Reviewerly/exHarmony huggingface dataset:
Block chain technology for digital financial inclusion in the industry 4.0, towards sustainable development?

There is a lot of hope that blockchain technology may be used to standardize money transactions and increase access to banking. It is believed that regulators and industry professionals have looked into the possibility of using blockchain technology to modernize and even replace the infrastructure that currently supports international payments and remittances, such as correspondent banking, in order to ensure that transactions can be verified and recorded using blockchain technology in a distributed ledger. The purpose of this study was to analyze how blockchain technology has helped to include previously underserved populations in the mainstream financial system, and to remark on the best practices and lessons learned from sustainable development. Using a systematic literature review, the study discovered the many ways in which blockchain technology can facilitate digital financial inclusion, including its application in financial transactions, its utility as a tool for increasing financial savings, its use in the provision of credit, and its application in the provision of insurance. According to the findings, even though the global goals do not specifically target financial inclusion, providing access to financial services for the majority of the population is a critical enabler for several of the global goals. Therefore, the study concluded that sustainable development can be ensured on many fronts if the technology behind blockchains can be successfully used to improve financial inclusion. If governments, especially in developing countries, are serious about increasing citizens’ access to financial services, they must prioritize blockchain investment.
Blockchain-enabled bioacoustics signal authentication for cloud-based electronic medical records

In this paper, we explore Blockchain technology can be used to build a reliable decentralised authentication system. High security for the bioacoustics signal authentication mechanism is guaranteed by using an optimised number of secured features from the bioacoustics signal rather than conventional biometric features for authentication, and by utilising a blockchain model to improve the robustness of multiple checks on the data. It allows for trustworthy authentication and the tracking of terminal activity. Then, light weighted cryptography (LWC) is developed to offer protection at each edge node and terminal. Finally, the belief propagation (BP) algorithm for retraining the features of the bioacoustics signal serves as the foundation for the catching method. It improves hit ratio while decreasing delay time. The experimental setup uses the bioacoustics signals for authentication instead of conventional biometric features, and the use of a blockchain model for data transparency improves the efficiency of multiple checks. When this happens, privacy and safety are both boosted.
Multimodal Data Augmentation for Visual-Infrared Person ReID with Corrupted Data

The re-identification (ReID) of individuals over a complex network of cameras is a challenging task, especially under real-world surveillance conditions. Several deep learning models have been proposed for visible-infrared (V-I) person ReID to recognize individuals from images captured using RGB and IR cameras. However, performance may decline considerably if RGB and IR images captured at test time are corrupted (e.g., noise, blur, and weather conditions). Although various data augmentation (DA) methods have been explored to improve the generalization capacity, these are not adapted for V-I person ReID. In this paper, a specialized DA strategy is proposed to address this multimodal setting. Given both the V and I modalities, this strategy allows to diminish the impact of corruption on the accuracy of deep person ReID models. Corruption may be modality-specific, and an additional modality often provides complementary information. Our multimodal DA strategy is designed specifically to encourage modality collaboration and reinforce generalization capability. For instance, punctual masking of modalities forces the model to select the informative modality. Local DA is also explored for advanced selection of features within and among modalities. The impact of training baseline fusion models for V-I person ReID using the proposed multimodal DA strategy is assessed on corrupted versions of the SYSUMM01, RegDB, and ThermalWORLD datasets in terms of complexity and efficiency. Results indicate that using our strategy provides V-I ReID models the ability to exploit both shared and individual modality knowledge so they can out-perform models trained with no or unimodal DA. GitHub code: https://github.com/art2611/ML-MDA.
Digital competence for emergency remote teaching in higher education: understanding the present and anticipating the future

Higher education has increasingly adopted online and blended models of teaching. Guided by institutional policy and digital competence frameworks, the integration of digital tools and competences is perceived as essential. The pivot to emergency remote teaching (ERT) in response to the COVID-19 pandemic increased the use of digital technologies and the need to deploy and support digital competences. Researchers captured a range of remote teaching practices in higher education across this period that highlight the adaptability of teachers despite a lack of preparation for such an event. This study reviewed empirical studies of ERT from the past 2 years to derive a conceptual frame for ERT digital competence, which was then applied as a lens to analyse teaching or digital competency frameworks from Australian universities. The findings of this paper demonstrate the pre-pandemic teaching and digital competency frameworks captured digital competencies relevant to ERT in varied ways. Practically, the findings provide a starting point for understanding digital competences needed for ERT to ensure future preparedness in responding to a crisis that disrupts educational provision. We also suggest universities can better support the development of teachers' digital competence through practical operationalisations that connect technical and pedagogical knowledge, make digital possibilities across modes of delivery explicit, and acknowledge the need to protect wellbeing of educators.
When Decentralized Optimization Meets Federated Learning

Federated learning is a new learning paradigm for extracting knowledge from distributed data. Due to its favorable properties in preserving privacy and saving communication costs, it has been extensively studied and widely applied to numerous data analysis applications. However, most existing federated learning approaches concentrate on the centralized setting, which is vulnerable to a single-point failure. An alternative strategy for addressing this issue is the decentralized communication topology. In this article, we systematically investigate the challenges and opportunities when renovating decentralized optimization for federated learning. In particular, we discussed them from the model, data, and communication sides, respectively, which can deepen our understanding about decentralized federated learning.
Hybrid Anomaly Detection Model on Trusted IoT Devices

Most machine learning proposals in the Internet of Things (IoT) are designed and evaluated on pre-processed datasets, where data acquisition and cleaning steps are often considered a black box.In addition, IoT environments have numerous challenges related to acquiring data from sensors, where sensitive data can be threatened by malicious users who seek to interfere with the communication channel or storage.Additionally, sensor data can also be affected by noise.Therefore, differentiating the type of threat/anomaly requires additional energy and computational resources.We propose to carry out data cleaning/anomaly techniques on the IoT device itself, not in the cloud servers but closer to the data source.Therefore, the IoT device sends trusted data to the Cloud.Among the benefits of this is the considerable reduction in the cost of implementation due to less movement of data between IoT devices and the Cloud.Consequently, we define three anomaly detection steps using smoothing filters, unsupervised learning, and deep learning techniques (i.e., hybrid model) to detect different variations of anomalies and threats while focusing on a small computational/memory footprint.The deployment of the hybrid model on AVR, Tensilica, and ARM microcontrollers showed that the last ones are an adequate target to implement the model because they best satisfy the necessary hardware requirements.The proposed model consumes 50KB of Flash and 12KB of RAM and processes data locally, achieving a bandwidth reduction of 60%.Finally, the hybrid model was tested in external datasets.
A Survey on Quantization Methods for Optimization of Deep Neural Networks

The Deep Neural Network (DNN) model has been used in a number of commercial applications and we benefit from its accuracy in numerous applications like virtual assistants and chatbots. Due to the high computational demands and significant memory requirements of those models, quantization approaches have been employed to minimise accuracy loss while reducing model size to address those problems. DNN’s has a number of problems, including a large model size and a high accuracy model, which have come at the cost of substantially increased computation and model storage resources, which consume more power. The study addresses these issues and focuses on enhancing model speed, reducing computational cost, compressing the size of the model, and making the model energy efficient by using some of the methods outlined below. We can use quantization techniques to accomplish our desired goals. These techniques are broadly categorised as quantification-aware training and post-training quantification. The former technique discusses full quan-tization and batch normalization, whereas the latter technique also discusses the weights, activations, weights and activations together for quantization.
Robust Video watermarking based on deep neural network and curriculum learning

With the rapid development of multimedia and short video, there is a growing concern for video copyright protection. Some work has been proposed to add some copyright or fingerprint information to the video to trace the source of the video when it is stolen and protect video copyright. This paper proposes a video watermarking method based on a deep neural network and curriculum learning for watermarking of sliced videos. The first frame of the segmented video is perturbed by an encoder network, which is invisible and can be distinguished by the decoder network. Our model is trained and tested on an online educational video dataset consisting of 2000 different video clips. Experimental results show that our method can successfully discriminate most watermarked and non-watermarked videos with low visual disturbance, which can be achieved even under a relatively high video compression rate(H.264 video compress with CRF 32).
Integrated Photonic Neural Networks: Opportunities and Challenges

Photonic neural networks benefit from the use of photons to perform intelligent inference computing with ultrafast and ultralow energy consumption in ultra-high-throughput, providing the efficient photonic hardware for the new generation of intelligent computing, and the effective way to support large-scale integration for on-chip all-optical computing chips. With the rapid development of photonic neural networks, demands for efficient computation power have increased dramatically. However, the weak and impractical optical nonlinear activations, the lack of suitable configurations for integrated photonic hardware, and proper optical storage mediums pose challenges to this field. In this Perspective, we propose our current point of view and a suggestive roadmap in the field of integrated photonic platform for optical neural networks. Throughout the discussion, we highlight recent progresses meeting with major challenges. We also identify some next challenges still ahead to realize integrated photonic neural networks capable of matching the current computational power of graphic cards.
Human-related anomalous event detection via memory-augmented Wasserstein generative adversarial network with gradient penalty

Timely detection of human-related anomaly in surveillance videos is a challenging task. Generally, the irregular human motion and action patterns can be regarded as abnormal human-related events. In this paper, we utilize the skeleton trajectories to learn the regularities of human motion and action in videos for anomaly detection. The skeleton trajectories are decomposed into global and local feature sequences, which are utilized to provide human motion and action information, respectively. Then, the global and local sequences are modeled as two separate sub-processes with our proposed Memory-augmented Wasserstein Generative Adversarial Network with Gradient Penalty (MemWGAN-GP). In each sub-process, the pre-trained MemWGAN-GP is employed to predict future feature sequences from corresponding input past sequences and reconstruct the input sequences simultaneously. The predicted and reconstructed feature sequences are compared with their groundtruth to identify anomalous sequences. The MemWGAN-GP integrates the autoencoder with a WGAN model to boost the reconstruction and prediction ability of the autoencoder. Besides, a memory module is employed in MemWGAN-GP to overcome high capacity of the autoencoder for anomalies reconstruction and prediction. Experimental results on four challenging datasets demonstrate advantages of the proposed method over other state-of-the-art algorithms.
A Load Balancing Protocol for Improved Video on Demand in SDN-Based Clouds

Video streaming has emerged to become an essential component of communication methods on the internet. However, streaming of video still faces a number of challenges. The transmission of video content over the Internet entails stringent bandwidth, delay and jitter requirements to guarantee better visual quality. The Internet, however, only offers best effort service that does not guarantee any requirement in terms of delay, loss and bandwidth, making video streaming very complicated to achieve. Existing routing protocols do not always offer assurance that certain quality of service will be met. In order to improve video streaming, it was proposed to transform the cloud architecture and its operations by incorporating the software-defined network approach. This also suggests a rethink in the design of routing protocols for video streaming. Several software-defined networks-based routing algorithms are proposed, however they do not always address the viewer's demand for a certain quality. This research introduces the Load Balancing Routing Protocol (LBRP) as an adaptive routing protocol that improves user-experience of stored video streaming applications. LBRP accomplishes routing by considering parameters such as link capacity and re-routing non-real-time traffic towards under utilized links. The performance of LBRP was tested using Mininet and taking into consideration metrics such as bandwidth distribution, throughput and delay. The results showed that LBRP gives precedence to real time traffic over non-real-time traffic in the network providing improved viewing experience for the user.
A sustainable Ethereum merge-based Big-Data gathering and dissemination in IIoT System

The Industrial Internet of Things (IIoT) has captured the attention of various smart farming industries in recent years by connecting physical objects of smart farms over the internet so that they can be discovered and queried globally using Enterprise Application Software (EAS). This continuous transmission and processing poses a significant challenge in dealing with the massive amount of Big-Data being gathered and disseminated. As more applications use cloud platforms to increase storage capacity by storing Big-Data on an untrusted cloud server, data security precautions should be taken. However, with the rapid increase in the number of IIoT devices and EAS end-users and their diverse categories, the existing device authentication mechanisms in IIoT suffer from single factor authentication and poor adaptability. In light of the security requirements in IIoT Big-Data sharing using untrusted cloud servers, we present in this paper a sustainable Ethereum merge-based Big-Data gathering and dissemination in IIoT system. Extensive theoretical and simulation results validate data gathering with a business incentive mechanism with a proper data propagation threshold is an important parameter for determining equilibrium and analysing dynamic properties of IIoT system, which directly affects data processing speed and its final state. The Ethereum merge mechanism controls data dissemination instances in both the industry and EAS through classification and integrity verification using distributed authentication devices. Proof-of-stake is used for this purpose, which uses randomly selected validators to confirm transactions and add new ledgers to consortium blockchains to transfer data using partial secrets, making it fast, secure, and sustainable. Our proposed scheme outperforms other state-of-the-art schemes in simulation over Hyperledger Fabric with improved security measures.
Intraday forecast of global horizontal irradiance using optical flow method and long short-term memory model

Short-term global horizontal irradiance (GHI) forecast methodologies are routinely employed to mitigate the instability of photovoltaic power and, more importantly, to secure early participation in the energy auction market. The intra-day forecast model substantially consists of the combination of a cloud motion vector (CMV) derivation and a GHI extraction model. This study utilized optical flow (OF) method to calculate CMV from two subsequent satellite images and predict cloud displacement up to 180 min ahead. Meanwhile, the conventional GHI extraction models (Hammer and the fast all-sky radiation model for solar applications (FARMS) models) were employed to extract future GHI. Moreover, to the best of our knowledge, only a few studies have been dedicated to combining OF method with deep learning model. Hence, this study proposed a novel combination based on OF method and long short-term memory model (LSTM) to enhance temporal horizon and accuracy. After all combinations of OF method and GHI models were simulated and assessed under all-sky conditions, the OF method that combined with LSTM outperformed the OF method combined with conventional models across all time horizons. For root mean square error (RMSE) and forecast skill (FS), the accuracy of OF and LSTM models for all time horizon ranges from 59.64 W/m2 to 120.63 W/m2 and 16.40 % to 54.42 %, respectively. The study findings are expected to encourage the inclusion of satellite data in GHI forecast as well as the optimization of energy management systems that integrate with a photovoltaic system.
A Systematical Evaluation for Next-Basket Recommendation Algorithms

Next basket recommender systems (NBRs) aim to recommend a user’s next (shopping) basket of items via modeling the user’s preferences towards items based on the user’s purchase history, usually a sequence of historical baskets. Due to its wide applicability in the real-world E-commerce industry, the studies NBR have attracted increasing attention in recent years. NBRs have been widely studied and much progress has been achieved in this area with a variety of NBR approaches having been proposed. However, an important issue is that there is a lack of a systematic and unified evaluation over the various NBR approaches. Different studies often evaluate NBR approaches on different datasets, under different experimental settings, making it hard to fairly and effectively compare the performance of different NBR approaches. To bridge this gap, in this work, we conduct a systematical empirical study in NBR area. Specifically, we review the representative work in NBR and analyze their cons and pros. Then, we run the selected NBR algorithms on the same datasets, under the same experimental setting and evaluate their performances using the same measurements. This provides a unified framework to fairly compare different NBR approaches. We hope this study can provide a valuable reference for the future research in this vibrant area.
A Survey of Recent Advances in Quantum Generative Adversarial Networks

Quantum mechanics studies nature and its behavior at the scale of atoms and subatomic particles. By applying quantum mechanics, a lot of problems can be solved in a more convenient way thanks to its special quantum properties, such as superposition and entanglement. In the current noisy intermediate-scale quantum era, quantum mechanics finds its use in various fields of life. Following this trend, researchers seek to augment machine learning in a quantum way. The generative adversarial network (GAN), an important machine learning invention that excellently solves generative tasks, has also been extended with quantum versions. Since the first publication of a quantum GAN (QuGAN) in 2018, many QuGAN proposals have been suggested. A QuGAN may have a fully quantum or a hybrid quantum–classical architecture, which may need additional data processing in the quantum–classical interface. Similarly to classical GANs, QuGANs are trained using a loss function in the form of max likelihood, Wasserstein distance, or total variation. The gradients of the loss function can be calculated by applying the parameter-shift method or a linear combination of unitaries in order to update the parameters of the networks. In this paper, we review recent advances in quantum GANs. We discuss the structures, optimization, and network evaluation strategies of QuGANs. Different variants of quantum GANs are presented in detail.
Artificial Intelligence (AI), Blockchain, and Cryptocurrency in Finance: Current Scenario and Future Direction

By combining artificial intelligence (AI), big data, and cloud computing, the blockchain revolutionises the production of both ascendable information technology systems and diverse applications. The combination of AI and blockchain has enormous potential for developing new financial service models that are enabled by digitalization. Although there are a few investigations on the application and conjunction of AI and blockchain, our extant literature findings on the financial services’ value of their integration are still fragmented. To close this gap, the goal of this research is to evaluate the uses and benefits of integrated AI and blockchain platforms across many financial service platforms. We further discussed the impact of decentralized cryptocurrency on financial sectors. This study revealed that AI and Blockchain have the potential of causing an increase in banking services, lending efficiency, collection and payment competency, and asset management. Furthermore, we highlight cryptocurrencies are encountering cybersecurity, mining, privacy, anonymity, and regulatory challenges that policymakers need to act on.