nils-herrmann/clean_scirepeval huggingface dataset:
Video content authentication techniques: a comprehensive survey

In this digital day and age, we are becoming increasingly dependent on multimedia content, especially digital images and videos, to provide a reliable proof of occurrence of events. However, the availability of several sophisticated yet easy-to-use content editing software has led to great concern regarding the trustworthiness of such content. Consequently, over the past few years, visual media forensics has emerged as an indispensable research field, which basically deals with development of tools and techniques that help determine whether or not the digital content under consideration is authentic, i.e., an actual, unaltered representation of reality. Over the last two decades, this research field has demonstrated tremendous growth and innovation. This paper presents a comprehensive and scrutinizing bibliography addressing the published literature in the field of passive-blind video content authentication, with primary focus on forgery/tamper detection, video re-capture and phylogeny detection, and video anti-forensics and counter anti-forensics. Moreover, the paper intimately analyzes the research gaps found in the literature, provides worthy insight into the areas, where the contemporary research is lacking, and suggests certain courses of action that could assist developers and future researchers explore new avenues in the domain of video forensics. Our objective is to provide an overview suitable for both the researchers and practitioners already working in the field of digital video forensics, and for those researchers and general enthusiasts who are new to this field and are not yet completely equipped to assimilate the detailed and complicated technical aspects of video forensics.
Reading Together : A Successful Reading Fluency Intervention

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Brain mechanisms in religion and spirituality: An integrative predictive processing framework

We present the theory of predictive processing as a unifying framework to account for the neurocognitive basis of religion and spirituality. Our model is substantiated by discussing four different brain mechanisms that play a key role in religion and spirituality: temporal brain areas are associated with religious visions and ecstatic experiences; multisensory brain areas and the default mode network are involved in self-transcendent experiences; the Theory of Mind-network is associated with prayer experiences and over attribution of intentionality; top-down mechanisms instantiated in the anterior cingulate cortex and the medial prefrontal cortex could be involved in acquiring and maintaining intuitive supernatural beliefs. We compare the predictive processing model with two-systems accounts of religion and spirituality, by highlighting the central role of prediction error monitoring. We conclude by presenting novel predictions for future research and by discussing the philosophical and theological implications of neuroscientific research on religion and spirituality.
Fog Computing: Enabling the Management and Orchestration of Smart City Applications in 5G Networks

Fog computing extends the cloud computing paradigm by placing resources close to the edges of the network to deal with the upcoming growth of connected devices. Smart city applications, such as health monitoring and predictive maintenance, will introduce a new set of stringent requirements, such as low latency, since resources can be requested on-demand simultaneously by multiple devices at different locations. It is then necessary to adapt existing network technologies to future needs and design new architectural concepts to help meet these strict requirements. This article proposes a fog computing framework enabling autonomous management and orchestration functionalities in 5G-enabled smart cities. Our approach follows the guidelines of the European Telecommunications Standards Institute (ETSI) NFV MANO architecture extending it with additional software components. The contribution of our work is its fully-integrated fog node management system alongside the foreseen application layer Peer-to-Peer (P2P) fog protocol based on the Open Shortest Path First (OSPF) routing protocol for the exchange of application service provisioning information between fog nodes. Evaluations of an anomaly detection use case based on an air monitoring application are presented. Our results show that the proposed framework achieves a substantial reduction in network bandwidth usage and in latency when compared to centralized cloud solutions.
Hardening Malware Detection Systems Against Cyber Maneuvers: An Adversarial Machine Learning Approach

The evolution of mobile malware poses a serious threat to smartphone security. Today, sophisticated attackers can adapt by maximally sabotaging machine-learning classifiers via polluting training data, rendering most recent machine learning-based malware detection tools (such as Drebin and DroidAPIMiner) ineffective. In this paper, we explore the feasibility of constructing crafted malware samples; examine how machine-learning classifiers can be misled under three different threat models; then conclude that injecting carefully crafted data into training data can significantly reduce detection accuracy. To tackle the problem, we propose KuafuDet, a two-phase learning enhancing approach that learns mobile malware by adversarial detection. KuafuDet includes an offline training phase that selects and extracts features from the training set, and an online detection phase that utilizes the classifier trained by the first phase. To further address the adversarial environment, these two phases are intertwined through a self-adaptive learning scheme, wherein an automated camouflage detector is introduced to filter the suspicious false negatives and feed them back into the training phase. We finally show KuafuDet significantly reduces false negatives and boosts the detection accuracy by at least 15%. Experiments on more than 250,000 mobile applications demonstrate that KuafuDet is scalable and can be highly effective as a standalone system.
Variational Deep Embedding: A Generative Approach to Clustering

Clustering is among the most fundamental tasks in computer vision and machine learning. In this paper, we propose Variational Deep Embedding (VaDE), a novel unsupervised generative clustering approach within the framework of Variational Auto-Encoder (VAE). Specifically, VaDE models the data generative procedure with a Gaussian Mixture Model (GMM) and a deep neural network (DNN): 1) the GMM picks a cluster; 2) from which a latent embedding is generated; 3) then the DNN decodes the latent embedding into an observable. Inference in VaDE is done in a variational way: a different DNN is used to encode observables to latent embeddings, so that the evidence lower bound (ELBO) can be optimized using Stochastic Gradient Variational Bayes (SGVB) estimator and the reparameterization trick. Quantitative comparisons with strong baselines are included in this paper, and experimental results show that VaDE significantly outperforms the stateof-the-art clustering methods on 4 benchmarks from various modalities. Moreover, by VaDE’s generative nature, we show its capability of generating highly realistic samples for any specified cluster, without using supervised information during training. Lastly, VaDE is a flexible and extensible framework for unsupervised generative clustering, more general mixture models than GMM can be easily plugged in.
High-Dimensional Feature Selection by Feature-Wise Kernelized Lasso

The goal of supervised feature selection is to find a subset of input features that are responsible for predicting output values. The least absolute shrinkage and selection operator (Lasso) allows computationally efficient feature selection based on linear dependency between input features and output values. In this letter, we consider a feature-wise kernelized Lasso for capturing nonlinear input-output dependency. We first show that with particular choices of kernel functions, nonredundant features with strong statistical dependence on output values can be found in terms of kernel-based independence measures such as the Hilbert-Schmidt independence criterion. We then show that the globally optimal solution can be efficiently computed; this makes the approach scalable to high-dimensional problems. The effectiveness of the proposed method is demonstrated through feature selection experiments for classification and regression with thousands of features.
dTrust: A Simple Deep Learning Approach for Social Recommendation

Rating prediction is a key task of e-commerce recommendation mechanisms. Recent studies in social recommendation enhance the performance of rating predictors by taking advantage of user relationships. However, these prediction approaches mostly rely on user personal information which is a privacy threat. In this paper, we present dTrust, a simple social recommendation approach that avoids using user personal information. It relies uniquely on the topology of an anonymized trust-user-item network that combines user trust relations with user rating scores. This topology is fed into a deep feed-forward neural network. Experiments on real-world data sets showed that dTrust outperforms state-of-the-art in terms of Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) scores for both warm-start and cold-start problems.
Comparison of Simulation Tools ATP-EMTP and MATLAB-Simulink for Time Domain Power System Transient Studies

Continuous development of appropriate software packages makes simulation of power engineering problems more and more effective. However, these analysis tools differ from each other considerably from the point of view of the applicability to a special problem. The authors compare two widespread environments: MATLAB-SIMULINK, which can be used to simulate a wide spectrum of dynamic systems, and ATP-EMTP, which is specific software to simulate power system transient problems. In the first part of the paper the components (function-blocks) that can be used to build a circuit, are listed. Then three examples are presented which demonstrate the capabilities and underline the advantages and drawbacks of both programs.
PWM control of dual active bridge: comprehensive analysis and experimental verification

The dual active bridge (DAB) topology is ideally suited for high power dc-dc conversion. This paper presents a comprehensive analysis and experimental results with PWM control of the DAB. The PWM control is in addition to phase shift modulation between the two H-bridges. The analysis includes PWM of one bridge at a time and of both bridges simultaneously. In the latter, five distinct modes arise based on the choice of PWM and load condition. The possibilities are analyzed for optimizing power density and efficiency for low load operation. An optimum PWM scheme is proposed that extends the soft-switching range, reduces rms and peak currents at low loads, and results in significant size reduction of the transformer. Experimental results are presented with a 10 kW prototype.
Sampling and Sampling Rate Conversion of Band Limited Signals in the Fractional Fourier Transform Domain

The fractional Fourier transform (FRFT) has become a very active area in signal processing community in recent years, with many applications in radar, communication, information security, etc., This study carefully investigates the sampling of a continuous-time band limited signal to obtain its discrete-time version, as well as sampling rate conversion, for the FRFT. Firstly, based on product theorem for the FRFT, the sampling theorems and reconstruction formulas are derived, which explain how to sample a continuous-time signal to obtain its discrete-time version for band limited signals in the fractional Fourier domain. Secondly, the formulas and significance of decimation and interpolation are studied in the fractional Fourier domain. Using the results, the sampling rate conversion theory for the FRFT with a rational fraction as conversion factor is deduced, which illustrates how to sample the discrete-time version without aliasing. The theorems proposed in this study are the generalizations of the conventional versions for the Fourier transform. Finally, the theory introduced in this paper is validated by simulations.
An Extensible Event Extraction System With Cross-Media Event Resolution

The automatic extraction of breaking news events from natural language text is a valuable capability for decision support systems. Traditional systems tend to focus on extracting events from a single media source and often ignore cross-media references. Here, we describe a large-scale automated system for extracting natural disasters and critical events from both newswire text and social media. We outline a comprehensive architecture that can identify, categorize and summarize seven different event types - namely floods, storms, fires, armed conflict, terrorism, infrastructure breakdown, and labour unavailability. The system comprises fourteen modules and is equipped with a novel coreference mechanism, capable of linking events extracted from the two complementary data sources. Additionally, the system is easily extensible to accommodate new event types. Our experimental evaluation demonstrates the effectiveness of the system.
Depression and Internet addiction in adolescents.

The aim of the study was to evaluate the relationship between depression and Internet addiction among adolescents.



A total of 452 Korean adolescents were studied. First, they were evaluated for their severity of Internet addiction with consideration of their behavioral characteristics and their primary purpose for computer use. Second, we investigated correlations between Internet addiction and depression, alcohol dependence and obsessive-compulsive symptoms. Third, the relationship between Internet addiction and biogenetic temperament as assessed by the Temperament and Character Inventory was evaluated.



Internet addiction was significantly associated with depressive symptoms and obsessive-compulsive symptoms. Regarding biogenetic temperament and character patterns, high harm avoidance, low self-directedness, low cooperativeness and high self-transcendence were correlated with Internet addiction. In multivariate analysis, among clinical symptoms depression was most closely related to Internet addiction, even after controlling for differences in biogenetic temperament.



This study reveals a significant association between Internet addiction and depressive symptoms in adolescents. This association is supported by temperament profiles of the Internet addiction group. The data suggest the necessity of the evaluation of the potential underlying depression in the treatment of Internet-addicted adolescents.
Decentralized Caching for Content Delivery Based on Blockchain: A Game Theoretic Perspective

Blockchains enable tamper-proof, ordered logging for transactional data in a decentralized manner over open-access, overlay peer-to-peer networks. In this paper, we propose a decentralized framework of proactive caching in a hierarchical wireless network based on blockchains. We employ the blockchain-based smart contracts to construct an autonomous content caching market. In the market, the cache helpers are able to autonomously adapt their caching strategies according to the market statistics obtained from the blockchain, and the truthfulness of trustless nodes are financially enforced by smart contract terms. Further, we propose an incentive-compatible consensus mechanism based on proof-of-stake to financially encourage the cache helpers to stay active in service. We model the interaction between the cache helpers and the content providers as a Chinese restaurant game. Based on the theoretical analysis regarding the Nash equilibrium of the game, we propose a decentralized strategy-searching algorithm using sequential best response. The simulation results demonstrate both the efficiency and reliability of the proposed equilibrium searching algorithm.
Effects of weight lifting training combined with plyometric exercises on physical fitness, body composition, and knee extension velocity during kicking in football.

The effects of a training program consisting of weight lifting combined with plyometric exercises on kicking performance, myosin heavy-chain composition (vastus lateralis), physical fitness, and body composition (using dual-energy X-ray absorptiometry (DXA)) was examined in 37 male physical education students divided randomly into a training group (TG: 16 subjects) and a control group (CG: 21 subjects). The TG followed 6 weeks of combined weight lifting and plyometric exercises. In all subjects, tests were performed to measure their maximal angular speed of the knee during in-step kicks on a stationary ball. Additional tests for muscle power (vertical jump), running speed (30 m running test), anaerobic capacity (Wingate and 300 m running tests), and aerobic power (20 m shuttle run tests) were also performed. Training resulted in muscle hypertrophy (+4.3%), increased peak angular velocity of the knee during kicking (+13.6%), increased percentage of myosin heavy-chain (MHC) type IIa (+8.4%), increased 1 repetition maximum (1 RM) of inclined leg press (ILP) (+61.4%), leg extension (LE) (+20.2%), leg curl (+15.9%), and half squat (HQ) (+45.1%), and enhanced performance in vertical jump (all p < or = 0.05). In contrast, MHC type I was reduced (-5.2%, p < or = 0.05) after training. In the control group, these variables remained unchanged. In conclusion, 6 weeks of strength training combining weight lifting and plyometric exercises results in significant improvement of kicking performance, as well as other physical capacities related to success in football (soccer).
Software Development Practices, Software Complexity, and Software Maintenance Performance: a Field Study

Software maintenance claims a large proportion of organizational resources. It is thought that many maintenance problems derive from inadequate software design and development practices. Poor design choices can result in complex software that is costly to support and difficult to change. However, it is difficult to assess the actual maintenance performance effects of software development practices because their impact is realized over the software life cycle. To estimate the impact of development activities in a more practical time frame, this research develops a two stage model in which software complexity is a key intermediate variable that links design and development decisions to their downstream effects on software maintenance. The research analyzes data collected from a national mass merchandising retailer on twenty-nine software enhancement projects and twenty-three software applications in a large IBM COBOL environment. Results indicate that the use of a code generator in development is associated with increased software complexity and software enhancement project effort. The use of packaged software is associated with decreased software complexity and software enhancement effort. These results suggest an important link between software development practices and maintenance performance.
Uncertainty in Deep Learning

Deep learning has attracted tremendous attention from researchers in various fields of information engineering such as AI, computer vision, and language processing [Kalchbrenner and Blunsom, 2013; Krizhevsky et al., 2012; Mnih et al., 2013], but also from more traditional sciences such as physics, biology, and manufacturing [Anjos et al., 2015; Baldi et al., 2014; Bergmann et al., 2014]. Neural networks, image processing tools such as convolutional neural networks, sequence processing models such as recurrent neural networks, and regularisation tools such as dropout, are used extensively. However, fields such as physics, biology, and manufacturing are ones in which representing model uncertainty is of crucial importance [Ghahramani, 2015; Krzywinski and Altman, 2013]. With the recent shift in many of these fields towards the use of Bayesian uncertainty [Herzog and Ostwald, 2013;  2014; Trafimow and Marks, 2015], new needs arise from deep learning. In this work we develop tools to obtain practical uncertainty estimates in deep learning, casting recent deep learning tools as Bayesian models without changing either the models or the optimisation. In the first part of this thesis we develop the theory for such tools, providing applications and illustrative examples. We tie approximate inference in Bayesian models to dropout and other stochastic regularisation techniques, and assess the approximations empirically. We give example applications arising from this connection between modern deep learning and Bayesian modelling such as active learning of image data and data efficient deep reinforcement learning. We further demonstrate the method’s practicality through a survey of recent applications making use of the suggested tools in language applications, medical diagnostics, bioinformatics, image processing, and autonomous driving. In the second part of the thesis we explore its theoretical implications, and the insights stemming from the link between Bayesian modelling and deep learning. We discuss what determines model uncertainty properties, analyse the approximate inference analytically in the linear case, and theoretically examine various priors such as spike and slab priors.
Image Denoising via CNNs: An Adversarial Approach

Is it possible to recover an image from its noisy version using convolutional neural networks? This is an interesting problem as convolutional layers are generally used as feature detectors for tasks like classification, segmentation and object detection. We present a new CNN architecture for blind image denoising which synergically combines three architecture components, a multi-scale feature extraction layer which helps in reducing the effect of noise on feature maps, an ℓp regularizer which helps in selecting only the appropriate feature maps for the task of reconstruction, and finally a three step training approach which leverages adversarial training to give the final performance boost to the model. The proposed model shows competitive denoising performance when compared to the state-of-the-art approaches.