nils-herrmann/clean_scirepeval huggingface dataset:
Efficient driver drowsiness detection at moderate levels of drowsiness.

Previous research on driver drowsiness detection has focused primarily on lane deviation metrics and high levels of fatigue. The present research sought to develop a method for detecting driver drowsiness at more moderate levels of fatigue, well before accident risk is imminent. Eighty-seven different driver drowsiness detection metrics proposed in the literature were evaluated in two simulated shift work studies with high-fidelity simulator driving in a controlled laboratory environment. Twenty-nine participants were subjected to a night shift condition, which resulted in moderate levels of fatigue; 12 participants were in a day shift condition, which served as control. Ten simulated work days in the study design each included four 30-min driving sessions, during which participants drove a standardized scenario of rural highways. Ten straight and uneventful road segments in each driving session were designated to extract the 87 different driving metrics being evaluated. The dimensionality of the overall data set across all participants, all driving sessions and all road segments was reduced with principal component analysis, which revealed that there were two dominant dimensions: measures of steering wheel variability and measures of lateral lane position variability. The latter correlated most with an independent measure of fatigue, namely performance on a psychomotor vigilance test administered prior to each drive. We replicated our findings across eight curved road segments used for validation in each driving session. Furthermore, we showed that lateral lane position variability could be derived from measured changes in steering wheel angle through a transfer function, reflecting how steering wheel movements change vehicle heading in accordance with the forces acting on the vehicle and the road. This is important given that traditional video-based lane tracking technology is prone to data loss when lane markers are missing, when weather conditions are bad, or in darkness. Our research findings indicated that steering wheel variability provides a basis for developing a cost-effective and easy-to-install alternative technology for in-vehicle driver drowsiness detection at moderate levels of fatigue.
Resources for building applications with Dependency Minimal Recursion Semantics

We describe resources aimed at increasing the usability of the semantic representations utilized within the DELPH-IN (Deep Linguistic Processing with HPSG) consortium. We concentrate in particular on the Dependency Minimal Recursion Semantics (DMRS) formalism, a graph-based representation designed for compositional semantic representation with deep grammars. Our main focus is on English, and specifically English Resource Semantics (ERS) as used in the English Resource Grammar. We first give an introduction to ERS and DMRS and a brief overview of some existing resources and then describe in detail a new repository which has been developed to simplify the use of ERS/DMRS. We explain a number of operations on DMRS graphs which our repository supports, with sketches of the algorithms, and illustrate how these operations can be exploited in application building. We believe that this work will aid researchers to exploit the rich and effective but complex DELPH-IN resources.
A hybrid computational approach to derive new ground-motion prediction equations

A novel hybrid method coupling genetic programming and orthogonal least squares, called GP/OLS, was employed to derive new ground-motion prediction equations (GMPEs). The principal ground-motion parameters formulated were peak ground acceleration (PGA), peak ground velocity (PGV) and peak ground displacement (PGD). The proposed GMPEs relate PGA, PGV and PGD to different seismic parameters including earthquake magnitude, earthquake source to site distance, average shear-wave velocity, and faulting mechanisms. The equations were established based on an extensive database of strong ground-motion recordings released by Pacific Earthquake Engineering Research Center (PEER). For more validity verification, the developed equations were employed to predict the ground-motion parameters of the Iranian plateau earthquakes. A sensitivity analysis was carried out to determine the contributions of the parameters affecting PGA, PGV and PGD. The sensitivity of the models to the variations of the influencing parameters was further evaluated through a parametric analysis. The obtained GMPEs are effectively capable of estimating the site ground-motion parameters. The equations provide a prediction performance better than or comparable with the attenuation relationships found in the literature. The derived GMPEs are remarkably simple and straightforward and can reliably be used for the pre-design purposes. & 2011 Elsevier Ltd. All rights reserved.
WEBSOM - Self-organizing maps of document collections

With the WEBSOM method a textual document collection may be organized onto a graphical map display that provides an overview of the collection and facilitates interactive browsing. Interesting documents can be located on the map using a content-directed search. Each document is encoded as a histogram of word categories which are formed by the self-organizing map (SOM) algorithm based on the similarities in the contexts of the words. The encoded documents are organized on another self-organizing map, a document map, on which nearby locations contain similar documents. Special consideration is given to the computation of very large document maps which is possible with general-purpose computers if the dimensionality of the word category histograms is first reduced with a random mapping method and if computationally efficient algorithms are used in computing the SOMs.
Measuring End-To-End Bandwidth with Iperf Using Web100

End-to-end bandwidth estimation tools like Iperf though fairly accurate are intrusive. In this paper, we describe how with an instrumented TCP stack (Web100), we can estimate the end-to-end bandwidth accurately, while consuming significantly less network bandwidth and time. We modified Iperf to use Web100 to detect the end of slow-start and estimate the end-toend bandwidth by measuring the amount of data sent for a short period (1 second) after the slow-start, when the TCP throughput is relatively stable. We obtained bandwidth estimates differing by less than 10% when compared to running Iperf for 20 seconds, and savings in bandwidth estimation time of up to 94% and savings in network traffic of up to 92%.
Architecture support for guest-transparent VM protection from untrusted hypervisor and physical attacks

The privacy and integrity of tenant's data highly rely on the infrastructure of multi-tenant cloud being secure. However, with both hardware and software being controlled by potentially curious or even malicious cloud operators, it is no surprise to see frequent reports of data leakages or abuses in cloud. Unfortunately, most prior solutions require intrusive changes to the cloud platform and none can protect a VM against adversaries controlling the physical machine. This paper analyzes the challenges of transparent VM protection against sophisticated adversaries controlling the whole software and hardware stack. Based on the analysis, this paper proposes HyperCoffer, a hardware-software framework that guards the privacy and integrity of tenant's VMs. HyperCoffer only trusts the processor chip and makes no security assumption on external memory and devices. Hyper-Coffer extends existing processor virtualization with memory encryption and integrity checking to secure data communication with off-chip memory. Unlike prior hardware-based approaches, HyperCoffer retains transparency with existing virtual machines (i.e., operating systems) and requires very few changes to the (untrusted) hypervisor. HyperCoffer introduces a mechanism called VM-Shim that runs in-between a guest VM and the hypervisor. Each VM-Shim instance for a VM runs in a separate protected context and only declassifies necessary information designated by the VM to the hypervisor and external environments (e.g., through NICs). We have implemented a prototype of HyperCoffer in a QEMU-based full-system emulator and the VM-Shim mechanism in a real machine. Performance measurement using trace-based simulation and on a real hardware platform shows that the performance overhead is small (ranging from 0.6% to 13.9% on simulated platform and 0.3% to 6.8% on real hardware for the VM-Shim mechanism).
Hot topic: physical-layer network coding

A main distinguishing feature of a wireless network compared with a wired network is its broadcast nature, in which the signal transmitted by a node may reach several other nodes, and a node may receive signals from several other nodes simultaneously. Rather than a blessing, this feature is treated more as an interference-inducing nuisance in most wireless networks today (e.g., IEEE 802.11). The goal of this paper is to show how the concept of network coding can be applied at the physical layer to turn the broadcast property into a capacity-boosting advantage in wireless ad hoc networks. Specifically, we propose a physical-layer network coding (PNC) scheme to coordinate transmissions among nodes. In contrast to "straightforward" network coding which performs coding arithmetic on digital bit streams after they have been received, PNC makes use of the additive nature of simultaneously arriving electromagnetic (EM) waves for equivalent coding operation. PNC can yield higher capacity than straight-forward network coding when applied to wireless networks. We believe this is a first paper that ventures into EM-wave-based network coding at the physical layer and demonstrates its potential for boosting network capacity. PNC opens up a whole new research area because of its implications and new design requirements for the physical, MAC, and network layers of ad hoc wireless stations. The resolution of the many outstanding but interesting issues in PNC may lead to a revolutionary new paradigm for wireless ad hoc networking.
Benchmark Functions for the CEC'2013 Special Session and Competition on Large-Scale Global Optimization

This report proposes 15 large-scale benchmark problems as an extension to the existing CEC’2010 large-scale global optimization benchmark suite. The aim is to better represent a wider range of realworld large-scale optimization problems and provide convenience and flexibility for comparing various evolutionary algorithms specifically designed for large-scale global optimization. Introducing imbalance between the contribution of various subcomponents, subcomponents with nonuniform sizes, and conforming and conflicting overlapping functions are among the major new features proposed in this report.
A literature survey on smart cities

Rapid urbanization creates new challenges and issues, and the smart city concept offers opportunities to rise to these challenges, solve urban problems and provide citizens with a better living environment. This paper presents an exhaustive literature survey of smart cities. First, it introduces the origin and main issues facing the smart city concept, and then presents the fundamentals of a smart city by analyzing its definition and application domains. Second, a data-centric view of smart city architectures and key enabling technologies is provided. Finally, a survey of recent smart city research is presented. This paper provides a reference to researchers who intend to contribute to smart city research and implementation. 世界范围内的快速城镇化给城市发展带来了很多新的问题和挑战, 智慧城市概念的出现, 为解决当前城市难题、提供更好的城市环境提供了有效的解决途径。论文介绍了智慧城市的起源, 总结了智慧城市领域的三个主要问题, 通过详细的综述性文献研究展开对这些问题的探讨。论文首先对智慧城市的定义和应用领域进行了归纳和分析, 然后研究了智慧城市的体系架构, 提出了智慧城市以数据为中心、多领域融合的相关特征, 并定义了以数据活化技术为核心的层次化体系架构, 并介绍了其中的关键技术, 最后选取了城市交通、城市群体行为、城市规划三个具有代表性的应用领域介绍了城市数据分析与处理的最新研究进展和存在问题。
A Probabilistic Short-Term Water Demand Forecasting Model Based on the Markov Chain

This paper proposes a short-term water demand forecasting method based on the use of the Markov chain. This method provides estimates of future demands by calculating probabilities that the future demand value will fall within pre-assigned intervals covering the expected total variability. More specifically, two models based on homogeneous and non-homogeneous Markov chains were developed and presented. These models, together with two benchmark models (based on artificial neural network and naïve methods), were applied to three real-life case studies for the purpose of forecasting the respective water demands from 1 to 24 h ahead. The results obtained show that the model based on a homogeneous Markov chain provides more accurate short-term forecasts than the one based on a non-homogeneous Markov chain, which is in line with the artificial neural network model. Both Markov chain models enable probabilistic information regarding the stochastic demand forecast to be easily obtained.
Who to follow and why: link prediction with explanations

User recommender systems are a key component in any on-line social networking platform: they help the users growing their network faster, thus driving engagement and loyalty.
 In this paper we study link prediction with explanations for user recommendation in social networks. For this problem we propose WTFW ("Who to Follow and Why"), a stochastic topic model for link prediction over directed and nodes-attributed graphs. Our model not only predicts links, but for each predicted link it decides whether it is a "topical" or a "social" link, and depending on this decision it produces a different type of explanation.
 A topical link is recommended between a user interested in a topic and a user authoritative in that topic: the explanation in this case is a set of binary features describing the topic responsible of the link creation. A social link is recommended between users which share a large social neighborhood: in this case the explanation is the set of neighbors which are more likely to be responsible for the link creation.
 Our experimental assessment on real-world data confirms the accuracy of WTFW in the link prediction and the quality of the associated explanations.
Surviving wi-fi interference in low power ZigBee networks

Frequency overlap across wireless networks with different radio technologies can cause severe interference and reduce communication reliability. The circumstances are particularly unfavorable for ZigBee networks that share the 2.4 GHz ISM band with WiFi senders capable of 10 to 100 times higher transmission power. Our work first examines the interference patterns between ZigBee and WiFi networks at the bit-level granularity. Under certain conditions, ZigBee activities can trigger a nearby WiFi transmitter to back off, in which case the header is often the only part of the Zig-Bee packet being corrupted. We call this the symmetric interference regions, in comparison to the asymmetric regions where the ZigBee signal is too weak to be detected by WiFi senders, but WiFi activity can uniformly corrupt any bit in a ZigBee packet. With these observations, we design BuzzBuzz to mitigate WiFi interference through header and payload redundancy. Multi-Headers provides header redundancy giving ZigBee nodes multiple opportunities to detect incoming packets. Then, TinyRS, a full-featured Reed Solomon library for resource-constrained devices, helps decoding polluted packet payload. On a medium-sized testbed, BuzzBuzz improves the ZigBee network delivery rate by 70%. Furthermore, BuzzBuzz reduces ZigBee retransmissions by a factor of three, which increases the WiFi throughput by 10%.
Simplification of the Nanosilver Sintering Process for Large-Area Semiconductor Chip Bonding: Reduction of Hot-Pressing Temperature Below 200/spl deg/C

Die attach by low-temperature sintering of nanoparticles of silver is an emerging lead-free joining solution for electronic packaging because of the high thermal/electrical conductivity and high reliability of silver. For bonding small chips, the attachment can be achieved by a simple heating profile under atmospheric pressure. However, for bonding chips with an area , an external pressure of a few MPa is reported necessary at the sintering temperature of ~ 250 °C. This hot-pressing process in excess of 200 °C can add significant complexity and costs to manufacturing and maintenance. In this paper, we conduct a fractional factorial design of experiments aimed at lowering the temperature at which pressure is required for the die-attach process. In particular, we examine the feasibility of applying pressure only during the drying stage of the process when the temperature is still at 180 °C. The experiments help to identify the importance and interaction of various processing parameters, such as pressure, temperature, and time, on the bonding strength and microstructure of sintered nanosilver joints. In addition, the positive effect of pressure applied during drying on the bonding quality is observed. With the results, a simpler process, consisting of pressure drying at 180 °C under 3 MPa pressure, followed by sintering at 275 °C under atmospheric pressure, is found to produce attachments with die-shear strengths in excess of 30 MPa.
Metamaterial-Based Design of Planar Compact MIMO Monopoles

A systematic design of planar MIMO monopole antennas with significantly reduced mutual coupling is presented, based on the concept of metamaterials. The design is performed by means of individual rectangular loop resonators, placed in the space between the antenna elements. The underlying principle is that resonators act like small metamaterial samples, thus providing an effective means of controlling electromagnetic wave propagation. The proposed design achieves considerably high levels of isolation between antenna elements, without essentially affecting the simplicity and planarity of the MIMO antenna.
Compressibility and Generalization in Large-Scale Deep Learning

Modern neural networks are highly overparameterized, with capacity to substantially overfit to training data. Nevertheless, these networks often generalize well in practice. It has also been observed that trained networks can often be “compressed” to much smaller representations. The purpose of this paper is to connect these two empirical observations. Our main technical result is a generalization bound for compressed networks based on the compressed size. Combined with off-the-shelf compression algorithms, the bound leads to state of the art generalization guarantees; in particular, we provide the first non-vacuous generalization guarantees for realistic architectures applied to the ImageNet classification problem. As additional evidence connecting compression and generalization, we show that compressibility of models that tend to overfit is limited: We establish an absolute limit on expected compressibility as a function of expected generalization error, where the expectations are over the random choice of training examples. The bounds are complemented by empirical results that show an increase in overfitting implies an increase in the number of bits required to describe a trained network.
Understanding Privacy Settings in Facebook with an Audience View

Users of online social networking communities are disclosing large amounts of personal information, putting themselves at a variety of risks. Our ongoing research investigates mechanisms for socially appropriate privacy management in online social networking communities. As a first step, we are examining the role of interface usability in current privacy settings. In this paper we report on our first iterative prototype, where presenting an audienceoriented view of profile information significantly improved the understanding of privacy settings.
Improving object proposals with multi-thresholding straddling expansion

Recent advances in object detection have exploited object proposals to speed up object searching. However, many of existing object proposal generators have strong localization bias or require computationally expensive diversification strategies. In this paper, we present an effective approach to address these issues. We first propose a simple and useful localization bias measure, called superpixel tightness. Based on the characteristics of superpixel tightness distribution, we propose an effective method, namely multi-thresholding straddling expansion (MTSE) to reduce localization bias via fast diversification. Our method is essentially a box refinement process, which is intuitive and beneficial, but seldom exploited before. The greatest benefit of our method is that it can be integrated into any existing model to achieve consistently high recall across various intersection over union thresholds. Experiments on PASCAL VOC dataset demonstrates that our approach improves numerous existing models significantly with little computational overhead.
Working Locally Thinking Globally - Part II: Stability and Algorithms for Convolutional Sparse Coding

The convolutional sparse model has recently gained increasing attention in the signal and image processing communities, and several methods have been proposed for solving the pursuit problem emerging from it – in particular its convex relaxation, Basis Pursuit. In the first of this two-part work, we have provided a theoretical back-bone for this model, providing guarantees for the uniqueness of the sparsest solution and for the success of pursuit algorithms by introducing the notion of stripe sparsity and other related measures. Herein, we extend the analysis to a noisy regime, thereby considering signal perturbations and model deviations. We address questions of stability of the sparsest solutions and the success of pursuit algorithms, both greedy and convex. Classical definitions such as the RIP are generalized to the convolutional model, and existing notions such as the ERC are connected to our setting. On the algorithmic side, we demonstrate how to solve the global pursuit problem by using simple local processing, thus offering a first of its kind bridge between global modeling of signals and their patch-based local treatment.
Information Flow within a Dispersed Agile Team: A Distributed Cognition Perspective

One of the hallmarks of a co-located agile team is the simple and open flow of information between its members. In a co-located setting, peripheral awareness, osmotic communication and simple information radiators support agile principles such as collective ownership, minimal documentation and simple design, and facilitate smooth collaboration. However in a dispersed agile team, where individual team members are distributed across several sites, these mechanisms are not available and information sharing has to be more explicit. Research into distributed software development has been tackling similar issues, but little work has been reported into dispersed agile teams. This paper reports on a field study of one successful partially dispersed agile team. Using a distributed cognition analysis which focuses on information propagation and transformation within the team we investigate how the team collaborates and compare our findings with those from co-located teams.
One Shot Learning via Compositions of Meaningful Patches

The task of discriminating one object from another is almost trivial for a human being. However, this task is computationally taxing for most modern machine learning methods, whereas, we perform this task at ease given very few examples for learning. It has been proposed that the quick grasp of concept may come from the shared knowledge between the new example and examples previously learned. We believe that the key to one-shot learning is the sharing of common parts as each part holds immense amounts of information on how a visual concept is constructed. We propose an unsupervised method for learning a compact dictionary of image patches representing meaningful components of an objects. Using those patches as features, we build a compositional model that outperforms a number of popular algorithms on a one-shot learning task. We demonstrate the effectiveness of this approach on hand-written digits and show that this model generalizes to multiple datasets.