nils-herrmann/clean_scirepeval huggingface dataset:
Towards Detection of Bus Driver Fatigue Based on Robust Visual Analysis of Eye State

Driver's fatigue is one of the major causes of traffic accidents, particularly for drivers of large vehicles (such as buses and heavy trucks) due to prolonged driving periods and boredom in working conditions. In this paper, we propose a vision-based fatigue detection system for bus driver monitoring, which is easy and flexible for deployment in buses and large vehicles. The system consists of modules of head-shoulder detection, face detection, eye detection, eye openness estimation, fusion, drowsiness measure percentage of eyelid closure (PERCLOS) estimation, and fatigue level classification. The core innovative techniques are as follows: 1) an approach to estimate the continuous level of eye openness based on spectral regression; and 2) a fusion algorithm to estimate the eye state based on adaptive integration on the multimodel detections of both eyes. A robust measure of PERCLOS on the continuous level of eye openness is defined, and the driver states are classified on it. In experiments, systematic evaluations and analysis of proposed algorithms, as well as comparison with ground truth on PERCLOS measurements, are performed. The experimental results show the advantages of the system on accuracy and robustness for the challenging situations when a camera of an oblique viewing angle to the driver's face is used for driving state monitoring.
Failure Identification in Smart Grids Based on Petri Net Modeling

This paper presents a method to identify and localize failures in smart grids. The method is based on a carefully designed Petri net (PN) that captures the modeling details of the protection system of the distribution network and allows the detection/identification of failures in data transmission and faults in the distribution network by means of simple matrix operations. The design of the PN model is carried out by carefully composing multiple PN models for single protection systems: Such an approach allows the identification of the faults despite possible strong penetration of distributed generation. In order to verify the method, two case studies are discussed. The results highlight that the proposed method can remove a lot of the complexity of the associated data analysis despite the possible presence of malfunctioning protection systems and misinformation due to communication and other errors.
Modeling a Paradigm Shift: From Producer Innovation to User and Open Collaborative Innovation

In this paper we assess the economic viability of innovation by producers relative to two increasingly important alternative models: innovations by single user individuals or firms, and open collaborative innovation. We analyze the design costs and architectures and communication costs associated with each model. We conclude that both innovation by individual users and open collaborative innovation increasingly compete with and may displace producer innovation in many parts of the economy. We explain why this represents a paradigm shift with respect to innovation research, policymaking, and practice. We discuss important implications and offer suggestions for further research.
On building an accurate stereo matching system on graphics hardware

This paper presents a GPU-based stereo matching system with good performance in both accuracy and speed. The matching cost volume is initialized with an AD-Census measure, aggregated in dynamic cross-based regions, and updated in a scanline optimization framework to produce the disparity results. Various errors in the disparity results are effectively handled in a multi-step refinement process. Each stage of the system is designed with parallelism considerations such that the computations can be accelerated with CUDA implementations. Experimental results demonstrate the accuracy and the efficiency of the system: currently it is the top performer in the Middlebury benchmark, and the results are achieved on GPU within 0.1 seconds. We also provide extra examples on stereo video sequences and discuss the limitations of the system.
Optimal Demand Response Using Device-Based Reinforcement Learning

Demand response (DR) for residential and small commercial buildings is estimated to account for as much as 65% of the total energy savings potential of DR, and previous work shows that a fully automated energy management system (EMS) is a necessary prerequisite to DR in these areas. In this paper, we propose a novel EMS formulation for DR problems in these sectors. Specifically, we formulate a fully automated EMS's rescheduling problem as a reinforcement learning (RL) problem, and argue that this RL problem can be approximately solved by decomposing it over device clusters. Compared with existing formulations, our new formulation does not require explicitly modeling the user's dissatisfaction on job rescheduling, enables the EMS to self-initiate jobs, allows the user to initiate more flexible requests, and has a computational complexity linear in the number of device clusters. We also demonstrate the simulation results of applying Q-learning, one of the most popular and classical RL algorithms, to a representative example.
Parallel implementation of artificial neural network training

In this paper we describe the implementation of a complete ANN training procedure for speech recognition using the block mode back-propagation learning algorithm. We exploit the high performance SIMD architecture of GPU using CUDA and its C-like language interface. We also compare the speed-up obtained implementing the training procedure only taking advantage of the multi-thread capabilities of multi-core processors. Our approach has been tested by training acoustic models for large vocabulary speech recognition tasks, showing a 6 times reduction of the time required to train real-world large size networks with respect to an already optimized implementation using the Intel MKL libraries.
Self-disclosure and liking: a meta-analytic review.

Self-disclosure plays a central role in the development and maintenance of relationships. One way that researchers have explored these processes is by studying the links between self-disclosure and liking. Using meta-analytic procedures, the present work sought to clarify and review this literature by evaluating the evidence for 3 distinct disclosure-liking effects. Significant disclosure-liking relations were found for each effect: (a) People who engage in intimate disclosures tend to be liked more than people who disclose at lower levels, (b) people disclose more to those whom they initially like, and (c) people like others as a result of having disclosed to them. In addition, the relation between disclosure and liking was moderated by a number of variables, including study paradigm, type of disclosure, and gender of the discloser. Taken together, these results suggest that various disclosure-liking effects can be integrated and viewed as operating together within a dynamic interpersonal system. Implications for theory development are discussed, and avenues for future research are suggested.
Dynamic vs. Static Flow-Sensitive Security Analysis

This paper seeks to answer fundamental questions about trade-offs between static and dynamic security analysis. It has been previously shown that flow-sensitive static information-flow analysis is a natural generalization of flow-insensitive static analysis, which allows accepting more secure programs. It has been also shown that sound purely dynamic information-flow enforcement is more permissive than static analysis in the flow-insensitive case. We argue that the step from flow-insensitive to flow-sensitive is fundamentally limited for purely dynamic information-flow controls. We prove impossibility of a sound purely dynamic information-flow monitor that accepts programs certified by a classical flow-sensitive static analysis. A side implication is impossibility of permissive dynamic instrumented security semantics for information flow, which guides us to uncover an unsound semantics from the literature. We present a general framework for hybrid mechanisms that is parameterized in the static part and in the reaction method of the enforcement (stop, suppress, or rewrite) and give security guarantees with respect to termination-insensitive noninterference for a simple language with output.
Exploring the Aesthetic Range for Humanoid Robots

Although the uncanny exists, the inherent, unavoidable dip (or valley) may be an illusion. Extremely abstract robots can be uncanny if the aesthetic is off, as can cosmetically atypical humans. Thus, the uncanny occupies a continuum ranging from the abstract to the real, although norms of acceptability may narrow as one approaches human likeness. However, if the aesthetic is right, any level of realism or abstraction can be appealing. If so, then avoiding or creating an uncanny effect just depends on the quality of the aesthetic design, regardless of the level of realism. The author’s preliminary experiments on human reaction to near-realistic androids appear to support this hypothesis.
FusionNet: 3D Object Classification Using Multiple Data Representations

High-quality 3D object recognition is an important component of many vision and robotics systems. We tackle the object recognition problem using two data representations, to achieve leading results on the Princeton ModelNet challenge. The two representations: • Volumetric representation: the 3D object is discretized spatially as binary voxels 1 if the voxel is occupied and 0 otherwise. • Pixel representation: the 3D object is represented as a set of projected 2D pixel images. Current leading submissions to the ModelNet Challenge use Convolutional Neural Networks (CNNs) on pixel representations. However, we diverge from this trend and additionally, use Volumetric CNNs to bridge the gap between the efficiency of the above two representations. We combine both representations and exploit them to learn new features, which yield a significantly better classifier than using either of the representations in isolation. To do this, we introduce new Volumetric CNN (V-CNN) architectures.
On the Design and Analysis of the Privacy-Preserving SVM Classifier

The support vector machine (SVM) is a widely used tool in classification problems. The SVM trains a classifier by solving an optimization problem to decide which instances of the training data set are support vectors, which are the necessarily informative instances to form the SVM classifier. Since support vectors are intact tuples taken from the training data set, releasing the SVM classifier for public use or shipping the SVM classifier to clients will disclose the private content of support vectors. This violates the privacy-preserving requirements for some legal or commercial reasons. The problem is that the classifier learned by the SVM inherently violates the privacy. This privacy violation problem will restrict the applicability of the SVM. To the best of our knowledge, there has not been work extending the notion of privacy preservation to tackle this inherent privacy violation problem of the SVM classifier. In this paper, we exploit this privacy violation problem, and propose an approach to postprocess the SVM classifier to transform it to a privacy-preserving classifier which does not disclose the private content of support vectors. The postprocessed SVM classifier without exposing the private content of training data is called Privacy-Preserving SVM Classifier (abbreviated as PPSVC). The PPSVC is designed for the commonly used Gaussian kernel function. It precisely approximates the decision function of the Gaussian kernel SVM classifier without exposing the sensitive attribute values possessed by support vectors. By applying the PPSVC, the SVM classifier is able to be publicly released while preserving privacy. We prove that the PPSVC is robust against adversarial attacks. The experiments on real data sets show that the classification accuracy of the PPSVC is comparable to the original SVM classifier.
Attacks on privacy and deFinetti's theorem

In this paper we present a method for reasoning about privacy using the concepts of exchangeability and deFinetti's theorem. We illustrate the usefulness of this technique by using it to attack a popular data sanitization scheme known as Anatomy. We stress that Anatomy is not the only sanitization scheme that is vulnerable to this attack. In fact, any scheme that uses the random worlds model, i.i.d. model, or tuple-independent model needs to be re-evaluated.
 The difference between the attack presented here and others that have been proposedin the past is that we do not need extensive background knowledge. An attacker only needs to know the nonsensitive attributes of one individual in the data, and can carry out this attack just by building a machine learning model over the sanitized data. The reason this attack is successful is that it exploits a subtle flaw in the way prior work computed the probability of disclosure of a sensitive attribute. We demonstrate this theoretically, empirically, and with intuitive examples. We also discuss how this generalizes to many other privacy schemes.
Exploiting time-based synonyms in searching document archives

Query expansion of named entities can be employed in order to increase the retrieval effectiveness. A peculiarity of named entities compared to other vocabulary terms is that they are very dynamic in appearance, and synonym relationships between terms change with time. In this paper, we present an approach to extracting synonyms of named entities over time from the whole history of Wikipedia. In addition, we will use their temporal patterns as a feature in ranking and classifying them into two types, i.e., time-independent or time-dependent. Time-independent synonyms are invariant to time, while time-dependent synonyms are relevant to a particular time period, i.e., the synonym relationships change over time. Further, we describe how to make use of both types of synonyms to increase the retrieval effectiveness, i.e., query expansion with time-independent synonyms for an ordinary search, and query expansion with time-dependent synonyms for a search wrt. temporal criteria. Finally, through an evaluation based on TREC collections, we demonstrate how retrieval performance of queries consisting of named entities can be improved using our approach.
Modeling and characterization of GPS spoofing

The Global Positioning System (GPS) grows into a ubiquitous utility that provides positioning, navigation, and timing (PNT) services. As an essential element of the global information infrastructure, cyber security of GPS faces serious challenges. Some mission-critical systems even rely on GPS as a security measure. However, civilian GPS itself has no protection against malicious acts such as spoofing. GPS spoofing breaches authentication by forging satellite signals to mislead users with wrong location/timing data that threatens homeland security. In order to make civilian GPS secure and resilient for diverse applications, we must understand the nature of attacks. This paper proposes a novel attack modeling of GPS spoofing with event-driven simulation package. Simulation supplements usual experiments to limit incidental harms and to comprehend a surreptitious scenario. We also provide taxonomy of GPS spoofing through characterization. The work accelerates the development of defense technology against GPS-based attacks.
DART: directed automated random testing

We present a new tool, named DART, for automatically testing software that combines three main techniques: (1) automated extraction of the interface of a program with its external environment using static source-code parsing; (2) automatic generation of a test driver for this interface that performs random testing to simulate the most general environment the program can operate in; and (3) dynamic analysis of how the program behaves under random testing and automatic generation of new test inputs to direct systematically the execution along alternative program paths. Together, these three techniques constitute Directed Automated Random Testing, or DART for short. The main strength of DART is thus that testing can be performed completely automatically on any program that compiles -- there is no need to write any test driver or harness code. During testing, DART detects standard errors such as program crashes, assertion violations, and non-termination. Preliminary experiments to unit test several examples of C programs are very encouraging.
LocalCom: A Community-based Epidemic Forwarding Scheme in Disruption-tolerant Networks

In disruption-tolerant networks (DTNs), network topology constantly changes and end-to-end paths can hardly be sustained. However, social network properties are observed in many DTNs and tend to be stable over time. To utilize the social network properties to facilitate packet forwarding, we present LocalCom, a community-based epidemic forwarding scheme that efficiently detects the community structure using limited local information and improves the forwarding efficiency based on the community structure. We define similarity metrics according to nodes’ encounter history to depict the neighboring relationship between each pair of nodes. A distributed algorithm which only utilizes local information is then applied to detect communities, and the formed communities have strong intra-community connections. We also present two schemes to mark and prune gateways that connect communities to control redundancy and facilitate inter-community packet forwarding. Extensive real-trace-driven simulation results are presented to support the effectiveness of our scheme.
Fake News Mitigation via Point Process Based Intervention

We propose the first multistage intervention framework that tackles fake news in social networks by combining reinforcement learning with a point process network activity model. The spread of fake news and mitigation events within the network is modeled by a multivariate Hawkes process with additional exogenous control terms. By choosing a feature representation of states, defining mitigation actions and constructing reward functions to measure the effectiveness of mitigation activities, we map the problem of fake news mitigation into the reinforcement learning framework. We develop a policy iteration method unique to the multivariate networked point process, with the goal of optimizing the actions for maximal total reward under budget constraints. Our method shows promising performance in real-time intervention experiments on a Twitter network to mitigate a surrogate fake news campaign, and outperforms alternatives on synthetic datasets.
The BSD Packet Filter: A New Architecture for User-level Packet Capture

Many versions of Unix provide facilities for user-level packet capture, making possible the use of general purpose workstations for network monitoring. Because network monitors run as user-level processes, packets must be copied across the kernel/user-space protection boundary. This copying can be minimized by deploying a kernel agent called a packet filter , which discards unwanted packets as early as possible. The original Unix packet filter was designed around a stack-based filter evaluator that performs sub-optimally on current RISC CPUs. The BSD Packet Filter (BPF) uses a new, registerbased filter evaluator that is up to 20 times faster than the original design. BPF also uses a straightforward buffering strategy that makes its overall performance up to 100 times faster than Sun’s NIT running on the same hardware.
A randomized trial of preexercise stretching for prevention of lower-limb injury.

This study investigated the effect of muscle stretching during warm-up on the risk of exercise-related injury.



1538 male army recruits were randomly allocated to stretch or control groups. During the ensuing 12 wk of training, both groups performed active warm-up exercises before physical training sessions. In addition, the stretch group performed one 20-s static stretch under supervision for each of six major leg muscle groups during every warm-up. The control group did not stretch.



333 lower-limb injuries were recorded during the training period, including 214 soft-tissue injuries. There were 158 injuries in the stretch group and 175 in the control group. There was no significant effect of preexercise stretching on all-injuries risk (hazard ratio [HR] = 0.95, 95% CI 0.77-1.18), soft-tissue injury risk (HR = 0.83, 95% CI 0.63-1.09), or bone injury risk (HR = 1.22, 95% CI 0.86-1.76). Fitness (20-m progressive shuttle run test score), age, and enlistment date all significantly predicted injury risk (P < 0.01 for each), but height, weight, and body mass index did not.



A typical muscle stretching protocol performed during preexercise warm-ups does not produce clinically meaningful reductions in risk of exercise-related injury in army recruits. Fitness may be an important, modifiable risk factor.
Feature sensitive surface extraction from volume data

The representation of geometric objects based on volumetric data structures has advantages in many geometry processing applications that require, e.g., fast surface interrogation or boolean operations such as intersection and union. However, surface based algorithms like shape optimization (fairing) or freeform modeling often need a topological manifold representation where neighborhood information within the surface is explicitly available. Consequently, it is necessary to find effective conversion algorithms to generate explicit surface descriptions for the geometry which is implicitly defined by a volumetric data set. Since volume data is usually sampled on a regular grid with a given step width, we often observe severe alias artifacts at sharp features on the extracted surfaces. In this paper we present a new technique for surface extraction that performs feature sensitive sampling and thus reduces these alias effects while keeping the simple algorithmic structure of the standard Marching Cubes algorithm. We demonstrate the effectiveness of the new technique with a number of application examples ranging from CSG modeling and simulation to surface reconstruction and remeshing of polygonal models.
Survival model predictive accuracy and ROC curves.

The predictive accuracy of a survival model can be summarized using extensions of the proportion of variation explained by the model, or R2, commonly used for continuous response models, or using extensions of sensitivity and specificity, which are commonly used for binary response models. In this article we propose new time-dependent accuracy summaries based on time-specific versions of sensitivity and specificity calculated over risk sets. We connect the accuracy summaries to a previously proposed global concordance measure, which is a variant of Kendall's tau. In addition, we show how standard Cox regression output can be used to obtain estimates of time-dependent sensitivity and specificity, and time-dependent receiver operating characteristic (ROC) curves. Semiparametric estimation methods appropriate for both proportional and nonproportional hazards data are introduced, evaluated in simulations, and illustrated using two familiar survival data sets.