nils-herrmann/clean_scirepeval huggingface dataset:
Data structure for representing a graph: combination of linked list and hash table

In this article we discuss a data structure, which combines advantages of two different ways for representing graphs: adjacency matrix and collection of adjacency lists. This data structure can fast add and search edges (advantages of adjacency matrix), use linear amount of memory, let to obtain adjacency list for certain vertex (advantages of collection of adjacency lists). Basic knowledge of linked lists and hash tables is required to understand this article. The article contains examples of implementation on Java.
Mining Educational Data to Improve Students' Performance: A Case Study

Educational data mining concerns with developing methods for discovering knowledge from data that come from educational domain. In this paper we used educational data mining to improve graduate students’ performance, and overcome the problem of low grades of graduate students. In our case study we try to extract useful knowledge from graduate students data collected from the college of Science and Technology – Khanyounis. The data include fifteen years period [1993-2007]. After preprocessing the data, we applied data mining techniques to discover association, classification, clustering and outlier detection rules. In each of these four tasks, we present the extracted knowledge and describe its importance in educational domain.
FlexiWeb: Network-Aware Compaction for Accelerating Mobile Web Transfers

To reduce page load times and bandwidth usage for mobile web browsing, middleboxes that compress page content are commonly used today. Unfortunately, this can hurt performance in many cases; via an extensive measurement study, we show that using middleboxes to facilitate compression results in up to 28% degradation in page load times when the client enjoys excellent wireless link conditions. We find that benefits from compression are primarily realized under bad network conditions. Guided by our study, we design and implement FlexiWeb, a framework that determines both when to use a middlebox and how to use it, based on the client's network conditions. First, FlexiWeb selectively fetches objects on a web page either directly from the source or via a middlebox, rather than fetching all objects via the middlebox. Second, instead of simply performing lossless compression of all content, FlexiWeb performs network-aware compression of images by selecting from among a range of content transformations. We implement and evaluate a prototype of FlexiWeb using Google's open source Chromium mobile browser and our implementation of a modified version of Google's open source compression proxy. Our extensive experiments show that, across a range of scenarios, FlexiWeb reduces page load times for mobile clients by 35-42% compared to the status quo.
Deep Multitask Learning for Railway Track Inspection

Railroad tracks need to be periodically inspected and monitored to ensure safe transportation. Automated track inspection using computer vision and pattern recognition methods has recently shown the potential to improve safety by allowing for more frequent inspections while reducing human errors. Achieving full automation is still very challenging due to the number of different possible failure modes, as well as the broad range of image variations that can potentially trigger false alarms. In addition, the number of defective components is very small, so not many training examples are available for the machine to learn a robust anomaly detector. In this paper, we show that detection performance can be improved by combining multiple detectors within a multitask learning framework. We show that this approach results in improved accuracy for detecting defects on railway ties and fasteners.
Event Detection in Social Streams

Social networks generate a large amount of text content over time because of continuous interaction between participants. The mining of such social streams is more challenging than traditional text streams, because of the presence of both text content and implicit network structure within the stream. The problem of event detection is also closely related to clustering, because the events can only be inferred from aggregate trend changes in the stream. In this paper, we will study the two related problems of clustering and event detection in social streams. We will study both the supervised and unsupervised case for the event detection problem. We present experimental results illustrating the effectiveness of incorporating network structure in event discovery over purely content-based
A bi-level view of inpainting - based image compression

Inpainting based image compression approaches, especially linear and non-linear diffusion models, are an active research topic for lossy image compression. The major challenge in these compression models is to find a small set of descriptive supporting points, which allow for an accurate reconstruction of the original image. It turns out in practice that this is a challenging problem even for the simplest Laplacian interpolation model. In this paper, we revisit the Laplacian interpolation compression model and introduce two fast algorithms, namely successive preconditioning primal dual algorithm and the recently proposed iPiano algorithm, to solve this problem efficiently. Furthermore, we extend the Laplacian interpolation based compression model to a more general form, which is based on principles from bi-level optimization. We investigate two different variants of the Laplacian model, namely biharmonic interpolation and smoothed Total Variation regularization. Our numerical results show that significant improvements can be obtained from the biharmonic interpolation model, and it can recover an image with very high quality from only 5% pixels.
Human-Robot Interaction Through Gesture-Free Spoken Dialogue

We present an approach to human-robot interaction through gesture-free spoken dialogue. Our approach is based on passive knowledge rarefication through goal disambiguation, a technique that allows a human operator to collaborate with a mobile robot on various tasks through spoken dialogue without making bodily gestures. A key assumption underlying our approach is that the operator and the robot share a common set of goals. Another key idea is that language, vision, and action share common memory structures.We discuss how our approach achieves four types of human-robot interaction: command, goal disambiguation, introspection, and instruction-based learning. We describe the system we developed to implement our approach and present experimental results.
Using Docker in high performance computing applications

Virtualization technology plays a vital role in cloud computing. In particular, benefits of virtualization are widely employed in high performance computing (HPC) applications. Recently, virtual machines (VMs) and Docker containers known as two virtualization platforms need to be explored for developing applications efficiently. We target a model for deploying distributed applications on Docker containers, among using well-known benchmarks to evaluate performance between VMs and containers. Based on their architecture, we propose benchmark scenarios to analyze the computing performance and the ability of data access on HPC system. Remarkably, Docker container has more advantages than virtual machine in terms of data intensive application and computing ability, especially the overhead of Docker is trivial. However, Docker architecture has some drawbacks in resource management. Our experiment and evaluation show how to deploy efficiently high performance computing applications on Docker containers and VMs.
Tensor decompositions for learning latent variable models

This work considers a computationally and statistically e?cient parameter estimation method for a wide class of latent variable models—including Gaussian mixture models, hidden Markov models, and latent Dirichlet allocation—which exploits a certain tensor structure in their loworder observable moments (typically, of secondand third-order). Speci?cally, parameter estimation is reduced to the problem of extracting a certain (orthogonal) decomposition of a symmetric tensor derived from the moments; this decomposition can be viewed as a natural generalization of the singular value decomposition for matrices. Although tensor decompositions are generally intractable to compute, the decomposition of these specially structured tensors can be e?ciently obtained by a variety of approaches, including power iterations and maximization approaches (similar to the case of matrices). A detailed analysis of a robust tensor power method is provided, establishing an analogue of Wedin’s perturbation theorem for the singular vectors of matrices. This implies a robust and computationally tractable estimation approach for several popular latent variable models Tensor Decompositions for Learning Latent Variable Models Anima Anandkumar, Rong Ge, Daniel Hsu, Sham M. Kakade, and Matus Telgarsky University of California, Irvine Princeton University Microsoft Research, New England University of California, San Diego
Inferring human activities from GPS tracks

The collection of huge amount of tracking data made possible by the widespread use of GPS devices, enabled the analysis of such data for several applications domains, ranging from traffic management to advertisement and social studies. However, the raw positioning data, as it is detected by GPS devices, lacks of semantic information since this data does not natively provide any additional contextual information like the places that people visited or the activities performed. Traditionally, this information is collected by hand filled questionnaire where a limited number of users are asked to annotate their tracks with the activities they have done. With the purpose of getting large amount of semantically rich trajectories, we propose an algorithm for automatically annotating raw trajectories with the activities performed by the users. To do this, we analyse the stops points trying to infer the Point Of Interest (POI) the user has visited. Based on the category of the POI and a probability measure based on the gravity law, we infer the activity performed. We experimented and evaluated the method in a real case study of car trajectories, manually annotated by users with their activities. Experimental results are encouraging and will drive our future works.
Atmospheric effects in interferometric synthetic aperture radar surface deformation and topographic maps

Interferogram images derived from repeat-pass paceborne synthetic aperture radar systems exhibit artifacts due to the time and space variations of atmospheric water vapor. Other tropospheric variations, such as pressure and temperature, also induce distortions, but the effects are smaller in magnitude and more evenly distributed throughout the interferogram than the wet troposphere term. Spatial and temporal changes of 20% in relative humidity lead to 10 cm errors in deformation products, and perhaps 100 rn of error in derived topographic maps for those pass pairs with unfavorable baseline geometries. In wet regions such as Hawaii, these are by far the dominant errors in the Spaceborne Imaging Radar-C and X Band Synthetic Aperature Radar (SIR-C/XSAR) interferometric products. The unknown time delay from tropospheric distortion is independent of frequency, and thus multiwavelength measurements, such as those commonly used to correct radar altimeter and Global Positioning System (GPS) ionospheric biases, cannot be used to rectify the error. In the topographic case, the errors may be mitigated by choosing interferometric pairs with relatively long baselines, as the error amplitude is inversely proportional to the perpendicular component of the interferometer baseline. For the SIR-C/X-SAR Hawaii data we found that the best (longest) baseline pair produced a map supporting 100 rn contouring, whereas the poorest baseline choice yielded an extremely noisy topographic map even at this coarse contour interval. In the case of deformation map errors the result is either independent of baseline parameters or else very nearly so. Here the only solution is averaging of independent interferograms, so in order to create accurate deformation products in wet regions many multiple passes may be required. Rules for designing optimal data acquisition and processing sequences for interferometric analyses in nondesert parts of the world are (1) to use the longest radar wavelengths possible, within ionospheric scintillation and Faraday rotation limits, (2) for topography, maximize interferometer baseline within decorrelation limits, and (3) for surface deformation, use multiple observations and average the derived products. Following the above recipe yields accuracies of 10 rn for digital elevation models and 1 cm for deformation maps even in very wet regions, such as Hawaii.
SUBSTRATE INTEGRATED WAVEGUIDE WILKINSON POWER DIVIDER WITH IMPROVED ISOLATION PERFORMANCE

This work proposes a substrate integrated waveguide (SIW) power divider employing the Wilkinson configuration for improving the isolation performance of conventional T-junction SIW power dividers. Measurement results at 15GHz show that the isolation (S23, S32) between output ports is about 17 dB and the output return losses (S22, S33) are about 14.5 dB, respectively. The Wilkinson-type performance has been greatly improved from those (7.0 dB ∼ 8.0 dB) of conventional T-junction SIW power dividers. The measured input return loss (23 dB) and average insertion loss (3.9 dB) are also improved from those of conventional ones. The proposed Wilkinson SIW divider will play an important role in high performance SIW circuits involving power divisions.
Latent entity space: a novel retrieval approach for entity-bearing queries

Analysis on Web search query logs has revealed that there is a large portion of entity-bearing queries, reflecting the increasing demand of users on retrieving relevant information about entities such as persons, organizations, products, etc. In the meantime, significant progress has been made in Web-scale information extraction, which enables efficient entity extraction from free text. Since an entity is expected to capture the semantic content of documents and queries more accurately than a term, it would be interesting to study whether leveraging the information about entities can improve the retrieval accuracy for entity-bearing queries. In this paper, we propose a novel retrieval approach, i.e., latent entity space (LES), which models the relevance by leveraging entity profiles to represent semantic content of documents and queries. In the LES, each entity corresponds to one dimension, representing one semantic relevance aspect. We propose a formal probabilistic framework to model the relevance in the high-dimensional entity space. Experimental results over TREC collections show that the proposed LES approach is effective in capturing latent semantic content and can significantly improve the search accuracy of several state-of-the-art retrieval models for entity-bearing queries.
Design and development of the wearable hand exoskeleton system for rehabilitation of hand impaired patients

In the past decade, the population of disability grew rapidly and became one of main problems in our society. The significant amounts of impaired patients include not only lower limb but also upper limb impairment such as the motor function of arm and hand. However, physical therapy requires high personal expenses and takes long time to complete the rehabilitation. In order to solve the problem mentioned above, a wearable hand exoskeleton system was developed in this paper. The hand exoskeleton system typically designed to accomplish the requirements for rehabilitation.  1 shows the prototype of hand exoskeleton system which can be easily worn on human hand. The developed exoskeleton finger can provide bi-directional movements in bending and extension motion for all joints of the finger through cable transmission. The kinematic relations between the fingertip and metacarpal was derived and verified. Moreover, the construction of control system is presented in this paper. The preliminary experiment results for finger's position control have demonstrated that the proposed device is capable of accommodating to the aforementioned variables.
Remote Sensing Image Scene Classification Using Bag of Convolutional Features

More recently, remote sensing image classification has been moving from pixel-level interpretation to scene-level semantic understanding, which aims to label each scene image with a specific semantic class. While significant efforts have been made in developing various methods for remote sensing image scene classification, most of them rely on handcrafted features. In this letter, we propose a novel feature representation method for scene classification, named bag of convolutional features (BoCF). Different from the traditional bag of visual words-based methods in which the visual words are usually obtained by using handcrafted feature descriptors, the proposed BoCF generates visual words from deep convolutional features using off-the-shelf convolutional neural networks. Extensive evaluations on a publicly available remote sensing image scene classification benchmark and comparison with the state-of-the-art methods demonstrate the effectiveness of the proposed BoCF method for remote sensing image scene classification.
Patch-Based Image Warping for Content-Aware Retargeting

Image retargeting is the process of adapting images to fit displays with various aspect ratios and sizes. Most studies on image retargeting focus on shape preservation, but they do not fully consider the preservation of structure lines, which are sensitive to human visual system. In this paper, a patch-based retargeting scheme with an extended significance measurement is introduced to preserve shapes of both visually salient objects and structure lines while minimizing visual distortions. In the proposed scheme, a similarity transformation constraint is used to force visually salient content to undergo as-rigid-as-possible deformation, while an optimization process is performed to smoothly propagate distortions. These processes enable our approach to yield pleasing content-aware warping and retargeting. Experimental results and a user study show that our results are better than those generated by state-of-the-art approaches.
Introduction to Industrial Control Networks

An industrial control network is a system of interconnected equipment used to monitor and control physical equipment in industrial environments. These networks differ quite significantly from traditional enterprise networks due to the specific requirements of their operation. Despite the functional differences between industrial and enterprise networks, a growing integration between the two has been observed. The technology in use in industrial networks is also beginning to display a greater reliance on Ethernet and web standards, especially at higher levels of the network architecture. This has resulted in a situation where engineers involved in the design and maintenance of control networks must be familiar with both traditional enterprise concerns, such as network security, as well as traditional industrial concerns such as determinism and response time. This paper highlights some of the differences between enterprise and industrial networks, presents a brief history of industrial networking, gives a high level explanation of some operations specific to industrial networks, provides an overview of the popular protocols in use and describes current research topics. The purpose of this paper is to serve as an introduction to industrial control networks, aimed specifically at those who have had minimal exposure to the field, but have some familiarity with conventional computer networks.
Swarm robotics: a review from the swarm engineering perspective

Swarm robotics is an approach to collective robotics that takes inspiration from the self-organized behaviors of social animals. Through simple rules and local interactions, swarm robotics aims at designing robust, scalable, and flexible collective behaviors for the coordination of large numbers of robots. In this paper, we analyze the literature from the point of view of swarm engineering: we focus mainly on ideas and concepts that contribute to the advancement of swarm robotics as an engineering field and that could be relevant to tackle real-world applications. Swarm engineering is an emerging discipline that aims at defining systematic and well founded procedures for modeling, designing, realizing, verifying, validating, operating, and maintaining a swarm robotics system. We propose two taxonomies: in the first taxonomy, we classify works that deal with design and analysis methods; in the second taxonomy, we classify works according to the collective behavior studied. We conclude with a discussion of the current limits of swarm robotics as an engineering discipline and with suggestions for future research directions.
Fast mining frequent itemsets using Nodesets

Node-list and N-list, two novel data structure proposed in recent years, have been proven to be very efficient for mining frequent itemsets. The main problem of these structures is that they both need to encode each node of a PPC-tree with pre-order and post-order code. This causes that they are memory consuming and inconvenient to mine frequent itemsets. In this paper, we propose Nodeset, a more efficient data structure, for mining frequent itemsets. Nodesets require only the pre-order (or post-order code) of each node, which makes it saves half of memory compared with N-lists and Node-lists. Based on Nodesets, we present an efficient algorithm called FIN to mining frequent itemsets. For evaluating the performance of FIN, we have conduct experiments to compare it with PrePost and FP-growth ⁄ , two state-of-the-art algorithms, on a variety of real and synthetic datasets. The experimental results show that FIN is high performance on both running time and memory usage. Frequent itemset mining, first proposed by Agrawal, Imielinski, and Swami (1993), has become a fundamental task in the field of data mining because it has been widely used in many important data mining tasks such as mining associations, correlations, episodes , and etc. Since the first proposal of frequent itemset mining, hundreds of algorithms have been proposed on various kinds of extensions and applications, ranging from scalable data mining methodologies, to handling a wide diversity of data types, various extended mining tasks, and a variety of new applications (Han, Cheng, Xin, & Yan, 2007). In recent years, we present two data structures called Node-list (Deng & Wang, 2010) and N-list (Deng, Wang, & Jiang, 2012) for facilitating the mining process of frequent itemsets. Both structures use nodes with pre-order and post-order to represent an itemset. Based on Node-list and N-list, two algorithms called PPV (Deng & Wang, 2010) and PrePost (Deng et al., 2012) are proposed, respectively for mining frequent itemsets. The high efficiency of PPV and PrePost is achieved by the compressed characteristic of Node-lists and N-lists. However, they are memory-consuming because Node-lists and N-lists need to encode a node with pre-order and post-order. In addition, the nodes' code model of Node-list and N-list is not suitable to join Node-lists or N-lists of two short itemsets to generate the Node-list or N-list of a long itemset. This may affect the efficiency of corresponding algorithms. Therefore, how to design an efficient data structure without …