nils-herrmann/clean_scirepeval huggingface dataset:
Integrating Containers into Workflows: A Case Study Using Makeflow, Work Queue, and Docker

Workflows are a widely used abstraction for representing large scientific applications and executing them on distributed systems such as clusters, clouds, and grids. However, workflow systems have been largely silent on the question of precisely what environment each task in the workflow is expected to run in. As a result, a workflow may run correctly in the environment in which it was designed, but when moved to another machine, is highly likely to fail due to differences in the operating system, installed applications, available data, and so forth. Lightweight container technology has recently arisen as a potential solution to this problem, by providing a well-defined execution environments at the operating system level. In this paper, we consider how to best integrate container technology into an existing workflow system, using Makeflow, Work Queue, and Docker as examples of current technology. A brief performance study of Docker shows very little overhead in CPU and I/O performance, but significant costs in creating and deleting containers. Taking this into account, we describe four different methods of connecting containers to different points of the infrastructure, and explain several methods of managing the container images that must be distributed to executing tasks. We explore the performance of a large bioinformatics workload on a Docker-enabled cluster, and observe the best configuration to be locally-managed containers that are shared between multiple tasks.
General Purpose Database Summarization

In this paper, a message-oriented architecture for large database summarization is presented. The summarization system takes a database table as input and produces a reduced version of this table through both a rewriting and a generalization process. The resulting table provides tuples with less precision than the original but yet are very informative of the actual content of the database. This reduced form can be used as input for advanced data mining processes as well as some specific application presented in other works. We describe the incremental maintenance of the summarized table, the system capability to directly deal with XML database systems, and finally scalability which allows it to handle very large datasets of a million record.
Auto-play: A Data Mining Approach to ODI Cricket Simulation and Prediction

Cricket is a popular sport played by 16 countries, is the second most watched sport in the world after soccer, and enjoys a multi-million dollar industry. There is tremendous interest in simulating cricket and more importantly in predicting the outcome of games, particularly in their one-day international format. The complex rules governing the game, along with the numerous natural parameters affecting the outcome of a cricket match present significant challenges for accurate prediction. Multiple diverse parameters, including but not limited to cricketing skills and performances, match venues and even weather conditions can significantly affect the outcome of a game. The sheer number of parameters, along with their interdependence and variance create a non-trivial challenge to create an accurate quantitative model of a game Unlike other sports such as basketball and baseball which are well researched from a sports analytics perspective, for cricket, these tasks have yet to be investigated in depth. In this paper, we build a prediction system that takes in historical match data as well as the instantaneous state of a match, and predicts future match events culminating in a victory or loss. We model the game using a subset of match parameters, using a combination of linear regression and nearestneighbor clustering algorithms. We describe our model and algorithms and finally present quantitative results, demonstrating the performance of our algorithms in predicting the number of runs scored, one of the most important determinants of match outcome.
Forum Thread Recommendation for Massive Open Online Courses

Recently, Massive Open Online Courses (MOOCs) have garnered a high level of interest in the media. With larger and larger numbers of students participating in each course, finding useful and informative threads in increasingly crowded course discussion forums becomes a challenging issue for students. In this work, we address this thread overload problem by taking advantage of an adaptive feature-based matrix factorization framework to make thread recommendations. A key component of our approach is a feature space design that effectively characterizes student behaviors in the forum in order to match threads and users. This effort includes content level modeling, social peer connections, and other forum activities. The results from our experiment conducted on one MOOC course show promise that our thread recommendation method has potential to direct students to threads they might be interested in.
Comparative Study of Classification Algorithms used in Sentiment Analysis

The field of information extraction and retrieval has grown exponentially in the last decade. Sentiment analysis is a task in which you identify the polarity of given text using text processing and classification. There are various approaches in the task of classification of text into various classes. Use of particular algorithms depends on the kind of input provided. Analyzing and understanding when to use which algorithm is an important aspect and can help in improving accuracy of results.  Sentiment Analysis, Classification Algorithms, Naïve Bayes, Max Entropy, Boosted Trees, Random Forest.
Community structure of the physical review citation network

We investigate the community structure of physics subfields in the citation network of all Physical Review publications between 1893 and August 2007. We focus on well-cited publications (those receiving more than 100 citations), and apply modularity maximization to uncover major communities that correspond to clearly identifiable subfields of physics. While most of the links between communities connect those with obvious intellectual overlap, there sometimes exist unexpected connections between disparate fields due to the development of a widely applicable theoretical technique or by cross fertilization between theory and experiment. We also examine communities decade by decade and also uncover a small number of significant links between communities that are widely separated in time.
Online Robust PCA via Stochastic Optimization

Robust PCA methods are typically based on batch optimization and have to load all the samples into memory during optimization. This prevents them from efficiently processing big data. In this paper, we develop an Online Robust PCA (OR-PCA) that processes one sample per time instance and hence its memory cost is independent of the number of samples, significantly enhancing the computation and storage efficiency. The proposed OR-PCA is based on stochastic optimization of an equivalent reformulation of the batch RPCA. Indeed, we show that OR-PCA provides a sequence of subspace estimations converging to the optimum of its batch counterpart and hence is provably robust to sparse corruption. Moreover, OR-PCA can naturally be applied for tracking dynamic subspace. Comprehensive simulations on subspace recovering and tracking demonstrate the robustness and efficiency advantages of the OR-PCA over online PCA and batch RPCA methods.
Evaluation of the Masked Logic Style MDPL on a Prototype Chip

MDPL has been proposed as a masked logic style that counteracts DPA attacks. Recently, it has been shown that the so-called “early propagation effect” might reduce the security of this logic style significantly. In the light of these findings, a 0.13 μm prototype chip that includes the implementation of an 8051-compatible microcontroller in MDPL has been analyzed. Attacks on the measured power traces of this implementation show a severe DPA leakage. In this paper, the results of a detailed analysis of the reasons for this leakage are presented. Furthermore, a proposal is made on how to improve MDPL with respect to the identified problems.
Power minimization in IC design: principles and applications

Low power has emerged as a principal theme in today's electronics industry. The need for low power has caused a major paradigm shift in which power dissipation is as important as performance and area. This article presents an in-depth survey of CAD methodologies and techniques for designing low power digital CMOS circuits and systems and describes the many issues facing designers at architectural, logical, and physical levels of design abstraction. It reviews some of the techniques and tools that have been proposed to overcome these difficulties and outlines the future challenges that must be met to design low power, high performance systems.
Teleo-Reactive Programs for Agent Control

A formalism is presented for computing and organizing actions for autonomous agents in dynamic environments. We introduce the notion of teleo-reactive (T-R) programs whose execution entails the construction of circuitry for the continuous computation of the parameters and conditions on which agent action is based. In addition to continuous feedback, T-R programs support parameter binding and recursion. A primary di erence between T-R programs and many other circuit-based systems is that the circuitry of T-R programs is more compact; it is constructed at run time and thus does not have to anticipate all the contingencies that might arise over all possible runs. In addition, T-R programs are intuitive and easy to write and are written in a form that is compatible with automatic planning and learning methods. We brie y describe some experimental applications of T-R programs in the control of simulated and actual mobile robots.
An experimental comparison of naive bayesian and keyword-based anti-spam filtering with personal e-mail messages

The growing problem of unsolicited bulk e-mail, also known as “spam”, has generated a need for reliable anti-spam e-mail filters. Filters of this type have so far been based mostly on manually constructed keyword patterns. An alternative approach has recently been proposed, whereby a Naive Bayesian classifier is trained automatically to detect spam messages. We test this approach on a large collection of personal e-mail messages, which we make publicly available in “encrypted” form contributing towards standard benchmarks. We introduce appropriate cost-sensitive measures, investigating at the same time the effect of attribute-set size, training-corpus size, lemmatization, and stop lists, issues that have not been explored in previous experiments. Finally, the Naive Bayesian filter is compared, in terms of performance, to a filter that uses keyword patterns, and which is part of a widely used e-mail reader.
Anomaly Detection in Embedded Systems

By employing fault tolerance, embedded systems can withstand both intentional and unintentional faults. Many fault-tolerance mechanisms are invoked only after a fault has been detected by whatever fault-detection mechanism is used, hence the process of fault detection must itself be dependable if the system is expected to be fault tolerant. Many faults are detectable only indirectly, as a result of performance disorders that manifest as anomalies in monitored system or sensor data. Anomaly detection, therefore, is often the primary means of providing early indications of faults. As with any other kind of detector, one seeks full coverage of the detection space with the anomaly detector being used. Even if coverage of a particular anomaly detector falls short of 100%, detectors can be composed to e ect broader coverage, once their respective sweet spots and blind regions are known. This paper provides a framework and a fault-injection methodology for mapping an anomaly detector's e ective operating space, and shows that two detectors, each designed to detect the same phenomenon, may not perform similarly, even when the event to be detected is unequivocally anomalous, and should be detected by either detector. Both synthetic and real-world data are used.
Transfer Learning for Reinforcement Learning Domains: A Survey

The reinforcement learning paradigm is a popular way to addr ess problems that have only limited environmental feedback, rather than correctly labeled exa mples, as is common in other machine learning contexts. While significant progress has been made t o improve learning in a single task, the idea oftransfer learninghas only recently been applied to reinforcement learning ta sks. The core idea of transfer is that experience gained in learning t o perform one task can help improve learning performance in a related, but different, task. In t his article we present a framework that classifies transfer learning methods in terms of their capab ilities and goals, and then use it to survey the existing literature, as well as to suggest future direct ions for transfer learning work.
Multiple resources and performance prediction

This paper describes the origins and history of multiple resource theory in accounting for di erences in dual task interference. One particular application of the theory, the 4-dimensional multiple resources model, is described in detail, positing that there will be greater interference between two tasks to the extent that they share stages (perceptual/cognitive vs response) sensory modalities (auditory vs visual), codes (visual vs spatial) and channels of visual information (focal vs ambient). A computational rendering of this model is then presented. Examples are given of how the model predicts interference di erences in operational environments. Finally, three challenges to the model are outlined regarding task demand coding, task allocation and visual resource competition.
Learning Depth with Convolutional Spatial Propagation Network

Depth prediction is one of the fundamental problems in computer vision. In this paper, we propose a simple yet effective convolutional spatial propagation network (CSPN) to learn the affinity matrix for various depth estimation tasks. Specifically, it is an efficient linear propagation model, in which the propagation is performed with a manner of recurrent convolutional operation, and the affinity among neighboring pixels is learned through a deep convolutional neural network (CNN). We can append this module to any output from a state-of-the-art (SOTA) depth estimation networks to improve their performances. In practice, we further extend CSPN in two aspects: 1) allow it to take sparse depth map as additional input, which is useful for the task of depth completion , ; 2) extend to 3D CSPN to handle features with one additional dimension, similar to the commonly used 3D convolution operation. It is effective in the task of stereo matching using 3D cost volume . For the task of single image depth estimation and depth completion, we experiment the proposed CPSN conjunct algorithms over the popular NYU v2  and KITTI  datasets, where we show that our proposed algorithms not only produce high quality (e.g., 30% more reduction in depth error), but also run faster (e.g., 2 to 5 × faster) than previous SOTA spatial propagation network [7]. We also evaluated our stereo matching algorithm on the Scene Flow  and KITTI Stereo datasets , , and rank 1st on both the KITTI Stereo 2012 and 2015 benchmarks , which demonstrates the effectiveness of the proposed module. The code of CSPN proposed in this work will be release at https://github.com/XinJCheng/CSPN.
Receding Horizon Model-Predictive Control for Mobile Robot Navigation of Intricate Paths

As mobile robots venture into more difficult environments, more complex state-space paths are required to move safely and efficiently. The difference between mission success and failure can be determined by a mobile robots capacity to effectively navigate such paths in the presence of disturbances. This paper describes a technique for mobile robot model predictive control that utilizes the structure of a regional motion plan to effectively search the local continuum for an improved solution. The contribution, a receding horizon model-predictive control (RHMPC) technique, specifically addresses the problem of path following and obstacle avoidance through geometric singularities and discontinuities such as cusps, turn-in-place, and multi-point turn maneuvers in environments where terrain shape and vehicle mobility effects are non-negligible. The technique is formulated as an optimal controller that utilizes a model-predictive trajectory generator to relax parameterized control inputs initialized from a regional motion planner to navigate safely through the environment. Experimental results are presented for a six-wheeled skid-steered field robot in natural terrain.
Password Cracking Using Probabilistic Context-Free Grammars

Choosing the most effective word-mangling rules to use when performing a dictionary-based password cracking attack can be a difficult task. In this paper we discuss a new method that generates password structures in highest probability order. We first automatically create a probabilistic context-free grammar based upon a training set of previously disclosed passwords. This grammar then allows us to generate word-mangling rules, and from them, password guesses to be used in password cracking. We will also show that this approach seems to provide a more effective way to crack passwords as compared to traditional methods by testing our tools and techniques on real password sets. In one series of experiments, training on a set of disclosed passwords, our approach was able to crack 28% to 129% more passwords than John the Ripper, a publicly available standard password cracking program.
EAST: An Efficient and Accurate Scene Text Detector

Previous approaches for scene text detection have already achieved promising performances across various benchmarks. However, they usually fall short when dealing with challenging scenarios, even when equipped with deep neural network models, because the overall performance is determined by the interplay of multiple stages and components in the pipelines. In this work, we propose a simple yet powerful pipeline that yields fast and accurate text detection in natural scenes. The pipeline directly predicts words or text lines of arbitrary orientations and quadrilateral shapes in full images, eliminating unnecessary intermediate steps (e.g., candidate aggregation and word partitioning), with a single neural network. The simplicity of our pipeline allows concentrating efforts on designing loss functions and neural network architecture. Experiments on standard datasets including ICDAR 2015, COCO-Text and MSRA-TD500 demonstrate that the proposed algorithm significantly outperforms state-of-the-art methods in terms of both accuracy and efficiency. On the ICDAR 2015 dataset, the proposed algorithm achieves an F-score of 0.7820 at 13.2fps at 720p resolution.
Integrating spatial fuzzy clustering with level set methods for automated medical image segmentation

The performance of the level set segmentation is subject to appropriate initialization and optimal configuration of controlling parameters, which require substantial manual intervention. A new fuzzy level set algorithm is proposed in this paper to facilitate medical image segmentation. It is able to directly evolve from the initial segmentation by spatial fuzzy clustering. The controlling parameters of level set evolution are also estimated from the results of fuzzy clustering. Moreover the fuzzy level set algorithm is enhanced with locally regularized evolution. Such improvements facilitate level set manipulation and lead to more robust segmentation. Performance evaluation of the proposed algorithm was carried on medical images from different modalities. The results confirm its effectiveness for medical image segmentation.
Compact Quarter-Wave Resonator and Its Applications to Miniaturized Diplexer and Triplexer

Novel and compact composite right/left-handed (CRLH) quarter-wave type resonators are proposed in this paper. The resonator can resonate at the frequency where the electrical length is phase-leading or negative, which results in a smaller size as compared to the conventional phase-delayed microstrip-line resonator. Furthermore, it is only half the size of the CRLH half-wave resonator resonating at the same frequency. In addition, the proposed resonator is capable of engineering the multiresonances very close to each other, which makes it suitable to implement the miniaturized multiband microwave components such as diplexers and triplexers. A very compact diplexer and a very compact triplexer are proposed based on the proposed CRLH quarter-wave resonators in this paper and both of them have demonstrated very good performance. Specifically, compared to the referenced works based on the conventional microstrip resonators, the proposed diplexer and triplexer are 50% and 76% smaller than their microstrip counterparts, respectively.
Some Effective Techniques for Naive Bayes Text Classification

While naive Bayes is quite effective in various data mining tasks, it shows a disappointing result in the automatic text classification problem. Based on the observation of naive Bayes for the natural language text, we found a serious problem in the parameter estimation process, which causes poor results in text classification domain. In this paper, we propose two empirical heuristics: per-document text normalization and feature weighting method. While these are somewhat ad hoc methods, our proposed naive Bayes text classifier performs very well in the standard benchmark collections, competing with state-of-the-art text classifiers based on a highly complex learning method such as SVM
Control of soft pneumatic finger-like actuators for affective motion generation

This paper investigates the design and implementation of a finger-like robotic structure capable of reproducing human hand gestural movements performed by a multi-fingered, hand-like structure. In this work, we present a pneumatic circuit and a closed-loop controller for a finger-like soft pneumatic actuator. Experimental results demonstrate the performance of the pneumatic and control systems of the soft pneumatic actuator, and its ability to track human movement trajectories with affective content.
Ways of Conditioning Generative Adversarial Networks

The GANs are generative models whose random samples realistically reflect natural images. It also can generate samples with specific attributes by concatenating a condition vector into the input, yet research on this field is not well studied. We propose novel methods of conditioning generative adversarial networks (GANs) that achieve state-of-the-art results on MNIST and CIFAR-10. We mainly introduce two models: an information retrieving model that extracts conditional information from the samples, and a spatial bilinear pooling model that forms bilinear features derived from the spatial cross product of an image and a condition vector. These methods significantly enhance log-likelihood of test data under the conditional distributions compared to the methods of concatenation.
3D smooth path planning for a UAV in cluttered natural environments

This paper presents a 3D path planing algorithm for an unmanned aerial vehicle (UAV) operating in cluttered natural environments. The algorithm satisfies the upper bounded curvature constraint and the continuous curvature requirement. In this work greater attention is placed on the computational complexity in comparison with other path-planning considerations. The rapidly-exploring random trees (RRTs) algorithm is used for the generation of collision free waypoints. The unnecessary waypoints are removed by a simple path pruning algorithm generating a piecewise linear path. Then a path smoothing algorithm utilizing cubic Bezier spiral curves to generate a continuous curvature path that satisfies the minimum radius of curvature constraint of UAV is implemented. The angle between two waypoints is the only information required for the generation of the continuous curvature path. The result shows that the suggested algorithm is simple and easy to implement compared with the Clothoids method.