinds of ship proposals are fed to the trained CNN for robust and efficient detection. Experimental results on a large amount of representative remote sensing images with different kinds of ships with varied poses, shapes and scales demonstrate the efficiency and robustness of our proposed S-CNN-Based ship detector.
REV2: Fraudulent User Prediction in Rating Platforms

Rating platforms enable large-scale collection of user opinion about items(e.g., products or other users). However, untrustworthy users give fraudulent ratings for excessive monetary gains. In this paper, we present REV2, a system to identify such fraudulent users. We propose three interdependent intrinsic quality metrics---fairness of a user, reliability of a rating and goodness of a product. The fairness and reliability quantify the trustworthiness of a user and rating, respectively, and goodness quantifies the quality of a product. Intuitively, a user is fair if it provides reliable scores that are close to the goodness of products. We propose six axioms to establish the interdependency between the scores, and then, formulate a mutually recursive definition that satisfies these axioms. We extend the formulation to address cold start problem and incorporate behavior properties. We develop the REV2 algorithm to calculate these intrinsic quality scores for all users, ratings, and products. We show that this algorithm is guaranteed to converge and has linear time complexity. By conducting extensive experiments on five rating datasets, we show that REV2 outperforms nine existing algorithms in detecting fair and unfair users. We reported the 150 most unfair users in the Flipkart network to their review fraud investigators, and 127 users were identified as being fraudulent(84.6% accuracy). The REV2 algorithm is being deployed at Flipkart.
Detecting spam tweets in Twitter using a data stream clustering algorithm

Social network sites are becoming great through millions of users and information have been gathered from the users. This information have equal advantages to their friends and spammers. Twitter is one of the most popular social networks that, users can send short textual messages namely tweet. Researches have shown that this network is subject to spammer's invasion more than other social networks and more than six percent of its tweets are spam. So diagnose of the spam tweets is very important. Firstly, in this research, we determine various features for spam detection and then by using a clustering algorithm based on the data stream, we identify spam tweets. previous works in the field of spam tweets were done by classification algorithms. it is the first time that for spam tweets detection, an data stream clustering algorithm is used. Den stream Algorithm can cluster tweets and consider Outliers as spam. Results show, when this algorithm is set properly the amount of accuracy and precision of spam tweets detection will improve and false positive rate will reach to the minimum value in comparison with previous works.
Position Sensorless Control for Four-Switch Three-Phase Brushless DC Motor Drives

This paper proposes a position sensorless control scheme for four-switch three-phase (FSTP) brushless DC (BLDC) motor drives using a field programmable gate array (FPGA). A novel sensorless control with six commutation modes and novel pulsewidth modulation scheme is developed to drive FSTP BLDC motors. The low cost BLDC driver is achieved by the reduction of switch device count, cost down of control, and saving of hall sensors. The feasibility of the proposed sensorless control for FSTP BLDC motor drives is demonstrated by analysis and experimental results.
Vehicle License Plate Recognition Based on Extremal Regions and Restricted Boltzmann Machines

This paper presents a vehicle license plate recognition method based on character-specific extremal regions (ERs) and hybrid discriminative restricted Boltzmann machines (HDRBMs). First, coarse license plate detection (LPD) is performed by top-hat transformation, vertical edge detection, morphological operations, and various validations. Then, character-specific ERs are extracted as character regions in license plate candidates. Followed by suitable selection of ERs, the segmentation of characters and coarse-to-fine LPD are achieved simultaneously. Finally, an offline trained pattern classifier of HDRBM is applied to recognize the characters. The proposed method is robust to illumination changes and weather conditions during 24 h or one day. Experimental results on thorough data sets are reported to demonstrate the effectiveness of the proposed approach in complex traffic environments.
PI-RADS Prostate Imaging - Reporting and Data System: 2015, Version 2.

The Prostate Imaging - Reporting and Data System Version 2 (PI-RADS™ v2) is the product of an international collaboration of the American College of Radiology (ACR), European Society of Uroradiology (ESUR), and AdMetech Foundation. It is designed to promote global standardization and diminish variation in the acquisition, interpretation, and reporting of prostate multiparametric magnetic resonance imaging (mpMRI) examination, and it is based on the best available evidence and expert consensus opinion. It establishes minimum acceptable technical parameters for prostate mpMRI, simplifies and standardizes terminology and content of reports, and provides assessment categories that summarize levels of suspicion or risk of clinically significant prostate cancer that can be used to assist selection of patients for biopsies and management. It is intended to be used in routine clinical practice and also to facilitate data collection and outcome monitoring for research.
A Decision Framework for Blockchain Platforms for IoT and Edge Computing

Blockchains started as an enabling technology in the area of digital currencies with the introduction of Bitcoin. However, blockchains have emerged as a technology that goes beyond financial transactions by providing a platform supporting secure and robust distributed public ledgers. We think that the Internet of Things (IoT) can also benefit from blockchain technology, especially in the areas of security, privacy, fault tolerance, and autonomous behavior. Here we present a decision framework to help practitioners systematically evaluate the potential use of blockchains in an IoT context.
Service-Oriented Architecture for Big Data Analytics in Smart Cities

A smart city has recently become an aspiration for many cities around the world. These cities are looking to apply the smart city concept to improve sustainability, quality of life for residents, and economic development. The smart city concept depends on employing a wide range of advanced technologies to improve the performance of various services and activities such as transportation, energy, healthcare, and education, while at the same time improve the city's resources utilization and initiate new business opportunities. One of the promising technologies to support such efforts is the big data technology. Effective and intelligent use of big data accumulated over time in various sectors can offer many advantages to enhance decision making in smart cities. In this paper we identify the different types of decision making processes involved in smart cities. Then we propose a service-oriented architecture to support big data analytics for decision making in smart cities. This architecture allows for integrating different technologies such as fog and cloud computing to support different types of analytics and decision-making operations needed to effectively utilize available big data. It provides different functions and capabilities to use big data and provide smart capabilities as services that the architecture supports. As a result, different big data applications will be able to access and use these services for varying proposes within the smart city.
Neuro-dynamic programming

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EVS 25 Shenzhen , China , Nov . 5-9 , 2010 Control of A high-Performance Z-Source Inverter for Fuel Cell / Supercapacitor Hybrid Electric Vehicles

This paper presents a supercapacitor (SC) module connected in parallel with fuel cell (FC) stack to supply a high-performance Z-Source Inverter (HP-ZSI) feeding a three phase induction motor for hybrid electric vehicles applications. The supercapacitor is connected between the input diode and the bidirectional switch of the highperformance ZSI topology. The indirect field-oriented control (IFOC) method is used to control an induction motor speed during motoring and regenerative braking operations to produce the modulation index and a dual loop controller is used to control the Z-network capacitor voltage to produce the shoot-through duty ratio. MATLAB simulation results verified the validity of the proposed control strategy during motoring and regenerative braking operations.