Reviewerly/exHarmony huggingface dataset:
Edge Devices and Blockchain Integration in IoT System: A Novel Design Approach

Blockchain, the technology that powers cryptocurrencies, has recently attracted a lot of interest. Numerous applications, including the smart grid and cryptocurrency, have adopted it. Blockchain is unable to handle services that require frequent transactions due to a substantial scalability constraint. Edge computing allows cloud-based resources to be dispersed at the edge of the network, however it presently has difficulties with its decentralized administration and privacy. By providing reliable control and access over the networking, storage and computation dispersed at the edges, edge computing and blockchain integrated into one system could enable computing validity of the blocks and provide huge of number of network servers and ample storage space for data in a secure method. In this paper an insight into blockchain technology is given followed by an introduction about edge computing. Next, the existing architecture about the integrating blockchain and edge computing is discussed and explore the challenges caused by the fusion of blockchain technology with edge computing systems from the angles of scalability and optimizing consensus mechanism. a novel SC-IBEC architecture for integrating blockchain technology with edge computing architecture is presented. Then possible remedies to overcome the existing challenges and how it can be implemented at the different layers of the SC-IBEC architecture. Finally, we list the current difficulties brought on by the combination of blockchain technology with edge computing architecture, as well as any potential future solutions.
Optimal Feed Forward Deep Neural Network for Lymph Disease Detection and Classification

Lymphatic system reinforces immune system by degrading as well as eliminating the cancer cells, and pathogens, rejecting unwanted sources, debris, and dead blood cells. It assists in assimilating the fat vitamins and fat-soluble from digestive system and delivers them to body tissues. Furthermore, the interstitial spaces amongst cells eradicate the extra fluids and redundant substances from body. Automatic diagnosis of cancer metastases in lymph nodes has the prospective to increase calculation of prognoses for patients. Machine learning¬based classification methods offer provision for the decision¬making method in various regions of healthcare, involving screening, diagnosis, prognosis, and so on. This study introduces an Optimal Feed Forward Deep Neural Network for Lymph Disease Detection and Classification (OFFDNN-LDC) model. The presented OFFDNN-LDC model intends to apply the classification model to determine the presence of lymph diseases in medical data. For attaining this, the presented OFFDNN-LDC model exploits the FFDNN model as a classifier to assign effective class labels. Besides, the presented OFFDNN-LDC model executes root mean square propagation (RMSProp) optimizer to properly elect the hyperparameter values of the FFDNN model. A series of simulations are performed for demonstrating the improved outcome of the OFFDNN-LDC model. The experimental values referred that the OFFDNN-LDC model is superior to other models.
Students’ Perception of the Digital Learning System for Junior High Schools in Padang, Indonesia

The learning differences before, during, and after the COVID-19 pandemic are very significant regarding the perceptions of offline education, where activeness in class activities is found to be excellent. These needs emphasize the positive effects of the learning method on the challenges of Indonesian education during the digital era. This study aims to evaluate the students' perception of the digital learning system from various phenomenal aspects, using the LMS Santiang. The method used in this research is descriptive qualitative and quantitative research. The informants or subjects of this study were 100 students were randomly selected as samples from class VIII Junior High Schools 7, 8, 12, and 25 Padang. The quantitative and qualitative data obtained from online Google-form questionnaires and WhatsApp interviews were also descriptively analyzed, respectively. According to the analysis of the questionnaire data, source and method triangulation techniques were analytically used. The findings showed that the students' perceptions of the digital learning system were quite good, regarding the use of the LMS Santiang with a percentage ≥ 70%. This analysis also exhibited the following results: (1) 75% of the students understood the LMS Santiang approach (75%); (2) 82% of them needed other learning resources or teaching materials; (3) 70% also demanded to use e-learning platforms; (4) 88% of the students needed e-learning in the digital era; (5) 73% of them were satisfied using LMS. This article is a novelty in developing a digital learning system using LMS Santiang post COVID-19.
Implementation of Energy efficient Blockchain using RUST

Blockchain technology has become a popular technology since the introduction of WWW. With the introduction of Blockchain 2.0 and Blockchain 3.0 the limited applications of Blockchain increased exponentially. The efficient implementation of blockchain requires a lot of energy and computational resources, wherein the backend of the consensus algorithm implementation makes it power hungry. In this paper, we have introduced a new programming environment for the implementation of a consensus algorithm with requires less power and computational devices. The RUST-based implementation of the consensus algorithm makes it more power efficient
EAARL: Enhanced Adaptive Authentication based on Reinforcement Learning

Secure authentication is a fundamental problem in any network. The size of networks within homes is rapidly increasing with the number of devices each person is now using. There is a need to develop a secure authentication technique that is versatile to different types of devices. In this paper, we review and analyze authentication techniques using reinforcement learning. Three techniques are reviewed in detail. The application area of these techniques is online money transactions. There are some issues with the existing methods, such as a lack of data confidentiality and vulnerability to insider attacks. To tackle these problems, we propose EAARL, Enhanced Adaptive Authentication based on Reinforcement Learning, implement it for an IoT application and provides a novel solution to the abovementioned problems. We use elliptic curve cryptography and reinforcement learning, which makes this solution unique. The implementation of this scheme is performed using Jupyter notebooks and Python 3.7.
A ship object detection algorithm based on improved RetinaNet

Visual ship image object detection has essential applications for near-shore ship management and military object location. In recent years, object detection technology based on a deep learning algorithm has been widely studied in object detection of visible ship images, and achieved outstanding results. However, due to the difference and overlap of nearshore ship objects, the object loss rate is high. Aiming at the above problems, this paper proposes an improved RetinaNet ship object detection algorithm. Firstly, channel attention is added after the residual network, and used to enhance the attention to low-frequency information. Secondly, the cyclical focal loss and the CIOU loss function are used to increase the training times of negative samples in the middle of training, which effectively improves object detection accuracy. The experimental results show that the improved RetinaNet algorithm improves the recognition accuracy of ship objects by 2.5%.
Weakly supervised text classification method based on transformer

The seed word-driven approach based on weakly supervised text classification (WTC) is the dominant approach. In existing seed word-driven methods，using metrics such as Term Frequency (TF), Inverse Document Frequency (IDF) and its combinations to update the seed words. the method assigns the same weight to all metrics, leading to the selection of common or poorly differentiated words as seed words; In addition most of the text classifiers used in the study have difficulty in capturing the correlation and global information between text information. In order to solve the above problems, Using Transformer as a text classifier first, The multi-headed self-attention mechanism allows capturing longrange dependencies while computing in parallel and fully learning the global semantic information of the input text. Then an improved TF-IDF method is proposed to increase the weight of IDF so that some common words that affect the classification can be filtered out. Its experimental results are improved on 20News and NYT datasets.
An improved YOLOv5 method for small object detection in high resolution images

The dense small objects detection is a challenging task in the scenario of UAV aerial surveillance. This paper proposes an improved YOLOv5 detection method for the dense small objects in high resolution images. To augment the dataset, a 20% overlap crop is used for the UAV aerial photography training set. In order to detect the tiny objects in the aerial photos of UAV, a tiny detection head is added on the basis of YOLOv5. The SPP and CBAM modules are introduced in the head of the model, SPP for feature fusion at different scales and CBAM for adding attention to spatial and channel dimensions. Multiple experiments are conducted on the VisDrone 2019 dataset, the results show that the mAP of 12 classes detected by the model is 30.4%, and 3.1% higher than the original YOLOv5.
Prediction of survival probability of cancer using machine learning models

As one of the major diseases, cancer has always been a hidden danger to human health. There has been a considerably improvement in the therapy for patients all over the world, as research and technology advance, medical care becomes more effective. In this regard, the cure rate and survival probability have increased positively compared with the last century. However, the incidence rate of cancer has not been effectively controlled, and lung cancer and breast cancer are still more common. Predicting the probability that a cancer patient will survive at their initial appointment is extremely important according to this report. In this case, doctors can not only have a more detailed understanding of the situation of patients, but also make the allocation of medical resources more reasonable; Secondly, it can also promote the improvement of medical treatment in cancer. This article will first import the relevant data sets and analyze the variables contained. Then, the next step will use logistic regression analysis and linear regression analysis to predict the survival probability of patients. Furthermore, completed the judgement which variable has a greater impact by comparing the data that affect this probability. By comparing the accuracy of these regression analysis, the accuracy of logical regression (93.14%) is higher than that of linear regression (77.12%). In this case, logistic regression analysis will be more applicable. Finally, this paper compares the influence of related variables. According to the findings, a patient's probability of survival is determined by the amount of lymph nodes inside the system.
Research on agile production technology for inducing maintenance of space application facilities

Space application facilities have complex systems and high operation requirements, making induction maintenance applications difficult and time-consuming based on augmented reality technology. To solve this problem, this paper puts forward the agile manufacturing technology framework and key technologies of induced maintenance application combined with the product characteristics of space application facilities and verifies the application effect of this technology in the actual scene through practical cases. The realization of the agile production technology of induced maintenance effectively improves the efficiency of application production. It provides a new auxiliary solution for the astronauts to solve emergencies and sudden missions in orbit.
Self-supervised learning for textured surface anomaly detection and localization

Aiming at the problems of insufficient labeled samples and high missed detection rate in common textured surface anomaly detection, the paper designs a self-supervised learning model based on masked Autoencoder, which can realize accurate detection and location of anomalies without providing mass anomaly samples. Autoencoder is widely used, but it is difficult to detect and locate anomalies by reconstruction error due to its strong generalization ability reconstructed anomalies with small errors. Then, masked reconstruction method is proposed to reduce the generalization performance. First, each input image is masked to obtain multiple masked input images which are sequentially reconstruct by the Autoencoder. Second, these reconstructed images are complementarily masked and recombined to obtain the final reconstructed image. Finally, anomaly detection and localization are achieved by evaluating the reconstruction error between the input and reconstructed image. The experiment results indicate that the anomaly detection rate of this method is 95.09 % and the anomaly location rate is 93.32% under the anomaly detection standard metric，and the performance can be significantly improved.
Chinese font generation based on deep learning

Font generation is a challenging problem. To address the existing problems of poor font style conversion models, which have missing structure, blurred glyphs and require paired datasets, this paper proposes a Chinese font style migration algorithm based on the improved CycleGan. The model introduces deformable convolution in the encoder part of the generator, which can learn the font features adaptively. A skip connection module, which fuses global and local features, was added to the model, and the features in the encoder are projected to the decoder using this module to avoid the structural error problem by reducing the information loss of the decoder. Meanwhile, using the attention mechanism, we can quickly and efficiently obtain the key information of the target region. On this basis, we can further complete the local and global feature fusion. According to the research results, this method can better achieve font generation in practice, so it has high application value.
Expansion in speech time can restore comprehension in a simultaneously speaking bilingual robot

Introduction: In this study, the development of a social robot, capable of giving speech simultaneously in more than one language was in mind. However, the negative effect of background noise on speech comprehension is well-documented in previous works. This deteriorating effect is more highlighted when the background noise has speech-like properties. Hence, the presence of speech as the background noise in a simultaneously speaking bilingual robot can be fatal for the speech comprehension of each person listening to the robot. Methods: To improve speech comprehension and consequently, user experience in the intended bilingual robot, the effect of time expansion on speech comprehension in a multi-talker speech scenario was investigated. Sentence recognition, speech comprehension, and subjective evaluation tasks were implemented in the study. Results: The obtained results suggest that a reduced speech rate, leading to an expansion in the speech time, in addition to increased pause duration in both the target and background speeches can lead to statistically significant improvement in both sentence recognition, and speech comprehension of participants. More interestingly, participants got a higher score in the time-expanded multi-talker speech than in the standard-speed single-talker speech in the speech comprehension and, in the sentence recognition task. However, this positive effect could not be attributed merely to the time expansion, as we could not repeat the same positive effect in a time-expanded single-talker speech. Discussion: The results obtained in this study suggest a facilitating effect of the presence of the background speech in a simultaneously speaking bilingual robot provided that both languages are presented in a time-expanded manner. The implications of such a simultaneously speaking robot are discussed.
An LDOP approach for face identification under unconstrained scenarios

In unconstrained environments, it encounters a number of challenges when considering handcrafted features for face recognition, including changes in pose, illumination and facial expression and plastic surgery variations, look-alike faces and selfie images. As majority of the published works on local descriptors are based on the relationship between the centre pixel and neighbourhood pixels at different radial widths, by including the relationship between different neighbourhood pixels of a target pixel with it can facilitate the decoupling of the discriminatory features further. To exploit such relationships and to mitigate the challenges, the proposed study reports a novel local descriptor called Local Directional Octa Pattern (LDOP) that makes use of derivative operation and three well-known metrics, namely, histogram intersection, K-nearest neighbour and Euclidean distance for face identification. Rigorous experiments have been conducted on six benchmark face databases, namely, Extended Yale Face B, Labeled Faces in the Wild, Plastic Surgery, Look-alike, UMDAA-02-FD and ORL, confirming the superiority of the proposed local descriptor over state-of-the-art descriptors with accuracies of 97.37%, 49.09%, 75.47%, 31.2% and 93.31% at Rank–10 and 100% at Rank-5 determined on the databases, respectively.
The b-symbol weight distributions of all semiprimitive irreducible cyclic codes

Abstract Up to a new invariant $$\mu (b)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>μ</mml:mi> <mml:mo>(</mml:mo> <mml:mi>b</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> , the complete b -symbol weight distribution of a particular kind of two-weight irreducible cyclic codes, was recently obtained by Zhu et al. (Des Codes Cryptogr 90(5):1113–1125, 2022). The purpose of this paper is to simplify and generalize the results of Zhu et al., and obtain the b -symbol weight distributions of all one-weight and two-weight semiprimitive irreducible cyclic codes.
Neural Network Image Segmentation for Sign Language Interpretation

The use of neural networks to recognize and classify objects in images is a popular field in computer science. It is highly likely that an object in an image chosen for classification will have a representation matrix with significantly less pixels than the background or other elements of the image. As a result, the initial plan would be to divide or segment that object from the other portions of the image that are not essential for categorization. This also serves as the study's objective, for which we employ segmentation to separate the components essential to the classification procedure and assess any room for improvement in the final classification outcome. Mask Region Convolutional Neural Network was the model used for segmentation, and Convolutional Neural Network was the model used for classification. The study's findings demonstrate a notable improvement in the classification in the case of sign language. Further advancement of image segmentation models implies better more accurate results for classification models once they are combined. Keywords— Neural network, Image segmentation, Sign language, Classification, Mask Regional Convolutional Neural Network.
Online Voting System using Face Recognition and Blockchain

Electronic voting (also known as electronic voting) refers to the use of electronic means to vote and to record and accurately count votes sent by users. Electronic voting systems must be secure, as they must not allow duplicate votes and be completely transparent, while protecting the privacy of participants. The disadvantage of the traditional voting system is that voting is not reliable and voters do not change until they are registered in the system. There is no transparency between the voters and the system. Electronic voting can be very useful because anyone can easily access the poll and cast their vote and express their choice. People can share a private link to the created poll (as long as they know the link) and the person with the link can vote and only one vote can be used per browser. In this proposed system, we design and develop a web-based application using python and flask framework for an online audio system using Face Recognition and Blockchain Technology with a decentralized data storage system.
Scene Text Detection Based on Multi-Dimensional Feature Fusion with Instance-Wise Loss

Over the past few years, scene text detection has witnessed rapid progress due to the development of deep neural networks. However, segmentation-based methods may fail to detect pixels near boundary well, and scale variations of text instances may lead to small text missing. To tackle these problems, we propose a novel segmentation-based detector for scene text detection, which can improve the quality of the detected texts. Specifically, a Multi-dimensional Feature Fusion module is used to extract structural and spatial text features from the perspective of height, width and channel, which helps to improve the representation ability of the network. In order to obtain more accurate boundaries of the detected text instances, a Boundary Refinement Branch is introduced to strengthen the supervision for pixels adjacent to boundary. Meanwhile, we propose an Instance-wise Loss to deal with text instances of different scales. Extensive ablation studies validate the effectiveness of these proposed modules. Experiments on several benchmark datasets show that our method achieves better results compared with the state-of-the-art methods.
CYBER RISK MANAGEMENT AND ISO 27005 APPLIED IN ORGANIZATIONS: A SYSTEMATIC LITERATURE REVIEW

With the advent of legislation and regulations in privacy and data protection, risk management provides information for organizations to plan actions and strategies in information security and cybersecurity. Thus, the use of international standards and references are utilized for the definition of programs and internal projects. This study aims to identify the motivations and goals for adoption of the ISO 27005 standard in organizations in productive systems. For the development of the research, a systematic literature review with the adoption of a protocol based on the PRISMA-P was conducted. The results achieved suggests that organizations seek to adopt ISO 27005 to attend functional and institutional motivations aiming to improve processes related to information security management, risk management, risk assessment, improve information security management and meet laws, regulations and stakeholders.
Research and application of template matching algorithm based on edge contour

In this paper, the ideas of shape template matching, multi-sample template matching, and ROI area processing are combined into an edge contour template matching algorithm module. Based on the OpenCV open source vision library, the area to be removed in the ROI area is colored polygons, and the noise points of this shape feature need to be removed, and only the feature points with obvious edge contour features are extracted; as a result, the matching time is transferred to the mapping time, thereby reducing the total matching time.