nils-herrmann/clean_scirepeval huggingface dataset:
Using image analytics to monitor retail store shelves

monitor retail store shelves M. Marder S. Harary A. Ribak Y. Tzur S. Alpert A. Tzadok Using image analytics to monitor the contents and status of retail store shelves is an emerging trend with increasing business importance. Detecting and identifying multiple objects on store shelves involves a number of technical challenges. The particular nature of product package design, the arrangement of products on shelves, and the requirement to operate in unconstrained environments are just a few of the issues that must be addressed. We explain how we addressed these challenges in a system for monitoring planogram compliance, developed as part of a project with Tesco, a large multinational retailer. The new system offers store personnel an instant view of shelf status and a list of action items for restocking shelves. The core of the system is based on its ability to achieve high rates of product recognition, despite the very small visual differences between some products. This paper covers how state-of-the-art methods for object detection behave when applied to this problem. We also describe the innovative aspects of our implementation for size-scale-invariant product recognition and fine-grained classification.
3D to 2D bijection for spherical objects under equidistant fisheye projection

The core problem addressed in this article is the 3D position detection of a spherical object of knownradius in a single image frame, obtained by a dioptric vision system consisting of only one fisheye lens camera that follows equidistant projection model. The central contribution is a bijection principle between a known-radius spherical object’s 3D world position and its 2D projected image curve, that we prove, thus establishing that for every possible 3D world position of the spherical object, there exists a unique curve on the image plane if the object is projected through a fisheye lens that follows equidistant projection model. Additionally, we present a setup for the experimental verification of the principle’s correctness. In previously published works we have applied this principle to detect and subsequently track a known-radius spherical object.
Statistical Learning Theory and ELM for Big Social Data Analysis

The science of opinion analysis based on data from social networks and other forms of mass media has garnered the interest of the scientific community and the business world. Dealing with the increasing amount of information present on the Web is a critical task and requires efficient models developed by the emerging field of sentiment analysis. To this end, current research proposes an efficient approach to support emotion recognition and polarity detection in natural language text. In this paper, we show how to exploit the most recent technological tools and advances in Statistical Learning Theory (SLT) in order to efficiently build an Extreme Learning Machine (ELM) and assess the resultant model's performance when applied to big social data analysis. ELM represents a powerful learning tool, developed to overcome some issues in back-propagation networks. The main problem with ELM is in training them to work in the event of a large number of available samples, where the generalization performance has to be carefully assessed. For this reason, we propose an ELM implementation that exploits the Spark distributed in memory technology and show how to take advantage of the most recent advances in SLT in order to address the issue of selecting ELM hyperparameters that give the best generalization performance.
Statistical feature matrix for texture analysis

A new approach using the statistical feature matrix, which measures the statistical properties of pixel pairs at several distances, within an image, is proposed for texture analysis. The major prop erties of this approach are that (1) the size of the matrix is dependent on the maximum distance used instead of the number of gray-levels, (2) the matrix can be expanded easily, and (3) some physical properties can be evaluated from the matrix. These properties have enhanced the practical applications of the matrix. In this paper, the matrix is applied to texture classification and visual-perceptual feature extraction. For texture classification, two experiments are performed. First, 16 Brodatz textures are employed to evaluate the performance of the matrix. A simple distance measure is defined to determine the similarity between two statistical feature matrices. Texture discrimination in an additive noise environment is also considered. Second, we apply the matrix to the classification of 150 sampled ultrasonic liver images. From experimental results it can be found that our approach is better than the spatial gray-level dependence method and the spatial frequency-based method. For visual-perceptual feature extraction, we evaluate five basic texture features, namely, coarseness, contrast, regularity, periodicity, and roughness, from the statistical feature matrix. It is shown that the statistical feature matrix is an excellent tool for texture analysis. Q 1992 ~eademic
Six years of systematic literature reviews in software engineering: An updated tertiary study

Since the introduction of evidence-based software engineering in 2004, systematic literature review (SLR) has been increasingly used as a method for conducting secondary studies in software engineering. Two tertiary studies, published in 2009 and 2010, identified and analysed 54 SLRs published in journals and conferences in the period between 1st January 2004 and 30th June 2008.  In this article, our goal was to extend and update the two previous tertiary studies to cover the period between 1st July 2008 and 31st December 2009. We analysed the quality, coverage of software engineering topics, and potential impact of published SLRs for education and practice.  We performed automatic and manual searches for SLRs published in journals and conference proceedings, analysed the relevant studies, and compared and integrated our findings with the two previous tertiary studies.  We found 67 new SLRs addressing 24 software engineering topics. Among these studies, 15 were considered relevant to the undergraduate educational curriculum, and 40 appeared of possible interest to practitioners. We found that the number of SLRs in software engineering is increasing, the overall quality of the studies is improving, and the number of researchers and research organisations worldwide that are conducting SLRs is also increasing and spreading.  Our findings suggest that the software engineering research community is starting to adopt SLRs consistently as a research method. However, the majority of the SLRs did not evaluate the quality of primary studies and fail to provide guidelines for practitioners, thus decreasing their potential impact on software engineering practice.
Safeguarding SCADA Systems with Anomaly Detection

This paper will show how the accuracy and security of SCADA systems can be improved by using anomaly detection to identify bad values caused by attacks and faults. The performance of invariant induction and ngram anomaly-detectors will be compared and this paper will also outline plans for taking this work further by integrating the output from several anomalydetecting techniques using Bayesian networks. Although the methods outlined in this paper are illustrated using the data from an electricity network, this research springs from a more general attempt to improve the security and dependability of SCADA systems using anomaly detection.
Interpersonal attraction and personality: what is attractive--self similarity, ideal similarity, complementarity or attachment security?

Little is known about whether personality characteristics influence initial attraction. Because adult attachment differences influence a broad range of relationship processes, the authors examined their role in 3 experimental attraction studies. The authors tested four major attraction hypotheses--self similarity, ideal-self similarity, complementarity, and attachment security--and examined both actual and perceptual factors. Replicated analyses across samples, designs, and manipulations showed that actual security and self similarity predicted attraction. With regard to perceptual factors, ideal similarity, self similarity, and security all were significant predictors. Whereas perceptual ideal and self similarity had incremental predictive power, perceptual security's effects were subsumed by perceptual ideal similarity. Perceptual self similarity fully mediated actual attachment similarity effects, whereas ideal similarity was only a partial mediator.
Multiple Sclerosis Lesion Segmentation from Brain MRI via Fully Convolutional Neural Networks

Multiple Sclerosis (MS) is an autoimmune disease that leads to lesions in the central nervous system. Magnetic resonance (MR) images provide sufficient imaging contrast to visualize and detect lesions, particularly those in the white matter. Quantitative measures based on various features of lesions have been shown to be useful in clinical trials for evaluating therapies. Therefore robust and accurate segmentation of white matter lesions from MR images can provide important information about the disease status and progression. In this paper, we propose a fully convolutional neural network (CNN) based method to segment white matter lesions from multi-contrast MR images. The proposed CNN based method contains two convolutional pathways. The first pathway consists of multiple parallel convolutional filter banks catering to multiple MR modalities. In the second pathway, the outputs of the first one are concatenated and another set of convolutional filters are applied. The output of this last pathway produces a membership function for lesions that may be thresholded to obtain a binary segmentation. The proposed method is evaluated on a dataset of 100 MS patients, as well as the ISBI 2015 challenge data consisting of 14 patients. The comparison is performed against four publicly available MS lesion segmentation methods. Significant improvement in segmentation quality over the competing methods is demonstrated on various metrics, such as Dice and false positive ratio. While evaluating on the ISBI 2015 challenge data, our method produces a score of 90.48, where a score of 90 is considered to be comparable to a human rater.
Foveated Real-Time Ray Tracing for Virtual Reality Headset

(a) Our method with 6.7M triangles Rungholt scene. 55K shaded samples. Inset picture was taken through the lens of the Oculus Rift HMD. (b) Naı̈ve ray tracing. 1M shaded samples. Visual quality in our method is equivalent to the one produced by the naı̈ve method when seen through the HMD. (c) Our foveated sampling pattern and k-NN filtering method. Each cell corresponds to a sampling point. Real-time rendering over 60 fps is achieved with the OpenCLray tracer, running on four RadeonR9 290X GPUs.
Unsupervised Spectral–Spatial Feature Learning via Deep Residual Conv–Deconv Network for Hyperspectral Image Classification

Supervised approaches classify input data using a set of representative samples for each class, known as training samples. The collection of such samples is expensive and time demanding. Hence, unsupervised feature learning, which has a quick access to arbitrary amounts of unlabeled data, is conceptually of high interest. In this paper, we propose a novel network architecture, fully Conv–Deconv network, for unsupervised spectral–spatial feature learning of hyperspectral images, which is able to be trained in an end-to-end manner. Specifically, our network is based on the so-called encoder–decoder paradigm, i.e., the input 3-D hyperspectral patch is first transformed into a typically lower dimensional space via a convolutional subnetwork (encoder), and then expanded to reproduce the initial data by a deconvolutional subnetwork (decoder). However, during the experiment, we found that such a network is not easy to be optimized. To address this problem, we refine the proposed network architecture by incorporating: 1) residual learning and 2) a new unpooling operation that can use memorized max-pooling indexes. Moreover, to understand the “black box,” we make an in-depth study of the learned feature maps in the experimental analysis. A very interesting discovery is that some specific “neurons” in the first residual block of the proposed network own good description power for semantic visual patterns in the object level, which provide an opportunity to achieve “free” object detection. This paper, for the first time in the remote sensing community, proposes an end-to-end fully Conv–Deconv network for unsupervised spectral–spatial feature learning. Moreover, this paper also introduces an in-depth investigation of learned features. Experimental results on two widely used hyperspectral data, Indian Pines and Pavia University, demonstrate competitive performance obtained by the proposed methodology compared with other studied approaches.
Attention , Distraction , and Cognitive Control Under Load

The extent to which people can focus attention in the face of irrelevant distractions has been shown to critically depend on the level and type of information load involved in their current task. The ability to focus attention improves under task conditions of high perceptual load but deteriorates under conditions of high load on cognitive control processes such as working memory. I review recent research on the effects of load on visual awareness and brain activity, including changing effects over the life span, and I outline the consequences for distraction and inattention in daily life and in clinical populations.
Non-orthogonal multiple access (NOMA) for future downlink radio access of 5G

Multiple access (MA) technology is of most importance for 5G. Non-orthogonal multiple access (NOMA) utilizing power domain and advanced receiver has been considered as a promising candidate MA technology recently. In this paper, the NOMA concept is presented toward future enhancements of spectrum efficiency in lower frequency bands for downlink of 5G system. Key component technologies of NOMA are presented and discussed including multiuser transmission power allocation, scheduling algorithm, receiver design and combination of NOMA with multi-antenna technology. The performance gains of NOMA are evaluated by system-level simulations with very practical assumptions. Under multiple configurations and setups, the achievable system-level gains of NOMA are shown promising even when practical considerations were taken into account.
A Vector Model for Syntagmatic and Paradigmatic Relatedness

This paper introduces context digests, high-dimensional real-valued representations for the typical left and right contexts of a word. Initial entries for the context digests are formed from the word’s close left and right neighbors. A singular value decomposition reduces the dimensionality of the space to enable subsequent efficient processing. In contrast to similar techniques, no preprocessor such as a parser is required. Context digests summarize both syntagmatic and paradigmatic relations between words: how typical they are as neighbors and how well they are substitutable for each other. We apply context digests to identifying collocations, to assessing the similarity of the arguments of different verbs, and to clustering occurrences of adjectives and verbs according to the words they modify in context.
Small-Signal Model-Based Control Strategy for Balancing Individual DC Capacitor Voltages in Cascade Multilevel Inverter-Based STATCOM

This paper presents a new feedback control strategy for balancing individual DC capacitor voltages in a three-phase cascade multilevel inverter-based static synchronous compensator. The design of the control strategy is based on the detailed small-signal model. The key part of the proposed controller is a compensator to cancel the variation parts in the model. The controller can balance individual DC capacitor voltages when H-bridges run with different switching patterns and have parameter variations. It has two advantages: 1) the controller can work well in all operation modes (the capacitive mode, the inductive mode, and the standby mode) and 2) the impact of the individual DC voltage controller on the voltage quality is small. Simulation results and experimental results verify the performance of the controller.
Dual Band U-Slotted Microstrip Patch Antenna for C Band and X Band Radar Applications

A compact dual band micro strip patch antenna is designed for C (4-8 GHz) band and X (8-12 GHz) band applications. The proposed antenna consists of a rectangular patch having four U-slots and one I-slot with H-shaped DGS (Defected Ground Structure). The antenna has overall size of 25 mm by 23 mm and gives bandwidth of about 140 MHz from 5.85 GHz to 6 GHz and of about 1.21 GHz from 7.87 to 9 GHz at resonating frequency of 5.9 GHz and 8.8 GHz respectively with DGS. The antenna without DGS mainly resonates at 6 GHz and 8.7 GHz. The antenna with DGS has return losses -16.29 dB at 5.9 GHz and -18.28 dB at 8.8 GHz, gain 1.2 dBi for 5.9 GHz and 4.4 dBi for 8.8 GHz. This antenna has been analyzed using IE3D electromagnetic solver.
LabVIEW-Based UHF RFID Tag Test and Measurement System

In this paper, we describe a UHF radio-frequency-identification tag test and measurement system based on National Instruments LabVIEW-controlled PXI RF hardware. The system operates in 800-1000-MHz frequency band with a variable output power up to 30 dBm and is capable of testing tags using Gen2 and other protocols. We explain testing methods and metrics, describe in detail the construction of our system, show its operation with real tag measurement examples, and draw general conclusions.
The web centipede: understanding how web communities influence each other through the lens of mainstream and alternative news sources

As the number and the diversity of news outlets on the Web grows, so does the opportunity for "alternative" sources of information to emerge. Using large social networks like Twitter and Facebook, misleading, false, or agenda-driven information can quickly and seamlessly spread online, deceiving people or influencing their opinions. Also, the increased engagement of tightly knit communities, such as Reddit and 4chan, further compounds the problem, as their users initiate and propagate alternative information, not only within their own communities, but also to different ones as well as various social media. In fact, these platforms have become an important piece of the modern information ecosystem, which, thus far, has not been studied as a whole.
 In this paper, we begin to fill this gap by studying mainstream and alternative news shared on Twitter, Reddit, and 4chan. By analyzing millions of posts around several axes, we measure how mainstream and alternative news flows between these platforms. Our results indicate that alt-right communities within 4chan and Reddit can have a surprising level of influence on Twitter, providing evidence that "fringe" communities often succeed in spreading alternative news to mainstream social networks and the greater Web.
Situation entity types: automatic classification of clause-level aspect

This paper describes the first robust approach to automatically labeling clauses with their situation entity type (Smith, 2003), capturing aspectual phenomena at the clause level which are relevant for interpreting both semantics at the clause level and discourse structure. Previous work on this task used a small data set from a limited domain, and relied mainly on words as features, an approach which is impractical in larger settings. We provide a new corpus of texts from 13 genres (40,000 clauses) annotated with situation entity types. We show that our sequence labeling approach using distributional information in the form of Brown clusters, as well as syntactic-semantic features targeted to the task, is robust across genres, reaching accuracies of up to 76%.
A general framework for road marking detection and analysis

Road markings are paintings on road surface to provide traffic guidance information for vehicles and pedestrians. In this paper, we propose a general framework for road marking detection and analysis, which is able to support various types of markings. Marking contours of different types are extracted indiscriminately from a image processing procedure, and sent to respective modules for independent classification and analysis. Four common types of markings are studied as examples in this paper, including lanes, arrows, zebra-crossings, and words. Our proposed method is tested through experiments, and shows good performance.
Calibrating the sociometer: the relationship between interpersonal appraisals and state self-esteem.

Four experiments examined the functional relationship between interpersonal appraisal and subjective feelings about oneself. Participants imagined receiving one of several positive or negative reactions from another person (Experiments 1, 2, and 3) or actually received interpersonal evaluations (Experiment 4), then completed measures relevant to state self-esteem. All 4 studies showed that subjective feelings were a curvilinear, ogival function of others' appraisals. Although trait self-esteem correlated with state reactions as a main effect, it did not moderate participants' reactions to interpersonal feedback.
The LFM-1b Dataset for Music Retrieval and Recommendation

We present the LFM-1b dataset of more than one billion music listening events created by more than 120,000 users of Last.fm. Each listening event is characterized by artist, album, and track name, and further includes a timestamp. On the (anonymous) user level, basic demographics and a selection of more elaborate user descriptors are included.
 The dataset is foremost intended for benchmarking in music information retrieval and recommendation. To facilitate experimentation in a straightforward manner, it also includes a precomputed user-item-playcount matrix. In addition, sample Python scripts showing how to load the data and perform efficient computations are provided. An implementation of a simple collaborative filtering recommender rounds off the code package.
 We discuss in detail the LFM-1b dataset's acquisition, availability, statistics, and content, and place it in the context of existing datasets. We also showcase its usage in a simple artist recommendation task, whose results are intended to serve as baseline against which more elaborate techniques can be assessed. The two unique features of the dataset in comparison to existing ones are (i) its substantial size and (ii) a wide range of additional user descriptors that reflect their music taste and consumption behavior.