nils-herrmann/clean_scirepeval huggingface dataset:
Improving the Reliability of Series Resonant Inverters for Induction Heating Applications

This paper analyzes a high-power (100-kW) high-frequency (50-kHz) voltage-fed inverter with a series resonant load circuit for industrial induction heating applications which is characterized by a full-bridge inverter composed of isolated-gate bipolar transistors and a new power control based on phase-shift (PS) control. This power control circuit incorporates a load-adaptive variable-frequency controller and automated blanking time management in order to allow the inverter to work in zero-voltage switching for all output power levels and load conditions. An important improvement of the inverter reliability is achieved by choosing an appropriate and novel switching sequence for the PS inverter. The results are verified experimentally using a prototype for induction hardening applications. A comparative study between the proposed and standard PS power control will be made.
TOM-Net: Learning Transparent Object Matting from a Single Image

This paper addresses the problem of transparent object matting. Existing image matting approaches for transparent objects often require tedious capturing procedures and long processing time, which limit their practical use. In this paper, we first formulate transparent object matting as a refractive flow estimation problem. We then propose a deep learning framework, called TOM-Net, for learning the refractive flow. Our framework comprises two parts, namely a multi-scale encoder-decoder network for producing a coarse prediction, and a residual network for refinement. At test time, TOM-Net takes a single image as input, and outputs a matte (consisting of an object mask, an attenuation mask and a refractive flow field) in a fast feed-forward pass. As no off-the-shelf dataset is available for transparent object matting, we create a large-scale synthetic dataset consisting of 178K images of transparent objects rendered in front of images sampled from the Microsoft COCO dataset. We also collect a real dataset consisting of 876 samples using 14 transparent objects and 60 background images. Promising experimental results have been achieved on both synthetic and real data, which clearly demonstrate the effectiveness of our approach.
Role-Based Access Control Model Supporting Regional Division in Smart Grid System

Smart Grid is a modern electric power infrastructure that incorporates elements of traditional power systems and information and communication technology (ICT) with the aim to improve the reliability, efficiency and safety as crucial requirements of critical infrastructure systems. Relying on the ICT, Smart Grid exposes electric power systems to new security issues and vulnerabilities over the network. Therefore, the security is becoming an increasing concern at both physical and information levels. Access controls are one of the most important aspects of information security. Role-based access control (RBAC) model is widely used in complex systems that are characterized by many participants. However, the existing models are not suitable for critical infrastructure systems with specialized features, such as a high number of devices dispersed over the vast geographical regions in “an Internetlike distributed environment”. In this paper, access control mechanism optimized for Smart Grid systems is developed considering the regional division and concept of areas of responsibility (AOR). To this end, the standardized RBAC model will be extended to provide an efficient and consistent policy with greater level of granularity from the aspect of resource protection. The proposed RBACAOR model is formally described and then applied to the smart grid environment.
A least squares support vector machine model optimized by moth-flame optimization algorithm for annual power load forecasting

Annual power load forecasting is essential for the planning, operation and maintenance of an electric power system, which can also mirror the economic development of a country to some extent. Accurate annual power load forecasting can provide valuable references for electric power system operators and economic managers. With the development of Energy Internet and further reformation of electric power market, power load forecasting has become a more difficult and challenging task. In this paper, a new hybrid annual power load forecasting model based on LSSVM (least squares support vector machine) and MFO (Moth-Flame Optimization algorithm) is proposed, which the parameters of LSSVM model are optimally determined by the latest nature-inspired metaheuristic algorithm MFO. Meanwhile, the rolling mechanism is also employed. The forecasting results of China’s annual electricity consumption indicate the proposed MFO-LSSVM model shows much better forecasting performance than single LSSVM, FOA-LSSVM (LSSVM optimized by fruit fly optimization), and PSO-LSSVM (LSSVM optimized by particle swarm optimization). MFO, as a new intelligence optimization algorithm, is attractive and promising. The LSSVM model optimized by MFO can significantly improve annual power load forecasting accuracy.
Migration transitions a theoretical and empirical inquiry into the developmental drivers of international migration

This paper aims to advance a conceptual framework on the developmental drivers of international migration processes and to provide an empirical test drawing on the global migrant origin database. Conventional ideas that development in origin countries will reduce international migration are ultimately based on “push-pull”, neoclassical and other equilibrium models which assume an inversely proportional relationship between absolute levels and relative differences of wealth and migration. By contrast, another group of theories postulate that development leads to generally increased levels of migration and that societies go through migration transitions characterised by an inverted U-shaped pattern of emigration. The paper discusses as yet unobserved conceptual parallels and differences between separately evolved ‘transition’ theories. It subsequently amends and synthesises prior theories, based on a criticism of their evolutionary character and sedentary bias, their inclination towards demographic determinism, their limited conceptualisation of structure and agency as well as the causal mechanisms underlying the correlations they describe. Sen’s capabilities-based development concept is applied to migration to create analytical room to analyse most forms of migration within a single perspective. Structure and agency are incorporated by conceptualising migration as a function of (1) capabilities, (2) aspirations and, on a macro-level, (3) opportunity rather than income differentials. Because of the contested nature of migration transition theory, the paper provides an empirical test. Drawing on the World Bank/University of Sussex global migrant origin database, it estimates the effect of theoretically relevant development indicators on immigrant, emigrant, net immigrant and total migrant stocks. The results largely confirm transition theory. Higher levels of economic and human development are associated to higher overall levels of migration and have the predicted U-curve effect on emigration. The results also suggest that demographic factors do not have a direct effect on migration. Although several empirical puzzles remain, particularly on the effects of political freedoms, the results suggest that take-off development in the least developed countries is likely to lead to take-off emigration. The analysis exemplifies the need to conceptualise migration as an integral part of broader development processes rather than as problem to be “solved”.
Model-based Analysis of ChIP-Seq (MACS)

We present Model-based Analysis of ChIP-Seq data, MACS, which analyzes data generated by short read sequencers such as Solexa's Genome Analyzer. MACS empirically models the shift size of ChIP-Seq tags, and uses it to improve the spatial resolution of predicted binding sites. MACS also uses a dynamic Poisson distribution to effectively capture local biases in the genome, allowing for more robust predictions. MACS compares favorably to existing ChIP-Seq peak-finding algorithms, and is freely available.
Development Emails Content Analyzer: Intention Mining in Developer Discussions (T)

Written development communication (e.g. mailing lists, issue trackers) constitutes a precious source of information to build recommenders for software engineers, for example aimed at suggesting experts, or at redocumenting existing source code. In this paper we propose a novel, semi-supervised approach named DECA (Development Emails Content Analyzer) that uses Natural Language Parsing to classify the content of development emails according to their purpose (e.g. feature request, opinion asking, problem discovery, solution proposal, information giving etc), identifying email elements that can be used for specific tasks. A study based on data from Qt and Ubuntu, highlights a high precision (90%) and recall (70%) of DECA in classifying email content, outperforming traditional machine learning strategies. Moreover, we successfully used DECA for re-documenting source code of Eclipse and Lucene, improving the recall, while keeping high precision, of a previous approach based on ad-hoc heuristics.
Sentiment analysis of movie reviews: A study on feature selection & classification algorithms

Sentiment analysis is a sub-domain of opinion mining where the analysis is focused on the extraction of emotions and opinions of the people towards a particular topic from a structured, semi-structured or unstructured textual data. In this paper, we try to focus our task of sentiment analysis on IMDB movie review database. We examine the sentiment expression to classify the polarity of the movie review on a scale of 0(highly disliked) to 4(highly liked) and perform feature extraction and ranking and use these features to train our multi-label classifier to classify the movie review into its correct label. Due to lack of strong grammatical structures in movie reviews which follow the informal jargon, an approach based on structured N-grams has been followed. In addition, a comparative study on different classification approaches has been performed to determine the most suitable classifier to suit our problem domain. We conclude that our proposed approach to sentiment classification supplements the existing rating movie rating systems used across the web and will serve as base to future researches in this domain. "Our approach using classification techniques has the best accuracy of 88.95%".
A Modern Take on the Bias-Variance Tradeoff in Neural Networks

We revisit the bias-variance tradeoff for neural networks in light of modern empirical findings. The traditional bias-variance tradeoff in machine learning suggests that as model capacity grows, bias decreases and variance increases. In contrast, we find that both bias and variance can decrease with the number of parameters. To better understand this, we decompose the total variance into variance due to training set sampling and variance due to initialization. Surprisingly, variance due to training set sampling is roughly constant with both network width and depth, in the over-parameterized setting. We provide theoretical analysis, in a simplified setting inspired by linear models, that is consistent with our empirical findings.
Intelligent Device-to-Device Communication in the Internet of Things

Analogous to the way humans use the Internet, devices will be the main users in the Internet of Things (IoT) ecosystem. Therefore, device-to-device (D2D) communication is expected to be an intrinsic part of the IoT. Devices will communicate with each other autonomously without any centralized control and collaborate to gather, share, and forward information in a multihop manner. The ability to gather relevant information in real time is key to leveraging the value of the IoT as such information will be transformed into intelligence, which will facilitate the creation of an intelligent environment. Ultimately, the quality of the information gathered depends on how smart the devices are. In addition, these communicating devices will operate with different networking standards, may experience intermittent connectivity with each other, and many of them will be resource constrained. These characteristics open up several networking challenges that traditional routing protocols cannot solve. Consequently, devices will require intelligent routing protocols in order to achieve intelligent D2D communication. We present an overview of how intelligent D2D communication can be achieved in the IoT ecosystem. In particular, we focus on how state-of-the-art routing algorithms can achieve intelligent D2D communication in the IoT.
The Scalability of Trustless Trust

Permission-less blockchains can realise trustless trust, albeit at the cost of limiting the complexity of computation tasks. To explain the implications for scalability, we have implemented a trust model for smart contracts, described as agents in an open multi-agent system. Agent intentions are not necessarily known and autonomous agents have to be able to make decisions under risk. The ramifications of these general conditions for scalability are analysed for Ethereum and then generalised to other current and future platforms.
Consensus of Hybrid Multi-Agent Systems

In this brief, we consider the consensus problem of hybrid multiagent systems. First, the hybrid multiagent system is proposed, which is composed of continuous-time and discrete-time dynamic agents. Then, three kinds of consensus protocols are presented for the hybrid multiagent system. The analysis tool developed in this brief is based on the matrix theory and graph theory. With different restrictions of the sampling period, some necessary and sufficient conditions are established for solving the consensus of the hybrid multiagent system. The consensus states are also obtained under different protocols. Finally, simulation examples are provided to demonstrate the effectiveness of our theoretical results.
Outpatient approach to palpitations.

Palpitations are a common problem seen in family medicine; most are of cardiac origin, although an underlying psychiatric disorder, such as anxiety, is also common. Even if a psychiatric comorbidity does exist, it should not be assumed that palpitations are of a noncardiac etiology. Discerning cardiac from noncardiac causes is important given the potential risk of sudden death in those with an underlying cardiac etiology. History and physical examination followed by targeted diagnostic testing are necessary to distinguish a cardiac cause from other causes of palpitations. Standard 12-lead electrocardiography is an essential initial diagnostic test. Cardiac imaging is recommended if history, physical examination, or electrocardiography suggests structural heart disease. An intermittent event (loop) monitor is preferred for documenting cardiac arrhythmias, particularly when they occur infrequently. Ventricular and atrial premature contractions are common cardiac causes of palpitations; prognostic significance is dictated by the extent of underlying structural heart disease. Atrial fibrillation is the most common arrhythmia resulting in hospitalization; such patients are at increased risk of stroke. Patients with supraventricular tachycardia, long QT syndrome, ventricular tachycardia, or palpitations associated with syncope should be referred to a cardiologist.
Closed-Loop Control of a Three-Phase Neutral-Point-Clamped Inverter Using an Optimized Virtual-Vector-Based Pulsewidth Modulation

This paper presents a closed-loop control scheme for the three-level three-phase neutral-point-clamped dc-ac converter using the optimized nearest three virtual-space-vector pulsewidth modulation, which is a modulation that produces low output-voltage distortion with a significant reduction of the dc-link capacitance. A new specific loop modifying the modulating waveforms is proposed to rapidly control possible perturbations in the neutral-point voltage balance. An online estimation of the load displacement angle and load linear/nonlinear nature is introduced at no extra cost. The remaining part of the control is analogous to the control for a two-level converter with an appropriate interfacing to the selected modulation. The closed-loop control is designed for the case of a renewable-energy source connected to the ac mains, and its performance is analyzed through simulation and experiments.
Toward Delay-Efficient Game-Aware Data Centers for Cloud Gaming

Gaming on demand is an emerging service that has recently started to garner prominence in the gaming industry. Cloud-based video games provide affordable, flexible, and high-performance solutions for end-users with constrained computing resources and enables them to play high-end graphic games on low-end thin clients. Despite its advantages, cloud gaming's Quality of Experience (QoE) suffers from high and varying end-to-end delay. Since the significant part of computational processing, including game rendering and video compression, is performed in data centers, controlling the transfer of information within the cloud has an important impact on the quality of cloud gaming services. In this article, a novel method for minimizing the end-to-end latency within a cloud gaming data center is proposed. We formulate an optimization problem for reducing delay, and propose a Lagrangian Relaxation (LR) time-efficient heuristic algorithm as a practical solution. Simulation results indicate that the heuristic method can provide close-to-optimal solutions. Also, the proposed model reduces end-to-end delay and delay variation by almost 11% and 13.5%, respectively, and outperforms the existing server-centric and network-centric models. As a byproduct, our proposed method also achieves better fairness among multiple competing players by almost 45%, on average, in comparison with existing methods.
Experiences Using the RISC-V Ecosystem to Design an Accelerator-Centric SoC in TSMC 16 nm

The recent trend towards accelerator-centric architectures has renewed the need for demonstrating new research ideas in prototype systems with custom chips. Unfortunately, building such research prototypes is tremendously challenging, but the emerging RISC-V open-source software and hardware ecosystem can partly address this challenge by reducing design, implementation, and verification effort. This paper briefly describes the Celerity system-on-chip (SoC), a 5× 5mm 385M-transistor chip in TSMC 16 nm, which uses a tiered parallel accelerator fabric to improve both the performance and energy efficiency of embedded applications. The Celerity SoC includes five RV64G cores, a 496-core RV32IM tiled manycore processor, and a complex BNN (binarized neural network) accelerator implemented as a Rocket custom co-processor (RoCC). We describe our experiences using the RISC-V ecosystem to build Celerity, and highlight both key benefits and challenges in leveraging the RISCV instruction set, RISC-V software stack, RISC-V processor and memory system generators, RISC-V on-chip network interfaces, RISC-V verification suite, and RISC-V system-level hardware infrastructure. The RISC-V ecosystem played an important role in enabling a team of junior graduate students to design and tapeout the highest-performance RISC-V SoC to date in just nine months.
Using attack graphs for correlating, hypothesizing, and predicting intrusion alerts

To defend against multi-step intrusions in high-speed networks, efficient algorithms are needed to correlate isolated alerts into attack scenarios. Existing correlation methods usually employ an in-memory index for fast searches among received alerts. With finite memory, the index can only be built on a limited number of alerts inside a sliding window. Knowing this fact, an attacker can prevent two attack steps from both falling into the sliding window by either passively delaying the second step or actively injecting bogus alerts between the two steps. In either case, the correlation effort is defeated. In this paper, we first address the above issue with a novel queue graph (QG) approach. Instead of searching all the received alerts for those that prepare for a new alert, we only search for the latest alert of each type. The correlation between the new alert and other alerts is implicitly represented using the temporal order between alerts. Consequently, our approach can correlate alerts that are arbitrarily far away, and it has a linear (in the number of alert types) time complexity and quadratic memory requirement. Then, we extend the basic QG approach to a unified method to hypothesize missing alerts and to predict future alerts. Finally, we propose a compact representation for the result of alert correlation. Empirical results show that our method can fulfill correlation tasks faster than an IDS can report alerts. Hence, the method is a promising solution for administrators to monitor and predict the progress of intrusions and thus to take appropriate countermeasures in a timely manner.
Locating the fovea center position in digital fundus images using thresholding and feature extraction techniques

A new methodology for detecting the fovea center position in digital retinal images is presented in this paper. A pixel is firstly searched for within the foveal region according to its known anatomical position relative to the optic disc and vascular tree. Then, this pixel is used to extract a fovea-containing subimage on which thresholding and feature extraction techniques are applied so as to find fovea center. The methodology was evaluated on 1200 fundus images from the publicly available MESSIDOR database, 660 of which present signs of diabetic retinopathy. In 93.92% of these images, the distance between the methodology-provided and actual fovea center position remained below 1/4 of one standard optic disc radius (i.e., 17, 26, and 27 pixels for MESSIDOR retinas of 910, 1380 and 1455 pixels in size, respectively). These results outperform all the reviewed methodologies available in literature. Its effectiveness and robustness with different illness conditions makes this proposal suitable for retinal image computer analyses such as automated screening for early diabetic retinopathy detection.
SeriesNet:A Generative Time Series Forecasting Model

Time series forecasting is emerging as one of the most important branches of big data analysis. However, traditional time series forecasting models can not effectively extract good enough sequence data features and often result in poor forecasting accuracy. In this paper, a novel time series forecasting model, named SeriesNet, which can fully learn features of time series data in different interval lengths. The SeriesNet consists of two networks. The LSTM network aims to learn holistic features and to reduce dimensionality of multi-conditional data, and the dilated causal convolution network aims to learn different time interval. This model can learn multi-range and multi-level features from time series data, and has higher predictive accuracy compared those models using fixed time intervals. Moreover, this model adopts residual learning and batch normalization to improve generalization. Experimental results show our model has higher forecasting accuracy and has greater stableness on several typical time series data sets.