nils-herrmann/clean_scirepeval huggingface dataset:
On-road vehicle detection: a review

Developing on-board automotive driver assistance systems aiming to alert drivers about driving environments, and possible collision with other vehicles has attracted a lot of attention lately. In these systems, robust and reliable vehicle detection is a critical step. This paper presents a review of recent vision-based on-road vehicle detection systems. Our focus is on systems where the camera is mounted on the vehicle rather than being fixed such as in traffic/driveway monitoring systems. First, we discuss the problem of on-road vehicle detection using optical sensors followed by a brief review of intelligent vehicle research worldwide. Then, we discuss active and passive sensors to set the stage for vision-based vehicle detection. Methods aiming to quickly hypothesize the location of vehicles in an image as well as to verify the hypothesized locations are reviewed next. Integrating detection with tracking is also reviewed to illustrate the benefits of exploiting temporal continuity for vehicle detection. Finally, we present a critical overview of the methods discussed, we assess their potential for future deployment, and we present directions for future research.
Technology assessment for mission-critical services on automotive virtual edge communicator (AVEC)

Mission critical services are beginning to migrate to broadband from the trusted-but-limited existing systems. They need assured availability and confidentiality for communication in areas affected by a disaster, flash crowding or any catastrophic network failure. This paper describes a vision for an automotive virtual edge Communication scheme that helps healing stricken networks, utilizing any available resources for hosting network functions on vehicles. Emergency and essential service organizations who rush to the area would ‘bring their own network’, i.e. computing capacity and connectivity tools, aboard their service vehicles. These vehicles form a dynamic community that shares resources, information and services, using the combined processing capacity via virtualized core functions. To realize such a scheme, innovative features are required, such as cooperative hosting, opportunistic vehicular resource virtualization, context-based edge SDN traffic prioritization, and ad-hoc vehicular community management, including multi-entity authentication.
Brand Community

This article introduces the idea of brand community. A brand community is a specialized, non-geographically bound community, based on a structured set of social relations among admirers of a brand. Grounded in both classic and contemporary sociology and consumer behavior, this article uses ethnographic and computer mediated environment data to explore the characteristics, processes, and particularities of three brand communities (those centered on Ford Bronco, Macintosh, and Saab). These brand communities exhibit three traditional markers of community: shared consciousness, rituals and traditions, and a sense of moral responsibility. The commercial and mass-mediated ethos in which these communities are situated affects their character and structure and gives rise to their particularities. Implications for branding, sociological theories of community, and consumer behavior are offered.
Comparing Software Architecture Recovery Techniques Using Accurate Dependencies

Many techniques have been proposed to automatically recover software architectures from software implementations. A thorough comparison among the recovery techniques is needed to understand their effectiveness and applicability. This study improves on previous studies in two ways.
 First, we study the impact of leveraging more accurate symbol dependencies on the accuracy of architecture recovery techniques. Previous studies have not seriously considered how the quality of the input might affect the quality of the output for architecture recovery techniques.
 Second, we study a system (Chromium) that is substantially larger (9.7 million lines of code) than those included in previous studies. Obtaining the ground-truth architecture of Chromium involved two years of collaboration with its developers. As part of this work we developed a new submodule-based technique to recover preliminary versions of ground-truth architectures.
 The other systems that we study have been examined previously. In some cases, we have updated the ground-truth architectures to newer versions, and in other cases we have corrected newly discovered inconsistencies.
 Our evaluation of nine variants of six state-of-the-art architecture recovery techniques shows that symbol dependencies generally produce architectures with higher accuracies than include dependencies. Despite this improvement, the overall accuracy is low for all recovery techniques. The results suggest that (1) in addition to architecture recovery techniques, the accuracy of dependencies used as their inputs is another factor to consider for high recovery accuracy, and (2) more accurate recovery techniques are needed.
 Our results show that some of the studied architecture recovery techniques scale to the 10M lines-of-code range (the size of Chromium), whereas others do not.
A 280-KBytes Twin-Bit-Cell Embedded NOR Flash Memory With a Novel Sensing Current Protection Enhanced Technique and High-Voltage Generating Systems

A novel sensing current protection enhanced (SCPE) technique and high-voltage (HV) generating systems are adopted to greatly enhance the advantages of the twin-bit cell. The SCPE technique provides the tradeoff of sensing margin loss between “1” and “0” bits sensing case to realize fast read access. The read speed is improved by 7.7%. HV generating systems with parallel-series-transform and capacitance-shared techniques are proposed to satisfy the complicated requirements of output capability and current drivability with lower area penalty. The HV periphery area decreases by 71%. A 1.5-V 280-KBytes embedded NOR flash memory IP has been fabricated in HHGrace (Shanghai Huahong Grace Semiconductor Manufacturing Corporation) 90-nm 4 poly 4 metal CMOS process. The complicated operation voltages and access time of 23.5 ns have been achieved at 1.5 V. The die size of the proposed IP is 0.437 mm<inline-formula> <tex-math notation="LaTeX">$^{2}$ </tex-math></inline-formula> and the area size of charge pump has been reduced to 0.006 mm<inline-formula> <tex-math notation="LaTeX">$^{2}$ </tex-math></inline-formula> on average.
Evaluation of LBP and Deep Texture Descriptors with a New Robustness Benchmark

In recent years, a wide variety of different texture descriptors has been proposed, including many LBP variants. New types of descriptors based on multistage convolutional networks and deep learning have also emerged. In different papers the performance comparison of the proposed methods to earlier approaches is mainly done with some wellknown texture datasets, with differing classifiers and testing protocols, and often not using the best sets of parameter values and multiple scales for the comparative methods. Very important aspects such as computational complexity and effects of poor image quality are often neglected. In this paper, we propose a new extensive benchmark (RoTeB) for measuring the robustness of texture operators against different classification challenges, including changes in rotation, scale, illumination, viewpoint, number of classes, different types of image degradation, and computational complexity. Fourteen datasets from the eight most commonly used texture sources are used in the benchmark. An extensive evaluation of the recent most promising LBP variants and some non-LBP descriptors based on deep convolutional networks is carried out. The best overall performance is obtained for the Median Robust Extended Local Binary Pattern (MRELBP) feature. For textures with very large appearance variations, Fisher vector pooling of deep Convolutional Neural Networks is clearly the best, but at the cost of very high computational complexity. The sensitivity to image degradations and computational complexity are among the key problems for most of the methods considered.
Low RCS microstrip patch array: EM design and performance analysis

In low observable technology, antennas with low radar cross section (RCS) but with high gain are preferred. In this paper, a low RCS microstrip patch array is designed using the concept of phase cancellation. The patch elements are rotated sequentially or randomly keeping the feed fixed. This geometric arrangement of patch elements reduces the scattering effectively. The gain and the RCS of randomly rotated (RR) microstrip patch array is analyzed in comparison with a uniform array with no element rotation (NR) and sequentially rotated (SR) patch elements. It is shown that the RCS of the RR array is reduced significantly without any adverse effect on its radiation performance.
Quality Models in Software Engineering Literature : An Analytical and Comparative Study

The quality of the software is critical and essenti al in different types of organizations. In some typ es of software, poor quality of the software product in s e itive systems (such as: real-time systems, contr ol systems, etc.) may lead to loss of human life, permanent injury, m ission failure, or financial loss. In software engi neering literature, there are a number of quality models in which they contain a number of quality characteris ics (or factors, as called in some models). These quality characteri stics could be used to reflect the quality of the s oftware product from the view of that characteristic. Selecting whi ch one of the quality models to use is a real chall enge. In this paper, we will discuss the contents of the followin g quality models: McCall’s quality model, Boehm’s q uality model, Dromey's quality model, FURPS quality model and ISO 9126 quality model. In addition, we will fo cus on a comparison between these quality models, and find t he key differences between them.
Improving Performance in Neural Networks Using a Boosting Algorithm

Patrice Simard AT &T Bell Laboratories Holmdel, NJ 07733 A boosting algorithm converts a learning machine with error rate less than 50% to one with an arbitrarily low error rate. However, the algorithm discussed here depends on having a large supply of independent training samples. We show how to circumvent this problem and generate an ensemble of learning machines whose performance in optical character recognition problems is dramatically improved over that of a single network. We report the effect of boosting on four databases (all handwritten) consisting of 12,000 digits from segmented ZIP codes from the United State Postal Service (USPS) and the following from the National Institute of Standards and Testing (NIST): 220,000 digits, 45,000 upper case alphas, and 45,000 lower case alphas. We use two performance measures: the raw error rate (no rejects) and the reject rate required to achieve a 1% error rate on the patterns not rejected. Boosting improved performance in some cases by a factor of three.
Parallel and incremental credit card fraud detection model to handle concept drift and data imbalance

Real-time fraud detection in credit card transactions is challenging due to the intrinsic properties of transaction data, namely data imbalance, noise, borderline entities and concept drift. The advent of mobile payment systems has further complicated the fraud detection process. This paper proposes a transaction window bagging (TWB) model, a parallel and incremental learning ensemble, as a solution to handle the issues in credit card transaction data. TWB model uses a parallelized bagging approach, incorporated with an incremental learning model, cost-sensitive base learner and a weighted voting-based combiner to effectively handle concept drift and data imbalance. Experiments were performed with Brazilian Bank data and University of California, San Diego (UCSD) data, and results were compared with state-of-the-art models. Comparisons on Brazilian Bank data indicates increased fraud detection levels between 18–38% and 1.3–2 times lower cost levels, which exhibits the enhanced performances of TWB. Comparisons on UCSD data indicate improved precision levels ranging between 8 and 25%, indicating the robustness of the TWB model. Future extensions of the proposed model will be on incorporating feature engineering to improve performances.
Bayesian Variable Selection

Variable selection is an important task in Predictive Analytics as it aims at eliminating redundant or irrelevant variables from a predictive model (either supervised or unsupervised) before this model is deployed in production. When the number of variables exceeds the number of instances, any predictive model will likely overfit the data, implying poor generalization to new, previously unseen instances. Even if for some data sets the number of variables is (much) smaller than the number of instances, some of collected variables may still harm the model performance if left in a data set because these variables may mask (hide) good for prediction variables. Therefore these “harmful” variables need to be discovered and removed. There are hundreds techniques proposed for variable selection (see, for example, the book of (Liu & Motoda, 2008) entirely devoted to various variable selection methods). The purpose of this chapter is not to present as many of them as possible but concentrate on one type of algorithms, namely Bayesian variable selection (Lunn, Jackson, Best, Thomas, & Spiegelhalter, 2013). Again, as there are many such algorithms (see, for example, the survey in (O’Hara & Sillanpää, 2009)), we explain the general idea on the example of a particular representative algorithm described in this chapter. Why Bayesian variable selection? Bayesian variable selection methods come equipped with measures of uncertainty, such as the posterior probability of each model and the variable importance specified by marginal inclusion probabilities. Model uncertainly can be incorporated into prediction through model averaging, which usually improves prediction. Missing data and/ or non-Gaussian data distributions are easily handled by Markov Chain Monte Carlo (MCMC) simulations, which are the part of Bayesian variable selection. Bayesian methods such as “stochastic search variable selection” (George & McCulloch, 1996) have been proposed as alternatives to traditional stepwise variable selection procedures in regression models. Instead of either fixing a regression coefficient at zero or allowing it to be estimated by least squares, as in stepwise procedures, stochastic search variable selection assigns a mixture prior distribution for the given coefficient. Both components of this prior are centered at zero but one with a small variance and the other with a large variance. In general, there is a vector of regression coefficients β and a vector γ of the same length containing 0/1 indicators, where 1 means a variable is included in a model and 0 implies that a variable is omitted from a model. The classical Bayesian variable selection (George & McCulloch, 1996) thus corresponds to the following model:
Light-Weight Instruction Set Extensions for Bit-Sliced Cryptography

Bit-slicing is a non-conventional implementation technique for cryptographic software where an n-bit processor is considered as a collection of n 1-bit execution units operating in SIMD mode. Particularly when implementing symmetric ciphers, the bit-slicing approach has several advantages over more conventional alternatives: it often allows one to reduce memory footprint by eliminating large look-up tables, and it permits more predictable performance characteristics that can foil time based side-channel attacks. Both features are attractive for mobile and embedded processors, but the performance overhead that results from bit-sliced implementation often represents a significant disadvantage. In this paper we describe a set of light-weight Instruction Set Extensions (ISEs) that can improve said performance while retaining all advantages of bit-sliced implementation. Contrary to other crypto-ISE, our design is generic and allows for a high degree of algorithm agility: we demonstrate applicability to several well-known cryptographic primitives including four block ciphers (DES, Serpent, AES, and PRESENT), a hash function (SHA-1), as well as multiplication of ternary polynomials.
The Human Element in Autonomous Vehicles

Autonomous vehicle research has been prevalent for well over a decade but only recently has there been a small amount of research conducted on the human interaction that occurs in autonomous vehicles. Although functional software and sensor technology is essential for safe operation, which has been the main focus of autonomous vehicle research, handling all elements of human interaction is also a very salient aspect of their success. This paper will provide an overview of the importance of human vehicle interaction in autonomous vehicles, while considering relevant related factors that are likely to impact adoption. Particular attention will be given to prior research conducted on germane areas relating to control in the automobile, in addition to the different elements that are expected to affect the likelihood of success for these vehicles initially developed for human operation. This paper will also include a discussion of the limited research conducted to consider interactions with humans and the current state of published functioning software and sensor technology that exists.
CNT/PDMS Composite Flexible Dry Electrodesfor Long-Term ECG Monitoring

We fabricated a carbon nanotube (CNT)/ polydimethylsiloxane (PDMS) composite-based dry ECG electrode that can be readily connected to conventional ECG devices, and showed its long-term wearable monitoring capability and robustness to motion and sweat. While the dispersion of CNTs in PDMS is challenging, we optimized the process to disperse untreated CNTs within PDMS by mechanical force only. The electrical and mechanical characteristics of the CNT/PDMS electrode were tested according to the concentration of CNTs and its thickness. The performances of ECG electrodes were evaluated by using 36 types of electrodes which were fabricated with different concentrations of CNTs, and with a differing diameter and thickness. The ECG signals were obtained by using electrodes of diverse sizes to observe the effects of motion and sweat, and the proposed electrode was shown to be robust to both factors. The CNT concentration and diameter of the electrodes were critical parameters in obtaining high-quality ECG signals. The electrode was shown to be biocompatible from the cytotoxicity test. A seven-day continuous wearability test showed that the quality of the ECG signal did not degrade over time, and skin reactions such as itching or erythema were not observed. This electrode could be used for the long-term measurement of other electrical biosignals for ubiquitous health monitoring including EMG, EEG, and ERG.
A Reference Model for the Blockchain-Based

Blockchain is a distributed, transactional database that is shared across all the nodes participating in the network. This is the main technical innovation of Bitcoin and it acts as a public ledger for the transactions. Every node in the system has a full copy of the current chain, which contains every transaction ever executed. Every block contains a hash of the previous block, linking these two together. The linked blocks become a blockchain. However, this technology lacks standardisation and uniform understanding. This is due to a few studies, that would provide a comprehensive model of the blockchain and the distributed ledger technology. In this thesis we compare four blockchain technology platforms and focus on their business level properties including actors and roles, services, processes and data model. Our comparison results in a reference model, which could potentially guide the business and system analysts and software developers when developing new blockchain platforms or their supported implementations. Accuracy of the proposed reference model is validated by considering it against selected blockchain platforms. The reference model is also validated via application, showing its usefulness for a risk-related blockchain security assessment.
Bagging to Improve the Accuracy of A Clustering Procedure

The microarray technology is increasingly being applied in biological and medical research to address a wide range of problems such as the classification of tumors. An important statistical question associated with tumor classification is the identification of new tumor classes using gene expression profiles. Essential aspects of this clustering problem include identifying accurate partitions of the tumor samples into clusters and assessing the confidence of cluster assignments for individual samples.



Two new resampling methods, inspired from bagging in prediction, are proposed to improve and assess the accuracy of a given clustering procedure. In these ensemble methods, a partitioning clustering procedure is applied to bootstrap learning sets and the resulting multiple partitions are combined by voting or the creation of a new dissimilarity matrix. As in prediction, the motivation behind bagging is to reduce variability in the partitioning results via averaging. The performances of the new and existing methods were compared using simulated data and gene expression data from two recently published cancer microarray studies. The bagged clustering procedures were in general at least as accurate and often substantially more accurate than a single application of the partitioning clustering procedure. A valuable by-product of bagged clustering are the cluster votes which can be used to assess the confidence of cluster assignments for individual observations.



For supplementary information on datasets, analyses, and software, consult http://www.stat.berkeley.edu/~sandrine and http://www.bioconductor.org.
May I help you? - Design of Human-like Polite Approaching Behavior-

When should service staff initiate interaction with a visitor? Neither simply-proactive (e.g. talk to everyone in a sight) nor passive (e.g. wait until being talked to) strategies are desired. This paper reports our modeling of polite approaching behavior. In a shopping mall, there are service staff members who politely approach visitors who need help. Our analysis revealed that staff members are sensitive to "intentions" of nearby visitors. That is, when a visitor intends to talk to a staff member and starts to approach, the staff member also walks a few steps toward the visitors in advance to being talked. Further, even when not being approached, staff members exhibit "availability" behavior in the case that a visitor's intention seems uncertain. We modeled these behaviors that are adaptive to pedestrians' intentions, occurred prior to initiation of conversation. The model was implemented into a robot and tested in a real shopping mall. The experiment confirmed that the proposed method is less intrusive to pedestrians, and that our robot successfully initiated interaction with pedestrians.