nils-herrmann/clean_scirepeval huggingface dataset:
Obstacle detection using stereo vision for self-driving cars

Detected obstacles overlaid on the original image Polar map: The agent is at the center of the map, facing 00. The blue points correspond to polar positions of the obstacle points around the agent. 1. Talukder, A., et al. "Fast and reliable obstacle detection and segmentation for cross-country navigation." Intelligent Vehicle SympoTalukder, A., et al. "Fast and reliable obstacle detection and segmentation for cross-country navigation." Intelligent Vehicle Symposium, 2002.  2002. 2. Sun, Deqing, Stefan Roth, and Michael J. Black. "Secrets of optical flow estimation and their principles." Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on.  2010. 3. Bernini, Nicola, et al. "Real-time obstacle detection using stereo vision for autonomous ground vehicles: A survey." Intelligent Transportation Systems (ITSC), 2014  2014. 4. Broggi, Alberto, et al. "Stereo obstacle detection in challenging environments: the VIAC experience." Intelligent Robots and Systems (IROS), 2011  2011.
Efficient discovery of overlapping communities in massive networks.

Detecting overlapping communities is essential to analyzing and exploring natural networks such as social networks, biological networks, and citation networks. However, most existing approaches do not scale to the size of networks that we regularly observe in the real world. In this paper, we develop a scalable approach to community detection that discovers overlapping communities in massive real-world networks. Our approach is based on a Bayesian model of networks that allows nodes to participate in multiple communities, and a corresponding algorithm that naturally interleaves subsampling from the network and updating an estimate of its communities. We demonstrate how we can discover the hidden community structure of several real-world networks, including 3.7 million US patents, 575,000 physics articles from the arXiv preprint server, and 875,000 connected Web pages from the Internet. Furthermore, we demonstrate on large simulated networks that our algorithm accurately discovers the true community structure. This paper opens the door to using sophisticated statistical models to analyze massive networks.
Hypervisor-vs . Container-based Virtualization

For a long time, the term virtualization implied talking about hypervisor-based virtualization. However, in the past few years container-based virtualization got mature and especially Docker gained a lot of attention. Hypervisor-based virtualization provides strong isolation of a complete operating system whereas container-based virtualization strives to isolate processes from other processes at little resource costs. In this paper, hypervisor and container-based virtualization are differentiated and the mechanisms behind Docker and LXC are described. The way from a simple chroot over a container framework to a ready to use container management solution is shown and a look on the security of containers in general is taken. This paper gives an overview of the two different virtualization approaches and their advantages and disadvantages.
Procrastination of Enjoyable

of Management, University of California, Los Angeles (e-mail: suzanne. ). Ayelet Gneezy is Assistant Professor of Marketing, Rady School of Management, University of California, San Diego (e-mail: agneezy@ucsd.edu). This article is based in part on Suzanne Shu’s doctoral dissertation. The authors thank the excellent JMR review team; Gal Zauberman; and dissertation committee members Richard Thaler, George Wu, Yuval Rottenstreich, and France Leclerc for their insightful comments and guidance. They also thank Alessandro Pevitero, the Museum of Contemporary Art in San Diego, the French Pastry Café, and AMC Theaters for providing data. Dilip Soman served as associate editor for this article. SUZANNE B. SHU and AYELET GNEEZY
Learning Sense-specific Word Embeddings By Exploiting Bilingual Resources

Recent work has shown success in learning word embeddings with neural network language models (NNLM). However, the majority of previous NNLMs represent each word with a single embedding, which fails to capture polysemy. In this paper, we address this problem by representing words with multiple and sense-specific embeddings, which are learned from bilingual parallel data. We evaluate our embeddings using the word similarity measurement and show that our approach is significantly better in capturing the sense-level word similarities. We further feed our embeddings as features in Chinese named entity recognition and obtain noticeable improvements against single embeddings.
The Good, The Bad and the Ugly: A Meta-analytic Review of Positive and Negative Effects of Violent Video Games

Video game violence has become a highly politicized issue for scientists and the general public. There is continuing concern that playing violent video games may increase the risk of aggression in players. Less often discussed is the possibility that playing violent video games may promote certain positive developments, particularly related to visuospatial cognition. The objective of the current article was to conduct a meta-analytic review of studies that examine the impact of violent video games on both aggressive behavior and visuospatial cognition in order to understand the full impact of such games.  A detailed literature search was used to identify peer-reviewed articles addressing violent video game effects. Effect sizes r (a common measure of effect size based on the correlational coefficient) were calculated for all included studies. Effect sizes were adjusted for observed publication bias.  Results indicated that publication bias was a problem for studies of both aggressive behavior and visuospatial cognition. Once corrected for publication bias, studies of video game violence provided no support for the hypothesis that violent video game playing is associated with higher aggression. However playing violent video games remained related to higher visuospatial cognition (r x = 0.36).  Results from the current analysis did not support the conclusion that violent video game playing leads to aggressive behavior. However, violent video game playing was associated with higher visuospatial cognition. It may be advisable to reframe the violent video game debate in reference to potential costs and benefits of this medium.
Teaching ethical hacking in information security curriculum: A case study

Denial of Service (DoS) attacks are important topics for security courses that teach ethical hacking techniques and intrusion detection. This paper presents a case study of the implementation of comprehensive offensive hands-on lab exercises about three common DoS attacks. The exercises teach students how to perform practically the DoS attacks in an isolated network laboratory environment. The paper discuses also some ethical and legal issues related to teaching ethical hacking, and then lists steps that schools and educators should take to improve the chances of having a successful and problem free information security programs.
DESIGN OF A DISTRIBUTED SWITCHED RELUCTANCE MOTOR FOR A TIP-DRIVEN FAN

4.7.2015. Received in revised form: 9.1.2016.  29.1.2016. This paper presents a design of a distributed switched reluctance motor for an integrated motorfan system. Unlike a conventional compact motor structure, the rotor is distributed into the ends of the impeller blades. This distributed structure of motor makes more space for airflow to pass through so that the system efficiency is highly improved. Simultaneously, the distributed structure gives the motor a higher torque, better efficiency and heat dissipation. The paper first gives an initial design of a switched reluctance motor based on system structure constraints and output equations, then it predicts the machine performance and determines phase current and winding turns based on equivalent magnetic circuit analysis; finally it validates and refines the analytical design with 3D transient finite element analysis. It is found that the analytical performance prediction agrees well with finite element analysis results except for the weakness on core losses estimation. The results of the design shows that the distributed switched reluctance motor can produce a large torque of pretty high efficiency at specified speeds.
Hand-Writing Motion Tracking with Vision-Inertial Sensor Fusion: Calibration and Error Correction

The purpose of this study was to improve the accuracy of real-time ego-motion tracking through inertial sensor and vision sensor fusion. Due to low sampling rates supported by web-based vision sensor and accumulation of errors in inertial sensors, ego-motion tracking with vision sensors is commonly afflicted by slow updating rates, while motion tracking with inertial sensor suffers from rapid deterioration in accuracy with time. This paper starts with a discussion of developed algorithms for calibrating two relative rotations of the system using only one reference image. Next, stochastic noises associated with the inertial sensor are identified using Allan Variance analysis, and modeled according to their characteristics. Finally, the proposed models are incorporated into an extended Kalman filter for inertial sensor and vision sensor fusion. Compared with results from conventional sensor fusion models, we have shown that ego-motion tracking can be greatly enhanced using the proposed error correction model.
Modality-constrained statistical learning of tactile, visual, and auditory sequences.

The authors investigated the extent to which touch, vision, and audition mediate the processing of statistical regularities within sequential input. Few researchers have conducted rigorous comparisons across sensory modalities; in particular, the sense of touch has been virtually ignored. The current data reveal not only commonalities but also modality constraints affecting statistical learning across the senses. To be specific, the authors found that the auditory modality displayed a quantitative learning advantage compared with vision and touch. In addition, they discovered qualitative learning biases among the senses: Primarily, audition afforded better learning for the final part of input sequences. These findings are discussed in terms of whether statistical learning is likely to consist of a single, unitary mechanism or multiple, modality-constrained ones.
Opaque: An Oblivious and Encrypted Distributed Analytics Platform

Many systems run rich analytics on sensitive data in the cloud, but are prone to data breaches. Hardware enclaves promise data confidentiality and secure execution of arbitrary computation, yet still suffer from access pattern leakage. We propose Opaque, a distributed data analytics platform supporting a wide range of queries while providing strong security guarantees. Opaque introduces new distributed oblivious relational operators that hide access patterns, and new query planning techniques to optimize these new operators. Opaque is implemented on Spark SQL with few changes to the underlying system. Opaque provides data encryption, authentication and computation verification with a performance ranging from 52% faster to 3.3x slower as compared to vanilla Spark SQL; obliviousness comes with a 1.6–46x overhead. Opaque provides an improvement of three orders of magnitude over state-of-the-art oblivious protocols, and our query optimization techniques improve performance by 2–5x.
The elephant in the room: getting value from Big Data

Big Data, and its 4 Vs – volume, velocity, variety, and veracity – have been at the forefront of societal, scientific and engineering discourse. Arguably the most important 5th V, value, is not talked about as much. How can we make sure that our data is not just big, but also valuable? WebDB 2015 has as its theme “Freshness, Correctness, Quality of Information and Knowledge on the Web”. The workshop attracted 31 submissions, of which the best 9 were selected for presentation at the workshop, and for publication in the proceedings. To set the stage, we have interviewed several prominent members of the data management community, soliciting their opinions on how we can ensure that data is not just available in quantity, but also in quality. In this interview Serge Abiteboul, Oren Etzioni, Divesh Srivastava with Luna Dong, and Gerhard Weikum shared with us their motivation for doing research in the area of data quality, and discussed their current work and their view on the future of the field. This interview appeared as a SIGMOD Blog article.
Algorithmic graph theory

Consider the following communication model (slightly differing from that in the lecture): In each time step, every node in the network can still send or receive one arbitrarily large message over one single edge. It is neither possible for a node to send/receive messages over different edges at the same time, nor can it both send and receive something at the same time. In each time step, messages can now be sent along a collection of edge-disjoint paths in the graph instead of just a matching. On such a path, intermediate nodes will not see the message that is just passing them by.
A saliency-based search mechanism for overt and covert shifts of visual attention

Most models of visual search, whether involving overt eye movements or covert shifts of attention, are based on the concept of a saliency map, that is, an explicit two-dimensional map that encodes the saliency or conspicuity of objects in the visual environment. Competition among neurons in this map gives rise to a single winning location that corresponds to the next attended target. Inhibiting this location automatically allows the system to attend to the next most salient location. We describe a detailed computer implementation of such a scheme, focusing on the problem of combining information across modalities, here orientation, intensity and color information, in a purely stimulus-driven manner. The model is applied to common psychophysical stimuli as well as to a very demanding visual search task. Its successful performance is used to address the extent to which the primate visual system carries out visual search via one or more such saliency maps and how this can be tested.
A switching median filter with boundary discriminative noise detection for extremely corrupted images

A novel switching median filter incorporating with a powerful impulse noise detection method, called the boundary discriminative noise detection (BDND), is proposed in this paper for effectively denoising extremely corrupted images. To determine whether the current pixel is corrupted, the proposed BDND algorithm first classifies the pixels of a localized window, centering on the current pixel, into three groups-lower intensity impulse noise, uncorrupted pixels, and higher intensity impulse noise. The center pixel will then be considered as "uncorrupted," provided that it belongs to the "uncorrupted" pixel group, or "corrupted." For that, two boundaries that discriminate these three groups require to be accurately determined for yielding a very high noise detection accuracy-in our case, achieving zero miss-detection rate while maintaining a fairly low false-alarm rate, even up to 70% noise corruption. Four noise models are considered for performance evaluation. Extensive simulation results conducted on both monochrome and color images under a wide range (from 10% to 90%) of noise corruption clearly show that our proposed switching median filter substantially outperforms all existing median-based filters, in terms of suppressing impulse noise while preserving image details, and yet, the proposed BDND is algorithmically simple, suitable for real-time implementation and application.
SimplE Embedding for Link Prediction in Knowledge Graphs

Knowledge graphs contain knowledge about the world and provide a structured representation of this knowledge. Current knowledge graphs contain only a small subset of what is true in the world. Link prediction approaches aim at predicting new links for a knowledge graph given the existing links among the entities. Tensor factorization approaches have proved promising for such link prediction problems. Proposed in 1927, Canonical Polyadic (CP) decomposition is among the first tensor factorization approaches. CP generally performs poorly for link prediction as it learns two independent embedding vectors for each entity, whereas they are really tied. We present a simple enhancement of CP (which we call SimplE) to allow the two embeddings of each entity to be learned dependently. The complexity of SimplE grows linearly with the size of embeddings. The embeddings learned through SimplE are interpretable, and certain types of background knowledge can be incorporated into these embeddings through weight tying. We prove SimplE is fully expressive and derive a bound on the size of its embeddings for full expressivity. We show empirically that, despite its simplicity, SimplE outperforms several state-of-the-art tensor factorization techniques. SimplE’s code is available on GitHub at https://github.com/Mehran-k/SimplE.
Starbucks Canada: The Mobile Payments Decision

This case, written on the basis of published sources, concerns the decision facing management of Starbucks Canada about how to implement mobile payments. While Starbucks has currently been using a mobile app to accept payments through their proprietary Starbucks card, rival Tim Hortons has recently introduced a more advanced mobile payments solution and the company now has to consider its next moves. The case reviews various aspects of mobile payments technology and platforms that must be understood to make a decision about the best direction for Starbucks Canada.
Change Management in Adult Educational Organizations : A Slovenian Case Study

Successful implementing and managing of change is urgently necessary for each adult educational organization. During the process, leading of the staff is becoming a key condition and the most significant factor. Beside certain personal traits of the leader, change management demands also certain leadership knowledges, skills, versatilities and behaviour which may even border on changing the organizational culture. The paper finds the significance of certain values and of organizational climate and above all the significance of leadership style which a leader will adjust to the staff and to the circumstances. The author presents a multiple qualitative case study of managing change in three adult educational organizations. The paper finds that factors of successful leading of change exist which represent an adequate approach to leading the staff during the introduction of changes in educational organizations. Its originality/value is in providing information on the important relationship between culture, leadership styles and leader’s behaviour as preconditions for successful implementing and managing of strategic change.
ExFuse: Enhancing Feature Fusion for Semantic Segmentation

Modern semantic segmentation frameworks usually combine low-level and high-level features from pre-trained backbone convolutional models to boost performance. In this paper, we first point out that a simple fusion of low-level and high-level features could be less effective because of the gap in semantic levels and spatial resolution. We find that introducing semantic information into low-level features and highresolution details into high-level features is more effective for the later fusion. Based on this observation, we propose a new framework, named ExFuse, to bridge the gap between low-level and high-level features thus significantly improve the segmentation quality by 4.0% in total. Furthermore, we evaluate our approach on the challenging PASCAL VOC 2012 segmentation benchmark and achieve 87.9% mean IoU, which outperforms the previous state-of-the-art results.
What's in my closet?: Image classification using fuzzy logic

A fuzzy system was created in MATLAB to identify an item of clothing as a dress, shirt, or pair of pants from a series of input images. The system was initialized using a high-contrast vector-image of each item of clothing as the state closest to a direct solution. Nine other user-input images (three of each item) were also used to determine the characteristic function of each item and recognize each pattern. Mamdani inference systems were used for edge location and identification of characteristic regions of interest for each item of clothing. Based on these non-dimensional trends, a second Mamdani fuzzy inference system was used to characterize each image as containing a shirt, a dress, or a pair of pants. An outline of the fuzzy inference system and image processing techniques used for creating an image pattern recognition system are discussed.
Conditional Word Embedding and Hypothesis Testing via Bayes-by-Backprop

Conventional word embedding models do not leverage information from document metadata, and they do not model uncertainty. We address these concerns with a model that incorporates document covariates to estimate conditional word embedding distributions. Our model allows for (a) hypothesis tests about the meanings of terms, (b) assessments as to whether a word is near or far from another conditioned on different covariate values, and (c) assessments as to whether estimated differences are statistically significant.
Automated Receipt Image Identification , Cropping , and Parsing

The identification and extraction of unstructured data has always been one of the most difficult challenges of computer vision. This form of data exists all around us, from flashy advertisements to basic nutritional information on food products, and provides a large amount of information that exists nowhere else. Parsing this sort of data is very challenging; however, recent advancements in computer vision technology help make this feasible. In this paper, I introduce a novel data pipeline for the identification, cropping, and extraction of unstructured data within receipt images, a ubiquitous, commonplace item that many consumers receive, but one that is difficult to be transformed into raw data. Receipts contain a dense amount of data that is useful for future analysis, but there exists no widely available solution for transforming receipts into structured data. Existing solutions do exist; however, they are either very costly or inaccurate. The pipeline that is described in this paper outperforms existing solutions by a large margin, and offers the ability to automatically pull out semantic data such as transaction date and price from an image of a receipt. It achieves this success by using Line Segment Detection (LSD), Holistically-Nested Edge Detection (HED), and Hough Transform to crop the image. Then, Optical Character Recognition (OCR) is applied to detect chunks of text and process the cropped image. Finally, natural language processing and statistical techniques are used to extract useful sets of information from the recognition result.