Reviewerly/exHarmony huggingface dataset:
Leveraging Blockchain for Spatial Data Infrastructure

Blockchain technology is gaining increasing attention across various industries, including healthcare, supply chain, education, business, logistics, disaster management, agriculture, tourism, and energy. As such, it is also becoming of interest to the spatial data infrastructure (SDI). This chapter explored the potential of blockchain technology for SDI and examined its potential role in addressing four key areas of concern regarding SDI: governance structure, facilitating access, capacity building, and data interoperability. Furthermore, the chapter also delved into the challenges that may arise with integrating blockchain technology into SDI. These challenges include scalability, technical complexity, regulatory as well as legal constraints, and cost as well as resource constraints. In addition to discussing the potential and challenges of blockchain-based SDI, this chapter also presented the technical, legal, and social implications that may arise from its deployment.
A Quantitative Approach of Purposive Sampling Techniques for Security and Privacy Issues in IoT Healthcare Applications

The internet of things (IoT) offers several benefits to the healthcare industry, including the ability to actively monitor patients and use data for analytics. For medical device integration, the focus has moved to the consumer end of the IoT, that captures data on patient vital signs. Unfortunately, when healthcare centers link these devices to the internet, they typically overlook security concerns. Short-term monitoring and emergency alerting of healthcare signals are becoming increasingly accessible thanks to the Internet of Things (IoT). Data secrecy is critical hence, encryption is required out of real concern. In this book chapter, we explore the privacy and security issues of Internet of Things (IoT) healthcare applications for special needs users. IoT enables health-related enterprises to lift necessary data from diverse sources in real-time and this helps in accurate decision-making to reducing data vulnerability and therefore creating opportunities for secure patient data, particularly for special needs patients.
Generating t-Closed Partitions of Datasets with Multiple Sensitive Attributes

The popular t-closeness privacy model requires the “distance” between the distribution of sensitive attribute values in any given raw dataset and their distribution in every equivalence class created to not exceed some privacy threshold t. While most existing methods for achieving t-closeness handle data with just a single sensitive attribute, datasets with multiple sensitive attributes are very common in the real world. Here we demonstrate a technique for creating equivalence classes from a dataset containing multiple sensitive attributes. The equivalence classes generated by our method satisfy t-closeness without taking any <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$t$</tex> values as input. While generalization of quasi-identifier attributes leads to information loss, the size of generated classes is roughly identical and differs by at most one, which results in a lower information loss. Generating classes with minimum information loss for a given value of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$t$</tex> is NP-hard, the equivalence classes generated by our method takes O(r log r) time.
ENF Based Digital Multimedia Forensics: Survey, Application, Challenges and Future Work

The electric network frequency (ENF) represents the transmission frequency of the electrical grid and fluctuates constantly around 50 Hz or 60 Hz, subject to the region. This constant fluctuation, caused by the continuous mismatch in power demand and supply, makes the ENF a unique signature, which can be utilized for several applications. According to studies, the ENF may be intrinsically implanted in audio recordings captured by digital audio recorders (e.g., microphone-based voice recorder) plugged into the mains supply or are situated close to sources of power and power transmission cable due to electromagnetic field interference originated from power source or acoustic hum and mechanical vibrations emitted by electrically operated devices such as regular household appliances. Recent studies further observed that video recordings made under an illumination source powered by main power can pick up the ENF signal. Following this discovery, several research efforts have been invested towards successful and accurate extraction of the ENF signal, and utilizing the ENF signal retrieved for several applications, including time stamp verification, audio/video authentication, location of recording estimation, power grid identification, estimation of camera read-out time, and video record synchronization. To the best of our knowledge, there has been no comprehensive survey on ENF-based multimedia forensics. Thus, in this paper, we present a comprehensive survey of studies conducted in this field, identifying several application specifics, current challenges, and future research directions.
Exploring Sparse Spatial Relation in Graph Inference for Text-Based VQA

Text-based visual question answering (TextVQA) faces the significant challenge of avoiding redundant relational inference. To be specific, a large number of detected objects and optical character recognition (OCR) tokens result in rich visual relationships. Existing works take all visual relationships into account for answer prediction. However, there are three observations: (1) a single subject in the images can be easily detected as multiple objects with distinct bounding boxes (considered repetitive objects). The associations between these repetitive objects are superfluous for answer reasoning; (2) two spatially distant OCR tokens detected in the image frequently have weak semantic dependencies for answer reasoning; and (3) the co-existence of nearby objects and tokens may be indicative of important visual cues for predicting answers. Rather than utilizing all of them for answer prediction, we make an effort to identify the most important connections or eliminate redundant ones. We propose a sparse spatial graph network (SSGN) that introduces a spatially aware relation pruning technique to this task. As spatial factors for relation measurement, we employ spatial distance, geometric dimension, overlap area, and DIoU for spatially aware pruning. We consider three visual relationships for graph learning: object-object, OCR-OCR tokens, and object-OCR token relationships. SSGN is a progressive graph learning architecture that verifies the pivotal relations in the correlated object-token sparse graph, and then in the respective object-based sparse graph and token-based sparse graph. Experiment results on TextVQA and ST-VQA datasets demonstrate that SSGN achieves promising performances. And some visualization results further demonstrate the interpretability of our method.
The Style Transformer With Common Knowledge Optimization for Image-Text Retrieval

Image-text retrieval which associates different modalities has drawn broad attention due to its excellent research value and broad real-world application. However, most of the existing methods haven't taken the high-level semantic relationships (“style embedding”) and common knowledge from multi-modalities into full consideration. To this end, we introduce a novel style transformer network with common knowledge optimization (CKSTN) for image-text retrieval. The main module is the common knowledge adaptor (CKA) with both the style embedding extractor (SEE) and the common knowledge optimization (CKO) modules. Specifically, the SEE uses the sequential update strategy to effectively connect the features of different stages in SEE. The CKO module is introduced to dynamically capture the latent concepts of common knowledge from different modalities. Besides, to get generalized temporal common knowledge, we propose a sequential update strategy to effectively integrate the features of different layers in SEE with previous common feature units. CKSTN demonstrates the superiorities of the state-of-the-art methods in image-text retrieval on MSCOCO and Flickr30 K datasets. Moreover, CKSTN is constructed based on the lightweight transformer which is more convenient and practical for the application of real scenes, due to the better performance and lower parameters.
A Secure Client-Side Deduplication Scheme Based on Updatable Server-Aided Encryption

The server-aided encryption is widely used in encrypted deduplication systems to protect against brute-force attacks. However, it is non-trivial to update the master key managed by the key server in existing schemes. Once the master key is leaked, all user data are vulnerable to offline brute-force attacks. In this article, we extend the server-aided encryption with the updatable encryption (UE) and a dynamic proof of ownership (PoW) protocol to make it support efficient key updates and can be used in the client-side deduplication. Specifically, we design an updatable server-aided encryption scheme based on UE, which achieves efficient encryption and the user-transparent key update for a system-level master key. Besides, to further enable our updatable server-aided encryption to be applicable to the client-side deduplication, we propose a dynamic PoW protocol based on the Merkle tree. Compared to the state-of-the-art PoW scheme, our PoW protects data privacy and allows multi-time leakages for the target file. Finally, we analyze the security of our scheme and present the performance evaluation. The results show that our scheme provides comprehensive security protection for user data and achieves efficient encryption, PoW, and key update.
Improving Embedding Generalization in Few-Shot Learning With Instance Neighbor Constraints

Recently, metric-based meta-learning methods have been effectively applied to few-shot image classification. These methods classify images based on the relationship between samples in an embedding space, avoiding over-fitting that can occur when training classifiers with limited samples. However, finding an embedding space with good generalization properties remains a challenge. Our work highlights that having an initial manifold space that preserves sample neighbor relationships can prevent the metric model from reaching a suboptimal solution. We propose a feature learning method that leverages Instance Neighbor Constraints (INC). This theory is thoroughly evaluated and analyzed through experiments, demonstrating its effectiveness in improving the efficiency of learning and the overall performance of the model. We further integrate the INC into an alternate optimization training framework (AOT) that leverages both batch learning and episode learning to better optimize the metric-based model. We conduct extensive experiments on 5-way 1-shot and 5-way 5-shot settings on four popular few-shot image benchmarks: miniImageNet, tieredImageNet, Fewshot-CIFAR100 (FC100), and Caltech-UCSD Birds-200-2011(CUB). Results show that our method achieves consistent performance gains on benchmarks and state-of-the-art performance. Our findings suggest that initializing the embedding space appropriately and leveraging both batch and episode learning can significantly improve few-shot learning performance.
Blind Quality Assessment of Stereoscopic Images Considering Binocular Perception Based on Shearlet Decomposition

Due to the deficient knowledge of binocular vision properties, how to effectively evaluate stereoscopic images still remains a challenging task. Inspired by multichannel processing of human visual system (HVS), we propose a blind method for stereoscopic image quality assessment (SIQA) by extracting quality related features in sub-bands of the image. First of all, we introduce the shearlet transform to decompose the left- and right-view images into multiple sub-bands content with diverse combinations of scales and orientations, and obtain the combined view based on energy-weighted summation of the corresponding sub-bands of two eye views. Then, natural scene statistics (NSS) of the original left and right images are obtained as quality-sensitive features, followed by extracting NSS features of the sub-bands of left, right and combined views. Moreover, we calculate the gradient similarity between each sub-band pair to denote the asymmetric distortion and disparity information. Finally, all the extracted features are mapped into a quality score by support vector regression (SVR). experimental results on multiple benchmark databases verify the superiority of our method.
Quantum State Tomography in FPGA

We demonstrate an efficient solution for quantum state tomography implemented on FPGA. By observing the closed-form solution for complete measurement sets, we create a simple, accurate, reconfigurable solution for multi-qubit quantum states.
Active reset of superconducting qubits using the electronics based on RF switches

Active reset of qubits is a crucial step in achieving quantum algorithms, particularly in fault-tolerant quantum computing, as it allows qubits to be quickly initialized to the ground state. In this paper, we demonstrate active reset of superconducting qubits using the feedback electronics based on RF switches. We are able to reset the qubit to the ground state in 11.412 µs with a success rate of 99.82%, which is better than the natural relaxation method in terms of both time consumption (180 µs) and success rate (99.61%). The demonstrated method facilitates fast and high-fidelity qubit initialization, holding practical significance for large-scale quantum computation.
Computer assisted investigation of alienness of linear functional equations

Abstract In this paper, a computer program developed in the computer algebra system Maple is presented, which investigates alienness and strong alienness of linear functional equations.
Monitoring and Quality Evaluation Method of English Teaching in Machine Manufacturing Based on Machine Learning and Internet of Things

INTRODUCTION: With the continuous development of society, education is getting more and more attention, and all stages of education, such as elementary school, middle school, high school, Etc., are the fundamental objects of attention, while English in the university, as a critical foreign language, is also given considerable attention. Among the various majors in the university, different majors pay different attention to English, among which in the mechanical engineering major, the importance of English is still relatively front and centre, which is helpful for the improvement of students' mechanical engineering level. However, in the current teaching, there are still a series of problems in the English teaching of mechanical majors, which have certain obstacles to improving students' English level. OBJECTIVES: To give university English teachers more feedback and opinions through a reasonable and perfect teaching monitoring and quality evaluation method; combining their English reading teaching with relevant rules and regulations can better promote the educational quality level of university English and help students improve their English more reasonably. METHODS: To study and analyze the monitoring and quality evaluation of English teaching in mechanical majors to get the relevant optimization suggestions of teaching mode, and to solve the correlation problem based on multiple linear regression through the calculation of SPSS software, to analyze the final impact mechanism of each item on the quality of teaching. RESULTS: The results of the sample survey indicate that, firstly, five factors, namely, research direction, disciplinary ladder, scientific research, talent cultivation and management, will have a significant impact on the monitoring and quality evaluation of English teaching in the mechanical program. Secondly, further research on the influence mechanism suggests that the monitoring and quality evaluation of English language teaching in mechanical majors needs some improvement, affecting the English language proficiency of students in this field. CONCLUSION: After summarizing several factors that impact the evaluation of the quality of English teaching, it is clear that such English classroom teaching has a positive effect on the training of mechanical students.
Research on the development of an effective mechanism of using public online education resource platform: TOE model combined with FS-QCA

The paper describes the research of causal relationships between the factors of technological, organizational, environmental, and personal contexts and their influence on the development of learning intentions in potential students. Its purpose was to develop a mechanism for designing a public online educational resource platform based on the combined TOE/fsQCA model taking into account the synergy of factors influencing student behavior at all stages of the educational process from entering the course to its completion. The research identified 4 and 3 configurations with high to medium and low intention to start learning on the MOOC platform, respectively. Since focusing on consumer demand allows for designing a high-quality MOOC platform, it is advisable to use the combined TOE/fsQCA model both at the stage of constructing the platform structure and at the learning stage to identify optimal learning trajectories taking into account the personal needs of students. Prospects for further research lie in the refinement of conventions, in-depth analysis, and a more detailed study of causal relationships that influence the behavioral motives of MOOC platform students.
Chatbots and young people in emerging economies: Factors affecting user satisfaction

Digitization and the internet have been changing how companies and customers interact, forcing organizations to adapt more efficient services that generate value in the purchasing and communication processes. This research aims to identify key factors that influence satisfaction with the use of chatbots among young people from Colombia, based on 126 surveys administered via Google Forms. The methodological design adopted a quantitative approach, with an exploratory-descriptive scope that involved a structural equation model PLS-SEM of the survey data. The information analysis tested the hypotheses concerning the constructs of the conceptual model and chatbot user satisfaction by means of p value and path coefficients. The results suggest that there is an effect of robotic knowledge of use and resolution of pending cases on the perception of having access to up-to-date information during the chatbot experience. The main expectations regarding chatbot use were also identified, such as resolving the issue, being attended to quickly and the effectiveness of the service. This study also has practical implications for chatbot designers and developers in the governments of all countries, especially in developing countries like Colombia.
Security state estimation based on signal reconstruction for multi‐vehicle systems under malicious attack

Abstract Aiming at the reconnaissance task of unmanned vehicle formation under the malicious attack, a security state estimation method based on attack signal reconstruction is proposed. First the reconstruction of attack signal is transformed into a sparse error correction problem by stacking the measurement information of adjacent vehicles, and is solved by orthogonal matching pursuit (OMP) algorithm. Then the attack compensation based particle filter is designed to estimate the target state for each vehicle. An information fusion strategy is designed to obtain the final reconnaissance result based on agent centrality and the number of attacks on unmanned vehicles. Finally, simulations are provided to illustrate the effectiveness of the proposed method.
Analytical hierarchy process model for managing cloud security

Purpose This paper aims to explore key management actions for implementing security on the cloud, which is a critical issue as many organizations are moving business processes and data on it. The cloud is a flexible, low cost and highly available technology, but it comes with increased complexity in maintaining the cloud consumer’s security. In this research, a model was built to assist strategic decision-makers in choosing from a diverse range of actions that can be taken to manage cloud security. Design/methodology/approach Published research from 2010 to 2022 was reviewed to identify alternatives to management actions pertaining to cloud security. Analytical hierarchical process (AHP) was applied to rate the most important action(s). For this, the alternatives, along with selection criteria, were summarized through thematic analysis. To gauge the relative importance of the alternatives, a questionnaire was distributed among cloud security practitioners to poll their opinion. AHP was then applied to the aggregated survey responses. Findings It was found that the respondents gave the highest importance to aligning information security with business needs. Building a cloud-specific risk management framework was rated second, while the actions: enforce and monitor contractual obligations, and update organizational structure, were rated third and fourth, respectively. Research limitations/implications The research takes a general view without catering to specialized industry-based scenarios. Originality/value This paper highlights the role of management actions when implementing cloud security. It presents an AHP-based multi-criteria decision-making model that can be used by strategic decision-makers in selecting the optimum mode of action. Finally, the criteria used in the AHP model highlight how each alternative contributes to cloud security.
Regression model of students subjective well-being learning at technical universities

In this article we will talk about students studying at the Institute of Economics and Management in construction (IEMC). The article studies the relationship between educational activity at the institute, social activity of students, student adaptation and the level of emotional intelligence and subjective well-being. The main methods used in our research are: interviewing, survey, testing based on Google forms, correlation and regression analysis. Modeling of parameters influencing the educational process has been carried out, taking into account the emotional maturity and adaptation of students of the IEMC, Kazan. The model was based on 16 factors, such as: understanding one’s own and others' emotions, managing one’s emotions, self-motivation, influencing the emotional state of others (manipulation), empathy, autonomy and a number of others. The result on regression statistics showed multiple R 0.7 and R-square 0.5. Calculations made by the authors show that 50% of the influence on the factor «subjective well-being» is due to parameters, and the remaining 50% require further research. The model calculated by the authors shows that the subjective well-being of bachelor students is based on personal growth, with the skills of self-regulation and the ability to arouse in other people the desired (usually positive) emotions. Subjective well-being is strongly affected by the inability to manage one’s emotions and the lack of self-acceptance.