Reviewerly/exHarmony huggingface dataset:
Blockchain-Based and Value-Driven Enterprise Data Governance: A Collaborative Framework

The traditional mode of cooperation between enterprises still suffers from major problems, including data privacy leakage, data falsification, and inefficient collaboration in data sharing. These challenges make it difficult for enterprises to ensure that their cooperative suppliers adopt sustainable practices in standards identification and operation processes. This paper proposes a “Value–Standard–Process” collaborative framework for blockchain-based enterprise data governance that helps ensure a high degree of data security, a high reliability of collaborative tasks, and a high transparency of value transformation. First, this paper proposes a new collaborative mode for blockchain-based manufacturing in the sharing economy, including the non-linear dynamic evaluation and value balancing mechanism of data with multiple attributes, a trusted data governance mechanism for blockchain-based manufacturing, and a smart contract generation mechanism for value-driven collaboration. Second, this paper explains these three components and the implementation of the overall framework. Third, this paper verifies the applicability and achievability of the proposed framework through experiments. Establishing the value-driven multi-level blockchain-based collaboration mode facilitates the effective flow of production factors and promotes trust in the digital economy of sustainability.
BLAST-IoT: BLockchain Assisted Scalable Trust in Internet of Things

Internet of Things (IoT) paved the way to realize ubiquitous access towards manifold services and applications by enabling connectivity between different physical devices. The devices, having heterogeneous computation power, protocol stack, or storage power, can send or receive various services by communicating through the internet. These services inevitably demand a secure and reliable network system to interchange the data or information for their impressive utilization. One proven solution to attain this objective is embedding Trust Management System (TMS) that aims to reduce risk or uncertainty associated with interacting adversary devices by capturing their dynamic behavior in the system. With TMS, devices compute and maintain trust value on the interacting devices by observing their behavior with different interaction instances. This trust information is exchanged between the devices to assess the global reputation of devices and facilitate devices’ decisions when there is a lack of direct interactions. For systems having enormous low-power devices, one crucial aspect of TMS is secure and timely dissemination, management, and storage of trust information to accurately assess devices’ dynamic behavior. Addressing this issue, we present a blockchain-assisted scalable trust model, BLAST-IoT, for IoT systems of heterogeneous devices in this paper. BLAST-IoT intends to ensure secure dissemination and storage of trust information leveraging on the functionalities provided by blockchain technology. It adopts a semi-distributed approach where direct trust information, computed by IoT end devices, is processed and stored by some high-power edge devices running public blockchain networks to handle impediments caused by power-deficient IoT devices. The effectiveness of BLAST-IoT has been evaluated through implementation and simulation-based results for trust convergence and assessment accuracy in the dynamic IoT environment. Further, the throughput performance of BLAST-IoT has been examined with an increase in network load.
Adaptive Density Spatial Clustering Method Fusing Chameleon Swarm Algorithm

The density-based spatial clustering of application with noise (DBSCAN) algorithm is able to cluster arbitrarily structured datasets. However, the clustering result of this algorithm is exceptionally sensitive to the neighborhood radius (Eps) and noise points, and it is hard to obtain the best result quickly and accurately with it. To solve the above problems, we propose an adaptive DBSCAN method based on the chameleon swarm algorithm (CSA-DBSCAN). First, we take the clustering evaluation index of the DBSCNA algorithm as the objective function and use the chameleon swarm algorithm (CSA) to iteratively optimize the evaluation index value of the DBSCAN algorithm to obtain the best Eps value and clustering result. Then, we introduce the theory of deviation in the data point spatial distance of the nearest neighbor search mechanism to assign the identified noise points, which solves the problem of over-identification of the algorithm noise points. Finally, we construct color image superpixel information to improve the CSA-DBSCAN algorithm's performance regarding image segmentation. The simulation results of synthetic datasets, real-world datasets, and color images show that the CSA-DBSCAN algorithm can quickly find accurate clustering results and segment color images effectively. The CSA-DBSCAN algorithm has certain clustering effectiveness and practicality.
On the Use of a Convolutional Block Attention Module in Deep Learning-Based Human Activity Recognition with Motion Sensors

Sensor-based human activity recognition with wearable devices has captured the attention of researchers in the last decade. The possibility of collecting large sets of data from various sensors in different body parts, automatic feature extraction, and aiming to recognize more complex activities have led to a rapid increase in the use of deep learning models in the field. More recently, using attention-based models for dynamically fine-tuning the model features and, in turn, improving the model performance has been investigated. However, the impact of using channel, spatial, or combined attention methods of the convolutional block attention module (CBAM) on the high-performing DeepConvLSTM model, a hybrid model proposed for sensor-based human activity recognition, has yet to be studied. Additionally, since wearables have limited resources, analysing the parameter requirements of attention modules can serve as an indicator for optimizing resource consumption. In this study, we explored the performance of CBAM on the DeepConvLSTM architecture both in terms of recognition performance and the number of additional parameters required by attention modules. In this direction, the effect of channel and spatial attention, individually and in combination, were examined. To evaluate the model performance, the Pamap2 dataset containing 12 daily activities and the Opportunity dataset with its 18 micro activities were utilized. The results showed that the performance for Opportunity increased from 0.74 to 0.77 in the macro f1-score owing to spatial attention, while for Pamap2, the performance increased from 0.95 to 0.96 owing to the channel attention applied to DeepConvLSTM with a negligible number of additional parameters. Moreover, when the activity-based results were analysed, it was observed that the attention mechanism increased the performance of the activities with the worst performance in the baseline model without attention. We present a comparison with related studies that use the same datasets and show that we could achieve higher scores on both datasets by combining CBAM and DeepConvLSTM.
Quantum Computing in the Next-Generation Computational Biology Landscape: From Protein Folding to Molecular Dynamics

Modern biological science is trying to solve the fundamental complex problems of molecular biology, which include protein folding, drug discovery, simulation of macromolecular structure, genome assembly, and many more. Currently, quantum computing (QC), a rapidly emerging technology exploiting quantum mechanical phenomena, has developed to address current significant physical, chemical, biological issues, and complex questions. The present review discusses quantum computing technology and its status in solving molecular biology problems, especially in the next-generation computational biology scenario. First, the article explained the basic concept of quantum computing, the functioning of quantum systems where information is stored as qubits, and data storage capacity using quantum gates. Second, the review discussed quantum computing components, such as quantum hardware, quantum processors, and quantum annealing. At the same time, article also discussed quantum algorithms, such as the grover search algorithm and discrete and factorization algorithms. Furthermore, the article discussed the different applications of quantum computing to understand the next-generation biological problems, such as simulation and modeling of biological macromolecules, computational biology problems, data analysis in bioinformatics, protein folding, molecular biology problems, modeling of gene regulatory networks, drug discovery and development, mechano-biology, and RNA folding. Finally, the article represented different probable prospects of quantum computing in molecular biology.
Advanced weighted hybridized approach for recommendation system

Purpose This paper aims to describe the usage of a hybrid weightage-based recommender system focused on books and implementing it at an industrial level, using various recommendation approaches. Additionally, it focuses on integrating the model into the most widely used platform application. Design/methodology/approach It is an industrial level implementation of a recommendation system by applying different recommendation approaches. This study describes the usage of a hybrid weightage-based recommender system focused on books and putting a model into the most used platform application. Findings This paper deals with the phases of software engineering from the analysis of the requirements, the actual making of the recommender model to deployment and testing of the application at the user end. Finally, the hybridized system outperforms over other existing recommender system. Originality/value The proposed recommendation system is an industrial level implementation of a recommendation system by applying different recommendation approaches. The recommendation system is centralized to books and its recommendation. In this paper, the authors also describe the usage of a hybrid weightage-based recommender system focused on books and putting a model into the most used platform application. This paper deals with the phases of software engineering from the analysis of the requirements, the actual making of the recommender model to deployment and testing of the application at the user end. Finally, the newly created hybridized system outperforms the Netflix recommendation model as well as the Hybrid book recommendation system model as has been clearly shown in the Results Analysis section of the book. The source-code can be available at https://github.com/debajyoty/recomender-system.git .
High-Speed Hardware Architecture Based on Error Detection for KECCAK

The hash function KECCAK integrity algorithm is implemented in cryptographic systems to provide high security for any circuit requiring integrity and protect the transmitted data. Fault attacks, which can extricate confidential data, are one of the most effective physical attacks against KECCAK hardware. Several KECCAK fault detection systems have been proposed to counteract fault attacks. The present research proposes a modified KECCAK architecture and scrambling algorithm to protect against fault injection attacks. Thus, the KECCAK round is modified so that it consists of two parts with input and pipeline registers. The scheme is independent of the KECCAK design. Iterative and pipeline designs are both protected by it. To test the resilience of the suggested detection system approach fault attacks, we conduct permanent as well as transient fault attacks, and we evaluate the fault detection capabilities (99.9999% for transient faults and 99.999905% for permanent faults). The KECCAK fault detection scheme is modeled using VHDL language and implemented on an FPGA hardware board. The experimental results show that our technique effectively secures the KECCAK design. It can be carried out with little difficulty. In addition, the experimental FPGA results demonstrate the proposed KECCAK detection scheme's low area burden, high efficiency and working frequency.
Deep Learning for Human Activity Recognition on 3D Human Skeleton: Survey and Comparative Study

Human activity recognition (HAR) is an important research problem in computer vision. This problem is widely applied to building applications in human-machine interactions, monitoring, etc. Especially, HAR based on the human skeleton creates intuitive applications. Therefore, determining the current results of these studies is very important in selecting solutions and developing commercial products. In this paper, we perform a full survey on using deep learning to recognize human activity based on three-dimensional (3D) human skeleton data as input. Our research is based on four types of deep learning networks for activity recognition based on extracted feature vectors: Recurrent Neural Network (RNN) using extracted activity sequence features; Convolutional Neural Network (CNN) uses feature vectors extracted based on the projection of the skeleton into the image space; Graph Convolution Network (GCN) uses features extracted from the skeleton graph and the temporal-spatial function of the skeleton; Hybrid Deep Neural Network (Hybrid-DNN) uses many other types of features in combination. Our survey research is fully implemented from models, databases, metrics, and results from 2019 to March 2023, and they are presented in ascending order of time. In particular, we also carried out a comparative study on HAR based on a 3D human skeleton on the KLHA3D 102 and KLYOGA3D datasets. At the same time, we performed analysis and discussed the obtained results when applying CNN-based, GCN-based, and Hybrid-DNN-based deep learning networks.
Bridging adaptive management and reinforcement learning for more robust decisions

From out-competing grandmasters in chess to informing high-stakes healthcare decisions, emerging methods from artificial intelligence are increasingly capable of making complex and strategic decisions in diverse, high-dimensional and uncertain situations. But can these methods help us devise robust strategies for managing environmental systems under great uncertainty? Here we explore how reinforcement learning (RL), a subfield of artificial intelligence, approaches decision problems through a lens similar to adaptive environmental management: learning through experience to gradually improve decisions with updated knowledge. We review where RL holds promise for improving evidence-informed adaptive management decisions even when classical optimization methods are intractable and discuss technical and social issues that arise when applying RL to adaptive management problems in the environmental domain. Our synthesis suggests that environmental management and computer science can learn from one another about the practices, promises and perils of experience-based decision-making. This article is part of the theme issue 'Detecting and attributing the causes of biodiversity change: needs, gaps and solutions'.
Virtual Reality in Teacher Education from 2010 to 2020

Virtual reality (VR) is a technology that recreates realistic scenarios in a controllable virtual environment. VR has been widely adopted in professional training of situational, adaptive, and complex skills within various occupations, yet the field of teacher education has only just started to utilize this technology in the recent decade. In order to advance the application of VR in teacher education, the current review presents a synopsis of state-of-the-art literature from 2010 to 2020. We conducted a review regarding the details of program implementation (e.g., VR system and simulated situations), intended outcomes (e.g., factual/conceptual or procedural knowledge), and measures used to evaluate the effectiveness of teacher education programs that implemented various VR systems. Based on the review of 46 empirical studies, we found that (1) VR-based training programs mostly targeted pre-service teachers, and that they most frequently simulated instructional situations; (2) VR has been used most often for fostering procedural knowledge; (3) the majority of studies reported positive results regarding the intended outcomes and the effectiveness of VR has been measured mostly by teachers' self-reports. We also identified areas of improvement and future directions for the application of VR in teacher education and teacher education research.
Enhancing Social Media Platforms with Machine Learning Algorithms and Neural Networks

Network analysis aids management in reducing overall expenditures and maintenance workload. Social media platforms frequently use neural networks to suggest material that corresponds with user preferences. Machine learning is one of many methods for social network analysis. Machine learning algorithms operate on a collection of observable features that are taken from user data. Machine learning and neural network-based systems represent a topic of study that spans several fields. Computers can now recognize the emotions behind particular content uploaded by users to social media networks thanks to machine learning. This study examines research on machine learning and neural networks, with an emphasis on social analysis in the context of the current literature.
Python code smells detection using conventional machine learning models

Code smells are poor code design or implementation that affect the code maintenance process and reduce the software quality. Therefore, code smell detection is important in software building. Recent studies utilized machine learning algorithms for code smell detection. However, most of these studies focused on code smell detection using Java programming language code smell datasets. This article proposes a Python code smell dataset for Large Class and Long Method code smells. The built dataset contains 1,000 samples for each code smell, with 18 features extracted from the source code. Furthermore, we investigated the detection performance of six machine learning models as baselines in Python code smells detection. The baselines were evaluated based on Accuracy and Matthews correlation coefficient (MCC) measures. Results indicate the superiority of Random Forest ensemble in Python Large Class code smell detection by achieving the highest detection performance of 0.77 MCC rate, while decision tree was the best performing model in Python Long Method code smell detection by achieving the highest MCC Rate of 0.89.
WiFi-based indoor passive fall detection for medical Internet of Things

There are currently numerous automated fall detection devices available, but most require the deployment of additional specialized equipment, which can create inconvenience for elderly individuals in their daily lives. This paper proposes a solution that reuses existing smartphone and WiFi infrastructure, eliminating the need for extra equipment and greatly simplifying the implementation of fall event detection. Due to the lower quality of data collected by smartphones, we have developed an adaptive DBSCAN clustering algorithm to process and extract useful data. We then train a deep convolutional LSTM model to classify the collected data in real-time. When a fall is detected, the smartphone can transmit an alert via the internet to notify relatives or medical staff of the incident. Our experimental results demonstrate that utilizing smartphones for fall detection is both feasible and accurate.
Quantum state preparation using tensor networks

Abstract Quantum state preparation is a vital routine in many quantum algorithms, including solution of linear systems of equations, Monte Carlo simulations, quantum sampling, and machine learning. However, to date, there is no established framework of encoding classical data into gate-based quantum devices. In this work, we propose a method for the encoding of vectors obtained by sampling analytical functions into quantum circuits that features polynomial runtime with respect to the number of qubits and provides <?CDATA ${\gt}99.9\%$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"><mml:mrow><mml:mo>&gt;</mml:mo></mml:mrow><mml:mn>99.9</mml:mn><mml:mi mathvariant="normal">%</mml:mi></mml:math> accuracy, which is better than a state-of-the-art two-qubit gate fidelity. We employ hardware-efficient variational quantum circuits, which are simulated using tensor networks, and matrix product state representation of vectors. In order to tune variational gates, we utilize Riemannian optimization incorporating auto-gradient calculation. Besides, we propose a ‘cut once, measure twice’ method, which allows us to avoid barren plateaus during gates’ update, benchmarking it up to 100-qubit circuits. Remarkably, any vectors that feature low-rank structure—not limited by analytical functions—can be encoded using the presented approach. Our method can be easily implemented on modern quantum hardware, and facilitates the use of the hybrid-quantum computing architectures.
G2Retro as a two-step graph generative models for retrosynthesis prediction

Retrosynthesis is a procedure where a target molecule is transformed into potential reactants and thus the synthesis routes can be identified. Recently, computational approaches have been developed to accelerate the design of synthesis routes. In this paper, we develop a generative framework $\mathsf{G^2Retro}$ for one-step retrosynthesis prediction. $\mathsf{G^2Retro}$ imitates the reversed logic of synthetic reactions. It first predicts the reaction centers in the target molecules (products), identifies the synthons needed to assemble the products, and transforms these synthons into reactants. $\mathsf{G^2Retro}$ defines a comprehensive set of reaction center types, and learns from the molecular graphs of the products to predict potential reaction centers. To complete synthons into reactants, $\mathsf{G^2Retro}$ considers all the involved synthon structures and the product structures to identify the optimal completion paths, and accordingly attaches small substructures sequentially to the synthons. Here we show that $\mathsf{G^2Retro}$ is able to better predict the reactants for given products in the benchmark dataset than the state-of-the-art methods.
ChatGPT Chats on Computational Experiments: From Interactive Intelligence to Imaginative Intelligence for Design of Artificial Societies and Optimization of Foundational Models

Powered by the rapid development of Internet, the penetration of the Internet of Things, the emergence of big data, and the rise of social media, more and more complex systems are exhibiting the characteristics of social, physical, and information fusion. These systems are known as cyber-physical-social systems (CPSS) [1], [2]. These CPSS face unprecedented challenges in design, analysis, management, control and integration due to their involvement with human and social factors [3], [4]. To cope with this challenge, there are two main approaches to CPSS research: 1)Data driven analysis method. Regard complex systems as black boxes, focus on the relationship between inputs and outputs, without modeling and analyzing the complex processes within the system. In the practical application, complex systems tend to be replaced by statistical models based on data and intelligent algorithms, such as convolutional neural network (CNN), recurrent neural networks (RNN), foundation models, etc. The latest ChatGPT (chat generative pretrained transformer) is a typical example of this approach. 2)Knowledge driven analysis method. According to the principle of “simple consistency”, the complex system in practice can be recognized, understood and analyzed by designing and restoring the structure and function of each system component. The computational experiments method is a representative method [5]. Starting from the micro-scale, it cultivates an “artificial society” of the real system in the cyber world. Then, a variety of experiments can be conducted to identify the causal relationship between intervention variables and system emergence to realize the interpretation, understanding, guidance and regulation of macro phenomena.