Reviewerly/exHarmony huggingface dataset:
Optical flow and scene flow estimation: A survey

Motion analysis is one of the most fundamental and challenging problems in the field of computer vision, which can be widely applied in many areas, such as autonomous driving, action recognition, scene understanding, and robotics. In general, the displacement field between subsequent frames can be divided into two types: optical flow and scene flow. The optical flow represents the pixel motion of adjacent frames. In contrast, the scene flow is a 3D motion field of the dynamic scene between two frames. Traditional approaches for the estimation of optical flow and scene flow usually leverage the variational technique, which can be solved as an energy minimization process. In recent years, deep learning has emerged as a powerful technique for learning feature representations directly from data. It has led to remarkable progress in the field of optical flow and scene flow estimation. In this paper, we provide a comprehensive survey of optical flow and scene flow estimation. First, we briefly review the pioneering approaches that use variational technique and then we delve in detail into the deep learning-based approaches. Furthermore, we present insightful observations on evaluation issues, specifically benchmark datasets, evaluation metrics, and state-of-the-art performance. Finally, we give the promising directions for future research. To the best of our knowledge, we are the first to review both optical flow and scene flow estimation, and the first to cover both traditional and deep learning-based approaches.
Blockchain-Based IoT Access Control System: Towards Security, Lightweight, and Cross-Domain

Most IoT devices cannot afford to be a blockchain node due to the high computation and storage loads. Thus, the blockchain is usually deployed on one delegate node, e.g., the edge device or cloud, which may encounters three drawbacks: (1) The delegate node becomes the single failure point when the number of delegate notes are limited. (2) The delegate node replicating the blockchain data can lead to privacy information leak. (3) The delegate node is vulnerable to the Distributed Denial of Service (DDoS) attack. To tackle these drawbacks, we consider to minimize the redundant of blockchain to make the IoT devices as the specialized blockchain nodes. In this paper, we integrate a permissioned blockchain (HLF), an attribute-based access control (ABAC) and an identity-based signature (IBS) to build a security, lightweight, and cross-domain blockchain-based IoT access control system. Specifically, we divided the IoT system into different function domains, named IoT domains. Then, we establish a local blockchain ledger for each IoT domain to enable more IoT devices as blockchain nodes. The local blockchain ledger records the IoT domain entities' attributes, policy files' digests, and access decisions. Meanwhile, we use the channel technology of HLF to realize cross-domain access and use the IBS to filter the legal access requests for each IoT domain to prevent DDoS attacks. We also design a policy decision point (PDP) selection algorithm that select multiple IoT devices (blockchain nodes) to achieve the real-time distributed policy decisions (off-chain). Finally, we implement and evaluate the proposed system to demonstrate its practicality.
Weapon Detection in Real-Time CCTV Videos Using Deep Learning

Security and safety is a big concern for today's modern world. For a country to be economically strong, it must ensure a safe and secure environment for investors and tourists. Having said that, Closed Circuit Television (CCTV) cameras are being used for surveillance and to monitor activities i.e. robberies but these cameras still require human supervision and intervention. We need a system that can automatically detect these illegal activities. Despite state-of-the-art deep learning algorithms, fast processing hardware, and advanced CCTV cameras, weapon detection in real-time is still a serious challenge. Observing angle differences, occlusions by the carrier of the firearm and persons around it further enhances the difficulty of the challenge. This work focuses on providing a secure place using CCTV footage as a source to detect harmful weapons by applying the state of the art open-source deep learning algorithms. We have implemented binary classification assuming pistol class as the reference class and relevant confusion objects inclusion concept is introduced to reduce false positives and false negatives. No standard dataset was available for real-time scenario so we made our own dataset by making weapon photos from our own camera, manually collected images from internet, extracted data from YouTube CCTV videos, through GitHub repositories, data by university of Granada and Internet Movies Firearms Database (IMFDB) imfdb.org. Two approaches are used i.e. sliding window/classification and region proposal/object detection. Some of the algorithms used are VGG16, Inception-V3, Inception-ResnetV2, SSDMobileNetV1, Faster-RCNN Inception-ResnetV2 (FRIRv2), YOLOv3, and YOLOv4. Precision and recall count the most rather than accuracy when object detection is performed so these entire algorithms were tested in terms of them. Yolov4 stands out best amongst all other algorithms and gave a F1-score of 91% along with a mean average precision of 91.73% higher than previously achieved.
Sentiment Analysis Using Word2vec And Long Short-Term Memory (LSTM) For Indonesian Hotel Reviews

Generally, Online Travel Agent (OTA) has a review element where clients can give reviews of the facilities they have used. Availability of a huge volume of reviews makes it troublesome for service executives to know the percent of reviews that have an effect on their services. Thus, it is essential to develop a sentiment assessment technique with respect to hotel reviews, particularly in Indonesian language. This research makes use of Long-Short Term Memory (LSTM) model as well as the Word2Vec model. The integration of Word2Vec and LSTM variables used in this research are Word2Vec architecture, Word2Vec vector dimension, Word2Vec evaluation method, pooling technique, dropout value, and learning rate. On the basis of an experimental research performed through 2500 review texts as dataset, the best performance was obtained that had accuracy of 85.96%. The parameter combinations for Word2Vec are Skip-gram as architecture, Hierarchical Softmax as evaluation method, and 300 as vector dimension. Whereas the parameter combinations for LSTM are dropout value is 0.2, pooling type is average pooling, and learning rate is 0.001.
Band Representation-Based Semi-Supervised Low-Light Image Enhancement: Bridging the Gap Between Signal Fidelity and Perceptual Quality

It has been widely acknowledged that under-exposure causes a variety of visual quality degradation because of intensive noise, decreased visibility, biased color, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">etc</i> . To alleviate these issues, a novel semi-supervised learning approach is proposed in this paper for low-light image enhancement. More specifically, we propose a deep recursive band network (DRBN) to recover a linear band representation of an enhanced normal-light image based on the guidance of the paired low/normal-light images. Such design philosophy enables the principled network to generate a quality improved one by reconstructing the given bands based upon another learnable linear transformation which is perceptually driven by an image quality assessment neural network. On one hand, the proposed network is delicately developed to obtain a variety of coarse-to-fine band representations, of which the estimations benefit each other in a recursive process mutually. On the other hand, the extracted band representation of the enhanced image in the recursive band learning stage of DRBN is capable of bridging the gap between the restoration knowledge of paired data and the perceptual quality preference to high-quality images. Subsequently, the band recomposition learns to recompose the band representation towards fitting perceptual regularization of high-quality images with the perceptual guidance. The proposed architecture can be flexibly trained with both paired and unpaired data. Extensive experiments demonstrate that our method produces better enhanced results with visually pleasing contrast and color distributions, as well as well-restored structural details.
Blockchain System in the Higher Education

Blockchain has emerged as an important concept at the interface of ICT and higher education. It is a system in which a record of transactions is maintained across several computers that are linked in a peer-to-peer network. Hence, it allows the creation of a decentralized environment, where data are not under the control of any third-party organization. This study presents a Systematic Bibliometric Literature Review (LRSB in further text) of research on blockchain applications in the higher education field. The review integrated 37 articles presenting up-to-date knowledge on current implications pertaining to the use of blockchain technology for improving higher education processes. The LRSB findings indicate that blockchain is being used to build up new interventions to improve the prevailing ways of sharing, delivering and securing knowledge data and personal student records. The application of blockchain technology is carrying on a conceptual progress in the higher education sector where it has added substantial value by ameliorated efficiency, effectiveness, privacy control, technological improvement and security of data management mechanisms. Challenges posed by current literature and further research directions are suggested.
Shift from blended learning to distance one during the lockdown period using Moodle: test control of students’ academic achievement and analysis of its results

Abstract The article highlights the urgency of the problem of introducing blended learning into the educational process of institutions of higher education and ensuring the quality of education using the tools of e-learning management system. The experience of using electronic testing during the final control of students' educational achievements is covered. A thorough analysis of the results was carried out, in particular the relationship between current and final grades, test scores and its duration were established. The reliability of the test tasks separately and the test as a whole, among other indicators, were tested using mathematical statistics methods.
Augmented self - The effects of virtual face augmentation on consumers' self-concept

Augmented reality mirrors are popular marketing tools that allow virtual try-on of products, such as makeup. We study how such sensory experiences affect consumer perception of the self, specifically the gap between actual and ideal attractiveness, and we conceptualise this change as augmented self. Over three lab experiments we show that viewing oneself in an AR mirror (as opposed to the regular mirror) affects the ideal-actual attractiveness gap and that this effect differs depending on a consumer’s self-esteem. Furthermore, we uncover that ideal self-congruence mediates this process. We also demonstrate that augmentation significantly changes variety-seeking. An additional survey-based study shows downstream effects of ideal self-congruence and ideal-actual gap on product choice and psychological well-being. While commercial immersive technologies are deployed to generate responses related to brands and products, this study demonstrates that the effects extend to consumers’ self-concept. We offer implications for academics and practitioners in marketing and human–computer interaction.
Nightcore: efficient and scalable serverless computing for latency-sensitive, interactive microservices

The microservice architecture is a popular software engineering approach for building flexible, large-scale online services. Serverless functions, or function as a service (FaaS), provide a simple programming model of stateless functions which are a natural substrate for implementing the stateless RPC handlers of microservices, as an alternative to containerized RPC servers. However, current serverless platforms have millisecond-scale runtime overheads, making them unable to meet the strict sub-millisecond latency targets required by existing interactive microservices.
To Talk or to Work: Flexible Communication Compression for Energy Efficient Federated Learning over Heterogeneous Mobile Edge Devices

Recent advances in machine learning, wireless communication, and mobile hardware technologies promisingly enable federated learning (FL) over massive mobile edge devices, which opens new horizons for numerous intelligent mobile applications. Despite the potential benefits, FL imposes huge communication and computation burdens on participating devices due to periodical global synchronization and continuous local training, raising great challenges to battery constrained mobile devices. In this work, we target at improving the energy efficiency of FL over mobile edge networks to accommodate heterogeneous participating devices without sacrificing the learning performance. To this end, we develop a convergence-guaranteed FL algorithm enabling flexible communication compression. Guided by the derived convergence bound, we design a compression control scheme to balance the energy consumption of local computing (i.e., "working") and wireless communication (i.e., "talking") from the long-term learning perspective. In particular, the compression parameters are elaborately chosen for FL participants adapting to their computing and communication environments. Extensive simulations are conducted using various datasets to validate our theoretical analysis, and the results also demonstrate the efficacy of the proposed scheme in energy saving.
Who is Real Bob? Adversarial Attacks on Speaker Recognition Systems

Speaker recognition (SR) is widely used in our daily life as a biometric authentication or identification mechanism. The popularity of SR brings in serious security concerns, as demonstrated by recent adversarial attacks. However, the impacts of such threats in the practical black-box setting are still open, since current attacks consider the white-box setting only.In this paper, we conduct the first comprehensive and systematic study of the adversarial attacks on SR systems (SRSs) to understand their security weakness in the practical black-box setting. For this purpose, we propose an adversarial attack, named FAKEBOB, to craft adversarial samples. Specifically, we formulate the adversarial sample generation as an optimization problem, incorporated with the confidence of adversarial samples and maximal distortion to balance between the strength and imperceptibility of adversarial voices. One key contribution is to propose a novel algorithm to estimate the score threshold, a feature in SRSs, and use it in the optimization problem to solve the optimization problem. We demonstrate that FAKEBOB achieves 99% targeted attack success rate on both open-source and commercial systems. We further demonstrate that FAKEBOB is also effective on both open-source and commercial systems when playing over the air in the physical world. Moreover, we have conducted a human study which reveals that it is hard for human to differentiate the speakers of the original and adversarial voices. Last but not least, we show that four promising defense methods for adversarial attack from the speech recognition domain become ineffective on SRSs against FAKEBOB, which calls for more effective defense methods. We highlight that our study peeks into the security implications of adversarial attacks on SRSs, and realistically fosters to improve the security robustness of SRSs.
A type-3 logic fuzzy system: Optimized by a correntropy based Kalman filter with adaptive fuzzy kernel size

In this study, a self-organizing interval type-3 fuzzy logic system (SO-IT3FLS) with a new learning algorithm is presented. An adaptive kernel size using fuzzy systems is introduced to improve the robustness of conventional correntropy based Kalman filters against non-Gaussian noise. The maximum correntropy Kalman filter (MCKF) and maximum correntropy unscented Kalman filter (MCUKF) with the proposed adaptive fuzzy kernel size are reformulated to optimize both rule and antecedent parameters, respectively. In addition to the rule parameters, the proposed membership function (MF) parameters and the level of α-cuts are also optimized. Five simulation examples with real-world data sets are given for examination. The simulations show that the introduced SO-IT3FLS and learning algorithm result in better accuracy in contrast to the other kind of fuzzy neural networks and conventional learning techniques. Furthermore, it is verified that the robustness of the proposed learning method against non-Gaussian noise is improved in contrast to the conventional Kalman filter, maximum correntropy Kalman filter and unscented Kalman filter.
Adopting distributed ledger technology for the sustainable construction industry: evaluating the barriers using Ordinal Priority Approach

Construction 4.0 has become a buzzword since the penetration of building information modeling (BIM), cyber-physical systems, and digital and computing technologies into the construction industry. Among emerging technologies, distributed ledger technology (DLT), or blockchain, is a powerful business enhancer whose potential can disrupt projects, AEC (architecture, engineering, and construction) firms, and construction supply chain, and in a broader sense, the whole construction industry. This technology has not reached the plateau of productivity due to several barriers and challenges. Previous studies have started to investigate the barriers to implementing DLT in various sectors and segmentations. However, we still need further surveys in the construction industry. This study evaluates the applicability of identified challenges and barriers based on a sustainability perspective. Precisely, we will answer which challenges need to be addressed for the sustainability of the construction industry. To meet the research objective, the ordinal priority approach (OPA) in multiple attributes decision-making (MADM) was utilized. This novel method determines the weight of sustainability attributes and barriers simultaneously. The results show that DLT implementation needs (i) infrastructure for data management, (ii) advanced applications and archetypes, and (iii) customers' demand, interest, and tendency, and (iv) taxation and reporting. Solving high-ranked challenges is the key to social sustainability from the aspects of "supply chain management and procurement"; "transparency, anti-corruption, and anti-counterfeiting"; and "fair operation and honest competition."
HistoGAN: Controlling Colors of GAN-Generated and Real Images via Color Histograms

While generative adversarial networks (GANs) can successfully produce high-quality images, they can be challenging to control. Simplifying GAN-based image generation is critical for their adoption in graphic design and artistic work. This goal has led to significant interest in methods that can intuitively control the appearance of images generated by GANs. In this paper, we present HistoGAN, a color histogram-based method for controlling GAN-generated images’ colors. We focus on color histograms as they provide an intuitive way to describe image color while remaining decoupled from domain-specific semantics. Specifically, we introduce an effective modification of the recent StyleGAN architecture [31] to control the colors of GAN-generated images specified by a target color histogram feature. We then describe how to expand HistoGAN to recolor real images. For image recoloring, we jointly train an encoder network along with HistoGAN. The recoloring model, ReHistoGAN, is an unsupervised approach trained to encourage the network to keep the original image’s content while changing the colors based on the given target histogram. We show that this histogram-based approach offers a better way to control GAN-generated and real images’ colors while producing more compelling results compared to existing alternative strategies.
Perceptual Image Quality Assessment with Transformers

In this paper, we propose an image quality transformer (IQT) that successfully applies a transformer architecture to a perceptual full-reference image quality assessment (IQA) task. Perceptual representation becomes more important in image quality assessment. In this context, we extract the perceptual feature representations from each of input images using a convolutional neural network (CNN) back-bone. The extracted feature maps are fed into the transformer encoder and decoder in order to compare a reference and distorted images. Following an approach of the transformer-based vision models [18], [55], we use extra learnable quality embedding and position embedding. The output of the transformer is passed to a prediction head in order to predict a final quality score. The experimental results show that our proposed model has an outstanding performance for the standard IQA datasets. For a large-scale IQA dataset containing output images of generative model, our model also shows the promising results. The proposed IQT was ranked first among 13 participants in the NTIRE 2021 perceptual image quality assessment challenge [23]. Our work will be an opportunity to further expand the approach for the perceptual IQA task.
Complete &amp; Label: A Domain Adaptation Approach to Semantic Segmentation of LiDAR Point Clouds

We study an unsupervised domain adaptation problem for the semantic labeling of 3D point clouds, with a particular focus on domain discrepancies induced by different LiDAR sensors. Based on the observation that sparse 3D point clouds are sampled from 3D surfaces, we take a Complete and Label approach to recover the underlying surfaces before passing them to a segmentation network. Specifically, we design a Sparse Voxel Completion Network (SVCN) to complete the 3D surfaces of a sparse point cloud. Unlike semantic labels, to obtain training pairs for SVCN requires no manual labeling. We also introduce local adversarial learning to model the surface prior. The recovered 3D surfaces serve as a canonical domain, from which semantic labels can transfer across different LiDAR sensors. Experiments and ablation studies with our new benchmark for cross-domain semantic labeling of LiDAR data show that the proposed approach provides 6.3-37.6% better performance than previous domain adaptation methods.