Reviewerly/exHarmony huggingface dataset:
UAV small target detection algorithm based on an improved YOLOv5s model

The targets of UAV target detection are usually small targets, and the backgrounds are complex. In this work, aiming at the problem that small targets are easy to be missed or misdetected during the UAV detection, an improved YOLOv5s_MSES target detection algorithm based on YOLOv5s is proposed. First of all, to solve the problem of UAV’s difficulty in detecting small targets, the detection layer is ameliorated into the small target detection layer STD, which makes the model more easily detect the small targets. Then, the multi-scale feature fusion module is added to improve the detection accuracy of the small targets. Furthermore, by combining multi-scale module and attention module, a new connection method is proposed to retain the large scale of feature information. Finally, in contrast with some existent methods, the experimental results of VisDrone2019 UAV target detection dataset show that our proposed YOLOv5s_MSES can achieve the better detection effect, and more effectively complete the small target detection task for UAV aerial photography images.
Design and analysis of cyber warfare model with intelligent predictive stochastic networks for attack–defend strategies on critical infrastructures

The purpose of this study is to develop a novel multi-class differential system (target network, protected network, inside attacker, and outside attacker) for a cyber war model that imitating the cyber war scenarios on different attack surfaces in the presence of multifarious attack vectors and analysis has been presented by stupendous knacks of intelligent predictive stochastic networks (IPSNs). The theoretical convergence analysis through attack-free and attack-invasion equilibrium in terms of basic reproduction number R0 has been presented along with the global stability of attack-free equilibrium points using the Lyapunov function. The reference solutions for studying the dynamics of all four classes of cyber war models in an air-gapped environment have also been determined in this paper for various scenarios by varying the protection strategy parameter ρ, the recovery strategy parameter ɛ, and the rate of attack from an outside/inside attacker using the Adams numerical method. The obtained data has been used in IPSNs as input/targets for training, validation, and testing in order to compute the approximate solution of the model. Extensive simulations have been run to determine the accuracy of the proposed framework for a complex advanced persistent attack targeting industrial systems. The outcomes of IPSNs have shown a fine resemblance with the matching of order 10−7 from the observed data. Also, the analysis based on mean square error convergence, error histogram, and regression index has further verified the strength and worth of the presented strategy.
CAISED: A Protocol for Cast-as-Intended Verifiability with a Second Device

Abstract Numerous approaches for cast-as-intended verifiability have been proposed in the literature on electronic voting, balancing practical aspects and security guarantees in different ways. One of the well-established methods involves the use of a second device that allows voters to audit their submitted ballots. This approach offers several benefits, including support for flexible ballot and election types and an intuitive user experience. Moreover, solutions based on this approach are generally adaptable to a range of existing voting protocols, rather than being restricted to a particular election scheme. This approach has been successfully implemented in real-life elections, such as in Estonia [18]. In this work, we improve the existing solutions for cast-as-intended verifiability based on the use of a second device. We propose a solution that preserves the advantageous practical properties outlined above, while providing stronger security guarantees. Our method does not increase the risk of vote selling compared to the underlying voting protocol to be augmented, and it requires only comparatively weak trust assumptions to achieve this. It can be combined with various voting protocols, including commitment-based systems that provide everlasting privacy. Overall, our work offers a new option to strengthen cast-as-intended and end-to-end verifiability for real-world Internet elections.
CORE-GPT: Combining Open Access Research and Large Language Models for Credible, Trustworthy Question Answering

In this paper, we present CORE-GPT, a novel question-answering platform that combines GPT-based language models and more than 32 million full-text open access scientific articles from CORE ( https://core.ac.uk ). We first demonstrate that GPT3.5 and GPT4 cannot be relied upon to provide references or citations for generated text. We then introduce CORE-GPT which delivers evidence-based answers to questions, along with citations and links to the cited papers, greatly increasing the trustworthiness of the answers and reducing the risk of hallucinations. CORE-GPT's performance was evaluated on a dataset of 100 questions covering the top 20 scientific domains in CORE, resulting in 100 answers and links to 500 relevant articles. The quality of the provided answers and relevance of the links were assessed by two annotators. Our results demonstrate that CORE-GPT can produce comprehensive and trustworthy answers across the majority of scientific domains, complete with links to genuine, relevant scientific articles.
A screen-shooting resilient data-hiding algorithm based on two-level singular value decomposition

This paper presents a screen-shooting resilient data-hiding scheme for JPEG images based on a two-level Singular Value Decomposition (SVD). The mid-to-high frequency coefficients are chosen to balance the data-embedding robustness and the image's visual quality. At the same time, the two-level singular value decomposition (SVD) is additionally utilized to construct the feature matrices, thereby enhancing the robustness of the embedded message. The combination of DCT and two-level SVD enables achieving a high-performance screen-shooting resilient data-hiding algorithm. Moreover, the affine transformation is employed to rectify the distortion that caused by screen-shooting, thus further improving the data extraction accuracy. Experimental results demonstrate a minimum 3 % improvement in confidential information extraction accuracy, with the data extraction rate exceeding 92 % across screen-shooting angles ranging from −40 to 40°. The performance of the proposed algorithm outperforms other state-of-the-art schemes apparently.
On understanding and predicting issue links

Abstract Stakeholders in software projects use issue trackers like JIRA or Bugzilla to capture and manage issues, including requirements, feature requests, and bugs. To ease issue navigation and structure project knowledge, stakeholders manually connect issues via links of certain types that reflect different dependencies, such as Epic-, Block-, Duplicate-, or Relate- links. Based on a large dataset of 16 JIRA repositories, we study the commonalities and differences in linking practices and link types across the repositories. We then investigate how state-of-the-art machine learning models can predict common link types. We observed significant differences across the repositories and link types, depending on how they are used and by whom. Additionally, we observed several inconsistencies, e.g., in how Duplicate links are used. We found that a transformer model trained on titles and descriptions of linked issues significantly outperforms other optimized models, achieving an encouraging average macro F1-score of 0.64 for predicting nine popular link types across all repositories (weighted F1-score of 0.73). For the specific Subtask- and Epic- links, the model achieves top F1-scores of 0.89 and 0.97, respectively. If we restrict the task to predict the mere existence of links, the average macro F1-score goes up to 0.95. In general, the shorter issue text, possibly indicating precise issues, seems to improve the prediction accuracy with a strong negative correlation of $$-$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>-</mml:mo> </mml:math> 0.73. We found that Relate-links often get confused with the other links, which suggests that they are likely used as default links in unclear cases. Our findings particularly on the quality and heterogeinity of issue link data have implications for researching and applying issue link prediction in practice.
Quantum algorithm offers faster way to hack internet encryption

Scheme to factor giant numbers could be more efficient than 30-year-old Shor’s algorithm
The Impact of Scrollytelling on the Reading Experience of Long-Form Journalism

Scrollytelling is the most common form of long-form journalism and has become a popular communication device because of its ability to capture an audience's attention. This could make scrollytelling a suitable format for younger audiences, whose attitudes towards news differ from older generations; it is more individualistic and is driven by progress and entertainment. This paper investigates the effect scrollytelling has on journalistic content according to a younger audience. This was done by letting participants consume an article in both a normal static format, i.e., non-scrollytelling, and a scrollytelling format, and then provide insights through a questionnaire and interview. The results showed a significant difference in perceived engagement favoring the scrollytelling format and clear indications of which features scrollytelling can produce and how desirable these are. It also showed the strength of scrollytelling in its ability to invoke an emotional response.
Enhancing Feature Detection and Matching in Low-Pixel-Resolution Hyperspectral Images Using 3D Convolution-Based Siamese Networks

Today, hyperspectral imaging plays an integral part in the remote sensing and precision agriculture field. Identifying the matching key points between hyperspectral images is an important step in tasks such as image registration, localization, object recognition, and object tracking. Low-pixel resolution hyperspectral imaging is a recent introduction to the field, bringing benefits such as lower cost and form factor compared to traditional systems. However, the use of limited pixel resolution challenges even state-of-the-art feature detection and matching methods, leading to difficulties in generating robust feature matches for images with repeated textures, low textures, low sharpness, and low contrast. Moreover, the use of narrower optics in these cameras adds to the challenges during the feature-matching stage, particularly for images captured during low-altitude flight missions. In order to enhance the robustness of feature detection and matching in low pixel resolution images, in this study we propose a novel approach utilizing 3D Convolution-based Siamese networks. Compared to state-of-the-art methods, this approach takes advantage of all the spectral information available in hyperspectral imaging in order to filter out incorrect matches and produce a robust set of matches. The proposed method initially generates feature matches through a combination of Phase Stretch Transformation-based edge detection and SIFT features. Subsequently, a 3D Convolution-based Siamese network is utilized to filter out inaccurate matches, producing a highly accurate set of feature matches. Evaluation of the proposed method demonstrates its superiority over state-of-the-art approaches in cases where they fail to produce feature matches. Additionally, it competes effectively with the other evaluated methods when generating feature matches in low-pixel resolution hyperspectral images. This research contributes to the advancement of low pixel resolution hyperspectral imaging techniques, and we believe it can specifically aid in mosaic generation of low pixel resolution hyperspectral images.
A Hybrid Human Activity Recognition Method Using an MLP Neural Network and Euler Angle Extraction Based on IMU Sensors

Inertial measurement unit (IMU) technology has gained popularity in human activity recognition (HAR) due to its ability to identify human activity by measuring acceleration, angular velocity, and magnetic flux in key body areas like the wrist and knee. It has propelled the extensive application of HAR across various domains. In the healthcare sector, HAR finds utility in monitoring and assessing movements during rehabilitation processes, while in the sports science field, it contributes to enhancing training outcomes and preventing exercise-related injuries. However, traditional sensor fusion algorithms often require intricate mathematical and statistical processing, resulting in higher algorithmic complexity. Additionally, in dynamic environments, sensor states may undergo changes, posing challenges for real-time adjustments within conventional fusion algorithms to cater to the requirements of prolonged observations. To address these limitations, we propose a novel hybrid human pose recognition method based on IMU sensors. The proposed method initially calculates Euler angles and subsequently refines them using magnetometer and gyroscope data to obtain the accurate attitude angle. Furthermore, the application of FFT (Fast Fourier Transform) feature extraction facilitates the transition of the signal from its time-based representation to its frequency-based representation, enhancing the practical significance of the data. To optimize feature fusion and information exchange, a group attention module is introduced, leveraging the capabilities of a Multi-Layer Perceptron which is called the Feature Fusion Enrichment Multi-Layer Perceptron (GAM-MLP) to effectively combine features and generate precise classification results. Experimental results demonstrated the superior performance of the proposed method, achieving an impressive accuracy rate of 96.13% across 19 different human pose recognition tasks. The proposed hybrid human pose recognition method is capable of meeting the demands of real-world motion monitoring and health assessment.
Remote Sensing Image Scene Classification in Hybrid Classical–Quantum Transferring CNN with Small Samples

The scope of this research lies in the combination of pre-trained Convolutional Neural Networks (CNNs) and Quantum Convolutional Neural Networks (QCNN) in application to Remote Sensing Image Scene Classification(RSISC). Deep learning (RL) is improving by leaps and bounds pretrained CNNs in Remote Sensing Image (RSI) analysis, and pre-trained CNNs have shown remarkable performance in remote sensing image scene classification (RSISC). Nonetheless, CNNs training require massive, annotated data as samples. When labeled samples are not sufficient, the most common solution is using pre-trained CNNs with a great deal of natural image datasets (e.g., ImageNet). However, these pre-trained CNNs require a large quantity of labelled data for training, which is often not feasible in RSISC, especially when the target RSIs have different imaging mechanisms from RGB natural images. In this paper, we proposed an improved hybrid classical-quantum transfer learning CNNs composed of classical and quantum elements to classify open-source RSI dataset. The classical part of the model is made up of a ResNet network which extracts useful features from RSI datasets. To further refine the network performance, a tensor quantum circuit is subsequently employed by tuning parameters on near-term quantum processors. We tested our models on the open-source RSI dataset. In our comparative study, we have concluded that the hybrid classical-quantum transferring CNN has achieved better performance than other pre-trained CNNs based RSISC methods with small training samples. Moreover, it has been proven that the proposed algorithm improves the classification accuracy while greatly decreasing the amount of model parameters and the sum of training data.
Kung Fu Metaverse: A Movement Guidance Training System

There are a large number of Chinese Kung Fu enthusiasts worldwide. Offline learning has been the primary method for teaching and learning Chinese Kung Fu due to the inability of online 2-D videos to convey spatial information and provide feedback. To address the issues of the high cost of offline teaching, fixed perspectives in 2-D videos, lack of spatial path feedback, and the absence of practical Kung Fu teaching systems, this article proposes Kung Fu Metaverse: A new movement guidance training system. A digital character in the system is given nearly a hundred kinds of Chinese Kung Fu. Learners can observe the static postures and dynamic transitions of martial arts from various angles. Composite navigation module of our system provides learners with a feedback of spatial path teaching method so that they can receive real-time feedback when training. We recruited volunteers and conducted two experiments. The first compared the cognitive experience of volunteers in three different modes, while the second investigated the impact of voice prompts, trajectory settings, arrow indicators, and free gestures on learning outcomes. The results indicate that volunteers demonstrated significantly higher levels of satisfaction, comfort, and interactivity in the mixed reality (MR) mode. Compared to virtual reality, MR allows free movement and significantly reduces adverse effects such as sickness and dizziness. The addition of the composite navigation module improves the movement precision during practice. This work is a pioneering system that integrates nearly a hundred sets of traditional Chinese Kung Fu routines with a low-cost device.
Investigating Transparency Dimensions for Internet Voting

Abstract While Internet voting is argued to have the potential to improve election processes, concerns about security risks remain one of its main adoption barriers. These concerns are furthermore aggravated by the lack of transparency of Internet voting systems that are often perceived as a “black box”. Moreover, there is a research gap in conceptualizing the idea of transparency and in studying voters’ attitudes towards transparency in Internet voting. In this work, we aim to address this gap by (1) Conducting a systematic literature review, from which we identified five dimensions of transparency; (2) Developing a questionnaire (Transparency Dimensions of Internet Voting, TDIV) to assess voters’ attitudes regarding the correlation of these dimensions with transparency; and (3) Conducting an online study (N = 500) to investigate voters’ attitudes towards transparency in Internet voting. We conclude that providing information about the security of the Internet voting system; testing it by independent experts for security vulnerabilities prior to the election; monitoring the election process and verifying its integrity; and providing a remedy for security breaches while holding the responsible parties accountable, are perceived by voters as important, and enhance transparency in Internet voting systems.
From Textual to Visual Image Searching: User Experience of Advanced Image Search Tool

This paper reports findings from a study focusing on user experience of image search tool utilizing content-based image retrieval methods. Previous studies have indicated challenges in textual image search especially in the historical domain. As a part of the project, a prototype tool was created for searching digitized historical images based on their visual contents to provide support for user needs identified in earlier studies. The tool was tested by 15 participants who evaluated their user experience with User Experience Scale and by verbal feedback. Our results indicate that participants derived benefits from the search capabilities provided by the tool, which went beyond relying on textual image descriptions. However, problems occurred, for example, in evaluating the search results and in user skills. Results also emphasize the value of intellectually produced metadata for image searching and use. Therefore, future developments should focus on creating hybrid systems supporting both textual and visual image searching.
Analysis of Imaging Characteristics of Wide-field Lobster Eye Lens

Abstract The lobster-eye lens is a reflected type focusing optics. It can be used for backscattering imaging that is a single-sided access technology for homeland security, detection of improvised explosive devices, non-destructive testing, and medical imaging. The aim of this study was to investigate the imaging characteristic of meridional lobster-eye lens with a planar imaging surface. The larger the field of view, the greater the spherical aberration, and the larger the field curvature of the edge field of view. Both spherical aberration and field curvature increase the size of the focal spot, and the images become blurry. Rays with a large angle of grazing incidence have large spherical aberration and curvature. The reflectivity of these rays drops sharply as the energy of photons increases. Therefore, spherical aberration and field curvature have a greater effect on photons with low energy. These conclusions can help improve the design of a lobster-eye lens for wide-field imaging applications.
Postdigital Positionality

Positionality refers to the individual social and political context that creates a person’s identity, e.g., race, class, gender, sexuality, ability, status, and how such markers influence the stance people take, or what they can access, in society. In a postdigital era, it becomes necessary to expand these markers of people’s positions to include numerous effects and potential bias from data-driven systems and related discourse that now fluidly interact with people’s positions. Postdigital positionality is a concept that develops former theoretical framings of positionality, to reflexively acknowledge a complex interplay of new postdigital knowledge ecologies, now intersecting with human identity.
Tailoring Fusion-Based Error Correction for High Thresholds to Biased Fusion Failures

We introduce fault-tolerant (FT) architectures for error correction with the $XZZX$ cluster state based on performing measurements of two-qubit Pauli operators $Z\ensuremath{\bigotimes}Z$ and $X\ensuremath{\bigotimes}X$, or fusions, on a collection of few-body entangled resource states. Our construction is tailored to effectively correct noise that predominantly causes faulty $X\ensuremath{\bigotimes}X$ measurements during fusions. This feature offers a practical advantage in linear optical quantum computing with dual-rail photonic qubits, where failed fusions only erase $X\ensuremath{\bigotimes}X$ measurement outcomes. By applying our construction to this platform, we find a record-high threshold to fusion failures exceeding 25% in the experimentally relevant regime of nonzero loss rate per photon, considerably simplifying hardware requirements.