nils-herrmann/clean_scirepeval huggingface dataset:
Single-Stage Soft-Switching Converter With Boost Type of Active Clamp for Wide Input Voltage Ranges

A single-stage soft-switching converter is proposed for universal line voltage applications. A boost type of active-clamp circuit is used to achieve zero-voltage switching operation of the power switches. A simple DC-link voltage feedback scheme is applied to the proposed converter. A resonant voltage-doubler rectifier helps the output diodes to achieve zero-current switching operation. The reverse-recovery losses of the output diodes can be eliminated without any additional components. The DC-link capacitor voltage can be reduced, providing reduced voltage stresses of switching devices. Furthermore, power conversion efficiency can be improved by the soft-switching operation of switching devices. The performance of the proposed converter is evaluated on a 160-W (50 V/3.2 A) experimental prototype. The proposed converter complies with International Electrotechnical Commission (IEC) 1000-3-2 Class-D requirements for the light-emitting diode power supply of large-sized liquid crystal displays, maintaining the DC-link capacitor voltage within 400 V under the universal line voltage (90-265 Vrms).
ArcTrees: Visualizing Relations in Hierarchical Data

In this paper we present, ARCTREES, a novel way of visualizing hierarchical and non-hierarchical relations within one interactive visualization. Such a visualization is challenging because it must display hierarchical information in a way that the user can keep his or her mental map of the data set and include relational information without causing misinterpretation. We propose a hierarchical view derived from traditional Treemaps and augment this view with an arc diagram to depict relations. In addition, we present interaction methods that allow the exploration of the data set using Focus+Context techniques for navigation. The development was motivated by a need for understanding relations in structured documents but it is also useful in many other application domains such as project management and calendars.
Learning-Based Compressive MRI

In the area of magnetic resonance imaging (MRI), an extensive range of non-linear reconstruction algorithms has been proposed which can be used with general Fourier subsampling patterns. However, the design of these subsampling patterns has typically been considered in isolation from the reconstruction rule and the anatomy under consideration. In this paper, we propose a learning-based framework for optimizing MRI subsampling patterns for a specific reconstruction rule and anatomy, considering both the noiseless and noisy settings. Our learning algorithm has access to a representative set of training signals, and searches for a sampling pattern that performs well on average for the signals in this set. We present a novel parameter-free greedy mask selection method and show it to be effective for a variety of reconstruction rules and performance metrics. Moreover, we also support our numerical findings by providing a rigorous justification of our framework via statistical learning theory.
Combining Phase Identification and Statistic Modeling for Automated Parallel Benchmark Generation

Parallel application benchmarks are indispensable for evaluating/optimizing HPC software and hardware. However, it is very challenging and costly to obtain high-fidelity benchmarks reflecting the scale and complexity of state-of-the-art parallel applications. Hand-extracted synthetic benchmarks are time- and labor-intensive to create. Real applications themselves, while offering most accurate performance evaluation, are expensive to compile, port, reconfigure, and often plainly inaccessible due to security or ownership concerns. This work contributes APPrime, a novel tool for trace-based automatic parallel benchmark generation. Taking as input standard communication-I/O traces of an application's execution, it couples accurate automatic phase identification with statistical regeneration of event parameters to create compact, portable, and to some degree reconfigurable parallel application benchmarks. Experiments with four NAS Parallel Benchmarks (NPB) and three real scientific simulation codes confirm the fidelity of APPrime benchmarks. They retain the original applications' performance characteristics, in particular their relative performance across platforms. Also, the result benchmarks, already released online, are much more compact and easy-to-port compared to the original applications.
The estimated seroprevalence of HSV-2 Update on the Treatment of Genital Herpes

Genital herpes is a chronic infection characterized by periodic reactivation. It can produce symptomatic disease in the host although asymptomatic viral excretion can also occur. It is currently the main cause of genital ulceration and an important public health problem that has substantial clinical, psychological, and economic repercussions. This review analyzes the currently available therapeutic options and regimens, which are based mainly on systemic use of antiviral agents such as aciclovir, valacyclovir, and famciclovir. In addition, special emphasis is placed on the prevention and management of this infection in specific situations, such as pregnant, pediatric, and immunocompromised patients.
Computational Soundness of Non-Malleable Commitments

This paper aims to find a proper security notion for commitment schemes to give a sound computational interpretation of symbolic commitments. We introduce an indistinguishability based security definition of commitment schemes that is equivalent to non-malleability with respect to commitment. Then, we give a construction using tag-based encryption and one-time signatures that is provably secure assuming the existence of trapdoor permutations. Finally, we apply this new machinery to give a sound interpretation of symbolic commitments in the Dolev-Yao model while considering active adversaries.
A blueprint for introducing disruptive technology into the Internet

This paper argues that a new class of geographically distributed network services is emerging, and that the most effective way to design, evaluate, and deploy these services is by using an overlay-based testbed. Unlike conventional network testbeds, however, we advocate an approach that supports both researchers that want to develop new services, and clients that want to use them. This dual use, in turn, suggests four design principles that are not widely supported in existing testbeds: services should be able to run continuously and access a slice of the overlay's resources, control over resources should be distributed, overlay management services should be unbundled and run in their own slices, and APIs should be designed to promote application development. We believe a testbed that supports these design principles will facilitate the emergence of a new service-oriented network architecture. Towards this end, the paper also briefly describes PlanetLab, an overlay network being designed with these four principles in mind.
A Pilot Study on the Combination of Applied Behavior Analysis and Bumetanide Treatment for Children with Autism.

The purpose of this study was to investigate the therapeutic effects of combined bumetanide and applied behavior analysis (ABA) treatment in children with autism.



Sixty children diagnosed with autism according to the International Classification of Diseases, Tenth Revision (ICD-10) criteria (mean age of 4.5 years) were randomly divided into two groups: A single treatment group (n=28) and a combined treatment group (n=32). The combined treatment group received ABA training combined with oral bumetanide (0.5 mg twice a day). The single treatment group received ABA training only. Autism symptoms were evaluated with the Autism Behavior Checklist (ABC) and the Childhood Autism Rating Scale (CARS), whereas severity of disease (SI) and global improvement (GI) were measured with the Clinical Global Impressions (CGI). Assessment of ABC, CARS, and CGI was performed immediately before and 3 months after initiation of the treatment(s).



Prior to intervention(s) no statistically significant differences in scores on the ABC, CARS, SI, or GI were found between the two groups. Total scores of the ABC, CARS, and SI were decreased in both groups after 3 months (p<0.05) compared with the scores prior to treatment. The total scores of the ABC and the CGI were significantly (p<0.05) lower in the combined treatment group than in the single treatment group. Although the total and item scores of the CARS in the combined treatment group were lower than in the single treatment group after a 3 month intervention, they did not reach statistical significance. No adverse effects of bumetanide were observed.



Treatment with bumetanide combined with ABA training may result in a better outcome in children with autism than ABA training alone.
Learning Answer Embeddings for Visual Question Answering

We propose a novel probabilistic model for visual question answering (Visual QA). The key idea is to infer two sets of embeddings: one for the image and the question jointly and the other for the answers. The learning objective is to learn the best parameterization of those embeddings such that the correct answer has higher likelihood among all possible answers. In contrast to several existing approaches of treating Visual QA as multi-way classification, the proposed approach takes the semantic relationships (as characterized by the embeddings) among answers into consideration, instead of viewing them as independent ordinal numbers. Thus, the learned embedded function can be used to embed unseen answers (in the training dataset). These properties make the approach particularly appealing for transfer learning for open-ended Visual QA, where the source dataset on which the model is learned has limited overlapping with the target dataset in the space of answers. We have also developed large-scale optimization techniques for applying the model to datasets with a large number of answers, where the challenge is to properly normalize the proposed probabilistic models. We validate our approach on several Visual QA datasets and investigate its utility for transferring models across datasets. The empirical results have shown that the approach performs well not only on in-domain learning but also on transfer learning.
LFGraph: simple and fast distributed graph analytics

Distributed graph analytics frameworks must offer low and balanced communication and computation, low preprocessing overhead, low memory footprint, and scalability. We present LFGraph, a fast, scalable, distributed, in-memory graph analytics engine intended primarily for directed graphs. LFGraph is the first system to satisfy all of the above requirements. It does so by relying on cheap hash-based graph partitioning, while making iterations faster by using publish-subscribe information flow along directed edges, fetch-once communication, single-pass computation, and in-neighbor storage. Our analytical and experimental results show that when applied to real-life graphs, LFGraph is faster than the best graph analytics frameworks by factors of 1x--5x when ignoring partitioning time and by 1x--560x when including partitioning time.
GPR91 senses extracellular succinate released from inflammatory macrophages and exacerbates rheumatoid arthritis

When SUCNR1/GPR91-expressing macrophages are activated by inflammatory signals, they change their metabolism and accumulate succinate. In this study, we show that during this activation, macrophages release succinate into the extracellular milieu. They simultaneously up-regulate GPR91, which functions as an autocrine and paracrine sensor for extracellular succinate to enhance IL-1β production. GPR91-deficient mice lack this metabolic sensor and show reduced macrophage activation and production of IL-1β during antigen-induced arthritis. Succinate is abundant in synovial fluids from rheumatoid arthritis (RA) patients, and these fluids elicit IL-1β release from macrophages in a GPR91-dependent manner. Together, we reveal a GPR91/succinate-dependent feed-forward loop of macrophage activation and propose GPR91 antagonists as novel therapeutic principles to treat RA.
Survey on Automatic Number Plate Recognition (ANR)

Day by day we have been heard about the news of vehicle getting stolen from parking or from any other place in the city. So, to keep track of that stolen vehicle we should have to install the CCTV camera on every signal in the city. Also we have to install the number plate detection system which can detect the number plate of every vehicle on the traffic signal. For detecting the number plate from the moving vehicle there are many algorithm has been developed but still this area always remain evolving each every year, In number plate detection system image processing plays an important role, the system consist of basic operations preprocessing, image conversion from RGB to Gray, apply edge detection, apply morphological operators on same image then extract plate region from image and last process the plate region using OCR (optical character recognition).Every algorithm in this category always follows this basic steps, each algorithm has some pros and cons, because same algorithm cannot be useful for different environmental condition. The Algorithm’s efficiency is totally depends upon the quality of input image. E.g. resolution of camera, intensity of the image, illumination of image, shadow effect etc. In this paper we are focusing on which are different algorithm has been developed so far to
AMRZone: A Runtime AMR Data Sharing Framework for Scientific Applications

Frameworks that facilitate runtime data sharingacross multiple applications are of great importance for scientificdata analytics. Although existing frameworks work well overuniform mesh data, they can not effectively handle adaptive meshrefinement (AMR) data. Among the challenges to construct anAMR-capable framework include: (1) designing an architecturethat facilitates online AMR data management, (2) achievinga load-balanced AMR data distribution for the data stagingspace at runtime, and (3) building an effective online indexto support the unique spatial data retrieval requirements forAMR data. Towards addressing these challenges to supportruntime AMR data sharing across scientific applications, wepresent the AMRZone framework. Experiments over real-worldAMR datasets demonstrate AMRZone's effectiveness at achievinga balanced workload distribution, reading/writing large-scaledatasets with thousands of parallel processes, and satisfyingqueries with spatial constraints. Moreover, AMRZone's performance and scalability are even comparable with existing state-of-the-art work when tested over uniform mesh data with up to16384 cores, in the best case, our framework achieves a 46% performance improvement.
Implementation of Smart Restaurant with e-menu Card

Today&apos;s era is said to be the world of technology. So many efforts have been taken by restaurants owners also to adopt information and communication technologies such as PDA, wireless LAN, costly multi-touch screens etc. to enhance dining experience. This paper highlights some of the limitations of the conventional paper based and PDA-based food ordering system and proposed the low cost touch screen based Restaurant Management System using an android Smartphone or tablet as a solution. The system consists of a Smartphone/tablet at the customer table contains the android application with all the menu details. The customer tablet, kitchen display connects directly with each other through Wi-Fi. Orders made by the customers will be instantly reach the kitchen module. This wireless application is user-friendly, improves efficiency and accuracy for restaurants by saving time, reduces human errors and provides customer feedback. This system successfully overcomes the drawbacks in earlier automated food ordering systems and is less expensive as it requires a one-time investment for gadgets.
Search Engine Guided Neural Machine Translation

In this paper, we extend an attention-based neural machine translation (NMT) model by allowing it to access an entire training set of parallel sentence pairs even after training. The proposed approach consists of two stages. In the first stage– retrieval stage–, an off-the-shelf, black-box search engine is used to retrieve a small subset of sentence pairs from a training set given a source sentence. These pairs are further filtered based on a fuzzy matching score based on edit distance. In the second stage–translation stage–, a novel translation model, called search engine guided NMT (SEG-NMT), seamlessly uses both the source sentence and a set of retrieved sentence pairs to perform the translation. Empirical evaluation on three language pairs (En-Fr, En-De, and En-Es) shows that the proposed approach significantly outperforms the baseline approach and the improvement is more significant when more relevant sentence pairs were retrieved.
Automated timetabling for small colleges and high schools using huge integer programs

We formulate an integer program to solve a highly constrained academic timetabling problem at the United States Merchant Marine Academy. The IP instance that results from our real case study has approximately both 170,000 rows and columns and solves to optimality in 4–24 hours using a commercial solver on a portable computer (near optimal feasible solutions were often found in 4–12 hours). Our model is applicable to both high schools and small colleges who wish to deviate from group scheduling. We also solve a necessary preprocessing student subgrouping problem, which breaks up big groups of students into small groups so they can optimally fit into small capacity classes.
Calibration of a rotating multi-beam lidar

This paper presents a technique for the calibration of multi-beam laser scanners. The technique is based on an optimization process, which gives precise estimation of calibration parameters starting from an initial estimate. The optimization process is based on the comparison of scan data with the ground truth environment. Detailed account of the optimization process and suitability analysis of optimization objective function is described, and results are provided to show the efficacy of calibration technique.
A Novel Approach For Face Liveness Detection To Avoid Face Spoofing Attacks

Face Liveness Detection To Avoid Face Spoofing Attacks Meenakshi Research Scholar, Department of Computer Science & Applications, Kurukshetra University, Kurukshetra-136119 Email: minakshisaini26@gmail.com Dr. Chander Kant Assistant Professor, Department of Computer Science & Applications, Kurukshetra University, Kurukshetra-136119 Email: ckverma@rediffmail.com --------------------------------------------------------------------ABSTRACT---------------------------------------------------------Face recognition is one of the most commonly used biometric technology to recognize an individual from a digital image or a video frame from a video source. Face recognition is more user friendly and cost-effective solution than other biometric technologies. Face recognition system can be spoofed by placing photo/video/mask or dummy face of the enrolled user in front of the camera. In order to enhance the security of system and minimize the spoofing attacks, liveness detection is integrated within the system and is added just before the face recognition module. The aim of liveness detection is to verify that the data is from a live user and is not generated by the artificial sources. In the proposed approach, the facial features of human face such as eye blinking and lip movement are used for liveness detection. User is asked to perform some activities such as eye movement and lip movement according to some random challenge generated by the system and then sequence of images are captured by the camera. After that, the facial features are located and variations in these facial features are computed. If the variations are greater than threshold value and also, the given challenge is equal to the desired response, then the user is considered as a “Live User” otherwise it is considered as a “Fake User”.
A Report from the U.S. National Science Foundation Blue Ribbon Panel on Cyberinfrastructure

This talk will review the mission, activities, and recommendations of the Blue Ribbon Panel on Cyberinfrastructure recently appointed by the leadership on the U.S. National Science Foundation (NSF). The NSF invests in people, ideas, and tools and in particular is a major investor in basic research to produce communication and information technology (ICT) as well as its use in supporting basic research and education in most all areas of science and engineering. The NSF through its Directorate for Computer and Information Science and Engineering (CISE) has provided substantial funding for high-end computing resources, initially by awards to five supercomputer centers and later through $70 M per year investments in two partnership alliances for advanced computation infrastructures centered at the University of Illinois and the University of California, San Diego. It has also invested in an array of complementary R&D initiatives in networking, middleware, digital libraries, collaboratories, computational and visualization science, and distributed terascale grid environments.
Distributed Data Mining in Credit Card FraudDetectionPhilip

Credit card transactions continue to grow in number, taking a larger share of the US payment system, and have led to a higher rate of stolen account numbers and subsequent losses by banks. Hence, improved fraud detection has become essential to maintain the viability of the US payment system. Banks have been elding early fraud warning systems for some years. We seek to improve upon the state-of-the-art in commercial practice via large scale data mining. Scalable techniques to analyze massive amounts of transaction data to compute eecient fraud detectors in a timely manner is an important problem, especially for e-commerce. Besides scalability and eeciency, the fraud detection task exhibits technical problems that include skewed distributions of training data and non-uniform cost per error, both of which have not been widely studied in the KDD/DM community. In this article we survey and evaluate a number of techniques that we have proposed and implemented that address these three main issues concurrently. Our proposed methods of combining multiple learned fraud detectors under a \cost model" are general and demonstrably useful; our empirical results demonstrate that we can signiicantly reduce loss due to fraud through distributed data mining of fraud models.
A Survey of Deep Learning: Platforms, Applications and Emerging Research Trends

Deep learning has exploded in the public consciousness, primarily as predictive and analytical products suffuse our world, in the form of numerous human-centered smart-world systems, including targeted advertisements, natural language assistants and interpreters, and prototype self-driving vehicle systems. Yet to most, the underlying mechanisms that enable such human-centered smart products remain obscure. In contrast, researchers across disciplines have been incorporating deep learning into their research to solve problems that could not have been approached before. In this paper, we seek to provide a thorough investigation of deep learning in its applications and mechanisms. Specifically, as a categorical collection of state of the art in deep learning research, we hope to provide a broad reference for those seeking a primer on deep learning and its various implementations, platforms, algorithms, and uses in a variety of smart-world systems. Furthermore, we hope to outline recent key advancements in the technology, and provide insight into areas, in which deep learning can improve investigation, as well as highlight new areas of research that have yet to see the application of deep learning, but could nonetheless benefit immensely. We hope this survey provides a valuable reference for new deep learning practitioners, as well as those seeking to innovate in the application of deep learning.