Reviewerly/exHarmony huggingface dataset:
Peta-Scale Embedded Photonics Architecture for Distributed Deep Learning Applications

As Deep Learning (DL) models grow larger and more complex, training jobs are increasingly distributed across multiple Computing Units (CU) such as GPUs and TPUs. Each CU processes a sub-part of the model and synchronizes results with others. Communication among these CUs has emerged as a key bottleneck in the training process. In this work, we present SiPAC, a Silicon Photonic Accelerated Compute cluster. SiPAC accelerates distributed DL training by means of two co-designed components: a photonic physical layer and a novel collective algorithm. The physical layer exploits embedded photonics to bring peta-scale I/O directly to the CUs of a DL optimized cluster and uses resonator-based optical wavelength selectivity to realize hardware multi-casting. The collective algorithm builds on the hardware multi-casting primitive. This combination expedites a variety of collective communications commonly employed in DL training and has the potential to drastically ease the communication bottlenecks. We demonstrate the feasibility of realizing the SiPAC architecture through 1) an optical testbed experiment where an array of comb laser wavelengths are shuffled by a cascaded ring switch, with each ring selecting and forwarding multiple wavelengths to increase the effective communication bandwidth and hence demonstrating the hardware multicasting primitive, and 2) a four-GPU testbed running a realistic DL workload that achieves 22% system-level performance improvement relative to a similarly-sized leaf-spine topology. Large scale simulations show that SiPAC achieves a 1.4× to 5.9× communication time reduction compared to state-of-the-art compute clusters for representative collective communications.
Digital Transformation Blueprint in Higher Education: A Case Study of PSU

Digital transformation is a rapidly growing trend that has captured the attention of institutions and stakeholders worldwide. This phenomenon has become a top priority for higher education institutions, as universities play a critical role in the development and empowerment of societies. To address this pressing need, this paper proposes the establishment of a sustainable digital transformation environment based on the best national and international practices and recommendations. The goal of this proposal is to overcome the challenges faced by universities and provide the most convenient mechanisms to achieve their duties. Furthermore, this proposal aims to be a successful part of achieving Saudi Arabia Kingdom’s Vision 2030, which seeks to revolutionize the education and scientific research system. By adopting this proposed approach, universities in Saudi Arabia can proactively anticipate and address emerging trends and thereby ensure that they are offering the highest quality education to their students. Digital transformation is a crucial trend that cannot be ignored. By embracing this trend and implementing sustainable digital transformation environments, universities can ensure that they are providing the best possible education to their students and contributing to the development and empowerment of societies.
Factors Affecting Avatar Customization Behavior in Virtual Environments

This research aims to examine the psychology and behavior of users when customizing avatars from the standpoint of user experience and to provide constructive contributions to the Metaverse avatar customization platform. This study analyzed the factors that affect the behavior of user-customized avatars in different virtual environments and compared the differences in public self-consciousness, self-expression, and emotional expression among customized avatars in multiple virtual contexts. Methods: Using a between-subjects experimental design, two random groups of participants were asked to customize avatars for themselves in two contexts, a multiplayer online social game (MOSG) and a virtual meeting (VM). Results: When subjects perceived a more relaxed environment, the customized avatars had less self-similarity, and the subjects exhibited a stronger self-disclosure willingness and enhanced avatar wishful identification; nevertheless, public self-consciousness was not increased. When subjects perceived a more serious environment, the customized avatars exhibited a higher degree of self-similarity, and the subjects exhibited a greater self-presentation willingness, along with enhanced identification of avatar similarity and increased public self-consciousness. Conclusions: Participants in both experiment groups expressed positive emotions. The virtual context affects the self-similarity of user-customized avatars, and avatar self-similarity affects self-presentation and self-disclosure willingness, and these factors will affect the behavior of the user-customized avatar.
Reconstruction of sub‐mm 3D pavement images using recursive generative adversarial network for faster texture measurement

Abstract It is challenging to collect 3D pavement images with desired resolution for accurate texture measurement at driving speeds with current devices, particularly in the longitudinal direction. This paper presents a novel superresolution technique with recursive generative adversarial network, called Pavement Texture Super Resolution Generative Adversarial Network (PT‐SRGAN), to reconstruct 0.1‐mm pavement 3D image from low‐resolution data for faster texture measurement. With the proposed pseudo‐Laplacian pyramid and an array of learning strategies, the developed PT‐SRGAN reconstructs 0.1‐mm 3D texture images with multiple upscaling factors in longitudinal direction. Combined with the evaluation mask, the proposed method is substantially superior to other methods in terms of three metrics when comparing the image quality of reconstructed 0.1‐mm 3D images against ground truth. The preliminary results indicate that the proposed method enables data collection at driving speeds up to 24 mph to collect 3D pavement images at sub‐mm resolution for faster texture measurement.
Educational metaverse: an exploration and practice of VR wisdom teaching model in Chinese Open University English course

Purpose The paper aims to propose a virtual reality (VR) wisdom teaching model in open university English course from the perspective of “Metaverse”. The study aims to testify the stimulation for English learning and the effectiveness of English-expressing with VR tools for adult learners from the practice in a pilot reform project. Design/methodology/approach The paper opted for an exploratory study using ICARE Design Model as the framework, under the grounded theories of constructivism and multi-modal teaching. The study compared the evaluation data of one-semester English learning performance between the experimental class (67 students) with VR practice and the controlled class (67 students), including speaking test score, qualitative feedback and in-depth experience analysis. The data were complemented by reflection paper analysis, including manual evaluation (the criteria of semantics, pronunciation, fluency and completeness), questionnaire survey (in the form of five-point Likert scale) and semi-structured interview. Findings The paper provides empirical insights about the VR wisdom teaching model in English language teaching and learning in a Chinese Open University. The empirical results suggest that “3I” features of VR technology could make up for the shortcomings of traditional English classes in open universities in China, and VR resources designed with curriculum teaching materials could also be helpful for students’ command of knowledge points and language skills. What’s more, the sense of authentic experience in virtual could promote the teaching and learning effect in college English classes. Research limitations/implications The present study focuses on a wisdom mode of foreign language teaching and learning for adult learners in open education, so the research results may lack generalizability. Therefore, researchers are encouraged to further explore the deep integration of VR/artificial intelligence in foreign language teaching and learning. Originality/value This paper fulfills an identified need to study how VR tools provide an engaging, fun and immersive language learning environment, to enhance autonomous learning and learning engagement.
SNRCN2: Steganalysis noise residuals based CNN for source social network identification of digital images

Identifying the processing history of digital images related to source acquisition device, image manipulations, and Source Social Media Network (SSMN) identification is important for a forensic analyst to verify image source, trustworthiness, and integrity. Nowadays, social media networks have become a major medium of image sharing and identifying the SSMN of digital images has allured attention in the image forensic scientific community. With the precedence of deep Convolutional Neural Networks (CNNs) in the domain of image forensics, we propose a novel Steganalysis Noise Residuals based CNN (SNRCN2) for digital images SSMN identification. Inspired by the fact that image content can heavily obscures the artifacts of post-processing operations, the proposed CNN utilizes the features from steganalysis-based noise residuals to highlight the artifacts introduced by social media networks. Therefore, the given input image is high-pass filtered using well-known 30 Spatial Rich Model (SRM) filters to obtain noise residuals. Afterward, these noise residuals are passed to an efficient CNN for the extraction of high-level features related to social media networks. Lastly, a fully-connected layer is used for classification purposes. The experimental results show that the proposed model outperforms the existing techniques by providing an image-level accuracy of 99.53% and 100% on the VISION and Forchheim datasets, respectively. To further evaluate the robustness of the proposed model, we create a combined dataset that includes the images of common classes from both datasets. The results of the combined dataset further confirm the efficacy of the proposed model.
DMS-YOLOv5: A Decoupled Multi-Scale YOLOv5 Method for Small Object Detection

Small objects detection is a challenging task in computer vision due to the limited semantic information that can be extracted and the susceptibility to background interference. In this paper, we propose a decoupled multi-scale small object detection algorithm named DMS-YOLOv5. The algorithm incorporates a receptive field module into the feature extraction network for better focus on low-resolution small objects. The coordinate attention mechanism, which combines spatial and channel attention information, is introduced to reduce interference from background information and enhance the network’s attention to object information. A detection layer tailored to small-sized objects is added to compensate for the loss of small object information in multiple downsampling operations, greatly improving the detection capability of small objects. Next, The decoupled network is introduced into the detection head network for branch processing of classification and bounding box regression tasks. Finally, the bounding box loss function is improved to alleviate missed detection problems caused by the concentration of small objects and mutual occlusion between objects. The improved method achieved a mean average precision improvement of 12.1% on VisDrone2019-DET dataset compared to the original method. In comparison experiments with similar methods, our proposed method also demonstrated good performance, validating its effectiveness.
Multimodal Interaction and Fused Graph Convolution Network for Sentiment Classification of Online Reviews

An increasing number of people tend to convey their opinions in different modalities. For the purpose of opinion mining, sentiment classification based on multimodal data becomes a major focus. In this work, we propose a novel Multimodal Interactive and Fusion Graph Convolutional Network to deal with both texts and images on the task of document-level multimodal sentiment analysis. The image caption is introduced as an auxiliary, which is aligned with the image to enhance the semantics delivery. Then, a graph is constructed with the sentences and images generated as nodes. In line with the graph learning, the long-distance dependencies can be captured while the visual noise can be filtered. Specifically, a cross-modal graph convolutional network is built for multimodal information fusion. Extensive experiments are conducted on a multimodal dataset from Yelp. Experimental results reveal that our model obtains a satisfying working performance in DLMSA tasks.
A Chaos-Based Image Encryption Scheme Is Proposed Using Multiple Chaotic Maps

Everybody wants to maintain solitariness to some extent or entirely in his dealings with other people during different modes of communication. To retain privacy, researchers materialized distinct image encryption algorithms using chaotic maps. Due to their extraordinary features, most researchers employed multidimensional chaotic maps to barricade clandestine information or digital images from potential invaders. Still, multidimensional chaotic maps have many impediments conferred in the literature review. In this paper, we developed a cryptosystem utilizing multiple chaotic maps to mitigate the shortcoming of multidimensional chaotic maps. A distinctive approach is adopted to sire a key stream using a combination of chaotic maps and create a sequence of random integers linked with the pixels of the plain image to shatter the association between neighboring pixels of a plain image. Finally, diffusion is accomplished using the previously diffused pixels at a decimal level. Security and statistical analysis demonstrate that the presented encryption algorithm is robust against well-known attacks. An ample key space indicates that it is best suited for secure communication.
Accuracy–Security Tradeoff With Balanced Aggregation and Artificial Noise for Wireless Federated Learning

In federated learning (FL), a number of devices train their local models and upload the corresponding parameters or gradients to the base station (BS) for global model updates. However, the eavesdropper can recover data from parameters or gradients, resulting in data leakage. To defend against eavesdropping attacks, in this article, we propose an algorithm that divides the transmit power proportionally between the transmitted signal and artificial noise (AN) to counteract the eavesdropper for wireless FL. In this algorithm, due to the limited communication resources, the ratio of signal power to total power and the aggregation frequency need to be carefully chosen, to guarantee the model accuracy and security at the same time. In order to achieve this goal, we maximize the secrecy rate with the system/user power and model performance constraints. To make this problem tractable, we derive two bounds of the secrecy rate and loss function, which allows us to obtain closed-form expressions for the power of AN and the aggregation frequency. Furthermore, in order to make our analysis more realistic, we consider the FL model with channel fading and additive white Gaussian noise (AWGN) over uplink and downlink, respectively. Specifically, we discuss the convergence of FL over noisy multiple access channels (MACs). Simulation results confirm the convergence and the effectiveness of the proposed algorithm.
Parameter-Free Robust Ensemble Framework of Fuzzy Clustering

The ensemble of fuzzy clustering can address the problems presented in the base clustering, such as fluctuations in results due to random initialization and performance degradation due to outliers. However, the performance of fuzzy clustering ensembles is still hampered by some challenges that include misaligned membership matrices, loss of information in the cosimilarity matrix, large storage space, unstable ensemble results due to an additional reclustering, the need for original data information for assistance, etc. To address these issues, we propose a parameter-free robust ensemble framework for fuzzy clustering. After obtaining the set of membership matrices, we cascade these membership matrices and mine the latent spectral matrix of the raw data. Benefiting from this step, we obtain global features of the dataset without knowing the specific data. Then, our framework uses transition matrices to solve the alignment problem, avoiding the storage of large-scale matrices. Most importantly, we introduce a robust weighted mechanism in the optimization model, where each base clustering is adaptively adjusted and the effect of outliers is suppressed by a robust function. In addition, the model yields the results as a membership matrix, which produces the exact partition results directly without any subsequent clustering operations. Finally, since our model is a parameter-free model, the setting of hyperparameters is avoided and the applicability of the model is improved as well. The effective algorithm of the optimization model is derived and its time complexity and convergence are analyzed. The results of competitive experiments on benchmark data show that the proposed ensemble framework is effective compared to state-of-the-art methods.
Blockchain Based Secure Interoperable Framework for the Internet of Medical Things

Internet of Medical Things (IoMT) has revolutionized the way medical infrastructure has been managed in the past. Multiple platforms in IoMT have disparate communication standards, data format requirements, and access policies, which produce immense overhead during data transfer among these platforms. In order to provide seamless healthcare services using IoMT, interoperability concerns of heterogeneous devices need to be addressed. Smart contracts using blockchain provide a secure communication for distributed objects to interact in a secure way. We propose a Blockchain-based Secure Interoperable Framework (BSIIoMT) using smart contracts for secure communication in IoMT. We present components, workflow, and design considerations of the BSIIoMT framework to show the feasibility of using edge-enabled blockchain for secure interoperability in IoMT. The BSIIoMT framework is an ongoing project where we present the framework and its components in this research where further results and evaluation will be presented in future.
A blockchain‐enabled security management framework for mobile edge computing

Abstract Mobile edge computing (MEC) integrates mobile and edge computing technologies to provide efficient computing services with low latency. It includes several Internet of Things (IoT) and edge devices that process the user data at the network's edge. The architectural characteristic of MEC supports many internet‐based services, which attract more number of users, including attackers. The safety and privacy of the MEC environment, especially user information is a significant concern. A lightweight accessing and sharing protocol is required because edge devices are resource constraints. This paper addresses this issue by proposing a blockchain‐enabled security management framework for MEC environments. This approach provides another level of security and includes blockchain security features like temper resistance, immutable, transparent, traceable, and distributed ledger in the MEC environment. The framework guarantees secure data storage in the MEC environment. The contributions of this paper are twofold: (1) We propose a blockchain‐enabled security management framework for MEC environments that address the security and privacy concerns, and (2) we demonstrate through simulations that the framework has high performance and is suitable for resource‐constrained MEC devices. In addition, a smart contract‐based access and sharing mechanism is proposed. Our research uses a combination of theoretical analysis and simulation experiments to demonstrate that the proposed framework offers high security, low latency, legitimate access, high throughput, and low operations cost.
A consider unscented particle filter with genetic algorithm for UAV multi-source integrated navigation

Abstract The unscented particle filter (UPF) combines the advantages of both the unscented Kalman filter and particle filter (PF) algorithms. However, during the fusion of multi-source information for UAV integrated navigation, the UPF may encounter problems related to measurement biases and particle depletion. These issues can lead to algorithm results that are unsatisfactory or divergent. To address these challenges, a novel approach called the consider unscented particle filter with genetic algorithm (GA-CUPF) is proposed. The GA-CUPF algorithm incorporates covariance and co-covariance of biases into the state estimation covariance through the ‘consider’ method employed by the CUPF, and it should be noted that the biases are not estimated in this process. Moreover, the GA-CUPF algorithm enhances the importance probability density of particles to avoid the degeneracy of PF algorithm and improves particle diversity through the application of selection, crossover, and mutation methods in GA. Simulation results demonstrate that the proposed GA-CUPF algorithm can effectively mitigate the adverse effects of measurement biases and particle depletion that the UPF algorithm faces in the UAV navigation system. Additionally, it significantly improves the accuracy of UAV integrated navigation.
Experimental generation and characterization of partially spatially coherent qubits

Partially spatially coherent qubits are more immune to turbulent atmospheric conditions than coherent qubits, which makes them excellent candidates for free-space quantum communication. In this article, we report the generation of partially spatially coherent qubits in a spontaneous parametric down-conversion (SPDC) process using a Gaussian Schell model (GSM) pump beam. For this non-linear process, we demonstrate experimentally for the first time, the transfer of spatial coherence features of the pump (classical) to the biphotons (quantum) field. Also, the spatial profiles of partially coherent qubits generated in type-I and type-II non-collinear SPDC process are experimentally observed and multi-mode nature of partially coherent photons (qubit) is ascertained. These investigations pave the way toward the efficient generation of partially spatially coherent qubits with a tunable degree of spatial coherence, which lead to wide range of applications in frontier areas such as quantum cryptography, teleportation, imaging, and lithography.