nils-herrmann/clean_scirepeval huggingface dataset:
Efficient online coflow routing and scheduling

A coflow is a collection of related parallel flows that occur typically between two stages of a multi-stage compute task in a network, such as shuffle flows in MapReduce. The coflow abstraction allows applications to convey their semantics to the network so that application-level requirements (e.g., minimizing the completion time of the slowest flow) can be better satisfied. In this paper, we study the routing and scheduling of multiple coflows to minimize the average coflow completion time (CCT). We first propose a rounding-based randomized approximation algorithm, called OneCoflow, for single coflow routing and scheduling. The multiple coflow problem is more challenging as coexisting coflows will compete for the same network resources such as link bandwidths. To minimize the average CCT, we derive an online multiple coflow routing and scheduling algorithm, called OMCoflow, and prove that it has a reasonably good competitive ratio. To the best of our knowledge, this is the first online algorithm with theoretical performance guarantees which considers routing and scheduling simultaneously for multi-coflows. Compared with existing methods, OMCoflow runs more efficiently, and it avoids the problem of frequently rerouting the flows. Extensive simulations on a Facebook data trace show that OMCoflow outperforms the state-of-the-art heuristic schemes significantly (e.g., reducing the average CCT by up to 41.8% and the execution time by up to 99.2% against RAPIER [28]).
Shape from Focus

Rough surfaces pose a challenging shape extraction problem. Images of rough surfaces are often characterized by high frequency intensity variations, and it is difficult to perceive the shapes of these surfaces from their images. The shape-from-focus method described in this paper uses different focus levels to obtain a sequence of object images. The sum-modified-Laplacian (SML) operator is developed to compute local measures of the quality of image focus. The SML operator is applied to the image sequence, and the set of focus measures obtained at each image point are used to compute local depth estimates. We present two algorithms for depth estimation. The first algorithm simply looks for the focus level that maximizes the focus measure at each point. The other algorithm models the SML focus measure variations at each point as a Gaussian distribution and use this model to interpolate the computed focus measures to obtain more accurate depth estimates. The algorithms were implemented and tested using surfaces of different roughness and reflectance properties. We conclude with a brief discussion on how the proposed method can be applied to smooth textured and smooth non-textured surfaces.
In-Vehicle Networks Outlook: Achievements and Challenges

This paper presents a comprehensive survey of five most widely used in-vehicle networks from three perspectives: system cost, data transmission capacity, and fault-tolerance capability. The paper reviews the pros and cons of each network, and identifies possible approaches to improve the quality of service (QoS). In addition, two classifications of automotive gateways have been presented along with a brief discussion about constructing a comprehensive in-vehicle communication system with different networks and automotive gateways. Furthermore, security threats to in-vehicle networks are briefly discussed, along with the corresponding protective methods. The survey concludes with highlighting the trends in future development of in-vehicle network technology and a proposal of a topology of the next generation in-vehicle network.
A Head-Wearable Short-Baseline Stereo System for the Simultaneous Estimation of Structure and Motion

This paper presents a short-baseline real-time stereo vision system that is capable of the simultaneous and robust estimation of the ego-motion and of the 3D structure and the independent motion of thousands of points of the environment. Kalman filters estimate the position and velocity of world points in 3D Euclidean space. The six degrees of freedom of the ego-motion are obtained by minimizing the projection error of the current and previous clouds of static points. Experimental results with real data in indoor and outdoor environments demonstrate the robustness, accuracy and efficiency of our approach. Since the baseline is as short as 13cm, the device is head-mountable, and can be used by a visually impaired person. Our proposed system can be used to augment the perception of the user in complex dynamic environments.
K-isomorphism: privacy preserving network publication against structural attacks

Serious concerns on privacy protection in social networks have been raised in recent years; however, research in this area is still in its infancy. The problem is challenging due to the diversity and complexity of graph data, on which an adversary can use many types of background knowledge to conduct an attack. One popular type of attacks as studied by pioneer work  is the use of embedding subgraphs. We follow this line of work and identify two realistic targets of attacks, namely, NodeInfo and LinkInfo. Our investigations show that k-isomorphism, or anonymization by forming k pairwise isomorphic subgraphs, is both sufficient and necessary for the protection. The problem is shown to be NP-hard. We devise a number of techniques to enhance the anonymization efficiency while retaining the data utility. A compound vertex ID mechanism is also introduced for privacy preservation over multiple data releases. The satisfactory performance on a number of real datasets, including HEP-Th, EUemail and LiveJournal, illustrates that the high symmetry of social networks is very helpful in mitigating the difficulty of the problem.
Characterizing the impact of end-system affinities on the end-to-end performance of high-speed flows

Multi-core end-systems use Receive Side Scaling (RSS) to parallelize protocol processing. RSS uses a hash function on the standard flow descriptors and an indirection table to assign incoming packets to receive queues which are pinned to specific cores. This ensures flow affinity in that the interrupt processing of all packets belonging to a specific flow is processed by the same core. A key limitation of standard RSS is that it does not consider the application process that consumes the incoming data in determining the flow affinity. In this paper, we carry out a detailed experimental analysis of the performance impact of the application affinity in a 40 Gbps testbed network with a dual hexa-core end-system. We show, contrary to conventional wisdom, that when the application process and the flow are affinitized to the same core, the performance (measured in terms of end-to-end TCP throughput) is significantly lower than the line rate. Near line rate performance is observed when the flow and the application process are affinitized to different cores belonging to the same socket. Furthermore, affinitizing the application and the flow to cores on different sockets results in significantly lower throughput than the line rate. These results arise due to the memory bottleneck, which is demonstrated using preliminary correlational data on the cache hit rate in the core that services the application process.
Automated sleep stage identification system based on time-frequency analysis of a single EEG channel and random forest classifier

In this work, an efficient automated new approach for sleep stage identification based on the new standard of the American academy of sleep medicine (AASM) is presented. The propose approach employs time-frequency analysis and entropy measures for feature extraction from a single electroencephalograph (EEG) channel. Three time-frequency techniques were deployed for the analysis of the EEG signal: Choi-Williams distribution (CWD), continuous wavelet transform (CWT), and Hilbert-Huang Transform (HHT). Polysomnographic recordings from sixteen subjects were used in this study and features were extracted from the time-frequency representation of the EEG signal using Renyi's entropy. The classification of the extracted features was done using random forest classifier. The performance of the new approach was tested by evaluating the accuracy and the kappa coefficient for the three time-frequency distributions: CWD, CWT, and HHT. The CWT time-frequency distribution outperformed the other two distributions and showed excellent performance with an accuracy of 0.83 and a kappa coefficient of 0.76.
Action Classification in Soccer Videos with Long Short-Term Memory Recurrent Neural Networks

In this paper, we propose a novel approach for action classification in soccer videos using a recurrent neural network scheme. Thereby, we extract from each video action at each timestep a set of features which describe both the visual content (by the mean of a BoW approach) and the dominant motion (with a key point based approach). A Long Short-Term Memory-based Recurrent Neural Network is then trained to classify each video sequence considering the temporal evolution of the features for each timestep. Experimental results on the MICC-Soccer-Actions-4 database show that the proposed approach outperforms classification methods of related works (with a classification rate of 77 %), and that the combination of the two features (BoW and dominant motion) leads to a classification rate of 92 %.
Task and domain specific modelling in the Carnegie Mellon communicator system

The Carnegie Mellon Communicator is a telephone-based dialog system that supports planning in a travel domain. The implementation of such a system requires two complimentary components, an architecture capable of managing interaction and the task, as well as a knowledge base that captures the speech, language and task characteristics specific to the domain. Given a suitable architecture, the principal effort in development in taken up in the acquisition and processing of a domain knowledge base. This paper describes a variety of techniques we have applied to modeling in acoustic, language, task, generation and synthesis components of the system.
Accurate camera calibration using iterative refinement of control points

We describe a novel camera calibration algorithm for square, circle, and ring planar calibration patterns. An iterative refinement approach is proposed that utilizes the parameters obtained from traditional calibration algorithms as initialization to perform undistortion and unprojection of calibration images to a canonical fronto-parallel plane. This canonical plane is then used to localize the calibration pattern control points and recompute the camera parameters in an iterative refinement until convergence. Undistorting and unprojecting the calibration pattern to the canonical plane increases the accuracy of control point localization and consequently of camera calibration. We have conducted an extensive set of experiments with real and synthetic images for the square, circle and ring pattern, and the pixel reprojection errors obtained by our method are about 50% lower than those of the OpenCV Camera Calibration Toolbox. Increased accuracy of camera calibration directly leads to improvements in other applications; we demonstrate recovery of fine object structure for visual hull reconstruction, and recovery of precise epipolar geometry for stereo camera calibration.
ThrowBot : Design Considerations for a Man-Portable Throwable Robot

The pocket-sized ThrowBot is a sub-kilogram-class robot that provides short-range remote eyes and ears for urban combat. This paper provides an overview of lessons learned from experience, testing, and evaluation of the iRobot ThrowBot developed under the Defense Advanced Research Projects Agency (DARPA) Tactical Mobile Robots (TMR) program. Emphasis has been placed on investigating requirements for the next generation of ThrowBots to be developed by iRobot Corporation and SPAWAR Systems Center, San Diego (SSC San Diego) Unmanned Systems Branch. Details on recent evaluation activities performed at the Military Operations in Urban Terrain (MOUT) test site at Fort Benning, GA, are included, along with insights obtained throughout the development of the ThrowBot since its inception in 1999 as part of the TMR program.
A Study of Grayware on Google Play

While there have been various studies identifying and classifying Android malware, there is limited discussion of the broader class of apps that fall in a gray area. Mobile grayware is distinct from PC grayware due to differences in operating system properties. Due to mobile grayware's subjective nature, it is difficult to identify mobile grayware via program analysis alone. Instead, we hypothesize enhancing analysis with text analytics can effectively reduce human effort when triaging grayware. In this paper, we design and implement heuristics for seven main categories of grayware. We then use these heuristics to simulate grayware triage on a large set of apps from Google Play. We then present the results of our empirical study, demonstrating a clear problem of grayware. In doing so, we show how even relatively simple heuristics can quickly triage apps that take advantage of users in an undesirable way.
Why sex matters for neuroscience

A rapidly burgeoning literature documents copious sex influences on brain anatomy, chemistry and function. This article highlights some of the more intriguing recent discoveries and their implications. Consideration of the effects of sex can help to explain seemingly contradictory findings. Research into sex influences is mandatory to fully understand a host of brain disorders with sex differences in their incidence and/or nature. The striking quantity and diversity of sex-related influences on brain function indicate that the still widespread assumption that sex influences are negligible cannot be justified, and probably retards progress in our field.
ML-MG: Multi-label Learning with Missing Labels Using a Mixed Graph

This work focuses on the problem of multi-label learning with missing labels (MLML), which aims to label each test instance with multiple class labels given training instances that have an incomplete/partial set of these labels (i.e. some of their labels are missing). To handle missing labels, we propose a unified model of label dependencies by constructing a mixed graph, which jointly incorporates (i) instance-level similarity and class co-occurrence as undirected edges and (ii) semantic label hierarchy as directed edges. Unlike most MLML methods, We formulate this learning problem transductively as a convex quadratic matrix optimization problem that encourages training label consistency and encodes both types of label dependencies (i.e. undirected and directed edges) using quadratic terms and hard linear constraints. The alternating direction method of multipliers (ADMM) can be used to exactly and efficiently solve this problem. To evaluate our proposed method, we consider two popular applications (image and video annotation), where the label hierarchy can be derived from Wordnet. Experimental results show that our method achieves a significant improvement over state-of-the-art methods in performance and robustness to missing labels.
Challenges in real-time virtualization and predictable cloud computing

Cloud computing and virtualization technology have revolutionized general-purpose computing applications in the past decade. The cloud paradigm offers advantages through reduction of operation costs, server consolidation, flexible system configuration and elastic resource provisioning. However, despite the success of cloud computing for general-purpose computing, existing cloud computing and virtualization technology face tremendous challenges in supporting emerging soft real-time applications such as online video streaming, cloud-based gaming, and telecommunication management. These applications demand real-time performance in open, shared and virtualized computing environments. This paper identifies the technical challenges in supporting real-time applications in the cloud, surveys recent advancement in real-time virtualization and cloud computing technology, and offers research directions to enable cloud-based real-time applications in the future.
Design and Analysis of Low-Voltage Current-Mode Logic Buffers

This paper investigates important problems involved in the design of a CML buffer as well as a chain of tapered CML buffers. A new design procedure to systematically design a chain of tapered CML buffers is proposed. The differential architecture of a CML buffer makes it functionally robust in the presence of environmental noise sources (e.g., crosstalk, power/ground noise). The circuit design issues in regard to the CML buffer are compared with those in a conventional CMOS inverter. It is shown, both through the experiments and by using efficient analytical models, why CML buffers are better than CMOS inverters in high-speed lowvoltage applications.
Design of tilted taper slot antenna for 5G base station antenna circular array

This communication presents the design of down tilted 3D Taper Slot Antennas suitable for base stations of future generation mobile communication. We designed a novel Vivaldi antenna with wide frequency band performance covering a larger band than the 4th Generation with a vertical tilting to suit the bases station coverage requirements. A downscaled version of this metal only antenna is advantageous for higher frequency bands of the 5th Generation. The available space inside of the 3D structure is suitable to integrate low noise amplifier as near as possible to the feeding point minimizing the losses in the cable while maintaining good performance in terms of gain and efficiency. Circular antenna array of (4G) and (5G) antenna elements are simulated in terms of reflection coefficient and inter-element coupling. We developed and measured a prototype of a single (4G) antenna. Good agreement is obtained between simulation and measurements.
Studying Information Technology in Organizations: Research Approaches and Assumptions

We examined 155 information systems research articles published from 1983 to 1988 and found that although this research is not rooted in a single overarching theoretical perspective, it does exhibit a single set of philosophical assumptions regarding the nature of the phenomena studied by information systems researchers, and what constitutes valid knowledge about those phenomena. We believe that a single research perspective for studying information systems phenomena is unnecessarily restrictive, and argue that there exist other philosophical assumptions that can inform studies of the relationships between information technology, people, and organizations. In this paper, we present two additional research philosophies for consideration-the interpretive and the critical-and for each we provide empirical examples to illustrate how they are used. We conclude by suggesting that much can be gained if a plurality of research perspectives is effectively employed to investigate information systems phenomena.
Knowledge-based power line detection for UAV surveillance and inspection systems

Spatial information captured from optical remote sensors on board unmanned aerial vehicles (UAVs) has great potential in the automatic surveillance of electrical power infrastructure. For an automatic vision based power line inspection system, detecting power lines from cluttered background an important and challenging task. In this paper, we propose a knowledge-based power line detection method for a vision based UAV surveillance and inspection system. A PCNN filter is developed to remove background noise from the images prior to the Hough transform being employed to detect straight lines. Finally knowledge based line clustering is applied to refine the detection results. The experiment on real image data captured from a UAV platform demonstrates that the proposed approach is effective.
Incentives for Privacy Tradeoff in Community Sensing

Community sensing, fusing information from populations of privately-held sensors, presents a great opportunity to create efficient and cost-effective sensing applications. Yet, reasonable privacy concerns often limit the access to such data streams. How should systems valuate and negotiate access to private information, for example in return for monetary incentives? How should they optimally choose the participants from a large population of strategic users with privacy concerns, and compensate them for information shared? In this paper, we address these questions and present a novel mechanism, SEQTGREEDY, for budgeted recruitment of participants in community sensing. We first show that privacy tradeoffs in community sensing can be cast as an adaptive submodular optimization problem. We then design a budget feasible, incentive compatible (truthful) mechanism for adaptive submodular maximization, which achieves near-optimal utility for a large class of sensing applications. This mechanism is general, and of independent interest. We demonstrate the effectiveness of our approach in a case study of air quality monitoring, using data collected from the Mechanical Turk platform. Compared to the state of the art, our approach achieves up to 30% reduction in cost in order to achieve a desired level of utility.
Hard Drive Failure Prediction Using Classification and Regression Trees

Some statistical and machine learning methods have been proposed to build hard drive prediction models based on the SMART attributes, and have achieved good prediction performance. However, these models were not evaluated in the way as they are used in real-world data centers. Moreover, the hard drives deteriorate gradually, but these models can not describe this gradual change precisely. This paper proposes new hard drive failure prediction models based on Classification and Regression Trees, which perform better in prediction performance as well as stability and interpretability compared with the state-of the-art model, the Back propagation artificial neural network model. Experiments demonstrate that the Classification Tree (CT) model predicts over 95% of failures at a false alarm rate (FAR) under 0.1% on a real-world dataset containing 25,792 drives. Aiming at the practical application of prediction models, we test them with different drive families, with fewer number of drives, and with different model updating strategies. The CT model still shows steady and good performance. We propose a health degree model based on Regression Tree (RT) as well, which can give the drive a health assessment rather than a simple classification result. Therefore, the approach can deal with warnings raised by the prediction model in order of their health degrees. We implement a reliability model for RAID-6 systems with proactive fault tolerance and show that our CT model can significantly improve the reliability and/or reduce construction and maintenance cost of large-scale storage systems.
Ambiguity and Coherence

Several recent theories of linguistic representation treat the lexicon as a highly structured object, incorporating fairly detailed semantic information, and allowing multiple aspects of meaning to be represented in a single entry (e.g. Pustejovsky 1991; Copestake 1992; Copestake and Briscoe 1995). One consequence of these approaches is that word senses cannot be thought of as discrete units which are in one-to-one correspondence with lexical entries. This has many advantages in allowing an account of systematic polysemy, but leaves the problem of accounting for effects such as zeugma and the absence of crossed readings, which have traditionally been explained in terms of multiple lexial entries, but which can also arise in examples where other criteria demand that a single entry be involved. Copestake and Briscoe (1995) claimed that these cases could be explained by discourse coherence, but did not describe how this might work. We remedy this here by formalizing a general pragmatic principle which encapsulates discourse effects on word meaning. We demonstrate how it contributes to the creation of zeugma and the non-availability of crossed readings.