Reviewerly/exHarmony huggingface dataset:
Robust time-varying Kalman predictor for uncertain singular system with missing measurement and colored noises

For the linear stochastic singular system with missing measurement, colored noise and uncertain-variance noises, the robust Kalman prediction problem is addressed. Applying the singular value decomposition (SVD) method, the fictitious noise approach, the augmented state approach and difference transformation approach, the original singular system is transformed to new reduced-order standard system only with uncertain-variance fictitious noises. By the augmented state approach and difference transformation approach, the new augmented state space model with correlated white noise is presented. Applying the minimax robust estimation principle, the minmax robust time-varying Kalman predictor is presented. Its robustness is proved by the Lyapunov equation approach in the sense that its actual prediction error variance is guaranteed to have the corresponding minimal upper bound for all admissible uncertainties. A simulation example about circuits system verifies the correctness and effectiveness of the proposed results.
Robot Path Planning Based on Deep Reinforcement Learning

Deep Q-Network (DQN) algorithm has some problems such as overestimation and poor algorithm stability in the process of robot path planning. To solve the above problems, Double Deep Q-Network (DDQN) algorithm is combined with the Averaged Deep Q-Network (ADQN) algorithm to reduce the overestimation problem and improve the stability of the algorithm. Using priority experience replay instead of average sampling can improve the utilization rate of valuable samples and enhance the learning efficiency of the robot. The improved algorithm is compared with traditional DDQN algorithm in simple and complex simulation environment. Experimental results show that the improved algorithm has higher convergence rate, higher reward value and better path planning.
Another look at SSIM image quality metric

We review the design of the SSIM quality metric and offer an alternative model of SSIM computation, utilizing subband decomposition and identical distance measures in each subband. We show that this model performs very close to the original and offers many advantages from a methodological standpoint. It immediately brings several possible explanations of why SSIM is effective. It also suggests a simple strategy for band noise allocation optimizing SSIM scores. This strategy may aid the design of encoders or pre-processing filters for video coding. Finally, this model leads to more straightforward mathematical connections between SSIM, MSE, and SNR metrics, improving previously known results.
Unveiling the Markovian to non-Markovian transition with quantum collision models

The concept of non-Markovianity in open quantum systems is traditionally associated with the existence of information backflows from the environment to the system. Meanwhile, the mechanisms through which such backflows emerge are still a subject of debate. In this work, we use collision models to study memory effects in the dynamics of a qubit system in contact with a thermal bath made up of few ancillas, in which system-ancilla and ancilla-ancilla interactions are considered. In the single-ancilla limit case, we show that the system-bath information flow exhibits an interesting mixture of chaotic and regular oscillatory behavior, which depends on the interaction probabilities. In parallel, our results clearly indicate that the information backflows decrease when new ancillas are added to the bath, which sheds light on the nature of the Markovian to non-Markovian transition.
Robust Manipulation Detection Scheme for Post-JPEG Compressed Images using CNN

Due to the popularity of JPEG as a compression standard, the importance of detecting manipulation in JPEG images has increased. Many data-driven approaches are implemented to detect manipulation of uncompressed scenario; however, the performance degraded significantly in re-compressed images due to lossy compression. To achieve this, a deep residual framework is designed to provide the highest-quality manipulation detection in re-compressed images (MDRNet). The framework undergoes three folds: noise residual extraction, feature extraction, and classification. Firstly, the noise residual extraction stage significantly expands the front-end detector, including three residual blocks with skip connections that can generate noise residuals by suppressing the image content and enhancing the manipulation traces. Next, two efficient residual blocks with cross feature learning strategy to obtain the deep manipulation features and fed to fully connected layers for classification. Experiments are performed on ten alterations followed by different quality factors to enhance forgery detection in the real-world scenario. Further, the proposed MDRNet achieves superior performance compared to the state-of-the-art baselines, precisely the most challenging case with lossy post-JPEG compression.
Partial label learning: Taxonomy, analysis and outlook

Partial label learning (PLL) is an emerging framework in weakly supervised machine learning with broad application prospects. It handles the case in which each training example corresponds to a candidate label set and only one label concealed in the set is the ground-truth label. In this paper, we propose a novel taxonomy framework for PLL including four categories: disambiguation strategy, transformation strategy, theory-oriented strategy and extensions. We analyze and evaluate methods in each category and sort out synthetic and real-world PLL datasets which are all hyperlinked to the source data. Future work of PLL is profoundly discussed in this article based on the proposed taxonomy framework.
An Assistance Teachers of Development Google Slide-Based Learning Media at School

This community service is motivated by the lack of skills, insight and competence of teachers in the use of technology as a learning medium. The learning activities of students only listen to explanations from the teacher and take notes on learning materials. So that learning seems less interesting, monotonous, and only teacher-cantered. Therefore, the purpose of this community service is to develop Google Slide for school or madrasah teachers in Lampung Province, apply and utilize Google Slides, and seek to improve the quality of teachers in using technology as a learning media tool. This study uses the lecture method and the method of practice (drill practice) the manufacture and development of learning media based on Google Slides. The drill practice is an activity of doing the same thing, and is done repeatedly and earnestly with the aim of perfecting a skill so that it becomes permanent. The results of this community service are in the form of Google Slide that can be used as a learning media tool to convey information and learning materials are more interesting. In addition, this service activity also received a positive response, as evidenced by the enthusiasm of the teachers in following through to completion so that they could develop Google Slides learning media.
Breast cancer detection from histopathological image dataset using hybrid convolution neural network

Cancer of the breast is a deadly disease that can take a person’s life in many different ways. Predicting breast cancer at an early stage is crucial in the fight to end the disease. The usage of deep learning and blockchain technology has been implemented with the intention of integrating optimal prediction with clinical diagnostics and protecting private health information. Patients’ medical records are encrypted and stored in the blockchain for maximum safety. As a result, a large portion of time and energy is spent on feature engineering, a tedious process that requires prior expert domain knowledge of the data to develop effective features, and is crucial to the success of most conventional classification systems. Deep learning, on the other hand, can arrange the discriminative information in the data without the need for a domain expert to develop feature extractors. The research community and industry have paid attention to deep, feedforward networks like convolutional neural networks (CNNs) because of their empirical results in areas including speech recognition, signal processing, object recognition, natural language processing, and transfer learning. For the best breast cancer prediction, a new method called a “Hybrid CNN” combining the Sine Cosine Algorithm (SCA) with a transfer learning algorithm has been presented. Mini-batch size and drop-out rate are just two of the factors that the SCA algorithm may fine-tune. To stop the model from overfitting, we employ a transfer learning strategy. The hyperparameters found using sine cosine algorithm is used in Visual geometry Group (VGG 16) architecture. ImageNet is used to pretrain the network and last three convolutional layers are trained using transfer learning. The integration of sine cosine algorithm and transfer learning together increases the accuracy thereby preventing the model from overfitting. The experimentation is performed in Google Colab and the proposed Hybrid CNN is compared with existing methodologies such as K-NN, SVM. Also, the proposed Hybrid CNN is compared with CNN without transfer learning and CNN without SCA. The metrics taken into account for comparison are accuracy, sensitivity, specificity, F-score. The proposed Hybrid CNN achieves 96.9% accuracy that shows the effectiveness of the integration of SCA and transfer learning.
About criteria of modern art: cultural approach

XX century introduced significant adjustments in the understanding of art. Art lost its special “aura” (V. Benjamin) with the development of visual practices, the growing role of science and the recipient in the design of artistic reality, which led to a change in the notion of the criteria of art up to the denial of its existence. The scientific novelty is determined by the change in the status of art in modern culture. The changes that took place in the art of the 19th century expanded the range of artistic value by recognizing new values and involving the figure of the recipient in the process of creating art. The acceleration of the development of culture in the 20th century led to a further change in the perception of art, which began to rely more on a rethinking of a tradition that had been enriched by the discovery of previously little-known cultures. In this regard, the criteria of art, representing the artwork as a cultural text aimed at understanding the experience of previous cultural eras, are changed. Purpose of the study describe the changes in the role of art in human life, which were reflected primarily in the rethinking of art criteria. In the twentieth century, there is a large number of contradictory phenomena called to be called culture and going beyond the framework of art, which requires their mastering. Visual mass culture, which does not have the signs of a new one, has trained the viewer to pay attention to the already realized reality, presented in a new shell from a different angle. Herewith the recipient ascribes traditional values and meanings to the mastered reality. Thus, the replicability and reproducibility of works of art change the person's idea of the subject of art. Methods: The analysis of art, through the lens of its criteria, took a cultural approach, which allowed to consider art in the context of the cultural text. To represent the specificities of contemporary art, descriptive and comparative methods were used to fit contemporary art into the cultural context and to identify its main criteria. The concepts of “art” and “culture” become largely synonymous in the consciousness of modern man. Peculiarities of the perception of a work of art are associated with the fusion of the concepts “work of art” and “cultural text”, when culture began to substitute for itself art, so people in the assessments of art are guided by the same concepts that apply to culture. Recognition of the conscious, as the main criterion of the art of mass consciousness, translates the work of art into the rank of a cultural text, for which other values and aesthetic criteria already act. We can draw a conclusion on the basis of the perception of the cultural text, that the toolkit for evaluating the cultural text passes to art, and therefore the main criteria are not the aesthetic, but the anthropological, associated with the creative activity of the recipient and his participation in the creation of a work of art.
Secure Blockchain-Based Electronic Voting Mechanism

Many countries have strived to popularise electronic voting (e-voting), but owing to various security concerns, large-scale elections are still invariably held using paper ballots. Electronic voting systems must find solutions to various issues with authentication, data privacy and integrity, transparency, and verifiability. On the other hand, Blockchain technology offers an innovative solution to many of these problems. In this study, we constructed a private blockchain network with a large number of nodes, which is only accessible to the relevant voters. Because of its decentralised design, the system is robust against attacks by malicious actors. The security of the system was enhanced using an elliptic curve discrete logarithm problem-based blind multi-document signcryption mechanism. As this mechanism can be used to blindly sign and encrypt multiple voting documents in a single pass, it will minimise redundant signing processes and thus improve efficiency. Furthermore, a self-certification mechanism was used in lieu of centralised certificate servers, so that the voters can participate in the computation of public and private keys. In summary, we designed an electronic voting mechanism that is sufficiently secure for practical purposes, which will improve trust in e-voting, and reduce the costs associated with vote checking.
A Semi-Fragile, Inner-Outer Block-Based Watermarking Method Using Scrambling and Frequency Domain Algorithms

Image watermarking is most often used to prove that an image belongs to someone and to make sure that the image is the same as was originally produced. The type of watermarking used for the detection of originality and tampering is known as authentication-type watermarking. In this paper, a blind semi-fragile authentication watermarking method is introduced. Although the main concern in this paper is authenticating the image, watermarking for proving ownership is additionally implemented. The method considers the image as two main parts: an inner part and an outer part. The inner and outer parts are divided into non-overlapping blocks. The block size of the inner and outer part are different. The outer blocks have a greater area than the inner blocks so that their watermark-holding capacity is greater, providing enough robustness for semi-fragility. The method is semi-fragile and the watermarked image is authenticated despite the JPEG being compressed to 75% quality. The embedded watermark also survives innocent types of image operations, such as intensity adjustment, histogram equalization and gamma correction. Semi-fragile and selectively fragile authentication is valuable and in high demand specifically because it survives these innocent image operations while detecting ill-intentioned tampering. In this work, we embed a binary watermark into the inner and outer parts of images using a scrambling algorithm, discrete wavelet transform (DWT) and discrete cosine transform (DCT) in the blocks. The proposed methodology has high image quality after watermarking, with a PSNR value of 40.577, and high quality is also achieved after JPEG compression. The embedding process provides acceptable image quality after tamper attacks, including JPEG compression, Gaussian noise, average filtering, and scaling attacks with PSNR values greater than 29. Experimental results obtained show that the proposed semi-fragile watermarking algorithm is more robust, secure and resistant than other algorithms in the literature.
An Efficient Segmentation of U-area and T-area on Facial Images by Using Matlab with Hough Transform and Viola-Jones Algorithm Base

The left and right cheeks and chin (U-area) and the forehead and nose (T-area) are useful for examining skin types.These areas provide crucial information to determine facial characteristics.The Matlab application function, particularly the Viola-Jones Algorithm base, is helpful in detecting the U and T areas.This application accurately detects normal images for face, including the eyes, nose, and mouth position.The T-area was determined by referring to the position of the eyes and nose, while the U-area was identified based on the eyes, nose, and mouth position.The accuracy in determining the two areas using this method was close to 100%.
Face Mask Detection using Convolutional Neural Networks

COVID-19 pandemic has rapidly affected our day-to-day life disrupting the world trade and movements. Wearing a protective face mask has become a new normal. Soon, many public service providers will ask the customers to wear masks correctly to avail of their services. Therefore, face mask detection has become a crucial task to help global society. This paper presents a simplified approach to achieve this purpose using some basic Machine Learning packages like Tensor Flow, Keras, Open CV and Scikit-Learn. The proposed method detects the face from the image correctly and then identifies if it has a mask on it or not. As a surveillance task performer, it can also detect a face along with a mask in motion. The method attains accuracy up to 92.75%. We explore optimized values of parameters using the Sequential Convolutional Neural Network model to detect the presence of masks correctly.
Blockchain based Roaming fraud prevention using LSTM model in 4G LTE Network

Fraud and Identity Theft constitute for huge financial loss to the telecom industry and telecom fraud accounts for a major cause of revenue loss. Among them, roaming fraud has the highest impact over network service providers, it has also become major concern for few companies. The present paper intends to provide a solution on roaming fraud with the help of a block chain-based network, which is used as secure data storage. This technology helps in minimizing financial loss, improving trust and ensures the reduction of latency in communication between the host and visitor network service provider. This is achieved by uploading the customers service report to the blockchain network as a transaction, using Inter Planetary File System (IPFS), and the service providers act as the accounts present in the network for accessing the report. It has been verified that, when the data is present in the blockchain, any outside entity cannot tamper or get hold of the data, which is an advantage of using blockchain technology.
Pill Box Text Identification Using DBNet-CRNN

The recognition process of natural scenes is complicated at present, and images themselves may be complex owing to the special features of natural scenes. In this study, we use the detection and recognition of pill box text as an application scenario and design a deep-learning-based text detection algorithm for such natural scenes. We propose an end-to-end graphical text detection and recognition model and implement a detection system based on the B/S research application for pill box recognition, which uses DBNet as the text detection framework and a convolutional recurrent neural network (CRNN) as the text recognition framework. No prior image preprocessing is required in the detection and recognition processes. The recognition result from the back-end is returned to the front-end display. Compared with traditional methods, this recognition process reduces the complexity of preprocessing prior to image detection and improves the simplicity of the model application. Experiments on the detection and recognition of 100 pill boxes demonstrate that the proposed method achieves better accuracy in text localization and recognition results than the previous CTPN + CRNN method. The proposed method is significantly more accurate and easier to use than the traditional approach in terms of both training and recognition processes.
A Key-Based Multi-Mode Clock-Controlled Stream Cipher for Real-Time Secure Communications of IoT

With the rapid development of the Internet and wireless communications, as well as the popularization of personal communication systems, the security of real-time communications is demanded. The efficient technology of stream ciphers can satisfy this requirement of security. In this paper, to enhance the security strength of stream ciphers, we design a key-based multi-mode clock-controlled stream cipher for real-time secure communications of the Internet of things (IoT). The proposed stream cipher is equipped with a multi-mode depending on the key. The different working modes are shipped with different encrypting circuits depending on the user’s key. We analyze the period, the linear complexity, and use known attacks to verify the security strength of the proposed cipher. Compared with existing dual mode clock-controlled stream ciphers, the merits of our proposed cipher are its long period, high linear complexity, low hardware complex, low initialization clock, and simplicity in mode switching. Furthermore, the proposed cipher passes the FIPS PUB 140-1 and SP800-22 tests, obtaining at least 97.00%.
An Intelligent Arabic Model for Recruitment Fraud Detection Using Machine Learning

Over the last years, with the tremendous growth of digital transformation and the constant need for companies to hire employees, huge amounts of fraudulent jobs have been posted on the internet. A cleverly planned sort of scam aimed at job searchers for a variety of unprofessional purposes is a false job posting. It can lead to a loss of money and effort. An Arabic intelligent model has been built to avoid fraudulent jobs on the Internet using machine learning, data mining, and classification techniques. The proposed model is applied to the Arabic version of the EMSCAD dataset. It is available on the Internet in the English version and it has been retrieved from the use of a real-life system and consists of several features such as company profile, company logo, interview questions, and more features depending on job offer ads, Firstly, EMSCAD is translated into the Arabic language. Then, a set of different classifiers such as Support Vector Machine (SVM), Random Forest (RF), Naïve Bayes (NB), and K-Nearest Neighbor (KNN) was used to detect the fraudulent jobs. Finally, the results were compared to determine the best classifier used for detecting fraudulent jobs. The proposed model achieved better results when using a Random Forest classifier with 97% accuracy.