Reviewerly/exHarmony huggingface dataset:
iSample: Intelligent Client Sampling in Federated Learning

The pervasiveness of AI in society has made machine learning (ML) an invaluable tool for mobile and internet-of-things (IoT) devices. While the aggregate amount of data yielded by those devices is sufficient for training an accurate model, the data available to any one device is limited. Therefore, augmenting the learning at any of the devices with the experience from observations associated with the rest of the devices will be necessary. This, however, can dramatically increase the bandwidth requirements. Prior work has led to the development of Federated Learning (FL), where instead of exchanging data, client devices can only share weights to learn from one another. However, het-erogeneity in device resource availability and network conditions still impose limitations on training performance. In order to improve performance while maintaining good levels of accuracy, we introduce iSample. iSample, an intelligent sampling technique, selects clients by jointly considering known network performance and model quality parameters, allowing the minimization of training time. We compare iSample with other federated learning approaches and show that iSample improves the performance of the global model, especially in the earlier stages of training, while decreasing the training time for both CNN and VGG by 27% and 39%, respectively.
The optimisation research of Blockchain application in the financial institution-dominated supply chain finance system

As an information technology that could significantly improve supply chain visibility and process automation, blockchain has been extensively applied in the field of supply chain finance (SCF). However, tradeoffs among the security, the operation cost, and the efficiency of the blockchain system may cause the SCF system dominated by a financial institution to inevitably fall into the dilemmas of risky or un-economic if the blockchain technology is applied inappropriately. Therefore, the objective of this paper is to optimise the blockchain application in the financial institution-based SCF system. We first analyse the application of blockchain security in SCF, and then the performance tradeoffs of blockchain and its impact on the performance of the supported SCF system. Based on the analysis above, an optimisation approach has been proposed and a corresponding non-linear integer programming (NIP) model has been constructed to select the best blockchain design schemes for the SCF system to achieve overall optimal in terms of security, cost, and efficiency. A designed ant colony algorithm is used to solve the optimisation problem. An application case analysis is used to verify the feasibility and effectiveness of the optimisation model.
An explainable semi-personalized federated learning model

Training a model using batch learning requires uniform data storage in a repository. This approach is intrusive, as users have to expose their privacy and exchange sensitive data by sending them to central entities to be preprocessed. Unlike the aforementioned centralized approach, training of intelligent models via the federated learning (FEDL) mechanism can be carried out using decentralized data. This process ensures that privacy and protection of sensitive information can be managed by a user or an organization, employing a single universal model for all users. This model should apply average aggregation methods to the set of cooperative training data. This raises serious concerns for the effectiveness of this universal approach and, therefore, for the validity of FEDL architectures in general. Generally, it flattens the unique needs of individual users without considering the local events to be managed. This paper proposes an innovative hybrid explainable semi-personalized federated learning model, that utilizes Shapley Values and Lipschitz Constant techniques, in order to create personalized intelligent models. It is based on the needs and events that each individual user is required to address in a federated format. Explanations are the assortment of characteristics of the interpretable system, which, in the case of a specified illustration, helped to bring about a conclusion and provided the function of the model on both local and global levels. Retraining is suggested only for those features for which the degree of change is considered quite important for the evolution of its functionality.
Secure Token–Key Implications in an Enterprise Multi-Tenancy Environment Using BGV–EHC Hybrid Homomorphic Encryption

Authentication, authorization, and data access control are playing major roles in data security and privacy. The proposed model integrated the multi-factor authentication–authorization process with dependable and non-dependable factors and parameters based on providing security for tenants through a hybrid approach of fully homomorphic encryption methodology: the enhanced homomorphic cryptosystem (EHC) and the Brakersky–Gentry–Vaikuntanathan (BGV) scheme. This research was composed of four major elements: the fully homomorphic encryption blended schemes, EHC and BGV; secure token and key implications based on dependable and don-dependable factors; an algorithm for generating the tokens and the suitable keys, depending on the user’s role; and the execution of experimental test cases by using the EHC algorithm for key and token generation, based on dependable and non-dependable parameters and time periods. The proposed approach was tested with 152 end-users by integrating six multi-tenants, five head tenants, and two enterprise levels; and achieved a 92 percent success rate. The research integrated 32-bit plain text in the proposed hybrid approach by taking into consideration the encryption time, decryption time, and key generation time of data transmission via cloud servers. The proposed blended model was efficient in preventing data from ciphertext attacks and achieved a high success rate for transmitting data between the multi-tenants, based on the user-role-user type of enterprise cloud servers.
Evolving objective function for improved variational quantum optimization

A promising approach to useful computational quantum advantage is to use variational quantum algorithms for optimisation problems. Crucial for the performance of these algorithms is to ensure that the algorithm converges with high probability to a near-optimal solution in a small time. In Barkoutsos et al (Quantum 2020) an alternative class of objective functions, called Conditional Value-at-Risk (CVaR), was introduced and it was shown that they perform better than standard objective functions. Here we extend that work by introducing an evolving objective function, which we call Ascending-CVaR and that can be used for any optimisation problem. We test our proposed objective function, in an emulation environment, using as case-studies three different optimisation problems: Max-Cut, Number Partitioning and Portfolio Optimisation. We examine multiple instances of different sizes and analyse the performance using the Variational Quantum Eigensolver (VQE) with hardware-efficient ansatz and the Quantum Approximate Optimization Algorithm (QAOA). We show that Ascending-CVaR in all cases performs better than standard objective functions or the "constant" CVaR of Barkoutsos et al (Quantum 2020) and that it can be used as a heuristic for avoiding sub-optimal minima. Our proposal achieves higher overlap with the ideal state in all problems, whether we consider easy or hard instances -- on average it gives up to ten times greater overlap at Portfolio Optimisation and Number Partitioning, while it gives an 80% improvement at Max-Cut. In the hard instances we consider, for the number partitioning problem, standard objective functions fail to find the correct solution in almost all cases, CVaR finds the correct solution at 60% of the cases, while Ascending-CVaR finds the correct solution in 95% of the cases.
Training of practicing teachers for the application of STEM education

Abstract The modern informational society poses new challenges to the education system, and one of the ways to solve them is STEM education, which has become one of the popular learning trends. The solution of the set tasks encourages not only the growth of the economic indicators, but also the general stable development of society with a careful attitude to resources, both material and immaterial. The practitioners are the leaders in high-tech changes of the education system, that is why they are the ones who can implement the STEM approach in the most effective way. This is important because the scientific component of technology is evolving over time and, accordingly, the skills of employees that are needed are changing, too. And the STEM approach in education, in turn, is the best to meet this requirement. The professional development of practicing teachers means to update knowledge in the field and acquire new teaching methods and technologies. Thus, in addition to professional necessity, professional development of practicing teachers is of innovative importance. The proposed certificate program of advanced training aims to develop students’ skills to implement STEM education in a scientifically sound and critical way; widely use interdisciplinary interaction; to introduce methods and means of STEM education in professional activity. The target audience of the program is future and practicing teachers of general and professional higher education.
Unsupervised Anomaly Detection for Time Series Data of Spacecraft Using Multi-Task Learning

Although in-orbit anomaly detection is extremely important to ensure spacecraft safety, the complex spatial-temporal correlation and sparsity of anomalies in the data pose significant challenges. This study proposes the new multi-task learning-based time series anomaly detection (MTAD) method, which captures the spatial-temporal correlation of the data to learn the generalized normal patterns and hence facilitates anomaly detection. First, four proxy tasks are implemented for feature extraction through joint learning: (1) Long short-term memory-based data prediction; (2) autoencoder-based latent representation learning and data reconstruction; (3) variational autoencoder-based latent representation learning and data reconstruction; and (4) joint latent representation-based data prediction. Proxy Tasks 1 and 4 capture the temporal correlation of the data by fusing the latent space, whereas Tasks 2 and 3 fully capture the spatial correlation of the data. The isolation forest algorithm then detects anomalies from the extracted features. Application to a real spacecraft dataset reveals the superiority of our method over existing techniques, and further ablation testing for each task proves the effectiveness of fusing multiple tasks. The proposed MTAD method demonstrates promising potential for effective in-orbit anomaly detection for spacecraft.
AFAR: A Real-Time Vision-based Activity Monitoring and Fall Detection Framework using 1D Convolutional Neural Networks

In recent years, there has been an increased interest in the use of telemedicine as an option to avail proper healthcare. However, one of the main issues of activity monitoring and fall detection in telehealth systems is the scalability of the technology for areas with inadequate technology infrastructure. As a potential solution, this study proposes an efficient activity monitoring and fall detection framework which can run real-time on CPU devices. In comparison to previous works, this study makes use of an efficient pose estimator called MediaPipe and leverages the pose joints as the main inputs of the model for activity monitoring and fall detection. This allows the framework to be used on cost-effective devices. To ensure the quality of the framework, it was evaluated on three (3) publicly available datasets: Adhikari Dataset, UP Fall Dataset, and UR Fall Dataset by looking at accuracy, precision, recall, and F1 scores. Based from the results, the framework was able to achieve both state-of-the-art and real-time performance on these datasets.
A Comprehensive Analysis of Privacy-Preserving Solutions Developed for Online Social Networks

Owning to the massive growth in internet connectivity, smartphone technology, and digital tools, the use of various online social networks (OSNs) has significantly increased. On the one hand, the use of OSNs enables people to share their experiences and information. On the other hand, this ever-growing use of OSNs enables adversaries to launch various privacy attacks to compromise users’ accounts as well as to steal other sensitive information via statistical matching. In general, a privacy attack is carried out by the exercise of linking personal data available on the OSN site and social graphs (or statistics) published by the OSN service providers. The problem of securing user personal information for mitigating privacy attacks in OSNs environments is a challenging research problem. Recently, many privacy-preserving solutions have been proposed to secure users’ data available over OSNs from prying eyes. However, a systematic overview of the research dynamics of OSN privacy, and findings of the latest privacy-preserving approaches from a broader perspective, remain unexplored in the current literature. Furthermore, the significance of artificial intelligence (AI) techniques in the OSN privacy area has not been highlighted by previous research. To cover this gap, we present a comprehensive analysis of the state-of-the-art solutions that have been proposed to address privacy issues in OSNs. Specifically, we classify the existing privacy-preserving solutions into two main categories: privacy-preserving graph publishing (PPGP) and privacy preservation in application-specific scenarios of the OSNs. Then, we introduce a high-level taxonomy that encompasses common as well as AI-based privacy-preserving approaches that have proposed ways to combat the privacy issues in PPGP. In line with these works, we discuss many state-of-the-art privacy-preserving solutions that have been proposed for application-specific scenarios (e.g., information diffusion, community clustering, influence analysis, friend recommendation, etc.) of OSNs. In addition, we discuss the various latest de-anonymization methods (common and AI-based) that have been developed to infer either identity or sensitive information of OSN users from the published graph. Finally, some challenges of preserving the privacy of OSNs (i.e., social graph data) from malevolent adversaries are presented, and promising avenues for future research are suggested.
CL-ECPE: contrastive learning with adversarial samples for emotion-cause pair extraction

The existing Emotion-Cause Pair Extraction (ECPE) has made some achievements, and it is applied in many tasks, such as criminal investigations. Previous approaches realised extraction by constructing different networks, but they did not fully exploit the original information of the data, which led to low extraction precision. Moreover, the extraction precision will also be decreased when the model is attacked by adversarial samples. To address the above problems, a new model CL-ECPE is proposed in this article to improve the extraction precision through contrastive learning. First, contrastive sets are constructed by adversarial samples. The contrastive sets are used as the raw data of adversarial training and the test data of the pilot experiment. Then, adversarial training is used to get contrastive features according to the training target. The acquisition of contrastive features can improve extraction precision. Experimental results on the benchmark emotion cause corpus show our method outperforms the state-of-the-art method by over 12.49%, as well as demonstrates the strong robustness of CL-ECPE.
Mapping of favourable mining areas in the Tiouit area by multispectral remote sensing and airborne gamma-ray spectrometry coupled with geochemical data (Eastern Anti-Atlas, Morocco)

The current investigation concern Tiouit region in order to perceive potential mining zones. Remote sensing data were the main component to process. In order to spot geochemical anomalies associated with the target zones, multispectral ASTER data, gamma-ray spectrometry, and stream sediment data were used. Crosta and SAM applications granted to map a spatial distribution of propylitic, phyllic, clay, and ferruginous alteration zones. The normalized ratios let the delineation of potassic alteration zones, including the Tiouit gold mine characterized by an intense alteration in the Ikniwn granodiorite. Thus, the potassic alteration zones are consistently co-located with the tectonic structures materialized by strong fracturing densities. The stream sediment geochemical investigation revealed that the cited areas have an important potential of mineralization, with high concentrations of As (136 g/t), Cu (91 g/t), Pb (141 g/t), Zn (392 g/t), Ag (3 g/t), Mo (11 g/t), Mn (0.2 g/t), and Fe (10.63 %).
An in-depth study of continuous subgraph matching

Continuous subgraph matching (CSM) algorithms find the occurrences of a given pattern on a stream of data graphs online. A number of incremental CSM algorithms have been proposed. However, a systematical study on these algorithms is missing to identify their advantages and disadvantages on a wide range of workloads. Therefore, we first propose to model CSM as incremental view maintenance (IVM) to capture the design space of existing algorithms. Then, we implement six representative CSM algorithms, including InclsoMatch, SJ-Tree, Graphflow, IEDyn, TurboFlux, and SymBi, in a common framework based on IVM. We further conduct extensive experiments to evaluate the overall performance of competing algorithms as well as study the effectiveness of individual techniques to pinpoint the key factors leading to the performance differences. We obtain the following new insights into the performance: (1) existing algorithms start the search from an edge in the query graph that maps to an updated data edge, potentially leading to many invalid partial results; (2) all matching orders are based on simple heuristics, which appear ineffective at times; (3) index updates dominate the query time on some queries; and (4) the algorithm with constant delay enumeration bears significant index update cost. Consequently, no algorithm dominate the others in all cases. Therefore, we give a few recommendations based on our experiment results. In particular, the SymBi index is useful for sparse queries or long running queries. The matching orders of IEDyn and TurboFlux work well on tree queries, those of Graphflow on dense queries or when both query and data graphs are sparse, and otherwise, we recommend SymBi's matching orders.
Preventive Measures for Digital Learning Spaces’ Security Issues

The COVID-19 pandemic affected humanity and changed impacted people’s habits worldwide. The necessity for social distancing has led to increasing remote working and online learning. As a result, many schools, institutions, and universities quickly adopted online learning to ensure the continuity of their training. Unfortunately, security was not a significant factor during the migration to digital learning. Consequently, security risks increased significantly with the advent of COVID-19 and cyberattacks do not spare education systems. This paper highlights security issues of digital learning spaces from the perspective of the Learning Management System and video conferencing tools. Also, we propose preventive measures to overcome each vulnerability. In addition, beyond the prevention measures, digital learning stakeholders need to be aware of the security principles and best practices to adopt before, during and after the sessions.
Transforming and extending library services by embracing technology and collaborations: A case study

Technology advances and collaborations with information technology and computer science groups have enabled library services to expand into new domains. Listening to user needs, eliminating administrative burden and saving users time remain strong foundations on which to build new library services enabled by technology. Examples of what is now possible is described, including service to user groups, successes, failures and challenges. Although technology advances have enabled library service enhancements to all user groups, special emphasis on new library services in support of the research enterprise is discussed. As Lindberg and Humphreys predicted in 2015, the research enterprise's need for responsible curation of research data has created new opportunities for library services and examples of those services are discussed. As technology continues to advance, new library services are expected to emerge. These may include regulatory and compliance services. By developing these services with user feedback to save users time and expedite their work, and in collaboration with technology experts, libraries can expect to offer sustainable and valued services for years to come.
Extraction of Joint Entity and Relationships with Soft Pruning and GlobalPointer

In recent years, scholars have paid increasing attention to the joint entity and relation extraction. However, the most difficult aspect of joint extraction is extracting overlapping triples. To address this problem, we propose a joint extraction model based on Soft Pruning and GlobalPointer, short for SGNet. In the first place, the BERT pretraining model is used to obtain the text word vector representation with contextual information, and then the local and non-local information of the word vector is obtained through graph operations. Specifically, to address the lack of information caused by the rule-based pruning strategies, we utilize the Gaussian Graph Generator and the attention-guiding layer to construct a fully connected graph. This process is called soft pruning for short. Then, to achieve node message passing and information integration, we employ GCNs and a thick connection layer. Next, we use the GlobalPointer decoder to convert triple extraction into quintuple extraction to tackle the problem of problematic overlapping triples extraction. The GlobalPointer decoder, unlike the typical feedforward neural network (FNN), can perform joint decoding. In the end, to evaluate the model performance, the experiment was carried out on two public datasets: the NYT and WebNLG. The experiments show that SGNet performs substantially better on overlapping extraction and achieves good results on two publicly available datasets.