Reviewerly/exHarmony huggingface dataset:
A framework for diagnosis and management of development and implementation of cloud-based energy communities - Energy cloud communities

The energy sector is in a technological transition process towards the massive insertion of new computing technologies aimed to assist in operational, management, and commercialization tasks, transforming itself into cloud-based energy environments or Energy Cloud (EC). In this sense, companies should seek the best management practices for implementing this concept in environments such as Energy Cloud Communities (ECCs). Thus, the objective of this article is to propose a framework for the diagnosis and management of the development and implementation of ECCs. The framework was developed through a systematic literature review and two surveys with experts in the fields of energy, computer science, and management. The framework integrates variables that influence and impact the business processes related to the EC and its implementation, such as Performance Objectives (POs) and Critical Success Factors (CSFs). The framework's input variables are composed of a series of challenges and opportunities that impact the implementation of these ECCs. The framework is aimed at organizations that work by contributing to the implementation of ECCs, showing what the needs are, and pointing out the effects of these deficiencies on POs and CSFs, directing the focus on what needs to be improved for the successful implementation of the ECCs.
Training a Two-Layer ReLU Network Analytically

Neural networks are usually trained with different variants of gradient descent-based optimization algorithms such as the stochastic gradient descent or the Adam optimizer. Recent theoretical work states that the critical points (where the gradient of the loss is zero) of two-layer ReLU networks with the square loss are not all local minima. However, in this work, we will explore an algorithm for training two-layer neural networks with ReLU-like activation and the square loss that alternatively finds the critical points of the loss function analytically for one layer while keeping the other layer and the neuron activation pattern fixed. Experiments indicate that this simple algorithm can find deeper optima than stochastic gradient descent or the Adam optimizer, obtaining significantly smaller training loss values on four out of the five real datasets evaluated. Moreover, the method is faster than the gradient descent methods and has virtually no tuning parameters.
A framework for co-evolutionary algorithm using Q-learning with meme

A large number of metaheuristic algorithms have been proposed in the last three decades, but no metaheuristic algorithm is superior to the others for all the optimization problems. It is laborious to select an appropriate metaheuristic algorithm to solve a problem, especially for laymen. In this paper, a framework for co-evolutionary algorithm using Q-learning with the meme is proposed, which is called as QLMA. The solution generation method in the metaheuristic algorithm is named meme, which is also viewed as the action for an agent in Q-learning, and multiple memes form the action set. In the initial stage, the tent map and opposition based learning are employed to obtain the initial population. In updating stage, a new population is generated by an action that is chosen from the action set by the agent in Q-learning, then the disruption operation is applied, avoiding excessive aggregation of solutions around the current global optimal solution and improving the balance between exploration and exploitation. QLMA is compared to sixteen algorithms on 23 classical benchmark functions, CEC 2017, and CEC 2019 benchmark functions. The experimental results demonstrate that QLMA is superior to the peer algorithms and has a good balance between exploration and exploitation.
Iterative constraint score based on hypothesis margin for semi-supervised feature selection

To remove redundant features and avoid the curse of dimensionality, the most important features should be selected for downstream tasks, including semi-supervised learning. Several semi-supervised constraint scores using pairwise constraints have been proposed to estimate the relevance of features. However, these methods evaluate the features individually and ignore the correlations between them. Thus, we propose a semi-supervised feature selection method called the iterative constraint score based on the hypothesis margin (HM-ICS), which uses forward sequential selection to select an optimal feature subset with a good ability to maintain the constraint structure of the data and distinguish samples that belong to different classes. HM-ICS iteratively modifies the classical constraint score method to measure the relevance between features and maintain the constraint structure of the data. By introducing the hypothesis margin, HM-ICS can ensure strong discriminative power of the optimal feature subset. Extensive experiments were conducted on nine UCI and five high-dimensional datasets, and the experimental results confirmed that HM-ICS can achieve better performance than state-of-the-art supervised and semi-supervised methods.
Influence of Data Distribution on Federated Learning Performance in Tumor Segmentation

Purpose To investigate the correlation between differences in data distributions and federated deep learning (Fed-DL) algorithm performance in tumor segmentation on CT and MR images. Materials and Methods Two Fed-DL datasets were retrospectively collected (from November 2020 to December 2021): one dataset of liver tumor CT images (Federated Imaging in Liver Tumor Segmentation [or, FILTS]; three sites, 692 scans) and one publicly available dataset of brain tumor MR images (Federated Tumor Segmentation [or, FeTS]; 23 sites, 1251 scans). Scans from both datasets were grouped according to site, tumor type, tumor size, dataset size, and tumor intensity. To quantify differences in data distributions, the following four distance metrics were calculated: earth mover’s distance (EMD), Bhattacharyya distance (BD), χ2 distance (CSD), and Kolmogorov-Smirnov distance (KSD). Both federated and centralized nnU-Net models were trained by using the same grouped datasets. Fed-DL model performance was evaluated by using the ratio of Dice coefficients, θ, between federated and centralized models trained and tested on the same 80:20 split datasets. Results The Dice coefficient ratio (θ) between federated and centralized models was strongly negatively correlated with the distances between data distributions, with correlation coefficients of −0.920 for EMD, −0.893 for BD, and −0.899 for CSD. However, KSD was weakly correlated with θ, with a correlation coefficient of −0.479. Conclusion Performance of Fed-DL models in tumor segmentation on CT and MRI datasets was strongly negatively correlated with the distances between data distributions. Keywords: CT, Abdomen/GI, Liver, Comparative Studies, MR Imaging, Brain/Brain Stem, Convolutional Neural Network (CNN), Federated Deep Learning, Tumor Segmentation, Data Distribution Supplemental material is available for this article. © RSNA, 2023 See also the commentary by Kwak and Bai in this issue.
Contact lens embedded holographic pointer

In this paper we present an infrared laser pointer, consisting of a vertical-cavity surface-emitting laser (VCSEL) and a diffractive optical element (DOE), encapsulated into a scleral contact lens (SCL). The VCSEL is powered remotely by inductive coupling from a primary antenna embedded into an eyewear frame. The DOE is used either to collimate the laser beam or to project a pattern image at a chosen distance in front of the eye. We detail the different SCL constitutive blocks, how they are manufactured and assembled. We particularly emphasize the various technological challenges related to their encapsulation in the reduced volume of the SCL, while keeping the pupil free. Finally, we describe how the laser pointer operates, what are its performances (e.g. collimation, image formation) and how it can be used efficiently in various application fields such as visual assistance and augmented reality.
A VOS analysis of LSTM Learners Classification for Recommendation System

In response to the growing popularity of social web apps, much research has gone into analyzing and developing an AI-based responsive suggestion system. Machine learning and neural networks come in many forms that help online students choose the best texts for their studies. However, when training recommendation models to deal with massive amounts of data, traditional machine learning approaches require additional training models. As a result, they are deemed inappropriate for the personalized recommender generation of learning systems. In this paper, we examine LSTM-based strategies in order to make useful recommendations for future research.
THE USE OF YOUTUBE AS THE SOURCE OF SUPPLEMENTARY MATERIAL IN LEARNING NEWS WRITING

The incorporation of technology in teaching and learning has been a growing trend. Appropriate and well-managed application of technological tools in instructional activities can give great value to student learning. YouTube is a popular technological tool which can be used to enhance student learning by assisting material retention as it contains considerable videos that can be used as supplementary learning materials. This study aims to investigate the use of YouTube as the source of supplementary material in learning news writing from the perspective of students. Twenty-seven undergraduate students in the Department of Language Education and Indonesian Literature at Universitas Lambung Mangkurat were involved in this study. They were assigned to search for news videos, review and learn the content of the videos to understand the concept of news and news writing. They were then assigned to write news report using appropriate journalistic language. This study is a descriptive study that employ mix-method data analysis. A questionnaire was subsequently administered to the students to explore their perception on the use of YouTube in learning news writing. The results of data analysis revealed that the students attained benefits of using YouTube in learning news writing and completing the whole assignment. The perceived benefits include enhanced learning, comprehension on news concept, enriched knowledge, practice of learning autonomy, and assistance in developing listening as well as writing skill. It is suggested that optimal benefits of technology incorporation in teaching and learning can be achieved if teachers design activities that fit the functions offer by technological tools in accordance with the desired learning outcome.
Research on Improved DNA Coding and Multidirectional Diffusion Image Encryption Algorithm

In order to make the security and operating efficiency of an image encryption algorithm coexist, this study proposed a color image encryption algorithm with improved DNA coding and rapid diffusion. During the stage of improving DNA coding, the chaotic sequence was used to form a look-up table to complete the base substitutions. In the replacement process, several encoding methods were combined and interspersed to make the randomness higher, thereby improving the security performance of the algorithm. In the diffusion stage, three-dimensional and six-directional diffusion was performed on the three channels of the color image by taking the matrix and the vector as the diffusion unit successively. This method not only ensures the security performance of the algorithm, but also improves the operating efficiency in the diffusion stage. From the simulation experiments and performance analysis, it was shown that the algorithm has good encryption and decryption effects, large key space, high key sensitivity, and strong security. The algorithm can effectively resist differential attacks and statistical attacks, and has good robustness.
Towards Asynchronous Mixed Reality Remote Guidance supported by a Virtual Assistant: Proposal of a Conceptual Model

Activities of remote guidance are essential in the Industry sector to overcome emerging problems. One important technology to cre-ate a common ground between distributed team-members is Mixed Reality (MR). On-site collaborators can receive instructions from remote experts, informing where to act, and what to do. Neverthe-less, most studies have focused on synchronous use-cases, where team-members can interact in real-time. Hence, an opportunity exists to explore asynchronous scenarios, in which collaborative actions take place at different times. This position paper proposes a vision for combining MR and a Virtual Assistant (VA) for sup-porting asynchronous remote guidance in Industrial Scenarios. This combination can be highly flexible in supporting most simpler tasks, taking advantage of existing instructions from previous sessions. Also important, a VA can be constantly available, reducing time-to-response, while supporting various individuals with distinct needs. These and other arguments in favor of this position are presented and future directions are proposed.
CRVideo: Cross-reality 360° Video Social Systems Exploration

Cross-reality systems provide visual representations at different levels of virtuality/physicality and offer smooth transitions between them to users. These systems also support multiple modes of interaction, and coordinate collaboration between users with multiple realities. In this paper, we explore a cross-reality 360° video social system, aiming to enable users with different personalities to get a social experience consistent with their psychological changes and behavioral habits when watching 360° video. We designed the prototype system CRVideo, conducted a preliminary user survey and a user study to research the social requirements of users and verify the effectiveness of our cross-reality system. The results show that the social habits of extroverted and introverted users in video platforms differ greatly, and the system across realistic levels can meet the psychological change curve of users in different periods.
Minimal distance index — A new clustering performance metrics

We define a new index for measuring clustering performance called the Minimal Distance Index. The index is based on representing clusters by characteristic objects containing the majority of cluster points. It performs well for both spherical and ellipsoidal clusters. This method can recognize all acceptable partitions with well-separated clusters. Among such partitions, our minimal distance index may identify the most appropriate one. The proposed index is compared with other most frequently used indexes in numerous examples with spherical and ellipsoidal clusters. It turned out that our proposed minimal distance index always recognizes the most appropriate partition, whereas the same cannot be said for other indexes found in the literature. Furthermore, among all acceptable partitions, the one with the largest number of clusters, not necessarily the most appropriate ones, has a special significance in image analysis. Namely, following Mahalanobis image segmentation, our index recognizes partitions that might not be the most appropriate ones but are the ones using colors that significantly differ from each other. The minimal distance index recognizes partitions with dominant colors, thus making it possible to select specific details in the image. We apply this approach to some real-world applications such as the plant rows detection problem, painting analysis, and iris detection. This may also be useful for medical image analysis.
Trends and Applications of Gamification in E-Commerce: A Systematic Literature Review

Background: Gamification is a trend that has emerged with the growth of e-commerce. Given the wide range of human characteristics, determining which gamification elements perform well and what impact those gamification elements have can be challenging. Objective: This study aims to conduct a systematic literature review to broadly review the impact that can be caused by the application of gamification elements in e-commerce. This study also attempts to identify the current trends in using gamification elements. Methods: This study was carried out based on the Kitchenham approach and analyzes 25 research papers extracted from a total of 550 papers. The articles were gathered from ACM, Emerald, ScienceDirect, and Scopus and were published between 2016 and 2021. Results: This study found that the trend of research in the field of gamification in e-commerce continues to grow every year. Also, this study found that the most frequently used gamification elements are achievement-oriented (such as rewards, points, badges, and leaderboards). Meanwhile, immersion-related gamification elements (such as avatars, fantasy, etc.) are emerging as a new trend for new gamification elements to be incorporated in e-commerce. This study also found three major themes, namely consumer loyalty, consumer engagement, and user behavior, as a result of the application of gamification in e-commerce. Conclusion: This study helps to improve knowledge of various gamification elements, trends, and impacts on e-commerce. Future studies need to examine the challenges that may arise in the application of gamification elements to the three major themes found in this study and find potential solutions to overcome them. Keywords: E-Commerce, Gamification, Gamification trends and applications, Kitchenham, Systematic literature review.
Hand Gesture Recognition Based on Various Deep Learning YOLO Models

Some varieties of sign languages are used by deaf or hard-of-hearing people worldwide to interact with others more effectively, consequently sign language's automatic translation is expressive and important.Significant improvements in computer vision have been made recently, notably in tasks based on object detection using deep learning.By locating things in visual photos or videos, the genuine cuttingedge one-step object detection approach greatly provides exceptional detection accuracy.With the help of messaging or video calling, this study suggests a technique to get beyond these obstacles and enhance communication for such persons, regardless of their disability.To recognize motions and classes, we provide an enhanced model based on Yolo (You Look Only Once) V3, V4, V4-tiny, and V5.The dataset is clustered using the suggested algorithm, requiring only manual annotation of a reduced number of classes and analysis for patterns that aid in target prediction.The suggested method outperforms the current object detection approaches based on the YOLO model, according to experimental results.
Machine Learning Algorithm to Estimate Distant Breast Cancer Recurrence at the Population Level with Administrative Data

Purpose: High-quality population-based cancer recurrence data are scarcely available, mainly due to complexity and cost of registration. For the first time in Belgium, we developed a tool to estimate distant recurrence after a breast cancer diagnosis at the population level, based on real-world cancer registration and administrative data. Methods: Data on distant cancer recurrence (including progression) from patients diagnosed with breast cancer between 2009– 2014 were collected from medical files at 9 Belgian centers to train, test and externally validate an algorithm (i.e., gold standard). Distant recurrence was defined as the occurrence of distant metastases between 120 days and within 10 years after the primary diagnosis, with follow-up until December 31, 2018. Data from the gold standard were linked to population-based data from the Belgian Cancer Registry (BCR) and administrative data sources. Potential features to detect recurrences in administrative data were defined based on expert opinion from breast oncologists, and subsequently selected using bootstrap aggregation. Based on the selected features, classification and regression tree (CART) analysis was performed to construct an algorithm for classifying patients as having a distant recurrence or not. Results: A total of 2507 patients were included of whom 216 had a distant recurrence in the clinical data set. The performance of the algorithm showed sensitivity of 79.5% (95% CI 68.8– 87.8%), positive predictive value (PPV) of 79.5% (95% CI 68.8– 87.8%), and accuracy of 96.7% (95% CI 95.4– 97.7%). The external validation resulted in a sensitivity of 84.1% (95% CI 74.4– 91.3%), PPV of 84.1% (95% CI 74.4– 91.3%), and an accuracy of 96.8% (95% CI 95.4– 97.9%). Conclusion: Our algorithm detected distant breast cancer recurrences with an overall good accuracy of 96.8% for patients with breast cancer, as observed in the first multi-centric external validation exercise. Keywords: machine learning, breast cancer, distant metastases, recurrences, algorithm, administrative data
Super-Resolution Processing of Synchrotron CT Images for Automated Fibre Break Analysis of Unidirectional Composites

Fibre breaks govern the strength of unidirectional composite materials under tension. The progressive development of fibre breaks is studied using in situ X-ray computed tomography, especially with synchrotron radiation. However, even with synchrotron radiation, the resolution of the time-resolved in situ images is not sufficient for a fully automated analysis of continuous mechanical deformations. We therefore investigate the possibility of increasing the quality of low-resolution in situ scans by means of super-resolution (SR) using 3D deep learning techniques, thus facilitating the subsequent fibre break identification. We trained generative neural networks (GAN) on datasets of high—(0.3 μm) and low-resolution (1.6 μm) statically acquired images. These networks were then applied to a low-resolution (1.1 μm) noisy image of a continuously loaded specimen. The statistical parameters of the fibre breaks used for the comparison are the number of individual breaks and the number of 2-plets and 3-plets per specimen volume. The fully automated process achieves an average accuracy of 82% of manually identified fibre breaks, while the semi-automated one reaches 92%. The developed approach allows the use of faster, low-resolution in situ tomography without losing the quality of the identified physical parameters.
Online Model Compression for Federated Learning with Large Models

This paper addresses the challenges of training large neural networks under federated learning settings: high on-device memory usage and communication cost. The proposed Online Model Compression (OMC) provides a framework that stores model parameters in a compressed format and decompresses them only when needed. We use quantization as the compression method in this paper and propose three methods, (1) per-variable transformation, (2) weight-matrix-only quantization, and (3) partial variable quantization, to minimize its impact on model accuracy. Our experiments on two recent neural networks for speech recognition and two different datasets show that OMC can reduce memory usage and communication cost of model parameters by up to 59% while attaining comparable accuracy and training speed when compared with full-precision federated learning.