Reviewerly/exHarmony huggingface dataset:
ENCOVIZ: An open-source, secure and multi-role energy consumption visualisation platform

The need for a more energy efficient future is now more evident than ever and has led to the continuous growth of sectors with greater potential for energy savings, such as smart buildings, energy consumption meters, etc. The large volume of energy related data produced is a huge advantage but, at the same time, it creates a new problem; The need to structure, organize and efficiently present this meaningful information. In this context, we present the ENCOVIZ platform, a multi-role, extensible, secure, energy consumption visualization platform with built-in analytics. ENCOVIZ has been built in accordance with the best visualisation practices, on top of open source technologies and includes (i) multi-role functionalities, (ii) the automated ingestion of energy consumption data and (iii) proper visualisations and information to support effective decision making both for energy providers and consumers.
‘A man is known by the company he keeps’: A critical overview of semantic prosody

The discipline of corpus linguistics has revolutionized the study of language in a variety of ways. In this respect, semantic prosody is one of the rapidly evolving research streams that encapsulates the incorporation of digital technology into linguistic research and deserves more scholarly attention. The paper aims to offer new insights into the study of this theoretical concept. It presents a critical review of the existing literature, delineating the major merits and challenges of this area of inquiry, and suggests novel research avenues. This article has theoretical and practical implications for the future research agenda on semantic prosody. It underscores the importance of endowing this corpus linguistics concept with more clarity and consistency as to its labeling, conceptualization, and delimitation. The paper also proposes some new applications pertaining to the investigation of the discursive construction of human referents across different text types.
Application of digital technology in music teaching

Modern teaching of any subject, including music teaching, with regard to the new technological age and digitization implies, in addition to the use of traditional teaching aids (e.g., traditional textbooks), the application of digital technology (e.g., digital textbooks). The computer has become an almost irreplaceable aid of digital technology in teaching, but its purpose is not to replace the teacher but to supplement his teaching. Currently, when the Internet is available almost everywhere and almost everyone has access to a digital technology device with Internet access, distance learning has become a common way of teaching and learning. Digital technology played a large role in remote music lessons during the COVID-19 pandemic. As part of the work, an anonymous questionnaire survey was conducted on a sample of 43 music students. The goal of the research was to determine students' attitudes about the use of digital technologies in future educational work and their self-assessment of knowledge and skills for using digital technologies. The results showed that the majority of students were assessed as digitally competent but that they mostly developed their competence through independent work. At the same time, they expressed positive attitudes toward the use of digital technologies in their future educational work. It can be concluded that academic institutions should consider introducing courses that would develop the digital competence necessary for successful operation in today's digital age.
Empirical Study on Sentiment Analysis

Sentiment analysis (SA), generally known as Opinion Mining (OM), is really the process of gathering and evaluating people's ideas, thoughts, feelings, beliefs, including views about various subjects, goods, as well as services. Individuals produce large amounts of comments and evaluations about products, services, and day-to-day tasks as Internet-based applications such as webpages, online sites, social networking sites, and blog posts continue to evolve at a rapid pace. Firms, government institutions medical researchers and scholars may use sentiment analysis to collect and evaluate mood of the people and perspectives, obtain business information, and make smarter and more informed choices. The approaches for sentiment analysis are thoroughly examined in this work, problems, trends and features in order to provide academics with a worldwide overview of sentiment analysis and topics of interest. The paper discusses the various uses of sentiment analysis as well as the general procedure for performing this assignment. The report subsequently examines, analyses, and analyses the various techniques in order to gain a comprehensive understanding of its benefits and downsides. Furthermore, to elucidate long term prospects, the constraints of sentiment analysis have been highlighted.
Inflation Dynamics in the Gulf Cooperation Council (GCC): What is the Role of External Factors?

Inflationary pressures have intensified in the Gulf Cooperation Council (GCC) in 2021-2022, mainly driven by a pick-up in tradeable goods inflation. Despite this increase, inflation remained relatively contained as compared to regional comparators. This paper aims to provide a comprehensive analysis of inflation dynamics in the region, with a focus on external factors because of GCC’s high reliance on international trade. Using a Global Vector Autoregressive model with quarterly data from 1987 to 2022, we find that external factors such as the imported inflation from main trading partners, mainly driven by China, and nominal effective exchange rate (NEER) are the main drivers of inflation in the GCC region. Additionally, we find that the direct pass-through of international commodity price shocks such as oil and raw agricultural materials is somewhat limited, after controlling for trading partners’ inflation, which can be explained by the prevalence of subsidies and administered prices in the region. Overall, since external factors are the main drivers of domestic inflation in the GCC, an increased focus on diversification, promoting food security, and ensuring prudent central bank policies, including through effective liquidity management frameworks, can play a key role in managing this impact.
A Blockchain-Based Hierarchical Storage Method for Supply Chain Data

Cross-organizational cooperation is becoming more and more common in modern engineering. For example, different departments in the supply chain need to share data resources, but there is a lack of trust between the organizations, which brings challenges to the security of some sensitive data. In this context, the hierarchical storage of data becomes an important need. By storing data in a hierarchical manner, the privacy and security of sensitive data can be protected. In this paper, we combine hierarchical technology and role-based access control technology to realize multi-form storage for supply chain data. Specifically, we deploy a data hierarchical storage model in the blockchain environment using smart contracts to hierarchically store data from different sources, and then adopt different storage methods according to the different hierarchical levels and ensure that the sensitive data can only be accessed by users with corresponding permissions, thus protecting the security of sensitive data. We evaluated our proposed approach on the Fabric platform and proved the effectiveness of our approach.
A Federated Learning Approach to Breast Cancer Prediction in a Collaborative Learning Framework

Breast cancer continues to pose a substantial worldwide public health concern, necessitating the use of sophisticated diagnostic methods to enable timely identification and management. The present research utilizes an iterative methodology for collaborative learning, using Deep Neural Networks (DNN) to construct a breast cancer detection model with a high level of accuracy. By leveraging Federated Learning (FL), this collaborative framework effectively utilizes the combined knowledge and data assets of several healthcare organizations while ensuring the protection of patient privacy and data security. The model described in this study showcases significant progress in the field of breast cancer diagnoses, with a maximum accuracy rate of 97.54%, precision of 96.5%, and recall of 98.0%, by using an optimum feature selection technique. Data augmentation approaches play a crucial role in decreasing loss and improving model performance. Significantly, the F1-Score, a comprehensive metric for evaluating performance, turns out to be 97%. This study signifies a notable advancement in the field of breast cancer screening, fostering hope for improved patient outcomes via increased accuracy and reliability. This study highlights the potential impact of collaborative learning, namely, in the field of FL, in transforming breast cancer detection. The incorporation of privacy considerations and the use of diverse data sources contribute to the advancement of early detection and the treatment of breast cancer, hence yielding significant benefits for patients on a global scale.
Uncertainty Quantification in Neural Networks Using Stochastic Differential Equations

Uncertainty quantification plays a crucial role in reduction of uncertainties during optimization and decision making, however, it is not solved yet in deep learning. Bayesian network is often used for uncertainty estimation but is only applicable to cases with a small number of parameters because the prior probability of the parameters is needed. On the other hand, while some non-Bayesian models have solved the parameter limitation problem, they often fail to separate different sources of uncertainties. In order to address these issues, we propose the Reduce Bias Stochastic Differential Equation Network (RB-SDENet) by adding a stochastic term to the existing ODE network, which enables us to capture epistemic uncertainty through the variance of random motion. And the probabilistic distribution of output can represent the aleatoric uncertainty for the model. Additionally, we ensured the stability of the model through the mathematical analysis of uniqueness.
An Improved Dueling Double Deep Q Network Algorithm and Its Application to the Optimized Path Planning for Unmanned Ground Vehicle

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;The traditional Double Deep Q-Network (DDQN) algorithm suffers from slow convergence and instability when dealing with complex environments. Besides, it is often susceptible to getting stuck in a local optimal solution and may fail to discover the optimal strategy. As a result, Unmanned Ground Vehicle (UGV) cannot search for the optimal path. To address these issues, the study presents an Improved Dueling Double Deep Q Network (ID3QN) algorithm, which adopts dynamic ε-greed strategy, priority experience replay (PER) and Dueling DQN structure. Where, UGV solves the problem of insufficient exploration and overexploitation according to the dynamic ε-greed strategy. Moreover, high-priority experience examples are extracted using the priority experience replay approach. Meanwhile, the Dueling DQN method can effectively manage the relationship between state values and dominance values. According to the experiment’s accomplishments, the ID3QN method outperforms the DDQN approach in terms of stability and rate of convergence, and obtains a better path in UGV path planning.&lt;/div&gt;&lt;/div&gt;
TCGNet: Type-Correlation Guidance for Salient Object Detection

Contrast and part-whole relations induced by deep neural networks like Convolutional Neural Networks (CNNs) and Capsule Networks (CapsNets) have been known as two types of semantic cues for deep salient object detection. However, few works pay attention to their complementary properties in the context of saliency prediction. In this paper, we probe into this issue and propose a Type-Correlation Guidance Network (TCGNet) for salient object detection. Specifically, a Multi-Type Cue Correlation (MTCC) covering CNNs and CapsNets is designed to extract the contrast and part-whole relational semantics, respectively. Using MTCC, two correlation matrices containing complementary information are computed with these two types of semantics. In return, these correlation matrices are used to guide the learning of the above semantics to generate better saliency cues. Besides, a Type Interaction Attention (TIA) is developed to interact semantics from CNNs and CapsNets for the aim of saliency prediction. Experiments and analysis on five benchmarks show the superiority of the proposed approach. Codes has been released on https://github.com/liuyi1989/TCGNet.
Fall detection paradigm for embedded devices based on YOLOv8

Human-action recognition attempts to determine which deed occurs by people. Owing to the wide spectrum of human activities, action recognition spans a wide range. Among all, falls the conduct gathering special interest, especially for the elderly, as 37.3 million accidents happen annually, with 1.83% being fatal. Over the years, several sensors, data types, and classification techniques have been explored to cope with this problem. Nowadays, ambient assisting living environments employ cameras and companion robots to monitor human activity. The need for low computing power platforms in such environments is essential. To this end, we propose here a lightweight fall detection pipeline for processing sequences-of-images in real-time, which is deployed on an embedded device, namely the Raspberry Pi 4. The proposed system is built upon YOLOv8, having been trained to locate people on the well-established COCO dataset, and a binary classification support vector machine trained on E-FDPS. Through an extensive experimental protocol on LE2I, we show that high detection accuracy and fast timing capabilities are achieved. Lastly, aiming to facilitate the community, our framework is open-sourced at https://github.com/smoutsis/real_time_fall_detection.
E-Learning as an Educational Strategy in University: A Systematic Review

Objective: Analyze the existing literature on the interaction that e-learning allows for university students to develop competencies in learning through the platform, ensuring continuous communication between university teachers and students. Theoretical framework: E-learning has emerged as a powerful transformative tool in university education. This educational approach leverages information and communication technologies to provide students with flexible and remote access to academic content. Method: The method used is a descriptive analysis of a literature review with the application of a strategy in the search for scientific information. For this, the analysis of the results and conclusions of 5 articles was applied for problem 1: "Implementation of E-learning," in addition to the analysis of 5 articles for problem 2: "Teacher-Student Interaction". Results and conclusions: The findings demonstrate that university students interact effectively in a virtual network when it is correctly implemented. Therefore, it can be concluded that the E-learning platform enables the improvement and achievement of teaching and learning through online communication. Implications of the research: It is expected that the research will contribute to university students' utilization of the e-learning platform as a strategy for the teacher-student dyad, fostering academic and learning development. Originality/value: This study demonstrates that currently, e-learning has gained significant importance in higher education, as it facilitates teacher-student interaction and enhances the teaching-learning process.
Comparison of K-Means, K-Means++, X-Means and Single Value Decomposition for Image Compression

Uncompressed digital images need huge storage space and large transmission bandwidth for the transmission over the network. Image compression reduces the image size and enables the efficient storage and transmission of image files. The main goal of image compression is the best image quality at the given compression rate. In this paper, the performance of Singular Value Decomposition (SVD), K-means, K-means++, and X-means for image compression is discussed. K-means, K-means++, and X-means clustering reduce the number of colors of images. Singular Value Decomposition performs a low-rank approximation of the image matrix. Image compression parameters mean-square error (MSE), peak signal-to-noise ratio (PSNR). and compression ratio (CR) are evaluated for the considered compression methods.
Type-LDD: A Type-Driven Lite Concept Drift Detector for Data Streams

Concept drift is a phenomenon that the distribution of data streams changes with time. When this happens, model predictions become less accurate. Hence, concept drift needs to be detected and adapted. Existing drift detection methods are good at determining when drift has occurred, but few retrieve information about how the drift came to be present in the stream, i.e., what type of drift has occurred. Hence, discussing the impact of the type of drift on adaptation is a difficult thing. To fill this gap, we propose a pre-trained framework for training a drift detector called a type-driven lite concept drift detector (Type-LDD) that retrieves information about both when and how a drift has occurred. In our proposed pre-trained framework, the Type-LDD including a drift-type identifier and a drift-point locator was based on a synthetic dataset containing a range of drift types. When repurposing the pre-trained model for detecting new data streams, a knowledge distillation module fine-tunes the proposed Type-LDD to speed up inference and keep detection accuracy. The proposed Type-LDD is validated on both synthetic data and real-world data, and demonstrated that accurately identifying the type of drift that has occurred can improve adaptation accuracy.
Cavity-Enhanced Emission from a Silicon T Center

Silicon T centers present the promising possibility of generating optically active spin qubits in an all-silicon device. However, these color centers exhibit long excited state lifetimes and a low Debye–Waller factor, making them dim emitters with low efficiency into the zero-phonon line. Nanophotonic cavities can solve this problem by enhancing radiative emission into the zero-phonon line through the Purcell effect. In this work, we demonstrate cavity-enhanced emission from a single T center in a nanophotonic cavity. We achieve a 2 order of magnitude increase in the brightness of the zero-phonon line relative to waveguide-coupled emitters, a 23% collection efficiency from emitter to fiber, and an overall emission efficiency into the zero-phonon line of 63.4%. We also observe a lifetime enhancement of 5, corresponding to a Purcell factor exceeding 18 when correcting for the emission to the phonon sideband. These results pave the way toward efficient spin–photon interfaces in silicon photonics.
The Impact of AI-Enhanced Social Media Strategies on Entrepreneurial Performance

This research aims to explore the influence of AI-enhanced social media strategies on entrepreneurial performance. The study incorporates a bibliometric analysis in the research methodology to provide a comprehensive understanding of existing literature, identify key themes, and contribute to the current knowledge base. Both China and the United States emerge as frontrunners in contributing to the scholarly discourse. The suggested insights offer promising avenues for future research, encouraging scholars to delve deeper into the strategic, human-centric, precision-oriented, collaborative, ethical, and methodological dimensions of AI-enhanced social media strategies in the entrepreneurial context.
Research on vehicle detection based on improved YOLOX_S

Abstract Aiming at the problem of easy misdetection and omission of small targets of long-distance vehicles in detecting vehicles in traffic scenes, an improved YOLOX_S detection model is proposed. Firstly, the redundant part of the original YOLOX_S network structure is clipped using the model compression strategy, which improves the model inference speed while maintaining the detection accuracy; secondly, the Resunit_CA structure is constructed by incorporating the coordinate attention module in the residual structure, which reduces the loss of feature information and improves the attention to the small target features; thirdly, in order to obtain richer small target features, the PAFPN structure tail to add an adaptive feature fusion module, which improves the model detection accuracy; finally, the loss function is optimized in the decoupled head structure, and the Focal Loss loss function is used to alleviate the problem of uneven distribution of positive and negative samples. The experimental results show that compared with the original YOLOX_S model, the improved model proposed in this paper achieves an average detection accuracy of 77.19% on this experimental dataset. However, the detection speed decreases to 29.73 fps, which is still a large room for improvement in detection in real-time. According to the visualization experimental results, it can be seen that the improved model effectively alleviates the problems of small-target missed detection and multi-target occlusion.
Consistent Valid Physically-Realizable Adversarial Attack Against Crowd-Flow Prediction Models

Recent works have shown that deep learning (DL) models can effectively learn city-wide crowd-flow patterns, which can be used for more effective urban planning and smart city management. However, DL models have been known to perform poorly on inconspicuous adversarial perturbations. Although many works have studied these adversarial perturbations in general, the adversarial vulnerabilities of deep CFP models in particular have remained largely unexplored. In this paper, we perform a rigorous analysis of the adversarial vulnerabilities of DL-based CFP models under multiple threat settings, making three-fold contributions; 1) we propose by formally identifying two novel properties—Consistency and Validity—of the CFP inputs that enable the detection of standard adversarial inputs with 0% false acceptance rate (FAR); 2) we leverage universal adversarial perturbations and an adaptive adversarial loss to present adaptive adversarial attacks to evade defense; 3) we propose, a Consistent, Valid and Physically-realizable adversarial attack, that explicitly inducts the consistency and validity priors in the perturbation generation mechanism. We find out that although the crowd-flow models are vulnerable to adversarial perturbations, it is extremely challenging to simulate these perturbations in physical settings, notably when is in place. We also show that attack considerably outperforms the adaptively modified standard attacks in FAR and adversarial loss metrics. We conclude with useful insights emerging from our work and highlight promising future research directions.