nils-herrmann/clean_scirepeval huggingface dataset:
Memristor based memories: Technology, design and test

Today's memory technologies, such as DRAM, SRAM, and NAND Flash, are facing major challenges with regard to their continued scaling. For instance, ITRS projects that DRAM cannot scale easily below 40nm as the cost and energy/power are hard -if not impossible- to scale. Fortunately, the international memory technology community has been researching other alternative for more than fifteen years. Apparently, non-volatile resistive memories are promising to replace the today's memories for many reasons such as better scalability, low cost, higher capacity, lower energy, CMOS compatibility, better configurability, etc. This paper discusses and highlights three major aspects of resistive memories, especially memristor based memories: (a) technology and design constraints, (b) architectures, and (c) testing and design-for-test. It shows the opportunities and the challenges.
Stereo Matching via Dual Fusion

We present a two-fold fusion framework that constructs comprehensive cost volumes for stereo matching. To this end, we develop fusion schemes at two key steps, i.e., a) the raw cost computation and b) the cost aggregation. Specifically, we commence by fusing both structure- and data-oriented features as raw costs. We then incorporate the guided filtered costs into the cross-based cost aggregation and obtain the fused aggregated costs. The fusion schemes at the two steps effectively complement each other and result in an accurate disparity map. Experiments on the Middlebury benchmark v3 demonstrate the state-of-the-art performance of our framework in terms of various metrics.
6K Effective Resolution with 4K HEVC Decoding Capability for OMAF-compliant 360° Video Streaming

The recent Omnidirectional MediA Format (OMAF) standard specifies delivery of 360° video content. OMAF supports only equirectangular (ERP) and cubemap projections and their region-wise packing with a limitation on video decoding capability to the maximum resolution of 4K (e.g., 4096x2048). Streaming of 4K ERP content allows only a limited viewport resolution, which is lower than the resolution of many current head-mounted displays (HMDs). In order to take the full advantage of those HMDs, this work proposes a specific mixed-resolution packing of 6K (6144x3072) ERP content and its realization in tile-based streaming, while complying with the 4K-decoding constraint and the High Efficiency Video Coding (HEVC) standard. Experimental results indicate that, using Zonal-PSNR test methodology, the proposed layout decreases the streaming bitrate up to 32% in terms of BD-rate, when compared to mixed-quality viewport-adaptive streaming of 4K ERP as an alternative solution.
Propagation of Delays in the National Airspace System

The National Airspace System (NAS) is a large and complex system with thousands of interrelated components: administration, control centers, airports, airlines, aircraft, passengers, etc. The complexity of the NAS creates many difficulties in management and control. One of the most pressing problems is flight delay. Delay creates high cost to airlines, complaints from passengers, and difficulties for airport operations. As demand on the system increases, the delay problem becomes more and more prominent. For this reason, it is essential for the Federal Aviation Administration to understand the causes of delay and to find ways to reduce delay. Major contributing factors to delay are congestion at the origin airport, weather, increasing demand, and air traffic management (ATM) decisions such as the Ground Delay Programs (GDP). Delay is an inherently stochastic phenomenon. Even if all known causal factors could be accounted for, macro-level national airspace system (NAS) delays could not be predicted with certainty from micro-level aircraft information. This paper presents a stochastic model that uses Bayesian Networks (BNs) to model the relationships among different components of aircraft delay and the causal factors that affect delays. A case study on delays of departure flights from Chicago O’Hare international airport (ORD) to Hartsfield-Jackson Atlanta International Airport (ATL) reveals how local and system level environmental and human-caused factors combine to affect components of delay, and how these components contribute to the final arrival delay at the destination airport.
A Review of the Crown Lengthening Surgery; The Basic Concepts.

Clinical crown of the tooth is the distance from gingival margin to incisal edge or occlusal surface of the tooth. A short clinical crown is defined as any tooth with less than 2 mm of sound, opposing parallel walls remaining after occlusal and axial reduction. Although implants have reasonably high success rate, recent literature showed that keeping patient’s tooth has numerous benefits. Crown lengthening is a surgical procedure designed to increase the extent of the supragingival tooth structure, so that the clinician can restore the tooth. The aim of the current study is to review the implications of CL in routine dental practice. To reach this aim, diagnosis requirements, restorative procedures after crown lengthening, stability of crown lengthening and esthetic crown lengthening are discussed in different sessions. Mini -review Article Talebi et al.; BJMMR, 13(3): 1-7, 2016; Article no.BJMMR.22602 2
Electrical Impedance Spectro-Tomography Based on Dielectric Relaxation Model

An electrical impedance spectro-tomography consists of a quasi-adjacent sensing method and a frequency-time difference imaging for relatively complex dielectric object monitoring has been proposed. A quasi-adjacent sensing method is designed to measure an electrical impedance spectroscopy based on four-wire measurement technique at the multi-electrodes electrical impedance tomography sensor. A frequency-time difference imaging for spatial distribution monitoring is needed, as the electrochemical reaction incur during the short time meanwhile the spatial geometry tends to be constant. An artificial set of phantoms were made and the proposed technique was capable to show the Cole–Cole parameters as a dielectric relaxation model on each measurement pair. Compare with the adjacent method, using the quasi-adjacent method show Re(Z) is two times higher, Imag(Z) is ten times higher, and a higher and homogeneous sensitivity map area are 60% wider. A negative resistance at high-frequency measurement also can be eliminated successfully by using the proposed method.
Deep Learning-based Feature Selection for Intrusion Detection System in Transport Layer 1 )

Numerous machine learning algorithms applied on Intrusion Detection System (IDS) to detect enormous attacks. However, it is difficult for machine to learn attack properties globally since there are huge and complex input features. Feature selection can overcome this problem by selecting the most important features only to reduce the dimensionality of input features. We leverage Artificial Neural Network (ANN) for the feature selection. In addition, in order to be suitable for resource-constrained devices, we can divide the IDS into smaller parts based on TCP/IP layer since different layer has specific attack types. We show the IDS for transport layer only as a prove of concept. We apply Stacked Auto Encoder (SAE) which belongs to deep learning algorithm as a classifier for KDD99 Dataset. Our experiment shows that the reduced input features are sufficient for classification task. 한국정보보호학회 하계학술대회 논문집 Vol. 26, No.
Estimation of breathing rate and heart rate from photoplethysmogram

Photoplethysmograph (PPG) is a simple and cost effective method to assess cardiovascular related parameters such as heart rate, arterial blood oxygen saturation and blood pressure. PPG signal consists of not only synchronized heart beats, but also the rhythms of respiration. The PPG sensor, which consists of infrared light-emitting diodes (LEDs) and a photodetector, allows a simple, reliable and low-cost means of monitoring the pulse rate. In this project, PPG signals are acquired through a customized data acquisition process using Arduino board to control the pulse circuit and to obtain the PPG signals from human subjects. Using signal processing techniques, including filters, peak detections, wavelet transform analysis and power spectral density, the heart rate (HR) and breathing rate (BR) are be obtained simultaneously. Estimations of HR and BR are conducted using MATLAB algorithm developed based on the wavelet decomposition techniques to extract the heart and respiration activities from the PPG signals. The values of HR and BR obtained using the algorithm are similar to the values obtained by manual estimation for seven sample subjects where the range of percentage errors are small about 0–9.5% for the breathing rate and 2.1–5.7% for the heart rate.
MQTT based air quality monitoring

Air quality plays very important role in safety, security, and health of the mankind. While increasing large-scale industrialization and urbanization creates huge cities, these activities have been creating various ill-effects to the environment. Likewise, one problem is deterioration of air quality in various Indian cities. The primary contributor to air pollution is the particulate matter (PM2.5), causing human health issues, like asthma and other respiratory illnesses. A study has found out that the persons inhaling particulate matter in air are at more risk of lung cancer and comparable to non-smoker susceptible to secondhand smoke. In this work, GP2Y1010AU0F, a sharp dust sensor is used for measuring smoke and dust particles in the environment. The measured data is sent using ESP8266 based NodeMCU-12E, a wireless micro-controller to the cloud server by using Message Queue Telemetry Transport protocol. The MQTT uses publish, subscriber, and broker services. Also, carbon monoxide sensor (MQ-7) is used for sensing carbon monoxide level in the air. The measured values of carbon monoxide (MQ-7) and of sharp dust sensor GP2Y1010AU0F are shown using web page and mobile application.
Accelerating Convolutional Neural Networks via Activation Map Compression

The deep learning revolution brought us an extensive array of neural network architectures that achieve state-of-the-art performance in a wide variety of Computer Vision tasks including among others classification, detection and segmentation. In parallel, we have also been observing an unprecedented demand in computational and memory requirements, rendering the efficient use of neural networks in low-powered devices virtually unattainable. Towards this end, we propose a threestage compression and acceleration pipeline that sparsifies, quantizes and entropy encodes activation maps of Convolutional Neural Networks. Sparsification increases the representational power of activation maps leading to both acceleration of inference and higher model accuracy. Inception-V3 and MobileNet-V1 can be accelerated by as much as 1.6× with an increase in accuracy of 0.38% and 0.54% on the ImageNet and CIFAR-10 datasets respectively. Quantizing and entropy coding the sparser activation maps lead to higher compression over the baseline, reducing the memory cost of the network execution. Inception-V3 and MobileNet-V1 activation maps, quantized to 16 bits, are compressed by as much as 6× with an increase in accuracy of 0.36% and 0.55% respectively.
Visual objects in context

We see the world in scenes, where visual objects occur in rich surroundings, often embedded in a typical context with other related objects. How does the human brain analyse and use these common associations? This article reviews the knowledge that is available, proposes specific mechanisms for the contextual facilitation of object recognition, and highlights important open questions. Although much has already been revealed about the cognitive and cortical mechanisms that subserve recognition of individual objects, surprisingly little is known about the neural underpinnings of contextual analysis and scene perception. Building on previous findings, we now have the means to address the question of how the brain integrates individual elements to construct the visual experience.
Staining protocol for organotypic hippocampal slice cultures

This protocol details a method to immunostain organotypic slice cultures from mouse hippocampus. The cultures are based on the interface method, which does not require special equipment, is easy to execute and yields slice cultures that can be imaged repeatedly, from the time of isolation at postnatal day 6–9 up to 6 months in vitro. The preserved tissue architecture facilitates the analysis of defined hippocampal synapses, cells and entire projections. Time-lapse imaging is based on transgenes expressed in the mice or on constructs introduced through transfection or viral vectors; it can reveal processes that develop over periods ranging from seconds to months. Subsequent to imaging, the slices can be processed for immunocytochemistry to collect further information about the imaged structures. This protocol can be completed in 3 d.
Price Programmatic Guarantee in Display Advertising

This paper presents a revenue maximisation model for sales channel allocation based on dynamic programming. It helps the media seller to determine how to distribute the sales volume of page views between guaranteed and nonguaranteed channels for display advertising. The model can algorithmically allocate and price the future page views via standardised guaranteed contracts in addition to real-time bidding (RTB). This is one of a few studies that investigates programmatic guarantee (PG) with posted prices. Several assumptions are made for media buyers’ behaviour, such as risk-aversion, stochastic demand arrivals, and time and price effects. We examine our model with an RTB dataset and find it increases the seller’s expected total revenue by adopting different pricing and allocation strategies depending the level of competition in RTB campaigns. The insights from this research can increase the allocative efficiency of the current media sellers’ sales mechanism and thus improve their revenue.
MIURA FOLDING : APPLYING ORIGAMI TO SPACE EXPLORATION

Miura folding is famous all over the world. It is an element of the ancient Japanese tradition of origami and reaches as far as astronautical engineering through the construction of solar panels. This article explains how to achieve the Miura folding, and describes its application to maps. The author also suggests in this context that nature may abhor the right angle, according to observation of the wing base of a dragonfly.
Desire to Be Underweight: Exploratory Study on a Weight Loss App Community and User Perceptions of the Impact on Disordered Eating Behaviors

Mobile health (mHealth) apps for weight loss (weight loss apps) can be useful diet and exercise tools for individuals in need of losing weight. Most studies view weight loss app users as these types of individuals, but not all users have the same needs. In fact, users with disordered eating behaviors who desire to be underweight are also utilizing weight loss apps; however, few studies give a sense of the prevalence of these users in weight loss app communities and their perceptions of weight loss apps in relation to disordered eating behaviors.



The aim of this study was to provide an analysis of users' body mass indices (BMIs) in a weight loss app community and examples of how users with underweight BMI goals perceive the impact of the app on disordered eating behaviors.



We focused on two aspects of a weight loss app (DropPounds): profile data and forum posts, and we moved from a broader picture of the community to a narrower focus on users' perceptions. We analyzed profile data to better understand the goal BMIs of all users, highlighting the prevalence of users with underweight BMI goals. Then we explored how users with a desire to be underweight discussed the weight loss app's impact on disordered eating behaviors.



We found three main results: (1) no user (regardless of start BMI) starts with a weight gain goal, and most users want to lose weight; (2) 6.78% (1261/18,601) of the community want to be underweight, and most identify as female; (3) users with underweight BMI goals tend to view the app as positive, especially for reducing bingeing; however, some acknowledge its role in exacerbating disordered eating behaviors.



These findings are important for our understanding of the different types of users who utilize weight loss apps, the perceptions of weight loss apps related to disordered eating, and how weight loss apps may impact users with a desire to be underweight. Whereas these users had underweight goals, they often view the app as helpful in reducing disordered eating behaviors, which led to additional questions. Therefore, future research is needed.
Mindless statistics

Statistical rituals largely eliminate statistical thinking in the social sciences. Rituals are indispensable for identification with social groups, but they should be the subject rather than the procedure of science. What I call the “null ritual” consists of three steps: (1) set up a statistical null hypothesis, but do not specify your own hypothesis nor any alternative hypothesis, (2) use the 5% significance level for rejecting the null and accepting your hypothesis, and (3) always perform this procedure. I report evidence of the resulting collective confusion and fears about sanctions on the part of students and teachers, researchers and editors, as well as textbook writers.
Algorithms for feature selection: An evaluation

A large number of algorithms have been proposed for doing feature subset selection. The goal of this paper is to evaluate the quality of feature subsets generated by the various algorithms, and also compare their computational requirements. Our results show that the sequential forward floating selection (SFFS) algorithm, proposed by Pudil et al., dominates the other algorithms tested. This paper also illustrates the dangers of using feature selection in small sample size situations. It gives the results of applying feature selection to land use classification of SAR satellite images using four different texture models. Pooling features derived from different texture models, followed by a feature selection results in a substantial improvement in the classification accuracy. Application of feature selection to classification of handprinted characters illustrates the value of feature selection in reducing the number of features needed for classifier design.
Offline Recognition of Devanagari Script: A Survey

In India, more than 300 million people use Devanagari script for documentation. There has been a significant improvement in the research related to the recognition of printed as well as handwritten Devanagari text in the past few years. State of the art from 1970s of machine printed and handwritten Devanagari optical character recognition (OCR) is discussed in this paper. All feature-extraction techniques as well as training, classification and matching techniques useful for the recognition are discussed in various sections of the paper. An attempt is made to address the most important results reported so far and it is also tried to highlight the beneficial directions of the research till date. Moreover, the paper also contains a comprehensive bibliography of many selected papers appeared in reputed journals and conference proceedings as an aid for the researchers working in the field of Devanagari OCR.
An Integration of CoTraining and Affinity Propagation for PU Text Classification

Under the framework of PU(Positive data and Unlabeled data), this paper originally proposes a three-setp algorithm. First, CoTraining is employed for filtering out the likely positive data from the unlabeled dataset U. Second, affinity propagation (AP) approach attempts to pick out the strong positive from likely positive set which is produced in first step. Those data picked out can be supplied to positive dataset P. Finally, a linear One-Class SVM will learn from both the purified U as negative and the expanded P as positive. Because of the algorithm's characteristic of automatic expanding positive dataset, the proposed algorithm especially performs well in situations where given positive dataset P is insufficient. A comprehensive experiment had proved that our algorithm is preferable to the existing ones.
Sample Acquisition and Caching architecture for the Mars Sample Return mission

This paper presents a Mars Sample Return (MSR) Sample Acquisition and Caching (SAC) study developed for the three rover platforms: MER, MER+, and MSL. The study took into account 26 SAC requirements provided by the NASA Mars Exploration Program Office. For this SAC architecture, the reduction of mission risk was chosen by us as having greater priority than mass or volume. For this reason, we selected a “One Bit per Core” approach. The enabling technology for this architecture is Honeybee Robotics' “eccentric tubes” core breakoff approach. The breakoff approach allows the drill bits to be relatively small in diameter and in turn lightweight. Hence, the bits could be returned to Earth with the cores inside them with only a modest increase to the total returned mass, but a significant decrease in complexity. Having dedicated bits allows a reduction in the number of core transfer steps and actuators. It also alleviates the bit life problem, eliminates cross contamination, and aids in hermetic sealing. An added advantage is faster drilling time, lower power, lower energy, and lower Weight on Bit (which reduces Arm preload requirements). Drill bits are based on the BigTooth bit concept, which allows re-use of the same bit multiple times, if necessary. The proposed SAC consists of a 1) Rotary-Percussive Core Drill, 2) Bit Storage Carousel, 3) Cache, 4) Robotic Arm, and 5) Rock Abrasion and Brushing Bit (RABBit), which is deployed using the Drill. The system also includes PreView bits (for viewing of cores prior to caching) and Powder bits for acquisition of regolith or cuttings. The SAC total system mass is less than 22 kg for MER and MER+ size rovers and less than 32 kg for the MSL-size rover.
Devising Effective Novelty Search Algorithms: A Comprehensive Empirical Study

Novelty search is a state-of-the-art evolutionary approach that promotes behavioural novelty instead of pursuing a static objective. Along with a large number of successful applications, many different variants of novelty search have been proposed. It is still unclear, however, how some key parameters and algorithmic components influence the evolutionary dynamics and performance of novelty search. In this paper, we conduct a comprehensive empirical study focused on novelty search's algorithmic components. We study the "k" parameter -- the number of nearest neighbours used in the computation of novelty scores; the use and function of an archive; how to combine novelty search with fitness-based evolution; and how to configure the mutation rate of the underlying evolutionary algorithm. Our study is conducted in a simulated maze navigation task. Our results show that the configuration of novelty search can have a significant impact on performance and behaviour space exploration. We conclude with a number of guidelines for the implementation and configuration of novelty search, which should help future practitioners to apply novelty search more effectively.