Reviewerly/exHarmony huggingface dataset:
A New Principle toward Robust Matching in Human-like Stereovision

Visual signals are the upmost important source for robots, vehicles or machines to achieve human-like intelligence. Human beings heavily depend on binocular vision to understand the dynamically changing world. Similarly, intelligent robots or machines must also have the innate capabilities of perceiving knowledge from visual signals. Until today, one of the biggest challenges faced by intelligent robots or machines is the matching in stereovision. In this paper, we present the details of a new principle toward achieving a robust matching solution which leverages on the use and integration of top-down image sampling strategy, hybrid feature extraction, and Restricted Coulomb Energy (RCE) neural network for incremental learning (i.e., cognition) as well as robust match-maker (i.e., recognition). A preliminary version of the proposed solution has been implemented and tested with data from Maritime RobotX Challenge. The contribution of this paper is to attract more research interest and effort toward this new direction which may eventually lead to the development of robust solutions expected by future stereovision systems in intelligent robots, vehicles, and machines.
Language Politeness Education through Language Behaviour Habits: Concerning the Indonesian and Japanese Viewpoints

Language politeness has universal characteristics. It means that speech communities from any country have their own language politeness based on the applicable norms. Language politeness education starts from family. Meanwhile, outside family communities, such as schools or society, language politeness education is greatly needed to create positive interactions between language users and certain communities. This research discussed language politeness education in two languages (Indonesian and Japanese) using a comparative qualitative case study in library research. The results of the study informed that Indonesian and Japanese are two languages that implement language politeness through both verbal and non-verbal languages. Verbal language is shown by the chosen polite words, while non-verbal language is shown by the speaking body gestures.
Distributed Knowledge Transfer for Evolutionary Multitask Multimodal Optimization

Evolutionary multitasking Optimization (EMTO) is a paradigm that optimizes multiple tasks simultaneously to improve the overall performance of all tasks by seamlessly transferring useful knowledge among them. Although EMTO has received significant interest, rare studies consider handling tasks that are multimodal optimization problems (MMOPs) with multiple global optimal solutions. Due to the multiple different modalities of each task, a major challenge of solving multiple MMOPs is how to extract and transfer knowledge across modalities of different tasks. To this end, this paper designs a distributed knowledge transfer based evolutionary multitask multimodal optimization (EMTMO-DKT) approach for solving multiple MMOPs simultaneously by discovering and utilizing local knowledge across modalities of different tasks. Specifically, we first divide the population of each task into multiple subpopulations, where each subpopulation explores a modality. Then, we propose an evolution path based similarity measurement to measure the local similarities between subpopulations of different tasks. Since the modalities can be locally similar across tasks, we develop a subpopulation cross matching strategy according to the obtained similarities to pair subpopulations of different tasks. In this stage, the successfully paired subpopulations are allowed to transfer knowledge. Finally, the knowledge transfer probability self-adjusting strategy is applied to each subpopulation to balance knowledge transfer and self-evolution, so as to improve search efficiency. In this paper, a set of multitask multimodal optimization test problems are constructed to assess the efficacy of compared algorithms. Experimental results on both the benchmark functions and the real-world optimization problem demonstrate that the proposed algorithm can quickly locate more global optima in comparison with state-of-the-art EMTO and multimodal optimization algorithms.
A Framework for Personalized Human Activity Recognition

In today’s world, Human Activity Recognition (HAR) through video streams is actively used in every aspect of our life, such as automated surveillance systems and sports statistics are computed according to the videos with the help of HAR. Activity detection is not a new subject, and several methods are available. However, the most recent and most promising techniques rely on Convolutional Neural Networks (CNNs). CNNs primary usage is based on a single image frame to perform logical or categorical identification of an object, scene, or activity. We exploit this feature to adapt CNN on video streams to achieve HAR. In this study, we present a Personalized HAR (PHAR) framework that increases activity recognition accuracy with Object Detection (OD). First, we demonstrate the state-of-the-art HAR and OD methods in the literature. Then we illustrate our framework with two new Single Person Human Activity Recognition models. Finally, the performance of the new framework is evaluated with the well-known activity detection methods. Results show that our new PHAR model with 95% accuracy ratio outperforms the CNN-LSTM-based reference model (90%). Moreover, a new metric Average Accuracy Score (AAS) is described in this study, PHAR models approximately have 94% AAS, which is better than the reference model with 89% AAS.
TRANSVERZALNO UČENJE: MEDIJSKA I INFORMACIJSKA PISMENOST NA UNIVERZITETU U SARAJEVU / TRANSVERSAL LEARNING: MEDIA AND INFORMATION LITERACY AT THE UNIVERSITY OF SARAJEVO

In the process of building a platform for media and information literacy, the Institute for Social Science Research, Faculty of Political Sciences, University of Sarajevo included research, development strategy, content design and methodology, public policy, and the future of education as integral components of adapting a specific model, multi-component, hybrid learning for the digital age. This paper presents an overview of the activities and processes operationalized to integrate lifelong learning competencies for the digital age.
Machine-Learning-Based Password-Strength-Estimation Approach for Passwords of Lithuanian Context

In an information-security-assurance system, humans are usually the weakest link. It is partly related to insufficient cybersecurity knowledge and the ignorance of standard security recommendations. Consequently, the required password-strength requirements in information systems are the minimum of what can be done to ensure system security. Therefore, it is important to use up-to-date and context-sensitive password-strength-estimation systems. However, minor languages are ignored, and password strength is usually estimated using English-only dictionaries. To change the situation, a machine learning approach was proposed in this article to support a more realistic model to estimate the strength of Lithuanian user passwords. A newly compiled dataset of password strength was produced. It integrated both international- and Lithuanian-language-specific passwords, including 6 commonly used password features and 36 similarity metrics for each item (4 similarity metrics for 9 different dictionaries). The proposed solution predicts the password strength of five classes with 77% accuracy. Taking into account the complexity of the accuracy of the Lithuanian language, the achieved result is adequate, as the availability of intelligent Lithuanian-language-specific password-cracking tools is not widely available yet.
Maintaining and Monitoring AIOps Models Against Concept Drift

AIOps solutions enable faster discovery of failures in operational large-scale systems through machine learning models trained on operation data. These models become outdated during the occurrence of concept drift, a term used to describe shifts in data distributions. In operation data concept drift is inevitable and it impacts the performance of AIOps solutions over time. Therefore, concept drift should be closely monitored and immediate maintenance to prevent erroneous predictions is required. In this work, we propose an automated maintenance pipeline for AIOps models that monitors the occurrence of concept drift and chooses the most appropriate model retraining technique according to the drift type.
The Significance of Educational Application of Artificial Intelligence and Its Current State in China

The educational application of artificial intelligence (AI) is to integrate AI technology into the educational system including educational components and processes to achieve personalized, inclusive, and more effective education. The adoption of AI technology in Chinese education has considerably improved its efficiency and equity. The purpose of this article is to present the significance of educational application of AI and the status quo of the use of AI technologies in Chinese education.
Subband Thresholding for Near-Lossless Medical Image Compression

Medical images play a prominent role in diagnostic and treatment planning in the medical arena.Medical images are acquired using several medical imaging modalities like Computed Tomography (CT), Magnetic Resonance Imaging (MRI), UltraSound (US), Positron Emission Tomography (PET), and X-Ray.These are used as an image acquisition tool to capture the medical images.Numerous volumes of medical data are produced for processing, storing and transmission over the network for telemedicine services every day.Those images are in the form of multidimensional data with high resolution.Hence, it certainly yields higher bandwidth and storage.Thus, it is very essential for medical image applications to reduce storage and to solve transmission problems.Every information it holds is a matter of human lives.The fast and safe transmission of these data will decrease the mortality rate.Lossy type of image compression technique achieves better compression rate than image quality where lossless compression can only retain the image quality.Therefore, there is an existing need for near-lossless compression which compromises both the compression rate and image quality.In this paper, we have proposed a near-lossless medical image compression technique that thresholds the sub-bands to increase the number of zero coefficients.And as an entropy encoder run-length encoding is applied to compress the medical image to retain the diagnostic information with high compression efficiency with better image quality.Compression Ratio (CR), Peak Signal to Noise Ratio (PSNR), Time Complexity and Bits Per Pixel (BPP) were used as assessment parameters to examine the performance of our proposed method.We achieved an average of 2 BPP and PSNR of 42.43 dB which is higher than the existing methods.
Blockchain-Based Organ Donation Platform: Defeating Trafficking and Ensuring Transparency

Organ donation and organ trafficking are significant global health issues that pose ethical and legal challenges. There is a significant shortage of available organs for transplantation compared to the demand, resulting in the emergence of an illegal market for organs. This black market exploits and takes advan- tage of vulnerable individuals, who are coerced into selling their organs for transplant purposes. To address these issues, this research proposes using blockchain technology as a tool for improving transparency and traceabil- ity in the organ donation and transplantation process. The platform is built on top of the Polygon blockchain, using smart contracts to automate various aspects of the process, such as verifying donor and recipient identities, man- aging organ matching, and releasing organ donation records. Patient infor- mation is securely stored using MongoDB, while decentralized digital identi- ties ensure data security and privacy. An administrative dashboard provides a user-friendly interface for managing the system, and regular data analytics and monitoring track key metrics. The use of blockchain technology has the poten- tial to improve the safety and ethical integrity of the organ transplantation process, combat organ trafficking, and improve the availability of lifesaving organ transplants.
Open Government Data in Pandemic Disease: Encouraging Spatial Policy-Making in East Java and Jakarta

This article aims to elaborate on whether the openness of spatial data by the government has an implication for the formulation of government policies. The case was taken in two provinces, namely the East Java provincial and the Special Capital Region (DKI) Jakarta. The case study method was conducted by referring to a database of documents, journals, news and books relating to the spatial data visualization and provincial government policies. ArcGis application is also used as one method of presenting data. As a result of the elaboration by using the concept of models open government data (OGD) and spatial policy-making, this article discovers facts that models of OGD in East Java are at the four-stage which is data integration between government and social data. However, DKI Jakarta shows the stage of OGD models is in integration government data stage. Both provincial showed that the policy-formulation for Covid-19 are not based on spatial data. Therefore, it can be assumed policy for handling Covid-19 is not effective. Both of provinces shows continually increasing cases of Covid-19 positive patients. These two provinces are predicated as the highest level of Covid-19 cases in Indonesia. From a practical point of view, Indonesia needs to implementing spatial data-based policies to break the Covid-19 chain, as this kind of policy orientation has been successfully implemented by Taiwan which has been successful tackling Covid-19.
A multi-strategy improved tree–seed algorithm for numerical optimization and engineering optimization problems

Abstract Tree–seed algorithm is a stochastic search algorithm with superior performance suitable for solving continuous optimization problems. However, it is also prone to fall into local optimum and slow in convergence. Therefore, this paper proposes an improved tree–seed algorithm based on pattern search, dimension permutation, and elimination update mechanism (PDSTSA). Firstly, a global optimization strategy based on pattern search is used to promote detection ability. Secondly, in order to maintain the diversity of the population, a random mutation strategy of individual dimension replacement is introduced. Finally, the elimination and update mechanism based on inferior trees is introduced in the middle and later stages of the iteration. Subsequently, PDSTSA is compared with seven representative algorithms on the IEEE CEC2015 test function for simulation experiments and convergence curve analysis. The experimental results indicate that PDSTSA has better optimization accuracy and convergence speed than other comparison algorithms. Then, the Wilcoxon rank sum test demonstrates that there is a significant difference between the optimization results of PDSTSA and each comparison algorithm. In addition, the results of eight algorithms for solving engineering constrained optimization problems further prove the feasibility, practicability, and superiority of PDSTSA.
Eulertigs: minimum plain text representation of k-mer sets without repetitions in linear time

Abstract A fundamental operation in computational genomics is to reduce the input sequences to their constituent k -mers. For maximum performance of downstream applications it is important to store the k -mers in small space, while keeping the representation easy and efficient to use (i.e. without k -mer repetitions and in plain text). Recently, heuristics were presented to compute a near-minimum such representation. We present an algorithm to compute a minimum representation in optimal (linear) time and use it to evaluate the existing heuristics. Our algorithm first constructs the de Bruijn graph in linear time and then uses a Eulerian-cycle-based algorithm to compute the minimum representation, in time linear in the size of the output.
Cultural gems linked open data: Mapping culture and intangible heritage in European cities

The recovery and resilience of the cultural and creative sectors after the COVID-19 pandemic is a current topic with priority for the European Commission. Cultural gems is a crowdsourced web platform managed by the Joint Research Centre of the European Commission aimed at creating community-led maps as well as a common repository for cultural and creative places across European cities and towns. More than 130,000 physical locations and online cultural activities in more than 300 European cities and towns are currently tracked by the application. The main objective of Cultural gems consists in raising a holistic vision of European culture, reinforcing a sense of belonging to a common European cultural space. This data article describes the ontology developed for Cultural gems, adopted to represent the domain of knowledge of the application by means of FAIR (Findable, Accessible, Interoperable, Reusable) principles and following the paradigms of Linked Open Data (LOD). We provide an overview of this dataset, and describe the ontology model, along with the services used to access and consume the data.
Adding Semantics for Solving ‘PP Attachment’ in Spanish

This paper describes a semantic module called Disambiguator of prepositions (DISPRE) in English, DESambiguador de PREposiciones in Spanish (hereinafter, DESPRE), that solves the prepositional phrase attachment for Spanish, and complements the syntactic analysis (based on tags and syntactic rules) of project FreeLing.Syntactic analysis is not enough to correctly disambiguate prepositions and attach each prepositional phrase to the appropriate head.This is because, depending on the context, the same preposition conveys different meanings.DESPRE analyzes the preposition and its surroundings, assigning it to its correct sense; once the preposition sense is disambiguated, parsing rules can easily determine which is the right head, thus building the correct dependency tree.In this manner, with the help of the preposition semantics, the sentence is disambiguated, and the prepositional phrase is correctly attached.DESPRE uses an ontology with semantic frames (for each prepositional sense) to disambiguate and solve prepositional phrase attachment.New rules have been added to the FreeLing dependency parser to handle the different preposition senses.Thus, DESPRE, the modified version of FreeLing, uses syntactic and semantic analysis.Several examples show the effectiveness of DESPRE, which was able to attach correctly prepositional phrases that the syntactic analysis of Freeling alone could not do.
A Cross-Domain Generative Data Augmentation Framework for Aspect-Based Sentiment Analysis

Aspect-based sentiment analysis (ABSA) is a crucial fine-grained sentiment analysis task that aims to determine sentiment polarity in a specific aspect term. Recent research has advanced prediction accuracy by pre-training models on ABSA tasks. However, due to the lack of fine-grained data, those models cannot be trained effectively. In this paper, we propose the cross-domain generative data augmentation framework (CDGDA) that utilizes a generation model to produce in-domain, fine-grained sentences by learning from similar, coarse-grained datasets out-of-domain. To generate fine-grained sentences, we guide the generation model using two prompt methods: the aspect replacement and the aspect–sentiment pair replacement. We also refine the quality of generated sentences by an entropy minimization filter. Experimental results on three public datasets show that our framework outperforms most baseline methods and other data augmentation methods, thereby demonstrating its efficacy.
Development of a mathematical model of a stacker crane with regard to energy dissipation

Stacker cranes are widely used in automated warehouses. The actual task is to increase their energy efficiency and productivity. Simulation mathematical models are used for the solution of this problem at the stage of research and development works. We have developed a mathematical model of a rack stacker crane in long spatial displacements taking into account energy dissipation of linear coordinates of the cart and the cargo carriage. The model is a system of two second-order Lagrange differential equations. Partial derivatives of analytical expressions of kinetic and potential energies of the dynamic stacker crane system as well as dissipative Rayleigh function are used for derivation of the differential equations. Different values of dissipation coefficients for two linear coordinates of the stacker crane can be used. Using SimInTech we develop a simulation model of a conventional stacker crane based on the suggested system of differential equations and represented in the form of a block diagram. The developed simulation model is described and an example of its use is given. A complex model of a shelf stacker crane includes as a constituent part a procedure of determining time intervals of equivalent-accelerated motion of the links. Examples of time dependences of the crane bogie and cargo carriage coordinates, drive forces providing the set coordinate dependences, drives work and total work are given. The developed mathematical model of the stacker-crane can be used for the modelling of the processes of the cargo moving along the rack, its raising to the given height corresponding to the rack target cell, its lowering as well as for the estimation of the energy input of the crane.
Towards Adversarial Robustness for Multi-Mode Data through Metric Learning

Adversarial attacks have become one of the most serious security issues in widely used deep neural networks. Even though real-world datasets usually have large intra-variations or multiple modes, most adversarial defense methods, such as adversarial training, which is currently one of the most effective defense methods, mainly focus on the single-mode setting and thus fail to capture the full data representation to defend against adversarial attacks. To confront this challenge, we propose a novel multi-prototype metric learning regularization for adversarial training which can effectively enhance the defense capability of adversarial training by preventing the latent representation of the adversarial example changing a lot from its clean one. With extensive experiments on CIFAR10, CIFAR100, MNIST, and Tiny ImageNet, the evaluation results show the proposed method improves the performance of different state-of-the-art adversarial training methods without additional computational cost. Furthermore, besides Tiny ImageNet, in the multi-prototype CIFAR10 and CIFAR100 where we reorganize the whole datasets of CIFAR10 and CIFAR100 into two and ten classes, respectively, the proposed method outperforms the state-of-the-art approach by 2.22% and 1.65%, respectively. Furthermore, the proposed multi-prototype method also outperforms its single-prototype version and other commonly used deep metric learning approaches as regularization for adversarial training and thus further demonstrates its effectiveness.
Transmission distance in the space of quantum channels

We analyze two ways to obtain distinguishability measures between quantum maps by employing the square root of the quantum Jensen-Shannon divergence, which forms a true distance in the space of density operators. The arising measures are the transmission distance between quantum channels and the entropic channel divergence. We investigate their mathematical properties and discuss their physical meaning. Additionally, we establish a chain rule for the entropic channel divergence, which implies the amortization collapse, a relevant result with potential applications in the field of discrimination of quantum channels and converse bounds. Finally, we analyze the distinguishability between two given Pauli channels and study exemplary Hamiltonian dynamics under decoherence.