Reviewerly/exHarmony huggingface dataset:
EVOLVE: Towards Converging Big-Data, High-Performance and Cloud-Computing Worlds

EVOLVE is a pan European Innovation Action that aims to fully-integrate High-Performance-Computing (HPC) hardware with state-of-the-art software technologies under a unique testbed, that enables the convergence of HPC, Cloud and Big-Data worlds and increases our ability to extract value from massive and demanding datasets. EVOLVE's advanced compute platform combines HPC-enabled capabilities, with transparent deployment in high abstraction level, and a versatile Big-Data processing stack for end-to-end workflows. Hence, domain experts have the potential to improve substantially the efficiency of existing services or introduce new models in the respective domains, e.g., automotive services, bus transportation, maritime surveillance and others. In this paper, we describe EVOLVE's testbed, and evaluate the performance of the integrated pilots from different domains.
Asynchronous Federated Learning Empowered Computation Offloading in Collaborative Vehicular Networks

Collaborative vehicular networks (CVNs) provide on-demand data processing via computation offloading empowered by edge and fog computing. However, intelligent computation offloading in CVNs still faces several challenges such as long-term quality of service (QoS) guarantee, inefficient utilization of environmental information, and asynchronous information exchange. In this paper, we aim at maximizing the throughput under long-term QoS constraints such as queuing delay. The problem of server-side computation resource allocation and user vehicle (UV)-side task offloading is decoupled by Lyapunov optimization. Firstly, we propose an asynchronous federated deep Q-learning network based task offloading (AF-DQN) algorithm to solve the task offloading subproblem by exploring the semi-distributed learning framework. Secondly, we develop a heuristic queue backlog-aware algorithm to solve the computation resource allocation subproblem. Simulation results demonstrate that the proposed algorithm effectively reduces end-to-end queuing delay.
The Study of Immersive Physiology Courses Based on Intelligent Network through Virtual Reality Technology in the Context of 5G

The current boom in Internet technology has paved the way for the research and evolution of various technologies related to it. One such technology is immersive virtual reality (IVR). Immersive technology is referred to as creating a reality-like experience by combining the physical world with digital reality. There are two main types of immersive technologies. Immersion in virtual reality is the perception of being physically present in an artificially created world. Perception is artificially created by images, videos, sounds, or other stimuli with the help of a virtual reality (VR) system that the user is connected to. VR uses rendered computer-generated simulations and results in a complete sense of immersion. Immersive virtual reality (immersive VR) refers to engaging users in an artificial environment that replaces their natural surroundings and fully engages them with the artificially created environment. In this research, we will research immersive physiology courses based on artificial intelligence combined with wireless network VR technology in the context of 5G. The teaching methodology has been kept up-to-date along with the technology. Teaching physiology courses also incorporate new technologies like immersive technologies. The use of technology in anatomy and physiology courses allows students to view structures and physiological concepts in a realistic environment. Virtual dissection in 3D is available with a life-like artificial environment. Students can attend the classes with VR headsets, laptops, or smartphones to experience immersive and interactive 3D classes. This advanced technology enhances and empowers the students to learn from real-life situations like those available in the classes. In this research, CNN with AI is proposed for effective learning of physiology courses. This algorithm is compared with the existing NNGA, KNN, and Random Forest, and it is observed that the proposed model has obtained an accuracy of 99%.
Feature pyramid-based graph convolutional neural network for graph classification

As an important task of Graph Neural Networks (GNN), graph classification has received increasing attention, as it can be widely used in numerous fields, such as protein prediction and community prediction. The GNN models for graph classification usually require aggregating the structure and feature information of an input graph into a hidden representation vector. Such a technique can be generally referred as graph pooling, which intends to maximize the removal of noise while minimize the loss of important features. Therefore, there exists a trade-off between the noise reduction and information maximization that needs to be balanced for better performance. However, the existing pooling-based GNNs for graph classification depends on the manual setting of the graph-pooling degree, making it difficult to balance the trade-off. To this end, we propose a Feature Pyramid-based Graph Convolutional Neural network for Graph Classification (FPGCN-GC), which constructs multi-scale hierarchical information fusion to reduce information loss, and achieves an adaptive feature fusion through a learnable weighted residual connection and self-attention mechanism. The superior performance of the proposed method is verified on multiple graph classification datasets, thus illustrating the effectiveness and superiority of FPGCN-GC. In addition, we visualize FPGCN-GC by T-SNE to further analyze the underlying reason for its effectiveness.
DograNet – a comprehensive offline dogra handwriting character dataset

Abstract Handwritten Text Recognition is an important area of research because of growing demand to process and convert a huge data and information available in handwritten form to Digital form. The digital data instead of handwritten form can prove to be highly useful in different fields. Handwritten text recognition plays an important role in applications involved in, postal services, banks for cheque processing, searching of information and organization dealing with such applications. In text recognition application dataset of the specified script is required for training purpose. Datasets of the different languages could be found online but dataset of dogra script characters is still not available. This paper presents a Dogra handwriting character dataset which contains around 38690 character images etc grouped in 73 character classes extracted from 530 one-page handwritings of 265 individuals of having variable age, sex, qualification, location. The dogra character dataset would be freely accessible by scholars and researchers which could also be used for further recognition improvement and updating with more characters and word, Identification of writer, dogra word segmentation. Dogra dataset could also be used for extracting variation of handwriting according to age and gender.
Multi-scale Multi-instance Multi-feature Joint Learning Broad Network (M3JLBN) for gastric intestinal metaplasia subtype classification

According to worldwide statistics, gastric intestinal metaplasia (GIM) is one of the most important characteristics for detecting the lesions in early gastric cancer. Currently, there are many detection methods employing multi-instance learning (MIL) on clinical medical images. For gastrointestinal endoscope (GE) images, there are very few existing works, which suffer from several issues: (i) only binary (i.e., healthy and GIM) cases are handled; (ii) the inter-instance correlation (i.e., the correlation between image patches or instances) cannot be captured; (iii) the multi-class label correlations among GIM subtype labels are neglected; (iv) multi-scale information is not considered. To address these issues, a novel Multi-scale Multi-instance Multi-feature Joint Learning Broad Network (M3JBLN) is proposed for practically required GIM subtype classification, which includes two new modules: (i) A multi-class MIL prediction module is designed based on probabilistic representation which can provide multi-class label correlations (pseudo label) through labeled multi-scaling training samples; (ii) A novel multiple features joint learning broad network (MFJLBN) based on broad learning system (BLS) is designed by integrating a new representation layer and multi-scaling module, which can jointly obtain inter-instance correlation in feature space under multiple scales. Under these two modules, the proposed M3JBLN jointly considers the multi-features of each instance at multiple scales to achieve more accurate subtype classification. The proposed M3JBLN is evaluated on a limited available GIM dataset acquired from patients who visited the Endoscopy Center of Kiang Wu Hospital (Macau, China) between January 2017 and April 25, 2021. Our method can respectively improve the performance with at least 7.5%, 7.6%, 7.6% and 6.6% under the four-evaluation metrics (accuracy, precision, recall and F1-score) compared to other classic methods.
Trace metals in saline waters and brines from China: Implications for tectonic and climatic controls on basin-related mineralization

Research into mineralization using a deposit-centric approach has identified three principal factors (evolution of the hydrosphere–atmosphere, secular decrease in global heat production, and long-term global tectonic trends) that have influenced the global pattern of metallogenesis. However, these factors do not explain the peak occurrences of basin-related mineralization during particular periods of Earth's evolution. Here we shift the research perspective from an ore-deposit-centric approach to one focusing on the formation processes of ore-forming fluids by using mass spectrometry to analyze the concentrations of metallic elements (U, Cr, Fe, Mn, Co, Ni, Cu, Mo, Cd, Sn, Pb, Zn, and V) of naturally occurring saline water bodies (brines) in currently developing basins in China. Our aim is to determine the presence of metal-bearing fluids and establish the formation processes of ore-forming fluids that can subsequently generate mineral deposits. The concentrations of trace metals in evaporated seawater and surficial saline waters (brines) in continental basins can reach up to mg/L level, with the increase in salinity resulting from the significant evaporation that occurs in semi-arid to arid environments. The high salinities increase the density of metal-bearing brines, which in turn causes these brines to circulate and mix with groundwater, resulting in enriched metal element concentrations in the groundwater. Groundwater containing dissolved evaporites also shows relatively high concentrations of metal elements (up to mg/L level) that compare with fresh groundwater. These metal-bearing brines can be regarded as the initial ore-forming fluids of basin-related mineral deposits, such as unconformity-type U deposits, red-bed Cu deposits, and some stratiform Pb–Zn deposits. The metal-bearing brines are the result of substantial evaporation under arid to semi-arid climates. Therefore, we conclude that climate is an important control on the formation of basin-related mineralization and that the tectonoclimatic setting plays an essential role in the formation of metal-bearing brines in closed inland basins. The major climatic events, such as the snowball and greenhouse event can affect the basin-related mineralization globally. The super mantle plume event could affect the global basin-related mineralization by changing the global climate significantly.
DIET: A Dynamic Energy Management Approach for Wearable Health Monitoring Devices

Wearable devices are becoming increasingly popular for health and activity monitoring applications. These devices typically include small rechargeable batteries to improve user comfort. However, the small battery capacity leads to limited operating life, requiring frequent recharging. Recent research has proposed energy harvesting using light and user motion to improve the lifetime of wearable devices. Most energy harvesting approaches assume that the placement of the energy harvesting device and sensors required for health monitoring are the same. However, this assumption does not hold for several real-world applications. For example, motion energy harvesting using piezoelectric sensors is limited to the knees and elbows, while a sensor for heart rate monitoring must be placed on the chest for optimal performance. To address this challenge, we propose a novel dynamic energy management approach referred to as DIET for wearable health applications enabled by multiple sensors and energy harvesting devices. The key idea behind DIET is to harvest energy from multiple sources and optimally allocate it to each sensor using a lightweight optimization algorithm such that the overall utility for applications is maximized. Experiments on real-world data from four users over 30 days show that the DIET approach achieves utility within 10% of an offline Oracle.
Gradual adaption with memory mechanism for image-based 3D model retrieval

With the development of 3D modeling technology and its wide application in different fields, the number of 3D models increases rapidly, making 3D model retrieval a hot topic in current research. Compared with other 3D model retrieval methods, 2D image-based unsupervised 3D model retrieval takes the 2D images which have rich labels and are easy to obtain as the queries, and also takes into account the difficulties of labeling 3D models. 2D image-based unsupervised 3D model retrieval is a retrieval task involving cross-domain adaptation problem, which main challenge is the excessive domain gap. In this paper, we propose a cross-domain 3D model retrieval method of memory mechanism based on disentangled feature learning. The disentangled feature learning enables to disentangle the twisted original features into the isolated domain-invariant features and domain-specific features, where the former is to be aligned to narrow the domain gap. On this basis, the memory mechanism selects feature vectors from class memory modules constructed by class representative features of the opposite domain for every sample, which are used to update the domain-invariant features with gradient weight. The memory mechanism can gradually improve the adaptability of the model to the very different two domains. Experiments are conducted on the public datasets MI3DOR and MI3DOR-2 to verify the feasibility and the superiority of the proposed method. Especially on MI3DOR-2 dataset, our method outperforms the current state-of-the-art methods with gains of 7.71% for the strictest retrieval metric NN.
Joining Sustainable Design and Internet of Things Technologies on Campus: The IPVC Smartbottle Practical Case

Higher education institutions (HEIs) are favored environments for the implementation of technological solutions that accelerate the generation of smart campi, given the dynamic ecosystem they create based on the involvement of inspired and motivated human resources (students, professors, and researchers), moving around in an atmosphere of advanced digital infrastructures and services. Moreover, HEIs have, in their mission, not only the creation of integrated knowledge through Research and Development (R&amp;D) activities but also solving societal problems that address the academic community expectations concerning environmental issues, contributing, therefore, towards a greener society embodied within the United Nations (UN) Sustainable Development Goals (SDGs). This article addresses the design and implementation of a Smartbottle Ecosystem in which an interactive and reusable water bottle communicates with an intelligent water refill station, both integrated by the Internet of Things (IoT) and Information and Communications Technologies (ICT), to eliminate the use of single-use plastic water bottles in the premises of the Polytechnical Institute of Viana do Castelo (IPVC), an HEI with nearly 6000 students. Three main contributions were identified in this research: (i) the proposal of a novel methodology based on the association of Design Thinking and Participatory Design as the basis for Sustainable Design; (ii) the design and development of an IoT-enabled smartbottle prototype; and (iii) the usability evaluation of the proposed prototype. The adopted methodology is rooted in Design Thinking and mixes it with a Participatory Design approach, including the end-user opinion throughout the Smartbottle Ecosystem design process, not only for the product design requirements but also for its specification. By promoting a participatory solution tailored to the IPVC academic community, recycled plastic has been identified as the preferential material and a marine mammal was selected for the smartbottle shape, in the process of developing a solution to replace the single-use plastic bottles.
Simplifying Text Mining Activities: Scalable and Self-Tuning Methodology for Topic Detection and Characterization

In recent years, the number and heterogeneity of large scientific datasets have been growing steadily. Moreover, the analysis of these data collections is not a trivial task. There are many algorithms capable of analyzing large datasets, but parameters need to be set for each of them. Moreover, larger datasets also mean greater complexity. All this leads to the need to develop innovative, scalable, and parameter-free solutions. The goal of this research activity is to design and develop an automated data analysis engine that effectively and efficiently analyzes large collections of text data with minimal user intervention. Both parameter-free algorithms and self-assessment strategies have been proposed to suggest algorithms and specific parameter values for each step that characterizes the analysis pipeline. The proposed solutions have been tailored to text corpora characterized by variable term distributions and different document lengths. In particular, a new engine called ESCAPE (enhanced self-tuning characterization of document collections after parameter evaluation) has been designed and developed. ESCAPE integrates two different solutions for document clustering and topic modeling: the joint approach and the probabilistic approach. Both methods include ad hoc self-optimization strategies to configure the specific algorithm parameters. Moreover, novel visualization techniques and quality metrics have been integrated to analyze the performances of both approaches and to help domain experts interpret the discovered knowledge. Both approaches are able to correctly identify meaningful partitions of a given document corpus by grouping them according to topics.
An empirical analysis of image augmentation against model inversion attack in federated learning

Abstract Federated Learning (FL) is a technology that facilitates a sophisticated way to train distributed data. As the FL does not expose sensitive data in the training process, it was considered privacy-safe deep learning. However, a few recent studies proved that it is possible to expose the hidden data by exploiting the shared models only. One common solution for the data exposure is differential privacy that adds noise to hinder such an attack, however, it inevitably involves a trade-off between privacy and utility. This paper demonstrates the effectiveness of image augmentation as an alternative defense strategy that has less impact of the trade-off. We conduct comprehensive experiments on the CIFAR-10 and CIFAR-100 datasets with 14 augmentations and 9 magnitudes. As a result, the best combination of augmentation and magnitude for each image class in the datasets was discovered. Also, our results show that a well-fitted augmentation strategy can outperform differential privacy.
A Hash-Based Quantum-Resistant Designated Verifier Signature Scheme

Digital signatures are unsuitable for specific applications that are sensitive on a personal or commercial level because they are universally verifiable. Jakobsson et al. proposed the Designated Verifier Signature (DVS) system, which only allows the intended verifier to validate a message’s signature. It prohibits the disclosure of a conviction to a third party. This functionality is useful in applications that require both authenticity and signer privacy, such as electronic voting and tender calls. The vast majority of current DVS schemes are based on difficult number theory problems such as integer factorization or discrete log problems over various groups. The development of a large-scale quantum computer would render these schemes unsafe. As a result, it is critical to develop quantum-resistant DVS methods. In both quantum and classical computers, signatures based on one-way functions are more efficient and secure. They have several advantages over digital signatures based on trapdoor functions. As a result, hash-based signatures are now considered viable alternatives to number-theoretic signatures. Existing hash-based signatures, on the other hand, are easily verifiable by anyone. As a result, they do not protect the signer’s identity. In addition, they are one-time signatures. This paper presents a hash-based multi-time designated verifier signature scheme that ensures signer anonymity. The unforgeability of the signature scheme is also tested in the random oracle model under chosen message attack. The properties such as non-transferability and non-delegatability are investigated.
A Novel Binary Hybrid PSO-EO Algorithm for Cryptanalysis of Internal State of RC4 Cipher

Cryptography protects privacy and confidentiality. So, it is necessary to guarantee that the ciphers used are secure and cryptanalysis-resistant. In this paper, a new state recovery attack against the RC4 stream cipher is revealed. A plaintext attack is used in which the attacker has both the plaintext and the ciphertext, so they can calculate the keystream and reveal the cipher's internal state. To increase the quality of answers to practical and recent real-world global optimization difficulties, researchers are increasingly combining two or more variations. PSO and EO are combined in a hybrid PSOEO in an uncertain environment. We may also convert this method to its binary form to cryptanalyze the internal state of the RC4 cipher. When solving the cryptanalysis issue with HBPSOEO, we discover that it is more accurate and quicker than utilizing both PSO and EO independently. Experiments reveal that our proposed fitness function, in combination with HBPSOEO, requires checking 104 possible internal states; however, brute force attacks require checking 2128 states.
Secure and efficient two-party collaborative SM9 signature scheme suitable for smart home

The smart home usually has poor security and is vulnerable to attack since it adopts embedded processors that are limited by volume and power consumption. To improve the communication security of the smart home system, identity-based signature schemes are widely used in wireless network communications. However, the user’s signature private key is generally stored in a single device, it is easy to be stolen by attackers to control the smart home devices. To reduce the risk of leakage of the signature private key, a two-party collaborative signature scheme based on the SM9 algorithm is proposed in this paper. The user’s signature private key is generated through the collaboration of the two-party key generation center (KGC), and the integer secrets related to the signature private key are stored in two devices respectively. During the signing process, the two devices sign collaboratively to prevent the complete private key from being leaked. The security of the scheme is proved in the random oracle model. Theoretical analysis and experimental results show that our proposed scheme can achieve higher security with lower computation cost and communication cost when compared with the existing two-party SM9 signature schemes.