Reviewerly/exHarmony huggingface dataset:
Analisis Kesalahan Penggunaan Bahasa Indonesia dalam Proses Diskusi Siswa Sekolah Dasar

This study aims to describe some of the errors in the use of the Indonesian language that were made to the sixth graders of SD Suka Cita. This research uses a descriptive research type, where the source of the data obtained is from discussion activities during class VI SD Suka Cita students. The instrument used in this study is to use observation sheets and tape recorders. The analysis technique used to analyze the Indonesian language error data is a qualitative data analysis technique. The results of this study indicate that there are errors in the use of Indonesian including phonological, morphological, syntactic, and semantic errors in the discussion process carried out by elementary school students. Suggestions from this study, teachers can use the results of analysis regarding language errors made by students as material for learning reflection.&#x0D; &#x0D; Keywords: Error Analysis of Language Usage, Discussion, Students, Elementary School
An Enhanced Dynamic Ensemble Selection Classifier for Imbalance Classification With Application to China Corporation Bond Default Prediction

China corporation bond default prediction is important and can be formulated as an imbalance classification problem solved by static ensemble classifiers.However, dynamic ensemble selection (DES) classifiers have not been applied to this typical problem in the context of business research.DES classifiers are capable of selecting an ensemble classifier for each test sample, leading to better classification performance than static ensemble classifiers.Most existing DES classifiers can not address imbalance classification optimally and only use single criteria of competent for classifier selection, resulting in sub-optimal classification performance.In this paper, we propose an enhanced DES classifier, named META-DES-Diversity, that inherits strengths of data sampling, meta-learning, criteria of diversity, and dynamic weighting fusion scheme to alleviate such limitations.Specifically, the synthetic minority over-sampling technique is initially used to balance the training set before generating a candidate classifier pool.To select an ensemble classifier with a highly competent, the meta-learning framework META-DES is utilized to consider multiple criteria of competence.In complement with the meta-leaning framework, a two-phase selection strategy is utilized to perform competence and diverse ensemble classifier selection.Note that a competencedriven decision fusion scheme is employed to effectively fuse classification results from selected ensemble classifiers.Experiments on 14 two-class imbalanced data sets from the KEEL repository and one self-collected China corporation bond data set show the effectiveness and superiority of the proposed enhanced DES classifier.
Transition Factors of Power Consumption Models for CPA Attacks on Cryptographic RISC-V SoC

Physical cryptographic devices are vulnerable to side-channel information leakages during operation. They are widely used in software as well as hardware implementations, ranging from microcontrollers and microprocessors to hardware accelerators in System on Chips (SoCs). Nowadays, cryptographic RISC-V SoCs are becoming the most prominent solution compared to the rest. Cryptographic accelerators provide users with a very high level of flexibility and customization of chips suited to specific applications in these systems. First, this research aims to confirm the effectiveness of the Correlation Power Analysis attack on cryptographic SoCs based on three different power consumption models. In each model, the effectiveness of an attack depends on the transition factor, which is a ratio related to different characteristics of the device's power consumption. Then, we focus on modifying the configuration on the SoC and attacking the AES hardware implementation on these designs. The experimental results show that applying the Switching Distance model brings the highest performance. With our suggested range of transition factors, the number of traces needed to find the secret key can be reduced by 13.35% in the best case.
A Pioneer Image Steganography Method Using the SOD Algorithm

Steganography is a technology that has gained popularity in recent years as people’s fears and susceptibilities have increased in today’s digital environment. As a result of this work, we have shed light on the existing popular techniques of Image Steganography in great detail, and we have also demonstrated a new Spatial Domain Image Steganography technique based on the ‘SOD’—Sum-Of-Digit method, wherein secret data is embedded in an image through the use of the proposed algorithm. Moreover, the presented method is compared with existing methodologies on a variety of parameters and found to be efficient and robust, which makes this steganography technique effective. The experimental results can demonstrate the effectiveness and accuracy of the proposed technique in terms of several image similarity metrics, which is a significant benefit.
Quantum Neural Network for Image Classification Using TensorFlow Quantum

Quantum neural networks are artificial neural networks that are designed using the principles of classical artificial neural networks and quantum information. Developing a quantum neural network requires knowledge of both classical neural networks and quantum computing techniques. TensorFlow Quantum is a library for developing quantum and hybrid neural network models. The MNIST handwritten digits dataset was used as a sample dataset for quantum neural network development. This chapter introduces the design and development process of a simple quantum neural network for image classification tasks using the TensorFlow quantum library. Also, it compared the classification performances of the quantum neural network and classical neural network on the MNIST handwritten digits classification. This chapter identified that the quantum neural network performed better than the classical neural network on digit classification. Also, the chapter discussed the advantages and limitations of quantum neural networks in image classification. The first section of the chapter introduced the quantum neural network models and the TensorFlow quantum library. Afterward, the data preparation steps such as data loading, downscaling, contradictory removal, and Tensorflow Quantum circuit conversions were discussed. Furthermore, the building process of quantum neural networks for image classification, such as quantum neural network designing, the model circuit to the Tensorflow Quantum model binding, and model training, was discussed in the third section of the chapter. Subsequently, the testing performance of the simple quantum neural network was discussed in section four of this chapter. Finally, the conclusions of the chapter were discussed in the fifth section.
Risk function analysis using COX regression model for children with brain cancer in Basra

This study aims to identify the most important factors affecting the incidence of brain cancer in children, The Cox model was used to determine the most important factors affecting brain cancer, and the partial probability method was used to estimate the model coefficients.A sample size of (250) patients with brain cancer from Basra Children's Specialized Hospital was used. The results showed that the test Wald value for the age variable is (6.734025) and its probabilistic value is (0.00946), which is a value less than 5% This indicates that the age variable has a significant effect, meaning that there are significant differences for brain cancer patients between different age groups on the risk of death. The results of the Chi-square test to test the maximum potential ratio of the Cox regression model is (11.24) and its probability value is (2%), which is less than (5%), and this indicates that the estimated model is a significant model statistic, and that the estimated parameters are negative, and this indicates that when the independent variables increase by one unit, the risk ratio decreases by one unit This shows the condition of the patient with brain cancer, as it is improving for the better.
Knowledge-Enhanced Medical Visual Question Answering: A Survey (Invited Talk Summary)

Medical Visual Question Answering (Med-VQA) is a task in the field of Artificial Intelligence where a medical image is given with a related question, and the task is to provide an accurate answer to the question. It involves the integration of computer vision, natural language processing, and medical domain knowledge. Furthermore, incorporating medical knowledge in Med-VQA can improve the reasoning ability and accuracy of the answers. While knowledge-enhanced Visual Question Answering (VQA) in the general domain has been widely researched, medical VQA requires further examination due to its unique features. In the paper, we gather information on and analyze the current publicly accessible Med-VQA datasets with external knowledge. We also critically review the key technologies combined with knowledge in Med-VQA tasks in terms of the advancements and limitations. Finally, we discuss the existing challenges and future directions for Med-VQA.
Efficient Quantum Circuit for Karatsuba Multiplier

The fundamental element of quantum computing is the quantum circuit. An efficient quantum circuit saves quantum hardware resources by reducing the number of gates without increasing the number of qubits. Quantum circuits with many qubits are very difficult to realize. Thus, the number of qubits is an important parameter in a quantum circuit design. Using reversible logic in quantum circuits has many advantages such as diminishing power consumption, reducing heat propagation and decreasing quantum cost, ancilla inputs, and garbage outputs that lead to increased performance of quantum computers. Quantum circuits for arithmetic operations such as addition, subtraction, and multiplication are required in the implementation of quantum circuits for many quantum algorithms in this area. In this article two novel designs for GF(2n) multiplier using Karatsuba algorithm have been proposed that have been proved to have an improvement in qubits, garbage outputs, and ancilla inputs when it comes to comparison with recent research that have been done concerning this field. Bennett’s garbage removal strategy with the SWAP gate is used to remove garbage output from existing works in order to establish a fair comparison to existing work.
SCA-ECPE: Emotion-Cause Pair Extraction Based on Sentiment Clustering Analysis

As a branch task of cause-effect extraction, ECPE (Emotion-Cause Pair Extraction) focuses on analyzing the causes of emotional effects. The existing models are usually directly extracted by neural networks, resulting in low recognition accuracy of emotion clauses. To further improve the extraction accuracy, we proposed an ECPE extraction model named SCA-ECPE. Firstly, the k-means clustering method is used to execute a separate cluster analysis of emotion clauses to obtain prior knowledge. Then, the clauses of the original document are classified and matched, and then filtered based on prior knowledge. Finally, the probability is calculated by a multi-layer neural network to extract the emotion-cause pairs. Experimental results show that prior sentimental knowledge can effectively enhance extraction performance.
Exploring the Potential of eXplainable AI in Identifying Errors and Biases

Artificial intelligence has virtually pervaded every field and its adaptation is a catalyst for organizational growth. However, the potential of artificial intelligence is often associated with a difficulty to understand the logic veiling behind its decision making. This is essentially the premise upon which XAI or eXplainable AI functions. In this field of study, researchers attempt to streamline techniques to provide an explanation for the decisions that the machines make. We endeavor to delve deeper into what explainable means and the repercussions of the lack of definition associated with the term. We intend to show in this paper that an evaluation system based solely on how easy it is to understand an explanation, without taking into account aspects such as fidelity, might produce potentially harmful explanation interfaces.
Study of Security for Edge Detection Based Image Steganography 

Due to the increase in the speed of the computer networks, the advantage in information communication has also increased and thus, the importance of information security cannot be overstated. The method of steganography has the purpose of making the communication hidden by wrapping the data into some other form. Many steganography formats of files are available, but still images in digital form are considered most famous due to their internet frequency. There are plenty of algorithms for data hiding but might have a compromise in quality of image. In this paper, a new technique is proposed for performing image steganography by utilizing edge detection techniques for grayscale images. In this proposed method, edges are detected by converting the image into grayscale and then, text is embedded into the digital image. Different methods like Canny, Sobel, Prewitt and Laplacian are applied here for having better secrecy and also for enhancing the stego image as well for getting correct data that was embedding.
An Encryption Key Sharing Method for Contact Tracing Application

The world has seen many pandemics in the past. COVID-19, SARS, and H1N1 are some of them. During the period of epidemic prevention and control, tracing the source becomes a challenge to control the disease, and contact tracing applications are developed by many countries to slow down the spread of pandemics. However, the privacy problem is becoming one of the important issues in contact tracing systems nowadays. To protect the private information for infected persons and their potential contacts in the scenario which described in our paper, the effective encryption key sharing method can be applied to contact tracing systems. In this paper, we propose a key sharing mechanism for contact tracing application, it is allows a confirmed patient to hide their sensitive information from others when send the notification messages. Our mechanism is used to achieve such a user’s privacy functionality. We present the security analysis and prove the security of the mechanism.
Design Scheme Analysis of Text Duplicate Search Optimization Algorithm in Natural Language Processing

In order to optimize text retrieval results in natural language processing, this paper will carry out relevant research. Firstly, the key technologies of the algorithm scheme are analyzed, and then the scheme flow is designed and tested. Through the research of this paper, a more effective text search optimization algorithm for natural language processing is obtained, which can obtain the search results more accurately compared with previous algorithms.
Singular Value Decomposition and Rivest–Shamir–Adleman Algorithm-Based Image Authentication Using Watermarking Technique

Digital watermarking is an approach where some information is embedded into the digital data like images, audio, video by its rightful owner. These days, most of the digital data is forged and copied by using various methods. Among this digital data, digital images are acting as the major subject for the modifications and malicious attacks. Digital watermarking can be used as a method to identify the unauthorized data and also for the copyright protection. The information embedded into the digital data by the owner is known as a watermark. Watermarking is helpful in the authentication of the image and to verify if the image has undergone any tampering. The watermark that is embedded needs to be robust and imperceptible against various attacks. In this paper, we are using Singular Value Decomposition technique (SVD), Rivest–Shamir–Adleman (RSA) encryption techniques for generating the robust watermarking technique which tolerates various image manipulation attacks and adds more strength to our authentication system.
Estimating the amounts of the solar energy: A multiple linear regression approach

The solar energy has some kind of effects in many fields connected to the human life as health, electricity generation and industrial, in a lot of countries around the world. Iraq is one of the countries, which has a large amount of the solar radiation throughout a year that can be invested to compensate the shortfall in the electric power particularly. In this paper, the analysis models in the four years from 2017 to 2020 are applied to estimate the amounts of the daily solar radiation using Multiple Linear Regression (Mtp_LR) method in Anbar, Iraq. Furthermore, two climate variables relative humidity (%) and air pressure Pascal unit (Pa) have been implemented as an input dataset to the predictive models. The results are evaluated via two statistical indicators Root Mean Square Error (RMSE) and Relative Root Mean Square Error (RRMSE). The models of the four years acquire remarkable accuracy in solar energy forecasting with RMSE values of the statistical indicators RMSE (0.8915, 1.0827, 0.7757 and 0.5055) kilowatt hours per square meter (kWh/m2) in 2017, 2018, 2019, and 2020 respectively, that determines a strong estimation outcome with the best relationship between the independent variables.
Restored texture segmentation using Markov random fields

Texture segmentation plays a crucial role in the domain of image analysis and its recognition. Noise is inextricably linked to images, just like it is with every signal received by sensing, which has an impact on how well the segmentation process performs in general. Recent literature reveals that the research community has started recognizing the domain of noisy texture segmentation for its work towards solutions for the automated quality inspection of objects, decision support for biomedical images, facial expressions identification, retrieving image data from a huge dataset and many others. Motivated by the latest work on noisy textures, during our work being presented here, Brodatz and Prague texture images are contaminated with Gaussian and salt-n-pepper noise. A three-phase approach is developed for the segmentation of textures contaminated by noise. In the first phase, these contaminated images are restored using techniques with excellent performance as per the recent literature. In the remaining two phases, segmentation of the restored textures is carried out by a novel technique developed using Markov Random Fields (MRF) and objective customization of the Median Filter based on segmentation performance metrics. When the proposed approach is evaluated on Brodatz textures, an improvement of up to 16% segmentation accuracy for salt-n-pepper noise with 70% noise density and 15.1% accuracy for Gaussian noise (with a variance of 50) has been made in comparison with the benchmark approaches. On Prague textures, accuracy is improved by 4.08% for Gaussian noise (with variance 10) and by 2.47% for salt-n-pepper noise with 20% noise density. The approach in the present study can be applied to a diversified class of image analysis applications spanning a wide spectrum such as satellite images, medical images, industrial inspection, geo-informatics, etc.
An intelligent algorithm for fast machine translation of long English sentences

Abstract Translation of long sentences in English is a complex problem in machine translation. This work briefly introduced the basic framework of intelligent machine translation algorithm and improved the long short-term memory (LSTM)-based intelligent machine translation algorithm by introducing the long sentence segmentation module and reordering module. Simulation experiments were conducted using the public corpus and the local corpus containing self-collected linguistic data. The improved algorithm was compared with machine translation algorithms based on a recurrent neural network and LSTM. The results suggested that the LSTM-based machine translation algorithm added with the long sentence segmentation module and reordering module effectively segmented long sentences and translated long English sentences more accurately, and the translation was more grammatically correct.
Investigation on the Infusion of Cybersecurity and Smart City

In order to escalate the operational efficiency and improve the two-way quality-of-service between government and citizens, as well as sharing significant information with the general public by means of information and communication technologies (ICT) is what is referred to as a Smart City. Not every city in the world is yet a smart one but it is almost on the verge of becoming one by the absolute application of smart components. Smart technological components involve the personal and private data of any user which makes them risky and vulnerable as there is a higher risk of increase in several issues and challenges. One cyber-attack can vividly destroy all the security aspects of the smart city and put the whole arena at risk. There are several other cyber-threats that in completion can majorly affect the behavioral strategies of a smart city. Thus, to prevent the malicious manipulation of the use of data and infrastructure of smart cities, it is important to introduce cyber security in the same direction. The objective of this book chapter is to introduce the steps presently taken or are required to be taken to ensure cyber security in this particular technological area. We aim to focus on the following major smart components of a smart city: grid, transportation system, building, and healthcare and their relevant cyber-security aspects that need to be framed so as to assure safe prevention from serious security breach incidents.
Overview and Application-Driven Motivations of Evolutionary Multitasking

A plethora of EMT algorithms have been proposed lately. Some of these either directly or indirectly make use of the probabilistic formulation of MTO discussed in Chap. 1 .
Fake Face Image Classification by Blending the Scalable Convolution Network and Hierarchical Vision Transformer

A face has been used as a primary and unique attribute to authenticate individual users in emerging security approaches. Cybercriminals use the double-edged sword “image processing” capabilities to deceive innocent users. The underlying technology is based on advanced machine learning and deep learning algorithms. The intentions of cyber criminals range from simple mimicking or trolling to creating violent situations in society. Hence, it is necessary to resolve such problems by identifying the fake face images generated by expert humans or artificial intelligent algorithms. Machine learning and artificial neural networks are used to resolve the issue. In this work, we have designed an approach for detecting deep learning-generated fake face images by combining the capabilities of the scalable convolutional neural networks (CNN) “EfficientNet” and hierarchical vision transformer (ViT) “shifted window transformer”. The proposed method accurately classifies the fake face images with a 98.04% accuracy and a validation loss of 0.1656 on the 140 k_real_fake_faces image dataset.