nils-herrmann/clean_scirepeval huggingface dataset:
MEAL: Multi-Model Ensemble via Adversarial Learning

Often the best performing deep neural models are ensembles of multiple base-level networks. Unfortunately, the space required to store these many networks, and the time required to execute them at test-time, prohibits their use in applications where test sets are large (e.g., ImageNet). In this paper, we present a method for compressing large, complex trained ensembles into a single network, where knowledge from a variety of trained deep neural networks (DNNs) is distilled and transferred to a single DNN. In order to distill diverse knowledge from different trained (teacher) models, we propose to use adversarial-based learning strategy where we define a block-wise training loss to guide and optimize the predefined student network to recover the knowledge in teacher models, and to promote the discriminator network to distinguish teacher vs. student features simultaneously. The proposed ensemble method (MEAL) of transferring distilled knowledge with adversarial learning exhibits three important advantages: (1) the student network that learns the distilled knowledge with discriminators is optimized better than the original model; (2) fast inference is realized by a single forward pass, while the performance is even better than traditional ensembles from multi-original models; (3) the student network can learn the distilled knowledge from a teacher model that has arbitrary structures. Extensive experiments on CIFAR-10/100, SVHN and ImageNet datasets demonstrate the effectiveness of our MEAL method. On ImageNet, our ResNet-50 based MEAL achieves top1/5 21.79%/5.99% val error, which outperforms the original model by 2.06%/1.14%. Code and models are available at: https://github.com/AaronHeee/MEAL.
Real-Time Big Data Processing Framework: Challenges and Solutions

Data type and amount in human society is growing in amazing sp eed which is caused by emerging new services as cloud computing, internet of things and location-based services , th era of big data has arrived. As data has been fundamental r source, how to manage and utilize big data better has attracted much attent ion. Especially, with the development of internet of things , how to processing large amount real-time data has become a great challenge in r esearch and applications. Recently, cloud computing techn ology has attracted much attention with high-performance, but how to use cloud computing technology for large-scale real-time d ata processing has not been studied. This paper studied the challenges of bi g data firstly and concludes all these challenges into six iss ues. In order to improve the performance of real-time processing of large data, this paper builds a kind of real-time big data processi ng (RTDP) architecture based on the cloud computing technology and th en proposed the four layers of the architecture, and hierarc hical computing model. This paper proposed a multi-level storage model and t he LMA-based application deployment method to meet the real -time and heterogeneity requirements of RTDP system. We use DSMS, CEP batch-based MapReduce and other processing mode and FPGA, GPU, CPU, ASIC technologies differently to processing the d ata at the terminal of data collection. We structured the dat a and then upload to the cloud server and MapReduce the data combined wi th the powerful computing capabilities cloud architecture . This paper points out the general framework for future RTDP system and c alculation methods, is currently the general method RTDP sy stem
A 16 Gb/s receiver with DC wander compensated rail-to-rail AC coupling and passive linear-equalizer in 22 nm CMOS

A 16 Gb/s receiver implemented in 22 nm SOI CMOS technology is reported. The analog frontend accepts a rail-to-rail input common-mode imposed from the transmitter side. It consists of a baseline wander compensated passive linear equalizer that AC-couples the received signal to the subsequent active CTLE with a regulated common-mode level. The programmable passive linear equalizer features a frequency response suitable for low-frequency equalization such as for skin-effect losses. When its zero is programmed at 200 MHz minimum frequency, the measured maximum mid-band peaking is 7 dB. The receiver architecture is half-rate and comprises an 8-tap DFE and a baud-rate CDR. With no FFE at the transmitter, 0.9 Vppd PRBS31 NRZ data are recovered error-free (BER<;10-12) across a copper channel with 34 dB attenuation at 8 GHz.
A droop control strategy of parallel-inverter-based microgrid

In this paper, the control strategy for a parallel-inverters-based microgrid is presented. The control strategy includes outer active and reactive power control loop based on frequency and voltage droop method to avoid critical communications among distributed generation units (DGs). The inner inverter control loop is employed to produce the required inverter output voltage provided by the outer power loop. In addition, two inverter control schemes are introduced and compared. This microgrid can operate at both grid-connected and islanded mode with proper power sharing capability between parallel DG systems. Moreover, smooth transition between operating modes is achieved without causing negative effect on the utility and critical loads. The performance of this control strategy is verified in simulation using Matlab/Simulink.
06-025 Multi-agent reinforcement learning : A survey ∗

Multi-agent systems are rapidly finding applications in a variety of domains, including robotics, distributed control, telecommunications, economics. Many tasks arising in these domains require that the agents learn behaviors online. A significant part of the research on multi-agent learning concerns reinforcement learning techniques. However, due to different viewpoints on central issues, such as the formal statement of the learning goal, a large number of different methods and approaches have been introduced. In this paper we aim to present an integrated survey of the field. First, the issue of the multi-agent learning goal is discussed, after which a representative selection of algorithms is reviewed. Finally, open issues are identified and future research directions are outlined. multi-agent systems, reinforcement learning, game theory, distributed control
Feature Clustering for Anomaly Detection Using Improved Fuzzy Membership Function

Earlier research focus towards anomaly detection has been towards using classifiers such as kNN, SVM and using existing distance measures to perform classification. Traditionally IDSs (Intrusion detection systems) have been developed by applying machine learning techniques and adopted single learning mechanism. This is later extended by developing Intrusion Detection Systems by adopting multiple learning mechanisms. Such systems have addressed better detection rates compared to single learning Intrusion Detection Systems. Dimensionality is one more serious concern which affects the performance of classification algorithms. Approaches such as "Feature selection" have been studied and adopted which selects a subset features from the feature set. However, the feature extraction approach for dimensionality reduction has proved to be better compared to feature selection and achieved better classification and detection rates. In this research, we address "Feature extraction" using "Evolutionary feature clustering" by proposing a "Novel fuzzy membership function" which addresses Dimensionality Reduction (DR). The idea is to transform the initial connection representation so that its equivalent representation has reduced noise affect and achieves better classification or detection rates. Experimental results on KDD datasets with 19 and 41 attributes, prove that the proposed approach has improved detection rates for R2L and U2R attack classes when compared to CANN, CLAPP, and SVM approaches. CANN approach recorded lower detection rates w.r.t U2R and R2L attacks. This failure is addressed in our earlier studies through proposing, CLAPP which proved comparatively better accuracy rates to CANN. The fuzzy membership function proposed in this paper, recorded better classification and detection rates in experiments conducted
Study and Analysis of K-Means Clustering Algorithm Using Rapidminer A CASE STUDY ON STUDENTS ’ EXAM RESULT

Institution is a place where teacher explains and student just understands and learns the lesson. Every student has his own definition for toughness and easiness and there isn’t any absolute scale for measuring knowledge but examination score indicate the performance of student. In this case study, knowledge of data mining is combined with educational strategies to improve students’ performance. Generally, data mining (sometimes called data or knowledge discovery) is the process of analysing data from different perspectives and summarizing it into useful information. Data mining software is one of a number of analytical tools for data. It allows users to analyse data from many different dimensions or angles, categorize it, and summarize the relationships identified. Technically, data mining is the process of finding correlations or patterns among dozens of fields in large relational database. Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters).This project describes the use of clustering data mining technique to improve the efficiency of academic performance in the educational institutions .In this project, a live experiment was conducted on students .By conducting an exam on students of computer science major using MOODLE(LMS) and analysing that data generated using RapidMiner(Datamining Software) and later by performing clustering on the data. This method helps to identify the students who need special advising or counselling by the teacher to give high quality of education.
From Motion to Photons in 80 Microseconds: Towards Minimal Latency for Virtual and Augmented Reality

We describe an augmented reality, optical see-through display based on a DMD chip with an extremely fast (16 kHz) binary update rate. We combine the techniques of post-rendering 2-D offsets and just-in-time tracking updates with a novel modulation technique for turning binary pixels into perceived gray scale. These processing elements, implemented in an FPGA, are physically mounted along with the optical display elements in a head tracked rig through which users view synthetic imagery superimposed on their real environment. The combination of mechanical tracking at near-zero latency with reconfigurable display processing has given us a measured average of 80 μs of end-to-end latency (from head motion to change in photons from the display) and also a versatile test platform for extremely-low-latency display systems. We have used it to examine the trade-offs between image quality and cost (i.e. power and logical complexity) and have found that quality can be maintained with a fairly simple display modulation scheme.
Percutaneous Endoscopic Lumbar Foraminotomy: An Advanced Surgical Technique and Clinical Outcomes

Although several authors have reported the use of endoscopic techniques to treat lumbar foraminal stenosis, the practical application of these techniques has been limited to soft disc herniation.



To describe the details of the percutaneous endoscopic lumbar foraminotomy (ELF) technique for bony foraminal stenosis and to demonstrate the clinical outcomes.



Two years of prospective data were collected from 33 consecutive patients with lumbar foraminal stenosis who underwent ELF. The surgical outcomes were assessed using the visual analog scale, Oswestry Disability Index, and modified MacNab criteria. The procedure begins at the safer extraforaminal zone rather than the riskier intraforaminal zone. Then, a full-scale foraminal decompression can be performed using a burr and punches under endoscopic control.



The mean age of the 18 female and 15 male patients was 64.2 years. The mean visual analog scale score for leg pain improved from 8.36 at baseline to 3.36 at 6 weeks, 2.03 at 1 year, and 1.97 at 2 years post-surgery (P < .001). The mean Oswestry Disability Index improved from 65.8 at baseline to 31.6 at 6 weeks, 19.7 at 1 year, and 19.3 at 2 years post-surgery (P < .001). Based on the modified MacNab criteria, excellent or good results were obtained in 81.8% of the patients, and symptomatic improvements were obtained in 93.9%.



Percutaneous ELF under local anesthesia could be an efficacious surgical procedure for the treatment of foraminal stenosis. This procedure may offer safe and reproducible results, especially for elderly or medically compromised patients.
WAVELET BASED WATERMARKING ON DIGITAL IMAGE

Safeguarding creative content and intellectual property in a digital form has become increasingly difficult as technologies, such as the internet, broadband availability and mobile access advance. It has grown to be progressively easier to copy, modify and redistribute digital media, resulting in great declines in business profits. Digital watermarking is a technique which has been proposed as a possible solution to this problem. Digital Watermarking is a technology which is used to identify the creator, owner, distributor of a given video or image by embedding copyright marks into the digital content, hence digital watermarking is a powerful tool used to check the copy right violation. In this paper a robust watermarking technique based on DWT (Discrete Wavelet Transform) is presented. In this technique the insertion and extraction of the watermark in the grayscale image is found to be simpler than other transform techniques. This paper successfully explains the digital watermarking technique on digital images based on discrete wavelet transform by analyzing various values of PSNR’s and MSE’s..
Physician Burnout and Well-Being: A Systematic Review and Framework for Action.

Physician burnout in the United States has reached epidemic proportions and is rising rapidly, although burnout in other occupations is stable. Its negative impact is far reaching and includes harm to the burned-out physician, as well as patients, coworkers, family members, close friends, and healthcare organizations.



The purpose of this review is to provide an accurate, current summary of what is known about physician burnout and to develop a framework to reverse its current negative impact, decrease its prevalence, and implement effective organizational and personal interventions.



I completed a comprehensive MEDLINE search of the medical literature from January 1, 2000, through December 28, 2016, related to medical student and physician burnout, stress, depression, suicide ideation, suicide, resiliency, wellness, and well-being. In addition, I selectively reviewed secondary articles, books addressing the relevant issues, and oral presentations at national professional meetings since 2013.



Healthcare organizations within the United States were studied.



The literature review is presented in 5 sections covering the basics of defining and measuring burnout; its impact, incidence, and causes; and interventions and remediation strategies.



All US medical students, physicians in training, and practicing physicians are at significant risk of burnout. Its prevalence now exceeds 50%. Burnout is the unintended net result of multiple, highly disruptive changes in society at large, the medical profession, and the healthcare system. Both individual and organizational strategies have been only partially successful in mitigating burnout and in developing resiliency and well-being among physicians. Two highly effective strategies are aligning personal and organizational values and enabling physicians to devote 20% of their work activities to the part of their medical practice that is especially meaningful to them. More research is needed.
Megatrend and Intervention Impact Analyser for Jobs: A European Big Data Hackathon Entry

This paper presents results of a European Big Data Hackathon entry to facilitate data analysis and visualization of patterns in provided and external datasets about European jobs and skills mismatch and labour market in general. The main contributions of this work are: the development of a method to represent the complex labour market internal structure from the perspective of occupations sharing skills; developing and presenting the prototype, together with an extended description of constructing a graph and related necessary data processing. Since labour market is not an isolated phenomenon and is constantly impacted with external trends and interventions, the presented tool is designed to enable adding extra layers of external information: what is the impact of a megatrend or an intervention to the labour market? Which parts of labour market of what country is most vulnerable to approaching megatrend or planned intervention? A case of analysing the labour market together with the megatrend of automation and computerization of jobs is presented. The source code of the prototype is released as open source for better repeat-ability.
Ship Detection From Optical Satellite Images Based on Saliency Segmentation and Structure-LBP Feature

Automatic ship detection from optical satellite imagery is a challenging task due to cluttered scenes and variability in ship sizes. This letter proposes a detection algorithm based on saliency segmentation and the local binary pattern (LBP) descriptor combined with ship structure. First, we present a novel saliency segmentation framework with flexible integration of multiple visual cues to extract candidate regions from different sea surfaces. Then, simple shape analysis is adopted to eliminate obviously false targets. Finally, a structure-LBP feature that characterizes the inherent topology structure of ships is applied to discriminate true ship targets. Experimental results on numerous panchromatic satellite images validate that our proposed scheme outperforms other state-of-the-art methods in terms of both detection time and detection accuracy.
IMPROVED DROOP CONTROL FOR PARALLEL INVERTER SYSTEM WITH LOAD

DC-AC converters are electronic devices used to change DC direct current to alternating current. Three-phase inverter is widely used in power electronics system applications consequently, the DC-AC converters requires a controller with a high degree. Therefore the structure of two parallel three phase inverter with load system has presented. In order to achieve load sharing between parallel inverters, the linear transformed droop control is applied. This control strategy combines frequency and voltage droop method and inverter voltage regulation control scheme. In the external power control structure, the references frequency and magnitude of inverter output voltage are obtained according to the droop characteristics. The improvement of the droop control is made to obtain a more stable voltage and better load sharing between two parallel inverters. The performance of the control strategy is verified in simulation using Matlab/Simulink.
A Scalable SIEM Correlation Engine and Its Application to the Olympic Games IT Infrastructure

The security event correlation scalability has become a major concern for security analysts and IT administrators when considering complex IT infrastructures that need to handle gargantuan amounts of events or wide correlation window spans. The current correlation capabilities of Security Information and Event Management (SIEM), based on a single node in centralized servers, have proved to be insufficient to process large event streams. This paper introduces a step forward in the current state of the art to address the aforementioned problems. The proposed model takes into account the two main aspects of this field: distributed correlation and query parallelization. We present a case study of a multiple-step attack on the Olympic Games IT infrastructure to illustrate the applicability of our approach.
Azhary: An Arabic Lexical Ontology

Arabic language is the most spoken languages in the Semitic languages group, and one of the most common languages in the world spoken by more than 422 million. It is also of paramount importance to Muslims, it is a sacred language of the Islamic Holly Book (Quran) and prayer (and other acts of worship) in Islam is performed only by mastering some of Arabic words. Arabic is also a major ritual language of a number of Christian churches in the Arab world and it is also used in writing several intellectual and religious Jewish books in the Middle Ages. Despite this, there is no semantic Arabic lexicon which researchers can depend on. In this paper we introduce Azhary as a lexical ontology for the Arabic language. It groups Arabic words into sets of synonyms called synsets, and records a number of relationships between words such as synonym, antonym, hypernym, hyponym, meronym, holonym and association relations. The ontology contains 26,195 words organized in 13,328 synsets. It has been developed and contrasted against AWN which is the most common available Arabic lexical ontology.  Arabic Language, Ontology, WordNet, Semantic Web, Arabic Lexicon.
A Scalable and Adaptive Method for Finding Semantically Equivalent Cue Words of Uncertainty

Scientific knowledge is constantly subject to a variety of changes due to new discoveries, alternative interpretations, and fresh perspectives. Understanding uncertainties associated with various stages of scientific inquiries is an integral part of scientists’ domain expertise and it serves as the core of their metaknowledge of science. Despite the growing interest in areas such as computational linguistics, systematically characterizing and tracking the epistemic status of scientific claims and their evolution in scientific disciplines remains a challenge. We present a unifying framework for the study of uncertainties explicitly and implicitly conveyed in scientific publications. The framework aims to accommodate a wide range of uncertain types, from speculations to inconsistencies and controversies. We introduce a scalable and adaptive method to recognize semantically equivalent cues of uncertainty across different fields of research and accommodate individual analysts’ unique perspectives. We demonstrate how the new method can be used to expand a small seed list of uncertainty cue words and how the validity of the expanded candidate cue words are verified. We visualize the mixture of the original and expanded uncertainty cue words to reveal the diversity of expressions of uncertainty. These cue words offer a novel resource for the study of uncertainty in scientific assertions.