Reviewerly/exHarmony huggingface dataset:
Segmentation of Cell Periphery from Blood Smear Images Using Dark Contrast Algorithm and K-Medoid Clustering

Computer-Aided Analysis of Blood Smear Images helps to identify several cell features which cannot be analyzed with the existing manual techniques. For this purpose, segmentation of required cell component is very important. The motive of this work is to segment the Cell Periphery, which holds the cytoplasm, from the Blood Smear Images. Primarily, these images are enhanced to increase the observability of various cell components. The enhancement is done using Dark Contrast Algorithm (DCA). This enhanced image is further segmented using K-Medoid Clustering, a technique based on spatial clustering. The number of clusters obtained as output are predefined. This technique clusters the data with the help of Similarity Index, to distribute it according to their similarities or dissimilarities by updating the medoids. The image hence obtained gives us the segmented Cell Periphery. The assessment of this proposed approach is done using parameters like Second Derivative like Measure of Enhancement (SDME) and Measure of Enhancement (EME) for enhancement and Dice Coefficient (DC) for segmentation.
Performance Analysis of Breast Cancer Data Using Mann–Whitney U Test and Machine Learning

Breast cancer is the most prevalent cause of mortality among women worldwide. Early detection and treatment can reduce the rate of mortality. Late prognosis and treatment of breast cancer (BC) patients lead to irreparable diseases and even death. As a result, in recent years, early BC diagnosis methods based on pathological breast images have been in high demand. In recent years, various models have been put up by the researcher for the early diagnosis of breast cancer. In this article, Wisconsin breast cancer (diagnostic) dataset (WDBC) is employed to categorize tumors into benign or malignant. Statistical-based Mann–Whitney U Test is applied for feature selection, followed by machine learning models for the classification of tumors. We compare two methods: a machine learning method with feature selection and one without. Finally, the results demonstrate that on selecting pertinent features, enhances the overall performance when tested on the WDBC dataset. The classification accuracy, sensitivity, and specificity obtained were 97.2%, 98.8%, and 94.5% using Random Forest with feature selection.
Measuring Board Game Distance

This paper presents a general approach for measuring distances between board games within the Ludii general game system. These distances are calculated using a previously published set of general board game concepts, each of which represents a common game idea or shared property. Our results compare and contrast two different measures of distance, highlighting the subjective nature of such metrics and discussing the different ways that they can be interpreted.
An Efficient Chinese Checkers Implementation: Ranking, Bitboards, and BMI2 pext and pdep Instructions

The game of Chinese Checkers has a computationally expensive move generation function. Finding legal moves dominates the performance of a Chinese Checkers program. This paper describes a bitboard representation of the Chinese Checkers board, how to efficiently generate and apply moves to the board, and how to rank and unrank states. When available, the BMI2 PDEP (parallel bits deposit) and PEXT (parallel bit extract) instructions offer significant efficiency gains, especially over a non-bitboard based implementation.
A Parallel Gannet Optimization Algorithm with Communication Strategies (PGOA)

Meta-heuristic algorithms have gained remarkable success in solving complex and large-scale problems. However, as the dimension of the problem increases, their elaborate implementations may lead to lower convergence speed and struggle with local optima easily. Therefore, it is time-consuming for people to tune for high-dimensional problems. In this paper, a parallel Gannet Optimization Algorithm (GOA) for two novel communication strategies is proposed, and comparisons with the original GOA on 13 100-dimension benchmark functions are committed. Comprehensive experimental results indicate that the improved algorithm outperforms the original algorithm in not only better escaping local optima, but shorter running time.
Scheduling Classifiers for Real-Time Hazard Perception Considering Functional Uncertainty

This paper addresses the problem of real-time classification-based machine perception, exemplified by a mobile autonomous system that must continually check that a designated area ahead is free of hazards. Such hazards must be identified within a specified time. In practice, classifiers are imperfect; they exhibit functional uncertainty. In the majority of cases, a given classifier will correctly determine whether there is a hazard or the area ahead is clear. However, in other cases it may produce false positives, i.e. indicate hazard when the area is clear, or false negatives, i.e. indicate clear when there is in fact a hazard. The former are undesirable since they reduce quality of service, whereas the latter are a potential safety concern. A stringent constraint is therefore placed on the maximum permitted probability of false negatives. Since this requirement may not be achievable using a single classifier, one approach is to (logically) OR the outputs of multiple disparate classifiers together, setting the final output to hazard if any of the classifiers indicates hazard. This reduces the probability of false negatives; however, the trade-off is an inevitably increase in the probability of false positives and an increase in the overall execution time required.
Ancilla-driven blind quantum computation for clients with different quantum capabilities

Abstract Blind quantum computation (BQC) allows a client with limited quantum power to delegate his quantum computational task to a powerful server and still keep his input, output, and algorithm private. There are mainly two kinds of models about BQC, namely circuit-based and measurement-based models. In addition, a hybrid model called ancilla-driven universal blind quantum computation (ADBQC) was proposed by combining the properties of both circuit-based and measurement-based models, where all unitary operations on the register qubits can be realized with the aid of single ancilla coupled to the register qubits. However, in the ADBQC model, the quantum capability of the client is strictly limited to preparing single qubits. If a client can only perform single-qubit measurements or a few simple quantum gates, he will not be able to perform ADBQC. This paper solves the problem and extends the existing model by proposing two types of ADBQC protocols for clients with different quantum capabilities, such as performing single-qubit measurements or single-qubit gates. Furthermore, in the two proposed ADBQC protocols, clients can detect whether servers are honest or not with a high probability by using corresponding verifiable techniques.
The Analysis of Ontology-Based Neuro-Symbolic Intelligence Methods for Collaborative Decision Support

The neural network approach to AI, which has become especially widespread in the last decade, has two significant limitations – training of a neural network, as a rule, requires a very large number of samples (not always available), and the resulting models often are not well interpretable, which can reduce their credibility. The use of symbols as the basis of collaborative processes, on the one hand, and the proliferation of neural network AI, on the other hand, necessitate the synthesis of neural network and symbolic paradigms in relation to the creation of collaborative decision support systems. The article presents the results of an analytical review in the field of ontology-oriented neuro-symbolic artificial intelligence with an emphasis on solving problems of knowledge exchange during collaborative decision support. Specifically, the review attempts to answer two questions: 1. how symbolic knowledge, represented as an ontology, can be used to improve AI agents operating on the basis of neural networks (knowledge transfer from a person to AI agents); 2. how symbolic knowledge, represented as an ontology, can be used to interpret decisions made by AI agents and explain these decisions (transfer of knowledge from an AI agent to a person). As a result of the review, recommendations were formulated on the choice of methods for introducing symbolic knowledge into neural network models, and promising areas of ontology-oriented methods for explaining neural networks were identified.
DEMANDE: Density Matrix Neural Density Estimation

Density estimation is a fundamental task in statistics and machine learning that aims to estimate, from a set of samples, the probability density function of the distribution that generated them.There are different methods for addressing this problem but recently deep-neural density estimation methods have emerged as a powerful alternative.This paper presents a novel method for neural density estimation based on density matrices and adaptive Fourier features.Density matrices are commonly used in quantum mechanics to represent the quantum state of a physical system.In this work, they are used to estimate probability densities using an operation called quantum measurement.The proposed method can be trained without optimization using an averaging operation over the samples of the training dataset.It can also be integrated with deep learning architectures and trained using gradient descent.The performance of the proposed method was evaluated on a range of synthetic and real datasets and compared with fast kernel density estimation and state-of-the-art neural density estimation methods.The results demonstrate that the proposed method achieves competitive performance while being faster and more efficient than existing methods.
Upgrading script watermarking robustness of unusual-to-tolerate functional confirmation by secret-sharing

Watermarking is currently critical to prove copyrights authentication. This paper proposes upgrading the counting-based secret-sharing strategy for smart trust of unusual Arabic script robustness validation. The study analyzes the accuracy of watermarking tolerated against common threats of tampering possibilities. It investigated effective comparison of the accurateness of the watermark writing via implementing two novel approaches for script watermarking tested on standard Arabic benchmark. The advantage of this unusual watermarking exploration is tuned for tolerating probable intimidations, showing high functionality robustness strategies with different outcomes. The research effectively addresses the watermarking challenges, remarking variances in the proposed models contributions, showing authentication remarks very attractive to tolerate functional confirmation for slightly-tampered unfamiliar e-writing watermarking.
GWS-Geo: A graph neural network based model for street-level IPv6 geolocation

IP geolocation is a technique for inferring geolocation of a device based on characteristics of its IP addresses such as network measurements or queryable information. Traditional IPv4 geolocation methods are often inapplicable to IPv6 networks or produce unsatisfactory results due to the imperfection of network routing and the sparsity of IPv6 addresses. To improve the accuracy of IPv6 geolocation, we proposed GWS-Geo, a street-level IPv6 geolocation model based on Graph Neural Network(GNN). Our model includes preprocessing, pre-training, an improved GraphSAGE algorithm, and hierarchical classification. In the preprocessing step, we process node features and transform them into node embeddings, and transform landmarks’ latitude and longitude coordinates into area numbers. During pretraining, we assign weights to edges between IP addresses and input node information into the improved GraphSAGE algorithm. After pruning according to edge weights, the improved GraphSAGE executes graph convolution operations. Finally, we use hierarchical classification to divide the geolocation into finer granularity to obtain the location of the target IP address. Experimental results on three datasets (covering the Tokyo, New York, and Shanghai areas) show that the median error distance is within a range of 5.53km to 9.46 km, which is close to the level of street-level accuracy. Compared to popular geolocation algorithms such as SLG, IRLD, and MLP-Geo, our model reduces the median error distance by at least 15.99% and the average error distance by at least 16.36%.
Correction of Banding Errors in Satellite Images With Generative Adversarial Networks (GAN)

This research proposes an innovative method for correcting banding errors in satellite images based on Generative Adversarial Networks (GAN). Small satellites are frequently launched into space to obtain images that can be used in scientific or military research, commercial activities, and urban planning, among other applications. However, its small cameras are more susceptible to radiometric, geometric errors, and other distortions caused by atmospheric interference. The proposed method was compared to the conventional correction technique using experimental data, showing the similar performance (92.64% and 90.05% accuracy, respectively). These experimental results suggest that generative models utilizing Artificial Intelligence (AI) techniques, specifically Deep Learning, are getting closer to achieving automatic correction close to conventional methods. Advantages of the GAN models include automating the task of correcting banding in satellite images, reducing the required time, and facilitating the processing without requiring prior technical knowledge in handling Geographic Information Systems (GIS). Potentially, this technique could represent a valuable tool for satellite image processing, improving the accuracy of the results and making the process more efficient. The research is particularly relevant to the field of remote sensing and can have practical applications in various industries.
A fast and precise filament winding path planning method based on discrete non-iterative semi-geodesic algorithm for all applicable mandrel

Obtaining reliable and stable winding paths for complex shapes with current filament winding technologies is still a challenge. Aiming at a practical design of the filament winding process, a novel discrete non-iterative semi-geodesic (NIS) algorithm is proposed, in which a first-order ordinary different equation (ODE) of the tangent vector is directly solved over triangle meshes and then the tangent vector is parallel transported along a geodesic path. Compared to the existing discrete iterative semi-geodesic algorithm, the NIS algorithm does not require trial-and-error to determine the next path point and the time complexity of the NIS algorithm to generate a semi-geodesic path is O(n). The relative error of the NIS algorithm compared with analytical method is less than 1% in terms of mandrel’s rotation angle and winding angle. Then, a complete discrete winding path planning method based on the NIS algorithm is presented, including constant-wall-thickness winding paths at regular winding region, semi-geodesic winding paths at return regions and certain winding pattern construction. This winding path planning method does not require knowledge of the parametric equations of mandrel and is particularly useful for designing winding path for all applicable mandrels, such as vessel, rocket motor and so on.
Technological Devices and Digital Competences: A Look into the Digital Divides for University Continuity during the COVID-19 Pandemic

Virtual education has gained great relevance in recent years, due to the pandemic. The access to electronic devices and services represents an urgent necessity and thus the concern for acquiring digital competences, which allow a proper interaction within the teaching–learning process. Recent studies have demonstrated the importance of having digital resources and the adaptability of their use from the university students’ homes during the pandemic crisis. This research intends to identify the relevant challenges regarding the accessibility to technological devices and digital competences that university students had to face to obtain suitable learning during the lockdown, due to the pandemic. The sample information consisted of 9326 Peruvian university students. The data was obtained from the National Homes Survey from the Statistics and Information National Institute, and it was distributed in twenty-five regions (in groups of five macro-regions) over a period of three years (2019–2021). The results showed significant differences in the number of students with internet access from home: between 40% and 60% access classes with a desktop or laptop, and digital competences have improved in the last year. This is evidence that digital divides set limits on the opportunities for a quality education.
Design of Advanced Encryption Standard using Verilog HDL

Cryptography deals with the security and integrity of the data. Initially many algorithms were developed to encode and decode the data but for securing large and confidential data, the existing algorithms are not reliable, so AES was developed as a new standard for encrypting and decrypting data. Initially it is mainly used to protect highly confidential data, later many applications in networking began using AES as a standard to protect their data. It is primarily used to protect sensitive data, though it is also applied to network backends to enhance data security. AES employs blocks that are 16 bytes long, and its keys can range in size from 128 bits to 256 bits. The main purpose of using Verilog instead of standard VHDL is that it provides very less operation time and the propagation delay to encode and decode the data are comparatively less than other HDL languages. Before AES, DES was used as the encryption standard. The main drawback of DES is that the fixed key size of 56 bits. This problem is solved by AES by providing the flexibility of using required variable key size.
Smart Autonomous Voice Control Obstacle Avoiding Robot

The study aims to build voice-controlled robots with autonomous braking, obstacle avoidance, and speed reduction capabilities. This research will enable the development of robots that can accurately and autonomously assess and respond to environmental conditions, such as obstacles in their path. The primary objective of the robot is to use voice commands to steer the robotic vehicle and analyze speech input to carry out the necessary actions to avoid obstacles. The research is expected to significantly improve the safety of autonomous vehicles by allowing them to detect and respond in real time to changes in their environment. The robot is voice-activated and connected to an Android smartphone through Bluetooth. The robot will also be able to recognize obstacles with the help of an ultrasonic sensor. The physically disabled and the primarily blind will benefit from further developments in this technology, among many other groups of people. This technology can fundamentally alter how people view mobility and provide many opportunities to those who would otherwise need to rely on other modes of transportation.
HIEF: a holistic interpretability and explainability framework

ABSTRACTABSTRACTMany applications are driven by Machine Learning (ML) today. While complex ML models lead to an accurate prediction, their inner decision-making is obfuscated. However, especially for high-stakes decisions, interpretability and explainability of the model are necessary. Therefore, we develop a holistic interpretability and explainability framework (HIEF) to objectively describe and evaluate an intelligent system's explainable AI (XAI) capacities. This guides data scientists to create more transparent models. To evaluate our framework, we analyse 50 real estate appraisal papers to ensure the robustness of HIEF. Additionally, we identify six typical types of intelligent systems, so-called archetypes, which range from explanatory to predictive, and demonstrate how researchers can use the framework to identify blind-spot topics in their domain. Finally, regarding comprehensiveness, we used a random sample of six intelligent systems and conducted an applicability check to provide external validity.KEYWORDS: Explainable AI (XAI)machine learninginterpretabilityreal estate appraisalframeworktaxonomy Disclosure statementNo potential conflict of interest was reported by the author.
Azeroth: Auditable Zero-Knowledge Transactions in Smart Contracts

With the rapid growth of the blockchain market, privacy and security issues for digital assets are becoming more important. In the most widely used public blockchains, such as Bitcoin and Ethereum, all activities on user accounts are publicly disclosed, which violates privacy regulations such as EU GDPR. Encryption of accounts and transactions may protect privacy, but it also raises issues of validity and transparency. While encrypted information can protect privacy, it cannot alone verify the validity of a transaction. Additionally, encryption makes it difficult to meet anti-money laundering regulations, such as auditability. In this paper, we propose <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\sf Azeroth}$ </tex-math></inline-formula> , an auditable zero-knowledge transfer framework. <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\sf Azeroth}$ </tex-math></inline-formula> connects a zero-knowledge proof to an encrypted transaction, enabling it to check its validation while protecting its privacy. <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\sf Azeroth}$ </tex-math></inline-formula> also allows authorized auditors to audit transactions. <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\sf Azeroth}$ </tex-math></inline-formula> is designed as a smart contract for flexible deployment on existing blockchains. We implement the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\sf Azeroth}$ </tex-math></inline-formula> smart contract, and execute it on various platforms including an Ethereum testnet blockchain, and measure the time to show the practicality of our proposal. The end-to-end latency of a privacy-preserving transfer takes about <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4.4s$ </tex-math></inline-formula> . In particular, the client’s transaction generation time with a proof only takes about <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.9s$ </tex-math></inline-formula> . The security of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\sf Azeroth}$ </tex-math></inline-formula> is proven under the cryptographic assumptions.