Reviewerly/exHarmony huggingface dataset:
Toward a collaborative governance model for distributed ledger technology adoption in organizations

Abstract Distributed ledger technology (DLT) is one of the emerging technologies adopted in organizations. Unlike traditional databases, the integrity of the DLT is maintained automatically by an algorithmic consensus mechanism and not by any dominant authority. Thus, the consensus mechanism controls the decision-making and governance process. But the adoption of DLT is faced with issues regarding how to ensure that governance decisions in distributed ledger systems in the interest of all actors and stakeholders involved in the operations of organizational operations. Therefore, it is imperative to provides a better understanding of the governance of DLT adoption in organizations. Accordingly, this study conducts an extensive literature review to investigate the governance issues and control of DLT adoption in intra-organizational domain. Findings from this study presents state-of-the-art governance practices to offer a comprehensive understanding on key governance issues in organizations. Additionally, the findings present factors associated with governance of DLT adoption solutions. More importantly, a governance model is developed to enhance the adoption of DLT adoption to accelerate the digitalization of organizational operations.
An ANN-Fuzzy Cognitive Map-Based Z-Number Theory to Predict Flyrock Induced by Blasting in Open-Pit Mines

Abstract Blasting is widely employed as an accepted mechanism for rock breakage in mining and civil activities. As an environmental side effect of blasting, flyrock should be investigated precisely in open-pit mining operations. This paper proposes a novel integration of artificial neural network and fuzzy cognitive map (FCM) with Z -number reliability information to predict flyrock distance in open-pit mine blasting. The developed model is called the artificial causality-weighted neural networks, based on reliability (ACWNNsR). The reliability information of Z -numbers is used to eliminate uncertainty in expert opinions required for the initial matrix of FCM, which is one of the main advantages of this method. FCM calculates weights of input neurons using the integration of nonlinear Hebbian and differential evolution algorithms. Burden, stemming, spacing, powder factor, and charge per delay are used as the input parameters, and flyrock distance is the output parameter. Four hundred sixteen recorded basting rounds are used from a real large-scale lead–zinc mine to design the architecture of the models. The performance of the proposed ACWNNsR model is compared with the Bayesian regularized neural network and multilayer perceptron neural network and is proven to result in more accurate prediction in estimating blast-induced flyrock distance. In addition, the results of a sensitivity analysis conducted on effective parameters determined the spacing as the most significant parameter in controlling flyrock distance. Based on the type of datasets used in this study, the presented model is recommended for flyrock distance prediction in surface mines where buildings are close to the blasting site.
On Supervised Class-Imbalanced Learning: An Updated Perspective and Some Key Challenges

The problem of class imbalance has always been considered as a significant challenge to traditional machine learning and the emerging deep learning research communities. A classification problem can be considered as class imbalanced if the training set does not contain an equal number of labeled examples from all the classes. A classifier trained on such an imbalanced training set is likely to favor those classes containing a larger number of training examples than the others. Unfortunately, the classes that contain a small number of labelled instances usually correspond to rare and significant events. Thus, poor classification accuracy on these classes may lead to severe consequences. In this article, we aim to provide a comprehensive summary of the rich pool of research works attempting to combat the adversarial effects of class imbalance efficiently. Specifically, following a formal definition of the problem of class imbalance, we explore the plethora of traditional machine learning approaches aiming to mitigate its adversarial effects. We further discuss the state-of-the-art deep-learning-based approaches for improving a classifier’s resilience against class imbalance and highlight the need for techniques tailored for such a paradigm. Moreover, we look at the emerging applications where class imbalance can be a major concern. Finally, we outline a few open problems along with the various challenges emerging with the advent of modern applications, deep learning paradigm, and new sources of data.
Hybrid training of optical neural networks

Optical neural networks are emerging as a promising type of machine learning hardware capable of energy-efficient, parallel computation. Today’s optical neural networks are mainly developed to perform optical inference after in silico training on digital simulators. However, various physical imperfections that cannot be accurately modeled may lead to the notorious “reality gap” between the digital simulator and the physical system. To address this challenge, we demonstrate hybrid training of optical neural networks where the weight matrix is trained with neuron activation functions computed optically via forward propagation through the network. We examine the efficacy of hybrid training with three different networks: an optical linear classifier, a hybrid opto-electronic network, and a complex-valued optical network. We perform a study comparative to in silico training, and our results show that hybrid training is robust against different kinds of static noise. Our platform-agnostic hybrid training scheme can be applied to a wide variety of optical neural networks, and this work paves the way towards advanced all-optical training in machine intelligence.
Deep learning and RGB-D based human action, human–human and human–object interaction recognition: A survey

Human activity recognition is one of the most studied topics in the field of computer vision. In recent years, with the availability of RGB-D sensors and powerful deep learning techniques, research on human activity recognition has gained momentum. From simple human atomic actions, the research has advanced towards recognizing more complex human activities using RGB-D data. This paper presents a comprehensive survey of the advanced deep learning based recognition methods and categorizes them in human atomic action, human–human interaction, human–object interaction. The reviewed methods are further classified based on the individual modality used for recognition i.e. RGB based, depth based, skeleton based, and hybrid. We also review and categorize recent challenging RGB-D datasets for the same. In addition, the paper also briefly reviews RGB-D datasets and methods for online activity recognition. The paper concludes with a discussion on limitations, challenges, and recent trends for promising future directions.
Use of Corporate Disclosures to Identify the Stage of Blockchain Adoption

SYNOPSIS Several studies have pointed to the transformative effects of blockchain on a wide spectrum of firms, industries, and professions. Despite the arguable consensus within the business community that blockchain will have a real impact on the way firms do business, views diverge when it comes to the timing of diffusion (i.e., when blockchain will achieve mass adoption). We propose that information gathering helps potential adopters form expectations regarding payoffs from blockchain adoption. Information-gathering activities and the resulting information sources, such as web searches, news articles, book titles, and corporate disclosures, can proxy the expectations of potential adopters. Corporate disclosures directly reflect firms' expectations and interests in the new technology. We leverage the corporate disclosure data from the SEC Edgar database to identify the current stage of blockchain adoption. Our analysis shows that while blockchain adoption is still nascent, the focus has been shifting from cryptocurrencies to business applications. Data Availability: Data are available from public sources cited in the text. JEL Classifications: M15.
Malicious Traffic classification Using Long Short-Term Memory (LSTM) Model

Malicious traffic classification is the initial and primary step for any network-based security systems. This traffic classification systems include behavior-based anomaly detection system and Intrusion Detection System. Existing methods always relies on the conventional techniques and process the data in the fixed sequence, which may leads to performance issues. Furthermore, conventional techniques require proper annotation to process the volumetric data. Relying on the data annotation for efficient traffic classification may leads to network loops and bandwidth issues within the network. To address the above-mentioned issues, this paper presents a novel solution based on artificial intelligence perspective. The key idea of this paper is to propose a novel malicious classification system using Long Short-Term Memory (LSTM) model. To validate the efficiency of the proposed model, an experimental setup along with experimental validation is carried out. From the experimental results, it is proven that the proposed model is better in terms of accuracy, throughput when compared to the state-of-the-art models. Further, the accuracy of the proposed model outperforms the existing state of the art models with increase in 5% and overall 99.5% in accuracy.
A vision transformer based approach for analysis of plasmodium vivax life cycle for malaria prediction using thin blood smear microscopic images

Microscopic images are an important part for haematologists in diagnosing various diseases in the blood cell. Changes in blood cells are caused by malaria disease, and early diagnosis can prevent the disease from entering its severe stage.In this paper, an automated non-invasive and efficient deep learning-based framework is developed for multi-class plasmodium vivax life cycle classification and malaria diagnosis. A multi-class microscopic blood cell of different plasmodium vivax life cycle stage dataset is analysed, and a diagnostic framework is designed. Several stages of the disease are examined and augmented through various techniques to make the framework robust in real-time. Generative adversarial network is specially designed to generate extended training samples of various life cycle stages to increase robustness of the resulting model. A special transformer-based neural network vision transformer is designed to improve generalisation capabilities. Microscopic images are classified into multi classes of plasmodium vivax life cycle stage, where the keystone transformer layers extract relevant disease features from microscopic colour images, and the extracted relevant features are used to make predictive diagnostic decisions.The capabilities of the vision transformer are computed and analysed by statistical parameters, and the performance of the vision transformer model is compared with baseline architectures, where it was evident that the performance of the vision transformer was significantly better, reaching 90.03% accuracy.A comprehensive comparison of the proposed framework to the state-of-the-art methods proves its efficiency in the classification of plasmodium vivax life cycle for malaria disease identification through thin blood smear microscopic images.
A novel image cryptosystem using Gray code, quantum walks, and Henon map for cloud applications

Abstract Cloud computing plays a vital task in our daily lives, in which an enormous amount of data is stored daily on cloud storage. The highest priority for cloud data storage is guaranteeing the security of confidential data. The security of confidential data can be realised through utilising one of the cryptographic mechanisms including encryption and data hiding. With the rapid development for the realization of quantum computers, modern cryptosystems may be cracked including cloud systems. Accordingly, it is a crucial task for achieving confidentiality of data stored on cloud storage before the availability of quantum computers. Therefore, this study aims to utilise one of the quantum computational models, as a quantum-inspired system, to layout a new data confidentiality technique that can be applied in digital devices to have the capability for resisting the potential attacks from quantum and digital computers. In this paper, a new image security algorithm for real-time cloud applications using Gray code, quantum walks (QW), and Henon map is proposed. In the proposed image cryptosystem, the generated key streams from QW and Henon map is related to the plain image with high sensitivity of slight bit changes on the plain image. The outcomes based on deep analysis proves that the presented algorithm is efficient with high security for real-time application.
Blockchain-Empowered Collaborative Task Offloading for Cloud-Edge-Device Computing

How to enable high-performance task offloading and preserve the trust between participants is imperative yet nontrivial to the Cloud-Edge-Device (CED) computing, mainly because the resources are geo-distributed and operated by different parties. Also, the CED participants are highly dynamic and heterogeneous in resource provision and may conflict in interest. This paper proposes BlockChain-empowered CED (BC-CED), a blockchain-empowered collaborative task offloading for CED computing. In BC-CED, blockchain plays a central role in the main functionality of CED, including task offloading, brokerage of resource usage, and incentives. We distinguish the BC-CED from the existing solutions by modifying the blockchain consensus process, enabling the participants to reach an agreement via solving the task offloading problem. For this purpose, we formulate the offloading problem by considering the computation capabilities of candidate nodes and the network performance. BC-CED allows each participant to apply reinforcement learning-based methods to solve this problem and compete for the right of block output by comparing the offloading policy performance and accepting the best policy as the offloading scheme within the next period. We also propose a truthful incentive mechanism to encourage resource contributions in BC-CED and force them to be honest. Extensive tests by implementing our solutions in a commercialized blockchain platform have shown how BC-CED achieves a superior performance in task offloading and blockchain maintenance.
Multi-task learning on the edge for effective gender, age, ethnicity and emotion recognition

More and more real-world applications, especially in cognitive robotics, require the running of different computer vision algorithms in parallel on board of embedded devices with limited GPU and memory resources. Multi-task learning, namely the usage of the same model to perform multiple classification and/or regression tasks by learning a shared low level representation, revealed to be a valid solution to reduce computation and required memory space while preserving the accuracy. In this paper, we propose a solution for real-time user profiling based on a multi-task convolutional neural network (CNN) for gender, age, ethnicity and emotion recognition from face images. To find the best trade-off between accuracy and processing time, we evaluate three different architectures, specifically designed for the purpose, and backbones, based on MobileNet, ResNet and SENet, which include convolutional layers, residual blocks and attention modules that already demonstrated great potential in face analysis. We trained the multi-task neural network with a custom learning procedure, which solves the problems of missing labels, dataset imbalance and loss function imbalance through label masking, batch balancing and a custom weighted loss function; there are no other multi-task neural networks for face analysis that address all these challenges simultaneously.. The proposed solution demonstrated its effectiveness in the comparison with the corresponding single-task CNNs in terms of accuracy, processing time and memory space; in fact, the multi-task CNNs achieved a processing speed-up between 2.5 and 4 times and a reduction of the memory space between 2 and 4 times, while preserving the accuracy. Moreover, the useful insights that arise from the experiments allow to choose a solution for face analysis easily integrable into real applications on smart cameras and embedded systems and most suited for the specific application constraints in terms of computational resources.
Detecting human activity types from 3D posture data using deep learning models

Human action and posture identification are two of the most difficult challenges in computerized systems. Categorization of these tasks is particularly relevant to the health and robotics fields. It is feasible to define recurrent physical movements and postures by using technologies based on artificial intelligence. The kinect activity recognition dataset (KARD) and the microsoft research (MSR) Pairs dataset were utilized for this research. Classifying videos with convolutional neural networks (CNNs) can introduce some difficulties. Training performance of video-based data with CNN models is generally lower than image data. This study makes a contribution of different perspective to the detection of motions by using frames extracted from video-based datasets of CNN models. It has brought human poses to the fore in order to prevent unnecessary images in the obtained frames. The images of the videos were extracted sequentially at a certain frequency. Then, the poses of the human activities in each frame were extracted in accordance with the order of their occurrence and all the frames in each video were combined into a single image format with respect to their order. Input data was trained by CNN models and classified according to motion types. In the classification process, the performance of all CNN models was increased and the training time of the models was saved. The proposed approach achieved 100 % success in classifying the two datasets according to the activity types.
Few Shot Generative Model Adaption via Relaxed Spatial Structural Alignment

Training a generative adversarial network (GAN) with limited data has been a challenging task. A feasible solution is to start with a GAN well-trained on a large scale source domain and adapt it to the target domain with a few samples, termed as few shot generative model adaption. However, existing methods are prone to model overfitting and collapse in extremely few shot setting (less than 10). To solve this problem, we propose a relaxed spatial structural alignment (RSSA) method to calibrate the target generative models during the adaption. We design a cross-domain spatial structural consistency loss comprising the self-correlation and disturbance correlation consistency loss. It helps align the spatial structural information between the synthesis image pairs of the source and target domains. To relax the cross-domain alignment, we compress the original latent space of generative models to a subspace. Image pairs generated from the subspace are pulled closer. Qualitative and quantitative experiments show that our method consistently surpasses the state-of-the-art methods in few shot setting. Our source code: https://github.com/StevenShaw1999/RSSA.
The reality of virtual reality

Virtual reality (VR) has become a popular tool for investigating human behavior and brain functions. Nevertheless, it is unclear whether VR constitutes an actual form of reality or is more like an advanced simulation. Determining the nature of VR has been mostly achieved by self-reported presence measurements, defined as the feeling of being submerged in the experience. However, subjective measurements might be prone to bias and, most importantly, do not allow for a comparison with real-life experiences. Here, we show that real-life and VR height exposures using 3D-360° videos are mostly indistinguishable on a psychophysiological level (EEG and HRV), while both differ from a conventional 2D laboratory setting. Using a fire truck, three groups of participants experienced a real-life ( N = 25), a virtual ( N = 24), or a 2D laboratory ( N = 25) height exposure. Behavioral and psychophysiological results suggest that identical exogenous and endogenous cognitive as well as emotional mechanisms are deployed to process the real-life and virtual experience. Specifically, alpha- and theta-band oscillations in line with heart rate variability, indexing vigilance, and anxiety were barely indistinguishable between those two conditions, while they differed significantly from the laboratory setup. Sensory processing, as reflected by beta-band oscillations, exhibits a different pattern for all conditions, indicating further room for improving VR on a haptic level. In conclusion, the study shows that contemporary photorealistic VR setups are technologically capable of mimicking reality, thus paving the way for the investigation of real-world cognitive and emotional processes under controlled laboratory conditions. For a video summary, see https://youtu.be/fPIrIajpfiA .
Masked Relation Learning for DeepFake Detection

DeepFake detection aims to differentiate falsified faces from real ones. Most approaches formulate it as a binary classification problem by solely mining the local artifacts and inconsistencies of face forgery, which neglect the relation across local regions. Although several recent works explore local relation learning for DeepFake detection, they overlook the propagation of relational information and lead to limited performance gains. To address these issues, this paper provides a new perspective by formulating DeepFake detection as a graph classification problem, in which each facial region corresponds to a vertex. But relational information with large redundancy hinders the expressiveness of graphs. Inspired by the success of masked modeling, we propose Masked Relation Learning which decreases the redundancy to learn informative relational features. Specifically, a spatiotemporal attention module is exploited to learn the attention features of multiple facial regions. A relation learning module masks partial correlations between regions to reduce redundancy and then propagates the relational information across regions to capture the irregularity from a global view of the graph. We empirically discover that a moderate masking rate (e.g., 50%) brings the best performance gain. Experiments verify the effectiveness of Masked Relation Learning and demonstrate that our approach outperforms the state of the art by 2% AUC on the cross-dataset DeepFake video detection. Code will be available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/zimyang/MaskRelation</uri> .