nils-herrmann/clean_scirepeval huggingface dataset:
Monotony of road environment and driver fatigue: a simulator study.

Studies have shown that drowsiness and hypovigilance frequently occur during highway driving and that they may have serious implications in terms of accident causation. This paper focuses on the task induced factors that are involved in the development of these phenomena. A driving simulator study was conducted in order to evaluate the impact of the monotony of roadside visual stimulation using a steering wheel movement (SWM) analysis procedure. Fifty-six male subjects each drove during two different 40-min periods. In one case, roadside visual stimuli were essentially repetitive and monotonous, while in the other one, the environment contained disparate visual elements aiming to disrupt monotony without changing road geometry. Subject's driving performance was compared across these conditions in order to determine whether disruptions of monotony can have a positive effect and help alleviate driver fatigue. Results reveal an early time-on-task effect on driving performance for both driving periods and more frequent large SWM when driving in the more monotonous road environment, which implies greater fatigue and vigilance decrements. Implications in terms of environmental countermeasures for driver fatigue are discussed.
User needs for location-aware mobile services

Mobile contexts of use vary a lot, and may even be continuously changing during use. The context is much more than location, but its other elements are still difficult to identify or measure. Location information is becoming an integral part of different mobile devices. Current mobile services can be enhanced with location-aware features, thus providing the user with a smooth transition towards context-aware services. Potential application fields can be found in areas such as travel information, shopping, entertainment, event information and different mobile professions. This paper studies location-aware mobile services from the user's point of view. The paper draws conclusions about key issues related to user needs, based on user interviews, laboratory and field evaluations with users, and expert evaluations of location-aware services. The user needs are presented under five main themes: topical and comprehensive contents, smooth user interaction, personal and user-generated contents, seamless service entities and privacy issues.
Habanero-Java library: a Java 8 framework for multicore programming

With the advent of the multicore era, it is clear that future growth in application performance will primarily come from increased parallelism. We believe parallelism should be introduced early into the Computer Science curriculum to educate students on the fundamentals of parallel computation. In this paper, we introduce the newly-created Habanero-Java library (HJlib), a pure Java 8 library implementation of the pedagogic parallel programming model . HJlib has been used in teaching a sophomore-level course titled "Fundamentals of Parallel Programming" at Rice University.
 HJlib adds to the Java ecosystem a powerful and portable task parallel programming model that can be used to parallelize both regular and irregular applications. By relying on simple orthogonal parallel constructs with important safety properties, HJlib allows programmers with a basic knowledge of Java to get started with parallel programming concepts by writing or refactoring applications to harness the power of multicore architecture. The HJlib APIs make extensive use of lambda expressions and can run on any Java 8 JVM. HJlib runtime feedback capabilities, such as the abstract execution metrics and the deadlock detector, help the programmer to obtain feedback on theoretical performance as well as the presence of potential bugs in their program.
 Being an implementation of a pedagogic programming model, HJlib is also an attractive tool for both educators and researchers. HJlib is actively being used in multiple research projects at Rice and also by external independent collaborators. These projects include exploitation of homogeneous and heterogeneous multicore parallelism in big data applications written for the Hadoop platform [20, 43].
Kineticons: using iconographic motion in graphical user interface design

Icons in graphical user interfaces convey information in a mostly universal fashion that allows users to immediately interact with new applications, systems and devices. In this paper, we define Kineticons - an iconographic scheme based on motion. By motion, we mean geometric manipulations applied to a graphical element over time (e.g., scale, rotation, deformation). In contrast to static graphical icons and icons with animated graphics, kineticons do not alter the visual content or "pixel-space" of an element. Although kineticons are not new - indeed, they are seen in several popular systems - we formalize their scope and utility. One powerful quality is their ability to be applied to GUI elements of varying size and shape from a something as small as a close button, to something as large as dialog box or even the entire desktop. This allows a suite of system-wide kinetic behaviors to be reused for a variety of uses. Part of our contribution is an initial kineticon vocabulary, which we evaluated in a 200 participant study. We conclude with discussion of our results and design recommendations.
Multiple-Goal Heuristic Search

This paper presents a new framework for anytime heuristic search where the task is to achieve as many goals as possible within the allocated resources. We show the inadequacy of traditional distance-estimation heuristics for tasks of this type and present alternative heuristics that are more appropriate for multiple-goal search. In particular, we introduce the marginal-utility heuristic, which estimates the cost and the benefit of exploring a subtree below a search node. We developed two methods for online learning of the marginal-utility heuristic. One is based on local similarity of the partial marginal utility of sibling nodes, and the other generalizes marginal-utility over the state feature space. We apply our adaptive and non-adaptive multiple-goal search algorithms to several problems, including focused crawling, and show their superiority over existing methods.
Automatic classification of citation function

The automatic recognition of the rhetorical function of citations in scientific text has many applications, from improvement of impact factor calculations to text summarisation and more informative citation indexers. Citation function is defined as the author’s reason for citing a given paper (e.g. acknowledgement of the use of the cited method). We show that our annotation scheme for citation function is reliable, and present a supervised machine learning framework to automatically classify citation function, which uses several shallow and linguistically-inspired features. We find, amongst other things, a strong relationship between citation function and sentiment classification.
Photobook: Content-based manipulation of image databases

We describe the Photobook system, which is a set of interactive tools for browsing and searching images and image sequences. These query tools differ from those used in standard image databases in that they make direct use of the image content rather than relying on text annotations. Direct search on image content is made possible by use of semantics-preserving image compression, which reduces images to a small set of perceptually-significant coefficients. We discuss three types of Photobook descriptions in detail: one that allows search based on appearance, one that uses 2-D shape, and a third that allows search based on textural properties. These image content descriptions can be combined with each other and with text-based descriptions to provide a sophisticated browsing and search capability. In this paper we demonstrate Photobook on databases containing images of people, video keyframes, hand tools, fish, texture swatches, and 3-D medical data.
A systematic analysis of textual variability modeling languages

Industrial variability models tend to grow in size and complexity due to ever-increasing functionality and complexity of software systems. Some authors report on variability models specifying several thousands of variabilities. However, traditional variability modeling approaches do not seem to scale adequately to cope with size and complexity of such models. Recently, textual variability modeling languages have been advocated as one scalable solution.
 In this paper, we provide a systematic analysis of the capabilities of current textual variability modeling languages, in particular regarding variability management in the large. Towards this aim, we define a classification schema consisting of five dimensions, classify ten different textual variability modeling languages using the classification schema and provide an analysis. In summary, some textual variability modeling languages go beyond textual representations of traditional variability modeling approaches and provide sophisticated modeling concepts and constraint languages. Three textual variability modeling approaches already support mechanisms for large-scale variability modeling such as model composition, modularization, or evolution support.
EvalAI: Towards Better Evaluation Systems for AI Agents

We introduce EvalAI, an open source platform for evaluating and comparing machine learning (ML) and artificial intelligence algorithms (AI) at scale. EvalAI is built to provide a scalable solution to the research community to fulfill the critical need of evaluating machine learning models and agents acting in an environment against annotations or with a human-in-the-loop. This will help researchers, students, and data scientists to create, collaborate, and participate in AI challenges organized around the globe. By simplifying and standardizing the process of benchmarking these models, EvalAI seeks to lower the barrier to entry for participating in the global scientific effort to push the frontiers of machine learning and artificial intelligence, thereby increasing the rate of measurable progress in this domain. Our code is available here.
Understanding Mental States in Natural Language

Understanding mental states in narratives is an important aspect of human language comprehension. By “mental states” we refer to beliefs, states of knowledge, points of view, and suppositions, all of which may change over time. In this paper, we propose an approach for automatically extracting and understanding multiple mental states in stories. Our model consists of two parts: (1) a parser that takes an English sentence and translates it to some semantic operations; (2) a mental-state inference engine that reads in the semantic operations and produces a situation model that represents the meaning of the sentence. We present the performance of the system on a corpus of children stories containing both fictional and non-fictional texts.
Performance analysis of precoding techniques for Massive MU-MIMO systems

Massive Multi-user Multiple Input Multiple Output (MU-MIMO) systems can yield a considerable increase in the capacity and BER for the requirement of higher data rate by using large number of antennas at transmitter and receiver side. In case of multi-user MIMO, a multi-antenna transmitter communicates simultaneously with multiple receivers. But the MU-MIMO system suffers from huge inter-user interference. By using precoding techniques in massive MU-MIMO the inter-user interference of the system are analyzed and it can be substantially reduced. In precoding technique the transmit diversity is exploited by weighting information stream so that it reduces the corrupted effect in communication channel, for this purpose the CSI are needed to be known by the transmitter. It is analyzed that at 6dB of SNR MMSE precoding outperforms error rate of 10-3.65 compared to Block diagonalization and 10-3.62 to Tomlinson-Harashima precoding method for Massive MU-MIMO system.
The NEURON Simulation Environment

The moment-to-moment processing of information by the nervous system involves the propagation and interaction of electrical and chemical signals that are distributed in space and time. Biologically realistic modeling is needed to test hypotheses about the mechanisms that govern these signals and how nervous system function emerges from the operation of these mechanisms. The NEURON simulation program provides a powerful and flexible environment for implementing such models of individual neurons and small networks of neurons. It is particularly useful when membrane potential is nonuniform and membrane currents are complex. We present the basic ideas that would help informed users make the most efficient use of NEURON.
Winnowing: Local Algorithms for Document Fingerprinting

Digital content is for copying: quotation, revision, plagiarism, and file sharing all create copies. Document fingerprinting is concerned with accurately identifying copying, including small partial copies, within large sets of documents.We introduce the class of local document fingerprinting algorithms, which seems to capture an essential property of any finger-printing technique guaranteed to detect copies. We prove a novel lower bound on the performance of any local algorithm. We also develop winnowing, an efficient local fingerprinting algorithm, and show that winnowing's performance is within 33% of the lower bound. Finally, we also give experimental results on Web data, and report experience with MOSS, a widely-used plagiarism detection service.
An updated performance comparison of virtual machines and Linux containers

Cloud computing makes extensive use of virtual machines because they permit workloads to be isolated from one another and for the resource usage to be somewhat controlled. In this paper, we explore the performance of traditional virtual machine (VM) deployments, and contrast them with the use of Linux containers. We use KVM as a representative hypervisor and Docker as a container manager. Our results show that containers result in equal or better performance than VMs in almost all cases. Both VMs and containers require tuning to support I/Ointensive applications. We also discuss the implications of our performance results for future cloud architectures.
BOINC: a system for public-resource computing and storage

BOINC (Berkeley Open Infrastructure for Network Computing) is a software system that makes it easy for scientists to create and operate public-resource computing projects. It supports diverse applications, including those with large storage or communication requirements. PC owners can participate in multiple BOINC projects, and can specify how their resources are allocated among these projects. We describe the goals of BOINC, the design issues that we confronted, and our solutions to these problems.
Efficient Adaptive-Support Association Rule Mining for Recommender Systems

Collaborative recommender systems allow personalization for e-commerce by exploiting similarities and dissimilarities among customers' preferences. We investigate the use of association rule mining as an underlying technology for collaborative recommender systems. Association rules have been used with success in other domains. However, most currently existing association rule mining algorithms were designed with market basket analysis in mind. Such algorithms are inefficient for collaborative recommendation because they mine many rules that are not relevant to a given user. Also, it is necessary to specify the minimum support of the mined rules in advance, often leading to either too many or too few rules; this negatively impacts the performance of the overall system. We describe a collaborative recommendation technique based on a new algorithm specifically designed to mine association rules for this purpose. Our algorithm does not require the minimum support to be specified in advance. Rather, a target range is given for the number of rules, and the algorithm adjusts the minimum support for each user in order to obtain a ruleset whose size is in the desired range. Rules are mined for a specific target user, reducing the time required for the mining process. We employ associations between users as well as associations between items in making recommendations. Experimental evaluation of a system based on our algorithm reveals performance that is significantly better than that of traditional correlation-based approaches.
Modeling Complex Air Traffic Management Systems

In this work, we propose the use of multi-agent system (MAS) models as the basis for predictive reasoning about various safety conditions and the performance of Air Traffic Management (ATM) Systems. To this end, we describe the engineering of a domain-specific MAS model that provides constructs for creating scenarios related to ATM systems and procedures; we then instantiate the constructs in the ATM model for different scenarios. As a case study we generate a model for a concept that provides the ability to maximize departure throughput at La Guardia airport (LGA) without impacting the flow of the arrival traffic; the model consists of approximately 1.5 hours real time flight data. During this time, between 130 and 150 airplanes are managed by four en-route controllers, three TRACON controllers, and one tower controller at LGA who is responsible for departures and arrivals. The planes are landing at approximately 36 to 40 planes an hour. A key contribution of this work is that the model can be extended to various air-traffic management scenarios and can serve as a template for engineering large-scale models in other domains.
Identification of many microRNAs that copurify with polyribosomes in mammalian neurons.

Localized translation in mammalian dendrites may play a role in synaptic plasticity and contribute to the molecular basis for learning and memory. The regulatory mechanisms that control localized translation in neurons are not well understood. We propose a role for microRNAs (miRNAs), a class of noncoding RNAs, as mediators of neuronal translational regulation. We have identified 86 miRNAs expressed in mammalian neurons, of which 40 have not previously been reported. A subset of these miRNAs exhibits temporally regulated expression in cortical cultures. Moreover, all of the miRNAs that were tested cofractionate with polyribosomes, the sites of active translation. These findings indicate that a large, diverse population of miRNAs may function to regulate translation in mammalian neurons.
Multi-Language Identification Using Convolutional Recurrent Neural Network

Language Identification, being an important aspect of Automatic Speaker Recognition has had many changes and new approaches to ameliorate performance over the last decade. We compare the performance of using audio spectrum in the log scale and using Polyphonic sound sequences from raw audio samples to train the neural network and to classify speech as either English or Spanish. To achieve this, we use the novel approach of using a Convolutional Recurrent Neural Network using Long Short Term Memory (LSTM) or a Gated Recurrent Unit (GRU) for forward propagation of the neural network. Our hypothesis is that the performance of using polyphonic sound sequence as features and both LSTM and GRU as the gating mechanisms for the neural network outperform the traditional MFCC features using a unidirectional Deep Neural Network.
LTE evolution for vehicle-to-everything services

Wireless communication has become a key technology for competitiveness of next generation vehicles. Recently, the 3GPP has initiated standardization activities for LTE-based V2X services composed of vehicle-to-vehicle, vehicle- to-pedestrian, and vehicle-to-infrastructure/network. The goal of these 3GPP activities is to enhance LTE systems to enable vehicles to communicate with other vehicles, pedestrians, and infrastructure in order to exchange messages for aiding in road safety, controlling traffic flow, and providing various traffic notifications. In this article, we provide an overview of the service flow and requirements of the V2X services LTE systems are targeting. This article also discusses the scenarios suitable for operating LTE-based V2X services, and addresses the main challenges of high mobility and densely populated vehicle environments in designing technical solutions to fulfill the requirements of V2X services. Leveraging the spectral-efficient air interface, the cost-effective network deployment, and the versatile nature of supporting different communication types, LTE systems along with proper enhancements can be the key enabler of V2X services.
Comparative study of trust and reputation systems for wireless sensor networks

Wireless sensor networks (WSNs) are emerging as useful technology for information extraction from the surrounding environment by using numerous small-sized sensor nodes that are mostly deployed in sensitive, unattended, and (sometimes) hostile territories. Traditional cryptographic approaches are widely used to provide security in WSN. However, because of unattended and insecure deployment, a sensor node may be physically captured by an adversary who may acquire the underlying secret keys, or a subset thereof, to access the critical data and/or other nodes present in the network. Moreover, a node may not properly operate because of insufficient resources or problems in the network link. In recent years, the basic ideas of trust and reputation have been applied to WSNs to monitor the changing behaviors of nodes in a network. Several trust and reputation monitoring (TRM) systems have been proposed, to integrate the concepts of trust in networks as an additional security measure, and various surveys are conducted on the aforementioned system. However, the existing surveys lack a comprehensive discussion on trust application specific to the WSNs. This survey attempts to provide a thorough understanding of trust and reputation as well as their applications in the context of WSNs. The survey discusses the components required to build a TRM and the trust computation phases explained with a study of various security attacks. The study investigates the recent advances in TRMs and includes a concise comparison of various TRMs. Finally, a discussion on open issues and challenges in the implementation of trust-based systems is also presented.
The Role of Context for Object Detection and Semantic Segmentation in the Wild

In this paper we study the role of context in existing state-of-the-art detection and segmentation approaches. Towards this goal, we label every pixel of PASCAL VOC 2010 detection challenge with a semantic category. We believe this data will provide plenty of challenges to the community, as it contains 520 additional classes for semantic segmentation and object detection. Our analysis shows that nearest neighbor based approaches perform poorly on semantic segmentation of contextual classes, showing the variability of PASCAL imagery. Furthermore, improvements of existing contextual models for detection is rather modest. In order to push forward the performance in this difficult scenario, we propose a novel deformable part-based model, which exploits both local context around each candidate detection as well as global context at the level of the scene. We show that this contextual reasoning significantly helps in detecting objects at all scales.