nils-herrmann/clean_scirepeval huggingface dataset:
Abnormal Event Detection in Surveillance Video: A Compressed Domain Approach for HEVC

Recently, detecting abnormal events in surveillance videos has become one of the most important tasks of video analysis. There is a huge demand in developing fast and accurate abnormal event detection approach. However, traditional pixel-domain approaches are time-consuming and require fully decoding of the bit streams. On the other hand, the compression format may provide useful information to solve the challenge: compared to raw pixels, the advantage of the compression format is that it already contains some valuable clues for video analysis. In this work, we focus on anomaly detection in traffic video with the information provided in the HEVC compressed domain. There are mainly two contributions. The first is that we propose a novel feature namely motion energy intensity (MEI) to represent the motion intensity within coding unit, based on the MV fields, bit allocations and partitions of coding units in the HEVC compressed domain, which can provide energy and motion information of the LCU. The second contribution is that we have proposed an anomaly detection model based on the MEI. Furthermore, this approach can also serve as preprocess for pixel domain detection algorithms, by labeling suspicious part of the video which are likely to be abnormities. Figure 1 shows the flowchart of the proposed algorithm. Experimental results show that the proposed approach can obtain rather high detecting accuracy with very little overhead. The processing speed for detection is as fast as 1200 fps on 480x360 video. When served as a preprocess approach, the pixel-domain methods may also benefit with up to 50% time reduction.
Open challenges for data stream mining research

Every day, huge volumes of sensory, transactional, and web data are continuously generated as streams, which need to be analyzed online as they arrive. Streaming data can be considered as one of the main sources of what is called big data. While predictive modeling for data streams and big data have received a lot of attention over the last decade, many research approaches are typically designed for well-behaved controlled problem settings, overlooking important challenges imposed by real-world applications. This article presents a discussion on eight open challenges for data stream mining. Our goal is to identify gaps between current research and meaningful applications, highlight open problems, and define new application-relevant research directions for data stream mining. The identified challenges cover the full cycle of knowledge discovery and involve such problems as: protecting data privacy, dealing with legacy systems, handling incomplete and delayed information, analysis of complex data, and evaluation of stream mining algorithms. The resulting analysis is illustrated by practical applications and provides general suggestions concerning lines of future research in data stream mining.
Grid of the future

Many believe the electric power system is undergoing a profound change driven by a number of needs. There's the need for environmental compliance and energy conservation. We need better grid reliability while dealing with an aging infrastructure. And we need improved operational effi ciencies and customer service. The changes that are happening are particularly signifi cant for the electricity distribution grid, where "blind" and manual operations, along with the electromechanical components, will need to be transformed into a "smart grid." This transformation will be necessary to meet environmental targets, to accommodate a greater emphasis on demand response (DR), and to support plug-in hybrid electric vehicles (PHEVs) as well as distributed generation and storage capabilities. It is safe to say that these needs and changes present the power industry with the biggest challenge it has ever faced. On one hand, the transition to a smart grid has to be evolutionary to keep the lights on; on the other hand, the issues surrounding the smart grid are signifi cant enough to demand major changes in power systems operating philosophy.
Anonymity Properties of the Bitcoin P2P Network

Bitcoin is a popular alternative to fiat money, widely used for its perceived anonymity properties. However, recent attacks on Bitcoin’s peer-to-peer (P2P) network demonstrated that its gossip-based flooding protocols, which are used to ensure global network consistency, may enable user deanonymization— the linkage of a user’s IP address with her pseudonym in the Bitcoin network. In 2015, the Bitcoin community responded to these attacks by changing the network’s flooding mechanism to a different protocol, known as diffusion. However, no systematic justification was provided for the change, and it is unclear if diffusion actually improves the system’s anonymity. In this paper, we model the Bitcoin networking stack and analyze its anonymity properties, both preand post-2015. In doing so, we consider new adversarial models and spreading mechanisms that have not been previously studied in the source-finding literature. We theoretically prove that Bitcoin’s networking protocols (both preand post-2015) offer poor anonymity properties on networks with a regular-tree topology. We validate this claim in simulation on a 2015 snapshot of the real Bitcoin P2P network topology.
College students who have an eveningness preference report lower self-control and greater procrastination.

Previous research suggests a possible link between eveningness and general difficulties with self-regulation (e.g., evening types are more likely than other chronotypes to have irregular sleep schedules and social rhythms and use substances). Our study investigated the relationship between eveningness and self-regulation by using two standardized measures of self-regulation: the Self-Control Scale and the Procrastination Scale. We predicted that an eveningness preference would be associated with poorer self-control and greater procrastination than would an intermediate or morningness preference. Participants were 308 psychology students (mean age=19.92 yrs) at a small Canadian college. Students completed the self-regulation questionnaires and Morningness/Eveningness Questionnaire (MEQ) online. The mean MEQ score was 46.69 (SD=8.20), which is intermediate between morningness and eveningness. MEQ scores ranged from definite morningness to definite eveningness, but the dispersion of scores was skewed toward more eveningness. Pearson and partial correlations (controlling for age) were used to assess the relationship between MEQ score and the Self-Control Scale (global score and 5 subscale scores) and Procrastination Scale (global score). All correlations were significant. The magnitude of the effects was medium for all measures except one of the Self-Control subscales, which was small. A multiple regression analysis to predict MEQ score using the Self-Control Scale (global score), Procrastination Scale, and age as predictors indicated the Self-Control Scale was a significant predictor (accounting for 20% of the variance). A multiple regression analysis to predict MEQ scores using the five subscales of the Self-Control Scale and age as predictors showed the subscales for reliability and work ethic were significant predictors (accounting for 33% of the variance). Our study showed a relationship between eveningness and low self-control, but it did not address whether the relationship is a causal one.
High-Spectral-Efficiency Optical Modulation Formats

As 100-Gb/s coherent systems based on polarization- division multiplexed quadrature phase shift keying (PDM-QPSK), with aggregate wavelength-division multiplexed (WDM) capacities close to 10 Tb/s, are getting widely deployed, the use of high-spectral-efficiency quadrature amplitude modulation (QAM) to increase both per-channel interface rates and aggregate WDM capacities is the next evolutionary step. In this paper we review high-spectral-efficiency optical modulation formats for use in digital coherent systems. We look at fundamental as well as at technological scaling trends and highlight important trade-offs pertaining to the design and performance of coherent higher-order QAM transponders.
Social Computing and Social Media. Human Behavior

YouTube is one of the more powerful tools for self-learning and entertaining globally. Uploading and sharing on YouTube have increased recently as these are possible via a simple click. Moreover, some countries, including Saudi Arabia, use this technology more than others. While there are many Saudi channels and videos for all age groups, there are limited channels for people with disabilities such as Deaf and Hard of Hearing people (DHH). The utilization of YouTube among DHH people has not reached its full potential. To investigate this phenomenon, we conducted an empirical research study to uncover factors influencing DHH people’s motivations, perceptions and adoption of YouTube, based on the Technology Acceptance Model (TAM). The results showed that DHH people pinpoint some useful functions in YouTube, such as the captions in English and the translation in Arabic. However, Arab DHH people are not sufficiently motivated to watch YouTube due to the fact that the YouTube time-span is fast and DHH personnel prefer greater time to allow them to read and understand the contents. Hence, DHH people tend to avoid sharing YouTube videos among their contacts.
Repetitive and High Voltage Marx Generator Using Solid-state Devices

Repetitive high voltage pulsed power system proposed in this study originates from conventional Marx generators. This newly developed Marx modulator employs high voltage (HV) insulated gate bipolar transistors (IGBT) as switches and series- connected diodes as isolated components. Self-supplied IGBT drivers and optic signals are used in the system to avoid insulation problem. Experimental results of 20 stages generating pulses with 60 kV, 20-100 mus and 50~500 Hz are presented to validate the performance of the system in the paper.
Mitigating Evasion Attacks to Deep Neural Networks via Region-based Classification

Deep neural networks (DNNs) have transformed several artificial intelligence research areas including computer vision, speech recognition, and natural language processing. However, recent studies demonstrated that DNNs are vulnerable to adversarial manipulations at testing time. Specifically, suppose we have a testing example, whose label can be correctly predicted by a DNN classifier. An attacker can add a small carefully crafted noise to the testing example such that the DNN classifier predicts an incorrect label, where the crafted testing example is called adversarial example. Such attacks are called evasion attacks. Evasion attacks are one of the biggest challenges for deploying DNNs in safety and security critical applications such as self-driving cars.
 In this work, we develop new DNNs that are robust to state-of-the-art evasion attacks. Our key observation is that adversarial examples are close to the classification boundary. Therefore, we propose region-based classification to be robust to adversarial examples. Specifically, for a benign/adversarial testing example, we ensemble information in a hypercube centered at the example to predict its label. In contrast, traditional classifiers are point-based classification, i.e., given a testing example, the classifier predicts its label based on the testing example alone. Our evaluation results on MNIST and CIFAR-10 datasets demonstrate that our region-based classification can significantly mitigate evasion attacks without sacrificing classification accuracy on benign examples. Specifically, our region-based classification achieves the same classification accuracy on testing benign examples as point-based classification, but our region-based classification is significantly more robust than point-based classification to state-of-the-art evasion attacks.
A new method for violence detection in surveillance scenes

Violence detection is a hot topic for surveillance systems. However, it has not been studied as much as for action recognition. Existing vision-based methods mainly concentrate on violence detection and make little effort to determine the location of violence. In this paper, we propose a fast and robust framework for detecting and localizing violence in surveillance scenes. For this purpose, a Gaussian Model of Optical Flow (GMOF) is proposed to extract candidate violence regions, which are adaptively modeled as a deviation from the normal behavior of crowd observed in the scene. Violence detection is then performed on each video volume constructed by densely sampling the candidate violence regions. To distinguish violent events from nonviolent events, we also propose a novel descriptor, named as Orientation Histogram of Optical Flow (OHOF), which are fed into a linear SVM for classification. Experimental results on several benchmark datasets have demonstrated the superiority of our proposed method over the state-of-the-arts in terms of both detection accuracy and processing speed, even in crowded scenes.
A fast and robust method for web page template detection and removal

The widespread use of templates on the Web is considered harmful for two main reasons. Not only do they compromise the relevance judgment of many web IR and web mining methods such as clustering and classification, but they also negatively impact the performance and resource usage of tools that process web pages. In this paper we present a new method that efficiently and accurately removes templates found in collections of web pages. Our method works in two steps. First, the costly process of template detection is performed over a small set of sample pages. Then, the derived template is removed from the remaining pages in the collection. This leads to substantial performance gains when compared to previous approaches that combine template detection and removal. We show, through an experimental evaluation, that our approach is effective for identifying terms occurring in templates - obtaining F-measure values around 0.9, and that it also boosts the accuracy of web page clustering and classification methods.
Sensorless control of PM synchronous motors and brushless DC motors - an overview and evaluation

This paper provides a review of the literature addressing sensorless operation methods of PM brushless machines. The methods explained are state-of-the-art of open and closed loop control strategies. The closed loop review includes those methods based on voltage and current measurements, those methods based on back emf measurements, and those methods based on novel techniques not included in the previous categories. The paper concludes with a comparison table including all main features for all control strategies
Accurate Dead-Time Control for Synchronous Buck Converter With Fast Error Sensing Circuits

Dead-time controls for synchronous buck converter are challenging due to the difficulties in accurate sensing and processing the on/off dead-time errors. For the control of dead-times, an integral feedback control using switched capacitors and a fast timing sensing circuit composed of MOSFET differential amplifiers and switched current sources are proposed. Experiments for a 3.3 V input, 1.5 V-0.3 A output converter demonstrated 1.3 ~ 4.6% efficiency improvement over a wide load current range.
Leaky-Wave Antennas Based on Noncutoff Substrate Integrated Waveguide Supporting Beam Scanning From Backward to Forward

In this paper, we propose an approach to realize substrate integrated waveguide (SIW)-based leaky-wave antennas (LWAs) supporting continuous beam scanning from backward to forward above the cutoff frequency. First, through phase delay analysis, it was found that SIWs with straight transverse slots support backward and forward radiation of the -1-order mode with an open-stopband (OSB) in between. Subsequently, by introducing additional longitudinal slots as parallel components, the OSB can be suppressed, leading to continuous beam scanning at least from -40° through broadside to 35°. The proposed method only requires a planar structure and obtains less dispersive beam scanning compared with a composite right/left-handed (CRLH) LWA. Both simulations and measurements verify the intended beam scanning operation while verifying the underlying theory.
ACT: Attack Countermeasure Trees for Information Assurance Analysis

In modeling system response to security threats, researchers have made extensive use of state space models, notable instances including the partially observable stochastic game model proposed by Zonouz et.al. The drawback of these state space models is that they may suffer from state space explosion. Our approach in modeling defense makes use of a combinatorial model which helps avert this problem. We propose a new attack-tree (AT) model named attack-countermeasure trees (ACT) based on combinatorial modeling technique for modeling attacks and countermeasures. ACT enables one to (i) place defense mechanisms in the form of detection and mitigation techniques at any node of the tree, not just at the leaf nodes as in defense trees (DT) (ii) automate the generation of attack scenarios from the ACT using its mincuts and (iii) perform probabilistic analysis (e.g. probability of attack, attack and security investment cost, impact of an attack, system risk, return on attack (ROA) and return on investment (ROI)) in an integrated manner (iv) select an optimal countermeasure set from the pool of defense mechanisms using a method which is much less expensive compared to the state-space based approach (v) perform analysis for trees with both repeated and non-repeat events. For evaluation purposes, we suggest suitable algorithms and implement an ACT module in SHARPE. We demonstrate the utility of ACT using a practical case study (BGP attacks).
A Web Service Discovery Scheme Based on Structural and Semantic Similarity

With the increasing adoption of Web Services and service-oriented computing paradigm, matchmaking of web services with the request has become a significant task. This warrants the need to establish an effective and reliable Web Service discovery. Here reducing the service discovery time and increasing the quality of discovery are key issues. This paper proposes a new semantic Web Service discovery scheme where the similarity between the query and service is decided using the WSDL specification and ontology, and the improved Hungarian algorithm is applied to quickly find the maximum match. The proposed approach utilizes the structure of datatype and operation, and natural language description used for information retrieval. Computer simulation reveals that the proposed scheme substantially increases the quality of service discovery compared to the existing schemes in terms of precision, recall rate, and F-measure. Moreover, the proposed scheme allows consistently smaller discovery time, while the improvement gets more significant as the number of compared parameters increases.
Adaptive Sampling Rate Correction for Acoustic Echo Control in Voice-Over-IP

Hands-free terminals for speech communication employ adaptive filters to reduce echoes resulting from the acoustic coupling between loudspeaker and microphone. When using a personal computer with commercial audio hardware for teleconferencing, a sampling frequency offset between the loudspeaker output D/A converter and the microphone input A/D converter often occurs. In this case, state-of-the-art echo cancellation algorithms fail to track the correct room impulse response. In this paper, we present a novel least mean square (LMS-type) adaptive algorithm to estimate the frequency offset and resynchronize the signals using arbitrary sampling rate conversion. In conjunction with a normalized LMS-type adaptive filter for room impulse response tracking, the proposed system widely removes the deteriorating effects of a frequency offset up to several Hz and restores the functionality of echo cancellation.
Advanced control engineering methods in modern technological applications

The paper deals with new directions in research, development and applications of advanced control methods and structures based on the principles of optimality, robustness and intelligence. Present trends in the complex process control design demand an increasing degree of integration of numerical mathematics, control engineering methods, new control structures based of distribution, embedded network control structure and new information and communication technologies. Furthermore, increasing problems with interactions, process non-linearity's, operating constraints, time delays, uncertainties, and significant dead-times consequently lead to the necessity to develop more sophisticated control strategies. Advanced control methods and new distributed embedded control structures represent the most effective tools for realizing high performance of many technological processes. Main ideas covered in this paper are motivated namely by the development of new advanced control engineering methods (predictive, hybrid predictive, optimal, adaptive, robust, fuzzy logic, neural network) and new possibilities of their SW and HW realizations and successful implementation in industry.
Performance Evaluation of 3D Keypoint Detectors

In the past few years detection of repeatable and distinctive keypoints on 3D surfaces has been the focus of intense research activity, due on the one hand to the increasing diffusion of low-cost 3D sensors, on the other to the growing importance of applications such as 3D shape retrieval and 3D object recognition. This work aims at contributing to the maturity of this field by a thorough evaluation of several recent 3D keypoint detectors. A categorization of existing methods in two classes, that allows for highlighting their common traits, is proposed, so as to abstract all algorithms to two general structures. Moreover, a comprehensive experimental evaluation is carried out in terms of repeatability, distinctiveness and computational efficiency, based on a vast data corpus characterized by nuisances such as noise, clutter, occlusions and viewpoint changes.
Performance in competitive Environments : Gender differences *

In spite of the fact that equal opportunities for men and women have been a priority in many countries, enormous gender differences prevail in most competitive high-ranking positions. We conduct a series of controlled experiments to investigate whether women might react differently than men to competitive incentive schemes commonly used in job evaluation and promotion. We observe no significant gender difference in mean performance when participants are paid proportional to their performance. But in the competitive environment with mixed gender groups we observe a significant gender difference: the mean performance of men has a large and significant, that of women is unchanged. This gap is not due to gender differences in risk aversion. We then run the same test with homogeneous groups, to investigate whether women under-perform only when competing against men. Women do indeed increase their performance and gender differences in mean performance are now insignificant. These results may be due to lower skill of women, or more likely to the fact that women dislike competition, or alternatively that they feel less competent than their male competitors, which depresses their performance in mixed tournaments. Our last experiment provides support for this hypothesis.