Reviewerly/exHarmony huggingface dataset:
FEATURES OF METAHEURISTIC METHODS

The essence of tasks and classification, as well as the necessity of fulfillment of conditions and relations on the set are analyzed. The properties of the equivalence relation are specified. As elementary fragments are analyzed all edges of the graph. The conditions of attachment of an edge are the next: this edge is a ray of an existing star or has no common vertices with already constructed stars of coverage. Emphasis is placed on the lack of optimality of such a solution. The aim of this work is to construct metaheuristics for finding a suboptimal classification defined by a tolerance relation on a finite set. This approach allows one to construct partitions close to optimal sets in accordance with the relation of “proximity” of elements. Moreover, this relationship of proximity is not transitive. The proposed algorithms can find wide application in applied problems related to the problem of object classification by a number of attributes. Such problems often arise in the economic, social and technical sciences. The urgency of the fragmentary structure of the problem was emphasized. Specifies the ability to build classes by looking at the entire list of objects that are classified in a specific order. On the basis of fragmentary structure it is proposed to use evolutionary algorithm. The prospect of using a genetic algorithm to find the best classifications was evaluated. The step-by-step sequence of operations of the genetic algorithm with examples is shown: selection, crossing, mutation, selection. Examples of key operators are given, named crossovers and mutations. A detailed algorithm of the evolutionary model is clearly illustrated. The principle of action of the evolutionary fragmentary algorithm is described in detail. As a set of feasible solutions, a subset of maximal fragments on a given fragmentary structure is considered. The mechanism of checking the quality of the genetic algorithm on a fragmentary structure, which reduces to a lot of variants, is defined. The problem of finding optimal classifications on a finite set is investigated. It is shown that the problem of finding the optimal classification generated by the tolerance relation on a finite set is reduced to the problem of optimization on the set of permutations. A modification of the method of mixed jumping frogs for finding suboptimal solutions of the classification problem is proposed.
The novel combination lock algorithm for improving the performance of metaheuristic optimizers

Nature-inspired population-based metaheuristics are promising search methods for solving optimization problems. In this paper, a novel systematic pre-processing approach, called the combination lock algorithm, for obtaining a good starting point for population-based algorithms is proposed. The proposed algorithm is tested on 32 benchmark unconstrained multidimensional optimization problems of different characteristics that are either unimodal or multimodal, continuous or non-continuous, separable or non-separable, differentiable or non-differentiable. The tests also include four engineering constrained optimization benchmark problems. The experimental results of applying the proposed algorithm for the Particle Swarm Optimization, the Ant Colony Optimization for Continuous Domain, and the Grey Wolf Optimization were compared with the results obtained from the conventional approach of initializing the starting population of population-based metaheuristic methods. The simulation results show the potential of the proposed algorithm as an efficient and reliable approach to enhance the performance of population-based optimization algorithms such that, overall, 50% and up to 100% of the tested problems “across various population size” settings, had either improved or equalled the optimal values when the proposed algorithm was applied.
Seismic Inversion Problem Using a Multioperator Whale Optimization Algorithm

The whale optimization algorithm (WOA) is a metaheuristic algorithm based on swarm intelligence and it mimics the hunting behavior of whales. It has the imperfection of premature convergence into local optima. In order to overcome this disadvantage, a multioperator WOA (MOWOA) is proposed. Four main strategies are introduced to the MOWOA to heighten the search capacity of WOA. The strategies include nonlinear adaptive parameter design, an exploration mechanism of honey badger, Cauchy factor strategy, and greedy strategy. This paper tests the versatility of MOWOA with three different types of benchmark functions, and a kind of seismic inversion problem are trialed run. From the experimental results, the performance of MOWOA outperforms the compared algorithms in global optimization.
Designing togetherness: Mediating digital social touch through footwear

This article investigates the significance of digital social touch in creating and maintaining our relationships to intimate others. It presents ‘Project Re:Connect’ – a speculative design project centred around mediating digital social touch through footwear. The primary objective of the project was to use diegetic props to instigate debate and deliberate decision making regarding the design futures of technologically mediated communication.
A computational model for medical image retrieval using orthogonal moment

This Paper Deals With Image Retrieval Process Of Liver Computer Tomography (Ct) Scan Images Using Orthogonal Moment Features And Content Based Image Retrieval. Medical Images Are Useful Diagnostic Evidence As It Can Provide Vital Information In Anatomical Pathology. The Objective Is To Efficiently Retrieve Medical Images From The Database Using Orthogonal Moments And Content Based Image Retrieval Methods. The Orthogonal Moment Viz Discrete Racah Polynomial, Continuous Legendre Moments And Zernike Moments Are Computed For The Study. The Region Of Interest Based Segmentation And Watershed Segmentation Is Applied To The Preprocessed Input Images And Features Are Extracted Using Orthogonal Moments And Shape And Texture Features Are Extracted Using Content Based Image Retrieval (Cbir). The Performances Of Each Moment In Terms Of Accuracy And Error Rate Are Compared With Cbir.
Optimized Navigation of Mobile Robots Based on Faster R-CNN in Wireless Sensor Network

Background: In recent years, deep learning techniques have dramatically enhanced mobile robot sensing, navigation, and reasoning. Due to the advancements in machine vision technology and algorithms, visual sensors have become increasingly crucial in mobile robot applications in recent years. However, due to the low computing efficiency of current neural network topologies and their limited adaptability to the requirements of robotic experimentation, there will still be gaps in implementing these techniques on real robots. It is worth noting that AI technologies are used to solve several difficulties in mobile robotics using visuals as the sole source of information or with additional sensors like lasers or GPS. Over the last few years, many works have already been proposed, resulting in a wide range of methods. They built a reliable environment model, calculated the position of the model, and managed the robot's mobility from one location to another. Objective: The proposed method aims to detect an object in the smart home and office using optimized, faster R-CNN and improve accuracy for different datasets. Methods: The proposed methodology uses a novel clustering technique based on faster R-CNN networks, a new and effective method for detecting groups of measurements with a continuous similarity. The resulting communities are coupled with the metric information given by the robot's distance estimation through an agglomerative hierarchical clustering algorithm. The proposed method optimizes ROI layers for generating the optimized features. Results: The proposed approach is tested on indoor and outdoor datasets, producing topological maps that aid semantic location. We show that the system successfully categorizes places when the robot returns to the same area, despite potential lighting variations. The developed method provides better accuracy than VGG-19 and RCNN methods. Conclusion: The findings were positive, indicating that accurate categorization can be accomplished even under varying illumination circumstances by adequately designing an area's semantic map. The Faster R-CNN model shows the lowest error rate among the three evaluated models.
Research on Carbon Foam Image Segmentation Based on Deep Learning

As a substitute for metal, carbon foam is vital in the electromagnetic shielding industry. Nevertheless, the diameter and density of carbon foam cells are still mostly assessed manually. This research offers a Deep-Res-MixAttention segmentation method to effectively minimize manual labor and increase measurement efficiency. Moreover, the method consists of two modules: the MixAttention module is intended to improve feature extraction skills, and we use the multiscale deep residual module to collect edge information. In addition to enhancing the segmentation capability of incomplete carbon foam, the loss function is adjusted to address the dataset imbalance issue. Additionally, we propose the bidirectional selection rotation calipers algorithm to intelligently determine the density and diameter. The results reveal that the optimized network’s IoU and acc carbon reach 91.05% and 88.31%. Finally, the calculation errors of the average diameter and density are under control at 1.79% and 7.09%, respectively. The approach has a high application value for assessing the electromagnetic shielding effectiveness of carbon foam.
Few-Shot Specific Emitter Identification Based on Variational Mode Decomposition and Meta-Learning

Recently, deep learning has become the mainstream solution to solve specific emitter identification (SEI) problems. However, because large amounts of labeled signal samples cannot be obtained in noncooperative scenarios, the performance of deep learning-based data-driven methods for SEI was limited. As a result, a novel SEI method targeted on few-shot was proposed in this study. First, the received signal was preprocessed based on variational mode decomposition and the Hilbert analysis to obtain the Hilbert time-frequency spectrum. Subsequently, a classification neural network model was built and trained with a small number of Hilbert time-frequency spectrum samples through meta-learning. This model could identify specific emitters with limited training samples. The experimental results showed that this method accomplishes network training with as few as 80 training samples while obtaining a good level of generalization and effectively identifying different emitter individuals. In addition, this method exhibits a strong robustness to noise by maintaining an identification accuracy of more than 80% in channels with low signal-to-noise ratios. Finally, the proposed method demonstrated better identification performances than other existing methods with its capability to effectively solve SEI problems in the few-shot scenario.
Private Computing Offloading in Edge Cloud via Collaborative Online Learning

Computing offloading based on mobile edge computing (MEC) for mobile devices (MDs) has received great attentions in recent years. Strategy selection is an extremely important part of computing offloading, so how to make an optimal decision quickly and accurately during the computing offloading is a difficult point. Furthermore, MDs are likely to leak personal privacy when interacting with edge cloud, and there is also an issue about commercial privacy leakage between different cloud service suppliers. In this paper, we propose the privacy-protected edge cloud computing offloading (EPCO) algorithm based on online learning to improve the efficiency of computing offloading while ensuring the privacy of system users. Simultaneously, EPCO also supports different MDs customize their privacy level. We prove that adding privacy protection mechanism is almost no effect on the convergence of the algorithm. The simulation results validate our conclusion using a real-world dataset.
Automated multi-class segmentation of digital mammograms with deep convolutional neural networks

Digital mammography (DM) and digital breast tomosynthesis, the gold standards for breast cancer screening, requires correct breast positioning to ensure accuracy. Improper positioning can result in missed cancers, or can lead to additional imaging. We propose an automated deep learning (DL) segmentation approach to perform multi-class identification of regions of interest (ROI) commonly used for identification of poor positioning in mediolateral oblique (MLO) breast views. We hypothesize that by leveraging the capabilities of DL through the use of the well-founded U-Net model architecture, multi-class DL-based segmentation approaches can accurately identify air, parenchyma, pectoralis, and nipple locations within MLO images. In this study, we employed model hyperparameter searches to determine optimal model parameters for our proposed DL architecture, including the optimal loss function configuration; our best model achieved an average Sørensen-Dice coefficient of 0.919 &plusmn; 0.061 on the held-out test set. We identified high levels of localization performance in the nipple ROI. We believe our proposed segmentation model can be a foundational step in further mammogram analysis, such as for breast positioning and localized image processing tools.
OTKI-F: An efficient memory-secure multi-keyword fuzzy search protocol

Many breakthroughs on security and privacy-preserving techniques have emerged to mitigate the trust loss of cloud hosting environment caused by various types of attacks. To enhance memory-level security of multi-keyword fuzzy search, a widely occurred query request, we take the initiative to apply Trusted Execution Environment (a.k.a TEE) technology to our protocol design which provides hardware-based tamper-proof enclaves. Then we propose the Edit Distance-based Obfuscation Mechanism to further protect the query process executed outside TEE against access pattern leakage. With concerns of practicality and performance, we also propose the two-layer fuzzy index structure and Trend-aware Cache. The former addresses the space limitation of TEE memory for searching large datasets, while the latter optimizes the cache utility of TEE with trend-aware coordinator to effectively reduce the communication overhead.
Live coding choreography: real-time programming with Terpsicode

This article aims to explore live coding of choreography as real time scoring of the dance performance it is creating. It will define live coding as a performance practice, and way of creating choreographic scores, as well as consider some forms of esoteric programming languages (esolangs) as forms of multicoding and explanations of text. It will then explore the work Terpsicode, a live coding language created to make choreography in real-time resulting in duets for coder and dancer and discuss the ways in which Terpsicode is used in performance by creating visual scores from dance terminology. Terpsicode is a demonstration not only of live coding choreography but of multicoding, allowing for both text and visuals to notate the dance score in real-time while the dancers are interpreting the score into movement.
Explainability of Text Clustering Visualizations—Twitter Disinformation Case Study

While text clustering methods have been available for decades, there is a paucity of material that would help practitioners with the choice and configuration of suitable algorithms and visualizations. In this article, we present a case study analyzing two disinformation datasets composed of tweets from the era of the 2016 United States Presidential Election. We use this to demonstrate steps for selecting the best configuration of the clustering algorithm and consequently conduct a user experiment for evaluating the comprehensibility of three alternate visualizations. A supplementary GitHub repository contains source code with examples.
Cross-Border, Interoperable Cooperative Intelligent Transportation Systems: Could We Make Them Operational? [Editor’s Column]

Aimed at increasing people’s mobility, the Cooperative Intelligent Transportation Systems (C-ITS) paradigm postulates that all modes of transport, and their means, are interconnected and their operations synchronized in time. This principle is also imposed on C-ITS infrastructures as well as services provided by C-ITS solutions. All of these inevitably require interconnectivity and interoperability among all C-ITS hard and soft components, products, and services. In return, such operational conditions make C-ITS components interdependable.
A Modified Discretized Chaotic Map and Its Generated Pseudo Binary Random Number

In this manuscript, a modified discretized chaotic map is developed which generates a pseudo-random binary number. This Modified Discretized Chaotic Map (MDCM) not only enlarges the range of system parameter of conventional chaotic map but also improves its performance in terms of key space. The numbers generated from MDCM are random in nature and their randomness is investigated with the help of empirical Probability Density Function (PDF) and Lyapunov Exponent. To investigate the randomness of the numbers, these numbers are quantized into two levels and encoded into 1-bit pseudo random binary numbers and then their randomness behavior is investigated by NIST's (National Institute of Standards and Technology) randomness testing methods. The proposed MDCM is compared with the conventional chaotic map in terms of their Lyapunov Exponent. The numbers generated from MDCM can be used not only in various applications of image processing such as image cryptography, image encryption and others, but also applicable where pseudo numbers are necessarily required.
Inhomogeneous deep Q-network for time sensitive applications

Deep Q-network (DQN) has attracted increasing attention from both industry and academic communities. Existing methods mostly formulate the decision process as discrete agent-environment interactions, while the intervals between successive interactions are largely neglected, which may otherwise reveal important signals in real-world applications. To bridge this gap, this paper proposes to explicitly model the time intervals in DQN. Specifically, we first cast the agent-environment interactions onto a continuous time dimension, and then define a time-aware learning objective and the corresponding Bellman operator. For sample efficient training, we approximate the Q-function with a neural network, where the time information is modeled by the point process. The intensity function in point process and Q-function are seamlessly integrated by sharing the same history summarization module, such that the time interval information can directly influence the model optimization process. To close the gap between the approximated and optimal Q-function, we theoretically analyze the sample complexity of our model by deriving the finite time bound in continuous time. We conduct both simulation and real-world experiments to demonstrate our model's effectiveness.
Estimation of optimized window size for hybridized kNN-random forest algorithm based image demosaicing

Image Demosaicing is gaining popularity in the field of image processing as it helps in identifying the missing elements by using already known value of surrounding pixels obtained from color filter array overlaid on bayer pattern. The proposed work aims at calculating the image quality metrics (Signal to Noise Ratio and Peak Signal to Noise Ratio) for finding the optimized window size by performing image demosaicing based on two machine learning algorithms i.e. hybrid kNN and random forest algorithm with 20 learning cycles. To pick out the best out of many possible window sizes (3×3, 5x3, 5x5, 7x5), a set of standard images from Kodak database have been taken into consideration. The proposed work of hybrid algorithm has been implemented in MATLAB tool. The findings of the proposed work show that 7x5 window size outperforms the other window sizes for almost all the images as well as from its counterpart kNN only.
An Analysis of Errors on Koreans" Learning Pattern of "De" Complement Phrase : With HSK Corpus of Beijing Language and Culture University

This is a study on how Korean learners use ‘de’ complement, which is classified into ‘potential complement’, ‘degree complement’ and ‘stative complement’. Potential complement written by Korean learners include confusion with modal verbs sentences, auxiliary word errors, confusion with adverb phrases, incorrect use of complements, errors in ‘ba’ phrase, word order errors, and predicate omission or replacement errors. Degree complement written by Korean learners include incorrect use of complements, auxiliary word errors, redicate replacement errors, and confusion with adverb phrases. Stative complement written by Korean learners include auxiliary word errors, incorrect use of complements, errors in sentences with objects, errors in adverbs of degree, descriptive errors, and confusion with adverbial phrases.BR In order to reduce errors in potential complement, prior learning on resultative complement and directional complement, which are the basis for forming potential complement structure, is necessary. In order to distinguish between potential complement phrases and sentences using modal verbs, it is necessary to practice various situations with contexts such as ability, subjective or objective conditions, and permission. It is also essential to learn that potential complement is not used in ‘ba’ phrase. Since degree complement is not familiar to Korean learners, learners need to learn the detailed characteristics of each form to use what is appropriate for the context. It should also be emphasized that the words that can become term are limited, and in particular, action verb and degree complement cannot be used together. In the case of stative complement, it should be emphasized that the adverb of degree is used when adjective is used as a stative complement. In many cases, ‘de’ complement phrase and adverb phrase are easily confused. However, even if it has the same meaning in Korean, it should be taught that the sentence should be expressed differently depending on where the speaker
Method of Quantitative Analysis of Cybersecurity Risks Focused on Data Security in Financial Institutions

In this paper, a quantitative analysis method is proposed to calculate the risks from cyber-attacks focused on the domain of data security in the financial sector. Cybersecurity risks have increased in organizations due to the process of digital transformation they are going through, reflecting in a notorious way in the financial sector, where a considerable percentage of the attacks carried out on the various industries are concentrated. In this sense, risk assessment becomes a critical point for their proper management and, in particular, for organizations to have a risk analysis method that allows them to make cost-effective decisions. The proposed method integrates a layered architecture, a list of attacks to be prioritized, and a loss taxonomy to streamline risk analysis over the data security domain including: encryption, masking, deletion, and resiliency. The layered architecture considers: presentation layer, business logic layer, and data management layer. The method was validated and tested by 6 financial companies in Lima, Peru. The preliminary results identified the applicability of the proposed method collected through surveys of experts from the 6 entities surveyed, obtaining 85.7% who consider that the proposed three-layer architecture contains the assets considered critical.