Reviewerly/exHarmony huggingface dataset:
Enhancing grasshopper optimization algorithm (GOA) with levy flight for engineering applications

The grasshopper optimization algorithm (GOA) is a meta-heuristic algorithm proposed in 2017 mimics the biological behavior of grasshopper swarms seeking food sources in nature for solving optimization problems. Nonetheless, some shortcomings exist in the origin GOA, and GOA global search ability is more or less insufficient and precision also needs to be further improved. Although there are many different GOA variants in the literature, the problem of inefficient and rough precision has still emerged in GOA variants. Aiming at these deficiencies, this paper develops an improved version of GOA with Levy Flight mechanism called LFGOA to alleviate the shortcomings of the origin GOA. The LFGOA algorithm achieved a more suitable balance between exploitation and exploration during searching for the most promising region. The performance of LFGOA is tested using 23 mathematical benchmark functions in comparison with the eight well-known meta-heuristic algorithms and seven real-world engineering problems. The statistical analysis and experimental results show the efficiency of LFGOA. According to obtained results, it is possible to say that the LFGOA algorithm can be a potential alternative in the solution of meta-heuristic optimization problems as it has high exploration and exploitation capabilities.
High-resolution processing and sigmoid fusion modules for efficient detection of small objects in an embedded system

Recent advances in deep learning realized accurate, robust detection of various types of objects including pedestrians on the road, defect regions in the manufacturing process, human organs in medical images, and dangerous materials passing through the airport checkpoint. Specifically, small object detection implemented as an embedded system is gaining increasing attention for autonomous vehicles, drone reconnaissance, and microscopic imagery. In this paper, we present a light-weight small object detection model using two plug-in modules: (1) high-resolution processing module (HRPM ) and (2) sigmoid fusion module (SFM). The HRPM efficiently learns multi-scale features of small objects using a significantly reduced computational cost, and the SFM alleviates mis-classification errors due to spatial noise by adjusting weights on the lost small object information. Combination of HRPM and SFM significantly improved the detection accuracy with a low amount of computation. Compared with the original YOLOX-s model, the proposed model takes a two-times higher-resolution input image for higher mean average precision (mAP) using 57% model parameters and 71% computation in Gflops. The proposed model was tested using real drone reconnaissance images, and provided significant improvement in detecting small vehicles.
Artificial Hummingbird Algorithm with Transfer-Learning-Based Mitotic Nuclei Classification on Histopathologic Breast Cancer Images

Recently, artificial intelligence (AI) is an extremely revolutionized domain of medical image processing. Specifically, image segmentation is a task that generally aids in such an improvement. This boost performs great developments in the conversion of AI approaches in the research lab to real medical applications, particularly for computer-aided diagnosis (CAD) and image-guided operation. Mitotic nuclei estimates in breast cancer instances have a prognostic impact on diagnosis of cancer aggressiveness and grading methods. The automated analysis of mitotic nuclei is difficult due to its high similarity with nonmitotic nuclei and heteromorphic form. This study designs an artificial hummingbird algorithm with transfer-learning-based mitotic nuclei classification (AHBATL-MNC) on histopathologic breast cancer images. The goal of the AHBATL-MNC technique lies in the identification of mitotic and nonmitotic nuclei on histopathology images (HIs). For HI segmentation process, the PSPNet model is utilized to identify the candidate mitotic patches. Next, the residual network (ResNet) model is employed as feature extractor, and extreme gradient boosting (XGBoost) model is applied as a classifier. To enhance the classification performance, the parameter tuning of the XGBoost model takes place by making use of the AHBA approach. The simulation values of the AHBATL-MNC system are tested on medical imaging datasets and the outcomes are investigated in distinct measures. The simulation values demonstrate the enhanced outcomes of the AHBATL-MNC method compared to other current approaches.
Empirical analysis of PGA-MAP-Elites for Neuroevolution in Uncertain Domains

Quality-Diversity algorithms, among which are the Multi-dimensional Archive of Phenotypic Elites (MAP-Elites), have emerged as powerful alternatives to performance-only optimisation approaches as they enable generating collections of diverse and high-performing solutions to an optimisation problem. However, they are often limited to low-dimensional search spaces and deterministic environments. The recently introduced Policy Gradient Assisted MAP-Elites (PGA-MAP-Elites) algorithm overcomes this limitation by pairing the traditional Genetic operator of MAP-Elites with a gradient-based operator inspired by deep reinforcement learning. This new operator guides mutations toward high-performing solutions using policy gradients (PG). In this work, we propose an in-depth study of PGA-MAP-Elites. We demonstrate the benefits of PG on the performance of the algorithm and the reproducibility of the generated solutions when considering uncertain domains. We firstly prove that PGA-MAP-Elites is highly performant in both deterministic and uncertain high-dimensional environments, decorrelating the two challenges it tackles. Secondly, we show that in addition to outperforming all the considered baselines, the collections of solutions generated by PGA-MAP-Elites are highly reproducible in uncertain environments, approaching the reproducibility of solutions found by Quality-Diversity approaches built specifically for uncertain applications. Finally, we propose an ablation and in-depth analysis of the dynamic of the PG-based variation. We demonstrate that the PG variation operator is determinant to guarantee the performance of PGA-MAP-Elites but is only essential during the early stage of the process, where it finds high-performing regions of the search space.
A Day in the Life of ChatGPT as a researcher: Sustainable and Efficient Machine Learning - A Review of Sparsity Techniques and Future Research Directions

The International Conference on Machine Learning (ICML) recently decided to prohibit producing/generating ICML paper text using large-scale language models this year (2023) icm [2023]. This decision was made due to the conference organizers’ desire to thoroughly observe, investigate, and consider the implications of using these models for the reviewing and publication process. This led to a curiosity in evaluating the capabilities of large language models like ChatGPT in producing a full conference paper on their own. To our knowledge, this is the first time that a paper has been completely generated by ChatGPT, from selecting the topic to creating all of the content. The goal of this paper is for the community to evaluate its performance and welcome review for the same. The prompts used is mentioned in the appendix. Here, ChatGPT was tasked to write a paper for an International Conference on Learning Representations (ICLR) workshop on Sparsity in Neural Networks: On practical limitations and tradeoffs between sustainability and efficiency. The abstract for the paper is: The sustainability and efficiency of machine learning algorithms are becoming increasingly important as the demand for machine learning grows and the complexity and scale of models continue to increase. The incorporation of sparsity into machine learning algorithms has the potential to address these issues by reducing the size and complexity of models and improving their efficiency and sustainability. In this paper, we review the current state of the art in sparsity for machine learning and discuss the challenges and tradeoffs of its application. We also suggest potential directions for future research in this area, including the development of novel compression algorithms and hardware architectures that can support the efficient training and analysis of large-scale, compressed neural networks, as well as the exploration of domain-specific approaches to effectively incorporate sparsity into machine learning algorithms.
Support vector machine based emotional analysis of restaurant reviews

Most customers are frequently faced with the same question: "which restaurant serves the best food?" We employ sentiment analysis to address this question. Due to the high number of competitors in the restaurant industry, restaurant owners place a premium on customer feedback regarding the quality of the food and services they offer in order to increase customer satisfaction. Every word a customer utters carries a connotation and emotion. This procedure is essential for determining whether the customers' feelings toward our brand are positive, negative, or neutral. This study attempts to classify restaurant customer satisfaction in Jakarta using SVM (Support Vector Machine) and comparing it to the Nave Bayes classifier (NB). The results indicated a rise in SVM accuracy from 77% to 79%.
Region-adaptive magnetic resonance image enhancement for improving CNN-based segmentation of the prostate and prostatic zones

Automatic segmentation of the prostate of and the prostatic zones on MRI remains one of the most compelling research areas. While different image enhancement techniques are emerging as powerful tools for improving the performance of segmentation algorithms, their application still lacks consensus due to contrasting evidence regarding performance improvement and cross-model stability, further hampered by the inability to explain models' predictions. Particularly, for prostate segmentation, the effectiveness of image enhancement on different Convolutional Neural Networks (CNN) remains largely unexplored. The present work introduces a novel image enhancement method, named RACLAHE, to enhance the performance of CNN models for segmenting the prostate's gland and the prostatic zones. The improvement in performance and consistency across five CNN models (U-Net, U-Net++, U-Net3+, ResU-net and USE-NET) is compared against four popular image enhancement methods. Additionally, a methodology is proposed to explain, both quantitatively and qualitatively, the relation between saliency maps and ground truth probability maps. Overall, RACLAHE was the most consistent image enhancement algorithm in terms of performance improvement across CNN models with the mean increase in Dice Score ranging from 3 to 9% for the different prostatic regions, while achieving minimal inter-model variability. The integration of a feature driven methodology to explain the predictions after applying image enhancement methods, enables the development of a concrete, trustworthy automated pipeline for prostate segmentation on MR images.
WordNet Semantic Relations Based Enhancement of KNN Model for Implicit Aspect Identification in Sentiment Analysis

Abstract Opinion mining or sentiment analysis (SA) is a key component of real-world applications for e-commerce organizations, manufacturers, and customers. SA deals with the computational evaluation of people’s views, thoughts, and feelings in the text, whether they are visible or concealed. The Aspect based SA level is becoming one of the most active phases in this area. In this paper, we propose an approach to enrich K-Nearest Neighbors (KNN) to deal with Implicit Aspect Identification task (IAI). Through the use of WordNet semantic relations, we propose an enhancement for KNN distance computation to support the IAI task. For a conclusive empirical evaluation, experiments are conducted on two datasets of electronic products and restaurant reviews and the effects of our approach are examined and analyzed according to three criteria: KNN distance used to compute the similarity, the number of nearest neighbors (K) and the KNN behavior towards Overfitting and Underfitting. The experimental results show that our approach helps KNN improve its performance and better deal with Overfitting and Underfitting for Implicit Aspect Identification.
Evaluating the Potential of Federated Learning for Maize Leaf Disease Prediction

The diagnosis of diseases in food crops based on machine learning seemed satisfactory and suitable for use on a large scale. The Convolutional Neural Networks (CNNs) perform accurately in the disease prediction considering the image capture of the crop leaf, being extensively enhanced in the literature. These machine learning techniques fall short in data privacy, as they require sharing the data in the training process with a central server, disregarding competitive or regulatory concerns. Thus, Federated Learning (FL) aims to support distributed training to address recognized gaps in centralized training. As far as we know, this paper inaugurates the use and evaluation of FL applied in maize leaf diseases. We evaluated the performance of five CNNs trained under the distributed paradigm and measured their training time compared to the classification performance. In addition, we consider the suitability of distributed training considering the volume of network traffic and the number of parameters of each CNN. Our results indicate that FL potentially enhances data privacy in heterogeneous domains.
Performance Enhancement in Federated Learning by Reducing Class Imbalance of Non-IID Data

Due to the distributed data collection and learning in federated learnings, many clients conduct local training with non-independent and identically distributed (non-IID) datasets. Accordingly, the training from these datasets results in severe performance degradation. We propose an efficient algorithm for enhancing the performance of federated learning by overcoming the negative effects of non-IID datasets. First, the intra-client class imbalance is reduced by rendering the class distribution of clients close to Uniform distribution. Second, the clients to participate in federated learning are selected to make their integrated class distribution close to Uniform distribution for the purpose of mitigating the inter-client class imbalance, which represents the class distribution difference among clients. In addition, the amount of local training data for the selected clients is finely adjusted. Finally, in order to increase the efficiency of federated learning, the batch size and the learning rate of local training for the selected clients are dynamically controlled reflecting the effective size of the local dataset for each client. In the performance evaluation on CIFAR-10 and MNIST datasets, the proposed algorithm achieves 20% higher accuracy than existing federated learning algorithms. Moreover, in achieving this huge accuracy improvement, the proposed algorithm uses less computation and communication resources compared to existing algorithms in terms of the amount of data used and the number of clients joined in the training.
Review and Demonstration of the Potential of Bitcoin Mining as a Productive Use of Energy (PUE) to Aid Equitable Investment in Solar Micro- and Mini-Grids Worldwide

Despite the climate commitments made by countries in the Paris Climate Agreement adopted in 2015 and reinforced during COP 21 and with notably less success during COP 22, world carbon emissions increased in both 2021 and 2022. It is increasingly unlikely that the world will achieve the targeted 50% carbon reduction by 2030, the reduction approximately needed for reducing global temperature rise since the beginning of the Industrial Revolution to less than 1.5 deg. C. At the same time, there remain nearly 2 billion people in the world who have no or highly unreliable access to power. In developed countries, access to both clean energy and energy efficiency investment in residences within low to moderate income communities has also lagged. This paper provides a review of the “Productive Use of Energy (PUE)”, which is a means to add value to solar energy mini- and micro-grids to ensure investment worthiness and add more value to the communities being served. In this context, it posits an opportunity to leverage Bitcoin mining as a common PUE strategy applicable to new solar installations. Several actual pilot cases are described to demonstrate this potential throughout the world and at multiple scales. These include: (i) existing micro-grids with significant stranded energy to generate income that could be used to reduce the cost per kWh for the community; (ii) new solar micro-grids optimized to meet community load and mining operations; (iii) dedicated solar-powered Bitcoin mining mini-grids developed solely to create a funding stream for self-investment by communities for their benefit; and (iv) a low-income residential solar-powered Bitcoin miner to reduce the energy cost burden for residents. Several of these scenarios show significant potential to aid investment worthiness.
Fashion-Oriented Image Captioning with External Knowledge Retrieval and Fully Attentive Gates

Research related to fashion and e-commerce domains is gaining attention in computer vision and multimedia communities. Following this trend, this article tackles the task of generating fine-grained and accurate natural language descriptions of fashion items, a recently-proposed and under-explored challenge that is still far from being solved. To overcome the limitations of previous approaches, a transformer-based captioning model was designed with the integration of external textual memory that could be accessed through k-nearest neighbor (kNN) searches. From an architectural point of view, the proposed transformer model can read and retrieve items from the external memory through cross-attention operations, and tune the flow of information coming from the external memory thanks to a novel fully attentive gate. Experimental analyses were carried out on the fashion captioning dataset (FACAD) for fashion image captioning, which contains more than 130k fine-grained descriptions, validating the effectiveness of the proposed approach and the proposed architectural strategies in comparison with carefully designed baselines and state-of-the-art approaches. The presented method constantly outperforms all compared approaches, demonstrating its effectiveness for fashion image captioning.
A Personalized Explainable Learner Implicit Friend Recommendation Method

Abstract With the rapid development of social networks, academic social networks have attracted increasing attention. In particular, providing personalized recommendations for learners considering data sparseness and cold-start scenarios is a challenging task. An important research topic is to accurately discover potential friends of learners to build implicit learning groups and obtain personalized collaborative recommendations of similar learners according to the learning content. This paper proposes a personalized explainable learner implicit friend recommendation method (PELIRM). Methodologically, PELIRM utilizes the learner's multidimensional interaction behavior in social networks to calculate the degrees of trust between learners and applies the three-degree influence theory to mine the implicit friends of learners. The similarity of research interests between learners is calculated by cosine and term frequency–inverse document frequency. To solve the recommendation problem for cold-start learners, the learner's common check-in IP is used to obtain the learner's location information. Finally, the degree of trust, similarity of research interests, and geographic distance between learners are combined as ranking indicators to recommend potential friends for learners and give multiple interpretations of the recommendation results. By verifying and evaluating the proposed method on real data from Scholar.com, the experimental results show that the proposed method is reliable and effective in terms of personalized recommendation and explainability.
Classification of breast cancer images using new transfer learning techniques

Breast cancer is one of the most common types of cancer among women, which requires building smart systems to help doctors and early detection of cancer. Deep learning applications have emerged in many fields, especially in health care, but there are still some limitations in this technique, such as the small number of classified medical images needed to train deep learning models, so this paper aims to find a solution to this problem by using new techniques for transfer learning by taking advantage of the presence of unclassified medical images of the same disease. The proposed approach was applied to the modified Xception model to classify the histological images of breast cancer in the ICIAR 2018 dataset into four classes: invasive carcinoma, in situ carcinoma, benign tumor, and normal tissue. The proposed approach has obtained 99%,&nbsp; 99.003%,&nbsp; 98.995%,&nbsp; 99%, 98.55%, and&nbsp; 99.14% for accuracy, precision, recall, F1-score, sensitivity, and specificity respectively. Our work has been compared with previous works.
The effects of TPACK and facility condition on preservice teachers’ acceptance of virtual reality in science education course

Virtual reality (VR) is developing in line with the establishment of the learning metaverse, although the relationship between its acceptance and technological pedagogical content knowledge (TPACK) is very unclear. Therefore, this study aims to determine the effects of facility condition (FC), technological acceptance model (TAM), and TPACK on pre-service teachers’ use of VR in Indonesian science education courses. This condition emphasizes the description of these teachers’ readiness in designing VR for learning and teaching integration. The survey was conducted using 406 preservice teachers from 12 Indonesian universities, with confirmatory factor analysis (CFA) and partial least square-structural equation model (PLS-SEM) subsequently utilized. The results showed that PU (perceived usefulness), PEOU (perceived ease of use), behavior Intention (BI), TPACK, and FC were significantly and positively related. However, two hypotheses emphasizing the relationship between FC and TPACK to PEOU were rejected. These results are expected to facilitate preservice teachers in easily adopting VR learning in courses.
Application of Gamification with "SIKMA" to Increase Motivation and Learning Independence Attitudes

This Community Service Activity (PKM) aims to increase the motivation and independence of learning for students in Porisgaga Housing, Ambon Block, Tangerang City, so that the use of Smartphones can be used as a tool to support the learning process outside of class hours (outside of school). Most students use smartphones outside of school to play games, so their time is spent on the game, so the function of the smartphone, which was originally to support the learning process, turned into access to playing online games. One effective way to solve this problem is the application of gamification with the concept of "SikMa". SikMa is a gamification that is conceptualized based on the fun of learning mathematics, after applying gamification with the concept of sikma, more than 80% of students stated that learning using the SikMa concept is more fun, and their learning independence is increased. This can be seen from their atusias working on the exercises given through gamification, from the results of the survey after being carried out PKM it was found that more than 60% of students are motivated in learning and more than 50% of them have a good attitude of learning independence.