Reviewerly/exHarmony huggingface dataset:
Multi-Generator Discriminator Network Using Texture-Edge Information

In the proposed paper, a parallel structure type Generative Adversarial Network (GAN) using edge and texture information is proposed. In the existing GAN-based model, many learning iterations had to be given to obtaining an output that was somewhat close to the original data, and noise and distortion occurred in the output image even when learning was performed. To solve this problem, the proposed model consists of two generators and three discriminators to propose a network in the form of a parallel structure. In the network, each edge information and texture information were received as inputs, learning was performed, and each character was combined and outputted through the Combine Discriminator. Through this, edge information and distortion of the output image were improved even with fewer iterations than DCGAN, which is the existing GAN-based model. As a result of learning on the network of the proposed model, a clear image with improved contour and distortion of objects in the image was output from about 50,000 iterations.
Bayesian Deep Learning Enabled Sentiment Analysis on Web Intelligence Applications

In recent times, web intelligence (WI) has become a hot research topic, which utilizes Artificial Intelligence (AI) and advanced information technologies on the Web and Internet. The users post reviews on social media and are employed for sentiment analysis (SA), which acts as feedback to business people and government. Proper SA on the reviews helps to enhance the quality of the services and products, however, web intelligence techniques are needed to raise the company profit and user fulfillment. With this motivation, this article introduces a new modified pigeon inspired optimization based feature selection (MPIO-FS) with Bayesian deep learning (BDL), named MPIO-BDL model for SA on WI applications. The presented MPIO-BDL model initially involved preprocessing and feature extraction take place using Term Frequency—Inverse Document Frequency (TF-IDF) technique to derive a useful set of information from the user reviews. Besides, the MPIO-FS model is applied for the selection of optimal feature subsets, which helps to enhance classification accuracy and reduce computation complexity. Moreover, the BDL model is employed to allocate the proper class labels of the applied user review data. A comprehensive experimental results analysis highlighted the improved classification efficiency of the presented model.
Recommendation Algorithm Integrating CNN and Attention System in Data Extraction

With the rapid development of the Internet globally since the 21st century, the amount of data information has increased exponentially. Data helps improve people’s livelihood and working conditions, as well as learning efficiency. Therefore, data extraction, analysis, and processing have become a hot issue for people from all walks of life. Traditional recommendation algorithm still has some problems, such as inaccuracy, less diversity, and low performance. To solve these problems and improve the accuracy and variety of the recommendation algorithms, the research combines the convolutional neural networks (CNN) and the attention model to design a recommendation algorithm based on the neural network framework. Through the text convolutional network, the input layer in CNN has transformed into two channels: static ones and non-static ones. Meanwhile, the self-attention system focuses on the system so that data can be better processed and the accuracy of feature extraction becomes higher. The recommendation algorithm combines CNN and attention system and divides the embedding layer into user information feature embedding and data name feature extraction embedding. It obtains data name features through a convolution kernel. Finally, the top pooling layer obtains the length vector. The attention system layer obtains the characteristics of the data type. Experimental results show that the proposed recommendation algorithm that combines CNN and the attention system can perform better in data extraction than the traditional CNN algorithm and other recommendation algorithms that are popular at the present stage. The proposed algorithm shows excellent accuracy and robustness.
TECMH: Transformer-Based Cross-Modal Hashing For Fine-Grained Image-Text Retrieval

In recent years, cross-modal hash retrieval has become a popular research field because of its advantages of high efficiency and low storage. Cross-modal retrieval technology can be applied to search engines, cross-modal medical processing, etc. The existing main method is to use a multi-label matching paradigm to finish the retrieval tasks. However, such methods do not use fine-grained information in the multi-modal data, which may lead to sub-optimal results. To avoid cross-modal matching turning into label matching, this paper proposes an end-to-end fine-grained cross-modal hash retrieval method, which can focus more on the fine-grained semantic information of multi-modal data. First, the method refines the image features and no longer uses multiple labels to represent text features but uses BERT for processing. Second, this method uses the inference capabilities of the transformer encoder to generate global fine-grained features. Finally, in order to better judge the effect of the fine-grained model, this paper uses the datasets in the image text matching field instead of the traditional label-matching datasets. This article experiment on Microsoft COCO (MS-COCO) and Flickr30K datasets and compare it with the previous classical methods. The experimental results show that this method can obtain more advanced results in the cross-modal hash retrieval field.
Delivery Invoice Information Classification System for Joint Courier Logistics Infrastructure

With the growth of the online market, demand for logistics and courier cargo is increasing rapidly. Accordingly, in the case of urban areas, road congestion and environmental problems due to cargo vehicles are mainly occurring. The joint courier logistics system, a plan to solve this problem, aims to establish an efficient logistics transportation system by utilizing one joint logistics delivery terminal by several logistics and delivery companies. However, several courier companies use different types of courier invoices. Such a system has a problem of information data transmission interruption. Therefore, the data processing process was systematically analyzed, a practically feasible methodology was devised, and delivery invoice information processing standards were established for this. In addition, the importance of this paper can be emphasized in terms of data processing in the logistics sector, which is expected to grow rapidly in the future. The results of this study can be used as basic data for the implementation of the logistics joint delivery terminal system in the future. And it can be used as a basis for securing the operational reliability of the joint courier logistics system.
Proving Information Inequalities and Identities With Symbolic Computation

Proving linear inequalities and identities of Shannon’s information measures, possibly with linear constraints on the information measures, is an important problem in information theory. For this purpose, ITIP and other variant algorithms have been developed and implemented, which are all based on solving a linear program (LP). In particular, an identity <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$f = 0$ </tex-math></inline-formula> is verified by solving two LPs, one for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$f \ge 0$ </tex-math></inline-formula> and one for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$f \le 0$ </tex-math></inline-formula> . In this paper, we develop a set of algorithms that can be implemented by symbolic computation. Based on these algorithms, procedures for verifying linear information inequalities and identities are devised. Compared with LP-based algorithms, our procedures can produce analytical proofs that are both human-verifiable and free of numerical errors. Our procedures are also more efficient computationally. For constrained inequalities, by taking advantage of the algebraic structure of the problem, the size of the LP that needs to be solved can be significantly reduced. For identities, instead of solving two LPs, the identity can be verified directly with very little computation.
Sentiment Analysis of the Indonesian National Team in the 2020 AFF Cup Using Naïve Bayes and K-Nearest Neighbor Algorithms

The AFF Cup is a football competition organized by the ASEAN Football Federation, or AFF for short. The 2020 AFF Cup was held in 2021 due to the COVID-19 pandemic. The Indonesian National Team advanced to the final round and became runner-up in the championship. With the end of the championship and the Indonesian National Team having to accept defeat in the final, the public responded through tweets on Twitter. Through these tweets, it will be known how the public evaluates the performance of the Indonesian National Team in the 2020 AFF Cup. It is vital to carry out this research to obtain information regarding society's response. The research that will be conducted is sentiment analysis. Sentiment analysis will be carried out on Rapid Miner software, with the algorithms used being Naïve Bayes and K-Nearest Neighbor. The data used to perform sentiment analysis are tweets from Twitter taken using SNScrape. This research aims to analyze public responses to the Indonesian National Team in the 2020 AFF Cup. This research will determine the percentage of positive, neutral, and negative sentiments from public responses. So that later it can be concluded how the public responds to the Indonesian National Team, whether positive, neutral, or negative. It is also to find out which algorithm has the higher accuracy. The results obtained for Naive Bayes with an accuracy of 64.74% are 71.54% positive sentiment, 15.45% neutral sentiment, and 13.01% negative sentiment. For K-Nearest Neighbor, with an accuracy of 65.64% is 80.49% positive sentiment, 15.45% neutral sentiment, and 4.06% negative sentiment. Both algorithms have the highest accuracy compared to other algorithms in Rapid Miner when the sentiment analysis is performed, with K-Nearest Neighbor having slightly higher accuracy. Most tweets about the Indonesian National Team in the 2020 AFF Cup had positive sentiments. Based on these results, it can be concluded that even though the Indonesian National Team did not win the 2020 AFF Cup, the public still responded positively.&#x0D;
Combining Discrete and Continuous Representation: Scale-Arbitrary Super-Resolution for Satellite Images

The advancements in image super-resolution technology have led to its widespread use in remote sensing applications. However, there is currently a lack of a general solution for the reconstruction of satellite images at arbitrary resolutions. The existing scale-arbitrary super-resolution methods are primarily predicated on learning either a discrete representation (DR) or a continuous representation (CR) of the image, with DR retaining the sensitivity to resolution and CR guaranteeing the generalization of the model. In this paper, we propose a novel image representation that combines the discrete and continuous representation, known as CDCR, which enables the extension of images to any desired resolution in a plug-and-play manner. CDCR consists of two components: a CR-based dense prediction that gathers more available information and a DR-based resolution-specific refinement that adjusts the predicted values of local pixels. Furthermore, we introduce a scale cumulative ascent (SCA) method, which enhances the performance of the dense prediction and improves the accuracy of the generated images at ultra-high magnifications. The efficacy and dependability of CDCR are substantiated by extensive experiments conducted on multiple remote sensing datasets, providing strong support for scenarios that require accurate images.
Reconciling transparency, low <i>Δ</i>0-complexity and axiomatic weakness in undecidability proofs

Abstract In a first-order theory $\varTheta $, the decision problem for a class of formulae $\varPhi $ is solvable if there is an algorithmic procedure that can assess whether or not the existential closure $\varphi ^{\exists }$ of $\varphi $ belongs to $\varTheta $, for any $\varphi \in \varPhi $. In 1988, Parlamento and Policriti already showed how to tailor arguments à la Gödel to a very weak axiomatic set theory, referring them to the class of $\varSigma _{1}$-formulae with $(\forall \exists \forall )_{0}$-matrix, i.e. existential closures of formulae that contain just restricted quantifiers of the forms $(\forall x \in y)$ and $(\exists x \in y)$ and are writable in prenex form with at most two alternations of restricted quantifiers (the outermost quantifier being a ‘$\forall $’). While revisiting their work, we show slightly less weak theories under which incompleteness for recursively axiomatizable extensions holds with respect to existential closures of $(\forall \exists )_{0}$-matrices, namely formulae with at most one alternation of restricted quantifiers.
Fault-Tolerant Aggregate Signature Schemes against Bandwidth Consumption Attack

A fault-tolerant aggregate signature (FT-AS) scheme is a variant of an aggregate signature scheme with the additional functionality to trace signers that create invalid signatures in case an aggregate signature is invalid. Several FT-AS schemes have been proposed so far, and some of them trace such rogue signers in multi-rounds, i.e., the setting where the signers repeatedly send their individual signatures. However, it has been overlooked that there exists a potential attack on the efficiency of bandwidth consumption in a multi-round FT-AS scheme. Since one of the merits of aggregate signature schemes is the efficiency of bandwidth consumption, such an attack might be critical for multi-round FT-AS schemes. In this paper, we propose a new multi-round FT-AS scheme that is tolerant of such an attack. We implement our scheme and experimentally show that it is more efficient than the existing multi-round FT-AS scheme if rogue signers randomly create invalid signatures with low probability, which for example captures spontaneous failures of devices in IoT systems.
Lattice Folding Simulation of Peptide by Quantum Computation

Computational protein folding has attracted considerable interest over the years, including molecular simulations and artificial intelligence assisted methods. On the other hand, research and development of quantum computer hardware and software have been thriving recently. In this paper, we report a case study of peptide (PSVKMA) folding based on a two-dimensional lattice model, by using both the blueqat quantum simulator (called AutoQML) and the IonQ quantum device. As a result, it was found that the actual device was still susceptible to noises.
A lattice-transformer-graph deep learning model for Chinese named entity recognition

Abstract Named entity recognition (NER) is the localization and classification of entities with specific meanings in text data, usually used for applications such as relation extraction, question answering, etc. Chinese is a language with Chinese characters as the basic unit, but a Chinese named entity is normally a word containing several characters, so both the relationships between words and those between characters play an important role in Chinese NER. At present, a large number of studies have demonstrated that reasonable word information can effectively improve deep learning models for Chinese NER. Besides, graph convolution can help deep learning models perform better for sequence labeling. Therefore, in this article, we combine word information and graph convolution and propose our Lattice-Transformer-Graph (LTG) deep learning model for Chinese NER. The proposed model pays more attention to additional word information through position-attention, and therefore can learn relationships between characters by using lattice-transformer. Moreover, the adapted graph convolutional layer enables the model to learn both richer character relationships and word relationships and hence helps to recognize Chinese named entities better. Our experiments show that compared with 12 other state-of-the-art models, LTG achieves the best results on the public datasets of Microsoft Research Asia, Resume, and WeiboNER, with the F 1 score of 95.89%, 96.81%, and 72.32%, respectively.
Enhancing Police Officers’ Cybercrime Investigation Skills Using a Checklist Tool

This paper addressed the current state of police officers' capabilities, skills, and their readiness to deal with the developments of cybercrime.This study discussed definition of cybercrime, cybercrime categories as well as comparison between traditional criminal techniques and cybercrime.As the abilities and skills required for detectives to investigate cybercrime have been discussed.Additionally, literature review and related work, was addressed challenges role of the police in combating cybercrime and facing cybercrime policing.We proposed the main tool in the study which is "Checklist of essential skills for a cybercrime investigator".Thus, to gain the ability to Identify technical and practical requirements in terms of skills, programs, and equipment to achieve effective and professional results in fight cybercrimes.
ソフトウェア構造情報と動作ログを活用した企業情報システムの実装言語刷新支援方式

Legacy enterprise information systems are required to convert their implementation languages into modern languages to enhance their maintainability. However, in a planning phase, it is difficult for engineers to identify units of programs to be converted appropriately and quickly. In this paper, we propose a support method that can effectively identify the units of programs for language conversion and test data acquisition points by relating application logs to software structure. We implemented this method by using graph database, and through the application of this method to real enterprise information system, we confirmed that we can effectively identify units of program conversion and test data acquisition points.
Estimation of the User Interest While Viewing Content on Smartphones Using Smart Devices Considering Viewing Conditions

Conventional recommendation systems such as for books or movies are primarily based on purchase and browsing history, which do not reflect user evaluation. This study focuses on analyzing invisible and feeble physiological tremors to reflect user evaluation. While physiological tremors are generally measured by fixing joints and wearing a light accelerometer, considering implementation, we measured them without fixing joints. This study aims to examine whether it is possible to acquire acceleration data including physiological tremors with no limitation on the posture and extract features reflecting the user interest. In the experiment, subjects read comic books on a smartphone and the acceleration data were collected using the 3D accelerometer of a smartphone. Assuming the actual use environment, subjects read comic books in both sitting and standing with no instruction on their posture. As a result of performing fast Fourier transforms, it was suggested that the acceleration data included physiological tremors and could estimate user interest while reading.
Graph neural networks via contrast between separation and aggregation for self and neighborhood

Graph Neural Networks (GNNs) including Graph Convolutional Networks (GCNs) have demonstrated state-of-the-art performance in various analytical tasks. Current GNNs approaches focus on learning node representations based on the proximity similarity principle. However, they may fail to generalize to complex relations such as disassortative structure, because irrelevant information may be aggregated and generate unreasonable representations. To this end, this research introduces a novel approach – Graph neural networks via contrast between separation and aggregation for self and neighborhood, to effectively learn the node representations in the disassortative structure. To search friendly neighbor space with mitigating the unreasonable of connections, the proposed approach synthesizes the feature semantic space and the structure semantic space. To learn reasonable representations for disassortative graphs, the proposed approach stacks multiple GCNs and learns the intrinsic interrelationship between the optimal friendly aggregated information and the separated information originating from self and neighborhood by contrastive learning. At the same time, the proposed approach integrates the variational expectation maximization to train the whole framework for exploring the approximated posterior. Extensive experimental results on eight real-world graph datasets show that the proposed approach outperforms eight state-of-the-art GNN models in diverse graph mining tasks, including node classification and link prediction, demonstrating the proposed approach’s superior graph representation ability.
ハンズオンによる技術科教員養成の電気理論科目における教育実践

The purpose of the study was to practice teaching electric theory to first graders using a hands-on approach in a technology teacher training course at a teacher's education university, and to verify the effect. Hands-on activities included simple electrical experiments and simulations in a lecture room. To allow time for hands-on activities, flipped classrooms were used as a teaching method. In the flipped classroom, students were required to watch the video lectures in advance and then engaged in hands-on activities. Fifteen lessons were taught, and the effect was examined based on test and questionnaire results of the group using the hands-on activities (experimental group) and a group without the activity (control group). The results showed that the average score of the short tests and regular examinations was significantly higher than that in the control group, except in the case of the AC circuit field. Thus, the experiments and simulations contributed to improved interest and comprehension, therefore, demonstrating the educational effect of the hands-on approach.
パラメトリック共振回路を用いたイジングモデルのシミュレーション

Recently, a high-efficiency solution of combinational optimization problem using Ising machines has been studied for many labs. We have been proposed an Ising machine using parametric resonant circuit with capacitor and inductor. We took a simulation of the Ising model using these circuits, which have been shown in the reproducing of the feature physical phenomenon in the ferromagnetic and the antiferromagnetic Ising models. Furthermore, we calculated the ground-state-search of the Ising model with 100-spins circuits, which succeeded in solving the ground-state. From these results, we suggested that there was able to imitate the Ising Machine using the parametric resonance circuits.
モンテカルロフィルタに基づく眼球回旋運動の計測法

In recent years, various studies on anthropometry using video images have been actively conducted. Of those applications, the measurement of eye movements is expected in fields such as medical care, and human interface. The eye movements include horizontal and vertical translations due to the movement of the gaze and torsional eye movements. This paper proposes a method for accurately measuring the torsional eye movement, which is generally considered difficult to measure with high accuracy. We have improved the Monte Carlo filter, which was traditionally used for object tracking in translational motion, so that it can also measure rotational motion. Evaluation experiments indicated that the measurement error using the proposed method was in a range between 0.07±0.49° to -0.03±0.54°, while the measurement accuracy of the conventional matching method was 0.28±0.81°. In addition, the results suggest that the proposed method can measure even a blurred iris image with a relatively small error.