Reviewerly/exHarmony huggingface dataset:
Identification and segregation of genes with improved recurrent neural network trained with optimal gene level and mutation level features

Authors:
raw_author_name: Ranjeet Kumar Rout
raw_affiliation_string: Assistant Professor, Department of Computer Science and Engineering National Institute of Technology, Srinagar, Jammu and Kashmir 190006, India

Abstract: Even though many different approaches have been employed to address the complex mutational heterogeneity of cancer, finding driver genes is still problematic since other genomic factors cannot be fully integrated for combined analyses. This research paper presents a novel gene identification and segregation model with five key processes (a) pre-processing, (b) treatment of class imbalances, (c) feature extraction, (d) feature selection, and (e) gene classification. To increase the data quality, the gathered initial information is first pre-processed utilizing data cleaning and data normalization. This turns the raw data into something that is both useful and effective. In actuality, the sample is skewed against drivers because passenger mutation markers appear in proportionally less instances than drivers do. To address the Class Imbalance Problem, improved K-Means + SMOTE are applied to the preprocessed data. The most crucial characteristics, including those at the gene and mutation levels, are then extracted from the balanced dataset. To lessen the computational load in terms of time, the best features from the retrieved features are selected using Forensic interpretation tailored hunger food search optimization (FIHFSO). The ideal features are used to train the deep learning classifier that conducts the separation procedure. In this research, an Improved Recurrent Neural Network (I-RNN) is used to make a final decision about genes. At 90% of learning percentage, the accuracy of the proposed method achieves 0.98% of 0.83, 0.81, 0.65, 0.80, 0.92 and 0.63% which is compared to the other methods like HGS, FBIO, AOA, AO, GOA and PRO respectively.
Cost-benefit Analysis of Adopting Blockchain Technology in the Radiology Unit of a District Hospital

Authors:
raw_author_name: Siti Zaleha Abdul Rasid
raw_affiliation_string: Azman Hashim International Business School, Universiti Teknologi Malaysia, Malaysia
raw_author_name: Rohaida Basiruddin
raw_affiliation_string: Azman Hashim International Business School, Universiti Teknologi Malaysia, Malaysia

Abstract: Background: Blockchain technology has better security and immutable features, which are beneficial for healthcare data management. Decentralisation data management allows better sharing of medical images between units and departments. Objectives: The objectives of this study were to determine the cost of adopting the blockchain-based system for medical images and the cost-benefit analysis for five years of adoption of this technology at a district hospital in Malaysia. Methodology: Data regarding the yearly costs and workload for 2016 until 2018 were collected from a district hospital in Malaysia. The cost of blockchain implementation was obtained from a company that developed this technology. The cost-benefit analysis was done using the capital budgeting method for five years of this technology adoption. Sensitivity analysis was done to assess uncertainty conditions during this adoption. Results: The total yearly cost of Radiology Unit for 2018 was MYR490,168.96. The cost for adopting blockchain technology was MYR80,000 for the software, MYR115,000 for upgrading the assets and yearly maintenance cost was MYR16,000. The total present value of net benefits for five years of technology adoption was MYR865,295.01. Sensitivity analysis shows that consumables cost-saving is the most sensitive variable. Most pessimistic situation analyses showed positive net benefits. Conclusion: The adoption of blockchain technology in managing medical images offers numerous benefits to public hospitals in Malaysia. Investment in this technology can provide a good return to the organisation.
Photovoltaic Power Prediction Based on Hybrid Deep Learning Networks and Meteorological Data

Authors:
raw_author_name: Li Xu
raw_affiliation_string: School of Naval Architecture, Ocean and Energy Power Engineering, Wuhan University of Technology, Wuhan 430063, China
raw_author_name: Tian Wang
raw_affiliation_string: State Key Laboratory of Maritime Technology and Safety, Wuhan University of Technology, Wuhan 430063, China
raw_author_name: Danyang Zhao
raw_affiliation_string: School of Naval Architecture, Ocean and Energy Power Engineering, Wuhan University of Technology, Wuhan 430063, China

Abstract: Conventional point prediction methods encounter challenges in accurately capturing the inherent uncertainty associated with photovoltaic power due to its stochastic and volatile nature. To address this challenge, we developed a robust prediction model called QRKDDN (quantile regression and kernel density estimation deep learning network) by leveraging historical meteorological data in conjunction with photovoltaic power data. Our aim is to enhance the accuracy of deterministic predictions, interval predictions, and probabilistic predictions by incorporating quantile regression (QR) and kernel density estimation (KDE) techniques. The proposed method utilizes the Pearson correlation coefficient for selecting relevant meteorological factors, employs a Gaussian Mixture Model (GMM) for clustering similar days, and constructs a deep learning prediction model based on a convolutional neural network (CNN) combined with a bidirectional gated recurrent unit (BiGRU) and attention mechanism. The experimental results obtained using the dataset from the Australian DKASC Research Centre unequivocally demonstrate the exceptional performance of QRKDDN in deterministic, interval, and probabilistic predictions for photovoltaic (PV) power generation. The effectiveness of QRKDDN was further validated through ablation experiments and comparisons with classical machine learning models.
Protecting FPGA-Based Cryptohardware Implementations from Fault Attacks Using ADCs

Authors:
raw_author_name: Francisco Eugenio Potestad-Ordóñez
raw_affiliation_string: Escuela Politécnica Superior, Universidad de Sevilla, 41011 Seville, Spain; Instituto de Microelectrónica de Sevilla, IMSE-CNM (CSIC; Universidad de Sevilla), 41092 Seville, Spain
raw_author_name: Erica Tena-Sánchez
raw_affiliation_string: Escuela Politécnica Superior, Universidad de Sevilla, 41011 Seville, Spain; Instituto de Microelectrónica de Sevilla, IMSE-CNM (CSIC; Universidad de Sevilla), 41092 Seville, Spain

Abstract: The majority of data exchanged between connected devices are confidential and must be protected against unauthorized access. To ensure data protection, so-called cryptographic algorithms are used. These algorithms have proven to be mathematically secure against brute force due to the key length, but their physical implementations are vulnerable against physical attacks. The physical implementation of these algorithms can result in the disclosure of information that can be used to access confidential data. Some of the most powerful hardware attacks presented in the literature are called fault injection attacks. These attacks involve introducing a malfunction into the normal operation of the device and then analyzing the data obtained by comparing them with the expected behavior. Some of the most common methods for injecting faults are the variation of the supply voltage and temperature or the injection of electromagnetic pulses. In this paper, a hardware design methodology using analog-to-digital converters (ADCs) is presented to detect attacks on cryptocircuits and prevent information leakage during fault injection attacks. To assess the effectiveness of the proposed design approach, FPGA-based ADC modules were designed that detect changes in temperature and supply voltage. Two setups were implemented to test the scheme against voltage and temperature variations and injections of electromagnetic pulses. The results obtained demonstrate that, in 100% of the cases, when the correct operating voltage and temperature range were established, the detectors could activate an alarm signal when the cryptographic module was attacked, thus avoiding confidential information leakage and protecting data from being exploited.
Chiral resolution based on non-adiabatic holonomic quantum control via a transmon qutrit

Authors:
raw_author_name: Jianwen Xu
raw_affiliation_string: National Laboratory of Solid State Microstructures, School of Physics, Nanjing University 1 , Nanjing 210093, China
raw_author_name: Xinsheng Tan
raw_affiliation_string: Hefei National Laboratory 3 , Hefei 230088, China; National Laboratory of Solid State Microstructures, School of Physics, Nanjing University 1 , Nanjing 210093, China; Shishan Laboratory, Suzhou Campus of Nanjing University 2 , Suzhou 215000, China
raw_author_name: Yang Yu
raw_affiliation_string: Hefei National Laboratory 3 , Hefei 230088, China; National Laboratory of Solid State Microstructures, School of Physics, Nanjing University 1 , Nanjing 210093, China; Shishan Laboratory, Suzhou Campus of Nanjing University 2 , Suzhou 215000, China

Abstract: Once the concept of chiral molecules was proposed, the task of chiral resolution has accompanied around in the related area. Naturally, scientists need fast and accurate methods to distinguish different chiralities. Here, we show a feasible method based on non-adiabatic holonomic quantum computation (NHQC), which can achieve over 99% discrimination of different chiralities. Based on the original scheme, we studied the modified version, NHQC+. The experimental data proved that it has better robustness than the original protocol, indicating a promising development in the relevant fields.
Research on the Recognition Method of Dial Scales for Arrester Pointer Instruments Based on Deep Learning

Authors:
raw_author_name: Huaiwen Wang
raw_affiliation_string: Tianjin Key Laboratory of Refrigeration Technology, Tianjin University of Commerce, No. 409 Guangrong Road, Beichen District, Tianjin 300134, China
raw_author_name: Yang Hu
raw_affiliation_string: Tianjin Key Laboratory of Refrigeration Technology, Tianjin University of Commerce, No. 409 Guangrong Road, Beichen District, Tianjin 300134, China
raw_author_name: Yue Cui
raw_affiliation_string: Department of State-Owned Assets and Laboratory Management, Tianjin University of Commerce, No. 409 Guangrong Road, Beichen District, Tianjin 300134, China

Abstract: To address the recognition challenges faced by arrester pointer instruments’ dial scales in various scenarios, this paper introduces a deep learning-based recognition method for pointer instrument scales. An attention module is integrated into the YOLOv5 network architecture, enhancing the accuracy and robustness of the model. After correcting the dial, dial recognition is conducted with OpenCV to achieve precise identification of the instrument scales. The proposed method was tested using images of arrester pointer instruments against diverse backgrounds. The experimental results demonstrate that the method processes instrument data images in an average time of 0.662 s and achieves a successful recognition rate of 96% with an average error of 0.923%. This method provides a rapid and efficient approach for recognizing instrument scales and offers a novel solution for identifying similar types of instruments.
MgMViT: Multi-Granularity and Multi-Scale Vision Transformer for Efficient Action Recognition

Authors:
raw_author_name: Hua Huo
raw_affiliation_string: Information Engineering College, Henan University of Science and Technology, Luoyang 471000, China
raw_author_name: Bingjie Li
raw_affiliation_string: Information Engineering College, Henan University of Science and Technology, Luoyang 471000, China

Abstract: Nowadays, the field of video-based action recognition is rapidly developing. Although Vision Transformers (ViT) have made great progress in static image processing, they are not yet fully optimized for dynamic video applications. Convolutional Neural Networks (CNN) and related models perform exceptionally well in video action recognition. However, there are still some issues that cannot be ignored, such as high computational costs and large memory consumption. In the face of these issues, current research focuses on finding effective methods to improve model performance and overcome current limits. Therefore, we present a unique Vision Transformer model based on multi-granularity and multi-scale fusion to accomplish efficient action recognition, which is designed for action recognition in videos to effectively reduce computational costs and memory usage. Firstly, we devise a multi-scale, multi-granularity module that integrates with Transformer blocks. Secondly, a hierarchical structure is utilized to manage information at various scales, and we introduce multi-granularity on top of multi-scale, which allows for a selective choice of the number of tokens to enter the next computational step, thereby reducing redundant tokens. Thirdly, a coarse-fine granularity fusion layer is introduced to reduce the sequence length of tokens with lower information content. The above two mechanisms are combined to optimize the allocation of resources in the model, further emphasizing critical information and reducing redundancy, thereby minimizing computational costs. To assess our proposed approach, comprehensive experiments are conducted by using benchmark datasets in the action recognition domain. The experimental results demonstrate that our method has achieved state-of-the-art performance in terms of accuracy and efficiency.
Cooperative Coverage Path Planning for Multi-Mobile Robots Based on Improved K-Means Clustering and Deep Reinforcement Learning

Authors:
raw_author_name: Jianjun Ni
raw_affiliation_string: College of Artificial Intelligence and Automation, Hohai University, Changzhou 213200, China; College of Information Science and Engineering, Hohai University, Changzhou 213200, China
raw_author_name: Guangyi Tang
raw_affiliation_string: College of Artificial Intelligence and Automation, Hohai University, Changzhou 213200, China
raw_author_name: Chunyan Ke
raw_affiliation_string: College of Information Science and Engineering, Hohai University, Changzhou 213200, China
raw_author_name: Yang Gu
raw_affiliation_string: College of Artificial Intelligence and Automation, Hohai University, Changzhou 213200, China

Abstract: With the increasing complexity of patrol tasks, the use of deep reinforcement learning for collaborative coverage path planning (CPP) of multi-mobile robots has become a new hotspot. Taking into account the complexity of environmental factors and operational limitations, such as terrain obstacles and the scope of the task area, in order to complete the CPP task better, this paper proposes an improved K-Means clustering algorithm to divide the multi-robot task area. The improved K-Means clustering algorithm improves the selection of the first initial clustering point, which makes the clustering process more reasonable and helps to distribute tasks more evenly. Simultaneously, it introduces deep reinforcement learning with a dueling network structure to better deal with terrain obstacles and improves the reward function to guide the coverage process. The simulation experiments have confirmed the advantages of this method in terms of balanced task assignment, improvement in strategy quality, and enhancement of coverage efficiency. It can reduce path duplication and omission while ensuring coverage quality.
Powerlifting total score prediction based on an improved random forest regression algorithm

Authors:
raw_author_name: Vinh Huy Chau
raw_affiliation_string: Ho Chi Minh City University of Physical Education and Sport, Ho Chi Minh City, Vietnam
raw_author_name: Anh Thu Vo
raw_affiliation_string: Ho Chi Minh City University of Physical Education and Sport, Ho Chi Minh City, Vietnam
raw_author_name: Huu Phuc Ngo
raw_affiliation_string: Ho Chi Minh City University of Physical Education and Sport, Ho Chi Minh City, Vietnam

Abstract: This paper discusses the use of an improved random forest regression algorithm (RFRA) to predict the total score of powerlifters. The paper collected the age, weight, and total score of multiple powerlifters, and then used an improved RFRA to build a predictive model. The parameters of this model are optimized by a differential squirrel search algorithm. The experimental results show that our proposed method can effectively predict the total score of powerlifters with an error of less than 10%, which can provide a reference for experts and athletes before training or competition.
Chaotic fuzzy iterated function systems

Authors:
raw_author_name: P.B. Vinod Kumar
raw_affiliation_string: Centre for Topology and Applications, Department of Mathematics. Rajagiri School of Engineering and Technology, A P J Abdul Kalam Technical University, Kerala, India

Abstract: It is observed that IFSs are defined based on the concept that the iterates take only an integer number of times. This work studies the dynamics of functions, where a function can iterate r times for every r ∈ R. Utilizing concepts from fuzzy sets, r-times iterates of a function are defined for r ∈ R. The study demonstrates that the chaotic property can be generalized to this new iterative concept. The chaotic behavior of a function is then extended using this iterative concept.
A decentralized framework for enhancing security in power systems through blockchain technology and trading system

Authors:
raw_author_name: R. Venkatesan
raw_affiliation_string: School of Computing, SASTRA Deemed University, Thanjavur, India

Abstract: The primary objective of implementing secure smart power networks is to reduce the risk of data privacy breaches, including adversarial data poisoning and inference attacks. This study presents a novel approach, namely the Blockchain-based Power System Security Model (BC-PSSM), to augment security measures within power systems. The proposed method utilizes blockchain technology to enhance the security of data storage and verification and the mechanisms for data signing and authentication. It employs decentralized detection to improve the effectiveness and precision of intrusion detection with a Support Vector Machine (SVM). The BC-PSSM method exhibits promising outcomes in multiple facets of power system security detection, as evidenced by thorough simulations and evaluations. The simulation results demonstrate a significant decrease in computational burden and energy consumption by maintaining a high energy output level. The BC-PSSM algorithm performs remarkably in detection and false positive rates, resulting in heightened security and enhanced efficiency. The results also illustrate the positive impact of BC-PSSM in enhancing the security of power systems, offering robust protection against cybersecurity threats. BC-PSSM aims to enhance the security of power systems, thereby promoting a more robust and dependable energy infrastructure. This initiative plays a crucial role in protecting essential assets and mitigating the potential threats posed by cyber-attacks. The BC-PSSM algorithm enhanced efficiency to 92.15% and boosted overall security to 96.34%. It achieved this by achieving an amazing identification rate of 96.75% and an extraordinarily low false positive rate of 1.82%.
Broadband Diffractive Neural Networks Enabling Classification of Visible Wavelengths

Authors:
raw_author_name: Madhu Bhaskaran
raw_affiliation_string: ARC Centre of Excellence for Transformative Meta-Optical Systems RMIT University Melbourne VIC 3000 Australia; Functional Materials and Microsystems Research Group and the Micro Nano Research Facility RMIT University Melbourne VIC 3000 Australia

Abstract: Diffractive neural networks (DNNs) are emerging as a new machine learning hardware based on optical diffraction with parallel and high‐throughput information processing. The optical inputs to DNNs are spatially modulated by propagating through passive diffractive layers that work in succession to achieve an inference. Herein, visible wavelength classification using single‐ and two‐layer DNNs fabricated using direct laser writing is demonstrated. The proposed DNN approach accepts the point spread function of two different wavelengths modeled after a microscope objective as the input and modulates the input field toward the target detector for classification. Of the three models trained to classify different wavelength pairs, the highest performance observed is for the classification of 561 and 785 nm, achieving over 90% accuracy. This work demonstrates the potential of all‐optical artificial neural networks for applications requiring visible wavelengths, from visible light beam shaping to spectral analysis and optical imaging.
FPHO: Fractional Pelican Hawks optimization based container consolidation in CaaS cloud

Authors:
raw_author_name: Manoj Kumar Patra
raw_affiliation_string: Department of Computer Science and Engineering National Institute of Technology Rourkela India
raw_author_name: Bibhudatta Sahoo
raw_affiliation_string: Department of Computer Science and Engineering National Institute of Technology Rourkela India
raw_author_name: Ashok Kumar Turuk
raw_affiliation_string: Department of Computer Science and Engineering National Institute of Technology Rourkela India

Abstract: Abstract Containers in cloud computing provide a logical packaging technique for applications to be isolated from the computing environment in which they actually execute, allowing for efficient sharing of memory, processor, storage, and network resources at the Operating System (OS) level. Since they are so compact, container‐based clouds have recently gained significant popularity. In order to maximize resource usage and minimize energy consumption, the container consolidation technique is widely employed in the cloud environment. This work introduces container consolidation in cloud computing that exploits Fractional Pelican Hawks Optimization (FPHO). In Container as a Service (CaaS) model, containers are placed in the Virtual Machines (VMs), and virtual machines are hosted in Physical Machines (PMs) or servers. The proposed method for container consolidation consists of two modules, namely, the host status module and the consolidation module. In the host status module, the PM's load is predicted using Long Short Term Memory (LSTM) and checked whether the PM is overloaded or underloaded using a threshold. If it is overloaded, the container selection algorithm is performed, and the migration list is also generated. In the consolidation module, the created migration list which is employed for the destination list to be created by an overloaded destination selector. In the same way, the underloaded list is also generated by the underloaded destination selector. Finally, the container and VM migration is carried out by considering the multi‐objectives such as predicted load, migration cost, resource utilization, energy consumption, network, and bandwidth which are optimally selected by the proposed FHPO. Here, FHPO is the combination of Fractional Pelican Optimization (FPO) and Fire Hawk Optimizer (FHO). The designed model achieved the measures with minimum energy consumption, resource utilization, Service Level Agreement (SLA), and Makespan as 0.066, 0.019, 0.054, and 0.066, respectively for setup one.