Reviewerly/exHarmony huggingface dataset:
Improved Salp Swarm Optimization Algorithm for Engineering Problems

This study aims to increase the performance of the Salp Swarm optimization algorithm (SSA), which was inspired by nature. Lévy flight and Logarithmic Spiral are two new and successful techniques integrated into the SSA optimization algorithm concurrently to maintain a high level of global exploration and a good balance between global and local searches. The Lévy flight search technique is used to control the global search and salp-position updating mechanism. The Logarithmic Spiral method is used to improve the quality of the solution, balance local development abilities, and enhance the global search capabilities of the algorithm. The suggested ISSA’s performance is assessed by addressing different engineering problems. The results showed considerable effectiveness compared to the SSA technique and showed superiority and efficacy when comparing test results to other meta-heuristic algorithms.
Evaluation of Machine Learning Performance Based on BERT Data Representation with LSTM Model to Conduct Sentiment Analysis in Indonesian for Predicting Voices of Social Media Users in the 2024 Indonesia Presidential Election

In this era, the innovation of science and technology has changed rapidly, such as Artificial Intelligence (A.I.) which has helped a lot in human life. Deep learning (DL) as part of A.I. is the development of one of the machine learning models, namely the neural network. With many neural network layers, deep learning models can perform feature extraction and classification processes in a single architecture. This model has proven to perform state-of-the-art machine learning techniques in text classification, pattern recognition, speech, and imagery. Various text classification tasks, including sentiment analysis, have gone beyond AI-based approaches in Deep Learning models. Text data can come from multiple sources, including social media, such as comments on videos on YouTube. Sentiment analysis, one of the opinion mining, is a computational study that analyzes people's opinions from texts. In this research, machine learning performance analysis is carried out on a deep learning method based on BERT data representation with the LSTM method. The implementation of the model uses youtube commentary data on political videos related to the 2024 presidential election in Indonesia; performance analysis is carried out using accuracy, precision, and recall metrics. In this study, the accuracy of the BERT-LSTM model outperformed the BERT model with an accuracy of 0.8783.
The Impact of Virtual Reality Toward Telemedicine: A Qualitative Study

Telemedicine is quickly becoming an essential asset in the healthcare industry today. After COVID, the combination of virtual reality (VR) technology and telemedicine is quickly becoming a safe and effective solution for patients. Despite the advantages of employing VR in medical education and treatment, various problems and limits lead to the technology’s ineffectiveness or misuse. As a result, addressing potential problems associated with VR could be beneficial in the strategic decision-making process for implementing and developing this technology in the healthcare industry. This research used case study method to identify current issues of VR technology adoption at a large US hospital system. The findings of this qualitative study explore potential concerns and limitations of current VR technology. Suggestions and insights are highlighted to benefit researchers and practitioners.
Formation of metasubject results of primary school children in computer science lessons in a digital distributed information and educational environment

The article deals with the problem of creating conditions for the formation of metasubject educational results of primary schoolchildren, including communicative and regulatory universal educational actions, in the context of the digitalization of education. The analysis of scientific literature has shown that after the transition of the initial stage of education to the second generation standard in 2010, this problem still occurs. The article describes the possibilities of implementing joint network activities of elementary school students within the framework of the academic subject “informatics” in a specially organized digital educational environment that promotes the formation of universal educational actions by gradually increasing the complexity of network communication through the joint performance of network tasks based on a four-level model of network interaction. Positive influence of the inclusion of network interaction in lesson activities in elementary school on students’ readiness to carry out training in a remote mode has been substantiated.
Low Complexity Soft Decoding With Superposition of Hard Decision Vectors in Lattice Reduction-Aided Linear Receivers

This paper proposes a novel approach to low complexity soft input decoding for lattice reduction-aided MIMO receivers. The proposed approach feeds a soft input decoder with soft signals made from hard decision signals generated by using a lattice reduction-aided linear detector. The soft signal is a weighted-sum of some candidate vectors that are near by the hard decision signal coming out from the lattice reduction-aided linear detector. This paper proposes a technique to adjust the weight adapt to the channel for the higher transmission performance. The transmission performance is evaluated in a 4 x 4 MIMO channel. When a serial interference canceller is used as the linear detector, the proposed technique achieves about 2.0 dB better transmission performance at the BER of 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-5</sup> than the decoder fed with the hard decision signals, even though the proposed soft input decoding can be implemented with small additional complexity.
OLAP over Big COVID-19 Data: A Real-Life Case Study

This paper focuses the attention on a real-life case study represented by the design, the development and the practice of OLAP tools over big COVID-19 data in Canada. The OLAP tools developed in this context are further enriched by machine learning procedures that magnify the mining effect. The contribution presented in this paper also embeds an implicit methodology for OLAP over big COVID-19 data. Experimental analysis on the target case study is also provided.
Hazy-to-Clear Translation with Contrastive Regularization

Single Image Dehazing is an under-constrained challenge in computer vision due to the severe information degradation. Previous prior-based methods solve this problem based on various summarized observation on hazy or clear images while disciplined priors can hardly fit diverse complex hazy situations. Existing learning-based methods can produce more natural images while most of them focus on exploiting clear images while ignoring hazy images. Therefore, we proposed an novel Hazy-to-Clear translation network for single image dehazing, which equipped contrastive regularization built upon contrastive learning to make the best of both the hazy and clear images as negative and positive samples respectively. We termed our de-hazing network as HCTCR-Net, which consists of two cooperative branches: Hazy-to-Clear Translation Branch(HCTB) and Contrastive Regularization Branch(CRB). Specifically, HCTB take full advantage of the mutual resistance between the generator and discriminator to explore the latent feature distribution and guide the hazy-to-clear translation mapping by advesarial learning. Simultaneously, CRB devoted to pulling the restored images approach clear images and pushing far away from hazy images by contrastive learning, to implicitly regularize the embeddings of the restored image, hazy and clear images in representation space. Extensive experiments demonstrates that our method achieves outstanding dehazing performance on both synthetic and real-world dataset.
Towards Efficient Data Access Through Multiple Relationship in Graph-Structured Digital Archives

The research field of digital libraries mainly deals with data with graph structure. Graph database management systems (GDBMSs) are suitable for managing data in the digital library because the data size is large and its structure is complex. However, when performing a non-simple search or analysis on a graph, GDBMSs cannot avoid reaching already-scanned nodes from different starting nodes by repeatedly traversing edges such as property paths pattern in SPARQL. Therefore, when a GDBMS reaches high degree nodes, the number of graph traversals increases in proportion to the number of its adjacent nodes. Consequently, the cost of traversing multiple paths extremely increases affected by nodes connected enormous the number of edges in conventional GDBMSs. In this paper, we propose a data access approach by repeatedly traversing edges belonging to a specific relationship or anything one while distinguishing between high degree nodes and low degree ones. Finally, a result of our experiment indicated our approach can increase the speed of repeat traversals by a factor of a maximum of ten.
Development of engineering university educator’s creativity within the framework of the discipline “innovations in engineering pedagogy”

The research is intended to deal with the requirements that society and dynamically changing reality impose on engineering educator. The new generation of educators must ensure development and implementation of new pedagogical technologies based on rapidly developing information and telecommunication technologies, taking into account modern scientific and industrial technologies. To cope with these requirements engineering educator must be creative. Within the framework of ERASMUS + project “EngineeriNg educaTors pEdagogical tRaining” (“ENTER”), Kazan National Research Technological University in cooperation with thirteen universities from Europe, Kazakhstan and Russia, develop a Multi-Level Modular Curricula “Innovative Pedagogy for Engineering HEIs’ Educators” (iPET) for the continuing professional training of “educator of the future”. One of iPET’s disciplines “Innovations in Engineering Pedagogy” is designed to promote creativity of engineering educator and the process and the requirements to its content designing are shown in this article.
Addressing ICS Security Challenges using Simplex Architecture

Safety-critical services managed by Industrial Control Systems (ICS) are now far more exposed to external threats. Security frameworks that ensure that the physical systems are fail-safe when compromised due to unforeseen vulnerabilities such as zero-day exploits are desired. We survey security challenges and mitigation strategies used for protecting ICS. We explore different types of vulnerabilities and elucidate how they have been exploited in the past. We identify key ICS attributes that differentiate them from traditional IT systems deployed in enterprise networks. Further, we show how these attributes can be used by designing a security framework based on Simplex Architecture. We evaluate this framework on an ICS testbed and demonstrate its effectiveness in ensuring the safety of the physical process system even under external attacks.
Social Relationship Recognition Based on Shifted Windows Transformer and Dynamic Attention Graph Neural Network

Image-based social relationship recognition is a challenging computer vision task. Understanding social relationships in images helps to build better intelligent systems. The features of people or objects extracted from the image will greatly affect the recognition of social relationships. In addition, most previous studies are limited to independently inferring the relationships between people. That is, if there are three pairs of relationships in an image, the network will process them three times independently rather than jointly. In order to get more detailed features and avoid unreasonable social relationships caused by inferring social relationships independently, in this paper, we propose to use Shifted Windows Transformer (Swin Transformer) to extract more expressive features and graph neural network based on dynamic attention to jointly infer all relationships between people in images. Experimental results show that the proposed method improves the performance of social relationship recognition on two benchmark datasets.
Forward and Backward Propagation of Stereo Matching Cost for Incremental Refinement of Multiview Disparity Maps

This study proposes a multiview stereo (MVS) method that is based on the forward and backward propagation of matching costs for the incremental refinement of multiview disparity maps. The volume-based MVS method requires numerous three-dimensional (3D) memory volumes to compute, store, and process the stereo matching costs. However, owing to memory limitations, conventional MVS methods allocate the memory of the 3D cost volumes only to the reference and its neighbor views. Thus, each reference view can only use the matching costs from a limited number of neighbor views. This study addresses this inherent MVS problem and proposes a new method by employing a forward and backward cost propagation (FBCP). First, a subpart of the input views is used to obtain disparity maps with a dense MVS method. Once all matching costs of the subpart views are sufficiently refined, the FBCP is performed for a new neighbor view. Immediately after the cost volume of the new view is computed, all matching costs of the subpart are forward propagated and fused with the initial cost of the new view. Furthermore, the new fused cost is backward propagated into the subpart to refine the previous costs again using the new fused cost. All cost volumes can be incrementally computed and refined without any limitation on the number of views using the proposed FBCP scheme. In the final step, all disparity maps are obtained from the refined cost volumes and fused into single point clouds. Moreover, we propose the use of surface consensus to obtain accurate fused point clouds for the fusion of the disparity maps. The performance of the proposed method is evaluated using the fused point clouds. The proposed method achieves less than 0.5 mm in mean distance error and about 82 percentage F-score within 2 mm distance threshold value.
Human Fall Detection Based on Unidirectional Multi-Scale Spatial Encoder and Bidirectional Temporal Decoder Neural Network

According to a statistic report from World Health Organization (WHO), fall is the second leading cause of death. They even add that residents with a disability who live in the nursing home or chronic care facility have a higher risk for falls. Approximately 30 to 50% of them had encountered fall every year, about 40% of them had more than once. Based on a statistic report released by the National Health Insurance Administration of Taiwan in 2018, fall is the second leading cause of death for people above 65 years old. Based on the population record, Taiwan will turn Super-aged society in 2025. The increasing demand and recent success of deep learning technology in the various research fields, especially computer vision, encouraged us to develop a deep learning based fall detecting system. Through this system, we aim to reduce the detection time of falls. A Unidirectional Multi-Scale Spatial Encoder and Bidirectional Temporal Decoder neural network is proposed to enhance performance for human fall detection. Additionally, we also utilize an auxiliary dataset "UCF50" to enhance the model's spatial recognition ability. The proposed method was able to achieve accuracy of 99.7% and F1-score of 99.97 at URFD.
Multi-robot Safe Navigation Under Localization Uncertainty

This paper introduces a distributed avoidance collision algorithm for multi-robot that can account for the uncertain localization of each robot. The algorithm in this paper can safely drive the robot to its goal position by using local information while ensuring that it does not collide with other robots in the same environment during the movement. The algorithm achieves the above target by adding a buffer for each robot based on the Voronoi diagram called buffer Voronoi cell (BVC). The operational range of each robot retreat a safe distance based on the VD-based boundary to ensure avoidance of collision of robots. This paper demonstrates the efficacy of these algorithms via a series of simulations with different uncertain localization ranges of robots while exploring these effects on robot search time and path length. The results show that robots can safely move to their goal position in uncertain localization ranges. The algorithm also solves the deadlock problem that often occurs in multi-robot by obeying the right-hand rule for each robot in the same environment.
Detection of corpora lutea in ovary stained tissue with Deep Learning

In the field of health sciences, the study of the various organic tissues in living beings is of great importance. This can give indications of the physiological and/or pathological state, since many tissue investigations are carried out for the purposes of diagnosing and treating various pathologies as early as possible. However, such investigations are complex processes, often involving manual methods and traditional techniques requiring many working hours in order to obtain conclusive results. This is the case with ovarian tissue histology, where the results of a morphometric evaluation are of great importance as it is the basis on which ovarian failure is identified, thus allowing access to diagnosis and treatment. However, thanks to developments in computer science, these processes are increasingly being optimized through the integration of intelligent systems that perform tasks autonomously, improving working times while delivering highly efficient and accurate results. These intelligent models are machine learning algorithms trained with sets of representative samples of a study population. These sets of samples are called datasets and are the most critical and crucial stage in process automation. This is why we have created a dataset of images of ovarian preparations with labels for a structure called corporea lutea. We have also trained a machine learning algorithm with this dataset as an input in a supervised learning to, finally, provide a useful tool for research.
σ-threshold Bayes Filter in Unknown Birth Background with Multi-Bernoulli Finite Sets

Multiple object tracking faces a challenge of realistically modelling birth background in the premise of keeping the efficiency of filtering. Existing adaptive birth models only pay attention to modeling the birth density, simply assuming the birth probability (BP) constant, resulting inaccurate birth description and deteriorated tracking performance. Moreover, the adaptive birth models incur much heavier computational burden, which greatly limits the real-time capability. The paper gives an efficient adaptive birth intensity cardinality balanced multi-target multi-Bernoulli (CBMeMBer) filter, capable of truly adapting birth as well as effectively achieving good tracking performance via the adaptive calculation of the BP by pre-processing, and reducing the unnecessary likelihood calculations by a measurement noise (MN)-based threshold.
Using Spherical Harmonics for Navigating in Dynamic and Uncertain Environments

In this paper, we present a novel approach for local motion planning in an unknown environment populated by static and dynamic obstacles. A key component of our planner is the use of harmonic basis functions defined over the unit sphere, known as spherical harmonics (SH), to represent a collision-free space near an agent. In contrast with our previous approach, which only applies to unknown static environments, the approach proposed herein handles dynamic obstacles as well. It is done by efficiently calculating the coefficients of a spherical harmonics approximation of the local collision-free space for each step of a given planning horizon. Our method for approximating the collision-free space with spherical harmonics allows us to plan trajectories in challenging environments (e.g., planning through narrow passages) where other methods for generating search-spaces fail. To accurately approximate the collision-free space for future time steps along a planning horizon, we use a Kalman Filter (KF) to propagate the measured point cloud through time. Our use of the KF allows us to better approximate obstacles moving in space in comparison to inaccurate constant obstacle velocity assumptions used by other methods. Finally, we show the effectiveness of our proposed trajectory planner using two different non-trivial scenarios.
Visualization of Promising Directions in Semantic Cyberspace of Scientific Concepts and Papers

In this poster (paper) we describe a cyberspace of scientific concepts and papers, in which the distance between concepts and between documents in a virtual semantic space is proportional to their semantic similarity and color of the concepts depends on long-term trend prediction. Meaningful keywords and concepts were identified among the words included in the titles of scientific articles. The longevity of the citation trend growth was the target for the machine learning algorithm CatBoost. We conducted experiments on a dataset including 5 million scientific publications to demonstrate the effectiveness of the proposed model. The accuracy rate of 3-year forecasts for a number of experiments was about 60%. The proposed method of long-term forecasting of trending research topics outperforms best known methods in terms of the number of topics predicted. Trending topics are built from trending keywords located closely in the semantic vector space. The following trending topics in the field of citation trend prediction have been identified: neural networks, learning approach, market, motion, big data, weather and wind speed prediction. Trending keywords and topics were visualized in the cyberspace using the t-SNE, A-Frame and WebVR methods. Visualization helps to effectively navigate, see big picture, understand trending topics, reveals related keywords and identify promising directions.
Segment Augmentation and Differentiable Ranking for Logo Retrieval

Logo retrieval is a challenging problem since the definition of similarity is more subjective than image retrieval, and the set of known similarities is very scarce. In this paper, to tackle this challenge, we propose a simple but effective segment-based augmentation strategy to introduce artificially similar logos for training deep networks for logo retrieval. In this novel augmentation strategy, we first find segments in a logo and apply transformations such as rotation, scaling, and color change, on the segments, unlike the conventional strategies that perform augmentation at the image level. Moreover, we evaluate suitability of using ranking-based losses (namely Smooth-AP) for learning similarity for logo retrieval. On the METU and the LLD datasets, we show that (i) our segment-based augmentation strategy improves retrieval performance compared to the baseline model or image-level augmentation strategies, and (ii) Smooth-AP indeed performs better than conventional losses for logo retrieval.
An Efficient Grasshopper Optimization Algorithm Using Arithmetic Crossover for Global Optimization

This paper introduces an Improved Grasshopper Optimisation Algorithm (IGOA) in which the arithmetic crossover mechanism is integrated into the standard Grasshopper Optimisation Algorithm (GOA). The improvement aims to maintain a high level of global exploration, assure an effective balance between global and local search. The IGOA algorithm is tested initially with a benchmark test functions, to evaluate and illustrate its performance in terms of qualitative and quantitative aspects. To validate the proposed methodology, it is then used to determine the best design for a Cantilever Beam and Pressure Vessel design problems. The obtained results indicate that the proposed method outperforms the literature’s well-known and recently published techniques. Finally, the IGOA algorithm proves its usability and usefulness when it comes to solving real-world engineering problems.