Reviewerly/exHarmony huggingface dataset:
An Efficient Machine Learning Algorithm for Breast Cancer Prediction

Cancer is a leading cause of death worldwide, with breast cancer (BC) being the most common and prevalent with 2.26 million cases each year, and the main cause of women’s deaths, so early and correct detection to discover BC in its first phases, help to avoid death by describing the appropriate treatment and to maintain human life. Cancer cells are divided into two types Malignant and Benign. The first type is more dangerous and the second type is less dangerous. Due to the existence of artificial intelligence (AI) and the great direction to the use of machine learning in medicine, doctors get accurate results for diagnosis. In this paper, we tend to use the Wisconsin Breast Cancer Patients Database (WBCD) which has been collected from the UCI repository. In this paper, the WBCD dataset is divided into 75% for training and 25% for testing using a split test train. We addressed to research the performance of various well-known algorithms in the discovery of BC such as Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Naïve Bayes (NB), Decision Tree (DT), Random Forest (RF), Logistic Regression (LR) and Artificial Neural Networks (ANN). High results indicate that the RF algorithm is 98.2% superior to the rest of the machine learning algorithms.
Methodology

The chapter that focuses on methodology describes the structure of the data that provide a background for the research. Specifically, these are monthly data concerning the volume of export, import, and balance of trade between Russia and the Czech Republic for a specific period, or more precisely, for more than 27 years. In addition to the specification of the data set, this chapter also describes the methods used and the process based on which the results are obtained. As the main tool, the method of artificial neural networks is used, specifically, the multilayer perceptron (MLP) as one of the most commonly used networks in practice, and one of the latest models of neural networks, a three-layer feedforward neural network RBF.
Framework and Practice of Entertainment Engineering Education

エンタテインメントは私たちの生活を豊かにするとともに，経済的にも大きな影響力を持っている．近年のエンタテインメントは多様化しつつあり，従来のゲーム，アニメ，映画，体験型アトラクションなど個々が独立したものではなく，横断的に体験できるものも増えてきている．しかしアニメやゲームなどの特定分野の専門スキルを習得するカリキュラムは実施されているが，エンタテインメントは多様であるため必要となる知識やスキルも幅広く体系化が難しい．またxR(VR/MR/AR), リアルタイムCG, ゲームAI, IoT(internet of things)などの高度な技術を利用したものが増えているが，既に開発されたツールを活用するだけでなく，原理に遡って理解することや独自の新技術を開発できることも重要となる．本稿では，エンタテインメント工学教育のコンピテンシーを，基礎力，デザイン力，プロデュース力に整理するとともに，大学の学部や前期・後期課程の大学院を想定したカリキュラムの構成例について述べる．また筑波大学での2002年からの学部や大学院での授業例と研究事例を紹介するとともに，今後の課題について考察する．
An Efficient CNN-Based Method for Content-Based Image Retrieval

Image recovery has been one of the most fascinating and active study fields in the field of computer vision. The use of content-based image retrieval (CBIR) systems allows for the automatic indexing, searching, retrieval, and exploration of picture datasets. Important characteristics in content-based picture retrieval systems include colour - texture elements. As a result, content-based image retrieval (CBIR) is attractive as a source of precise and speedy retrieval in the modern era. The (CBIR) system uses a feature-based approach to retrieve images from image databases. Low grade characteristics and high grade characteristics are the two categories that image features fall under. Low level aspects of an image include colour, texture, and shape, whereas high level features define the image's semantic content. CBIR is a rapidly developing technology, and as datasets grow as a result of recent advancements in multimedia, it is crucial to enhance this technology to suit user needs.
The Impact of Blended Learning in Mathematics teaching at Rural Higher Educational Institutions

Blended learning is a new strategy for meeting the current needs of the teaching and learning process. Blended learning meets the need for online learning in cyberspace, particularly during the period of disruption caused by COVID-19 last year. Because blended learning incorporates various learning strategies, learners have a better learning experience and make academic progress. Students can advance at their own pace. This strategy's learning outcomes are assessed using standard assessment tools. Swayam Portal assists the learning community in gaining knowledge in a variety of disciplines. This portal incorporates subjects from all fields, such as Science, Social Science, Engineering, and so on. This study focuses on the impact of various pedagogies that rely on technology and how we use them effectively in mathematics teaching.
File Encryption and Decryption using AES and RSA Algorithm

In the present scenario, no technology that's connected to the internet is unhackable. All digital services like internet communication, military and medical imaging systems, multimedia systems require reliable security in storage and transmission of digital images. The importance of protecting digital images cannot be overstated. Therefore, there is a need for image encryption techniques in order to hide images from such attacks. In this encryption process, we use AES and RSA Algorithm in order to hide images. This encryption techniques helps us to avoid intrusive attacks. Since, the image is encrypted using the AES technique, only the receiver can view it, as the key is known only to the sender and receiver. Overall, image encryption improves data integrity and can raise consumer trust.
New Avenues for Automated Railway Safety Information Processing in Enterprise Architecture: An NLP Approach

Enterprise Architecture (EA) is crucial in any organisation as it defines the basic building blocks of a business. It is typically presented as a set of documents that help all departments understand the business model. In EA, safety documents are used to manage and understand safety risks. A novel similarity system for railway safety document processing is presented in this work. It measures the feasibility of automated updating of EA models with the Rule Book by verifying whether Rail Safety and Standards Board (RSSB’s) Rule Book clauses are present and complete in existing EA models. Additionally, a Natural Language Processing (NLP) based search feature was developed to drill through the database to find similar existing rules, principles, and clauses based on semantic similarity. The result will display the most similar clauses and rules with similarity scores and document names. In this study, different pre-trained Electra Small, DistilBERT (Distillation Bidirectional Encoder Representations from Transformers) Base and BERT (Bidirectional Encoder Representations from Transformers) Base were used to embed text. Additionally, the similarity between document rules was measured by cosine similarity metrics. With conclusive evidence, our findings show that BERT Base exceeds the other embedding methods in the semantic comparison of documents.
Automatic Generation of Visual Effects from Movement of Athletes in Sports Videos

Sports broadcasts can be made more entertaining by adding visual effects. Currently, such effect animations are added manually in a real-time or post process. Or many cases require depth information. Here, we automatically add visual effects to sports videos by extracting optical flow and automatically adjusting the brightness of regions that include athletes. The proposed method can be used to easily and quickly add effects to make sports broadcasts more enjoyable without depth information.
The Design and Implementation of the Lookup Table in Distortion Correction for the Optical System

Image geometric distortion is common in a large field of view optical system. It becomes a technical difficulty for high distortion rate optical systems and high resolution image geometric distortion correction. But, a crucial step in distortion correction is figuring out how to precisely obtain the index coordinates and correction parameters, otherwise known as the lookup table. In this paper, the problems in the traditional lookup table method are analyzed, and an improved lookup table method is proposed. The method is based on the traditional lookup table method with the addition of removing the wrong data of position coordinates in the correction image feature points, calculating the ideal image feature point position coordinates, handling the index coordinate out-of-bounds, and adjusting the output order of index coordinates and correction parameters. The experimental results show that the improved design method is feasible, and improves the accuracy of the correction image, compared with the traditional lookup table, the root mean square error is reduced by 93%, and the average accuracy of the fitted position over the full field of view is reduced by 51.9%, and the maximum deviation is only 0.2917%. For the correction of the large field of view optical display devices, it offers good correction effects.
Blockchain Voting with Tamper Verification using Telegram Bot

Blockchain technology has the potential to revolutionize the voting process by providing a secure and transparent method for recording votes. This paper proposes a voting system that utilizes blockchain technology, along with the Django web framework and Django administration for user profile updates, login using Aadhar number. The system incorporates a unique login feature that utilizes an Aadhar number, and two-step authentication through email OTP and private key authentication. The use of Merkle trees and SHA ensures a proof of work that prevents double voting, while tamper verification is achieved through a Telegram bot. The proposed system provides a secure, transparent, and efficient method for conducting elections, which is essential for ensuring a fair and democratic society.
Comparison of Efficiency Data Sorting Algorithms Based on Execution Time

In today's era, the development of information technology is increasingly rapid. This is because human life is currently very dependent on the needs of information technology. This can be proven by the number of human interactions with various gadgets, such as laptops, cellphones, computers, and so on. The development of information technology has made IT activists such as companies and programmers compete in making good applications. One of the most basic things that are mastered in making an application is making algorithms. Currently, there are many types of algorithms. One of them is the data sorting algorithm. In this study, we will try to examine 3 data sorting algorithms, namely Insertion Sort, Quick Sort, and Merge Sort. These three algorithms will be used to sort random data ranging from 1000 to 20,000 data. The three algorithms will be compared in terms of execution time. The results show that the Insertion Sort algorithm is a data sorting algorithm that has the fastest execution time compared to other algorithms, while the Merge Sort algorithm is the most time consuming algorithm compared to other algorithms.
NUMERICAL SOLUTION OF PARTIAL DIFFERENTIAL EQUATIONS THROUGH THE LAGUERRE NEURAL NETWORK

The Chebyshev and legendre neural networks developed to approximate the solution of ordinary differential equations were adapted to the numerical solution of a partial differential equation, but using Laguerre's orthogonal polynomial, which has been called by the authors as Laguerre Neural Network (LNN). The Laguerre Neural Network consists of a single neural layer and an input pattern expansion using Laguerre polynomials. LNN has been applied to solve a Poisson partial differential equation with Dirichlet boundary conditions. A comparison of the solution by LNN with the analytical solution of the equation has been perfomed. The result has been showed a great(using what measure??) approximation of the solution by the Laguerre Neural Network with the training of the network in a reduced number of times compared to the multilayer perceptron network.
A hybrid algorithm to bypass some weak features in the particle swarm and firefly algorithms...with an application to the traveling salesman problem

In this paper, the Traveling Salesman Problem (TSP) is solved through the use of some approximation techniques where the results of the previous work showed some defects in solving the problem to obtain an optimal or close to optimal solution,so the use of hybrid algorithms to solve some results from the use of intuitive and exact algorithms. A hybrid algorithm has been proposed that combines the characteristics of the firefly algorithm (FA) and Particle Swarm Optimization (PSO) to obtain an algorithm that works effectively in overcoming some of the problems resulting from the use of each algorithm separately. Then using an improvement factor to improve each solution within the resulting community and to obtain solutions with a high diversity. The efficiency of the proposed method was measured by solving some standard problems TSP, and the results showed a high convergence of the algorithm towards the known optimal solution for each problem by solving 13 standard problems.
複数の目標を持つ誘導対話のための話題管理部の階層型制御アーキテクチャ

In a dialogue system, when there is a dialogue goal for the dialogue system that is independent of the user's dialogue goal, a dialogue that tries to reach a topic for that goal while coming to terms with the user (i.e., target-guided dialogue) has the potential to be useful for applications such as advertising and recommendation. However, in the case of multiple target-guided strategies, the complexity and multiplicity of designs for switching between target-guided strategies is a challenge. In this paper, we propose the hierarchical structure for dialogue management method, which alleviates the complexity and multiplicity of designing such target-guided strategy switching. It is found that the proposed method automatically complements the topic transitions of skipping, modifying, and sliding, and enhances naturalness of transitions and interest in guided topics.
Problems and Possibilities for NACO Armed Forces Access Points: The Cases of Serbia and Yugoslavia

Creating NACO name authority records for Serbian and Yugoslav armed forces at the national level (Resource Description and Access (RDA) 11.2.2.22.1) exposes how cataloging codes often embody assumptions of particular cultural and national traditions. Catalogers may spend as much effort in authority work parsing the rules as simply identifying corporate bodies. Solutions depend upon cataloger judgment sometimes based upon research. In some cases identity management approaches might offer advantages over an authority control approach to organizing information resources about the armed forces in question. Even so, information organizers would likely still have to account for the particular historical circumstances of the corporate bodies involved.
Mind the Gap: Alleviating Local Imbalance for Unsupervised Cross-Modality Medical Image Segmentation

Unsupervised cross-modality medical image adaptation aims to alleviate the severe domain gap between different imaging modalities without using the target domain label. A key in this campaign relies upon aligning the distributions of source and target domain. One common attempt is to enforce the global alignment between two domains, which, however, ignores the fatal local-imbalance domain gap problem, i.e., some local features with larger domain gap are harder to transfer. Recently, some methods conduct alignment focusing on local regions to improve the efficiency of model learning. While this operation may cause a deficiency of critical information from contexts. To tackle this limitation, we propose a novel strategy to alleviate the domain gap imbalance considering the characteristics of medical images, namely Global-Local Union Alignment. Specifically, a feature-disentanglement style-transfer module first synthesizes the target-like source images to reduce the global domain gap. Then, a local feature mask is integrated to reduce the 'inter-gap' for local features by prioritizing those discriminative features with larger domain gap. This combination of global and local alignment can precisely localize the crucial regions in segmentation target while preserving the overall semantic consistency. We conduct a series of experiments with two cross-modality adaptation tasks, i,e. cardiac substructure and abdominal multi-organ segmentation. Experimental results indicate that our method achieves state-of-the-art performance in both tasks.
A reduction from an LWE problem to maximum independent set problems

The learning with errors (LWE) problem is a problem derived from machine learning that is believed to be intractable for quantum computers. This paper proposes a method that can reduce an LWE problem to a set of maximum independent set (MIS) problems, which are graph problems that are suitable for a quantum annealing machine to solve. The reduction algorithm can reduce an n-dimensional LWE problem to several small MIS problems with at most [Formula: see text] nodes when the lattice-reduction algorithm used in the LWE-reduction method successfully finds short vectors. The algorithm is useful for solving LWE problems in a quantum-classical hybrid manner by using an existing quantum algorithm to solve the MIS problems. For example, the smallest LWE challenge problem is reduced to MIS problems with about 40,000 vertices. This result means that the smallest LWE challenge problem will be within the scope of a real quantum computer in the near future.
Exploring Endless Space

Summary4X games are a genre of modern computer games, exemplified by the Civilization series, whose initial stages implicitly involve exploring an unknown graph. One exploration scheme is “move to nearest unvisited vertex” and this is in fact forced in the game Endless Space. We revisit the small body of old “worst case” results about the efficiency of this scheme, and comment on the unstudied “average case” problem.
Learning Block Structured Graphs in Gaussian Graphical Models

Within the framework of Gaussian graphical models, a prior distribution for the underlying graph is introduced to induce a block structure in the adjacency matrix of the graph and learning relationships between fixed groups of variables. A novel sampling strategy named Double Reversible Jumps Markov chain Monte Carlo is developed for block structural learning, under the conjugate G-Wishart prior. The algorithm proposes moves that add or remove not just a single link but an entire group of edges. The method is then applied to smooth functional data. The classical smoothing procedure is improved by placing a graphical model on the basis expansion coefficients, providing an estimate of their conditional independence structure. Since the elements of a B-Spline basis have compact support, the independence structure is reflected on well-defined portions of the domain. A known partition of the functional domain is exploited to investigate relationships among the substances within the compound.
Noise-robust graph-based semi-supervised learning with dynamic shaving label propagation

Graph-based semi-supervised classification is widely used because it effectively exploits the characteristics of unlabeled data. However, the existing methods have a drawback in that they do not account for the inherent noise of the data. Noise in graph data refers to nodes that are isolated from classes and overlapping nodes between different classes. Therefore, neglecting noise can distort the data manifold, leading to over-smoothing and overall performance degradation. In this paper, we propose a noise-robust model called the dynamic shaving label propagation algorithm. Our proposed method comprises three parts: graph construction, noise definition and cutting, and label propagation. First, in the graph construction process, k is determined at the point when reverse nearest neighbors are identified for the most isolated nodes. Second, the noise definition process identifies the nodes classified as noise based on the number and distance of their reverse nearest neighbors. Finally, label propagation is performed dynamically and iteratively by adjusting the noise removal intensity. Using various simulated and real-world datasets, we evaluate the accuracy and noise robustness of the proposed method with those of existing methods to evaluate its effectiveness and applicability. The comparison results demonstrate that the proposed method outperforms the existing alternatives.
Generating post-hoc explanations for Skip-gram-based node embeddings by identifying important nodes with bridgeness

Node representation learning in a network is an important machine learning technique for encoding relational information in a continuous vector space while preserving the inherent properties and structures of the network. Recently, unsupervised node embedding methods such as DeepWalk, LINE, struc2vec, PTE, UserItem2vec, and RWJBG have emerged from the Skip-gram model and perform better performance in several downstream tasks such as node classification and link prediction than the existing relational models. However, providing post-hoc explanations of Skip-gram-based embeddings remains a challenging problem because of the lack of explanation methods and theoretical studies applicable for embeddings. In this paper, we first show that global explanations to the Skip-gram-based embeddings can be found by computing bridgeness under a spectral cluster-aware local perturbation. Moreover, a novel gradient-based explanation method, which we call GRAPH-wGD, is proposed that allows the top-q global explanations about learned graph embedding vectors more efficiently. Experiments show that the ranking of nodes by scores using GRAPH-wGD is highly correlated with true bridgeness scores. We also observe that the top-q node-level explanations selected by GRAPH-wGD have higher importance scores and produce more changes in class label prediction when perturbed, compared with the nodes selected by recent alternatives, using five real-world graphs.
A Constant Time Secure and Private Evaluation of Decision Trees in Smart Cities Enabled by Mobile IoT

A server has an already trained decision tree machine learning model and one or more clients have unclassified query(ies) that they wish to classify using the server's model under strict security, privacy, and efficiency constraints. To do so, already existing secure building blocks are used, improved, and adjusted to fit this scenario. On top of the proposed building blocks, novel secure and private Decision Tree Evaluation (sDTE) algorithms are proposed. The proposed building blocks show better performances than the related ones in literature in terms of computation and communication costs. Consequently, experimental evaluations over benchmark datasets show that the proposed sDTE algorithms build on top of the proposed blocks, also outperform the state-of-the-art ones in terms of computation and communication costs as well as on security and privacy characteristics. Our theoretical analysis shows that if the whole decision tree can fit in a single ciphertext, which in the proposed sDTE algorithms is almost always the case, then private tree evaluations are done in constant time and do not depend on the tree depth. To the best of the author's knowledge, this is the first scheme in literature with such properties.