nils-herrmann/clean_scirepeval huggingface dataset:
Open source real-time control software for the Kuka light weight robot

The Kuka light weight robot offers unique features to researchers. Besides its 7 Degrees of Freedom (DOF), also torque sensing in every joint and a variety of compliance modes make the robot a good choice for robotic research. Unfortunately the interface to control the robot externally has its restrictions. In this paper, we present an open source solution (OpenKC) that will allow the control of the robot externally using a simple set of routines that can easily be integrated in existing software. All features and modes of the Kuka light weight robot can be used and triggered externally. Simultaneous control of several robots is explicitly supported. The software has proven its use in several applications.
Compact Design of Triple-Band Circularly Polarized Quadrifilar Helix Antennas

This letter introduces the compact design of triple-band circularly polarized quadrifilar helix antennas (QHAs) possessing the characteristics of wide beam, high gain at the low elevation, and high stability of the phase center. The available triple-band operation is achieved through the incorporation of a UHF-band QHA and an L/S-band QHA, which are assembled in a “piggyback” fashion. The UHF-band QHA is fed by a compact microstrip feed network, while the L/S-band QHA is fed by a split coax balun. The measured results coincide well with the simulated ones, which verified the effectiveness of the proposed compact design .
Enjoy and learn with educational games: Examining factors affecting learning performance

Educational games have enhanced the value of instruction procedures in institutions and business organizations. Factors that increase students’ adoption of learning games have been widely studied in past; however, the effect of these factors on learners’ performance is yet to be explored. In this study, factors of Enjoyment, Happiness, and Intention to Use were chosen as important attitudes in learning educational games and increasing learning performance. A two-step between group experiment was conducted: the first study compared game-based learning and traditional instruction in order to verify the value of the game. 41 Gymnasium (middle school) students were involved, and the control and experimental groups were formed based on a pretest method. The second study, involving 46 Gymnasium students, empirically evaluates whether and how certain attitudinal factors affect learners’ performance. The results of the two-part experiment showed that a) the game demonstrated good performance (as compared to traditional instruction) concerning the gain of knowledge, b) learners’ enjoyment of the game has a significant relation with their performance, and c) learners’ intention to use and happiness with the game do not have any relation with their performance. Our results suggest that there are attitudinal factors affecting knowledge acquisition gained by a game.
Image segmentation using fuzzy clustering: A survey

This paper presents a survey of latest image segmentation techniques using fuzzy clustering. Fuzzy C-Means (FCM) Clustering is the most wide spread clustering approach for image segmentation because of its robust characteristics for data classification. In this paper, four image segmentation algorithms using clustering, taken from the literature are reviewed. To address the drawbacks of conventional FCM, all these approaches have modified the objective function of conventional FCM and have incorporated spatial information in the objective function of the standard FCM. The techniques that have been reviewed in this survey are Segmentation for noisy medical images with spatial probability, Novel Fuzzy C-Means Clustering (NFCM), Fuzzy Local Information C-Means (FLICM) Clustering Algorithm and Improved Spatial Fuzzy C-Means Clustering (ISFCM) algorithm.
Automatic Mood-Transferring between Color Images

This paper presents a new color-conversion method that offers users an intuitive, one-click interface for style conversion. So, rather than having to supply a reference image, users simply select a color mood with a mouse click. To address the color-transfer quality problem, we associate each color mood with a set of up to 10 images from our image database. After selecting their color mood, users choose one associated image. Our histogram matching algorithm then uses the selected image to determine the input image's color distribution. We thereby achieve more accurate and justifiable color conversion results, while also preserving spatial coherence. Here, we further describe our solutions and their results and compare them to existing approaches.
Learning Memory Access Patterns

The explosion in workload complexity and the recent slow-down in Moore’s law scaling call for new approaches towards efficient computing. Researchers are now beginning to use recent advances in machine learning in software optimizations, augmenting or replacing traditional heuristics and data structures. However, the space of machine learning for computer hardware architecture is only lightly explored. In this paper, we demonstrate the potential of deep learning to address the von Neumann bottleneck of memory performance. We focus on the critical problem of learning memory access patterns, with the goal of constructing accurate and efficient memory prefetchers. We relate contemporary prefetching strategies to n-gram models in natural language processing, and show how recurrent neural networks can serve as a drop-in replacement. On a suite of challenging benchmark datasets, we find that neural networks consistently demonstrate superior performance in terms of precision and recall. This work represents the first step towards practical neural-network based prefetching, and opens a wide range of exciting directions for machine learning in computer architecture research.
UAV-assisted disaster management: Applications and open issues

The fast-paced development of Unmanned Aerial Vehicles (UAVs) and their use in different domains, opens a new paradigm on their use in natural disaster management. In UAV-assisted disaster management applications, UAVs not only survey the affected area but also assist in establishing the communication network between the disaster survivors, rescue teams and nearest available cellular infrastructure. This paper identifies main disaster management applications of UAV networks and discusses open research issues related to UAV-assisted disaster management.
High-voltage pulse generators based on capacitor-diode voltage multiplier

Along with rapid advancement of power semiconductors, voltage multipliers have introduced new series of pulsed power generators. In this paper, current topologies of capacitor-diode voltage multipliers (CDVM) are investigated. Alternative structures for voltage multiplier based on power electronics switches are presented in high voltage pulsed power supplies application. The new topology is able to generate the desired high voltage output without increasing the voltage rating of semiconductor devices as well as capacitors. Finally, a comparative analysis is carried out between different CDVM topologies. Experimental and simulation results are presented to verify the analysis.
Laplacian Regularized Low-Rank Representation and Its Applications

Low-rank representation (LRR) has recently attracted a great deal of attention due to its pleasing efficacy in exploring low-dimensional subspace structures embedded in data. For a given set of observed data corrupted with sparse errors, LRR aims at learning a lowest-rank representation of all data jointly. LRR has broad applications in pattern recognition, computer vision and signal processing. In the real world, data often reside on low-dimensional manifolds embedded in a high-dimensional ambient space. However, the LRR method does not take into account the non-linear geometric structures within data, thus the locality and similarity information among data may be missing in the learning process. To improve LRR in this regard, we propose a general Laplacian regularized low-rank representation framework for data representation where a hypergraph Laplacian regularizer can be readily introduced into, i.e., a Non-negative Sparse Hyper-Laplacian regularized LRR model (NSHLRR). By taking advantage of the graph regularizer, our proposed method not only can represent the global low-dimensional structures, but also capture the intrinsic non-linear geometric information in data. The extensive experimental results on image clustering, semi-supervised image classification and dimensionality reduction tasks demonstrate the effectiveness of the proposed method.
An Implementation of DES and AES, Secure against Some Attacks

Since Power Analysis on smart cards was introduced by Paul Kocher [7], many countermeasures have been proposed to protect implementations of cryptographic algorithms. In this paper we propose a new protection principle: the transformed masking method. We apply this method to protect two of the most popular block ciphers: DES and the AES Rijndael. To this end we introduce some transformed S-boxes for DES and a new masking method and its applications to the non-linear part of Rijndael.
Artificial muscle technology: physical principles and naval prospects

The increasing understanding of the advantages offered by fish and insect-like locomotion is creating a demand for muscle-like materials capable of mimicking nature's mechanisms. Actuator materials that employ voltage, field, light, or temperature driven dimensional changes to produce forces and displacements are suggesting new approaches to propulsion and maneuverability. Fundamental properties of these new materials are presented, and examples of potential undersea applications are examined in order to assist those involved in device design and in actuator research to evaluate the current status and the developing potential of these artificial muscle technologies. Technologies described are based on newly explored materials developed over the past decade, and also on older materials whose properties are not widely known. The materials are dielectric elastomers, ferroelectric polymers, liquid crystal elastomers, thermal and ferroelectric shape memory alloys, ionic polymer/metal composites, conducting polymers, and carbon nanotubes. Relative merits and challenges associated with the artificial muscle technologies are elucidated in two case studies. A summary table provides a quick guide to all technologies that are discussed.
Convolutional Network for Attribute-driven and Identity-preserving Human Face Generation

This paper focuses on the problem of generating human face pictures from specific attributes. The existing CNN-based face generation models, however, either ignore the identity of the generated face or fail to preserve the identity of the reference face image. Here we address this problem from the view of optimization, and suggest an optimization model to generate human face with the given attributes while keeping the identity of the reference image. The attributes can be obtained from the attribute-guided image or by tuning the attribute features of the reference image. With the deep convolutional network "VGG-Face", the loss is defined on the convolutional feature maps. We then apply the gradient decent algorithm to solve this optimization problem. The results validate the effectiveness of our method for attribute driven and identity-preserving face generation.
Self-Paced Cross-Modal Subspace Matching

Cross-modal matching methods match data from different modalities according to their similarities. Most existing methods utilize label information to reduce the semantic gap between different modalities. However, it is usually time-consuming to manually label large-scale data. This paper proposes a Self-Paced Cross-Modal Subspace Matching (SCSM) method for unsupervised multimodal data. We assume that multimodal data are pair-wised and from several semantic groups, which form hard pair-wised constraints and soft semantic group constraints respectively. Then, we formulate the unsupervised cross-modal matching problem as a non-convex joint feature learning and data grouping problem. Self-paced learning, which learns samples from 'easy' to 'complex', is further introduced to refine the grouping result. Moreover, a multimodal graph is constructed to preserve the relationship of both inter- and intra-modality similarity. An alternating minimization method is employed to minimize the non-convex optimization problem, followed by the discussion on its convergence analysis and computational complexity. Experimental results on four multimodal databases show that SCSM outperforms state-of-the-art cross-modal subspace learning methods.
An improved digital evidence acquisition model for the Internet of Things forensic I: A theoretical framework

Digital evidence plays a vital role in determining legal case admissibility in electronic- and cyber-oriented crimes. Considering the complicated level of the Internet of Things (IoT) technology, performing the needed forensic investigation will be definitely faced by a number of challenges and obstacles, especially in digital evidence acquisition and analysis phases. Based on the currently available network forensic methods and tools, the performance of IoT forensic will be producing a deteriorated digital evidence trail due to the sophisticated nature of IoT connectivity and data exchangeability via the “things”. In this paper, a revision of IoT digital evidence acquisition procedure is provided. In addition, an improved theoretical framework for IoT forensic model that copes with evidence acquisition issues is proposed and discussed.
Real-time modeling and 3D visualization of source dynamics and connectivity using wearable EEG

This report summarizes our recent efforts to deliver real-time data extraction, preprocessing, artifact rejection, source reconstruction, multivariate dynamical system analysis (including spectral Granger causality) and 3D visualization as well as classification within the open-source SIFT and BCILAB toolboxes. We report the application of such a pipeline to simulated data and real EEG data obtained from a novel wearable high-density (64-channel) dry EEG system.
The role of small cell technology in future Smart City applications

Meeting citizens’ requirements economically and efficiently is the most important objective of Smart Cities. As a matter of fact, they are considered a key concept both for future Internet and information and communications technology. It is expected that a wide range of services will be made available for residential users (e.g. intelligent transportation systems, e-government, e-banking, e-commerce and smart management of energy demand), public administration entities, public safety and civil protection agencies and so on with increased quality, lower costs and reduced environmental impact. In order to achieve these ambitious objectives, new technologies should be developed such as non-invasive sensing, highly parallel processing, smart grids and mobile broadband communications. This paper considers the communication aspects of Smart City applications, specifically, the role of the latest developments of Long-Term Evolution-Advanced standard, which forecast the increase of broadband coverage by means of small cells. We shall demonstrate that the novel concept of small cell fully meets the emerging communication and networking requirements of future Smart Cities. To this aim, a feasible network architecture for future Smart Cities, based on small cells, will be discussed in the framework of a future smarter and user-centric perspective of forthcoming 4G mobile technologies.
Particle PHD Filter Based Multiple Human Tracking Using Online Group-Structured Dictionary Learning

An enhanced sequential Monte Carlo probability hypothesis density (PHD) filter-based multiple human tracking system is presented. The proposed system mainly exploits two concepts: a novel adaptive gating technique and an online group-structured dictionary learning strategy. Conventional PHD filtering methods preset the target birth intensity and the gating threshold for selecting real observations for the PHD update. This often yields inefficiency in false positives and missed detections in a cluttered environment. To address this issue, a measurement-driven mechanism based on a novel adaptive gating method is proposed to adaptively update the gating sizes. This yields an accurate approach to discriminate between survival and residual measurements by reducing the clutter inferences. In addition, online group-structured dictionary learning with a maximum voting method is used to robustly estimate the target birth intensity. It enables the new-born targets to be automatically detected from noisy sensor measurements. To improve the adaptability of our group-structured dictionary to appearance and illumination changes, we employ the simultaneous code word optimization algorithm for the dictionary update stage. Experimental results demonstrate our proposed method achieves the best performance amongst state-of-the-art random finite set-based methods, and the second best online tracker ranked on the leaderboard of latest MOT17 challenge.
Early prediction of software component reliability

The ability to predict the reliability of a software system early in its development, e.g., during architectural design, can help to improve the system's quality in a cost-effective manner. Existing architecture-level reliability prediction approaches focus on system-level reliability and assume that the reliabilities of individual components are known. In general, this assumption is unreasonable, making component reliability prediction an important missing ingredient in the current literature. Early prediction of component reliability is a challenging problem because of many uncertainties associated with components under development. In this paper we address these challenges in developing a software component reliability prediction framework. We do this by exploiting architectural models and associated analysis techniques, stochastic modeling approaches, and information sources available early in the development lifecycle. We extensively evaluate our framework to illustrate its utility as an early reliability prediction approach.
EnhanceNet: Single Image Super-Resolution Through Automated Texture Synthesis

Single image super-resolution is the task of inferring a high-resolution image from a single low-resolution input. Traditionally, the performance of algorithms for this task is measured using pixel-wise reconstruction measures such as peak signal-to-noise ratio (PSNR) which have been shown to correlate poorly with the human perception of image quality. As a result, algorithms minimizing these metrics tend to produce over-smoothed images that lack highfrequency textures and do not look natural despite yielding high PSNR values.,,We propose a novel application of automated texture synthesis in combination with a perceptual loss focusing on creating realistic textures rather than optimizing for a pixelaccurate reproduction of ground truth images during training. By using feed-forward fully convolutional neural networks in an adversarial training setting, we achieve a significant boost in image quality at high magnification ratios. Extensive experiments on a number of datasets show the effectiveness of our approach, yielding state-of-the-art results in both quantitative and qualitative benchmarks.
Clustering Orders

We propose a method of using clustering techniques to partition a set of orders. We define the term order as a sequence of objects that are sorted according to some property, such as size, preference, or price. These orders are useful for, say, carrying out a sensory survey. We propose a method called the k-o’means method, which is a modified version of a k-means method, adjusted to handle orders. We compared our method with the traditional clustering methods, and analyzed its characteristics. We also applied our method to a questionnaire survey data on people’s preferences in types of sushi (a Japanese food).
Analysis and Design of Fully Integrated Planar Magnetics for Primary–Parallel Isolated Boost Converter

A highly efficient planar integrated magnetic (PIM) design approach for primary-parallel isolated boost converters is presented. All magnetic components in the converter, including two input inductors and two transformers with primary-parallel and secondary-series windings, are integrated into an E-I-E-core geometry, reducing the total ferrite volume and core loss. The transformer windings are symmetrically distributed into the outer legs of E-cores, and the inductor windings are wound on the center legs of E-cores with air gaps. Therefore, the inductor and the transformer can be operated independently. Due to the low-reluctance path provided by the shared I-core, the two input inductors can be integrated independently, and also, the two transformers can be partially coupled to each other. Detailed characteristics of the integrated structure have been studied in this paper. AC losses in the windings and the leakage inductance of the transformer are kept low by interleaving the primary and secondary turns of the transformers substantially. Because of the combination of inductors and transformers, the maximum output power capability of the fully integrated module needs to be investigated. Winding loss, core loss, and switching loss of MOSFETs are analyzed in-depth in this work as well. To verify the validity of the design approach, a 2-kW prototype converter with two primary power stages is implemented for fuel-cell-fed traction applications with 20-50-V input and 400-V output. An efficiency of 95.9% can be achieved during 1.5-kW nominal operating conditions. Experimental comparisons between the PIM module and three separated cases have illustrated that the PIM module has advantages of lower footprint and higher efficiencies.
Dexterous anthropomorphic robot hand with distributed tactile sensor: Gifu hand II

This paper presents an anthropomorphic robot hand called the Gifu hand II, which has a thumb and four fingers, all the joints of which are driven by servomotors built into the fingers and the palm. The thumb has four joints with four-degrees-of-freedom (DOF); the other fingers have four joints with 3-DOF; and two axes of the joints near the palm cross orthogonally at one point, as is the case in the human hand. The Gifu hand II can be equipped with six-axes force sensor at each fingertip and a developed distributed tactile sensor with 624 detecting points on its surface. The design concepts and the specifications of the Gifu hand II, the basic characteristics of the tactile sensor, and the pressure distributions at the time of object grasping are described and discussed herein. Our results demonstrate that the Gifu hand II has a high potential to perform dexterous object manipulations like the human hand.
Classification of histopathological images using convolutional neural network

In this work, classification of cellular structures in the high resolutional histopathological images and the discrimination of cellular and non-cellular structures have been investigated. The cell classification is a very exhaustive and time-consuming process for pathologists in medicine. The development of digital imaging in histopathology has enabled the generation of reasonable and effective solutions to this problem. Morever, the classification of digital data provides easier analysis of cell structures in histopathological data. Convolutional neural network (CNN), constituting the main theme of this study, has been proposed with different spatial window sizes in RGB color spaces. Hence, to improve the accuracies of classification results obtained by supervised learning methods, spatial information must also be considered. So, spatial dependencies of cell and non-cell pixels can be evaluated within different pixel neighborhoods in this study. In the experiments, the CNN performs superior than other pixel classification methods including SVM and k-Nearest Neighbour (k-NN). At the end of this paper, several possible directions for future research are also proposed.