nils-herrmann/clean_scirepeval huggingface dataset:
Data security and privacy in E-health Cloud: Comparative study

The use of cloud computing in the medical sector has many advantages such as: reducing costs, sharing and exchanging information between medical institutions, data availability ... However, the centralization of sensitive health data in the Cloud Computing raises numerous problems concerning security and data privacy. Since the importance and sensitive nature of health data, medical institutions are reluctant to move those data's patients in the Cloud. This paper analysis the issues of security and privacy raised by the use of cloud in e-health. We will describe the architectures of the eHealth cloud, the problems of security and privacy related to these architectures and some of the different approaches proposed to address these issues.
Jointly Embedding Entities and Text with Distant Supervision

Learning representations for knowledge base entities and concepts is becoming increasingly important for NLP applications. However, recent entity embedding methods have relied on structured resources that are expensive to create for new domains and corpora. We present a distantly-supervised method for jointly learning embeddings of entities and text from an unnanotated corpus, using only a list of mappings between entities and surface forms. We learn embeddings from open-domain and biomedical corpora, and compare against prior methods that rely on human-annotated text or large knowledge graph structure. Our embeddings capture entity similarity and relatedness better than prior work, both in existing biomedical datasets and a new Wikipedia-based dataset that we release to the community. Results on analogy completion and entity sense disambiguation indicate that entities and words capture complementary information that can be effectively combined for downstream use.
CubeSat Attitude Determination via Kalman Filtering of Magnetometer and Solar Cell Data

Attitude determination options are limited on a CubeSat due to power, mass, and volume constraints. This report documents the design and implementation of an extended Kalman filter (EKF) for attitude estimation using three-axis magnetometer and two-axis solar cell measurements. The motivation for such a system is to utilize sensors already present on most CubeSats, namely three-axis magnetometers for active magnetic detumbling and four faces of solar cell arrays for power generation. The system is developed and simulation-tested on a 1-U CubeSat in a 600 km dawn-dusk orbit. Results show the system can converge to an attitude accuracy of 2.5 ̊ within 1 orbit after detumbling.
Splitting approaches for context-aware recommendation: an empirical study

User and item splitting are well-known approaches to context-aware recommendation. To perform item splitting, multiple copies of an item are created based on the contexts in which it has been rated. User splitting performs a similar treatment with respect to users. The combination of user and item splitting: UI splitting, splits both users and items in the data set to boost context-aware recommendations. In this paper, we perform an empirical comparison of these three context-aware splitting approaches (CASA) on multiple data sets, and we also compare them with other popular context-aware collaborative filtering (CACF) algorithms. To evaluate those algorithms, we propose new evaluation metrics specific to contextual recommendation. The experiments reveal that CASA typically outperform other popular CACF algorithms, but there is no clear winner among the three splitting approaches. However, we do find some underlying patterns or clues for the application of CASA.
Vision-guided flight stability and control for micro air vehicles

Substantial progress has been made recently towards designing, building and test-flying remotely piloted Micro Air Vehicles (MAVs) and small UAVs. We seek to complement this progress in overcoming the aerodynamic obstacles to flight at very small scales with a vision-guided flight stability and autonomy system, based on a robust horizon detection algorithm. In this paper, we first motivate the use of computer vision for MAV autonomy, arguing that given current sensor technology, vision may be the only practical approach to the problem. We then describe our statistical vision-based horizon detection algorithm, which has been demonstrated at 30Hz with over 99.9% correct horizon identification. Next, we develop robust schemes for the detection of extreme MAV attitudes, where no horizon is visible, and for the detection of horizon estimation errors, due to external factors such as video transmission noise. Finally, we discuss our feedback controller for self-stabilized flight, and report results on vision-based autonomous flights of duration exceeding ten minutes. We conclude with an overview of our on-going and future MAV-related research.
An SIW-Based Large-Scale Corporate-Feed Array Antenna

A 16 × 16 substrate-integrated waveguide (SIW) cavity-backed array antenna is proposed in this paper. The array consists of two layers. The top layer employs novel SIW-based subarrays with a compact size. The bottom layer is an 8 × 8 SIW corporate-feed network to feed the subarrays. The Chebyshev amplitude weighting is employed in the feed network, which substantially reduces the side lobe level (SLL). The array antenna is fabricated using low-cost printed circuit board technology. The experimental results show that the proposed array antenna has a large bandwidth of 15% from 18.5 to 21.5 GHz with a peak gain of 29.1 dBi at 20.5 GHz. Across the entire band, high radiation efficiency above 62% and a low SLL below -17 dB are realized. The design principle can be used as a guidance for largescale planar array antenna design and the proposed antenna can be used as a receiving antenna located on the ground in satellite communication systems.
Discovering Opinion Changes in Online Reviews via Learning Fine-Grained Sentiments

Recent years have shown rapid advancement in understanding consumers' behaviors and opinions through collecting and analyzing data from online social media platforms. While abundant research has been undertaken to detect users' opinions, few tools are available for understanding events where user opinions change drastically. In this paper, we propose a novel framework for discovering consumer opinion changing events. To detect subtle opinion changes over time, we first develop a novel fine-grained sentiment classification method by leveraging word embedding and convolutional neural networks. The method learns sentiment-enhanced word embedding, both for words and phrases, to capture their corresponding syntactic, semantic, and sentimental characteristics. We then propose an opinion shift detection algorithm that is based on the Kullback-Leibler divergence of temporal opinion category distributions, and conducted experiments on online reviews from Yelp. The results show that the proposed approach can effectively classify fine-grained sentiments of reviews and can discover key moments that correspond to consumer opinion shifts in response to events that relate to a product or service.
CEFAM: Comprehensive Evaluation Framework for Agile Methodologies

Agile software development is regarded as an effective and efficient approach, mainly due to its ability to accommodate rapidly changing requirements, and to cope with modern software development challenges. There is therefore a strong tendency to use agile software development methodologies where applicable; however, the sheer number of existing agile methodologies and their variants hinders the selection of an appropriate agile methodology or method chunk. Methodology evaluation tools address this problem through providing detailed evaluations, yet no comprehensive evaluation framework is available for agile methodologies. We introduce the comprehensive evaluation framework for agile methodologies (CEFAM) as an evaluation tool for project managers and method engineers. The hierarchical (and mostly quantitative) evaluation criterion set introduced in this evaluation framework enhances the usability of the framework and provides results that are precise enough to be useful for the selection, adaptation and construction of agile methodologies.
Self-Improving Visual Odometry

We propose a self-supervised learning framework that uses unlabeled monocular video sequences to generate large-scale supervision for training a Visual Odometry (VO) frontend, a network which computes pointwise data associations across images. Our self-improving method enables a VO frontend to learn over time, unlike other VO and SLAM systems which require time-consuming hand-tuning or expensive data collection to adapt to new environments. Our proposed frontend operates on monocular images and consists of a single multi-task convolutional neural network which outputs 2D keypoints locations, keypoint descriptors, and a novel point stability score. We use the output of VO to create a self-supervised dataset of point correspondences to retrain the frontend. When trained using VO at scale on 2.5 million monocular images from ScanNet, the stability classifier automatically discovers a ranking for keypoints that are not likely to help in VO, such as t-junctions across depth discontinuities, features on shadows and highlights, and dynamic objects like people. The resulting frontend outperforms both traditional methods (SIFT, ORB, AKAZE) and deep learning methods (SuperPoint and LF-Net) in a 3D-to-2D pose estimation task on ScanNet.
Understanding Judgment of Information Quality and Cognitive Authority in the WWW

In the WWW, people are engaging in interaction with more, and more diverse information than ever before, so there is an increasing need for information “filtering.” But because of the diversity of information resources, and the lack of traditional quality control on the WWW, the criteria of authority and quality of information that people have used for this purpose in past contexts may no longer be relevant. This paper reports on a study of people’s decision making with respect to quality and authority in the WWW. Seven facets of judgment of information quality were identified: source, content, format, presentation, currency, accuracy, and speed of loading. People mentioned source credibility with two levels: institutional level and individual level. Authority was identified as a underlying theme in source credibility. Institutional authority involved: institutional domain identified by URL; institution type; and institution reputation recognized by names. Individual authority involved: identification of creator/author; creator/author affiliation; and creator/author’s name. People were more or less concerned with evaluating information quality depending upon: the consequence of use of information; act or commitment based on information; and the focus of inquiry. It was also found that people believed that the web, as an institution, was less authoritative and less credible than other types of information systems.
Classification of EEG signals by multi-scale filtering and PCA

High accuracy for the classification of electroencephalogram (EEG) signal is an important basis for a brain-computer interface (BCI) system. In this paper, we proposed a novel approach to enhance the classification performance in identifying EEG signals, which classify EEG by combining multi-scale filters and Principal Component Analysis (PCA). First, a multi-scale filter with different size of filter window was used to extract major frequency-band components from EEG signals. This might not only enhance the adaptability of filter to the EEG signals, but also satisfy the diversity of frequency resolution. Then PCA was utilized for feature extraction to reduce data dimension and improve the classification accuracy. The experimental results on EEG signals of motor imagery indicate that the proposed method is able to achieve a classification accuracy of 91.13%. Using this method might enhance the performance of a BCI system in signal recognition.
Mutual Inductance of a Precise Rogowski Coil in Dependence of the Position of Primary Conductor

The Rogowski coil is a well-known current-to-voltage transducer. To use it for the high-accuracy measurement of AC current (at power supply frequency), all influencing quantities and their contribution should be recognized and analyzed. Thus, the partial influence due to the deviation from the central position of the primary conductor for a real sensor, with the nonhomogeneous density of secondary turns, is given in this paper. The measured deviation of mutual inductance in this case showed very good agreement with the theoretical prediction, which is promising for the expected application of such a sensor.
An analogue approach to the travelling salesman problem using an elastic net method

The travelling salesman problem1 is a classical problem in the field of combinatorial optimization, concerned with efficient methods for maximizing or minimizing a function of many independent variables. Given the positions of N cities, which in the simplest case lie in the plane, what is the shortest closed tour in which each city can be visited once? We describe how a parallel analogue algorithm, derived from a formal model2–3 for the establishment of topographically ordered projections in the brain4–10, can be applied to the travelling salesman problem1,11,12. Using an iterative procedure, a circular closed path is gradually elongated non-uniformly until it eventually passes sufficiently near to all the cities to define a tour. This produces shorter tour lengths than another recent parallel analogue algorithm13, scales well with the size of the problem, and is naturally extendable to a large class of optimization problems involving topographic mappings between geometrical structures14.
Modeling and Simulation of Electric and Hybrid Vehicles

This paper discusses the need for modeling and simulation of electric and hybrid vehicles. Different modeling methods such as physics-based Resistive Companion Form technique and Bond Graph method are presented with powertrain component and system modeling examples. The modeling and simulation capabilities of existing tools such as Powertrain System Analysis Toolkit (PSAT), ADvanced VehIcle SimulatOR (ADVISOR), PSIM, and Virtual Test Bed are demonstrated through application examples. Since power electronics is indispensable in hybrid vehicles, the issue of numerical oscillations in dynamic simulations involving power electronics is briefly addressed
Diagnosis and compensation of amplitude imbalance, imperfect quadrant and offset in resolver signals

In this paper, a new method for detecting some errors in resolver signals is presented. Sin and cosine signals in resolver to digital converter's (RDC's) input might have errors, such as amplitude imbalance, imperfect quadrant or DC offset. Like other sensors some defects in mechanical structure and electrical or magnetic circuits cause to these errors in resolver output signals. The amplitude and phase of resolver output signals and consequently calculated angle are affected by these errors at any moment. Resolver output signals are more affected in their peak points than the other points. These peak points are easier and more accurate than the other points for analyzing, too. It is obvious that any error has unique effects on the signals. Therefore, by analyzing the effect on signals' peak points the error type and the value can be detected. To avoid wrong detection, error signal is defined, which detects the per unit signals deviation from the unit circle. Also, for error detection, there is no need to interrupt converter's operation. The proposed method is simulated in Matlab/Simulink environment, and the obtained results validate its effectiveness. Also, it is simulated on TMS320F2812 DSP using CCS 3.3, and the results are presented. Furthermore, by implementing the present RDC on motor controller DSP, it will be a low-cost approach.
Brain development and ADHD.

Attention-Deficit/Hyperactivity Disorder (ADHD) is characterized by excessive inattention, hyperactivity, and impulsivity, either alone or in combination. Neuropsychological findings suggest that these behaviors result from underlying deficits in response inhibition, delay aversion, and executive functioning which, in turn, are presumed to be linked to dysfunction of frontal-striatal-cerebellar circuits. Over the past decade, magnetic resonance imaging (MRI) has been used to examine anatomic differences in these regions between ADHD and control children. In addition to quantifying differences in total cerebral volume, specific areas of interest have been prefrontal regions, basal ganglia, the corpus callosum, and cerebellum. Differences in gray and white matter have also been examined. The ultimate goal of this research is to determine the underlying neurophysiology of ADHD and how specific phenotypes may be related to alterations in brain structure.
Non-invasive glucose monitoring in patients with diabetes: a novel system based on impedance spectroscopy.

The aim of this work was to evaluate the performance of a novel non-invasive continuous glucose-monitoring system based on impedance spectroscopy (IS) in patients with diabetes. Ten patients with type 1 diabetes (mean+/-S.D., age 28+/-8 years, BMI 24.2+/-3.2 kg/m(2) and HbA(1C) 7.3+/-1.6%) and five with type 2 diabetes (age 61+/-8 years, BMI 27.5+/-3.2 kg/m(2) and HbA(1C) 8.3+/-1.8%) took part in this study, which comprised a glucose clamp experiment followed by a 7-day outpatient evaluation. The measurements obtained by the NI-CGMD and the reference blood glucose-measuring techniques were evaluated using retrospective data evaluation procedures. Under less controlled outpatient conditions a correlation coefficient of r=0.640 and a standard error of prediction (SEP) of 45 mg dl(-1) with a total of 590 paired glucose measurements was found (versus r=0.926 and a SEP of 26 mg dl(-1) under controlled conditions). Clark error grid analyses (EGA) showed 56% of all values in zone A, 37% in B and 7% in C-E. In conclusion, these results indicate that IS in the used technical setting allows retrospective, continuous and truly non-invasive glucose monitoring under defined conditions for patients with diabetes. Technical advances and developments are needed to expand on this concept to bring the results from the outpatient study closer to those in the experimental section of the study. Further studies will not only help to evaluate the performance and limitations of using such a technique for non non-invasive glucose monitoring but also help to verify technical extensions towards a IS-based concept that offers improved performance under real life operating conditions.
Iterative Noise Injection for Scalable Imitation Learning

In Imitation Learning, a supervisor’s policy is observed and the intended behavior is learned. A known problem with this approach is covariate shift, which occurs because the agent visits different states than the supervisor. Rolling out the current agent’s policy, an on-policy method, allows for collecting data along a distribution similar to the updated agent’s policy. However this approach can become less effective as the demonstrations are collected in very large batch sizes, which reduces the relevance of data collected in previous iterations. In this paper, we propose to alleviate the covariate shift via the injection of artificial noise into the supervisor’s policy. We prove an improved bound on the loss due to the covariate shift, and introduce an algorithm that leverages our analysis to estimate the level of -greedy noise to inject. In a driving simulator domain where an agent learns an image-to-action deep network policy, our algorithm Dart achieves a better performance than DAgger with 75% fewer demonstrations.
Architecting phase change memory as a scalable dram alternative

Memory scaling is in jeopardy as charge storage and sensing mechanisms become less reliable for prevalent memory technologies, such as DRAM. In contrast, phase change memory (PCM) storage relies on scalable current and thermal mechanisms. To exploit PCM's scalability as a DRAM alternative, PCM must be architected to address relatively long latencies, high energy writes, and finite endurance.
 We propose, crafted from a fundamental understanding of PCM technology parameters, area-neutral architectural enhancements that address these limitations and make PCM competitive with DRAM. A baseline PCM system is 1.6x slower and requires 2.2x more energy than a DRAM system. Buffer reorganizations reduce this delay and energy gap to 1.2x and 1.0x, using narrow rows to mitigate write energy and multiple rows to improve locality and write coalescing. Partial writes enhance memory endurance, providing 5.6 years of lifetime. Process scaling will further reduce PCM energy costs and improve endurance.
THE LAST WILL AND TESTAMENT OF THE BGA VOID

The impact of voiding on the solder joint integrity of ball grid arrays (BGAs)/chip scale packages (CSPs) can be a topic of lengthy and energetic discussion. Detailed industry investigations have shown that voids have little effect on solder joint integrity unless they fall into specific location/geometry configurations. These investigations have focused on thermal cycle testing at 0°C-100°C, which is typically used to evaluate commercial electronic products. This paper documents an investigation to determine the impact of voids in BGA and CSP components using thermal cycle testing (-55°C to +125°C) in accordance with the IPC9701 specification for tin/lead solder alloys. This temperature range is more typical of military and other high performance product use environments. A proposed BGA void requirement revision for the IPC-JSTD-001 specification will be extracted from the results analysis.
Cyber–Physical Systems: A Perspective at the Centennial

Cyber-physical systems (CPSs) are the next generation of engineered systems in which computing, communication, and control technologies are tightly integrated. Research on CPSs is fundamentally important for engineered systems in many important application domains such as transportation, energy, and medical systems. We overview CPS research from both a historical point of view in terms of technologies developed for early generations of control systems, as well as recent results on CPSs in many relevant research domains such as networked control, hybrid systems, real-time computing, real-time networking, wireless sensor networks, security, and model-driven development. We outline the potential for CPSs in many societally important application domains.
Repetitive high voltage all-solid-state Marx generator for dielectric barrier discharge pulsed plasma

Dielectric barrier discharge (DBD) is used in plasma technology with wide applications, such as ozone generation, depollution of gas stream and surface treatment of plastics. Commonly used power supplies for DBD generate sinusoidal voltages with frequency up to 100 kHz. Recent research shows that apart from a primary discharge occurring at the rising front or during the pulse top, a secondary discharge occurs at the end of the falling voltage flank when unipolar square pulses are used. This excitation method of DBDs improves energy efficiency significantly compared with sinusoidal excitation. In this paper, an all-solid-state Marx generator that can generate nearly rectangular pulses with a DBD load is proposed. It operates at an output voltage of 30 kV, a repetition frequency of 1 kHz and 1 kV DC input voltage successfully. Both the rise and fall times are less than 200 ns, and a maximum pulse-width of 1 us is obtained. A transformer isolated gate drive circuit is employed. This prototype has been applied in gas treatment research using DBD. Preliminary experimental results show that it is able to deliver almost rectangular voltage pulse to resistive load and capacitive load. When driving a DBD load, it can output large current at the rising of a pulse.