Reviewerly/exHarmony huggingface dataset:
PRNU-based Deepfake Detection

As deepfakes become harder to detect by humans, more reliable detection methods are required to fight the spread of fake images and videos. In our work, we focus on PRNU-based detection methods, which, while popular in the image forensics scene, have not been given much attention in the context of deepfake detection. We adopt a PRNU-based approach originally developed for the detection of face morphs and facial retouching, and performed the first large scale test of PRNU-based deepfake detection methods on a variety of standard datasets. We show the impact of often neglected parameters of the face extraction stage on detection accuracy. We also document that existing PRNU-based methods cannot compete with state of the art methods based on deep learning but may be used to complement those in hybrid detection schemes.
Autonomous Mobile Robot Navigation in Sparse LiDAR Feature Environments

In the industrial environment, Autonomous Guided Vehicles (AGVs) generally run on a planned route. Among trajectory-tracking algorithms for unmanned vehicles, the Pure Pursuit (PP) algorithm is prevalent in many real-world applications because of its simple and easy implementation. However, it is challenging to decelerate the AGV’s moving speed when turning on a large curve path. Moreover, this paper addresses the kidnapped-robot problem occurring in spare LiDAR environments. This paper proposes an improved Pure Pursuit algorithm so that the AGV can predict the trajectory and decelerate for turning, thus increasing the accuracy of the path tracking. To solve the kidnapped-robot problem, we use a learning-based classifier to detect the repetitive pattern scenario (e.g., long corridor) regarding 2D LiDAR features for switching the localization system between Simultaneous Localization And Mapping (SLAM) method and Odometer method. As experimental results in practice, the improved Pure Pursuit algorithm can reduce the tracking error while performing more efficiently. Moreover, the learning-based localization selection strategy helps the robot navigation task achieve stable performance, with 36.25% in completion rate more than only using SLAM. The results demonstrate that the proposed method is feasible and reliable in actual conditions.
An energy‐aware virtual machines consolidation method for cloud computing: Simulation and verification

Abstract Cloud systems have become an essential part of our daily lives owing to various Internet‐based services. Consequently, their energy utilization has also become a necessary concern in cloud computing systems increasingly. Live migration, including several virtual machines (VMs) packed on in minimal physical machines (PMs) as virtual machines consolidation (VMC) technique, is an approach to optimize power consumption. In this article, we have proposed an energy‐aware method for the VMC problem, which is called energy‐aware virtual machines consolidation (EVMC), to optimize the energy consumption regarding the quality of service guarantee, which comprises: (1) the support vector machine classification method based on the utilization rate of all resource of PMs that is used for PM detection in terms of the amount' load; (2) the modified minimization of migration approach which is used for VM selection; (3) the modified particle swarm optimization which is implemented for VM placement. Also, the evaluation of the functional requirements of the method is presented by the formal method and the non‐functional requirements by simulation. Finally, in contrast to the standard greedy algorithms such as modified best fit decreasing, the EVMC decreases the active PMs and migration of VMs, respectively, 30%, 50% on average. Also, it is more efficient for the energy 30% on average, resources and the balance degree 15% on average in the cloud.
Designing for Trust in Blockchain Platforms

Trust is a crucial component for successful transactions regardless of whether they are executed in physical or virtual spaces. Blockchain technology is often discussed in the context of trust and referred to as a trust-free, trustless, or trustworthy technology. However, the question of how the trustworthiness of blockchain platforms should be demonstrated and proven to end users still remains open. While there may be some genuine trust in the blockchain technology itself, on an application level trust in an IT artifact needs to be established. In this article, we examine how trust-supporting design elements may be implemented to foster an end user's trust in a blockchain platform. We follow the design science paradigm and suggest a practically useful set of design elements that can help designers of blockchain platforms to build more trustworthy systems.
Cross-Domain Similarity Learning for Face Recognition in Unseen Domains

Face recognition models trained under the assumption of identical training and test distributions often suffer from poor generalization when faced with unknown variations, such as a novel ethnicity or unpredictable individual make-ups during test time. In this paper, we introduce a novel cross-domain metric learning loss, which we dub Cross-Domain Triplet (CDT) loss, to improve face recognition in unseen domains. The CDT loss encourages learning semantically meaningful features by enforcing compact feature clusters of identities from one domain, where the compactness is measured by underlying similarity metrics that belong to another training domain with different statistics. Intuitively, it discriminatively correlates explicit metrics derived from one domain, with triplet samples from another domain in a unified loss function to be minimized within a network, which leads to better alignment of the training domains. The network parameters are further enforced to learn generalized features under domain shift, in a model-agnostic learning pipeline. Unlike the recent work of Meta Face Recognition [18], our method does not require careful hard-pair sample mining and filtering strategy during training. Extensive experiments on various face recognition benchmarks show the superiority of our method in handling variations, compared to baseline and the state-of-the-art methods.
Effective Scaling of Blockchain Beyond Consensus Innovations and Moore’s Law: Challenges and Opportunities

As an emerging technology, blockchain has achieved great success in numerous application scenarios, from intelligent healthcare to smart cities. However, a long-standing bottleneck hindering its further development is the massive resource consumption attributed to the distributed storage and consensus mechanisms. This makes blockchain suffer from insufficient performance and poor scalability. In this article, we evaluate numerous representative blockchain proposals in the past decade and demonstrate that the potential of the widely adopted consensus-based scaling is seriously limited, especially in the current era when Moore’s law-based hardware evolution is about to end. We achieve this by developing an open-source benchmarking tool, called <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Prism</monospace> , with which we further investigate the resource-related factors that hinder the current scaling attempts. Furthermore, we discuss various topology and hardware innovations which could help to scale up blockchain. Generally speaking, this article provides blockchain researchers many valuable insights because it explores the next-generation scaling strategies by conducting a large-scale benchmarking.
Explainable Machine Learning and Mining of Influential Patterns from Sparse Web

With advancements in modern technology in the current era, very large volumes of big data have been generated and collected in numerous real-life applications. These have formed a connected world comprising webs of agents, data, people, things and trust. Some of these webs have also emerged in health and smart living. As valuable information and knowledge is embedded in these rich sets of webs, web intelligence is in demand. In this paper, we focus on a data science task of web usage mining. In particular, we present a web intelligent solution to conduct explainable machine learning and mining of influential patterns from sparse web. It provides a compressed representation of sparse web, discovers influential websites and/or web pages that are frequently browsed or surfed by web surfers, and recommends these influential websites and/or web pages to other web surfers. Evaluation results show the effectiveness (especially, in data compression), interpretability and practicality of our solution.
A Unified Approach for Patient Activity Recognition in Healthcare Using Depth Camera

Context-awareness is an essential part of pervasive computing. Video-based human activity recognition (HAR) has arisen as an imperative module to detect user’s situation for involuntary facility delivery in context-aware domains. The activity recognition systems are frequently employed for protective and practical health care. Most of the existing works utilize RGB (red, green, and blue) cameras which present confidentiality and security concerns in the health-care domain. The existing approaches also do not sustain their performance results under the presence of a depth camera. Moreover, the accuracy of an HAR system relies on the extraction and selection of the prominent features from the feature space. To address these limitations, in this research, we first employ a depth camera to resolve the confidentiality and security concerns, and propose an unsupervised segmentation algorithm that can accurately segment the human body from the video frame. Then, we propose a new feature selection technique that has the ability to excerpt and select the best features from the feature space. Our proposed feature selection method focuses on the selection of confined features from the series of images and discriminate their category based on reversion (i.e., regression) value. The proposed method extracts and selects the best features through the benefits of forward selection and backward regression algorithms. Finally, we have trained and tested our proposed system by employing hidden Markov model (HMM) to label the activities. The proposed approach presents a significant performance against the existing works using depth camera.
Traffic Incident Detection Based on Dynamic Graph Embedding in Vehicular Edge Computing

With a large of time series dataset from the Internet of Things in Ambient Intelligence-enabled smart environments, many supervised learning-based anomaly detection methods have been investigated but ignored the correlation among the time series. To address this issue, we present a new idea for anomaly detection based on dynamic graph embedding, in which the dynamic graph comprises the multiple time series and their correlation in each time interval. We propose an entropy for measuring a graph’s information injunction with a correlation matrix to define similarity between graphs. A dynamic graph embedding model based on the graph similarity is proposed to cluster the graphs for anomaly detection. We implement the proposed model in vehicular edge computing for traffic incident detection. The experiments are carried out using traffic data produced by the Simulation of Urban Mobility framework. The experimental findings reveal that the proposed method achieves better results than the baselines by 14.5% and 18.1% on average with respect to F1-score and accuracy, respectively.
Cyberbullying Detection Through Sentiment Analysis

In recent years with the widespread of social media platforms across the globe especially among young people, cyberbullying and aggression have become a serious and annoying problem that communities must deal with. Such platforms provide various ways for bullies to attack and threaten others in their communities. Various techniques and methodologies have been used or proposed to combat cyberbullying through early detection and alerts to discover and/or protect victims from such attacks. Machine learning (ML) techniques have been widely used to detect some language patterns that are exploited by bullies to attack their victims. Also. Sentiment Analysis (SA) of social media content has become one of the growing areas of research in machine learning. SA provides the ability to detect cyberbullying in real-time. SA provides the ability to detect cyberbullying in real-time. This paper proposes a SA model for identifying cyberbullying texts in Twitter social media. Support Vector Machines (SVM) and Naïve Bayes (NB) are used in this model as supervised machine learning classification tools. The results of the experiments conducted on this model showed encouraging outcomes when a higher n-grams language model is applied on such texts in comparison with similar previous research. Also, the results showed that SVM classifiers have better performance measures than NB classifiers on such tweets.
SentiXGboost: enhanced sentiment analysis in social media posts with ensemble XGBoost classifier

Sentiment Analysis has emerged as one of the most challenging and significant research directions in text mining. The first milestone of Sentiment Analysis involves identifying the polarity of sentiments in social media and classifying them as positive or negative. Most of the previous work on sentiment analysis has focused on designing a classifier, for a specific dataset, using supervised machine learning approaches and feature extraction methods. Notably, choosing the most appropriate classification technique to correctly classify the sentiment polarity of text is crucial. Thus, this study proposes a novel ensemble classifier approach, which utilizes a combination of multiple feature sets with ensemble classification by combining multiple base classifiers, which are weak learners, into an ensemble classifier. These feature sets include Bag of Words, Term Frequency–Inverse Document Frequency, Part of Speech, N-gram, Opinion Lexicon, and Term Frequency. The experiments confirm that the proposed ensemble method outperforms all individual classifiers and significantly improves the overall sentiment classification performance on the most frequently used datasets in Sentiment Analysis.
Automated Identification and Classification of Blur Images, Duplicate Images Using Open CV

The number of digital images increasing rapidly with the popularization of digital cameras and mobile phones, as they are now available at affordable prices in the market. There is various image quality degradants out of which the blur plays a vital role in the nature. To get the quality and quantified images users will move on with the clicking of same image repeatedly. With the rapid increment of images, the occupation of space in the storage devices also increases. Previously researchers are worked on these problems individually, so the model will provide them combinedly in a single methodology. As the identification of blur images, duplicate images manually by the users has become a critical task, we come up with a new idea of sequential model to identify blur, duplicate images sequentially and storing those images in their respective folders automatically which are created by this model such that the user can review if they want, else they can delete the folder. It also provides the video of original good images (without duplicates) to review the images quickly and easily by the user.
An Innovative Recommender System for Health Tourism

The recommender systems process data for extracting information relevant to the user profile. In this study, we present an innovative recommender system aiming at matching health tourist preferences to health/tourism providers. It focuses on providing complete health tourism products, by matching the user profile to characteristics of both health and tourism service providers, in order that users receive the treatment they choose in the right location, the right period and at the right cost. The proposed recommender system is implemented by applying a facility location problem which employs a parameter that controls the diversity of the recommendation list and thus the variety of the proposed results. It incorporates a database of cases, i.e., medical, wellness and tourism service providers. A case is described by a set of attributes such as medical service category, spa category, wellness category, cost, infrastructure, accreditations, communication languages and so on. Users that have already acquired a health tourism package provide ratings for certain categories of attributes. A new user expresses her preferences in the form of a query, and then, the system tries to match this query to the cases that exist in the database. At first, the best possible cases are extracted, by applying a sorting procedure based on comparisons to the ideal one, i.e., that containing the best ratings for each attribute of the database. Then, the facility location method is applied to provide the final recommended cases to the user that are both similar to the provided query and diverse to each other.
Learning Semantic-Aware Dynamics for Video Prediction

We propose an architecture and training scheme to predict video frames by explicitly modeling dis-occlusions and capturing the evolution of semantically consistent regions in the video. The scene layout (semantic map) and motion (optical flow) are decomposed into layers, which are predicted and fused with their context to generate future layouts and motions. The appearance of the scene is warped from past frames using the predicted motion in co-visible regions; dis-occluded regions are synthesized with content-aware inpainting utilizing the predicted scene layout. The result is a predictive model that explicitly represents objects and learns their class-specific motion, which we evaluate on video prediction benchmarks.
Mirror3D: Depth Refinement for Mirror Surfaces

Despite recent progress in depth sensing and 3D reconstruction, mirror surfaces are a significant source of errors. To address this problem, we create the Mirror3D dataset: a 3D mirror plane dataset based on three RGBD datasets (Matterpot3D, NYUv2 and ScanNet) containing 7,011 mirror instance masks and 3D planes. We then develop Mirror3DNet: a module that refines raw sensor depth or estimated depth to correct errors on mirror surfaces. Our key idea is to estimate the 3D mirror plane based on RGB input and surrounding depth context, and use this estimate to directly regress mirror surface depth. Our experiments show that Mirror3DNet significantly mitigates errors from a variety of input depth data, including raw sensor depth and depth estimation or completion methods.
Boosting the Performance of Video Compression Artifact Reduction with Reference Frame Proposals and Frequency Domain Information

Many deep learning based video compression artifact removal algorithms have been proposed to recover high-quality videos from low-quality compressed videos. Recently, methods were proposed to mine spatiotemporal information via utilizing multiple neighboring frames as reference frames. However, these post-processing methods take advantage of adjacent frames directly, but neglect the information of the video itself, which can be exploited. In this paper, we propose an effective reference frame proposal strategy to boost the performance of the existing multi-frame approaches. Besides, we introduce a loss based on fast Fourier transformation (FFT) to further improve the effectiveness of restoration. Experimental results show that our method achieves better fidelity and perceptual performance on MFQE 2.0 dataset than the state-of-the-art methods. And our method won Track 1 and Track 2, and was ranked the 2nd in Track 3 of NTIRE 2021 Quality enhancement of heavily compressed videos Challenge.
A business case for democracy: regime type, growth, and growth volatility

I present a business case for democracy, focusing on the impact of democracy on economic growth. This relationship is widely studied, and results have been less robust for growth than many other development outcomes such as literacy or infant mortality. I discuss four factors pertaining to data quality and modelling choices, suggesting that several previous studies have underestimated the growth-benefits of democracy. I further discuss the relationships between democracy and economic crises and variation in economic performance. By mitigating abysmal economic outcomes and ensuring more stable performance, democracy is generally of benefit to risk-averse entrepreneurs, investors, workers, and consumers alike.
Real-time Monocular Depth Estimation with Sparse Supervision on Mobile

Monocular (relative or metric) depth estimation is a critical task for various applications, such as autonomous vehicles, augmented reality and image editing. In recent years, with the increasing availability of mobile devices, accurate and mobile-friendly depth models have gained importance. Increasingly accurate models typically require more computational resources, which inhibits the use of such models on mobile devices. The mobile use case is arguably the most unrestricted one, which requires highly accurate yet mobile-friendly architectures. Therefore, we try to answer the following question: How can we improve a model without adding further complexity (i.e. parameters)?Towards this end, we systematically explore the design space of a relative depth estimation model from various dimensions and we show, with key design choices and ablation studies, even an existing architecture can reach highly competitive performance to the state of the art, with a fraction of the complexity. Our study spans an in-depth backbone model selection process, knowledge distillation, intermediate predictions, model pruning and loss rebalancing. We show that our model, using only DIW as the supervisory dataset, achieves 0.1156 WHDR on DIW with 2.6M parameters and reaches 37 FPS on a mobile GPU, without pruning or hardware-specific optimization. A pruned version of our model achieves 0.1208 WHDR on DIW with 1M parameters and reaches 44 FPS on a mobile GPU.
Meta-learning based relation and representation learning networks for single-image deraining

Single-image deraining is a kind of computer vision task that aims to restore the image that be degraded by rain streaks, which motivates existing methods to either directly translate the rainy image to its clean one, or indirectly learn the rain residual based on the prior information. However, both methodologies harm the generalization ability due to the limited diversity of the training samples, comparing with the endless varieties of the real-world rainy images. Such fact inspires us to take the merit of meta-learning and propose a meta-learning based representation learning network to learn the transferable embeddings of the rainy/clean images, while their discrepancies are characterized by the relation vector, which is generated by the subsequent meta-learning based relation learning network. These networks are leveraged into the meta-learning based deraining network (MLDN) to enhance the generalization ability by removing the latent relation vector from the transferable embedding of the rainy image and generate high-quality deraining result. Superior performance is achieved by MLDN, which has averaged 4% better than the state-of-the-arts.