nils-herrmann/clean_scirepeval huggingface dataset:
Integrating 3D structure into traffic scene understanding with RGB-D data

RGB Video now is one of the major data sources of traffic surveillance applications. In order to detect the possible traffic events in the video, traffic-related objects, such as vehicles and pedestrians, should be first detected and recognized. However, due to the 2D nature of the RGB videos, there are technical difficulties in efficiently detecting and recognizing traffic-related objects from them. For instance, the traffic-related objects cannot be efficiently detected in separation while parts of them overlap, and complex background will influence the accuracy of the object detection. In this paper, we propose a robust RGB-D data based traffic scene understanding algorithm. By integrating depth information, we can calculate more discriminative object features and spatial information can be used to separate the objects in the scene efficiently. Experimental results show that integrating depth data can improve the accuracy of object detection and recognition. We also show that the analyzed object information plus depth data facilitate two important traffic event detection applications: overtaking warning and collision
Definition and Classification of Power System Stability

The problem of defining and classifying power system stability has been addressed by several previous CIGRE and IEEE Task Force reports. These earlier efforts, however, do not completely reflect current industry needs, experiences and understanding. In particular, the definitions are not precise and the classifications do not encompass all practical instability scenarios. This report developed by a Task Force, set up jointly by the CIGRE Study Committee 38 and the IEEE Power System Dynamic Performance Committee, addresses the issue of stability definition and classification in power systems from a fundamental viewpoint and closely examines the practical ramifications. The report aims to define power system stability more precisely, provide a systematic basis for its classification, and discuss linkages to related issues such as power system reliability and security.
Agile virtualized infrastructure to proactively defend against cyber attacks

DDoS attacks have been a persistent threat to network availability for many years. Most of the existing mitigation techniques attempt to protect against DDoS by filtering out attack traffic. However, as critical network resources are usually static, adversaries are able to bypass filtering by sending stealthy low traffic from large number of bots that mimic benign traffic behavior. Sophisticated stealthy attacks on critical links can cause a devastating effect such as partitioning domains and networks. In this paper, we propose to defend against DDoS attacks by proactively changing the footprint of critical resources in an unpredictable fashion to invalidate an adversary's knowledge and plan of attack against critical network resources. Our present approach employs virtual networks (VNs) to dynamically reallocate network resources using VN placement and offers constant VN migration to new resources. Our approach has two components: (1) a correct-by-construction VN migration planning that significantly increases the uncertainty about critical links of multiple VNs while preserving the VN placement properties, and (2) an efficient VN migration mechanism that identifies the appropriate configuration sequence to enable node migration while maintaining the network integrity (e.g., avoiding session disconnection). We formulate and implement this framework using SMT logic. We also demonstrate the effectiveness of our implemented framework on both PlanetLab and Mininet-based experimentations.
A prototype architecture for cyber-physical systems

Cyber-Physical Systems (CPS) is an exciting emerging research area that has drawn the attention of many researchers. Although the question of "What is a CPS?" remains open, widely recognized and accepted attributes of a CPS include timeliness, distributed, reliability, fault-tolerance, security, scalability and autonomous. In this paper, a CPS definition is given and a prototype architecture is proposed. It is argued that this architecture captures the essential attributes of a CPS and lead to identification of many research challenges.
Simple regret for infinitely many armed bandits

We consider a stochastic bandit problem with infinitely many arms. In this setting, the learner has no chance of trying all the arms even once and has to dedicate its limited number of samples only to a certain number of arms. All previous algorithms for this setting were designed for minimizing the cumulative regret of the learner. In this paper, we propose an algorithm aiming at minimizing the simple regret. As in the cumulative regret setting of infinitely many armed bandits, the rate of the simple regret will depend on a parameter β characterizing the distribution of the near-optimal arms. We prove that depending on β, our algorithm is minimax optimal either up to a multiplicative constant or up to a log(n) factor. We also provide extensions to several important cases: when β is unknown, in a natural setting where the near-optimal arms have a small variance, and in the case of unknown time horizon.
On the Feasibility of Side-Channel Attacks with Brain-Computer Interfaces

Brain computer interfaces (BCI) are becoming increasingly popular in the gaming and entertainment industries. Consumer-grade BCI devices are available for a few hundred dollars and are used in a variety of applications, such as video games, hands-free keyboards, or as an assistant in relaxation training. There are application stores similar to the ones used for smart phones, where application developers have access to an API to collect data from the BCI devices. The security risks involved in using consumer-grade BCI devices have never been studied and the impact of malicious software with access to the device is unexplored. We take a first step in studying the security implications of such devices and demonstrate that this upcoming technology could be turned against users to reveal their private and secret information. We use inexpensive electroencephalography (EEG) based BCI devices to test the feasibility of simple, yet effective, attacks. The captured EEG signal could reveal the user’s private information about, e.g., bank cards, PIN numbers, area of living, the knowledge of the known persons. This is the first attempt to study the security implications of consumergrade BCI devices. We show that the entropy of the private information is decreased on the average by approximately 15 % 40 % compared to random guessing attacks.
Commerce, e-commerce, and m-commerce: what comes next?

In ancient times, people exchanged their goods and services to obtain what they needed (such as clothes and tools) from other people. This system of bartering compensated for the lack of currency. People offered goods/services and received in kind other goods/services. Now, despite the existence of multiple currencies and the progress of humanity from the Stone Age to the Byte Age, people still barter but in a different way. Mainly, people use money to pay for the goods they purchase and the services they obtain.
The Control-Value Theory of Achievement Emotions : An Integrative Approach to Emotions in Education

For students and teachers alike, educational settings are of critical import� ance. Over the years, many hours are spent in the classroom, social relation� ships are created there, and the attain ment of important life goals depends on individual and collective agency in educational institutions. Because of their subjective importance, educational settings are infused with intense emo� tional experiences that direct interactions, affect learning and performance, and influence personal growth in both students and teachers (Pekrun, Goetz, Titz, & Perry, 2002a, 2002b). The significance of emotions experienced in educational settings has been recognized by researchers in different fields, including personality research that has analyzed students' test anxiety since the 1930s (Zeidner, 1998), research on achievement motivation (Heckhausen, 1991), and more recent educational studies focusing on a variety of emotions in education (as evi� denced in the chapters of this volume). Emanating from these different
Machine Learning Framework for the Detection of Mental Stress at Multiple Levels

Mental stress has become a social issue and could become a cause of functional disability during routine work. In addition, chronic stress could implicate several psychophysiological disorders. For example, stress increases the likelihood of depression, stroke, heart attack, and cardiac arrest. The latest neuroscience reveals that the human brain is the primary target of mental stress, because the perception of the human brain determines a situation that is threatening and stressful. In this context, an objective measure for identifying the levels of stress while considering the human brain could considerably improve the associated harmful effects. Therefore, in this paper, a machine learning (ML) framework involving electroencephalogram (EEG) signal analysis of stressed participants is proposed. In the experimental setting, stress was induced by adopting a well-known experimental paradigm based on the montreal imaging stress task. The induction of stress was validated by the task performance and subjective feedback. The proposed ML framework involved EEG feature extraction, feature selection (receiver operating characteristic curve, t-test and the Bhattacharya distance), classification (logistic regression, support vector machine and naïve Bayes classifiers) and tenfold cross validation. The results showed that the proposed framework produced 94.6% accuracy for two-level identification of stress and 83.4% accuracy for multiple level identification. In conclusion, the proposed EEG-based ML framework has the potential to quantify stress objectively into multiple levels. The proposed method could help in developing a computer-aided diagnostic tool for stress detection.
Fast image dehazing using guided joint bilateral filter

In this paper, we propose a new fast dehazing method from single image based on filtering. The basic idea is to compute an accurate atmosphere veil that is not only smoother, but also respect with depth information of the underlying image. We firstly obtain an initial atmosphere scattering light through median filtering, then refine it by guided joint bilateral filtering to generate a new atmosphere veil which removes the abundant texture information and recovers the depth edge information. Finally, we solve the scene radiance using the atmosphere attenuation model. Compared with exiting state of the art dehazing methods, our method could get a better dehazing effect at distant scene and places where depth changes abruptly. Our method is fast with linear complexity in the number of pixels of the input image; furthermore, as our method can be performed in parallel, thus it can be further accelerated using GPU, which makes our method applicable for real-time requirement.
Reliable information retrieval evaluation with incomplete and biased judgements

Information retrieval evaluation based on the pooling method is inherently biased against systems that did not contribute to the pool of judged documents. This may distort the results obtained about the relative quality of the systems evaluated and thus lead to incorrect conclusions about the performance of a particular ranking technique.
 We examine the magnitude of this effect and explore how it can be countered by automatically building an unbiased set of judgements from the original, biased judgements obtained through pooling. We compare the performance of this method with other approaches to the problem of incomplete judgements, such as bpref, and show that the proposed method leads to higher evaluation accuracy, especially if the set of manual judgements is rich in documents, but highly biased against some systems.
ACCAMS: Additive Co-Clustering to Approximate Matrices Succinctly

Matrix completion and approximation are popular tools to capture a user's preferences for recommendation and to approximate missing data. Instead of using low-rank factorization we take a drastically different approach, based on the simple insight that an additive model of co-clusterings allows one to approximate matrices efficiently. This allows us to build a concise model that, per bit of model learned, significantly beats all factorization approaches in matrix completion. Even more surprisingly, we find that summing over small co-clusterings is more effective in modeling matrices than classic co-clustering, which uses just one large partitioning of the matrix. Following Occam's razor principle, the fact that our model is more concise and yet just as accurate as more complex models suggests that it better captures the latent preferences and decision making processes present in the real world. We provide an iterative minimization algorithm, a collapsed Gibbs sampler, theoretical guarantees for matrix approximation, and excellent empirical evidence for the efficacy of our approach. We achieve state-of-the-art results for matrix completion on Netflix at a fraction of the model complexity.
Effects of resistance training on endurance capacity and muscle fiber composition in young top-level cyclists.

Equivocal findings exist on the effect of concurrent strength (S) and endurance (E) training on endurance performance and muscle morphology. Further, the influence of concurrent SE training on muscle fiber-type composition, vascularization and endurance capacity remains unknown in top-level endurance athletes. The present study examined the effect of 16 weeks of concurrent SE training on maximal muscle strength (MVC), contractile rate of force development (RFD), muscle fiber morphology and composition, capillarization, aerobic power (VO2max), cycling economy (CE) and long/short-term endurance capacity in young elite competitive cyclists (n=14). MVC and RFD increased 12-20% with SE (P<0.01) but not E. VO2max remained unchanged. CE improved in E to reach values seen in SE. Short-term (5-min) endurance performance increased (3-4%) after SE and E (P<0.05), whereas 45-min endurance capacity increased (8%) with SE only (P<0.05). Type IIA fiber proportions increased and type IIX proportions decreased after SE training (P<0.05) with no change in E. Muscle fiber area and capillarization remained unchanged. In conclusion, concurrent strength/endurance training in young elite competitive cyclists led to an improved 45-min time-trial endurance capacity that was accompanied by an increased proportion of type IIA muscle fibers and gains in MVC and RFD, while capillarization remained unaffected.
Perception of Drug Abuse Amongst Nigerian Undergraduates

This study examined the perception of drug abuse amongst Nigerian undergraduates living off-campus. Students were surveyed at the Lagos State University, Ojo, allowing for a diverse sample that included a large percentage of the students from different faculties and departments. The undergraduate students were surveyed with a structured self-reporting anonymous questionnaire modified and adapted from the WHO student drug survey proforma. Of the 1000 students surveyed, a total of 807 responded to the questionnaire resulting in 80.7% response rate. Majority (77.9%) of the students were aged 19-30 years and unmarried. Six hundred and ninety eight (86.5%) claimed they were aware of drug abuse, but contrarily they demonstrated poor knowledge and awareness. Marijuana, 298 (45.7%) was the most common drug of abuse seen by most of the students. They were unable to identify very well the predisposing factors to drug use and the attending risks. Two hundred and sixty six (33.0%) students were currently taking one or more drugs of abuse. Coffee (43.1%) was the most commonly used drug, followed by alcohol (25.8%) and marijuana (7.4%). Despite chronic use of these drugs (5 years and above), addiction is not a common finding. The study also revealed the poor attitudes of the undergraduates to drug addicts even after rehabilitation. It was therefore concluded that the awareness, knowledge, practices and attitudes of Nigerian undergraduates towards drug abuse is very poor. Considerably more research is needed to develop effective prevention strategy that combines school-based interventions with those affecting the family, social institutions and the larger community.
Incremental Bloom Filters

A bloom filter is a randomized data structure for performing approximate membership queries. It is being increasingly used in networking applications ranging from security to routing in peer to peer networks. In order to meet a given false positive rate, the amount of memory required by a bloom filter is a function of the number of elements in the set. We consider the problem of minimizing the memory requirements in cases where the number of elements in the set is not known in advance but the distribution or moment information of the number of elements is known. We show how to exploit such information to minimize the expected amount of memory required for the filter. We also show how this approach can significantly reduce memory requirement when bloom filters are constructed for multiple sets in parallel. We show analytically as well as experiments on synthetic and trace data that our approach leads to one to three orders of magnitude reduction in memory compared to a standard bloom filter.
SDF: software-defined flash for web-scale internet storage systems

In the last several years hundreds of thousands of SSDs have been deployed in the data centers of Baidu, China's largest Internet search company. Currently only 40\% or less of the raw bandwidth of the flash memory in the SSDs is delivered by the storage system to the applications. Moreover, because of space over-provisioning in the SSD to accommodate non-sequential or random writes, and additionally, parity coding across flash channels, typically only 50-70\% of the raw capacity of a commodity SSD can be used for user data. Given the large scale of Baidu's data center, making the most effective use of its SSDs is of great importance. Specifically, we seek to maximize both bandwidth and usable capacity.
 To achieve this goal we propose {\em software-defined flash} (SDF), a hardware/software co-designed storage system to maximally exploit the performance characteristics of flash memory in the context of our workloads. SDF exposes individual flash channels to the host software and eliminates space over-provisioning. The host software, given direct access to the raw flash channels of the SSD, can effectively organize its data and schedule its data access to better realize the SSD's raw performance potential.
 Currently more than 3000 SDFs have been deployed in Baidu's storage system that supports its web page and image repository services. Our measurements show that SDF can deliver approximately 95% of the raw flash bandwidth and provide 99% of the flash capacity for user data. SDF increases I/O bandwidth by 300\% and reduces per-GB hardware cost by 50% on average compared with the commodity-SSD-based system used at Baidu.
Monopole Antenna With Inkjet-Printed EBG Array on Paper Substrate for Wearable Applications

In this letter, a novel electromagnetic band-gap structure (EBG) with single-ring resonators is inkjet-printed on the commercially available photo paper using conductive nano-silver ink. The printed EBG array is placed above a copper sheet, forming an artificial magnetic conductor (AMC) reflector at the designed frequency range (2.4 ~ 2.5 GHz). A microstrip monopole antenna is backed with the designed AMC reflector and is tested in free space and in contact with a human phantom. The antenna gain of a conventional microstrip monopole on human phantom is as low as -9 dBi. The gain of the proposed AMC backed monopole, measured on a human phantom is 0.95 dBi. The measurements demonstrate superior performance of the proposed monopole with EBG array compared to a conventional microstrip monopole antenna when they are considered for wearable applications.
Semantic Segmentation Based Traffic Light Detection at Day and at Night

Traffic light detection from a moving vehicle is an important technology both for new safety driver assistance functions as well as for autonomous driving in the city. In this paper we present a machine learning framework for detection of traffic lights that can handle in realtime both day and night situations in a unified manner. A semantic segmentation method is employed to generate traffic light candidates, which are then confirmed and classified by a geometric and color features based classifier. Temporal consistency is enforced by using a tracking by detection method. We evaluate our method on a publicly available dataset recorded at daytime in order to compare to existing methods and we show similar performance. We also present an evaluation on two additional datasets containing more than 50 intersections with multiple traffic lights recorded both at day and during nighttime and we show that our method performs consistently in those situations.
Automated epilepsy diagnosis using interictal scalp EEG

Over 50 million people worldwide suffer from epilepsy. Traditional diagnosis of epilepsy relies on tedious visual screening by highly trained clinicians from lengthy EEG recording that contains the presence of seizure (ictal) activities. Nowadays, there are many automatic systems that can recognize seizure-related EEG signals to help the diagnosis. However, it is very costly and inconvenient to obtain long-term EEG data with seizure activities, especially in areas short of medical resources. We demonstrate in this paper that we can use the interictal scalp EEG data, which is much easier to collect than the ictal data, to automatically diagnose whether a person is epileptic. In our automated EEG recognition system, we extract three classes of features from the EEG data and build Probabilistic Neural Networks (PNNs) fed with these features. We optimize the feature extraction parameters and combine these PNNs through a voting mechanism. As a result, our system achieves an impressive 94.07% accuracy.
Design and Analysis of a Spoke Type Motor With Segmented Pushing Permanent Magnet for Concentrating Air-Gap Flux Density

This paper proposes a new design of a SPOKE-type permanent magnet brushless direct current (BLDC) motor by using pushing magnet. A numerical analysis is developed to calculate the maximum value of air-gap flux density. First, the analytical model of the SPOKE-type motor was established, and Laplace equations of magnetic scalar potential and a series of boundary conditions were given. Then, the analytical expressions of magnet field strength and magnet flux density were obtained in the air gap produced by ferrite permanent magnets. The developed analytical model was obtained by solving the magnetic scalar potential. Finally, the air-gap field distribution and back-electromotive force of spoke type machine was analyzed. The analysis works for internal rotor motor topologies, and either radial or parallel magnetized permanent magnets. This paper validates results of the analytical model by finite-element analysis as well as with the experimental analysis for SPOKE-type BLDC motors.
Fast Calibration for Robot Welding System with Laser Vision

Camera calibration and robot hand-eye calibration are important parts in applications of robots with visions. A way to simultaneously carry out camera calibration and robot hand-eye calibration is presented in robot welding system with laser vision. It is based on a certain relation between hand-eye matrix and matrix of robot hand frame to robot base frame. Calibration experiments are performed in this way using specifically designed calibration objects. And all parameters to be calibrated are solved out in nonlinear least square optimization method. 3D world coordinates of 20 sample points selected from a v-groove seam are reconstructed with the calibration results. By contrasting their real coordinates, average error is 0.12 mm, which is sufficient to the requirement of robot seam tracking.