nils-herrmann/clean_scirepeval huggingface dataset:
Xpander: Towards Optimal-Performance Datacenters

Despite extensive efforts to meet ever-growing demands, today's datacenters often exhibit far-from-optimal performance in terms of network utilization, resiliency to failures, cost efficiency, incremental expandability, and more. Consequently, many novel architectures for high-performance datacenters have been proposed. We show that the benefits of state-of-the-art proposals are, in fact, derived from the fact that they are (implicitly) utilizing "expander graphs" (aka expanders) as their network topologies, thus unveiling a unifying theme of these proposals. We observe, however, that these proposals are not optimal with respect to performance, do not scale, or suffer from seemingly insurmountable deployment challenges. We leverage these insights to present Xpander, a novel datacenter architecture that achieves near-optimal performance and provides a tangible alternative to existing datacenter designs. Xpander's design turns ideas from the rich graph-theoretic literature on constructing optimal expanders into an operational reality. We evaluate Xpander via theoretical analyses, extensive simulations, experiments with a network emulator, and an implementation on an SDN-capable network testbed. Our results demonstrate that Xpander significantly outperforms both traditional and proposed datacenter designs. We discuss challenges to real-world deployment and explain how these can be resolved.
Swarm intelligence and robotics

The aim of this paper is to provide a review of recent developments in the application of swarm intelligence to robotics.  This paper initially considers swarm intelligence and then discusses its application to robotics through reference to a number of recent research programmes.  Based on the principles of swarm intelligence, which is derived from the swarming behaviour of biological entities, swarm robotics research is widespread but still at an early stage. Much aims to gain an understanding of biological swarming and apply it to autonomous, mobile multi-robot systems. European activities are particularly strong and several large, collaborative projects are underway. Research in the USA has a military bias and much is funded by defence agencies.  The paper provides an up-to-date insight into swarm robot research and development.
Detecting concept change in dynamic data streams

In this research we present a novel approach to the concept change detection problem. Change detection is a fundamental issue with data stream mining as classification models generated need to be updated when significant changes in the underlying data distribution occur. A number of change detection approaches have been proposed but they all suffer from limitations with respect to one or more key performance factors such as high computational complexity, poor sensitivity to gradual change, or the opposite problem of high false positive rate. Our approach uses reservoir sampling to build a sequential change detection model that offers statistically sound guarantees on false positive and false negative rates but has much smaller computational complexity than the ADWIN concept drift detector. Extensive experimentation on a wide variety of datasets reveals that the scheme also has a smaller false detection rate while maintaining a competitive true detection rate to ADWIN.
Multilayered Composite Right/Left-Handed Leaky-Wave Antenna With Consistent Gain

A multilayered composite right/left-handed (CRLH) based leaky-wave antenna is proposed for the continuous beam scanning from backward to forward direction with consistent gain. The antenna comprises a substrate integrated waveguide (SIW) with an array of the CRLH unit cells. The CRLH unit cell is formed by a slot cut on the upper ground of the SIW, and a parasitic patch beneath the slot. The antenna achieves continuous beam scanning against frequency and the optimal boresight gain at the balanced condition frequency. The antenna prototype with 15 unit cells exhibits the measured beam scanning angle of -66° to 78° over the frequency range from 8.0 to 13.0 GHz with a consistent gain of more than 9.0 dBi.
Fibonacci Exposure Bracketing for High Dynamic Range Imaging

Exposure bracketing for high dynamic range (HDR) imaging involves capturing several images of the scene at different exposures. If either the camera or the scene moves during capture, the captured images must be registered. Large exposure differences between bracketed images lead to inaccurate registration, resulting in artifacts such as ghosting (multiple copies of scene objects) and blur. We present two techniques, one for image capture (Fibonacci exposure bracketing) and one for image registration (generalized registration), to prevent such motion-related artifacts. Fibonacci bracketing involves capturing a sequence of images such that each exposure time is the sum of the previous N(N > 1) exposures. Generalized registration involves estimating motion between sums of contiguous sets of frames, instead of between individual frames. Together, the two techniques ensure that motion is always estimated between frames of the same total exposure time. This results in HDR images and videos which have both a large dynamic range and minimal motion-related artifacts. We show, by results for several real-world indoor and outdoor scenes, that the proposed approach significantly outperforms several existing bracketing schemes.
Video enhancement using per-pixel virtual exposures

We enhance underexposed, low dynamic range videos by adaptively and independently varying the exposure at each photoreceptor in a post-process. This virtual exposure is a dynamic function of both the spatial neighborhood and temporal history at each pixel. Temporal integration enables us to expand the image's dynamic range while simultaneously reducing noise. Our non-linear exposure variation and denoising filters smoothly transition from temporal to spatial for moving scene elements. Our virtual exposure framework also supports temporally coherent per frame tone mapping. Our system outputs restored video sequences with significantly reduced noise, increased exposure time of dark pixels, intact motion, and improved details.
Distinct Brain Systems for Processing Concrete and Abstract Concepts

Behavioral and neurophysiological effects of word imageability and concreteness remain a topic of central interest in cognitive neuroscience and could provide essential clues for understanding how the brain processes conceptual knowledge. We examined these effects using event-related functional magnetic resonance imaging while participants identified concrete and abstract words. Relative to nonwords, concrete and abstract words both activated a left-lateralized network of multimodal association areas previously linked with verbal semantic processing. Areas in the left lateral temporal lobe were equally activated by both word types, whereas bilateral regions including the angular gyrus and the dorsal prefrontal cortex were more strongly engaged by concrete words. Relative to concrete words, abstract words activated left inferior frontal regions previously linked with phonological and verbal working memory processes. The results show overlapping but partly distinct neural systems for processing concrete and abstract concepts, with greater involvement of bilateral association areas during concrete word processing, and processing of abstract concepts almost exclusively by the left hemisphere.
A Tangled Web: The Faint Signals of Deception in Text - Boulder Lies and Truth Corpus (BLT-C)

We present an approach to creating corpora for use in detecting deception in text, including a discussion of the challenges peculiar to this task. Our approach is based on soliciting several types of reviews from writers and was implemented using Amazon Mechanical Turk. We describe the multi-dimensional corpus of reviews built using this approach, available free of charge from LDC as the Boulder Lies and Truth Corpus (BLT-C). Challenges for both corpus creation and the deception detection include the fact that human performance on the task is typically at chance, that the signal is faint, that paid writers such as turkers are sometimes deceptive, and that deception is a complex human behavior; manifestations of deception depend on details of domain, intrinsic properties of the deceiver (such as education, linguistic competence, and the nature of the intention), and specifics of the deceptive act (e.g., lying vs. fabricating.) To overcome the inherent lack of ground truth, we have developed a set of semi-automatic techniques to ensure corpus validity. We present some preliminary results on the task of deception detection which suggest that the BLT-C is an improvement in the quality of resources available for this task.
Review of various methods in improvement in speed, power & efficiency of induction motor

Induction motors are mostly used motor in industrial motion control system. Earlier, the nature of industrial application of induction motor where off constant speed mechanical drives due to different speed control systems but the recent development in power electronics component have given the way for the development for the power electronics based variable speed induction motor drives replacing DC drives. This paper contains study of the different methodology of induction motor drives. The speed, power & efficiency of induction motor controlled by the process industrial motor drivesusing a various techniques. Among the no of methods of induction motor drive techniques, this paper explain the details about slip power recovery scheme by using multilevel inverters.
FindingHuMo: Real-Time Tracking of Motion Trajectories from Anonymous Binary Sensing in Smart Environments

In this paper we have proposed and designed FindingHuMo (Finding Human Motion), a real-time user tracking system for Smart Environments. FindingHuMo can perform device-free tracking of multiple (unknown and variable number of) users in the Hallway Environments, just from non-invasive and anonymous (not user specific) binary motion sensor data stream. The significance of our designed system are as follows: (a) fast tracking of individual targets from binary motion data stream from a static wireless sensor network in the infrastructure. This needs to resolve unreliable node sequences, system noise and path ambiguity, (b) Scaling for multi-user tracking where user motion trajectories may crossover with each other in all possible ways. This needs to resolve path ambiguity to isolate overlapping trajectories, FindingHumo applies the following techniques on the collected motion data stream: (i) a proposed motion data driven adaptive order Hidden Markov Model with Viterbi decoding (called Adaptive-HMM), and then (ii) an innovative path disambiguation algorithm (called CPDA). Using this methodology the system accurately detects and isolates motion trajectories of individual users. The system performance is illustrated with results from real-time system deployment experience in a Smart Environment.
Privacy as a Service: Privacy-Aware Data Storage and Processing in Cloud Computing Architectures

In this paper we present PasS (Privacy as a Service); a set of security protocols for ensuring the privacy and legal compliance of customer data in cloud computing architectures. PasS allows for the secure storage and processing of users’ confidential data by leveraging the tamper-proof capabilities of cryptographic coprocessors. Using tamper-proof facilities provides a secure execution domain in the computing cloud that is physically and logically protected from unauthorized access. PasS central design goal is to maximize users’ control in managing the various aspects related to the privacy of sensitive data. This is achieved by implementing user-configurable software protection and data privacy mechanisms. Moreover, PasS provides a privacy feedback process which informs users of the different privacy operations applied on their data and makes them aware of any potential risks that may jeopardize the confidentiality of their sensitive information. To the best of our knowledge, PasS is the first practical cloud computing privacy solution that utilizes previous research on cryptographic coprocessors to solve the problem of securely processing sensitive data in cloud computing infrastructures.
The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis

, doi: 10.1098/rspa.1998.0193 454 1998 , Chi Chao Tung and Henry H. Liu Norden E. Huang, Zheng Shen, Steven R. Long, Manli C. Wu, Hsing H. Shih, Quanan Zheng, Nai-Chyuan nonlinear and non-stationary time series analysis The empirical mode decomposition and the Hilbert spectrum for References http://rspa.royalsocietypublishing.org/content/454/1971/903#related-urls Article cited in: Email alerting service here corner of the article or click Receive free email alerts when new articles cite this article sign up in the box at the top right-hand
A Comparison of Tactile, Visual, and Auditory Warnings for Rear-End Collision Prevention in Simulated Driving

This study examined the effectiveness of rear-end collision warnings presented in different sensory modalities as a function of warning timing in a driving simulator.



The proliferation of in-vehicle information and entertainment systems threatens driver attention and may increase the risk of rear-end collisions. Collision warning systems have been shown to improve inattentive and/or distracted driver response time (RT) in rear-end collision situations. However, most previous rear-end collision warning research has not directly compared auditory, visual, and tactile warnings.



Sixteen participants in a fixed-base driving simulator experienced four warning conditions: no warning, visual, auditory, and tactile. The warnings activated when the time-to-collision (TTC) reached a critical threshold of 3.0 or 5.0 s. Driver RT was captured from a warning below critical threshold to brake initiation.



Drivers with a tactile warning had the shortest mean RT. Drivers with a tactile warning had significantly shorter RT than drivers without a warning and had a significant advantage over drivers with visual warnings.



Tactile warnings show promise as effective rear-end collision warnings.



The results of this study can be applied to the future design and evaluation of automotive warnings designed to reduce rear-end collisions.
Learning to Implement Enterprise Systems: An Exploratory Study of the Dialectics of Change

This paper reports on a comparative case study of 13 industrial firms that had implemented an enterprise resource planning (ERP) system. Firms were compared based on their dialectical learning process. All firms had to overcome knowledge barriers of two types: those associated with the configuration of the ERP package, and those associated with the assimilation of new work processes. We examined the mechanisms through which firms attempted to overcome each type of knowledge barrier. We also observed different ERP implementation approaches: piecemeal and concerted. In the former approach, firms concentrated on the technology first and on process changes second. In the latter approach, both the technology and the process changes were tackled together. The learning challenges associated with each of these approaches were found to be different.
Self-referenced laser system for optical 3D seafloor mapping

Underwater high resolution 3D mapping implies a very accurate sensor pose estimation to fuse the sensor readings over time into one consistent model. In the case of optical mapping systems, the needed accuracy can easily lie outside of the specification of Doppler Velocity Logs normally used for pose estimation by remotely operated and autonomous underwater vehicles. This is especially the case for difficult terrains or for low altitude situations. Therefore, to improve performance in such situations, a self-referenced laser system is proposed performing micro navigation to significantly improve the resolution of optical underwater 3D seabed mapping.
A cross-platform analysis of bugs and bug-fixing in open source projects: desktop vs. Android vs. iOS

As smartphones continue to increase in popularity, understanding how software processes associated with the smartphone platform differ from the traditional desktop platform is critical for improving user experience and facilitating software development and maintenance. In this paper we focus specifically on differences in bugs and bug-fixing processes between desktop and smartphone software. Our study covers 444,129 bug reports in 88 open source projects on desktop, Android, and iOS. The study has two main thrusts: a quantitative analysis to discover similarities and differences between desktop and smartphone bug reports/processes; and a qualitative analysis where we extract topics from bug reports to understand bugs' nature, categories, and differences between platforms. Our findings include: during 2011--2013, iOS bugs were fixed three times faster compared to Android and desktop; top smartphone bug fixers are more involved in reporting bugs than top desktop bug fixers; and most frequent high-severity bugs are due to build issues on desktop, concurrency on Android, and application logic on iOS. Our study, findings, and recommendations are potentially useful to smartphone researchers and practitioners.
Discriminative Multi-instance Multitask Learning for 3D Action Recognition

As the prosperity of low-cost and easy-operating depth cameras, skeleton-based human action recognition has been extensively studied recently. However, most of the existing methods partially consider that all 3D joints of a human skeleton are identical. Actually, these 3D joints exhibit diverse responses to different action classes, and some joint configurations are more discriminative to distinguish a certain action. In this paper, we propose a discriminative multi-instance multitask learning (MIMTL) framework to discover the intrinsic relationship between joint configurations and action classes. First, a set of discriminative and informative joint configurations for the corresponding action class is captured in multi-instance learning model by regarding the action and the joint configurations as a bag and its instances, respectively. Then, a multitask learning model with group structure constraints is exploited to further reveal the intrinsic relationship between the joint configurations and different action classes. We conduct extensive evaluations of MIMTL using three benchmark 3D action recognition datasets. Experimental results show that our proposed MIMTL framework performs favorably compared with several state-of-the-art approaches.
Image Segmentation by Nested Cuts

We present a new image segmentation algorithm based on graph cuts. Our main tool is separation of each pixel p from a special point outside the image by a cut of a minimum cost. Such a cut creates a group of pixels Cp around each pixel. We show that these groups Cp are either disjoint or nested in each other, and so they give a natural segmentation of the image. In addition this property allows an efficient implementation of the algorithm because for most pixelsp the computation of Cp is not performed on the whole graph. We inspect all Cp’s and discard those which are not interesting, for example if they are too small. This procedure automatically groups small components together or merges them into nearby large clusters. Effectively our segmentation is performed by extracting significant non-intersecting closed contours. We present interes ting segmentation results on real and artificial images.
Latent Aspect Mining via Exploring Sparsity and Intrinsic Information

We investigate latent aspect mining problem that aims at automatically discovering aspect information from a collection of review texts in a domain in an unsupervised manner. One goal is to discover a set of aspects which are previously unknown for the domain, and predict the user's ratings on each aspect for each review. Another goal is to detect key terms for each aspect. Existing works on predicting aspect ratings fail to handle the aspect sparsity problem in the review texts leading to unreliable prediction. We propose a new generative model to tackle the latent aspect mining problem in an unsupervised manner. By considering the user and item side information of review texts, we introduce two latent variables, namely, user intrinsic aspect interest and item intrinsic aspect quality facilitating better modeling of aspect generation leading to improvement on the accuracy and reliability of predicted aspect ratings. Furthermore, we provide an analytical investigation on the Maximum A Posterior (MAP) optimization problem used in our proposed model and develop a new block coordinate gradient descent algorithm to efficiently solve the optimization with closed-form updating formulas. We also study its convergence analysis. Experimental results on the two real-world product review corpora demonstrate that our proposed model outperforms existing state-of-the-art models.
Salt-and-pepper noise removal by median-type noise detectors and detail-preserving regularization

This paper proposes a two-phase scheme for removing salt-and-pepper impulse noise. In the first phase, an adaptive median filter is used to identify pixels which are likely to be contaminated by noise (noise candidates). In the second phase, the image is restored using a specialized regularization method that applies only to those selected noise candidates. In terms of edge preservation and noise suppression, our restored images show a significant improvement compared to those restored by using just nonlinear filters or regularization methods only. Our scheme can remove salt-and-pepper-noise with a noise level as high as 90%.
Reaching Agreement in the Presence of Faults

The problem addressed here concerns a set of isolated processors, some unknown subset of which may be faulty, that communicate only by means of two-party messages. Each nonfaulty processor has a private value of information that must be communicated to each other nonfaulty processor. Nonfaulty processors always communicate honestly, whereas faulty processors may lie. The problem is to devise an algorithm in which processors communicate their own values and relay values received from others that allows each nonfaulty processor to infer a value for each other processor. The value inferred for a nonfaulty processor must be that processor's private value, and the value inferred for a faulty one must be consistent with the corresponding value inferred by each other nonfaulty processor.
It is shown that the problem is solvable for, and only for, n ≥ 3m + 1, where m is the number of faulty processors and n is the total number. It is also shown that if faulty processors can refuse to pass on information but cannot falsely relay information, the problem is solvable for arbitrary n ≥ m ≥ 0. This weaker assumption can be approximated in practice using cryptographic methods.
Implications of integrating test-driven development into CS1/CS2 curricula

Many academic and industry professionals have called for more testing in computer science curricula. Test-driven development (TDD) has been proposed as a solution to improve testing in academia. This paper demonstrates how TDD can be integrated into existing course materials without reducing topic coverage. Two controlled experiments were conducted in a CS1/CS2 course in Winter 2008. Following a test-driven learning approach, unit testing was introduced at the beginning of the course and reinforced through example. Results indicate that while student work loads may increase with the incorporation of TDD, students are able to successfully develop unit tests while learning to program.