nils-herrmann/clean_scirepeval huggingface dataset:
Autonomous tour guide robot by using ultrasonic range sensors and QR code recognition in indoor environment

This paper addresses the challenge of mobile robot navigation in indoor environments. There is a critical need for cost-effective, reliable, and fairly accurate solutions to meet the demands of indoor robotic applications. Currently, researchers are exploring various approaches for this problem. The one we are presenting in this paper is based on QR (Quick Response) codes to provide location references for mobile robots. The mobile robot is equipped with a Smartphone that is programmed to detect and read information on QR codes that are strategically placed in the operating environment of the robot. The mobile robot can perform the autonomous run throughout the guide route by using real-time QR code recognition. The lab information on QR code is played to the visitors using Text-to-Speech provided through Android device. Ultrasonic range sensors which can detect objects and measure distances with high accuracy are used to implement the wall-following and obstacle-avoidance behaviors. The collected sonar range information by ultrasonic range sensors is processed by a microcontroller that autonomously controls a tour guide robot. An algorithm based on a proportional-integral-derivative (PID) control is applied to the tour guide robot to perform more accurate robot motion control. A Bluetooth technology is used to send stored information on QR codes from the Smartphone to the tour guide robot wirelessly. The experimental setup of the tour guide robot along with the successful implementation of the efficient method for a navigation technique is presented.
Reviewer recommendation for pull-requests in GitHub: What can we learn from code review and bug assignment?

The pull-based model, widely used in distributed software development, offers an extremely low barrier to entry for potential contributors (anyone can submit of contributions to any project, through pull-requests). Meanwhile, the project’s core team must act as guardians of code quality, ensuring that pull-requests are carefully inspected before being merged into the main development line. However, with pull-requests becoming increasingly popular, the need for qualified reviewers also increases. GitHub facilitates this, by enabling the crowd-sourcing of pull-request reviews to a larger community of coders than just the project’s core team, as a part of their social coding philosophy. However, having access to more potential reviewers does not necessarily mean that it’s easier to find the right ones (the “needle in a haystack” problem). If left unsupervised, this process may result in communication overhead and delayed pull-request processing.  This study aims to investigate whether and how previous approaches used in bug triaging and code review can be adapted to recommending reviewers for pull-requests, and how to improve the recommendation performance.  First, we extend three typical approaches used in bug triaging and code review for the new challenge of assigning reviewers to pull-requests. Second, we analyze social relations between contributors and reviewers, and propose a novel approach by mining each project’s comment networks (CNs). Finally, we combine the CNs with traditional approaches, and evaluate the effectiveness of all these methods on 84 GitHub projects through both quantitative and qualitative analysis.  We find that CN-based recommendation can achieve, by itself, similar performance as the traditional approaches. However, the mixed approaches can achieve significant improvements compared to using either of them independently.  Our study confirms that traditional approaches to bug triaging and code review are feasible for pull-request reviewer recommendations on GitHub. Furthermore, their performance can be improved significantly by combining them with information extracted from prior social interactions between developers on GitHub. These results prompt for novel tools to support process automation in social coding platforms, that combine social (e.g., common interests among developers) and technical factors (e.g., developers’ expertise).
Frankly, We Do Give a Damn: The Relationship Between Profanity and Honesty.

There are two conflicting perspectives regarding the relationship between profanity and dishonesty. These two forms of norm-violating behavior share common causes and are often considered to be positively related. On the other hand, however, profanity is often used to express one's genuine feelings and could therefore be negatively related to dishonesty. In three studies, we explored the relationship between profanity and honesty. We examined profanity and honesty first with profanity behavior and lying on a scale in the lab (Study 1; N = 276), then with a linguistic analysis of real-life social interactions on Facebook (Study 2; N = 73,789), and finally with profanity and integrity indexes for the aggregate level of U.S. states (Study 3; N = 50 states). We found a consistent positive relationship between profanity and honesty; profanity was associated with less lying and deception at the individual level and with higher integrity at the society level.
Above the Clouds : A View of Cloud Computing

Cloud Computing, the long-held dream of computing as a utility, has the potential to transform a large part of the IT industry, making software even more attractive as a service and shaping the way IT hardware is designed and purchased. Developers with innovative ideas for new Internet services no longer require the large capital outlays in hardware to deploy their service or the human expense to operate it. They need not be concerned about over-provisioning for a service whose popularity does not meet their predictions, thus wasting costly resources, or under-provisioning for one that becomes wildly popular, thus missing potential customers and revenue. Moreover, companies with large batch-oriented tasks can get results as quickly as their programs can scale, since using 1000 servers for one hour costs no more than using one server for 1000 hours. This elasticity of resources, without paying a premium for large scale, is unprecedented in the history of IT. As a result, Cloud Computing is a popular topic for blogging and white papers and been featured in the title of workshops, conferences, and even magazines. Nevertheless, confusion remains about exactly what it is and when it’s useful, causing Oracle’s CEO Larry Ellison to vent his frustration:
Designing efficient high performance server-centric data center network architecture

Data center network (DCN) architecture is regarded as one of the most important determinants of network performance. As the most typical representatives of DCN architecture designs, the server-centric scheme stands out due to its good performance in various aspects. In this paper, we firstly present the design, implementation and evaluation of SprintNet, a novel server-centric network architecture for data centers. SprintNet achieves high performance in network capacity, fault tolerance, and network latency. SprintNet is also a scalable, yet low-diameter network architecture where the maximum shortest distance between any pair of servers can be limited by no more than four and is independent of the number of layers. The specially designed routing schemes for SprintNet strengthen its merits. However, all of these kind of server-centric architectures still suffer from some critical shortcomings owing to the server’s responsibility of forwarding packets. With regard to these issues, in this paper, we then propose a hardware based approach, named ‘‘Forwarding Unit’’ to provide an effective solution to these drawbacks and improve the efficiency of server-centric architectures. Both theoretical analysis and simulations are conducted to evaluate the overall performance of SprintNet and the Forwarding Unit approach with respect to cost-effectiveness, fault-tolerance, system latency, packet loss ratio, aggregate bottleneck throughput, and average path length. The evaluation results convince the feasibility and good performance of both SprintNet and Forwarding Unit.
Twitter financial community modeling using agent based simulation

With the empirical evidence that Twitter influences the financial market, there is a need for a bottom-up approach focusing on individual Twitter users and their message propagation among a selected Twitter community with regard to the financial market. This paper presents an agent-based simulation framework to model the Twitter network growth and message propagation mechanism in the Twitter financial community. Using the data collected through the Twitter API, the model generates a dynamic community network with message propagation rates by different agent types. The model successfully validates against the empirical characteristics of the Twitter financial community in terms of network demographics and aggregated message propagation pattern. Simulation of the 2013 Associated Press hoax incident demonstrates that removing critical nodes of the network (users with top centrality) dampens the message propagation process linearly and critical node of the highest betweenness centrality has the optimal effect in reducing the spread of the malicious message to lesser ratio of the community.
3D scan-conversion algorithms for voxel-based graphics

An assortment of algorithms, termed three-dimensional (3D) scan-conversion algorithms, is presented. These algorithms scan-convert 3D geometric objects into their discrete voxel-map representation within a Cubic Frame Buffer (CFB). The geometric objects that are studied here include three-dimensional lines, polygons (optionally filled), polyhedra (optionally filled), cubic parametric curves, bicubic parametric surface patches, circles (optionally filled), and quadratic objects (optionally filled) like those used in constructive solid geometry: cylinders, cones, and spheres.
All algorithms presented here do scan-conversion with computational complexity which is linear in the number of voxels written to the CFB. All algorithms are incremental and use only additions, subtractions, tests and simpler operations inside the inner algorithm loops. Since the algorithms are basically sequential, the temporal complexity is also linear. However, the polyhedron-fill and sphere-fill algorithms have less than linear temporal complexity, as they use a mechanism for writing a voxel run into the CFB. The temporal complexity would then be linear with the number of pixels in the object's 2D projection. All algorithms have been implemented as part of the CUBE Architecture, which is a voxel-based system for 3D graphics. The CUBE architecture is also presented.
A Simple and Global Optimization Algorithm for Engineering Problems: Differential Evolution Algorithm

Differential Evolution (DE) algorithm is a new heuristic approach mainly having three advantages; finding the true global minimum regardless of the initial parameter values, fast convergence, and using few control parameters. DE algorithm is a population based algorithm like genetic algorithms using similar operators; crossover, mutation and selection. In this work, we have compared the performance of DE algorithm to that of some other well known versions of genetic algorithms: PGA, Grefensstette, Eshelman. In simulation studies, De Jong’s test functions have been used. From the simulation results, it was observed that the convergence speed of DE is significantly better than genetic algorithms. Therefore, DE algorithm seems to be a promising approach for engineering optimization problems.
Design of effective neural network ensembles for image classification purposes

In the field of pattern recognition, the combination of an ensemble of neural networks has been proposed as an approach to the development of high performance image classification systems. However, previous work clearly showed that such image classification systems are effective only if the neural networks forming them make different errors. Therefore, the fundamental need for methods aimed to design ensembles of "error-independent” networks is currently acknowledged. In this paper, an approach to the automatic design of effective neural network ensembles is proposed. Given an initial large set of neural networks, our approach is aimed to select the subset formed by the most error-independent nets. Reported results on the classification of multisensor remote-sensing images show that this approach allows one to design effective neural network ensembles.
GermEval-2014 : Nested Named Entity Recognition with Neural Networks

Collobert et al. (2011) showed that deep neural network architectures achieve stateof-the-art performance in many fundamental NLP tasks, including Named Entity Recognition (NER). However, results were only reported for English. This paper reports on experiments for German Named Entity Recognition, using the data from the GermEval 2014 shared task on NER. Our system achieves an F1-measure of 75.09% according to the official metric.
A Global Hypotheses Verification Method for 3D Object Recognition

We propose a novel approach for verifying model hypotheses in cluttered and heavily occluded 3D scenes. Instead of verifying one hypothesis at a time, as done by most state-of-the-art 3D object recognition methods, we determine object and pose instances according to a global optimization stage based on a cost function which encompasses geometrical cues. Peculiar to our approach is the inherent ability to detect significantly occluded objects without increasing the amount of false positives, so that the operating point of the object recognition algorithm can nicely move toward a higher recall without sacrificing precision. Our approach outperforms state-of-the-art on a challenging dataset including 35 household models obtained with the Kinect sensor, as well as on the standard 3D object recognition benchmark dataset.
Integrated cognitive architectures: a survey

This article aims to present an account of the state of the art research in the field of integrated cognitive architectures by providing a review of six cognitive architectures, namely Soar, ACT-R, ICARUS, BDI, the subsumption architecture and CLARION. We conduct a detailed functional comparison by looking at a wide range of cognitive components, including perception, memory, goal representation, planning, problem solving, reasoning, learning, and relevance to neurobiology. In addition, we study the range of benchmarks and applications that these architectures have been applied to. Although no single cognitive architecture has provided a full solution with the level of human intelligence, important design principles have emerged, pointing to promising directions towards generic and scalable architectures with close analogy to human brains.
Parallel Clustering Algorithms : Survey

Clustering is grouping input data sets into subsets, called ’clusters’ within which the elements are somewhat similar. In general, clustering is an unsupervised learning task as very little or no prior knowledge is given except the input data sets. The tasks have been used in many fields and therefore various clustering algorithms have been developed. Clustering task is, however, computationally expensive as many of the algorithms require iterative or recursive procedures and most of real-life data is high dimensional. Therefore, the parallelization of clustering algorithms is inevitable, and various parallel clustering algorithms have been implemented and applied to many applications. In this paper, we review a variety of clustering algorithms and their parallel versions as well. Although the parallel clustering algorithms have been used for many applications, the clustering tasks are applied as preprocessing steps for parallelization of other algorithms too. Therefore, the applications of parallel clustering algorithms and the clustering algorithms for parallel computations are described in this paper.
Frequency Based Chunking for Data De-Duplication

A predominant portion of Internet services, like content delivery networks, news broadcasting, blogs sharing and social networks, etc., is data centric. A significant amount of new data is generated by these services each day. To efficiently store and maintain backups for such data is a challenging task for current data storage systems. Chunking based deduplication (dedup) methods are widely used to eliminate redundant data and hence reduce the required total storage space. In this paper, we propose a novel Frequency Based Chunking (FBC) algorithm. Unlike the most popular Content-Defined Chunking (CDC) algorithm which divides the data stream randomly according to the content, FBC explicitly utilizes the chunk frequency information in the data stream to enhance the data deduplication gain especially when the metadata overhead is taken into consideration. The FBC algorithm consists of two components, a statistical chunk frequency estimation algorithm for identifying the globally appeared frequent chunks, and a two-stage chunking algorithm which uses these chunk frequencies to obtain a better chunking result. To evaluate the effectiveness of the proposed FBC algorithm, we conducted extensive experiments on heterogeneous datasets. In all experiments, the FBC algorithm persistently outperforms the CDC algorithm in terms of achieving a better dedup gain or producing much less number of chunks. Particularly, our experiments show that FBC produces 2.5 ~ 4 times less number of chunks than that of a baseline CDC which achieving the same Duplicate Elimination Ratio (DER). Another benefit of FBC over CDC is that the FBC with average chunk size greater than or equal to that of CDC achieves up to 50% higher DER than that of a CDC algorithm.
Pushing the Limits: A Very Compact and a Threshold Implementation of AES

Our contribution is twofold: first we describe a very compact hardware implementation of AES-128, which requires only 2400 GE. This is to the best of our knowledge the smallest implementation reported so far. Then we apply the threshold countermeasure by Nikova et al. to the AES S-box and yield an implementation of the AES improving the level of resistance against first-order side-channel attacks. Our experimental results on real-world power traces show that although our implementation provides additional security, it is still susceptible to some sophisticated attacks having enough number of measurements.
Coping: pitfalls and promise.

Coping, defined as the thoughts and behaviors used to manage the internal and external demands of situations that are appraised as stressful, has been a focus of research in the social sciences for more than three decades. The dramatic proliferation of coping research has spawned healthy debate and criticism and offered insight into the question of why some individuals fare better than others do when encountering stress in their lives. We briefly review the history of contemporary coping research with adults. We discuss three primary challenges for coping researchers (measurement, nomenclature, and effectiveness), and highlight recent developments in coping theory and research that hold promise for the field, including previously unaddressed aspects of coping, new measurement approaches, and focus on positive affective outcomes.
Functional imaging of brain responses to pain. A review and meta-analysis (2000)

Brain responses to pain, assessed through positron emission tomography (PET) and functional magnetic resonance imaging (fMRI) are reviewed. Functional activation of brain regions are thought to be reflected by increases in the regional cerebral blood flow (rCBF) in PET studies, and in the blood oxygen level dependent (BOLD) signal in fMRI. rCBF increases to noxious stimuli are almost constantly observed in second somatic (SII) and insular regions, and in the anterior cingulate cortex (ACC), and with slightly less consistency in the contralateral thalamus and the primary somatic area (SI). Activation of the lateral thalamus, SI, SII and insula are thought to be related to the sensory-discriminative aspects of pain processing. SI is activated in roughly half of the studies, and the probability of obtaining SI activation appears related to the total amount of body surface stimulated (spatial summation) and probably also by temporal summation and attention to the stimulus. In a number of studies, the thalamic response was bilateral, probably reflecting generalised arousal in reaction to pain. ACC does not seem to be involved in coding stimulus intensity or location but appears to participate in both the affective and attentional concomitants of pain sensation, as well as in response selection. ACC subdivisions activated by painful stimuli partially overlap those activated in orienting and target detection tasks, but are distinct from those activated in tests involving sustained attention (Stroop, etc.). In addition to ACC, increased blood flow in the posterior parietal and prefrontal cortices is thought to reflect attentional and memory networks activated by noxious stimulation. Less noted but frequent activation concerns motor-related areas such as the striatum, cerebellum and supplementary motor area, as well as regions involved in pain control such as the periaqueductal grey. In patients, chronic spontaneous pain is associated with decreased resting rCBF in contralateral thalamus, which may be reverted by analgesic procedures. Abnormal pain evoked by innocuous stimuli (allodynia) has been associated with amplification of the thalamic, insular and SII responses, concomitant to a paradoxical CBF decrease in ACC. It is argued that imaging studies of allodynia should be encouraged in order to understand central reorganisations leading to abnormal cortical pain processing. A number of brain areas activated by acute pain, particularly the thalamus and anterior cingulate, also show increases in rCBF during analgesic procedures. Taken together, these data suggest that hemodynamic responses to pain reflect simultaneously the sensory, cognitive and affective dimensions of pain, and that the same structure may both respond to pain and participate in pain control. The precise biochemical nature of these mechanisms remains to be investigated.
Device-Free Presence Detection and Localization With SVM and CSI Fingerprinting

Presence detection and localization are of importance to a variety of applications. Most previous approaches require the objects to carry electronic devices, while on many occasions device-free presence detection and localization are in need. This paper proposes a device-free presence detection and localization algorithm based on WiFi channel state information (CSI) and support vector machines (SVM). In the area of interest covered with WiFi, human movements may cause observable alteration of WiFi signals. By analyzing the CSI fingerprint patterns, the proposed algorithm is able to detect human presence through SVM classification. By establishing the nonlinear relationship between CSI fingerprints and locations through SVM regression, the proposed algorithm is able to estimate the object locations according to the measured CSI fingerprints. To cope with the noisy WiFi channels, the proposed algorithm applies density-based spatial clustering of applications with noise to reduce the noise in CSI fingerprints, and applies principal component analysis to extract the most contributing features and reduce the dimensionality of CSI fingerprints. Evaluations in two typical scenarios achieved the presence detection precision of over 97%, and the localization accuracy of 1.22 and 1.39 m, respectively.