Reviewerly/exHarmony huggingface dataset:
A Novel Design of Deep Learning Assisted Drug Recommendation Model using Sentimental Inspection

The field of Natural Language Processing includes Sentiment Analysis as one of its core algorithms for identifying emotional tone in written content. As a result of the corona virus, many people have died, and the medical community as a whole is in shock. As a result of the shortage, many began self-medicating without first consulting with a professional, worsening their health. In recent years, machine learning has proven useful in a wide range of contexts, and novel efforts have been made to automate formerly laborious tasks. Our group decided to focus on studying medicine evaluations written by others, with each review including a star rating from 1 to 10. Since there are several reviews for pharmaceuticals that treat the same medical problem, we thought it would be interesting to see if the reviews for these ailments use the same terms or if the conditions' specific vocabulary choices affect the products' overall scores. To that end, we set out to construct supervised ML categorization techniques that can deduce the rating's category from the review's content. Our primary focus had been on implementing various embedding’s, including Term Frequency Inverse Document Frequency (TFIDF). Accuracy, recall, f1score, reliability, and AUC were used to assess the anticipated emotions.
A Comparison of Spectral and Spatial Graph Convolutional Neural Network Kernels Using GraphSAGE-Sparse

Graph Convolutional Networks (GCNs) are widely successful architectures for performing deep learning on graphs, but their well-known scalability challenges have led to increased interest to develop both improved algorithms and hardware accelerators. In this paper, we present and evaluate GraphSAGE-Sparse, a variant of the paradigmatic GraphSAGE GCN that replaces the original’s spatial-based node convolution operation with a minibatch-aware sparse matrix multiply (SpMM) kernel. We find that this modification substantially reduces the per-batch memory cost for training and inference on a GPU accelerator, with the tradeoff of increased time and memory needed to preprocess the data structures used by the sparse kernel. On comparing both algorithms with datasets from the Open Graph Benchmark, we find that GraphSAGE-Sparse is able to obtain improved accuracy predictions in less than half of the total training time, even with the additional preprocessing work.
Redundancy Management in Federated Learning for Fast Communication

One of the most critical challenges of federated learning (FL) is to send data efficiently and reliably over the noisy wireless channels between the clients and server to achieve target learning accuracy as fast as possible. To achieve this goal, we design effective error correction coded FL with managed retransmissions. Rather than using Shannon capacity as the performance measure to design the communication mechanisms for FL, our approach relies critically on learning accuracy. Our fundamental idea is based on the observation that Stochastic Gradient Decent (SGD) and its family can tolerate some errors in the course of training. Inspired by this, to reduce the communication burden without degrading the learning accuracy, our FL framework with Managed Redundancy (FL-MR) has two phases: (i) the No-Retransmission phase, where retransmissions are never performed even in case of erroneous decoding of data and (ii) the Select Retransmission phase, where only some carefully selected data packets are retransmitted. Our extensive simulation results demonstrate that the proposed coded FL system achieves target accuracies much faster than the baseline coded approach.
Enhanced Electrical Wheel Chair based on Eye Blinking control

Wheelchair is an inevitable requirement for seriously impeded individuals to enable navigation. Driving a wheelchair is a drawn-out task for seriously incapacitated people, except if they utilize the tongue to control the joystick. All the while, visually impaired and paraplegic individuals manage two issues, which cause uncomfortable circumstance for them, i.e., headway and confinement. Various frameworks are being created to defeat the issues depicted above, permitting the end-client to perform safe developments and achieve some day to day existence of significant assignments. Our Automated wheelchair utilizes eye squint and head slant development to control the wheelchair. Likewise, we can give more freedom to the impaired individual by utilizing a similar head slant development to speak with the gadgets in a space for instance: a fan. This correspondence is finished utilizing a RF transmitter and beneficiary. Utilizing this, the individual have some control over different gadgets without any problem.
Occluded Face Recognition Using Deep Convolutional Neural Network with Sparse Representation

The restricted ability to distinguish faces under occlusions is a topic that has been around for a long time and offers a unique difficulty not just for face recognition algorithms but also for humans. When compared to other obstacles, such as position variation, varied expressions, and so on, the topic involving occlusion has received significantly less attention from researchers. Despite this, occluded face identification is absolutely necessary in order to realise the full potential of face recognition for use in real-world applications. Several factors have contributed to the rapid development and improvement of facial recognition algorithms throughout the years. Researchers have researched and created a multitude of algorithms for occluded face recognition because of the unexpected aspects encountered in real-world circumstances. Yet, as a result of the epidemic, masked face recognition research has emerged as a distinct branch of occluded face identification and a pressing challenge in the field. The feature extraction stage of a convolutional neural network (CNN) is recommended to benefit from the addition of a sparse representation layer in this paper. Our objective is to improve a target network's functionality by including sparse transforms into it, all without requiring the network to perform more mathematical operations. To begin, the method that will be shown was developed by beginning with the shallow layers of a target network and then proceeding to add the sparse representation layers. Following that, the network was trained with four distinct datasets. The Difference of Gaussian Filter, also known as the DoG Filter, is a pre-processing technique that is used to minimise noise before training. The accuracy of the model that was proposed is higher than the accuracy of the other model with 98.13%.
Block Chain and Distributed Computing Aided with Cloud Technology- A Specific Reference to Security Issues of Healthcare Industry

There has been a significant uptick in the need for user-centric electronic records to save the many characteristics of a patient's medical history as they are recorded at a hospital during treatment and to make those records available as and when they are needed for future treatment, care, or invoicing. Blockchain technology allows us to save these data on a distributed ledger, allowing us to begin and enable use at breakneck speeds while still maintaining a transparent and secure system. To save records safely and in an interoperable fashion, a distributed system with ledger functionality is used. Block chain technology has the potential to revolutionize the way digital assets are transacted by removing the need for a trusted third party to authenticate data authenticity, record asset ownership, or mediate financial or data transactions. Immutability, decentralisation, and transparency are three of its defining characteristics, making it well suited to resolving pressing problems in the healthcare industry, such as the lack of comprehensive peer-to-peer files and the inability to easily access patients' medical histories. Health care systems that are efficient and successful depend on interoperability, the capacity of different health organisations and manufacturers of different software products to connect and share data in a safe and smooth manner. Many problems in modern healthcare can be traced back to a lack of interoperability, such as siloed data, sluggish communications, and different workflow tools. To address this, it is recommended to implement a system that provides secure, pseudo-anonymous access to complete, recognised longitudinal medical records stored in silos. In this article, we conducted a system overview and literature review of block chain technology in a fog computing environment, both of which are capable of processing vast amounts of data in a decentralised fashion. Our in-progress experimental study reveals weaknesses in existing systems and foreshadows directions for future investigation.
Experiences in Quantum Software Engineering

The field of Quantum Computing (QC) is increasingly becoming applicable for a wide range of computationally intensive problems. Especially Variational Quantum Algorithms (VQA) are promising as they allow dealing with the practical challenges of current quantum devices (also referred to as Noisy Intermediate-Scale Quantum devices). However, developing VQA-based solutions is not a straightforward process but involves several development phases, each with different design decisions and implications. In this paper, we share experiences made in developing VQA-based solutions for two different industrial case studies. Based on the solutions, we extracted five generic development phases quantum engineers must take into consideration when engineering quantum solutions. We have embedded the phases in a model (i.e. a phase model) to show their natural order and to illustrate the phases that require more development iterations. The main objective of this paper is not only to share and discuss our findings and experiences but also to provide the phase model as a starting point or guideline to structure the development process.
Ethical Hacking and Penetration Testing: Securing Digital Assets and Networks

As technology evolves, so does the possibility of assaults on digital assets and networks. In reaction to this growing threat, ethical hacking and penetration testing have developed as key strategies for ensuring enterprises' cybersecurity. Ethical hacking, often known as penetration testing, is the process of attempting to uncover weaknesses in computer systems, applications, and networks with the help of authorized individuals. The relevance of ethical hacking and penetration testing as vital components of a complete cybersecurity architecture are examined in this research study. The presentation begins with an overview of ethical hacking and penetration testing, separating ethical hackers from malevolent hackers and emphasizing the importance of authorized security assessments. The ethical issues and legal elements of ethical hacking operations are studied, with an emphasis on adherence to ethical norms and regulatory frameworks. The study focuses light on the critical role professional hackers perform in identifying possible gaps and establishing security against cyberattacks by digging into their legal responsibilities and duties. Furthermore, the study examines the benefits and drawbacks of ethical hacking, as well as its possible problems and ethical issues concerning hacking activities. Considering the growing cybersecurity landscape and the increasing need for comprehensive cyber threat safety, the consequences of the future of ethical hacking are anticipated
Design of compound data acquisition gateway based on 5G network

With the wide application of industrial Internet of Things, the increasing amount of data and the complexity of data types, higher requirements are put forward for the performance of data acquisition gateway. In order to reduce the data acquisition time of the gateway and improve the data retrieval coverage of the gateway, a novel design method of composite data acquisition gateway based on 5G network is proposed. Based on the analysis of related technologies, the functional requirements of the composite data acquisition gateway are summarized, and the overall design of the gateway is completed. On this basis, the gateway hardware environment is constructed by designing the main control module, 5G module and FPGA program, and then the software program is designed by designing the data acquisition driver, 5G module driver, embedded software and protocol conversion process. The experimental results show that the data retrieval coverage of the gateway designed by this method is always above 92%, which is 6% higher than that of method 1. This shows that the method significantly improves the coverage of data search, speeds up the efficiency of data collection, and improves the performance of the data collection gateway, which proves the effectiveness and feasibility of the method and is conducive to promoting the intelligent development of the data collection gateway technology.
A Novel Conditional Connectivity and Hamiltonian Connectivity of BCube with Various Faulty Elements

BCube is one of the main data center networks because it has many attractive features. In practical applications, the failure of components or physical connections is inevitable. In data center networks in particular, switch failures are unavoidable. Fault-tolerance capability is one main aspect to measure the performance of data center networks. Connectivity, fault tolerance Hamiltonian connectivity, and fault tolerance Hamiltonicity are important parameters that assess the fault tolerance of networks. In general, the distribution of fault elements is scattered, and it is necessary to consider the distribution of fault elements in different dimensions. We research the fault tolerance of BCube when considering faulty switches and faulty links/edges that distribute in different dimensions. We also investigate the connectivity, fault tolerance Hamiltonian connectivity, and Hamiltonicity. This study better evaluates the fault-tolerant performance of data center networks.
Computer vision based human fall detection and classification for real-time videos

Most recently, Human fall detection systems using deep learning models find major applications in all fields, especially in the held of healthcare. Even without doctor analysis, most Neurological and musculoskeletal diseases such as oncoming strokes and gait problems can be identified using these models and computer vision. In this article, automatic human fall detection is proposed using a convolutional neural network by applying real-time videos. In general, most of the research has been carried out using standard videos which will not apply to real-time applications. Hence this work concentrates about using convolutional neural networks as a system has real-time videos for the Human Fall Detection and monitoring system using three pre-trained models: (i) TinyYOLOv3-ones, (ii) AlphaPose and (iii) ST-GCN. The proposed Spatial temporal graph convolutional networks produce better accuracy with captured real-time video for human fall detection. The same method was also utilized for classification with different epochs. The results were compared and maximum accuracy of 100% is obtained for 500 epochs. Hence it is proved that the existing method can be utilized for human fall detection with greater accuracy.
A Derivative-Incorporated Adaptive Gradient Method for Federated Learning

As a new machine learning technique, federated learning has received more attention in recent years, which enables decentralized model training across data silos or edge intelligent devices in the Internet of Things without exchanging local raw data. All kinds of algorithms are proposed to solve the challenges in federated learning. However, most of these methods are based on stochastic gradient descent, which undergoes slow convergence and unstable performance during the training stage. In this paper, we propose a differential adaptive federated optimization method, which incorporates an adaptive learning rate and the gradient difference into the iteration rule of the global model. We further adopt the first-order moment estimation to compute the approximate value of the differential term so as to avoid amplifying the random noise from the input data sample. The theoretical convergence guarantee is established for our proposed method in a stochastic non-convex setting under full client participation and partial client participation cases. Experiments for the image classification task are performed on two standard datasets by training a neural network model, and experiment results on different baselines demonstrate the effectiveness of our proposed method.
A novel frequency-range analysis (FRA) method for determining critical words among English high-stakes tests

Within the new era of artificial intelligence (AI), education industry should develop in the direction of intelligence and digitalization. For evaluating learners’ academic performances, English high-stakes test is not only a mere means for measuring what English as a Foreign Language (EFL) stakeholders know or do not know but also likely to bring life-changing consequences. Hence, effective test preparation for English high-stakes test is crucial for those who futures depend on attaining a particular score. However, traditional corpus-based approaches cannot simultaneously take words’ frequency and range variables into consideration when evaluating their importance level, which makes the word sorting results inaccurate. Thus, to effectively and accurately extract critical words among English high-stakes test for enhancing EFL stakeholders’ test performance, this paper integrates a corpus-based approach and a revised Importance-Performance Analysis (IPA) method to develop a novel frequency-range analysis (FRA) method. Taiwan College Entrance Exam of English Subject (TCEEES) from the year of 2001 to 2022 are adopted as an empirical case of English high stake test and the target corpus for verification. Results indicate that the critical words evaluated by FRA method are concentrated on Quadrant I including 1,576 word types that account for over 60% running words of TCEEES corpus. After compared with the three traditional corpus-based approaches and the Term Frequency-Inverse Document Frequency (TF-IDF) method, the significant contributions include: (1) the FRA method can use a machine-based function words elimination technique to enhance the efficiency; (2) the FRA method can simultaneously take words’ frequency and range variables into consideration; (3) the FRA method can effectively conduct cluster analysis by categorizing the words into the four quadrants that based on their relative importance level. The results will give EFL stakeholders a clearer picture of how to allocate their learning time and education resources into critical words acquisition.
A Fine-Tuned Hybrid Stacked CNN to Improve Bengali Handwritten Digit Recognition

Recognition of Bengali handwritten digits has several unique challenges, including the variation in writing styles, the different shapes and sizes of digits, the varying levels of noise, and the distortion in the images. Despite significant improvements, there is still room for further improvement in the recognition rate. By building datasets and developing models, researchers can advance state-of-the-art support, which can have important implications for various domains. In this paper, we introduce a new dataset of 5440 handwritten Bengali digit images acquired from a Bangladeshi University that is now publicly available. Both conventional machine learning and CNN models were used to evaluate the task. To begin, we scrutinized the results of the ML model used after integrating three image feature descriptors, namely Binary Pattern (LBP), Complete Local Binary Pattern (CLBP), and Histogram of Oriented Gradients (HOG), using principal component analysis (PCA), which explained 95% of the variation in these descriptors. Then, via a fine-tuning approach, we designed three customized CNN models and their stack to recognize Bengali handwritten digits. On handcrafted image features, the XGBoost classifier achieved the best accuracy at 85.29%, an ROC AUC score of 98.67%, and precision, recall, and F1 scores ranging from 85.08% to 85.18%, indicating that there was still room for improvement. On our own data, the proposed customized CNN models and their stack model surpassed all other models, reaching a 99.66% training accuracy and a 97.57% testing accuracy. In addition, to robustify our proposed CNN model, we used another dataset of Bengali handwritten digits obtained from the Kaggle repository. Our stack CNN model provided remarkable performance. It obtained a training accuracy of 99.26% and an almost equally remarkable testing accuracy of 96.14%. Without any rigorous image preprocessing, fewer epochs, and less computation time, our proposed CNN model performed the best and proved the most resilient throughout all of the datasets, which solidified its position at the forefront of the field.
Progressive Dunhuang murals inpainting based on recurrent feature reasoning network

The Dunhuang murals, notably the paintings on the interior walls of China’s Dunhuang Grottoes, are considered international cultural treasure. The Dunhuang murals were ruined to varied degrees after a lengthy period of erosion. Deep learning networks were utilized to reconstruct broken parts of murals in order to better preserve their important historical and cultural values. Due to the presence of various damages, such as large peeling, mold and scratches, and multi-scale objects in the mural, a simple porting of existing working methods is suboptimal. In this paper, we propose a progressive Dunhuang murals inpainting (PDMI) based on recurrent feature reasoning network to progressively infer the pixel values of hole centers by a progressive approach, aiming to obtain visually reasonable and semantically consistent inpainted results. PDMI consists mainly of the FFC-based recurrent feature reasoning (RFR) module and Multi-scale Knowledge Consistent Attention (MKCA) module. The RFR module first fills in the feature value at the feature map’s hole border, then utilizes the obtained feature value as a clue for further inference. The module steadily improved the limitation of hole centers, making the inpainted results more explicit; MKCA enables feature maps in RFR to handle richer background information from distant location information in a flexible manner while preventing misuse. After several round-robin inferences provide multiple feature maps, these feature maps are fused using an adaptive feature weighted fusion mechanism, then the fused feature maps decode back to RGB image. Experiments on a publicly available dataset and a self-made Dunhuang mural dataset reveal that the proposed method outperforms the comparison algorithm in both qualitative and quantitative aspects.
Latent Regression Bayesian Network for Speech Representation

In this paper, we present a novel approach for speech representation using latent regression Bayesian networks (LRBN) to address the issue of poor performance in low-resource language speech systems. LRBN, a lightweight unsupervised learning model, learns data distribution and high-level features, unlike computationally expensive large models, such as Wav2vec 2.0. To evaluate the effectiveness of LRBN in learning speech representations, we conducted experiments on five different low-resource languages and applied them to two downstream tasks: phoneme classification and speech recognition. Our experimental results demonstrate that LRBN outperforms prevailing speech representation methods in both tasks, highlighting its potential in the realm of speech representation learning for low-resource languages.