Reviewerly/exHarmony huggingface dataset:
An elitist whale optimization algorithm with the nonlinear parameter: Algorithm and application

Abstract To address the problem that the whale optimization algorithm tends to fall into the local optimum and fails to maintain a balance between exploration and exploitation, an elitist whale optimization algorithm with the nonlinear parameter (EWOANP) is proposed in this paper. An elitist strategy based on the random Cauchy mutation is used in the shrinking encircling mechanism to increase the chance of escaping the local optimum. Cleverly, the strategy is to generate mutation solutions based on the random Cauchy mutation, after which the better population is selected to proceed to the next iteration. Then, a nonlinear parameter is used in the logarithmic spiral mechanism to balance exploration and exploitation. Various numerical optimization experiments are performed based on the IEEE CEC2020 benchmark suite and compared with eleven other algorithms. The results show that EWOANP outperforms most competitors in numerical optimization. Finally, the backpropagation neural network is optimized by EWOANP to build a prediction model for the sulfur content in the molten iron. The experimental results based on production data indicate that the proposed prediction model has a relatively small fluctuation in errors. Compared to the other seven competitors, the proposed model has a better prediction performance with and =0.916619.
Applicability of deep neural networks for lithofacies classification from conventional well logs: An integrated approach

Parametric understanding for specifying formation characteristics can be perceived through conventional approaches. Significantly, attributes of reservoir lithology are practiced for hydrocarbon exploration. Well logging is conventional approach which is applicable to predict lithology efficiently as compared to geophysical modeling and petrophysical analysis due to cost effectiveness and suitable interpretation time. However, manual interpretation of lithology identification through well logging data requires domain expertise with an extended length of time for measurement. Therefore, in this study, Deep Neural Network (DNN) has been deployed to automate the lithology identification process from well logging data which would provide support by increasing time-effective for monitoring lithology. DNN model has been developed for forecasting formation lithology leading to the optimization of the model through the thorough evaluation of the best parameters and hyperparameters including the number of neurons, number of layers, optimizer, learning rate, dropout values, and activation functions. Accuracy of the model is examined by utilizing different evaluation metrics through the division of the dataset into the subdomains of training, validation and testing. Additionally, an attempt is contributed to remove interception for formation lithology prediction while addressing the imbalanced nature of the associated dataset as well in the training process using class weight. It is assessed that accuracy is not a true and only reliable metric to evaluate the lithology classification model. The model with class weight recognizes all the classes but has low accuracy as well as a low F1-score while LSTM based model has high accuracy as well as a high F1-score.
Planning Purposeful Activities Autonomous Intelligent Robot with Knowledge Update in Short-Term Memory

The main problems associated with the creation of autonomous intelligent robots capable of performing various complex tasks in a priori undescribed unstable problematic environments, based on the processing of knowledge presented in an abstract way, are outlined. To store typical elements of an abstract knowledge representation model, the article recommends using long-term and short-term memory. Long-term memory with associative search and data retrieval is designed to permanently store information necessary for planning a variety of purposeful activities that provide the robot with the ability to solve various complex behavioral tasks. In short-term memory, submodels of knowledge representation are entered from long-term memory, which are necessary for solving the current task of a certain type in the short term, related to the fulfillment of the task formulated for the autonomous intelligent robot. At the same time, with each change in the type of the current task of behavior being solved by an autonomous intelligent robot, a corresponding update of knowledge stored in short-term memory is simultaneously carried out. Original constructions of typical elements of the model for representing abstract knowledge in the form of various behavioral skills, set regardless of a particular subject area, have been developed. This approach to building a knowledge representation model allows autonomous intelligent robots to adapt to the current operating conditions and, on this basis, organize purposeful activities in complex unstable problematic environments. Various tools and rules for processing abstract knowledge are proposed, which endow autonomous intelligent robots with the ability to eliminate the differences between the current and target situation of the problem environment both in terms of the values of structurally equivalent relations of the same name in them, and in the current states of objects in the environment. This, in turn, makes it possible to create intelligent problem solvers for autonomous intelligent robots for various purposes, capable of performing complex tasks in unstable a priori uncertain conditions of a problematic environment.
An efficient multimodal sentiment analysis in social media using hybrid optimal multi-scale residual attention network

Abstract Sentiment analysis is a key component of many social media analysis projects. Additionally, prior research has concentrated on a single modality in particular, such as text descriptions for visual information. In contrast to standard image databases, social images frequently connect to one another, making sentiment analysis challenging. The majority of methods now in use consider different images individually, rendering them useless for interrelated images. We proposed a hybrid Arithmetic Optimization Algorithm- Hunger Games Search (AOA-HGS)-optimized Ensemble Multi-scale Residual Attention Network (EMRA-Net) technique in this paper to explore the modal correlations including texts, audio, social links, and video for more effective multimodal sentiment analysis. The hybrid AOA-HGS technique learns complementary and comprehensive features. The EMRA-Net uses two segments, including Ensemble Attention CNN (EA-CNN) and Three-scale Residual Attention Convolutional Neural Network (TRA-CNN), to analyze the multimodal sentiments. The loss of spatial domain image texture features can be reduced by adding the Wavelet transform to TRA-CNN. The feature-level fusion technique known as EA-CNN is used to combine visual, audio, and textual information. The proposed method performs significantly better than the existing multimodel sentimental analysis techniques of HALCB, HDF, and MMLatch when evaluated using the Multimodal Emotion Lines Dataset (MELD) and EmoryNLP datasets. Also, even though the size of the training set varies, the proposed method outperformed other techniques in terms of recall, accuracy, F score, and precision and takes less time to compute in both datasets.
Control of a Wheeled Robot on a Plane with Obstacles

The work proposes an algorithm for controlling a wheeled robot in an environment with static and dynamic obstacles. A wheeled robot (WR) consists of a platform, two wheels with a differential drive and one roller, which is used solely for the stability of the structure and does not affect the dynamics of the system. The robot’s motion algorithm assumes its movement from the starting point to the final point in an environment with obstacles. The robot’s motion program is specified through servo-constraints, and the algorithm that implements the motion program is based on the potential field method. In the case of a dynamic obstacle, a repulsive field of a shape elongated in the direction of movement of the obstacle is constructed, allowing the robot to safely go around it. It is possible to change the geometric dimensions of the field using the entered numerical parameters. An algorithm for overcoming a potential hole by a robot is presented, according to which the robot is taken out of the potential hole and directed to a global goal by an introduced fictitious point located outside the critical region (local minimum region) and having its own attractive field. The paper presents the results of numerical simulation of the robot’s movement both in an environment with static and dynamic obstacles, as well as the results of a numerical experiment with overcoming the region of a potential well. Graphs of the required mechanical parameters are presented. The results of numerical simulation confirm the effectiveness of the proposed algorithms.
Multidimensional Multi-Connected Fuzzy Interval-Logic Controller

The key features and the principle of operation of a multidimensional fuzzy interval-logic controller with interconnected adjustable parameters developed by the authors are considered. The scheme of interpretation of continuous physical quantities by an equivalent set of terms and the basic version of the block diagram of the regulator are presented. The main elements of the logical output block are described, including the block of production rules, which includes a predefined database of values of control actions on control objects in the form of ordinal numbers of terms of output continuous quantities. The advantages and features of setting up the mutual connections between the input and output continuous values of the regulator are considered, which is expressed in determining the functional dependencies between them. The set of parameters possessed by the terms of input continuous quantities in relation to specific output continuous quantities of the regulator is described. The methods of defuzzification of the values of output continuous quantities within the boundaries of their terms are shown. The possibility of combining input and output continuous quantities into groups when setting up mutual connections between them is described. The mechanism of determining groups of interrelated continuous quantities is illustrated, the total number of which is determined by forming a matrix of relationships, according to the author’s scheme. The algorithm of sequential processing of the contents of the cells of the matrix of relationships, consisting of three steps, is considered. Expressions are given for calculating the basic parameters of a multidimensional fuzzy interval-logic controller with interrelated adjustable parameters. The results of a computational experiment to reduce the system of production rules of the regulator in determining the relationships between input and output continuous quantities are presented.
Optimizing AI Social Chatbots for Relational Outcomes: The Effects of Profile Design, Communication Strategies, and Message Framing

With more corporations incorporating artificial intelligence (AI) tools like social chatbots into their public communication practices, we explore optimal chatbot designs for organization-public relational outcomes. Our 2 × 2 × 2 factorial web experiment with eight custom-designed chatbots showed that communication strategies (verbal and non-verbal social cues such as emoji, memes, filler words, and response delay) had a significant impact (Cohen’s d = 0.536, standardized coefficient β = 0.438) on chatbot social conversation, the central antecedent to the investigated relational outcomes such as trust in business. Furthermore, the effect of chatbot social conversation is partially mediated by perceived organizational listening, highlighting the importance of listening and its related theories and practices in automated business communication.
Challenging 21st-Century Competencies for STEM Students: Companies’ Vision in Slovenia and Norway in the Light of Global Initiatives for Competencies Development

Rapid technological innovations are changing the jobs that Science, Technology, Engineering, and Mathematics (STEM) students are employed in after completing their education. Therefore, employers are looking for applicants with skills beyond the specialised professional competencies that can be acquired in the chosen subjects during formal education. Higher education institutions (HEIs) have an important role to play in this process, as they endeavour to equip graduates with the skills and knowledge for future jobs. Our study aims to identify the key competencies that Slovenian and Norwegian companies expect from graduates of selected STEM degree programmes in both countries. Based on these findings, the participating HEIs can tailor their degree programmes to the employability requirements of companies. This study first analysed European and international recommendations and initiatives relevant to skills development in STEM employment and education. We selected 30 key competencies and included them in the questionnaires in which companies’ human resources managers (HRMs) defined their attitudes towards employability skills in the future. We focused on companies employing graduates of the Ecotechnologies and Civil and Environmental Engineering programmes of the participating HEIs. The analysis of the results showed that companies in the two countries have different attitudes towards skills. Four competencies were identified as more important in both countries: (i) management and design of processes, technologies, and people’s work, (ii) adaptation to changes, (iii) ability to manage uncertainty, and (iv) innovation. Based on the analysis, we have developed a framework of ten employability skills for STEM students. In addition, this study provides the basis for operating a decision support system (DSS) to monitor the development of students’ competencies. The DSS is being developed as part of the RESPO-VI project supported by the Norway Grants to better match the skills of STEM students as future job seekers with the needs of potential employers. The study’s results will also help other formal and non-formal education adapt their curricula and thus reduce the employability skill gaps between current STEM education and future labour market needs.
Fast Multi-User Searchable Encryption with Forward and Backward Private Access Control

Untrusted servers are servers or storage entities lacking complete trust from the data owner or users. This characterization implies that the server hosting encrypted data may not enjoy full trust from data owners or users, stemming from apprehensions related to potential security breaches, unauthorized access, or other security risks. The security of searchable encryption has been put into question by several recent attacks. Currently, users can search for encrypted documents on untrusted cloud servers using searchable symmetric encryption (SSE). This study delves deeply into two pivotal concepts of privacy within dynamic searchable symmetric encryption (DSSE) schemes: forward privacy and backward privacy. The former serves as a safeguard against the linkage of recently added documents to previously conducted search queries, whereas the latter guarantees the irretrievability of deleted documents in subsequent search inquiries. However, the provision of fine-grained access control is complex in existing multi-user SSE schemes. SSE schemes may also incur high computation costs due to the need for fine-grained access control, and it is essential to support document updates and forward privacy. In response to these issues, this paper suggests a searchable encryption scheme that uses simple primitive tools. We present a multi-user SSE scheme that efficiently controls access to dynamically encrypted documents to resolve these issues, using an innovative approach that readily enhances previous findings. Rather than employing asymmetric encryption as in comparable systems, we harness low-complexity primitive encryption tools and inverted index-based DSSE to handle retrieving encrypted files, resulting in a notably faster system. Furthermore, we ensure heightened security by refreshing the encryption key after each search, meaning that users are unable to conduct subsequent searches with the same key and must obtain a fresh key from the data owner. An experimental evaluation shows that our scheme achieves forward and Type II backward privacy and has much faster search performance than other schemes. Our scheme can be considered secure, as proven in a random oracle model.
Diffractive Deep-Neural-Network-Based Classifier for Holographic Memory

Holographic memory offers high-capacity optical storage with rapid data readout and long-term durability. Recently, read data pages have been classified using digital deep neural networks (DNNs). This approach is highly accurate, but the prediction time hinders the data readout throughput. This study presents a diffractive DNN (D2NN)-based classifier for holographic memory. D2NNs have so far attracted a great deal of attention for object identification and image transformation at the speed of light. A D2NN, consisting of trainable diffractive layers and devoid of electronic devices, facilitates high-speed data readout. Furthermore, we numerically investigated the classification performance of a D2NN-based classifier. The classification accuracy of the D2NN was 99.7% on 4-bit symbols, exceeding that of the hard decision method.
A Secure Blockchain-Enabled Remote Healthcare Monitoring System for Home Isolation

This article presents a secure framework for remote healthcare monitoring in the context of home isolation, thereby addressing the concerns related to untrustworthy client connections to a hospital information system (HIS) within a secure network. Our proposed solution leverages a public blockchain network as a secure distributed database to buffer and transmit patient vital signs. The framework integrates an algorithm for the secure gathering and transmission of vital signs to the Ethereum network. Additionally, we introduce a publish/subscribe paradigm, thus enhancing security using the TLS channel to connect to the blockchain network. An analysis of the maintenance cost of the distributed database underscores the cost-effectiveness of our approach. In conclusion, our framework provides a highly secure and economical solution for remote healthcare monitoring in home isolation scenarios.
40Ar/39Ar and (U-Th)/He constraints on emplacement, exhumation, and weathering of alkaline-carbonatite complexes in the Alto Paranaíba Igneous Province (APIP), Brazil

The timing and rate of emplacement, exhumation, and weathering of alkaline-carbonatite complexes associated with failed rift systems in the South Atlantic provide constraints on the thermotectonic conditions of West Gondwana before, during, and after break-up. 40Ar/39Ar geochronology of biotite crystals show that two alkaline-carbonatite complexes – Araxá and Catalão I – from the Alto Paranaíba Igneous Province were shallowly emplaced simultaneously at ∼ 85 Ma. After rapid cooling, the igneous complexes were rapidly exhumed between 85 and 66 Ma at Araxá (∼ 52 m.Ma-1) and 85 and 45 Ma at Catalão (∼ 25 m.Ma-1). Fast exhumation of the complexes occurred under relatively arid to semi-arid climates, conditions that favored physical weathering and erosion. The eroded material was transported in a NE-SW direction and deposited in the adjacent Paraná and São Sanfranciscana basins. After exhumation, wetter climates and more subdued tectonic conditions favored active widespread vegetation cover that favored reduced erosion and deepening of weathering profiles. After 66 Ma, the Araxá complex denuded at rates of 3.4 m.Ma-1; at Catalão I, denudation rates reduced to 4.7 m.Ma-1 after ∼ 45 Ma. Favorable geological and climatic conditions promoted the formation of deep and chemically stratified lateritic weathering profiles. The exhumed and weathered carbonatites underwent hypogene and supergene enrichment in Nb, P, Ti, and REEs, critical metals needed for the electrification of our energy base. The preservation of the lateritic profiles and their mineral wealth in the continental interior contrasts with the younger and shallower weathering profiles formed on similar lithologies and at equivalent elevations near the Brazilain South Atlantic margin, confirming that climatic and tectonic conditions along the margin were less conducive to the preservation of valuable mineralized systems because of enhaced erosion.
Error Analysis of Pretrained Language Models (PLMs) in English-to-Arabic Machine Translation

Abstract Advances in neural machine translation utilizing pretrained language models (PLMs) have shown promise in improving the translation quality between diverse languages. However, translation from English to languages with complex morphology, such as Arabic, remains challenging. This study investigated the prevailing error patterns of state-of-the-art PLMs when translating from English to Arabic across different text domains. Through empirical analysis using automatic metrics (chrF, BERTScore, COMET) and manual evaluation with the Multidimensional Quality Metrics (MQM) framework, we compared Google Translate and five PLMs (Helsinki, Marefa, Facebook, GPT-3.5-turbo, and GPT-4). Key findings provide valuable insights into current PLM limitations in handling aspects of Arabic grammar and vocabulary while also informing future improvements for advancing English–Arabic machine translation capabilities and accessibility.
Digital Transformation in Tunneling – A Project Report on TransIT

The digital transformation has significantly affected many industries. Recently, it has gained momentum in the Architecture, Engineering, and Construction (AEC) sector in general and tunneling in particular. To support these developments, TransIT represents an inter-university and interdisciplinary research initiative with the goal of advancing digitalization in tunneling. This article discusses the challenges connected with the digital transformation of the tunneling sector. We examine this topic with different use cases demonstrating solutions to those challenges.
Encrypted Video Search with Single/Multiple Writers

Video-based services have become popular. Clients often outsource their videos to the cloud to relieve local maintenance. However, privacy has become a major concern since many videos contain sensitive information. Although retrieving (unencrypted) videos has been extensively investigated, retrieving encrypted multimedia has received relatively rare attention, at best in a limitation of image-based similarity searches. We initiate the study of scalable encrypted video search, enabling clients to query videos similar to an image search. Our modular framework leverages intrinsic attributes of videos, such as semantics and visuals, to effectively capture their contents. We propose a two-step approach whereby lightweight searchable encryption techniques are used for pre-screening, followed by an interactive approach for fine-grained search. Furthermore, we present three instantiations, including one centralized-writer instantiation and two distributed-writer instantiations, to effectively cater to varying needs and scenarios– 1) The centralized one employs forward and backward private searchable encryption [CCS 2017] over deep hashing [CVPR 2020]. 2) Motivated by distributed computing, the multi-writer instantiations building atop HSE [Usenix Security 2022] allows searching the relevant videos contributed by multiple intuitions collaboratively. Our experimental results illustrate their practical performance over multiple real-world datasets, whether in a centralized setting or distributed setting.