Reviewerly/exHarmony huggingface dataset:
Multi-Scale Feature Fusion Based on PVTv2 for Deep Hash Remote Sensing Image Retrieval

For high-resolution remote sensing image retrieval tasks, single-scale features cannot fully express the complexity of the image information. Due to the large volume of remote sensing images, retrieval requires extensive memory and time. Hence, the problem of how to organically fuse multi-scale features and enhance retrieval efficiency is yet to be resolved. We propose an end-to-end deep hash remote sensing image retrieval model (PVTA_MSF) by fusing multi-scale features based on the Pyramid Vision Transformer network (PVTv2). We construct the multi-scale feature fusion module (MSF) by using a global attention mechanism and a multi-head self-attention mechanism to reduce background interference and enhance the representation capability of image features. Deformable convolution is introduced to address the challenge posed by varying target orientations. Moreover, an intra-class similarity (ICS) loss is proposed to enhance the discriminative capability of the hash feature by minimizing the distance among images of the same category. The experimental results show that, compared with other state-of-the-art methods, the proposed hash feature could yield an excellent representation of remote sensing images and improve remote sensing image retrieval accuracy. The proposed hash feature can gain an increase of 4.2% and 1.6% in terms of mAP on the UC Merced and NWPU-RESISC45 datasets, respectively, in comparison with other methods.
A Novel Robust Geolocation-Based Multi-Factor Authentication Method for Securing ATM Payment Transactions

Credit/debit cards are a ubiquitous form of payment at present. They offer a number of advantages over cash, including convenience, security, and fraud protection. In contrast, the inherent vulnerabilities of credit/debit cards and transaction methods have led many payment institutions to focus on strengthening the security of these electronic payment methods. Also, the increasing number of electronic payment transactions around the world have led to a corresponding increase in the amount of money lost due to fraud and cybercrime. This loss of money has a significant impact on businesses and consumers, and it necessitates the development of rigid and robust security designs for securing underlying electronic transaction methods. In this regard, this research introduces a novel geolocation-based multi-factor authentication method for improving the security of electronic payment transactions, especially ATM transactions. The proposed method leverages geolocation to verify the user’s identity and prevent fraudulent transactions. In addition, this research also proposes a novel design approach for further controlling the ownership of transactions in a convenient way (e.g., allowing users to deactivate/reactivate authentication at any time, block the card in case it is stolen or lost, and set up a withdrawal limit). Overall, this approach does not require any major modifications to the existing banking infrastructure, which would be an ideal solution for securing ATM transactions around the world.
Automatic Essay Evaluation Technologies in Chinese Writing—A Systematic Literature Review

Automatic essay evaluation, an essential application of natural language processing (NLP) technology in education, has been increasingly employed in writing instruction and language proficiency assessment. Because automatic Chinese Essay Evaluation (ACEE) has made some breakthroughs due to the rapid development of upstream Chinese NLP technology, many evaluation tools have been applied in teaching practice and high-risk evaluation processes. However, the development of ACEE is still in its early stages, with many technical bottlenecks and challenges. This paper systematically explores the current research status of corpus construction, feature engineering, and scoring models in ACEE through literature to provide a technical perspective for stakeholders in the ACEE research field. Literature research has shown that constructing the ACEE public corpus is insufficient and lacks an effective platform to promote the development of ACEE research. Various shallow and deep features can be extracted using statistical and NLP techniques in ACEE. However, there are still substantial limitations in extracting grammatical errors and features related to syntax and traditional Chinese Literary style. For the construction of scoring models, existing studies have shown that traditional machine learning and deep learning methods each have advantages in different corpora and feature selections. The deep learning model, which exhibits strong adaptability and multi-task joint learning potential, has broader development space regarding model scalability.
Probing dressed states and quantum nonlinearities in a strongly coupled three-qubit waveguide system under optical pumping

We study a three-qubit waveguide system in the presence of optical pumping, when the side qubits act as atomlike mirrors, manifesting in a strong light-matter coupling regime. The qubits are modeled as two-level systems, where we account for important saturation effects and quantum nonlinearities. Optically pumping this system into a nonlinear regime is shown to lead to a rich manifold of dressed states that can be seen in the emitted spectrum, and we show two different theoretical solutions using a medium-dependent master-equation model in the Markovian limit, as well as using matrix product states without invoking any Markov approximations. We demonstrate how a rich nonlinear spectrum is obtained by varying the relative decay rates of the mirror qubits as well as their spatial separation and show the limitations of using a Markovian master equation. Our model allows one to directly model giant-atom phenomena, while including important retardation effects and multiphoton nonlinearities. We also show how the excited three-qubit system, in a strong-coupling regime, deviates significantly from a Jaynes-Cummings model when entering the nonlinear regime.
Differentiation on Interval

Summary This article generalizes the differential method on intervals, using the Mizar system [2], [3], [12]. Differentiation of real one-variable functions is introduced in Mizar [13], along standard lines (for interesting survey of formalizations of real analysis in various proof-assistants like ACL2 [11], Isabelle/HOL [10], Coq [4], see [5]), but the differentiable interval is restricted to open intervals. However, when considering the relationship with integration [9], since integration is an operation on a closed interval, it would be convenient for differentiation to be able to handle derivates on a closed interval as well. Regarding differentiability on a closed interval, the right and left differentiability have already been formalized [6], but they are the derivatives at the endpoints of an interval and not demonstrated as a differentiation over intervals. Therefore, in this paper, based on these results, although it is limited to real one-variable functions, we formalize the differentiation on arbitrary intervals and summarize them as various basic propositions. In particular, the chain rule [1] is an important formula in relation to differentiation and integration, extending recent formalized results [7], [8] in the latter field of research.
Cloud-assisted collaborative inference of convolutional neural networks for vision tasks on resource-constrained devices

This work analyses the most relevant research conducted under the mobile cloud computing paradigm to bring vision tasks supported by state-of-the-art deep convolutional neural networks closer to the end-user through collaborative intelligence. In particular, this review aims to comprehensively address collaborative inference on convolutional networks, offering the reader a detailed explanation of the main methods and technologies used to partition and deploy such models on the UE-edge-cloud continuum, which have made it possible to leverage the capabilities of resource-constrained devices to alleviate, and ideally eliminate, the traditional dependence on the cloud for high-performance computing, thereby enabling a more rational exploitation of the supporting hardware infrastructure. The paper details the technical aspects of the different frameworks designed to support these tasks, examining and comparing the mechanisms and techniques used to conceive pertinent configurations. Moreover, the study outlines the various algorithmic solutions developed for synthesizing optimal co-inference schemes, also covering the design conventions adopted and the tweaks implemented to optimize the overall performance delivered, concluding with a discussion of the specific challenges that have arisen thus far, as well as the following steps to be taken in this field.
Word2Vec-based efficient privacy-preserving shared representation learning for federated recommendation system in a cross-device setting

Recommendation systems have required centralized storage of user data, but due to privacy concerns, recent studies adopted federated learning (FL) that discloses intermediate statistics instead of raw data to build privacy-preserving federated recommendation systems. However, they suffer from inefficiencies in privacy-preserving mechanisms and inaccuracies in simple algorithms that ignore sequential information. This study proposes an extension of Word2Vec for a privacy-preserving federated sequential recommendation system (PPFSRS). This method exploits sequential information to generate contextual item representations for accurate recommendations while concealing privacy-sensitive features efficiently. Specifically, we mixed updates from negative samples to inhibit the direct leakage of purchased items from model updates. In addition, our method computes approximate model updates that can occur when sensitive features only belong to negative samples to prevent inference attacks. In experiments, we used benchmark datasets for recommendation and simulated highly distributed data such that each user stores historical data locally. While preserving privacy with reasonable complexity, the proposed method showed little degradation in recommendation performance compared to FL-based Word2Vec without privacy-preserving mechanisms. Utilizing contextual item representations trained by our method from highly distributed data will be a practical starting point for PPFSRS in a cross-device setting.
User perception and self-disclosure towards an AI psychotherapy chatbot according to the anthropomorphism of its profile picture

Studies have displayed that the favorable image of a psychotherapist increases the therapy outcome by encouraging the self-disclosure of clients. Recently, in a situation where AI psychotherapy chatbots were actively employed, this study examines how the degree of anthropomorphism in profile pictures of AI psychotherapy chatbots (company logo, robot character, human character, real human photo) affects user self-disclosure. Furthermore, this study verifies that the relationship between anthropomorphism and self-disclosure is mediated by the user's liking and mind perception towards the chatbot. The study found that the anthropomorphism of the psychotherapy chatbot profile picture did not directly affect the user's self-disclosure, and the higher the degree of anthropomorphism of the profile picture, the more self-disclosure when the user had a favorable feeling for the chatbot. On the other hand, when the user perceived the chatbot to have thoughts and emotions, the higher the degree of anthropomorphism of the profile picture, the lower the degree of self-disclosure. This study has significance in examining the user's perception according to the chatbot profile picture given the situation in which a psychotherapy chatbot will be used in the actual market.
$$\text {C}^3$$BR: Category-Aware Cross-View Contrastive Learning Framework for Bundle Recommendation

Bundle recommendation aims to suggest a series of items that users are interested in, and MealRec is a novel bundle recommendation dataset recently introduced. While current methods utilize user, bundle, and item interaction data to create user and bundle representations, the connection between item view and bundle view has not received sufficient attention. This study addresses this gap by examining the relationship between item view and bundle view. To capture interactive cooperative relations, we present the Category-aware Cross-view Contrastive learning framework ( $$\text {C}^3$$ BR). We participated in the workshop of BundleRS 2023 and the team name is Reset. Our experiments on MealRec dataset prove the superiority of $$\text {C}^3$$ BR over baseline method.
Drones and special libraries in the fifth industrial revolution

Purpose This study aims to explore the potential use of drones in special library services, aiming to enhance accessibility, services and reliability. It examines how drones can provide library materials to individuals unable to access traditional services and addresses challenges associated with drone implementation. Design/methodology/approach This study involves a literature review and case studies to analyze the feasibility and benefits of incorporating drones into special libraries. This study also discusses the synergy between drone technology and artificial intelligence (AI) in enhancing library operations. Findings Drones have the potential to transform special libraries by automating tasks, improving efficiency and expanding outreach. Their application ranges from inventory management and book retrieval to security, surveillance and outreach initiatives. AI-powered drones can provide real-time data on library usage and enhance cost-effectiveness. However, challenges including costs, privacy concerns and regulatory frameworks need to be addressed. Originality/value The integration of drones and AI in special library services presents a novel approach to revolutionizing library operations. This study uniquely combines these technologies, emphasizing the importance of proactive consideration of challenges and prospects for successful implementation.
Supporting claustrophobic patients during Magnetic Resonance Imaging examination– the patient perspective

Magnetic Resonance Imaging (MRI) is a common imaging modality used to diagnose disease and monitor treatment. However, some patients cannot complete their MRI examination as a result of claustrophobia. The recent developments in the MRI scanner design may have reduced the incidence of claustrophobia in MRI. This study aimed to explore the claustrophobic patients' experience to better understand how to support them.A qualitative descriptive approach using focus groups was deemed appropriate for the study. The research was advertised in a print newspaper and on social media platforms for eligible participants to contact the researcher. Six claustrophobic patients were recruited to the focus group meeting. The discussion was audio recorded and transcribed. Content analysis was used to analyse the data into code and categories.Four themes emerged from the data analysis, which included examination preparation, information provision, coping mechanisms and MR scanner design. Participants reported that the confined space in the MRI scanner bore is responsible for their anxiety during MRI. However, the majority of the participants believe that they would be able to complete the examination if, in advance of their scan, they were shown the MRI scanner and what they would be required to do.Several coping mechanisms were shared by the participants on how they were able to complete their MRI examination despite their claustrophobia. MRI radiographers should endeavour to use these mechanisms to support their patients in practice. Also, the findings emphasise the importance of virtual reality and clear communication with patients in preparing them for their MRI examinations.The findings of this study may be used to enhance claustrophobic patients' experience in MRI. It also identified those aspects of the MRI examination that matter most to patients with claustrophobia in order to provide a suitable intervention to support these patients.
Postdigital Collective Memory: Media Practices Against Total Design

Abstract The article presents the concept of postdigital collective memory—a proposal that opens possible research fields for postdigital science and education. Postdigital collective memory is co-created between human and nonhuman beings and technological media, with the latter treated as sensitive sensors. In order to exemplify this concept, the article presents research results from field practices and design workshops conducted by the Humanities/Art/Technology Research Center at Lake Elsensee-Rusałka in Poznań and the prototype of the Sensitive Data Lake (SDL)—a digital environment project incorporating human and nonhuman actants and attempting to restore a shared narrative about a place whose history has been suppressed and has faded from public memory. This lake is one of many examples of what Tony Fry calls ‘total design’: it was created during World War II, through the forced labor of Jewish prisoners, as part of the Nazi expansion into the East; and the project attempted to redesign the environment and remove the local inhabitants. Following the theories that analyze the long duration of ‘total design’ (Fry) and the concepts of transitions design (Escobar), the author’s own Critical Media Design (CMD) method was applied to develop various experimental strategies for design and educational work related to the history and memory of the Elsensee-Rusałka site in the postdigital reality.
Profiling consumers who reported mass marketing scams: demographic characteristics and emotional sentiments associated with victimization

Abstract We examine the characteristics of consumers who reported scams to the U.S. Federal Trade Commission. We assess how consumers vary demographically across six scam types, and how the overall emotional sentiment of a consumer’s complaint (positive, negative, neutral/mixed) relates to reporting victimization versus attempted fraud (no losses). For romance, tech support, and prize, sweepstakes, and lottery scams, more older than young and middle-aged adults reported victimization. Across all scam types, consumers classified as Black, Hispanic, and Asian/Asian Pacific Islander were more likely than non-Hispanic white consumers to report victimization than attempted fraud. Relative to complaints categorized as emotionally neutral or mixed, we find that emotionally positive complaints and emotionally negative complaints were significantly associated with victimization, but that these relationships differed by scam type. This study helps identify which consumer groups are affected by specific scams and the association between emotion and victimization.
Efficient Focus Autoencoders for Fast Autonomous Flight in Intricate Wild Scenarios

The autonomous navigation of aerial robots in unknown and complex outdoor environments is a challenging problem that typically requires planners to generate collision-free trajectories based on human expert rules for fast navigation. Presently, aerial robots suffer from high latency in acquiring environmental information, which limits the control strategies that the vehicle can implement. In this study, we proposed the SAC_FAE algorithm for high-speed navigation in complex environments using deep reinforcement learning (DRL) policies. Our approach consisted of a soft actor–critic (SAC) algorithm and a focus autoencoder (FAE). Our end-to-end DRL navigation policy enabled a flying robot to efficiently accomplish navigation tasks without prior map information by relying solely on the front-end depth frames and its own pose information. The proposed algorithm outperformed existing trajectory-based optimization approaches at flight speeds exceeding 3 m/s in multiple testing environments, which demonstrates its effectiveness and efficiency.
Defining Semantically Close Words of Kazakh Language with Distributed System Apache Spark

This work focuses on determining semantically close words and using semantic similarity in general in order to improve performance in information retrieval tasks. The semantic similarity of words is an important task with many applications from information retrieval to spell checking or even document clustering and classification. Although, in languages with rich linguistic resources, the methods and tools for this task are well established, some languages do not have such tools. The first step in our experiment is to represent the words in a collection in a vector form and then define the semantic similarity of the terms using a vector similarity method. In order to tame the complexity of the task, which relies on the number of word (and, consequently, of the vector) pairs that have to be combined in order to define the semantically closest word pairs, A distributed method that runs on Apache Spark is designed to reduce the calculation time by running comparison tasks in parallel. Three alternative implementations are proposed and tested using a list of target words and seeking the most semantically similar words from a lexicon for each one of them. In a second step, we employ pre-trained multilingual sentence transformers to capture the content semantics at a sentence level and a vector-based semantic index to accelerate the searches. The code is written in MapReduce, and the experiments and results show that the proposed methods can provide an interesting solution for finding similar words or texts in the Kazakh language.
A Novel Eighth-Order Hyperchaotic System and Its Application in Image Encryption

With the advancement in information and communication technologies (ICTs), the widespread dissemination and sharing of digital images has raised concerns regarding privacy and security. Traditional methods of encrypting images often suffer from limitations such as a small key space and vulnerability to brute-force attacks. To address these issues, this paper proposes a novel eighth-order hyperchaotic system. This hyperchaotic system exhibits various dynamic behaviors, including hyperchaos, sub-hyperchaos, and chaos. The encryption scheme based on this system offers a key space larger than 22338. Through a comprehensive analysis involving histogram analysis, key space analysis, correlation analysis, entropy analysis, key sensitivity analysis, differential attack analysis, and cropping attack analysis, it is demonstrated that the proposed system is capable of resisting statistical attacks, brute force attacks, differential attacks, and cropping attacks, thereby providing excellent security performance.