nils-herrmann/clean_scirepeval huggingface dataset:
Empirical Study of Usage and Performance of Java Collections

Collection data structures have a major impact on the performance of applications, especially in languages such as Java, C#, or C++. This requires a developer to select an appropriate collection from a large set of possibilities, including different abstractions (e.g. list, map, set, queue), and multiple implementations. In Java, the default implementation of collections is provided by the standard Java Collection Framework (JCF). However, there exist a large variety of less known third-party collection libraries which can provide substantial performance benefits with minimal code changes.
 In this paper, we first study the popularity and usage patterns of collection implementations by mining a code corpus comprised of 10,986 Java projects. We use the results to evaluate and compare the performance of the six most popular alternative collection libraries in a large variety of scenarios. We found that for almost every scenario and JCF collection type there is an alternative implementation that greatly decreases memory consumption while offering comparable or even better execution time. Memory savings range from 60% to 88% thanks to reduced overhead and some operations execute 1.5x to 50x faster.
 We present our results as a comprehensive guideline to help developers in identifying the scenarios in which an alternative implementation can provide a substantial performance improvement. Finally, we discuss how some coding patterns result in substantial performance differences of collections.
Modularity and architecture of PLC-based software for automated production Systems: An analysis in industrial companies

Adaptive and flexible production systems require modular and reusable software especially considering their long-term life cycle of up to 50 years. SWMAT4aPS, an approach to measure Software Maturity for automated Production Systems is introduced. The approach identifies weaknesses and strengths of various companies’ solutions for modularity of software in the design of automated Production Systems (aPS). At first, a self-assessed questionnaire is used to evaluate a large number of companies concerning their software maturity. Secondly, we analyze PLC code, architectural levels, workflows and abilities to configure code automatically out of engineering information in four selected companies. In this paper, the questionnaire results from 16 German world-leading companies in machine and plant manufacturing and four case studies validating the results from the detailed analyses are introduced to prove the applicability of the approach and give a survey of the state of the art in industry. factory automation, automated production systems, maturity, modularity, control software, Programmable Logic Controller.
Interleukin-33-Activated Islet-Resident Innate Lymphoid Cells Promote Insulin Secretion through Myeloid Cell Retinoic Acid Production.

Pancreatic-islet inflammation contributes to the failure of β cell insulin secretion during obesity and type 2 diabetes. However, little is known about the nature and function of resident immune cells in this context or in homeostasis. Here we show that interleukin (IL)-33 was produced by islet mesenchymal cells and enhanced by a diabetes milieu (glucose, IL-1β, and palmitate). IL-33 promoted β cell function through islet-resident group 2 innate lymphoid cells (ILC2s) that elicited retinoic acid (RA)-producing capacities in macrophages and dendritic cells via the secretion of IL-13 and colony-stimulating factor 2. In turn, local RA signaled to the β cells to increase insulin secretion. This IL-33-ILC2 axis was activated after acute β cell stress but was defective during chronic obesity. Accordingly, IL-33 injections rescued islet function in obese mice. Our findings provide evidence that an immunometabolic crosstalk between islet-derived IL-33, ILC2s, and myeloid cells fosters insulin secretion.
A matching pursuit based similarity measure for fuzzy clustering and classification of signals

Matching pursuits is a well known technique for signal representation and has also been used as a feature extractor for some classification systems. However, applications that use matching pursuits (MP) algorithm in their feature extraction stage are quite problem domain specific, making their adaptation for other types of problems quite hard. In this paper we propose a matching pursuits based similarity measure that uses only the dictionary, coefficients and residual information provided by the MP algorithm while comparing two signals. Hence it is easily applicable to a variety of problems. We show that using the MP based similarity measure for competitive agglomerative fuzzy clustering leads to an interesting and novel update equation that combines the standard fuzzy prototype updating equation with a term involving the error between approximated signals and approximated prototypes. The potential value of the similarity measure is investigated using the fuzzy k-nearest prototype algorithm of Frigui for a two-class, signal classification problem. It is shown that the new similarity measure significantly outperforms the Euclidean distance.
Sex Differences in Variability in General Intelligence: A New Look at the Old Question.

The idea that general intelligence may be more variable in males than in females has a long history. In recent years it has been presented as a reason that there is little, if any, mean sex difference in general intelligence, yet males tend to be overrepresented at both the top and bottom ends of its overall, presumably normal, distribution. Clear analysis of the actual distribution of general intelligence based on large and appropriately population-representative samples is rare, however. Using two population-wide surveys of general intelligence in 11-year-olds in Scotland, we showed that there were substantial departures from normality in the distribution, with less variability in the higher range than in the lower. Despite mean IQ-scale scores of 100, modal scores were about 105. Even above modal level, males showed more variability than females. This is consistent with a model of the population distribution of general intelligence as a mixture of two essentially normal distributions, one reflecting normal variation in general intelligence and one refecting normal variation in effects of genetic and environmental conditions involving mental retardation. Though present at the high end of the distribution, sex differences in variability did not appear to account for sex differences in high-level achievement.
Monitoring frequent items over distributed data streams.

MONITORING FREQUENT ITEMS OVER DISTRIBUTED DATA STREAMS Robert H. Fuller April 3, 2007 Many important applications require the discovery of items which have occurred frequently. Knowledge of these items is commonly used in anomaly detection and network monitoring tasks. Effective solutions for this problem focus mainly on reducing memory requirements in a centralized environment. These solutions, however, ignore the inherently distributed nature of many systems. Naively forwarding data to a centralized location is not practical when dealing with high speed data streams and will result in significant communication overhead. This thesis proposes a new approach designed for continuously tracking frequent items over distributed data streams, providing either exact or approximate answers. The method introduced is a direct modification to an existing communication efficient algorithm called Top-K Monitoring. Experimental results demonstrated that the proposed modifications significantly reduced communication cost and improved scalability. Also examined in this thesis is the applicability of frequent item monitoring at detecting distributed denial of service attacks. Simulation of the proposed tracking
Decision Tree and SVM-Based Data Analytics for Theft Detection in Smart Grid

Nontechnical losses, particularly due to electrical theft, have been a major concern in power system industries for a long time. Large-scale consumption of electricity in a fraudulent manner may imbalance the demand-supply gap to a great extent. Thus, there arises the need to develop a scheme that can detect these thefts precisely in the complex power networks. So, keeping focus on these points, this paper proposes a comprehensive top-down scheme based on decision tree (DT) and support vector machine (SVM). Unlike existing schemes, the proposed scheme is capable enough to precisely detect and locate real-time electricity theft at every level in power transmission and distribution (T&D). The proposed scheme is based on the combination of DT and SVM classifiers for rigorous analysis of gathered electricity consumption data. In other words, the proposed scheme can be viewed as a two-level data processing and analysis approach, since the data processed by DT are fed as an input to the SVM classifier. Furthermore, the obtained results indicate that the proposed scheme reduces false positives to a great extent and is practical enough to be implemented in real-time scenarios.
Revisiting Defenses against Large-Scale Online Password Guessing Attacks

Brute force and dictionary attacks on password-only remote login services are now widespread and ever increasing. Enabling convenient login for legitimate users while preventing such attacks is a difficult problem. Automated Turing Tests (ATTs) continue to be an effective, easy-to-deploy approach to identify automated malicious login attempts with reasonable cost of inconvenience to users. In this paper, we discuss the inadequacy of existing and proposed login protocols designed to address large-scale online dictionary attacks (e.g., from a botnet of hundreds of thousands of nodes). We propose a new Password Guessing Resistant Protocol (PGRP), derived upon revisiting prior proposals designed to restrict such attacks. While PGRP limits the total number of login attempts from unknown remote hosts to as low as a single attempt per username, legitimate users in most cases (e.g., when attempts are made from known, frequently-used machines) can make several failed login attempts before being challenged with an ATT. We analyze the performance of PGRP with two real-world data sets and find it more promising than existing proposals.
Study of single-phase HFAC microgrid based on Matlab/Simulink

Renewable energy is currently the main direction of development of electric power. Because of its own characteristics, the reliability of renewable energy generation is low. Renewable energy generation system needs lots of energy conversion devices which are made of power electronic devices. Too much power electronic components can damage power quality in microgrid. High Frequency AC (HFAC) microgrid is an effective way to solve the problems of renewable energy generation system. Transmitting electricity by means of HFAC is a novel idea in microgrid. Although the HFAC will cause more loss of power, it can improve the power quality in microgrid. HFAC can also reduce the impact of fluctuations of renewable energy in microgrid. This paper mainly simulates the HFAC with Matlab/Simulink and analyzes the feasibility of HFAC in microgrid.
Speaking to Read : The Effects of Speech Recognition Technology on the Reading and Spelling Performance of Children with Learning Disabilities

In recent literature on persons with learning disabilities (LD), speech recognition has been discussed primarily as an assistive technology to help compensate for writing difficulties. However, prior research by the authors has suggested that in addition to helping persons to compensate for poor writing skills, speech recognition also may enhance reading and spelling; that is, what was designed as assistive technology appears to serve remedial functions as well. The present study was conducted to determine whether elementary and secondary students with LD who used the technology to write self-selected compositions and class assignments would demonstrate improvements in reading and spelling. Thirty-nine students with LD (ages 9 to 18) participated. Nineteen participants used speech recognition 50 minutes a week for sixteen weeks, and twenty students in a control group received general computer instruction. Results indicated that the speech recognition group showed significantly more improvement than the control group in word recognition (p < .0001), spelling (p < .002) and reading
Using Facebook for Image Steganography

Because Facebook is available on hundreds of millions of desktop and mobile computing platforms around the world and because it is available on many different kinds of platforms (from desktops and laptops running Windows, Unix, or OS X to hand held devices running iOS, Android, or Windows Phone), it would seem to be the perfect place to conduct steganography. On Facebook, information hidden in image files will be further obscured within the millions of pictures and other images posted and transmitted daily. Facebook is known to alter and compress uploaded images so they use minimum space and bandwidth when displayed on Facebook pages. The compression process generally disrupts attempts to use Facebook for image steganography. This paper explores a method to minimize the disruption so JPEG images can be used as steganography carriers on Facebook.
State of the Art on 3D Reconstruction with RGB-D Cameras

The advent of affordable consumer grade RGB-D cameras has brought about a profound advancement of visual scene reconstruction methods. Both computer graphics and computer vision researchers spend significant effort to develop entirely new algorithms to capture comprehensive shape models of static and dynamic scenes with RGB-D cameras. This led to significant advances of the state of the art along several dimensions. Some methods achieve very high reconstruction detail, despite limited sensor resolution. Others even achieve real-time performance, yet possibly at lower quality. New concepts were developed to capture scenes at larger spatial and temporal extent. Other recent algorithms flank shape reconstruction with concurrent material and lighting estimation, even in general scenes and unconstrained conditions. In this state-of-the-art report, we analyze these recent developments in RGB-D scene reconstruction in detail and review essential related work. We explain, compare, and critically analyze the common underlying algorithmic concepts that enabled these recent advancements. Furthermore, we show how algorithms are designed to best exploit the benefits of RGB-D data while suppressing their often non-trivial data distortions. In addition, this report identifies and discusses important open research questions and suggests relevant directions for future work. CCS Concepts •Computing methodologies , . . ., Reconstruction; Appearance and texture representations; Motion capture;
A machine learning approach for prediction of on-time performance of flights

One of the major business problems that airlines face is the significant costs that are associated with flights being delayed due to natural occurrences and operational shortcomings, which is an expensive affair for the airlines, creating problems in scheduling and operations for the end-users thus causing bad reputation and customer dissatisfaction. In our paper, a two-stage predictive model was developed employing supervised machine learning algorithms for the prediction of flight on-time performance. The first stage of the model performs binary classification to predict the occurrence of flight delays and the second stage does regression to predict the value of the delay in minutes. The dataset used for evaluating the model was obtained from historical data which contains flight schedules and weather data for 5 years. It was observed that, in the classification stage, Gradient Boosting Classifier performed the best and in the regression stage, Extra-Trees Regressor performed the best. The performance of the other algorithms is also extensively documented in the paper. Furthermore, a real-time Decision Support Tool was built using the model which utilizes features that are readily available before the departure of an airplane and can inform passengers and airlines about flight delays in advance, helping them reduce possible monetary losses.
A survey of BRDF models for computer graphics

To produce photo-realistic images in computer graphics, we must effectively describe the interactions between light and surfaces. In this paper, we focus on Bidirectional Reflectance Distribution Functions (BRDFs), which characterize these interactions. We survey on most BRDF representations introduced so far and we investigate their usage, importance and applications. We look at in detail their two important usages; in GPU-based real-time renderings and in renderings of metallic car paints.
A Distributionally-robust Approach for Finding Support Vector Machines

The classical SVM is an optimization problem minimizing the hinge losses of mis-classified samples with the regularization term. When the sample size is small or data has noise, it is possible that the classifier obtained with training data may not generalize well to population, since the samples may not accurately represent the true population distribution. We propose a distributionally-robust framework for Support Vector Machines (DR-SVMs). We build an ambiguity set for the population distribution based on samples using the Kantorovich metric. DR-SVMs search the classifier that minimizes the sum of regularization term and the hinge loss function for the worst-case population distribution among the ambiguity set. We provide semi-infinite programming formulation of the DR-SVMs and propose a cutting-plane algorithm to solve the problem. Computational results on simulated data and real data from University of California, Irvine Machine Learning Repository show that the DR-SVMs outperform the SVMs in terms of the Area Under Curve (AUC) measures on several test problems.
Robust control of permanent magnet synchronous motors

In this paper, permanent magnet synchronous motors (PMSMs) are investigated. According to the feature of PMSMs, a novel state equation of PMSMs is obtained by choosing suitable state variables. Based on the state equation, robust controllers are designed via interval matrix and PI control idea. In terms of bilinear matrix inequations, sufficient conditions for the existence of the robust controller are derived. In order to reduce the conservation and the dependence on parameter, the control inputs of PMSMs are divided into two parts, a feedforward control input and a feedback control input, and relevant sufficient conditions for the existence of the controller are obtained. Because of the suitable choice of state variables, the proposed control strategies can cope with the load uncertainty and have robustness for disturbance. Finally, simulations are carried out via Matlab/Simulink soft to verify the effectiveness of the proposed control strategies. The performance of the proposed control strategies are demonstrated by the simulation results.
Content annotation for the semantic web: an automatic web-based approach

Semantic Annotation is required to add machine-readable content to natural language text. A global initiative such as the Semantic Web directly depends on the annotation of massive amounts of textual Web resources. However, considering the amount of those resources, a manual semantic annotation of their contents is neither feasible nor scalable. In this paper we introduce a methodology to partially annotate textual content of Web resources in an automatic and unsupervised way. It uses several well-established learning techniques and heuristics to discover relevant entities in text and to associate them to classes of an input ontology by means of linguistic patterns. It also relies on the Web information distribution to assess the degree of semantic co-relation between entities and classes of the input domain ontology. Special efforts have been put in minimizing the amount of Web accesses required to evaluate entities in order to ensure the scalability of the approach. A manual evaluation has been carried out to test the methodology for several domains showing promising results.
Diagnosis, staging and treatment of hepatocellular carcinoma.

Hepatocellular carcinomas are aggressive tumors with a high dissemination power. An early diagnosis of these tumors is of great importance in order to offer the possibility of curative treatment. For an early diagnosis, abdominal ultrasound and serum alpha-fetoprotein determinations at 6-month intervals are suggested for all patients with cirrhosis of the liver, since this disease is considered to be the main risk factor for the development of the neoplasia. Helicoidal computed tomography, magnetic resonance and/or hepatic arteriography are suggested for diagnostic confirmation and tumor staging. The need to obtain a fragment of the focal lesion for cytology and/or histology for a diagnosis of hepatocellular carcinoma depends on the inability of imaging methods to diagnose the lesion. Several classifications are currently available for tumor staging in order to determine patient prognosis. All take into consideration not only the stage of the tumor but also the degree of hepatocellular dysfunction, which is known to be the main factor related to patient survival. Classifications, however, fail to correlate treatment with prognosis and cannot suggest the ideal treatment for each tumor stage. The Barcelona Classification (BCLC) attempts to correlate tumor stage with treatment but requires prospective studies for validation. For single tumors smaller than 5 cm or up to three nodules smaller than 3 cm, surgical resection, liver transplantation and percutaneous treatment may offer good anti-tumoral results, as well as improved patient survival. Embolization or chemoembolization are therapeutic alternatives for patients who do not benefit from curative therapies.
Parametric t-Distributed Stochastic Exemplar-Centered Embedding

Parametric embedding methods such as parametric t-distributed Stochastic Neighbor Embedding (pt-SNE) enables out-of-sample data visualization without further computationally expensive optimization or approximation. However, pt-SNE favors small mini-batches to train a deep neural network but large minibatches to approximate its cost function involving all pairwise data point comparisons, and thus has difficulty in finding a balance. To resolve the conflicts, we present parametric t-distributed stochastic exemplar-centered embedding. Our strategy learns embedding parameters by comparing training data only with precomputed exemplars to indirectly preserve local neighborhoods, resulting in a cost function with significantly reduced computational and memory complexity. Moreover, we propose a shallow embedding network with high-order feature interactions for data visualization, which is much easier to tune but produces comparable performance in contrast to a deep feedforward neural network employed by pt-SNE. We empirically demonstrate, using several benchmark datasets, that our proposed method significantly outperforms pt-SNE in terms of robustness, visual effects, and quantitative evaluations.