nils-herrmann/clean_scirepeval huggingface dataset:
A Novel On-Road Vehicle Detection Method Using  $\pi$HOG

In this paper, a new on-road vehicle detection method is presented. First, a new feature named the Position and Intensity-included Histogram of Oriented Gradients (PIHOG or πHOG) is proposed. Unlike the conventional HOG, πHOG compensates the information loss involved in the construction of a histogram with position information, and it improves the discriminative power using intensity information. Second, a new search space reduction (SSR) method is proposed to speed up the detection and reduce the computational load. The SSR additionally decreases the false positive rate. A variety of classifiers, including support vector machine, extreme learning machine, and k-nearest neighbor, are used to train and classify vehicles using πHOG. The validity of the proposed method is demonstrated by its application to Caltech, IR, Pittsburgh, and Kitti datasets. The experimental results demonstrate that the proposed vehicle detection method not only improves detection performance but also reduces computation time.
Understanding Human-Smartphone Concerns: A Study of Battery Life

This paper presents a large, 4-week study of more than 4000 people to assess their smartphone charging habits to identify timeslots suitable for opportunistic data uploading and power intensive operations on such devices, as well as opportunities to provide interventions to support better charging behavior. The paper provides an overview of our study and how it was conducted using an online appstore as a software deployment mechanism, and what battery information was collected. We then describe how people charge their smartphones, the implications on battery life and energy usage, and discuss how to improve users’ experience with battery life.
CACHE MISSING FOR FUN AND PROFIT

Simultaneous multithreading — put simply, the sharing of the execution resources of a superscalar processor between multiple execution threads — has recently become widespread via its introduction (under the name “Hyper-Threading”) into Intel Pentium 4 processors. In this implementation, for reasons of efficiency and economy of processor area, the sharing of processor resources between threads extends beyond the execution units; of particular concern is that the threads share access to the memory caches. We demonstrate that this shared access to memory caches provides not only an easily used high bandwidth covert channel between threads, but also permits a malicious thread (operating, in theory, with limited privileges) to monitor the execution of another thread, allowing in many cases for theft of cryptographic keys. Finally, we provide some suggestions to processor designers, operating system vendors, and the authors of cryptographic software, of how this attack could be mitigated or eliminated entirely.
Reliable Crowdsourcing under the Generalized Dawid-Skene Model

Crowdsourcing systems provide scalable and cost-effectiv e human-powered solutions at marginal cost, for classification tasks where humans are significantly better than the machines. Although traditional approaches in aggregating crowdsourced labels have relied on the Dawid-Skene model, t his fails to capture how some tasks are inherently more difficult than the others. Several generalizations have bee n proposed, but inference becomes intractable and typical solutions resort to heuristics. To bridge this gap, we study a recently proposed generalize Dawid-Skene model, and propose a linear-time algorithm based on spectral methods. We show near-optimality of the proposed approach, by providing an upper bound on the error and comparing it to a fun damental limit. We provide numerical experiments on synthetic data matching our analyses, and also on real datas ets demonstrating that the spectral method significantly improves over simple majority voting and is comparable to ot her methods.
Analysis of a Denial of Service Attack on TCP

This paper analyzes a network-based denial of service attack for IF (Inteme.t Protocol) based networks. It is popularly called SYN flooding. It works by an attacker sending many TCP ( COIlllection requests with spoofed source addresses to a victim's machine. Each single request causes the targeted host to instantiate data structures out of a limited pool of resources. Once the target host's resources are exhausted, no more incoming TCP connections can be established, thus denying further legitimate access. The paper contributes a detailed analysis of the SYN flooding attack and a discussion of existing and proposed countermeasures. Furthermore, we introduce a new solution approach, explain its design, and evaluate its perfonnance. Our approach offers protection against SYN flooding for all hosts connected to the same local area network, independent of their operating system or networking stack implementation. It is highly portable, configurable, extensible, and neither requires special hardware, nor modifications in routers or protected end systems. attacks cun be launched with little effort. Presently, it is difficult to trace an attack uack to its originator. Several possible solutions to this attack have been proposed by others, und some implemented. We have developed an active monitoring tool that classifies IP source addresses with high probability as being falsified or genuine. Our approach finds connection establishment protocol messages that are coming from forged IP addresses, and takes actions to ensure that the resulting illegitimate half-open connections arc reset immediately. This paper is organized as follows. Section 2 describes backgroWld material, such as the IP and TCP protocols. Section 3 explains the SYN flooding attack. Section 4 discusses existing approaches to solve this problem, such as configuration improvements and firewall-based approaches. The technical details of our approach are described in Section 5, followed by a performance evaluation in Section 6. Sections 7 and B outline future work issues and present conclusions.
System-Optimal Routing of Traffic Flows with User Constraints in Networks with Congestion

The design of route guidance systems faces a well-known dilemma. The approach that theoretically yields the system-optimal traffic pattern may discriminate against some users in favor of others. Proposed alternate models, however, do not directly address the system perspective and may result in inferior performance. We propose a novel model and corresponding algorithms to resolve this dilemma. We present computational results on real-world instances and compare the new approach with the well-established traffic assignment model. The essence of this study is that system-optimal routing of traffic flow with explicit integration of user constraints leads to a better performance than the user equilibrium, while simultaneously guaranteeing superior fairness compared to the pure system optimum.
Occupational risks and challenges of seafaring.

Seafarers are exposed to a high diversity of occupational health hazards onboard ships.



The aim of this article is to present a survey of the current, most important hazards in seafaring including recommendations on measures how to deal with these problems.



The review is based on maritime expert opinions as well a PubMed analysis related to the occupational risks of seafaring.



Despite recent advances in injury prevention, accidents due to harmful working and living conditions at sea and of non-observance of safety rules remain a main cause of injury and death. Mortality in seafaring from cardiovascular diseases (CVD) is mainly caused by increased risks and impaired treatment options of CVD at sea. Further, shipboard stress and high demand may lead to fatigue and isolation which have an impact on the health of onboard seafarers. Communicable diseases in seafaring remain an occupational problem. Exposures to hazardous substances and UV-light are important health risks onboard ships. Because of harsh working conditions onboard including environmental conditions, sufficient recreational activities are needed for the seafarers' compensation both onboard and ashore. However, in reality there is often a lack of leisure time possibilities.



Seafaring is still an occupation with specific work-related risks. Thus, a further reduction of occupational hazards aboard ships is needed and poses a challenge for maritime health specialists and stakeholders. Nowadays, maritime medicine encompasses a broad field of workplaces with different job-related challenges.
Privacy-Preserving Datamining on Vertically Partitioned Databases

In a recent paper Dinur and Nissim considered a statistical database in which a trusted database administrator monitors queries and introduces noise to the responses with the goal of maintaining data privacy . Under a rigorous definition of breach of privacy, Dinur and Nissim proved that unless the total number of queries is sub-linear in the size of the database, a substantial amount of noise is required to avoid a breach, rendering the database almost useless. As databases grow increasingly large, the possibility of being able to query only a sub-linear number of times becomes realistic. We further investigate this situation, generalizing the previous work in two important directions: multi-attribute databases (previous work dealt only with single-attribute databases) and vertically partitioned databases, in which different subsets of attributes are stored in different databases. In addition, we show how to use our techniques for datamining on published noisy statistics.
Learning from Observation Paradigm: Leg Task Models for Enabling a Biped Humanoid Robot to Imitate Human Dances

This paper proposes a framework that achieves the Learning from Observation paradigm for learning dance motions. The framework enables a humanoid robot to imitate dance motions captured from human demonstrations. This study especially focuses on leg motions to achieve a novel attempt in which a biped-type robot imitates not only upper body motions but also leg motions including steps. Body differences between the robot and the original dancer make the problem difficult because the differences prevent the robot from straightforwardly following the original motions and they also change dynamic body balance. We propose leg task models, which play a key role in solving the problem. Low-level tasks in leg motion are modelled so that they clearly provide essential information required for keeping dynamic stability and important motion characteristics. The models divide the problem of adapting motions into the problem of The International  No. 00, Xxxxxxxx 2007,  We have developed a method for recognizing the tasks from captured motion data and a method for generating the motions of the tasks that can be executed by existing robots including HRP-2. HRP-2 successfully performed the generated motions, which imitated a traditional folk dance performed by human dancers. learning from observation, imitation, biped humanoid robot, motion capture, entertainment robotics
Automatic Online Calibration of Cameras and Lasers

The combined use of 3D scanning lasers with 2D cameras has become increasingly popular in mobile robotics, as the sparse depth measurements of the former augment the dense color information of the latter. Sensor fusion requires precise 6DOF transforms between the sensors, but hand-measuring these values is tedious and inaccurate. In addition, autonomous robots can be rendered inoperable if their sensors’ calibrations change over time. Yet previously published camera-laser calibration algorithms are offline only, requiring significant amounts of data and/or specific calibration targets; they are thus unable to correct calibration errors that occur during live operation. In this paper, we introduce two new real-time techniques that enable camera-laser calibration online, automatically, and in arbitrary environments. The first is a probabilistic monitoring algorithm that can detect a sudden miscalibration in a fraction of a second. The second is a continuous calibration optimizer that adjusts transform offsets in real time, tracking gradual sensor drift as it occurs. Although the calibration objective function is not globally convex and cannot be optimized in real time, in practice it is always locally convex around the global optimum, and almost everywhere else. Thus, the local shape of the objective function at the current parameters can be used to determine whether the sensors are calibrated, and allows the parameters to be adjusted gradually so as to maintain the global optimum. In several online experiments on thousands of frames in real markerless scenes, our method automatically detects miscalibrations within one second of the error exceeding .25 deg or 10cm, with an accuracy of 100%. In addition, rotational sensor drift can be tracked in real-time with a mean error of just .10 deg. Together, these techniques allow significantly greater flexibility and adaptability of robots in unknown and potentially harsh environments.
Student See Versus Student Do: A Comparative Study of Two Online Tutorials

This study examines the impact on student performance after interactive and non-interactive tutorials using a 2×2 treatment-control design. In an undergraduate management course, a control group watched a video tutorial while the treatment group received the same content using a dynamic tutorial. Both groups received the same quiz questions. Using effect size to determine magnitude of change, it was found that those in the treatment condition performed better than those in the control condition. Students were able to take the quiz up to two times. When examining for change in performance from attempt one to attempt two, the treatment group showed a greater magnitude of change. Students who consistently performed lowest on the quizzes outperformed all students in learning gains.
InfoGather: entity augmentation and attribute discovery by holistic matching with web tables

The Web contains a vast corpus of HTML tables, specifically entity attribute tables. We present three core operations, namely entity augmentation by attribute name, entity augmentation by example and attribute discovery, that are useful for "information gathering" tasks (e.g., researching for products or stocks). We propose to use web table corpus to perform them automatically. We require the operations to have high precision and coverage, have fast (ideally interactive) response times and be applicable to any arbitrary domain of entities. The naive approach that attempts to directly match the user input with the web tables suffers from poor precision and coverage.
 Our key insight is that we can achieve much higher precision and coverage by considering indirectly matching tables in addition to the directly matching ones. The challenge is to be robust to spuriously matched tables: we address it by developing a holistic matching framework based on topic sensitive pagerank and an augmentation framework that aggregates predictions from multiple matched tables. We propose a novel architecture that leverages preprocessing in MapReduce to achieve extremely fast response times at query time. Our experiments on real-life datasets and 573M web tables show that our approach has (i) significantly higher precision and coverage and (ii) four orders of magnitude faster response times compared with the state-of-the-art approach.
Expert and exceptional performance: evidence of maximal adaptation to task constraints.

Expert and exceptional performance are shown to be mediated by cognitive and perceptual-motor skills and by domain-specific physiological and anatomical adaptations. The highest levels of human performance in different domains can only be attained after around ten years of extended, daily amounts of deliberate practice activities. Laboratory analyses of expert performance in many domains such as chess, medicine, auditing, computer programming, bridge, physics, sports, typing, juggling, dance, and music reveal maximal adaptations of experts to domain-specific constraints. For example, acquired anticipatory skills circumvent general limits on reaction time, and distinctive memory skills allow a domain-specific expansion of working memory capacity to support planning, reasoning, and evaluation. Many of the mechanisms of superior expert performance serve the dual purpose of mediating experts' current performance and of allowing continued improvement of this performance in response to informative feedback during practice activities.
Comparing and Evaluating epoll, select, and poll Event Mechanisms

This paper uses a high-performance, eventdriven, HTTP server (the μserver) to compare the performance of the select, poll, and epoll event mechanisms. We subject the μserver to a variety of workloads that allow us to expose the relative strengths and weaknesses of each event mechanism. Interestingly, initial results show that the select and poll event mechanisms perform comparably to the epoll event mechanism in the absence of idle connections. Profiling data shows a significant amount of time spent executing a large number of epoll_ctl system calls. As a result, we examine a variety of techniques for reducing epoll_ctl overhead including edge-triggered notification, and introducing a new system call (epoll_ctlv) that aggregates several epoll_ctl calls into a single call. Our experiments indicate that although these techniques are successful at reducing epoll_ctl overhead, they only improve performance slightly.
Duration prediction using multi-level model for GPR-based speech synthesis

This paper introduces frame-based Gaussian process regression (GPR) into phone/syllable duration modeling for Thai speech synthesis. The GPR model is designed for predicting framelevel acoustic features using corresponding frame information, which includes relative position in each unit of utterance structure and linguistic information such as tone type and part of speech. Although the GPR-based prediction can be applied to a phone duration model, the use of phone duration model only is not always sufficient to generate natural sounding speech. Specifically, in some languages including Thai, syllable durations affect the perception of sentence structure. In this paper, we propose a duration prediction technique using a multi-level model which includes syllable and phone levels for prediction. In the technique, first, syllable durations are predicted, and then they are used as additional contexts in phone-level model to generate phone duration for synthesizing. Objective and subjective evaluation results show that GPR-based modeling with multi-level model for duration prediction outperforms the conventional HMM-based speech synthesis.
A generalized back-door criterion

We generalize Pearl’s back-door criterion for directed acyclic graphs (DAGs) to more general types of graphs that describe Markov equivalence classes of DAGs and/or allow for arbitrarily many hidden variables. We also give easily checkable necessary and sufficient graphical criteria for the existence of a set of variables that satisfies our generalized back-door criterion, when considering a single intervention and a single outcome variable. Moreover, if such a set exists, we provide an explicit set that fulfills the criterion. We illustrate the results in several examples. R-code is available in the R-package pcalg.
Component behavior discovery from software execution data

Tremendous amounts of data can be recorded during software execution. This provides valuable information on software runtime analysis. Many crashes and exceptions may occur, and it is a real challenge to understand how software is behaving. Software is usually composed of various components. A component is a nearly independent part of software that full-fills a clear function. Process mining aims to discover, monitor and improve real processes by extracting knowledge from event logs. This paper presents an approach to utilize process mining as a tool to discover the real behavior of software and analyze it. The unstructured software execution data may be too complex, involving multiple interleaved components, etc. Applying existing process mining techniques results in spaghetti-like models with no clear structure and no valuable information that can be easily understood by end. In this paper, we start with the observation that software is composed of components and we use this information to decompose the problem into smaller independent ones by discovering a behavioral model per component. Through experimental analysis, we illustrate that the proposed approach facilitates the discovery of more understandable software models. All proposed approaches have been implemented in the open-source process mining toolkit ProM.
A feedback framework for improved chord recognition based on NMF-based approximate note transcription

This paper presents a feedback framework that can improve chord recognition for music audio signals by performing approximate note transcription with Bayesian non-negative matrix factorization (NMF) using prior knowledge on chords. Although the names and note compositions of chords are intrinsically linked with each other (e.g., C major chords are highly likely to include C, E, and G notes, and those notes are highly likely to be in C major chords), chord recognition and note transcription (multipitch analysis) have been studied independently. To solve this chicken-and-egg problem, our framework iterates chord recognition and approximate note transcription using each other's results. More specifically, we first perform approximate note transcription based on Bayesian NMF that forces basis spectra to respectively correspond to different semitone-level pitches covering the whole range. We then execute chord recognition based on Bayesian hidden Markov models (HMMs) that use chroma features obtained from the activation patterns of those pitches. To improve note transcription, we again perform Bayesian NMF that encourages certain kinds of pitches in each chord region to be activated. Experimental results showed that our feedback framework gradually improved the accuracy of chord recognition.
Semi-supervised Learning Meets Factorization: Learning to Recommend with Chain Graph Model

Recently, latent factor model (LFM) has been drawing much attention in recommender systems due to its good performance and scalability. However, existing LFMs predict missing values in a user-item rating matrix only based on the known ones, and thus the sparsity of the rating matrix always limits their performance. Meanwhile, semi-supervised learning (SSL) provides an effective way to alleviate the label (i.e., rating) sparsity problem by performing label propagation, which is mainly based on the smoothness insight on affinity graphs. However, graph-based SSL suffers serious scalability and graph unreliable problems when directly being applied to do recommendation. In this article, we propose a novel probabilistic chain graph model (CGM) to marry SSL with LFM. The proposed CGM is a combination of Bayesian network and Markov random field. The Bayesian network is used to model the rating generation and regression procedures, and the Markov random field is used to model the confidence-aware smoothness constraint between the generated ratings. Experimental results show that our proposed CGM significantly outperforms the state-of-the-art approaches in terms of four evaluation metrics, and with a larger performance margin when data sparsity increases.