Reviewerly/exHarmony huggingface dataset:
Ensemble Learning With Symbiotic Organism Search Optimization Algorithm for Breast Cancer Classification and Risk Identification of Other Organs on Histopathological Images

Breast cancer (BC) is closely linked with the maximum mortality rate for cancer detection across the globe and has become a predominant public health issue. Earlier detection might increase the possibility of survival and successful treatment. However, it is a time-consuming and very challenging task that depends on the diagnostician’s experience. For patients and their prognosis, it is essential that BC cancer can be automatically detected by the analysis of histopathological images. Conventional feature extraction method extracts some lower-level features of images, and preceding knowledge is essential for selecting suitable features that could be heavily impacted by human beings. The deep learning (DL) technique extracts higher-level abstract features from an image automatically. Therefore, this study develops a new Ensemble Learning with Symbiotic Organism Search Optimization Algorithm for Breast Cancer Classification (ELSOSA-BCC) technique on Histopathological Images. In the ELSOSA-BCC technique, the noise is removed using Gabor filtering (GF). In addition, the ELSOSA-BCC technique employs the EfficientNet-B0 model for feature extraction and optimal hyperparameter tuning using the SOS algorithm. Finally, the ensemble learning-based classification process is performed by three classifiers namely deep stacked autoencoder (DSAE), kernel extreme learning machine (KELM), and bidirectional long short-term memory (BiLSTM). In this study, ELSOSA-BCC simulation values are tested on a medical dataset. ELSOSA-BCC has been shown to perform better than other models in the experimental results.
Investigating the emerging and future trends of knowledge management in small and medium enterprises: a science mapping approach

Purpose This study aims to evaluate the knowledge structure of knowledge management (KM) applications within small and medium enterprises (SMEs). In pursuit of excellence, SMEs must adopt KM like large corporations, even with limited resources. Design/methodology/approach Through a bibliometric approach, this study evaluates current themes based on bibliographic coupling analysis and predicts future trends based on coword analysis. A total of 726 documents and 2,658 keywords were derived from the Web of Science database. Findings Findings show four clusters in bibliographic coupling and three in coword analysis. A common theme discovered converged on digital technology to ensure SMEs follow current market trends. Research limitations/implications Advanced application of Industry 4.0 requires digital transformation for SMEs’ pursuit of excellence. Implications include sustaining SMEs through a considerable KM system integrated with digital technology for sustainable business activities. Originality/value To the best of the authors’ knowledge, this study is among the first to provide a science mapping analysis on KM in SMEs by applying a bibliometric analysis.
Solving the Sensor-based Activity Recognition Problem (SOAR): Self-supervised, Multi-modal Recognition of Activities from Wearable Sensors

Feature extraction lies at the core of Human Activity Recognition (HAR): the automated inference of what activity is being performed. Traditionally, the HAR community used statistical metrics and distribution-based representations to summarize the movement present in windows of sensor data into feature vectors. More recently, learned representations have been used successfully in lieu of such handcrafted and manually engineered features. In particular, the community has shown substantial interest in self-supervised methods, which leverage large-scale unlabeled data to first learn useful representations that are subsequently fine-tuned to the target applications. In this tutorial, we focus on representations for single-sensor and multi-modal setups, and go beyond the current de facto of learning representations. We also discuss the economic use of existing representations, specifically via transfer learning and domain adaptation. The proposed tutorial will introduce state-of-the-art methods for representation learning in HAR, and provide a forum for researchers from mobile and ubiquitous computing to not only discuss the current state of the field but to also chart future directions for the field itself, including answering what it would take to finally solve the activity recognition problem.
Don't freeze: Finetune encoders for better Self-Supervised HAR

Recently self-supervised learning (SSL) has been proposed in the field of human activity recognition (HAR) as a solution to the labelled data availability problem. The idea being that by using pretext tasks such as reconstruction or contrastive predictive coding, useful representations can be learned that then can be used for classification. Those approaches follow the pretrain, freeze and fine-tune procedure. In this work we investigate how a simple change - not freezing the representation - leads to substantial performance gains across pretext tasks. The improvement was found in all four investigated datasets and across all four pretext tasks and is inversely proportional to amount of labelled data. Moreover the effect is present whether the pretext task is carried on the Capture24 dataset or directly in unlabelled data of the target dataset.
Interpreting Sign Language Recognition using Transformers and MediaPipe Landmarks

Sign Language Recognition (SLR) is a challenging task that aims to bridge the communication gap between the deaf and hearing communities. In recent years, deep learning-based approaches have shown promising results in SLR. However, the lack of interpretability remains a significant challenge. In this paper, we seek to understand which hand and pose MediaPipe Landmarks are deemed the most important for prediction as estimated by a Transformer model. We propose to embed a learnable array of parameters into the model that performs an element-wise multiplication of the inputs. This learned array highlights the most informative input features that contributed to solve the recognition task. Resulting in a human-interpretable vector that lets us interpret the model predictions. We evaluate our approach on public datasets called WLASL100 (SRL) and IPNHand (gesture recognition). We believe that the insights gained in this way could be exploited for the development of more efficient SLR pipelines.
Automatic Detection Method for Black Smoke Vehicles Considering Motion Shadows

Various statistical data indicate that mobile source pollutants have become a significant contributor to atmospheric environmental pollution, with vehicle tailpipe emissions being the primary contributor to these mobile source pollutants. The motion shadow generated by motor vehicles bears a visual resemblance to emitted black smoke, making this study primarily focused on the interference of motion shadows in the detection of black smoke vehicles. Initially, the YOLOv5s model is used to locate moving objects, including motor vehicles, motion shadows, and black smoke emissions. The extracted images of these moving objects are then processed using simple linear iterative clustering to obtain superpixel images of the three categories for model training. Finally, these superpixel images are fed into a lightweight MobileNetv3 network to build a black smoke vehicle detection model for recognition and classification. This study breaks away from the traditional approach of “detection first, then removal” to overcome shadow interference and instead employs a “segmentation-classification” approach, ingeniously addressing the coexistence of motion shadows and black smoke emissions. Experimental results show that the Y-MobileNetv3 model, which takes motion shadows into account, achieves an accuracy rate of 95.17%, a 4.73% improvement compared with the N-MobileNetv3 model (which does not consider motion shadows). Moreover, the average single-image inference time is only 7.3 ms. The superpixel segmentation algorithm effectively clusters similar pixels, facilitating the detection of trace amounts of black smoke emissions from motor vehicles. The Y-MobileNetv3 model not only improves the accuracy of black smoke vehicle recognition but also meets the real-time detection requirements.
Perancangan Prediksi Produksi Teh Menggunakan Metode Fuzzy Tsukamoto Berbasis Web

Nowadays, technology is also widely applied in various fields with various needs. Innovations will continue to be developed to create even more efficient technologies for various fields. Based on data from. Various sectors, such as education, business, tourism, to the agricultural sector, widely use websites. There are various implementations or applications of technological developments in agriculture, one of which is the application of calculating an agricultural product—tea production in Kaligua in 2018 experienced ups and downs due to less stable weather conditions. Calculations in determining an agricultural product can use several calculation methods, one of which is the Tsukamoto fuzzy method. Tsukamoto's fuzzy method is an extension of monotone reasoning. In Tsukamoto's method, every consequence has a rule in the form of IF-THEN that must be represented in a fuzzy set with a monotone arrangement function. So this requires a system that can produce predictions in tea production in Kaligua tea plantations to meet market needs. The solution offered is a production prediction system in Kaligua plantations using the Tsukamoto fuzzy method. The prediction results obtained in this study were a 36% increase in tea sales production.
Graph Clustering with High-Order Contrastive Learning

Graph clustering is a fundamental and challenging task in unsupervised learning. It has achieved great progress due to contrastive learning. However, we find that there are two problems that need to be addressed: (1) The augmentations in most graph contrastive clustering methods are manual, which can result in semantic drift. (2) Contrastive learning is usually implemented on the feature level, ignoring the structure level, which can lead to sub-optimal performance. In this work, we propose a method termed Graph Clustering with High-Order Contrastive Learning (GCHCL) to solve these problems. First, we construct two views by Laplacian smoothing raw features with different normalizations and design a structure alignment loss to force these two views to be mapped into the same space. Second, we build a contrastive similarity matrix with two structure-based similarity matrices and force it to align with an identity matrix. In this way, our designed contrastive learning encompasses a larger neighborhood, enabling our model to learn clustering-friendly embeddings without the need for an extra clustering module. In addition, our model can be trained on a large dataset. Extensive experiments on five datasets validate the effectiveness of our model. For example, compared to the second-best baselines on four small and medium datasets, our model achieved an average improvement of 3% in accuracy. For the largest dataset, our model achieved an accuracy score of 81.92%, whereas the compared baselines encountered out-of-memory issues.
Nonequilibrium Effects on the Precision of Parameter Estimation

Abstract The impact of nonequilibrium environment effects on the accuracy of quantum parameter estimation is investigated, and it is found that these effects can significantly affect estimation accuracy. Using an individual estimation strategy reveals that the nonequilibrium effects consistently enhance accuracy, regardless of the coupling strength between the probe and its environment. In contrast, weak memory effects undermine estimation accuracy. When employing a multi‐parameter simultaneous estimation strategy, it is observed that the nonequilibrium effects consistently improve the advantages of simultaneous estimation, as analyzed by the ratio of total variances between the two estimation scenarios. However, the memory effects on these advantages depend on the coupling strength between the qubit and the environment. These findings suggest that appropriate parameters of a nonequilibrium environment can increase the quantum Fisher information (QFI), thereby enhancing the accuracy of quantum parameter estimation. These significant results are essential for improving parameter estimation accuracy in quantum systems interacting with nonequilibrium environments.
Bird’s–Eye View

This chapter presents an overview of VR systems from hardware (Section 2.1) to software (Section 2.2), including the introduction of the Virtual World Generator (VWG), which maintains the geometry and physics of the virtual world, to human perception (Section 2.3). The purpose is to quickly provide a sweeping perspective so that the detailed subjects in the remaining chapters will be understood within the larger context.
Estimation of hourly solar irradiation on tilted surfaces

Tilted photovoltaic panel (PVP) are extensively utilized in solar power installations. To model and design them, you must understand the solar irradiation on a tilted surface over time scales of an hour, day, week, month, or other period. However, most meteorological stations only record global horizontal irradiance (GHI). In this research, a method is proposed for translating hourly worldwide horizontal irradiation from the NASA POWER database into hourly global sun irradiation on a tilted surface utilizing regression analysis methods and numerical modeling methods based on an isotropic model of solar irradiation. In addition, this work proposes a method for establishing the regression reliance of the diffuse transmittance index on the clearness index for geographic areas where this relationship has not yet been empirically proven. To assess effectiveness, the results of the proposed technique and other authors' methods are compared with monthly average NASA POWER climatological data using evaluation methods such as mean bias error (MBE), mean absolute bias error (MABE), and root-mean-square error (RMSE). The study's findings can be used to construct, optimize, or anticipate the functioning of solar power facilities at any angle of tilt and in any geographic area.
Digitalization of Lifelong Education as a Means for Forming Professional Competences of Students

The article is devoted to the study of the role of digitalization in the organization of lifelong education as a means of developing professional competencies in the context of informatization of education. The relevance of the study lies in the study of the system for implementing the effective functioning of the mechanism of continuous education at the university, as one of the effective means in implementing the strategy for the sustainable development of students’ professional competencies. The issues of continuous education should be considered as a multifaceted phenomenon in solving educational and cognitive problems, the essence of which is the formation of professional competence of students in conditions of independent cognitive activity. In this regard, it is especially important that students, mastering knowledge and methods of obtaining it, realize that independent work on writing scientific papers is designed to fulfill the tasks of all other types of educational work, since no knowledge that has not become the subject of one’s own activity can be considered a true property of professionalism and competence. Considerable interest in the development of continuous education allows us to conclude that the topic of the formation of professional competencies is in demand in the conditions of informatization of education. The article develops recommendations on the formation of professional competence of students in the context of digitalization of education, as a form of active organization of the “culture of educational and labor activity of a student”.
The Audible Universe Workshop: An Interdisciplinary Approach to the Design and Evaluation of Tools for Astronomical Data Sonification

Even if images of astrophysical objects are used by professional astronomers for research and by the public for outreach, we are all basically blind to the Universe. Challenging the idea that we should always use visualisations, there has been a growing interest in converting astronomical phenomena into sound, motivated by: making astronomy more accessible to people who are blind or visually impaired (BVI); creating more engaging educational resources, and enabling a deeper understanding of complex astronomical data. The Audible Universe (AU) workshop focuses on consolidating what has been done in the field so far and identifying the areas where most effort is required to make progress over the coming years. The second edition of the AU workshop (AU2) took place in 2022, and brought together 50 experts, among whom astronomers interested in sonification, sound designers, experts in sound perception and educators. This community started a multi-disciplinary discussion about how to properly design and evaluate sonification tools. In this methodological and position paper, we present and discuss the main activities of the AU2 workshop, with a particular focus on activities concerned with the development of collaborative design processes, and the implementation of methods for evaluation. While this workshop was dedicated to fostering exchanges between the sonification community and astronomers, the structure and the methods used within the workshop are transferable to other application areas, and a contribution to the effort to develop interdisciplinary strategies for the development of the field of sonification.
Smart-Charge-Pro: Innovating Energy Metering and Monitoring for Sustainable EV Stations

A new solution that makes use of IoT technology to optimize energy usage and cost at EV charging stations is the GPS- based net meter and cost monitoring system. The technology offers in-the-moment monitoring of energy use and cost, empowering EV users to choose their charging habits wisely. Additionally, the system may be set up to change the charging rate dependent on the cost and availability of electricity, thereby saving money for both EV owners and the owners of charging stations. Additionally, the Proposed system offers a better user interface, remote management, data analytics, customization, and scalability, making it appropriate for a variety of EV charging ecosystem applications. Due to the rising demand for EV charging infrastructure, the system's cost- saving potential, scalability, and favorable market trends towards smart cities and IoT technologies in the energy sector, the system's commercial viability is optimistic. The Internet of things, smart energy meter and cost monitoring system for EV charging stations is a useful addition to the ecosystem for EV charging, supporting environment friendly transportation and advancing the growth of smart cities.
Classification of Arabic Social Media Texts Based on a Deep Learning Multi-Tasks Model

The proliferation of social networking sites and their user base has led to an exponential increase in the amount of data generated on a daily basis. Textual content is one type of data that is commonly found on these platforms, and it has been shown to have a significant impact on decision-making processes at the individual, group, and national levels. One of the most important and largest part of this data are the texts that express human intentions, feelings and condition. Understanding these texts is one of the biggest challenges that facing data analysis. It is the backbone for understanding people, their orientations, and making decisions in many cases and thus predicting their behavior. In this paper, a model was proposed for understanding texts that written by people on social media platforms, and hence knowing people's attitudes within specific topics, the emotion of those people, positivity, negativity, and neutrality. Also, it extracts emotion of those people. In this context, the system solves many tasks in natural language processing therefore it uses many techniques including topic classifier, sentiment analyzer, sarcasm detector and emotion classifier. CNN-BiLSTM was used for topic classifier, sentiment analyzer, sarcasm detector, and emotion classifier where (f-measure, accuracy) were (97,97.58) %, (84,86) %, (95,97) %, and (82,81.6) % respectively.
Distributed Facial Recognition Facial Recognition in Visual Internet of Things (VIoT)-- An Intelligent Approach

In the rapidly evolving landscape of the Visual Internet of Things (VIoT), this paper presents a pioneering approach to distributed facial expression recognition—an intelligent system that holds transformative potential for security, human-computer interaction, and personalized services. Our journey unfolds with the development of the Light Vision Transformer (LVT) model, specifically engineered to operate on the resource-constrained edges of the VIoT network. Differentially private federated training ensures both the model's prowess and the preservation of user privacy. Through meticulous experimental evaluations, we validate the effectiveness and efficiency of our approach, shedding light on its scalability and ethical implications. This work is more than a technical endeavor; it symbolizes a commitment to responsible AI, balancing innovation with the preservation of individual rights. Our findings resonate beyond facial expression recognition, serving as a beacon for the VIoT community to explore the dynamic interplay between distributed computing, edge intelligence, and ethical considerations. As we stride towards a more connected and responsive world, this research paves the way for continued exploration, propelling VIoT technology towards a future that is both intelligent and ethically attuned.
Comparative Analysis of Machine Learning Technique to Improve Software Defect Prediction

One of the most active areas of research in the software engineering community is defect prediction. The gap between data mining and software engineering must be bridged to increase the rate of software success. Before the testing phase, software defect prediction predicts where these flaws will most likely occur in the source code. Methods for predicting software defects are widely used to investigate the impact area in software using various techniques (clustering, statistical methods, neural networks, and machine learning models). The goal of this research is to examine various machine learning algorithms for predicting software defects. There have been many fault prediction techniques introduced, but no single technique or approach can be used for all types of datasets. To achieve maximum accuracy, different machine learning algorithms such as Bayesian Net, Logistic Regression, Multilayer Perceptron, Ruler Zero-R, J48, Lazy IBK, Support Vector Machine, Neural Networks, Random Forest, and Decision stump were used to uncover the maximum subset of defects that could be predicted. This research concern is to find out defects using five NASA data sets JM1, CM1, KC1, KC2, and PC1. Logistic Regression has been shown to have the best output relative to others (93%).