Reviewerly/exHarmony huggingface dataset:
A correlation graph attention network for classifying chromosomal instabilities from histopathology whole-slide images

The chromosome instability (CIN) is one of the hallmarks of cancer and is closely related to tumor metastasis. However, the sheer size and resolution of histopathology whole-slide images (WSIs) already challenges the capabilities of computational pathology. In this study, we propose a correlation graph attention network (MLP-GAT) that can construct graphs for classifying multi-type CINs from the WSIs of breast cancer. We construct a WSIs dataset of breast cancer from the Cancer Genome Atlas Breast Invasive Carcinoma (TCGA-BRCA). Extensive experiments show that MLP-GAT far outperforms accepted state-of-the-art methods and demonstrate the advantages of the constructed graph networks for analyzing WSI data. The visualization shows the difference among the tiles in a WSI. Furthermore, the generalization performance of the proposed method was verified on the stomach cancer. This study provides guidance for studying the relationship between CIN and cancer from the perspective of image phenotype.
Editorial on New Challenges in Solar Radiation, Modeling and Remote Sensing

Accurate estimations or measurements of solar radiation are frequently required in many activities and studies in areas such as climatology, atmospheric physics and chemistry, energy and environment, ecosystems, and human health [...]
Risk-Based Cybersecurity Compliance Assessment System (RC2AS)

Cybersecurity attacks are still causing significant threats to individuals and organizations, affecting almost all aspects of life. Therefore, many countries worldwide try to overcome this by introducing and applying cybersecurity regularity frameworks to maintain organizations’ information and digital resources. Saudi Arabia has taken practical steps in this direction by developing the essential cybersecurity control (ECC) as a national cybersecurity regulation reference. Generally, the compliance assessment processes of different international cybersecurity standards and controls (ISO2700x, PCI, and NIST) are generic for all organizations with different scopes, business functionality, and criticality level, where the overall compliance score is absent with no consideration of the security control risk. Therefore, to address all of these shortcomings, this research takes the ECC as a baseline to build a comprehensive and customized risk-based cybersecurity compliance assessment system (RC2AS). ECC has been chosen because it is well-defined and inspired by many international standards. Another motive for this choice is the limited related works that have deeply studied ECC. RC2AS is developed to be compatible with the current ECC tool. It offers an offline self-assessment tool that helps the organization expedite the assessment process, identify current weaknesses, and provide better planning to enhance its level based on its priorities. Additionally, RC2AS proposes four methods to calculate the overall compliance score with ECC. Several scenarios are conducted to assess these methods and compare their performance. The goal is to reflect the accurate compliance score of an organization while considering its domain, needs, resources, and risk level of its security controls. Finally, the outputs of the assessment process are displayed through rich dashboards that comprehensively present the organization’s cybersecurity maturity and suggest an improvement plan for its level of compliance.
Real-Time Deep Recognition of Standardized Liver Ultrasound Scan Locations

Liver ultrasound (US) plays a critical role in diagnosing liver diseases. However, it is often difficult for examiners to accurately identify the liver segments captured in US images due to patient variability and the complexity of US images. Our study aim is automatic, real-time recognition of standardized US scans coordinated with reference liver segments to guide examiners. We propose a novel deep hierarchical architecture for classifying liver US images into 11 standardized US scans, which has yet to be properly established due to excessive variability and complexity. We address this problem based on a hierarchical classification of 11 US scans with different features applied to individual hierarchies as well as a novel feature space proximity analysis for handling ambiguous US images. Experiments were performed using US image datasets obtained from a hospital setting. To evaluate the performance under patient variability, we separated the training and testing datasets into distinct patient groups. The experimental results show that the proposed method achieved an F1-score of more than 93%, which is more than sufficient for a tool to guide examiners. The superior performance of the proposed hierarchical architecture was demonstrated by comparing its performance with that of non-hierarchical architecture.
Predicting Consumer Personalities from What They Say

This study mapped personality based on the newly proposed extraction method from consumers’ textual data and revealed the relevance (attention) and polarity (affection) of words associated with a specific personality trait. Furthermore, we illustrate how unique words are used to predict a consumer’s behavior associated with certain personality traits. In this study, we employed the scales of the Kaggle MBTI Personality dataset to examine the methodology’s effectiveness, extract the personality traits from the textual data into features, and map them into the traits/dimensions of the existing scale. Based on the results obtained in this study, we assert that using the TF-IDF algorithm is a good way to generate a custom dictionary. Furthermore, sentiment scoring with an AI-empowered machine learning algorithm provides useful data to filter and validate more coherent words to understand and, thus, communicate a particular aspect of personality. Finally, we proposed that four situations involving the interaction between attention (frequency) and affection (sentiment) allow us to better understand the consumer and how to use the feature words in terms of the interaction between attention (TF-IDF score) and affection (sentiment score).
Application of Internet of Things and Blockchain in Information Security and Privacy Protection of Global Organizations

Access control data will continue to be exposed to the threat of privacy leakage even if blockchain technology currently offers a new solution for the security and privacy of the internet of things (IoT). However, its usability and privacy are not completely leveraged. This paper first discusses the IoT and blockchain technology and then examines each technology's structural models in order to address the issue of information security and privacy protection for the global organization IoT based on blockchain. Second, the information security and privacy guarantee system based on blockchain is built with ZKP and TEE at its heart after problems with zero-knowledge proof (ZKP) and trusted execution environment (TEE) in information security guarantee based on blockchain are investigated. By comparing the simulation trials, the proposed system's viability is finally confirmed. The results demonstrate that the suggested algorithm's evidence generation time is 352 ms when it reaches the experiment's highest node 28, which is clearly faster than previous techniques.
Optimization Simulation of Match between Technical Actions and Music of National Dance Based on Deep Learning

In the match between technical movements and music of folk dance, the most important thing is to extract features effectively. DL algorithm is one of the most efficient methods to extract video features at present. In this study, the DL method is applied to the matching optimization of technical movements and music in folk dance. Using DL to train the corresponding relationship between the technical movements and music of national dance, the given dance movements and corresponding movements are adapted to the musical beat points. To better reflect the degree of correlation between music and movement changes, the change rate of feature value is used instead of feature value itself in correlation calculation. The matching degree between this method and genetic theory method and spatial skeleton timing diagram method is compared. The experiment shows that the matching method of technical movements and music of national dance optimized by DL can achieve 95.78% accuracy, and the matching synchronization of technical movements and music of national dance can reach 96.17%. Therefore, the method proposed in this study can fully reflect the synchronization of music and movement changes, and the optimized movement matching method matches the national dance technical movements—music matching quality is better. This study expands a new perspective for the research of dance and music matching technology. It has certain practical and theoretical significance.
Online Video Super-resolution using Information Replenishing Unidirectional Recurrent Model

Recurrent Neural Networks (RNN) are widespread for Video Super-Resolution (VSR) because of their proven ability to learn spatiotemporal inter-dependencies across the temporal dimension. Despite RNN’s ability to propagate memory across longer sequences of frames, vanishing gradient and error accumulation remain major obstacles to unidirectional RNNs in VSR. Several bi-directional recurrent models are suggested in the literature to alleviate this issue; however, these models are only applicable to offline use cases due to heavy demands for computational resources and the number of frames required per input. This paper proposes a novel unidirectional recurrent model for VSR, namely “Replenished Recurrency with Dual-Duct” (R2D2), that can be used in an online application setting. R2D2 incorporates a recurrent architecture with a sliding-window-based local alignment resulting in a recurrent hybrid architecture. It also uses a dual-duct residual network for concurrent and mutual refinement of local features along with global memory for full utilisation of the information available at each timestamp. With novel modelling and sophisticated optimisation, R2D2 demonstrates competitive performance and efficiency despite the lack of information available at each time-stamp compared to its offline (bi-directional) counterparts. Ablation analysis confirms the additive benefits of the proposed sub-components of R2D2 over baseline RNN models.The PyTorch-based code for the R2D2 model will be released at R2D2 GitRepo.
Developing a Supplementary Diagnostic Tool for Breast Cancer Risk Estimation Using Ensemble Transfer Learning

Breast cancer is the most prevalent cancer worldwide. Thus, it is necessary to improve the efficiency of the medical workflow of the disease. Therefore, this study aims to develop a supplementary diagnostic tool for radiologists using ensemble transfer learning and digital mammograms. The digital mammograms and their associated information were collected from the department of radiology and pathology at Hospital Universiti Sains Malaysia. Thirteen pre-trained networks were selected and tested in this study. ResNet101V2 and ResNet152 had the highest mean PR-AUC, MobileNetV3Small and ResNet152 had the highest mean precision, ResNet101 had the highest mean F1 score, and ResNet152 and ResNet152V2 had the highest mean Youden J index. Subsequently, three ensemble models were developed using the top three pre-trained networks whose ranking was based on PR-AUC values, precision, and F1 scores. The final ensemble model, which consisted of Resnet101, Resnet152, and ResNet50V2, had a mean precision value, F1 score, and Youden J index of 0.82, 0.68, and 0.12, respectively. Additionally, the final model demonstrated balanced performance across mammographic density. In conclusion, this study demonstrates the good performance of ensemble transfer learning and digital mammograms in breast cancer risk estimation. This model can be utilised as a supplementary diagnostic tool for radiologists, thus reducing their workloads and further improving the medical workflow in the screening and diagnosis of breast cancer.
The Influence of Knowledge Worker Salary Satisfaction on Employee Job Performance

Knowledge workers are quite crucial to every enterprise, so exploring the relationship between their salary satisfaction and job performance is significant. Hence, this work observes their salary satisfaction by identifying the employees' emotions at the time of salary announcement. The relationship between salary satisfaction and job performance is studied through the obtained satisfaction. First, the convolutional neural network (CNN) model is introduced. Then, it is optimized by adding an attention mechanism to improve the accuracy of the emotion recognition model. Finally, through comparative experiments, the effectiveness of the model proposed and the impact of employee's salary satisfaction on job performance are verified. The experimental results show that the recognition accuracy of the model is much higher than that of the traditional model. In particular, the recognition accuracy of neutral emotions is as high as 95%. It verifies the effectiveness of the model.
A Secure Scheme Based on a Hybrid of Classical-Quantum Communications Protocols for Managing Classical Blockchains

Blockchain technology affords data integrity protection and building trust mechanisms in transactions for distributed networks, and, therefore, is seen as a promising revolutionary information technology. At the same time, the ongoing breakthrough in quantum computation technology contributes toward large-scale quantum computers, which might attack classic cryptography, seriously threatening the classic cryptography security currently employed in the blockchain. As a better alternative, a quantum blockchain has high expectations of being immune to quantum computing attacks perpetrated by quantum adversaries. Although several works have been presented, the problems of impracticality and inefficiency in quantum blockchain systems remain prominent and need to be addressed. First, this paper develops a quantum-secure blockchain (QSB) scheme by introducing a consensus mechanism-quantum proof of authority (QPoA) and an identity-based quantum signature (IQS)-wherein QPoA is used for new block generation and IQS is used for transaction signing and verification. Second, QPoA is developed by adopting a quantum voting protocol to achieve secure and efficient decentralization for the blockchain system, and a quantum random number generator (QRNG) is deployed for randomized leader node election to protect the blockchain system from centralized attacks like distributed denial of service (DDoS). Compared to previous work, our scheme is more practical and efficient without sacrificing security, greatly contributing to better addressing the challenges in the quantum era. Extensive security analysis demonstrates that our scheme provides better protection against quantum computing attacks than classic blockchains. Overall, our scheme presents a feasible solution for blockchain systems against quantum computing attacks through a quantum strategy, contributing toward quantum-secured blockchain in the quantum era.
Categorical classifiers in multiclass classification with imbalanced datasets

Abstract This paper discusses, in a multiclass classification setting, the issue of the choice of the so‐called categorical classifier, which is the procedure or criterion that transforms the probabilities produced by a probabilistic classifier into a single category or class. The standard choice is the Bayes Classifier (BC), but it has some limits with rare classes. This paper studies the classification performance of the BC versus two alternatives, that are the Max Difference Classifier (MDC) and Max Ratio Classifier (MRC), through an extensive simulation and some case studies. The results show that both MDC and MRC are preferable to BC in a multiclass setting with imbalanced data.
A visual analytics workflow for probabilistic modeling

Probabilistic programming is a powerful means for formally specifying machine learning models. The inference engine of a probabilistic programming environment can be used for serving complex queries on these models. Most of the current research in probabilistic programming is dedicated to the design and implementation of highly efficient inference engines. Much less research aims at making the power of these inference engines accessible to non-expert users. Probabilistic programming means writing code. Yet many potential users from promising application areas such as the social sciences lack programming skills. This prompted recent efforts in synthesizing probabilistic programs directly from data. However, working with synthesized programs still requires the user to read, understand, and write some code, for instance, when invoking the inference engine for answering queries. Here, we present an interactive visual approach to synthesizing and querying probabilistic programs that does not require the user to read or write code.
Sensitive product feature integrity and confidentiality using blockchain-based internet of things (IoT) architecture

Small-scale third-party accessories vendors face numerous challenges such as (i) warranty period covered for sensitive product, (ii) malicious induction, and (iii) extract root cause of product failure after customer experienced in the real time usage. Therefore, an advanced industrial environment is necessary to resolve these limitations. It offers strong security levels as well as intelligent root cause detection over failure. In this work, propose a blockchain-based internet of things (IoT) architecture for industry 4.0, which includes a voting decision mechanism for IoT device authentication and fault classification based on device and product trust values, respectively. These values are regularly updated in the historical trust value and machinery database for every successful product transaction and identify a new failure cause to set feature for next iteration set from the defective industrial cloud database. The analytical model and simulation results for correct authentication ensure that the proposed architecture achieves 86% eligibility of accuracy across a high ratio of malicious attacks in less converged time.
An underwater imaging method of enhancement via multi-scale weighted fusion

Blurring and color distortion are significant issues in underwater optical imaging, caused by light absorption and scattering impacts in the water medium. This hinders our ability to accurately perceive underwater imagery. Initially, we merge two images and enhance both the brightness and contrast of the secondary images. We also adjust their weights to ensure minimal effects on the image fusion process, particularly on edges, colors, and contrast. To avoid sharp weighting transitions leading to ghost images of low-frequency components, we then propose and use a multi-scale fusion method when reconstructing the images. This method effectively reduces scattering and blurring impacts of water, fixes color distortion, and improves underwater image contrast. The experimental results demonstrate that the image fusion method proposed in this paper effectively improves the fidelity of underwater images in terms of sharpness and color, outperforming the latest underwater imaging methods by comparison in PSNR, Gradient, Entropy, Chroma, AG, UCIQE and UIQM. Moreover, this method positively impacts our visual perception and enhances the quality of the underwater imagery presented.
A High-Throughput Processor for GDN-Based Deep Learning Image Compression

Deep learning-based image compression techniques can take advantage of the autoencoder’s benefits to achieve greater compression quality at the same bit rate as traditional image compression, which is more in line with user desires. Designing a high-performance processor that can increase the inference speed and efficiency of the deep learning image compression (DIC) network is important to make this technology more extensively employed in mobile devices. To the best of our knowledge, there is no dedicated processor that can accelerate DIC with low power consumption, and general-purpose network accelerators based on field programmable gate arrays (FPGA) cannot directly process compressed networks, so we propose a processor suitable for DIC in this paper. First, we analyze the image compression algorithm and quantize the data of the network into 16-bit fixed points using a dynamic hierarchical quantization. Then, we design an operation module, which is the core computational part for processing. It is composed of convolution, sampling, and normalization units, which pipeline the inference calculation for each layer of the network. To achieve high-throughput inference computing, the processing elements group (PEG) array with local buffers is developed for convolutional computation. Based on the common components in encoding and decoding, the sampling and normalization units are compatible with codec computation and utilized for image compression with time-sharing multiplexing. According to the control signal, the operation module could change the order of data flow through the three units so that they perform encoding and decoding operations, respectively. Based on these design methods and schemes, DIC is deployed into the Xilinx Zynq ZCU104 development board to achieve high-throughput image compression at 6 different bit rates. The experimental results show that the processor can run at 200 MHz and achieve 283.4 GOPS for the 16 bits fixed-point DIC network.
Real‐time face perception based encoding strategy optimization method for UHD videos

Abstract Face regions containing rich semantic information appear frequently in the videos. As the video resolution increase dramatically, the face regions will inevitably attract more attentions. This paper proposes a face perception based coding scheme to improve the visual quality of the face regions in UHD videos. A specially tailored face perception model is first utilized to precisely and quickly locate the face regions. Then, a face perception map is generated based on a hierarchical mapping algorithm. Finally, the face perception map is employed as a guidance to optimize the encoding process, including mode decision, block partition and bit allocation. The proposed method is implemented on HEVC to demonstrate the effectiveness. Experimental results on a set of 4K test sequences show that the proposed method can obviously improve the objective and subjective quality of the face regions, while causing only slight quality decline over the rest of the frame. Additionally, the computation required for mode decision and block partition is reduced, thereby saving encoding time cost.
"Admission Plus: The Flutter based student Enrolment System"

Admission Plus is online student registration system project developed using Flutter framework. It is a mobile application for students and Anna University affiliated colleges that facilitates online application process. It reduces the manpower needed to facilitate an enrolment process. This mobile application can be accessed by student and all the affiliated colleges. Our application has two types of accessing modes, administrator and student. Student application management database is managed by the college administrator. It is the job of the administrator to admit and monitor the whole application process. Student’s login facilitates to apply for desired colleges by setting preferences and uploading the needed documents.