Reviewerly/exHarmony huggingface dataset:
From U-Net to (U-Net) +, What Innovations Have We Made for the Treatment and Discovery of Breast Cancer?

Artificial Intelligence (AI) has achieved remarkable performance in the field of medical image analysis, particularly in tasks such as object detection, segmentation, and classification. In this paper, we introduce a solution for automatic breast cancer diagnosis based on the U-Net architecture, which we call (U-Net)+. The novel (U-Net)+ is designed to handle both segmentation and classification tasks within a signal framework. We retained the original U-Net architecture due to its strong learning capabilities and its advantages in semantic segmentation. Notable, we incorporated fully connected layers into the bottleneck layers, serving as a multi-functional classifier for both initial diagnoses based on raw images and further diagnoses for segmented images. The (U-Net)+ model is trained using a joint loss function. We conducted the experiments on breast ultrasound images, demonstrating that the (U-Net) performs well in both classification and segmentation tasks
APPLICATION OF GRAPHICAL REPRESENTATIONS FROM VIDEO GAMES TO AMPLIFY CO-PRESENCE IN GROUP VISITS TO VIRTUAL HOUSES

In real life, the ability that people have of observing the elements of their surrounding space contributes for the sense of presence and social co-presence in that physical space. Digital avatars are often the employed means for providing a similar
Practical Path of Cultivating College Students' Innovative and Entrepreneurial Ability from the Perspective of "Internet Plus"

In the era of "Internet plus", China's higher education should achieve new development and cultivate innovative and entrepreneurial talents that meet the needs of society, which is the inevitable requirement of the development of the times. However, there are still some problems in the concept, curriculum and teachers of innovation and entrepreneurship education in colleges and universities. Based on this, from the perspective of "Internet plus", this paper expounds the necessity of cultivating college students' innovative and entrepreneurial ability, analyzes the present situation of cultivating college students' innovative and entrepreneurial ability, and puts forward corresponding practical paths, aiming at further improving the quality and level of talent training in colleges and universities.
Exploring Pre-service Teachers' Perceptions and Experiences of Digital Literacy as Revealed during Metaverse Creation Activities

This study was conducted to explore the perceptions of digital literacy, which is considered to be a survival skill for future members of society, from the perspective of pre-service teachers, who are the practitioners of future education. For this purpose, in-depth interviews were conducted with nine pre-service teachers who participated in the process of creating activities an educational metaverse that requires digital literacy. The results of the analysis showed that digital literacy as perceived and experienced by pre-service teachers can be summarized in terms of 'understanding' and 'utilization'. Based on the results of the study, it was suggested that practical and academic efforts are needed to improve and develop teacher education institutions for pre-service teachers, such as improving the curriculum of subject and non-subject courses, expanding mixed online and offline classes, and revitalizing field-based research.
Research on Teaching Strategy of Primary School Chinese Reading Project-based

In the context of "double reduction", in order to deeply improve students' core Chinese literacy, this article explores project-based teaching for the problems existing in primary school reading. Project based learning makes knowledge structured and programmed, which is beneficial for students to acquire knowledge and further enhance their subject abilities and cultivate subject literacy through collaborative exploration. Project-based learning is used to reconstruct the reading classroom and build high-quality deep learning Chinese reading.
The Role of Implementing The Digital Strategy In Strengthen Organizational Performance - a Study Of The Opinions Of a Sample Of Workers In Telecommunications Companies In Nineveh Governorate

The research aims to know the impact of the digital strategy (cloud computing, the Internet of Things, social media, and big data) in organizational performance (financial performance, customer-market, internal operations, learning and growth) among a sample of employees at Asiacell Company, as it was distributed ( 256) questionnaire forms, and the data was analyzed using the following statistical programs (SPSSV.24) and (AMOSV.24), and testing the research hypotheses. The researcher reached a number of conclusions, the most important of which is the existence of a significant impact of the digital strategy on the organizational performance of the company under study, and based on the conclusions reached To her, the researcher presented a number of suggested recommendations, the most prominent of which was emphasizing the necessity of expanding the contents of the digital strategy, and trying to implement it to integrate with each other in a way that reflects positively on the reality of the work of the researched company, in addition to the necessity of increasing senior management’s awareness of their company’s performance levels, and trying to implement more There are common models for measuring it, in line with the nature of the work of the company under investigation. The literature has shown that there is a knowledge gap in explaining the relationship between digital strategy and organizational performance in organizations in general. Keywords: digital strategy, organizational performance, Asiacell company
An Efficient Non-parametric Item Selection Method for Polytomous Scoring CD-CAT

In educational evaluations at home and abroad, polytomous scoring items are becoming increasingly important. They can provide richer and more valuable information, with unmatched advantages compared to binary (0-1 scoring) items. If used as a tool for teachers to diagnose and assess students in the classroom, CD-CAT (Cognitive Diagnostic Computerized Adaptive Testing) has significant implications for improving teaching effectiveness. However, in classroom teaching situations, it is not feasible to estimate item parameters accurately with a large sample, as in large-scale assessments. In such cases, non-parametric CD-CAT becomes the only viable option. Compared to parametric CD-CAT, non-parametric CD-CAT started later and is particularly lacking in research related to polytomous scoring. Item selection method is at the core of CD-CAT, so it is essential to develop a non-parametric item selection method suitable for polytomous scoring CD-CAT. This study proposes a non-parametric item selection method for polytomous scoring cognitive diagnostic computerized adaptive testing (PCD-CAT): the Manhattan Distance Non-parametric Difference index item selection method (MD-NDI). The results of simulation studies indicate: (1) MD-NDI item selection method is suitable for PCD-CAT scenarios and exhibits better performance when the item bank quality is poor or the sample size for estimating item parameters is limited. (2) MD-NDI does not require pre-testing of items and distributes item usage more evenly, effectively ensuring the security of the item bank. (3) Even in cases of incorrectly specified item bank of Qc-matrix, MD-NDI still shows higher pattern correct classification rates. (4) In the study of variable-length PCD-CAT, MD-NDI not only reduces the test length in most conditions but also has a higher pattern correct classification rates when reaching the test termination rule.
Generalized Measure-Biased Sampling and Priority Sampling

Query with aggregates is one of the most important classes of ad-hoc queries. Since query response time is critical in many scenarios, small errors are usually tolerable for query processing. In this work, we adopt sampling to provide fast approximate answers to distribution query and subset-sum query. On the one hand, uniform sampler is sub-optimal. On the other hand, both measure-biased sampler and priority sampler need to create a sample for each measure column. It leads to expensive storage cost, when there are dozens or hundreds of measure columns in the table. To address this issue, we generalize both measure-biased sampler and priority sampler, which can compress the samples but still provide fast approximate answers to both distribution query and subset-sum query within a user-specified error bound. Besides, we establish the relationship between measure-biased sampler and priority sampler by constructing a measure-biased sample from a priority sample. We also extend the priority sampler to support multiple types of aggregates for arbitrary subset. In the extensive experimental evaluation, our generalized samplers achieve a remarkable improvement over the original samplers in terms of the error metrics.
BCE-FL: A Secure and Privacy-Preserving Federated Learning System for Device Fault Diagnosis Under Non-IID Condition in IIoT

Traditional device fault diagnostic methods in Industrial Internet of Things (IIoT) require nodes to upload local data to the cloud, which, however, may lead to privacy leakage issues. Although Federated learning (FL) methods can protect the privacy of data, many challenges still need to be addressed. For example, the non-independently and identically distributed (non-IID) issue in FL prevents the convergence of global model. Moreover, FL lacks detection mechanism to resist poisoning attacks from malicious nodes, and it requires incentive mechanism to encourage nodes to share their resources. To address these challenges, this paper proposes a secure and privacy-preserving FL system that leverages blockchain and edge computing technology. Specifically, a feature-contrastive loss function is constructed to train an unbiased global model under the non-IID condition. Additionally, a Byzantine-tolerance scoring mechanism is designed to resist poisoning attacks, and a reputation-based incentive algorithm is developed to estimate the rewards or penalties owed to nodes. The proposed method is applied to two case studies: chiller fault diagnosis for Heating, Ventilation and Air Conditioning systems and gearbox fault diagnosis for wind turbines in IIoT. Experimental results show the superior performance of the proposed method.
DCI-PFGL: Decentralized Cross-Institutional Personalized Federated Graph Learning for IoT Service Recommendation

The massive amount of data on the Internet of Things (IoT) drives recommendation systems (RS) based on graph neural network (GNN) to fully play a role in improving user experience. However, data sharing and centralized storage can pose serious security threats. Even though federated learning (FL) can render data “available but not visible," the heterogeneity of graph data within IoT institutions can result in limitations in recommendation performance. To address the issues, we propose a privacy-preserving decentralized cross-institutional federated graph learning framework called DCI-PFGL for IoT service recommendation, which alleviates the negative impact of data heterogeneity while protecting data security. Our approach extracts graph feature embeddings using the shortest path graph kernel. These embeddings are then anonymized and compared on a blockchain through smart contracts, which helps match partner IoT institutions with lower data heterogeneity. Subsequently, IoT institutions within the same partition collaborate in federated graph learning. We also ensure the protection of transmitted information through differential privacy measures. Finally, we conduct comprehensive experiments on two benchmark datasets. Results demonstrate that DCI-PFGL outperforms other approaches in terms of system accuracy and collaboration costs.
Online ML Self-adaptation in Face of Traps

Online machine learning (ML) is often used in selfadaptive systems to strengthen the adaptation mechanism and improve the system utility. Despite such benefits, applying online ML for self-adaptation can be challenging, and not many papers report its limitations. Recently, we experimented with applying online ML for self-adaptation of a smart farming scenario and we had faced several unexpected difficulties – traps – that, to our knowledge, are not discussed enough in the community. In this paper, we report our experience with these traps. Specifically, we discuss several traps that relate to the specification and online training of the ML-based estimators, their impact on selfadaptation, and the approach used to evaluate the estimators. Our overview of these traps provides a list of lessons learned, which can serve as guidance for other researchers and practitioners when applying online ML for self-adaptation.
Volumetric Video Experiences with MPEG V3C Decoder Platform for Efficient Delivery and Rendering on Client Devices

The demonstration will present a real-time implementation of a platform jointly developed by InterDigital and Philips that showcases use cases leveraging the MPEG volumetric video codecs (MPEG-I V3C).
Improving Knee Osteoarthritis Classification with Markerless Pose Estimation and STGCN Model

Knee osteoarthritis (KOA) is a debilitating disease that greatly impacts the quality of life, particularly among the elderly population. Conventional subjective assessment methods for KOA have limitations in terms of accuracy and objective diagnosis. This paper proposes an innovative approach by integrating advanced technologies, specifically the Spatio-Temporal Graph Convolutional Network (STGCN), applied to gait analysis from markerless videos, for precise and quantitative assessment of KOA. The STGCN network is applied to normalized data obtained from Blazepose, a markerless pose estimation technique. Evaluated on an academic dataset of 80 RGB videos, it provides an accuracy of 93.75%. By leveraging the capabilities of the STGCN network, this study significantly enhances the classification of KOA based on gait patterns, offering promising prospects for improved diagnosis and treatment strategies for individuals with KOA.
PEDAGOGICAL APPROACHES TO THE DEVELOPMENT OF GRAPHIC CULTURE OF 8-9 GRADE STUDENTS IN TECHNOLOGY LESSONS

The article analyzes pedagogical conditions for the effective formation of graphic culture among students in technology classes. Various pedagogical approaches that can be used to achieve this goal are highlighted: instructional, interactive, constructive, game, integrated. It was found that the pedagogical approach to the development of graphic culture in technology lessons requires a systematic and comprehensive approach, taking into account successive steps and teaching methods to ensure the effective formation of students' graphic abilities and skills. A comparison of various educational programs aimed at the development of graphic culture in technology lessons was made, both positive aspects that are already present in the programs and aspects that need improvement are highlighted. Based on the analysis, a conclusion was made about the need to create an author's program or a series of lessons taking into account the indicated pedagogical aspects to improve the development of students' graphic culture in technology lessons. It was determined that pedagogical approaches to the development of students' graphic culture in technology lessons are important from the point of view of students' intellectual development. Studies indicate that an invariant function of intellectual activity is the ability to operate imaginative graphic, artistic, schematic and symbolic models of objects. It is known that the level of training of a specialist is determined by the extent to which he can work with figurative and symbolic models and how developed his spatial thinking is.
Integrating IoT and Novel Approaches to Enhance Electromagnetic Image Quality using Modern Anisotropic Diffusion and Speckle Noise Reduction Techniques

Electromagnetic imaging is becoming more important in many sectors, and this requires high-quality pictures for reliable analysis. This study makes use of the complementary relationship between IoT and current image processing methods to improve the quality of electromagnetic images. The research presents a new framework for connecting Internet of Things sensors to imaging equipment, allowing for instantaneous input and adjustment. At the same time, the suggested system makes use of sophisticated anisotropic diffusion algorithms to bring out key details and hide noise in electromagnetic pictures. In addition, a cutting-edge technique for reducing speckle noise is used to combat this persistent issue in electromagnetic imaging. The effectiveness of the suggested system was determined via a comparison to standard imaging techniques. There was a noticeable improvement in visual sharpness, contrast, and overall clarity without any loss of information, as shown by the results. Incorporating IoT sensors also facilitated faster calibration and real-time modifications, which opened up new possibilities for use in contexts with a high degree of variation. In fields where electromagnetic imaging plays a crucial role, such as medicine, remote sensing, and aerospace, the ramifications of this study are far-reaching. Our research demonstrates how the Internet of Things (IoT) and cutting-edge image processing have the potential to dramatically improve the functionality and versatility of electromagnetic imaging systems.
Hybrid Encryption based Access Control Approach for Securing Cloud Computing

Cloud computing has become an integral part of modern technological infrastructure, facilitating the storage and processing of vast amounts of data. However, ensuring the security of sensitive information in the cloud remains a persistent challenge. This paper proposes a novel approach to enhance the security of cloud computing through hybrid encryption, leveraging the Whale Optimization Algorithm (WOA) and Cuckoo Search Optimization (CSO) algorithms. Hybrid encryption, combining symmetric and asymmetric cryptographic techniques, is employed to address the limitations of traditional encryption methods in cloud environments. The Whale Optimization Algorithm and Cuckoo Search Optimization are utilized to optimize key generation and management processes, enhancing the overall efficiency and security of the encryption scheme. The Whale Optimization Algorithm, inspired by the social behavior of humpback whales, is employed to optimize the parameters of the encryption algorithm. WOA's exploration and exploitation capabilities are leveraged to find an optimal balance in the encryption process, improving the overall robustness against potential attacks. Complementing WOA, the Cuckoo Search Optimization algorithm is applied to optimize the key distribution and update mechanisms. Modeled after the brood parasitism behavior of cuckoo birds, CSO excels in searching large solution spaces, making it suitable for refining the distribution of encryption keys and ensuring their constant adaptability to dynamic cloud environments.
An Efficient Authentication Approach with Optimization Algorithms and Elliptical Curve Cryptography for Cloud Environment

The fast-emerging of cloud computing technology today has sufficiently benefited its wide range of users from individuals to large organizations. It carries an attractive characteristic by renting myriad virtual storages, computing resources and platform for users to manipulate their data or utilize the processing resources conveniently over Internet without the need to know the exact underlying infrastructure which is resided remotely at cloud servers. Security is very important for any kind of networks. As a main communication mode, the security mechanism for multicast is not only the measure to ensure secured communications, but also the precondition for other security services. Attacks are one of the biggest concerns for security professionals. Attackers usually gain access to a large number of computers by exploiting their vulnerabilities to set up attack armies. This paper presents a dual optimizer based key generation method for the improving the authentication with Elliptical Curve Cryptography (ECC) encryption algorithm. The optimal private and secret key for the encryption and decryption are obtained with the optimization techniques like Animal Migration Optimization (AMO), and Brain Storm Optimization (BSO) for strengthening the security in the Cloud Computing environment.
Research on the Construction of Smart Teaching Mode with Artificial Intelligence Technology Facilitating Education Informatization in Colleges and Universities

Abstract Based on subject knowledge mapping, this paper dynamically collects learning data, portrays learners’ learning situations, and accurately regulates the learning process. Personalized learning path recommendations and learning communities are constructed through learner profiling and learning services. Secondly, structural equation modeling was used to hypothesize the three-level elements of the E-GPPE-C model. Finally, 103 college students in smart teaching classes were taken as research subjects, and the utility of the smart teaching model was analyzed separately through the steps of precondition validation and cross-lag model with random intercepts. The results show that the smart teaching model has β =0.286 for deep learning strategy, β =0.211 for the smart classroom, and β =0.20 for classroom participation, and they accurately indicate that smart teaching has a positive facilitating mechanism on the learning ability of college students. This study also provides a useful reference for the practice of smart teaching in various disciplines in colleges and universities.
Autoencoders and their application in removing masks

Images are frequently distorted by noises that have a negative impact on the quality of image data. In this study, the author focuses on coping with a specific type of noise that has arisen regularly in recent years as a result of the pandemic: masks covering portions of the photographs of human faces. The paper employs the autoencoder model, which offers unsupervised learning. It compresses or encodes original data input into a smaller latent vector, then decodes it back to its original size, learning and extracting relevant features from the data in the process. In a further phase, the author employs a combination of convolutional autoencoders and denoising autoencoders, treating masks as corruptions in order to get more accurate predictions regarding the image of a human face without any covering. After training on 2,500 image pairs with and without masks and validating on 200 such image pairs, the model presented in this research achieves an overall accuracy of 93%. The research demonstrates that the combination of convolutional and denoising autoencoders is an excellent method for removing masks from facial images, and the author believes it can also be used to effectively remove other types of noise. However, the study also reveals that the picture data generated in this manner are always inferior to the original, and that the autoencoder can only process data of the same or comparable type on which it has been trained. In the future, improved models will exist to address these shortcomings and be applied to more real-life situations.