nils-herrmann/clean_scirepeval huggingface dataset:
Cartography of complex networks: modules and universal roles.

Integrative approaches to the study of complex systems demand that one knows the manner in which the parts comprising the system are connected. The structure of the complex network defining the interactions provides insight into the function and evolution of the components of the system. Unfortunately, the large size and intricacy of these networks implies that such insight is usually difficult to extract. Here, we propose a method that allows one to systematically extract and display information contained in complex networks. Specifically, we demonstrate that one can (i) find modules in complex networks and (ii) classify nodes into universal roles according to their pattern of within- and between-module connections. The method thus yields a 'cartographic representation' of complex networks.
Epileptic Spike Detection Using a Kalman Filter Based Approach

The electroencephalogram (EEG) consists of an underlying background process with superimposed transient nonstationarities such as epileptic spikes (ESs). The detection of ESs in the EEG is of particular importance in the diagnosis of epilepsy. In this paper a new approach for detecting ESs in EEG recordings is presented. It is based on a time-varying autoregressive model (TVAR) that makes use of the nonstationarities of the EEG signal. The autoregressive (AR) parameters are estimated via Kalman filtering (KF). In our method, the EEG signal is first preprocessed to accentuate ESs and attenuate background activity, and then passed through a thresholding function to determine ES locations. The proposed method is evaluated using simulated signals as well as real inter-ictal EEGs
Analysis and Optimization of Back EMF Waveform of a Flux-Switching Permanent Magnet Motor

Flux-switching permanent magnet (FSPM) motors have a doubly salient structure, the magnets being housed in the stator and the stator winding comprising concentrated coils. They have attracted considerable interest due to their essentially sinusoidal phase back electromotive force (EMF) waveform. However, to date, the inherent nature of this desirable feature has not been investigated in detail. Thus, a typical three-phase FSPM motor with 12 stator teeth and ten rotor poles is considered. It is found that, since there is a significant difference in the magnetic flux paths associated with the coils of each phase, this results in harmonics in the coil back EMF waveforms being cancelled, resulting in essentially sinusoidal phase back EMF waveforms. In addition, the influence of the rotor pole-arc on the phase back EMF waveform is evaluated by finite-element analysis, and an optimal pole-arc for minimum harmonic content in the back EMF is obtained and verified experimentally.
A convolutional neural network approach for acoustic scene classification

This paper presents a novel application of convolutional neural networks (CNNs) for the task of acoustic scene classification (ASC). We here propose the use of a CNN trained to classify short sequences of audio, represented by their log-mel spectrogram. We also introduce a training method that can be used under particular circumstances in order to make full use of small datasets. The proposed system is tested and evaluated on three different ASC datasets and compared to other state-of-the-art systems which competed in the “Detection and Classification of Acoustic Scenes and Events” (DCASE) challenges held in 20161 and 2013. The best accuracy scores obtained by our system on the DCASE 2016 datasets are 79.0% (development) and 86.2% (evaluation), which constitute a 6.4% and 9% improvements with respect to the baseline system. Finally, when tested on the DCASE 2013 evaluation dataset, the proposed system manages to reach a 77.0% accuracy, improving by 1% the challenge winner's score.
A Tone Mapping Algorithm for High Contrast Images

A new method is presented that takes as an input a high dynamic range image and maps it into a limited range of luminance values reproducible by a display device. There is significant evidence that a similar operation is performed by early stages of human visual system (HVS). Our approach follows functionality of HVS without attempting to construct its sophisticated model. The operation is performed in three steps. First, we estimate local adaptation luminance at each point in the image. Then, a simple function is applied to these values to compress them into the required display range. Since important image details can be lost during this process, we then re-introduce details in the final pass over the image.
Using Paxos to Build a Scalable, Consistent, and Highly Available Datastore

Spinnaker is an experimental datastore that is designed to run on a large cluster of commodity servers in a single datacenter. It features key-based range partitioning, 3-way replication, and a transactional get-put API with the option to choose either strong or timeline consistency on reads. This paper describes Spinnaker’s Paxos-based replication protocol. The use of Paxos ensures that a data partition in Spinnaker will be available for reads and writes as long a majority of its replicas are alive. Unlike traditional master-slave replication, this is true regardless of the failure sequence that occurs. We show that Paxos replication can be competitive with alternatives that provide weaker consistency guarantees. Compared to an eventually consistent datastore, we show that Spinnaker can be as fast or even faster on reads and only 5% to 10% slower on writes.
A single sensor based PFC Zeta converter Fed BLDC motor drive for fan applications

A novel PFC (Power Factor Corrected) Converter using Zeta DC-DC converter feeding a BLDC (Brush Less DC) motor drive using a single voltage sensor is proposed for fan applications. A single phase supply followed by an uncontrolled bridge rectifier and a Zeta DC-DC converter is used to control the voltage of a DC link capacitor which is lying between the Zeta converter and a VSI (Voltage Source Inverter). Voltage of a DC link capacitor of Zeta converter is controlled to achieve the speed control of BLDC motor. The Zeta converter is working as a front end converter operating in DICM (Discontinuous Inductor Current Mode) and thus using a voltage follower approach. The DC link capacitor of the Zeta converter is followed by a VSI which is feeding a BLDC motor. A sensorless control of BLDC motor is used to eliminate the requirement of Hall Effect position sensors. A MATLAB/Simulink environment is used to simulate the developed model to achieve a wide range of speed control with high PF (power Factor) and improved PQ (Power Quality) at the supply.
Investigating the application of moving target defenses to network security

This paper presents a preliminary design for a moving-target defense (MTD) for computer networks to combat an attacker's asymmetric advantage. The MTD system reasons over a set of abstract models that capture the network's configuration and its operational and security goals to select adaptations that maintain the operational integrity of the network. The paper examines both a simple (purely random) MTD system as well as an intelligent MTD system that uses attack indicators to augment adaptation selection. A set of simulation-based experiments show that such an MTD system may in fact be able to reduce an attacker's success likelihood. These results are a preliminary step towards understanding and quantifying the impact of MTDs on computer networks.
Stock portfolio selection using learning-to-rank algorithms with news sentiment

In this study, we apply learning-to-rank algorithms to design trading strategies using relative performance of a group of stocks based on investors’ sentiment toward these stocks. We show that learning-to-rank algorithms are effective in producing reliable rankings of the best and the worst performing stocks based on investors’ sentiment. More specifically, we use the sentiment shock and trend indicators introduced in the previous studies, and we design stock selection rules of holding long positions of the top 25% stocks and short positions of the bottom 25% stocks according to rankings produced by learning-to-rank algorithms. We then apply two learning-to-rank algorithms, ListNet and RankNet, in stock selection processes and test long-only and long-short portfolio selection strategies using 10 years of market and news sentiment data. Through backtesting of these strategies from 2006 to 2014, we demonstrate that our portfolio strategies produce risk-adjusted returns superior to the S&P500 index return, the hedge fund industry average performance HFRIEMN, and some sentiment-based approaches without learning-to-rank algorithm during the same period.
The Web of Things : A Survey

In the vision of the Internet of Things (IoT), an increasing number of embedded devices of all sorts (e.g., sensors, mobile phones, cameras, smart meters, smart cars, traffic lights, smart home appliances, etc.) are now capable of communicating and sharing data over the Internet. Although the concept of using embedded systems to control devices, tools and appliances has been proposed for almost decades now, with every new generation, the ever-increasing capabilities of computation and communication pose new opportunities, but also new challenges. As IoT becomes an active research area, different methods from various points of view have been explored to promote the development and popularity of IoT. One trend is viewing IoT as Web of Things (WoT) where the open Web standards are supported for information sharing and device interoperation. By penetrating smart things into existing Web, the conventional web services are enriched with physical world services. This WoT vision enables a new way of narrowing the barrier between virtual and physical worlds. In this paper, we elaborate the architecture and some key enabling technologies of WoT. Some pioneer open platforms and prototypes are also illustrated. The most recent research results are carefully summarized. Furthermore, many systematic comparisons are made to provide the insight in the evolution and future of WoT. Finally, we point out some open challenging issues that shall be faced and tackled by research community.
Classifying NBA Offensive Plays Using Neural Networks

The amount of raw information available for basketball analytics has been given a great boost with the availability of player tracking data. This facilitates detailed analyses of player movement patterns. In this paper, we focus on the difficult problem of offensive playcall classification. While outstanding individual players are crucial for the success of a team, the strategies that a team can execute and their understanding of the opposing team’s strategies also greatly influence game outcomes. These strategies often involve complex interactions between players. We apply techniques from machine learning to directly process SportVU tracking data, specifically variants of neural networks. Our system can label as many sequences with the correct playcall given roughly double the data a human expert needs with high precision, but takes only a fraction of the time. We also show that the system can achieve good recognition rates when trained on one season and tested on the next.
Compact Antenna for Small Satellite Applications [Antenna Applications Corner]

The combined effort of the Universiti Kebangsaan Malaysia and the Kyushu Institute of Technology (KYUTECH) to develop a small-satellite antenna is presented in this paper. Microstrip antennas offer an ideal solution to satellite communication requirements due to their light weight and low profile. In this paper, a compact single-layer coaxial-probe-fed circularly polarized high-gain patch antenna designed for HORYU-IV nanosatellite S-band communication is presented. HORYU-IV aims to acquire data about high-voltage discharge phenomena in low Earth orbit (LEO). This will enhance the understanding of satellite charging, and overall satellite reliability can be improved for future high-power space programs. The proposed antenna consists of four asymmetric V-shaped slits, i.e., one at each corner of a rectangular patch, and a parasitic rectangular strip. The proposed antenna achieves a sufficient beamwidth for LEO satellite application and less than 3-dB axial ratio for the entire field of view. A prototype of the antenna was developed with a 1.57-mm-thick singlelayer Rogers substrate with a relative permittivity of 2.2, and the measured results are consistent with the simulation.
Non-Interactive Verifiable Computing: Outsourcing Computation to Untrusted Workers

We introduce and formalize the notion of Verifiable Computation , which enables a computationally weak client to “outsource” the computation of a functio n F on various dynamically-chosen inputs x1, ...,xk to one or more workers. The workers return the result of the fu nction evaluation, e.g., yi = F(xi), as well as a proof that the computation of F was carried out correctly on the given value xi . The primary constraint is that the verification of the proof should requi re substantially less computational effort than computingF(xi) from scratch. We present a protocol that allows the worker to return a compu tationally-sound, non-interactive proof that can be verified inO(m· poly(λ)) time, wherem is the bit-length of the output of F , andλ is a security parameter. The protocol requires a one-time pr e-processing stage by the client which takes O(|C| · poly(λ)) time, whereC is the smallest known Boolean circuit computing F . Unlike previous work in this area, our scheme also provides (at no additional cost) input and output privacy for the client, meaning that the workers do not learn any information about t hexi or yi values.
Performance characterization and acceleration of in-memory file systems for Hadoop and Spark applications on HPC clusters

For data-intensive computing, the low throughput of the existing disk-bound storage systems is a major bottleneck. Recent emergence of the in-memory file systems with heterogeneous storage support mitigates this problem to a great extent. Parallel programming frameworks, e.g. Hadoop MapReduce and Spark are increasingly being run on such high-performance file systems. However, no comprehensive study has been done to analyze the impacts of the in-memory file systems on various Big Data applications. This paper characterizes two file systems in literature, Tachyon [17] and Triple-H [13] that support in-memory and heterogeneous storage, and discusses the impacts of these two architectures on the performance and fault tolerance of Hadoop MapReduce and Spark applications. We present a complete methodology for evaluating MapReduce and Spark workloads on top of in-memory file systems and provide insights about the interactions of different system components while running these workloads. We also propose advanced acceleration techniques to adapt Triple-H for iterative applications and study the impact of different parameters on the performance of MapReduce and Spark jobs on HPC systems. Our evaluations show that, although Tachyon is 5x faster than HDFS for primitive operations, Triple-H performs 47% and 2.4x better than Tachyon for MapReduce and Spark workloads, respectively. Triple-H also accelerates K-Means by 15% over HDFS and 9% over Tachyon.
Leveraging User Interaction Signals for Web Image Search

User interfaces for web image search engine results differ significantly from interfaces for traditional (text) web search results, supporting a richer interaction. In particular, users can see an enlarged image preview by hovering over a result image, and an `image preview' page allows users to browse further enlarged versions of the results, and to click-through to the referral page where the image is embedded. No existing work investigates the utility of these interactions as implicit relevance feedback for improving search ranking, beyond using clicks on images displayed in the search results page. In this paper we propose a number of implicit relevance feedback features based on these additional interactions: hover-through rate, 'converted-hover' rate, referral page click through, and a number of dwell time features. Also, since images are never self-contained, but always embedded in a referral page, we posit that clicks on other images that are embedded on the same referral webpage as a given image can carry useful relevance information about that image. We also posit that query-independent versions of implicit feedback features, while not expected to capture topical relevance, will carry feedback about the quality or attractiveness of images, an important dimension of relevance for web image search. In an extensive set of ranking experiments in a learning to rank framework, using a large annotated corpus, the proposed features give statistically significant gains of over 2% compared to a state of the art baseline that uses standard click features.
Solving LEGO brick layout problem using Evolutionary Algorithms

LEGO® presented the following problem at the SCAI’01 conference in February 2001: Given any 3D body, how can it be built from LEGO bricks? We apply methods of Evolutionary Algorithms (EA) to solve this optimization problem. For this purpose several specific operators are defined, applied, and their use is compared. In addition, mutation operators with dynamic rate of mutation updated based on their contribution to progress of evolution are proposed. Different population organization strategies are compared. Early results indicate that EA is suitable for solving this hard optimization problem.
Fusion of color images and LiDAR data for lane classification

Lane classification is a fundamental problem for autonomous driving and map-aided localization. Many existing algorithms rely on special designed 1D or 2D filters to extract features of lane markings from either color images or LiDAR data. However, these handcrafted features could not be robust under various driving and lighting conditions.
 In this paper, we propose a novel algorithm to fuse color images and LiDAR data together. Our algorithm consists of two stages. In the first stage, we segment road surfaces and register LiDAR data with the corresponding color images. In the second stage, we train convolutional neural networks (CNNs) to classify image patches into lane markings and non-markings. Comparing with the algorithms based on handcrafted features, our algorithm learns a set of kernels to extract and integrate features from two different modalities. The pixel-level classification rate in our experiments shows that our algorithm is robust to different conditions such as shadows and occlusions.
Design and implementation of PLC-based monitoring control system for induction motor

The implementation of a monitoring and control system for the induction motor based on programmable logic controller (PLC) technology is described. Also, the implementation of the hardware and software for speed control and protection with the results obtained from tests on induction motor performance is provided. The PLC correlates the operational parameters to the speed requested by the user and monitors the system during normal operation and under trip conditions. Tests of the induction motor system driven by inverter and controlled by PLC prove a higher accuracy in speed regulation as compared to a conventional V/f control system. The efficiency of PLC control is increased at high speeds up to 95% of the synchronous speed. Thus, PLC proves themselves as a very versatile and effective tool in industrial control of electric drives.
The orbicularis retaining ligament of the medial orbit: closing the circle.

There exists some ambiguity regarding the exact anatomical limits of the orbicularis retaining ligament, particularly its medial boundary in both the superior and inferior orbits. Precise understanding of this anatomy is necessary during periorbital rejuvenation.



Sixteen fresh hemifacial cadaver dissections were performed in the anatomy laboratory to evaluate the anatomy of the orbicularis retaining ligament. Dissection was assisted by magnification with loupes and the operating microscope.



A ligamentous system was found that arises from the inferior and superior orbital rim that is truly periorbital. This ligament spans the entire circumference of the orbit from the medial to the lateral canthus. There exists a fusion line between the orbital septum and the orbicularis retaining ligament in the superior orbit, indistinguishable from the arcus marginalis of the inferior orbital rim. Laterally, the orbicularis retaining ligament contributes to the lateral canthal ligament, consistent with previous studies. No contribution to the medial canthus was identified in this study.



The orbicularis retaining ligament is a true, circumferential "periorbital" structure. This ligament may serve two purposes: (1) to act as a fixation point for the orbicularis muscle of the upper and lower eyelids and (2) to protect the ocular globe. With techniques of periorbital injection with fillers and botulinum toxin becoming ever more popular, understanding the orbicularis retaining ligament's function as a partitioning membrane is mandatory for avoiding ocular complications. As a support structure, examples are shown of how manipulation of this ligament may benefit canthopexy, septal reset, and brow-lift procedures as described by Hoxworth.
A Novel Classification Method for Diagnosis of Diabetes Mellitus Using Artificial Neural Networks

Many real world problems can be solved with Artificial Neural Networks in the areas of pattern recognition, signal processing and medical diagnosis. Most of the medical data set is seldom complete. Artificial Neural Networks require complete set of data for an accurate classification. This paper dwells on the various missing value techniques to improve the classification accuracy. The proposed system also investigates the impact on preprocessing during the classification. A classifier was applied to Pima Indian Diabetes Dataset and the results were improved tremendously when using certain combination of preprocessing techniques. The experimental system achieves an excellent classification accuracy of 99% which is best than before.
A survey of video processing techniques for traffic applications

Video sensors become particularly important in traffic applications mainly due to their fast response, easy installation, operation and maintenance, and their ability to monitor wide areas. Research in several fields of traffic applications has resulted in a wealth of video processing and analysis methods. Two of the most demanding and widely studied applications relate to traffic monitoring and automatic vehicle guidance. In general, systems developed for these areas must integrate, amongst their other tasks, the analysis of their static environment (automatic lane finding) and the detection of static or moving obstacles (object detection) within their space of interest. In this paper we present an overview of image processing and analysis tools used in these applications and we relate these tools with complete systems developed for specific traffic applications. More specifically, we categorize processing methods based on the intrinsic organization of their input data (feature-driven, area-driven, or model-based) and the domain of processing (spatial/frame or temporal/video). Furthermore, we discriminate between the cases of static and mobile camera. Based on this categorization of processing tools, we present representative systems that have been deployed for operation. Thus, the purpose of the paper is threefold. First, to classify image-processing methods used in traffic applications. Second, to provide the advantages and disadvantages of these algorithms. Third, from this integrated consideration, to attempt an evaluation of shortcomings and general needs in this field of active research.