nils-herrmann/clean_scirepeval huggingface dataset:
SiC solid state circuit breaker with an adjustable current-time tripping profile

This paper reports a SiC-based solid-state circuit breaker (SSCB) with an adjustable current-time (I-t) tripping profile for both ultrafast short circuit protection and overload protection. The tripping time ranges from 0.5 microsecond to 10 seconds for a fault current ranging from 0.8X to 10X of the nominal current. The I-t tripping profile, adjustable by choosing different resistance values in the analog control circuit, can help avoid nuisance tripping of the SSCB due to inrush transient current. The maximum thermal capability of the 1200V SiC JFET static switch in the SSCB is investigated to set a practical thermal limit for the I-t tripping profile. Furthermore, a low fault current ‘blind zone’ limitation of the prior SSCB design is discussed and a new circuit solution is proposed to operate the SSCB even under a low fault current condition. Both simulation and experimental results are reported.
FOR MUSIC AUTO-TAGGING USING RAW WAVEFORMS

Recent work has shown that the end-to-end approach using convolutional neural network (CNN) is effective in various types of machine learning tasks. For audio signals, the approach takes raw waveforms as input using an 1-D convolution layer. In this paper, we improve the 1-D CNN architecture for music auto-tagging by adopting building blocks from state-of-the-art image classification models, ResNets and SENets, and adding multi-level feature aggregation to it. We compare different combinations of the modules in building CNN architectures. The results show that they achieve significant improvements over previous state-of-the-art models on the MagnaTagATune dataset and comparable results on Million Song Dataset. Furthermore, we analyze and visualize our model to show how the 1-D CNN operates.
A technique for orthogonal frequency division multiplexing frequency offset correction

HE TECHNIQUE described in this paper has been deT veloped to correct frequency offset errors in digital communications systems employing orthogonal frequency division multiplexing (OFDM) as the method of modulation. The aim of the paper is twofold; to show the effect offset errors have on the signal-to-noise ratio of the OFDM carriers and to present an algorithm to estimate offset so that it may be removed prior to demodulhhn. OFDM is a bandwidth efficient signalling scheme for digital communications that was first proposed by Chang [l]. The main difference bepeen frequency division multiplexing (FDM) and OFDM, is that in OFDM the spectrum of the individual carriers mutually overlap, giving therefore an optimun spectrum efficiency (asymptotically Q b/Hz for 2Q-ary modulation of each carrier). Nevertheless, the OFDM carriers exhibit orthogonality on a symbol interval if synthesized such that they are spaced in frequency exactly at the reciprocal of the symbol interval. Fortunately, this synthesis can be accomplished perfectly, in principle, utilizing the discrete Fourier ttansform (dft) as first described by Darlington  and later, for data modems, by Weinstein and Ebert . With the recent evolution of integrated circuit digital signal processing (dsp) chips, OFDM has become practical to implement and
Computing Web Page Importance without Storing the Graph of the Web (extended abstract)

The computation of page importance in a huge dynamic graph has recently attracted a lot of attention because of the web. Page importance or page rank is defined as the fixpoint of a matrix equation. Previous algorithms compute it off-line and require the use of a lot of extra CPU as well as disk resources in particular to store and maintain the link matrix of the web. We briefly discuss a new algorithm that works on-line, and uses much less resources. In particular, it does not require storing the link matrix. It is on-line in that it continuously refines its estimate of page importance while the web/graph is visited. When the web changes, page importance changes as well. We modify the algorithm so that it adapts dynamically to changes of the web. We report on experiments on web data and on synthetic data.
Simultaneous merging multiple grid maps using the robust motion averaging

Mapping in the GPS-denied environment is an important and challenging task in the field of robotics. In the large environment, mapping can be significantly accelerated by multiple robots exploring different parts of the environment. Accordingly, a key problem is how to integrate these local maps built by different robots into a single global map. In this paper, we propose an approach for simultaneous merging of multiple grid maps by the robust motion averaging. The main idea of this approach is to recover all global motions for map merging from a set of relative motions. Therefore, it firstly adopts the pair-wise map merging method to estimate relative motions for grid map pairs. To obtain as many reliable relative motions as possible, a graph-based sampling scheme is utilized to efficiently remove unreliable relative motions obtained from the pair-wise map merging. Subsequently, the accurate global motions can be recovered from the set of reliable relative motions by the motion averaging. Experimental results carried on real robot data sets demonstrate that proposed approach can achieve simultaneous merging of multiple grid maps with good performances.
VRID-1: A basic vehicle re-identification dataset for similar vehicles

Vehicle re-identification is a process of recognising a vehicle at different locations. It has attracted increasing amounts of attention due to the rapidly-increasing number of vehicles. Identification of two vehicles of the same model is even more difficult than the identification of identical twin humans. Further-more, there is no vehicle re-identification dataset that considers the interference caused by the presence of other vehicles of the same model. Therefore, to provide a fair comparison and facilitate future research into vehicle re-identification, this paper constructs a new dataset called the vehicle re-identification dataset-1 (1 VRID-1). VRID-1 contains 10,000 images captured in daytime of 1,000 individual vehicles of the ten most common vehicle models. For each vehicle model, there are 100 individual vehicles, and for each of these, there are ten images captured at different locations. The images in VRID-1 were captured by 326 surveillance cameras, and thus there are various vehicles poses and levels of illumination. Yet, it provides images of good enough quality for the evaluation of vehicle re-identification in a practical surveillance environment. In addition, according to the characteristics of vehicle morphology, this paper proposes a deep learning-based method to extract multi-dimensional robust features for vehicle re-identification using convolutional neural networks. Experimental results on the VRID-1 dataset demonstrate that it can deal with interference from vehicles of the same model, and is effective and practical for vehicle re-identification.
Towards a Real-Time Campus-Scale Water Balance Monitoring System

In this paper, we describe the design of a real time water balance monitoring system, suitable for large campuses. The battery operated sensor nodes consist of an ultra-sound level sensor, a 16-bit microcontroller and a sub-gigahertz radio to setup a hub and spoke system. Real time data from the sensors is pushed to a server on the cloud to log as well as perform analytics. Industrial design of the device allows flexible mounting on a variety of tanks. Experimental results from a trial deployment in a medium sized campus are shown to illustrate the usefulness of such a system towards better management of campus water resources.
Congestion Management in Deregulated Power System Using Heuristic Search Algorithms Incorporating Wireless Technology

The impact of restructuring in the field of communication sector has brought an evolutionary change in power sector too. This revolutionary idea has brought about competition in this sector with an aim of reduction in the electricity price. The competitive environment not only benefits the utilities and customers however it kindles some of the technical issues, typical one being the transmission congestion. It is considered to be tenacious since it admonish system security andmay result in inflation of electricity prices effecting in feeblemarket condition.The explication to the dispute of congestion has been furnished in this paper. To minimize the congestion cost, an effective multi objective approach is proposed to endorse generator rescheduling and FACTS technology using a metaheurisitc optimization algorithm, symbiotic organic search algorithm. The choice of most sensitive generators to reschedule real and reactive power is realized using real power transmission congestion distribution factor. The proposed method has been tested on IEEE 14 bus system and IEEE 30 bus system.
The role and significance of potential lipid-binding regions in the mitochondrial protein import motor: an in-depth in silico study

Over the last two decades, an impressive progress has been made in the identification of novel factors in the translocation machineries of the mitochondrial protein import and their possible roles. The role of lipids and possible protein-lipids interactions remains a relatively unexplored territory. Investigating the role of potential lipid-binding regions in the sub-units of the mitochondrial motor might help to shed some more light in our understanding of protein-lipid interactions mechanistically. Bioinformatics results seem to indicate multiple potential lipid-binding regions in each of the sub-units. The subsequent characterization of some of those regions in silico provides insight into the mechanistic functioning of this intriguing and essential part of the protein translocation machinery. Details about the way the regions interact with phospholipids were found by the use of Monte Carlo simulations. For example, Pam18 contains one possible transmembrane region and two tilted surface bound conformations upon interaction with phospholipids. The results demonstrate that the presented bioinformatics approach might be useful in an attempt to expand the knowledge of the possible role of protein-lipid interactions in the mitochondrial protein translocation process.
Development of a Speech-Based Augmented Reality System to Support Exploration of Cityscape

When people explore new environments they often use landmarks as reference points to help navigate and orientate themselves. This research paper examines how spatial datasets can be used to build a system for use in an urban environment which functions as a city guide, announcing Features of Interest (FoI) as they become visible to the user (not just proximal), as the user moves freely around the city. Visibility calculations for the FoIs were pre-calculated based on a digital surface model derived from LIDAR (Light Detection and Ranging) data. The results were stored in a textbased relational database management system (RDBMS) for rapid retrieval. All interaction between the user and the system was via a speech-based interface, allowing the user to record and request further information on any of the announced FoI. A prototype system, called Edinburgh Augmented Reality System (EARS) , was designed, implemented and field tested in order to assess the effectiveness of these ideas. The application proved to be an innovating, ‘non-invasive’ approach to augmenting the user’s reality
Modern Computer Vision Techniques for X-Ray Testing in Baggage Inspection

X-ray screening systems have been used to safeguard environments in which access control is of paramount importance. Security checkpoints have been placed at the entrances to many public places to detect prohibited items, such as handguns and explosives. Generally, human operators are in charge of these tasks as automated recognition in baggage inspection is still far from perfect. Research and development on X-ray testing is, however, exploring new approaches based on computer vision that can be used to aid human operators. This paper attempts to make a contribution to the field of object recognition in X-ray testing by evaluating different computer vision strategies that have been proposed in the last years. We tested ten approaches. They are based on bag of words, sparse representations, deep learning, and classic pattern recognition schemes among others. For each method, we: 1) present a brief explanation; 2) show experimental results on the same database; and 3) provide concluding remarks discussing pros and cons of each method. In order to make fair comparisons, we define a common experimental protocol based on training, validation, and testing data (selected from the public  ${\mathbb {GDX}}$ ray database). The effectiveness of each method was tested in the recognition of three different threat objects: 1) handguns; 2) shuriken (ninja stars); and 3) razor blades. In our experiments, the highest recognition rate was achieved by methods based on visual vocabularies and deep features with more than 95% of accuracy. We strongly believe that it is possible to design an automated aid for the human inspection task using these computer vision algorithms.
Botanical compounds and their regulation of nuclear receptor action: The case of traditional Chinese medicine

Nuclear receptors (NRs) are major pharmacological targets that allow an access to the mechanisms controlling gene regulation. As such, some NRs were identified as biological targets of active compounds contained in herbal remedies found in traditional medicines. We aim here to review this expanding literature by focusing on the informative articles regarding the mechanisms of action of traditional Chinese medicines (TCMs). We exemplified well-characterized TCM action mediated by NR such as steroid receptors (ER, GR, AR), metabolic receptors (PPAR, LXR, FXR, PXR, CAR) and RXR. We also provided, when possible, examples from other traditional medicines. From these, we draw a parallel between TCMs and phytoestrogens or endocrine disrupting chemicals also acting via NR. We define common principle of action and highlight the potential and limits of those compounds. TCMs, by finely tuning physiological reactions in positive and negative manners, could act, in a subtle but efficient way, on NR sensors and their transcriptional network.
Childhood Experience of Care and Abuse Questionnaire (CECA.Q)

Interview measures for investigating adverse childhood experiences, such as the Childhood Experience of Care and Abuse (CECA) instrument, are comprehensive and can be lengthy and time-consuming. A questionnaire version of the CECA (CECA.Q) has been developed which could allow for screening of individuals in research settings. This would enable researchers to identify individuals with adverse early experiences who might benefit from an in-depth interview. This paper aims to validate the CECA.Q against the CECA interview in a clinical population.  One hundred and eight patients attending an affective disorders service were assessed using both the CECA interview and questionnaire measures. A follow-up sample was recruited 3 years later and sent the questionnaire. The questionnaire was also compared with the established Parental Bonding Instrument (PBI).  Agreement between ratings on the interview and questionnaire were high. Scales measuring antipathy and neglect also correlated highly with the PBI. The follow-up sample revealed the questionnaire to have a high degree of reliability over a long period of time.  The CECA.Q appears to be a reliable and valid measure which can be used in research on clinical populations to screen for individuals who have experienced severe adversity in childhood.
The effects of high- and moderate-resistance training on muscle function in the elderly.

The purpose of this study was to investigate the effects of a 12-week resistance-training program on muscle strength and mass in older adults. Thirty-three inactive participants (60-74 years old) were assigned to 1 of 3 groups: high-resistance training (HT), moderate-resistance training (MT), and control. After the training period, both HT and MT significantly increased 1-RM body strength, the peak torque of knee extensors and flexors, and the midthigh cross-sectional area of the total muscle. In addition, both HT and MT significantly decreased the abdominal circumference. HT was more effective in increasing 1-RM strength, muscle mass, and peak knee-flexor torque than was MT. These data suggest that muscle strength and mass can be improved in the elderly with both high- and moderate-intensity resistance training, but high-resistance training can lead to greater strength gains and hypertrophy than can moderate-resistance training.
IoT based parking system using Google

Today's world is of internet. Internet is a thing which is making our lives easier as well as faster. The use of smart phones has tremendous applications. The IoT (Internet of things) platforms allow us to connect the sensors with the internet. Hence using this we can implement many applications. The parking system is one of them. There are many problems not only in India but also in rest of the world because of unplanned parking. Hence we are trying to introduce a parking system based on IoT to make the travelling comfortable. The android applications are very user friendly interfaces can give better results for parking places although the Google maps are the key element of this system. From these maps not only user can locate the available spaces but also can find the direction for it. This system will show the efficient place for parking and it will also direct to reach there. This can reduce accidents as well as pollution.
Personal cloud storage: Usage, performance and impact of terminals

Personal cloud storage services such as Dropbox and OneDrive are popular among Internet users. They help in sharing content and backing up data by relying on the cloud to store files. The rise of mobile terminals and the presence of new providers question whether the usage of cloud storage is evolving. This knowledge is essential to understand the workload these services need to handle, their performance, and implications. In this paper we present a comprehensive characterization of personal cloud storage services. Relying on traces collected for one month in an operational network, we show that users of each service present distinct behaviors. Dropbox is now threatened by competitors, with OneDrive and Google Drive reaching large market shares. However, the popularity of the latter services seems to be driven by their integration into Windows and Android. Indeed, around 50% of their users do not produce any workload. Considering performance, providers show distinct trade-offs, with bottlenecks that hardly allow users to fully exploit their access line bandwidth. Finally, usage of cloud services is now ordinary among mobile users, thanks to the automatic backup of pictures and media files.
Analysis of navigability of Web applications for improving blind usability

Various accessibility activities are improving blind access to the increasingly indispensable WWW. These approaches use various metrics to measure the Web's accessibility. “Ease of navigation” (navigability) is one of the crucial factors for blind usability, especially for complicated webpages used in portals and online shopping sites. However, it is difficult for automatic checking tools to evaluate the navigation capabilities even for a single webpage. Navigability issues for complete Web applications are still far beyond their capabilities.
 This study aims at obtaining quantitative results about the current accessibility status of real world Web applications, and analyzes real users' behavior on such websites. In Study 1, an automatic analysis method for webpage navigability is introduced, and then a broad survey using this method for 30 international online shopping sites is described. The next study (Study 2) focuses on a fine-grained analysis of real users' behavior on some of these online shopping sites. We modified a voice browser to record each user's actions and the information presented to that user. We conducted user testing on existing sites with this tool. We also developed an analysis and visualization method for the recorded information. The results showed us that users strongly depend on scanning navigation instead of logical navigation. A landmark-oriented navigation model was proposed based on the results. Finally, we discuss future possibilities for improving navigability, including proposals for voice browsers.
Visual crowding: a fundamental limit on conscious perception and object recognition

Crowding, the inability to recognize objects in clutter, sets a fundamental limit on conscious visual perception and object recognition throughout most of the visual field. Despite how widespread and essential it is to object recognition, reading and visually guided action, a solid operational definition of what crowding is has only recently become clear. The goal of this review is to provide a broad-based synthesis of the most recent findings in this area, to define what crowding is and is not, and to set the stage for future work that will extend our understanding of crowding well beyond low-level vision. Here we define six diagnostic criteria for what counts as crowding, and further describe factors that both escape and break crowding. All of these lead to the conclusion that crowding occurs at multiple stages in the visual hierarchy.
State of Charge and State of Health Estimation for Lithium Batteries Using Recurrent Neural Networks

This paper presents an application of dynamically driven recurrent networks (DDRNs) in online electric vehicle (EV) battery analysis. In this paper, a nonlinear autoregressive with exogenous inputs (NARX) architecture of the DDRN is designed for both state of charge (SOC) and state of health (SOH) estimation. Unlike other techniques, this estimation strategy is subject to the global feedback theorem (GFT) which increases both computational intelligence and robustness while maintaining reasonable simplicity. The proposed technique requires no model or knowledge of battery's internal parameters, but rather uses the battery's voltage, charge/discharge currents, and ambient temperature variations to accurately estimate battery's SOC and SOH simultaneously. The presented method is evaluated experimentally using two different batteries namely lithium iron phosphate (<inline-formula> <tex-math notation="LaTeX">$\text{LiFePO}_4$</tex-math></inline-formula>) and lithium titanate (<inline-formula> <tex-math notation="LaTeX">$\text{LTO}$</tex-math></inline-formula>) both subject to dynamic charge and discharge current profiles and change in ambient temperature. Results highlight the robustness of this method to battery's nonlinear dynamic nature, hysteresis, aging, dynamic current profile, and parametric uncertainties. The simplicity and robustness of this method make it suitable and effective for EVs’ battery management system (BMS).
Hiding stars with fireworks: location privacy through camouflage

Individuals face privacy risks when providing personal location data to potentially untrusted location based services (LBSs). We develop and demonstrate CacheCloak, a system that enables realtime anonymization of location data. In CacheCloak, a trusted anonymizing server generates mobility predictions from historical data and submits intersecting predicted paths simultaneously to the LBS. Each new predicted path is made to intersect with other users' paths, ensuring that no individual user's path can be reliably tracked over time. Mobile users retrieve cached query responses for successive new locations from the trusted server, triggering new prediction only when no cached response is available for their current locations. A simulated hostile LBS with detailed mobility pattern data attempts to track users of CacheCloak, generating a quantitative measure of location privacy over time. GPS data from a GIS-based traffic simulation in an urban environment shows that CacheCloak can achieve realtime location privacy without loss of location accuracy or availability.