Reviewerly/exHarmony huggingface dataset:
A developed distributed ledger technology architectural layer framework for decentralized governance implementation in virtual enterprise

Abstract Distributed Ledger technology (DLT) has recently emerged as a disruptive system with a wide range of applicability, with prospect to improve societal interactions at large. In virtual enterprise (VE) context researchers and practitioners have started to investigate the deployment of DLT to automate the processing of data and implementation of decisions to support the provision of digital services. Although academic interest in this domain is growing, a practical analysis of DLT from a governance perspective is still lacking to date. Accordingly, this study aims to fill this gap and provide implications related to decentralized governance of DLT. This article develops an architectural governance-by-design framework that defines the governance of DLT as a combination of architectural layers and governance of DLT dimensions. Design science is employed, and IOTA tangle an open-source DLT which employs a decentralized asynchronous network is deployed to evaluate the applicability of the developed architectural governance-by-design framework through qualitative interviews and literature inquiry. The findings confirm the developed architectural governance-by-design framework and offer a shared discussion and insight surrounding the topic of governance of DLT. The findings also identify limitations associated with governance of DLT solutions and proposes policy recommendations to be used as guidelines for practitioners to improve the adoption of DLT to accelerate VE digitalization.
Meta-relation assisted knowledge-aware coupled graph neural network for recommendation

Currently, the sparsity of user–item interaction data limits the performance of recommender systems. To alleviate the problems caused by data sparsity, researchers are devoted to designing more powerful graph neural network models to fuse the auxiliary information of users and items (social relationships, item dependencies, etc.). However, the lack of in-depth exploration of auxiliary information and the lack of optimization of the spatially distributed properties of the generated representations (known as embeddings) make it difficult to further improve the recommendation performance of these models. To address the above challenges, we propose a meta-relation-assisted knowledge-aware coupled graph neural network (MKCGN). Specifically, we design meta-relationship-guided graph neural networks for capturing collaborative signals from user and item domains and introduce coupled graph neural networks to inject local relationships into user/item representations. In addition, we design an auxiliary task that optimizes the distribution properties (alignment and uniformity) of the representation on the unit hypersphere to improve the quality of the representation. To evaluate the performance of MKCGN, we compare it with the state-of-the-art model on two real-world datasets with average performance improvements of 5.26% and 4.87%(using HR@N and NDCG@N as evaluation metrics), respectively. Our implemented model is available via https://github.com/Ganoder/MKCGN.
Pattern Recognition of Acute Lymphoblastic Leukemia (ALL) Using Computational Deep Learning

Leukemia is a cancer of blood-producing cells, including the bone marrow. Abnormal white blood cells travel through blood vessels and multiply rapidly. Healthy cells in the body become a minority, and the imbalance increases the chances of infection in the body. Leukemia or blood cancer is the most common cancer in children ages 2 - 14. Most leukemia in children is treated. Acute lymphocytic leukemia (ALL) is a type of cancer in the blood and bone marrow. It progresses rapidly when immature white blood cells are formed instead of mature ones. Treatments for acute lymphocytic leukemia include drugs and blood transfusions directly into veins, chemotherapy, and all transplantation, which involve transferring organs or tissues within the body or from one person to another. In this paper, Pattern Recognition of Acute Lymphoblastic Leukemia has been proposed using Computational Deep Learning. Pattern recognition technology uses mathematical algorithms to identify patterns in large datasets of data. Analyzing the data, the algorithms can identify patterns indicative of certain states or conditions. In the case of ALL, the algorithm would look for patterns in white blood cell count data that indicate the presence of ALL. These patterns may include changes in the number of white blood cells over time, changes in the composition of the white blood cells, or changes in the levels of certain proteins or gene expressions associated with ALL. The proposed ALLDM model achieved 81.53% (DDS) and 87.92% (SDS) of chemotherapy management, 79.16% (DDS) and 94.31% (SDS) of Stem Cell Transplantation Management, 63.77% (DDS) and 87.37% (SDS) of Radiation therapy Management and 88.92% (DDS) and 85.86% (SDS) of Targeted therapy drugs management.
Enhancing Cybersecurity Policies with Blockchain Technology: A Survey

Cybersecurity is a major challenge in today’s era despite being equipped with multiple technologies, the world is lacking behind due to security issues. The domains like industries, manufacturing plants, healthcare organizations, educational institutes, etc are at the edge of shifting entirely to digitalization. This gives us immense improvement in existing processes but on the other hand, digitalized information is prone to hackers. Blockchain Technology is widely used for creating cryptocurrency as well as the research community is also utilizing this concept for numerous other applications. Supply chain, industry 4.0, defense services, agriculture industry, and many other fields exploit blockchain technology’s benefits. In this research paper, the recent work in the direction of boosting cybersecurity in different fields with blockchain technology is discussed, and also the future directions are outlined by depicting the research gap in that specific research area. Blockchain Technology is the master coin to provide reliable, transparent, and authorized access to information-sharing domains and it can be explored further for expanding the security of the system.
Personalized Prompt Learning for Explainable Recommendation

Providing user-understandable explanations to justify recommendations could help users better understand the recommended items, increase the system’s ease of use, and gain users’ trust. A typical approach to realize it is natural language generation. However, previous works mostly adopt recurrent neural networks to meet the ends, leaving the potentially more effective pre-trained Transformer models under-explored. In fact, user and item IDs, as important identifiers in recommender systems, are inherently in different semantic space as words that pre-trained models were already trained on. Thus, how to effectively fuse IDs into such models becomes a critical issue. Inspired by recent advancement in prompt learning, we come up with two solutions: find alternative words to represent IDs (called discrete prompt learning) and directly input ID vectors to a pre-trained model (termed continuous prompt learning). In the latter case, ID vectors are randomly initialized but the model is trained in advance on large corpora, so they are actually in different learning stages. To bridge the gap, we further propose two training strategies: sequential tuning and recommendation as regularization. Extensive experiments show that our continuous prompt learning approach equipped with the training strategies consistently outperforms strong baselines on three datasets of explainable recommendation.
AVoiD-DF: Audio-Visual Joint Learning for Detecting Deepfake

Recently, deepfakes have raised severe concerns about the authenticity of online media. Prior works for deepfake detection have made many efforts to capture the intra-modal artifacts. However, deepfake videos in real-world scenarios often consist of a combination of audio and visual. In this paper, we propose an Audio-Visual Joint Learning for Detecting Deepfake (AVoiD-DF), which exploits audio-visual inconsistency for multi-modal forgery detection. Specifically, AVoiD-DF begins by embedding temporal-spatial information in Temporal-Spatial Encoder. A Multi-Modal Joint-Decoder is then designed to fuse multi-modal features and jointly learn inherent relationships. Afterward, a Cross-Modal Classifier is devised to detect manipulation with inter-modal and intra-modal disharmony. Since existing datasets for deepfake detection mainly focus on one modality and only cover a few forgery methods, we build a novel benchmark DefakeAVMiT for multi-modal deepfake detection. DefakeAVMiT contains sufficient visuals with corresponding audios, where any one of the modalities may be maliciously modified by multiple deepfake methods. The experimental results on DefakeAVMiT, FakeAVCeleb, and DFDC demonstrate that the AVoiD-DF outperforms many state-of-the-arts in deepfake detection. Our proposed method also yields superior generalization on various forgery techniques.
Immersive Learning

Immersive learning conceptualizes education as a set of active phenomenological experiences that are based on presence. Immersive learning can be implemented using both physical and digital means, such as virtual reality and augmented reality.
The Influence of Emerging Technologies on Distance Education

Recently, during the COVID-19 pandemic, distance education became mainstream. Many students were not prepared for this situation—they lacked equipment or were not even connected to the Internet. Schools and government institutions had to react quickly to allow students to learn remotely. They had to provide students with equipment (e.g., computers, tablets, and goggles) but also provide them with access to the Internet and other necessary tools. On the other hand, teachers were trying to adopt new technologies in the teaching process to enable more interactivity, mitigate feelings of isolation and disconnection, and enhance student engagement. New technologies, including Virtual Reality (VR), Augmented Reality (AR), Mixed Reality (MR), Extended Reality (XR, so-called Metaverse), Big Data, Blockchain, and Free Space Optics (FSO) changed learning, teaching, and assessing. Despite that, some tools were implemented fast, and the COVID-19 pandemic was the trigger for this process; most of these technologies will be used further, even in classroom teaching in both schools and universities. This paper presents a concise review of the emerging technologies applied in distance education. The main emphasis was placed on their influence on the efficiency of the learning process and their psychological impact on users. It turned out that both students and teachers were satisfied with remote learning, while in the case of undergraduate children and high-school students, parents very often expressed their dissatisfaction. The limitation of the availability of remote learning is related to access to stable Internet and computer equipment, which turned out to be a rarity. In the current social context, the obtained results provided valuable insights into factors affecting the acceptance and emerging technologies applied in distance education. Finally, this paper suggests a research direction for the development of effective remote learning techniques.
AERO: AI-Enabled Remote Sensing Observation with Onboard Edge Computing in UAVs

Unmanned aerial vehicles (UAVs) equipped with computer vision capabilities have been widely utilized in several remote sensing applications, such as precision agriculture, environmental monitoring, and surveillance. However, the commercial usage of these UAVs in such applications is mostly performed manually, with humans being responsible for data observation or offline processing after data collection due to the lack of on board AI on edge. Other technical methods rely on the cloud computation offloading of AI applications, where inference is conducted on video streams, which can be unscalable and infeasible due to remote cloud servers’ limited connectivity and high latency. To overcome these issues, this paper presents a new approach to using edge computing in drones to enable the processing of extensive AI tasks onboard UAVs for remote sensing. We propose a cloud–edge hybrid system architecture where the edge is responsible for processing AI tasks and the cloud is responsible for data storage, manipulation, and visualization. We designed AERO, a UAV brain system with onboard AI capability using GPU-enabled edge devices. AERO is a novel multi-stage deep learning module that combines object detection (YOLOv4 and YOLOv7) and tracking (DeepSort) with TensorRT accelerators to capture objects of interest with high accuracy and transmit data to the cloud in real time without redundancy. AERO processes the detected objects over multiple consecutive frames to maximize detection accuracy. The experiments show a reduced false positive rate (0.7%), a low percentage of tracking identity switches (1.6%), and an average inference speed of 15.5 FPS on a Jetson Xavier AGX edge device.
Improved Healthcare System with Quantum Computing

Health care is playing a major role in designing any kind of prediction model and there is a large amount of data related to different diseases is available over the globe. A major part of machine learning lies in the identification of the data related to different diseases and make them helpfulfor people. The major implementation is in the identification ofthe safest medical data systems if the new challenging task where we can implement quantum computing as the main lead to process the medical data and making the new data systems which are most secured to manage the medical data which is useful to identify the fastest method to form a drug and also to manage in the identification of the safe drug mechanism for thedisease which is not even in identification. Quantum computingis a challenging area that is like an extension to the current scenario of medical imaging. Improved healthcare system using machine learning and quantum computing gives a new age to medical sciences to identify the approximate solutions to the different medical diseases. This article works on the implementation of such medical advancements to a specificdisorder based on human genetics and DNA sequencing. The Quantum computing approach succeeded in recognizing the approximate medical sequences for a particular disease.
Machine learning tuning by diversity oriented firefly metaheuristics for <scp>Industry 4.0</scp>

Abstract The progress of Industrial Revolution 4.0 has been supported by recent advances in several domains, and one of the main contributors is the Internet of Things. Smart factories and healthcare have both benefited in terms of leveraged quality of service and productivity rate. However, there is always a trade‐off and some of the largest concerns include security, intrusion, and failure detection, due to high dependence on the Internet of Things devices. To overcome these and other challenges, artificial intelligence, especially machine learning algorithms, are employed for fault prediction, intrusion detection, computer‐aided diagnostics, and so forth. However, efficiency of machine learning models heavily depend on feature selection, predetermined values of hyper‐parameters and training to deliver a desired result. This paper proposes a swarm intelligence‐based approach to tune the machine learning models. A novel version of the firefly algorithm, that overcomes known deficiencies of original method by employing diversification‐based mechanism, has been proposed and applied to both feature selection and hyper‐parameter optimization of two machine learning models—XGBoost and extreme learning machine. The proposed approach has been tested on four real‐world Industry 4.0 data sets, namely distributed transformer monitoring, elderly fall prediction, BoT‐IoT, and UNSW‐NB 15. Achieved results have been compared to the results of eight other cutting‐edge metaheuristics, that have been implemented and tested under the same conditions. The experimental outcomes strongly indicate that the proposed approach significantly outperformed all other competitor metaheuristics in terms of convergence speed and results' quality measured with standard metrics—accuracy, precision, recall, and f1‐score.
Towards a Secure and Sustainable Internet of Medical Things (IoMT): Requirements, Design Challenges, Security Techniques, and Future Trends

Recent advances in machine-to-machine (M2M) communications, mini-hardware manufacturing, and micro computing have led to the development of the Internet of Things (IoT). The IoT is integrated with medical devices in order to enable better treatment, cost-effective medical solutions, improved patient monitoring, and enhanced personalized healthcare. This has led to the development of more complex and heterogeneous Internet of Medical Things (IoMT) systems that have their own operating systems and protocols. Even though such pervasive and low-cost sensing devices can bring about enormous changes in the healthcare sector, these are prone to numerous security and privacy issues. Security is thus a major challenge in these critical systems, one that inhibits their widespread adoption. However, significant inroads have been made by the on-going research, which powers the IoMT applications by incorporating prevalent security measures. In this regard, this paper highlights the significance of implementing key security measures, and essential aspects of the IoMT that make it useful for interconnecting various internal and external working domains of healthcare. This paper presents state-of-the-art techniques for securing IoMT systems, in terms of data transmission, collection, and storage. Furthermore, the paper also explores various security requirements, inherent design challenges, and various security techniques that could make the IoMT more secure and sustainable. Finally, the paper gives a panoramic view of the current status of research in the field and outlines some future research directions in this area.
Integration Flows Modeling in the Context of Architectural Views

In an increasing number of software applications, the execution of their functions depends on communication with external systems. Messaging enables the integration of information technology (IT) systems in a loosely-coupled manner. The paper aims to show the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Integrated services</i> architectural view and two methods of modeling messaging flows at the service and business levels. Service flows allow for modeling the communication between systems/services at the level of sending messages. In contrast, business flows model the entire course of interaction among cooperating applications. The methods employ an Integration flows diagram, which is a specialized version of the Unified Modeling Language (UML) Activity diagram. In addition, the business process task from the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Integrated Processes</i> view defines the context for the business flow. Moreover, the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Use Cases</i> view identifies the integration scope within requirements. All those views belong to the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</i> + <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">5</i> architectural views model. The paper exerts extensibility mechanisms declared in the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">UML Profile for Messaging Patterns</i> . Besides, the profile has been augmented with stereotypes for messaging patterns for the Apache Camel framework, stakeholders involved in communication, and up-to-date stereotypes for structural components. The methods were used in the integration design of sending orders and confirmations between the business applications of the brokerage house and the stock exchange.
High-speed integrated QKD system

Quantum key distribution (QKD) is nowadays a well-established method for generating secret keys at a distance in an information-theoretically secure way, as the secrecy of QKD relies on the laws of quantum physics and not on computational complexity. In order to industrialize QKD, low-cost, mass-manufactured, and practical QKD setups are required. Hence, photonic and electronic integration of the sender’s and receiver’s respective components is currently in the spotlight. Here we present a high-speed (2.5 GHz) integrated QKD setup featuring a transmitter chip in silicon photonics allowing for high-speed modulation and accurate state preparation, as well as a polarization-independent low-loss receiver chip in aluminum borosilicate glass fabricated by the femtosecond laser micromachining technique. Our system achieves raw bit error rates, quantum bit error rates, and secret key rates equivalent to a much more complex state-of-the-art setup based on discrete components [ Boaron A. et al. , Phys. Rev. Lett. 121 , 190502 ( 2018 ) ].
Potential impacts of climate change on global solar radiation and PV output using the CMIP6 model in West Africa

In this study, six different PV power technology models from similar climatic environments in West Africa were used to estimate the potential of PV generation and apply the model to evaluate the effects of climate change on PV output in the region. This was achieved by using historical and projected satellite datasets obtained from the ECMWF database. It was found that, compared to other technologies, poly-crystalline silicon (p-Si) technology yielded the highest increase in solar PV output compared to other modules in both best-case and moderate-case scenarios. However, in the worst-case scenario, amorphous silicon (a-Si) technology produces less than a 1% increase in solar PV production, while other technologies produce less than a 1% decrease in solar PV output. The CMIP6 climate model was also used to assess the effects of climate change on the region's global solar radiation. With the exception of the near- and far-future Boreal winter seasons (DJF), as the impact of climate change intensifies, there may be a corresponding decrease in solar radiation in the moderate- and worst-case scenarios in different seasons between 2015-2050 and 2051–2100, as well as in the annual resolution implying that solar energy should be the main contributor to renewable energy and low-carbon economic planning in the region. In addition, the authors used historical datasets to develop and test 171 theoretical models to estimate West Africa's solar radiation potential. According to the error metrics, the CARIMA-LG hybrid surpassed other models developed in this study as well as those obtained from the literature.