Reviewerly/exHarmony huggingface dataset:
Radio Frequency Fingerprinting Identification of Few-Shot Wireless Signals Based on Deep Metric Learning

As a cross-protocol endogenous security mechanism, the physical layer-based radio frequency (RF) fingerprinting can effectively enhance the existing password-based application layer authentication utilizing the hardware differences of wireless devices, which is unique and cannot be counterfeited by a third party. However, the recognition performance of the deep learning physical layer fingerprint recognition algorithm drops sharply in the case of a small number of signal samples. This paper analyzes the feasibility and proposes the few-shot wireless signal classification network based on deep metric learning (FSig-Net). FSig-Net reduces the model’s dependence on big data by adaptively learning the feature distance metric. We use 8 mobile phones and 18 Internet of Things (IoT) modules as targets for identification. When the number of single-type samples is only 10, the recognition accuracy of mobile phones can reach 98.28%, and the recognition accuracy of IoT devices can reach 98.20%.
Transformer vehicle re-identification of intelligent transportation system under carbon neutral target

Vehicle re-identification technology plays a pivotal role in intelligent transportation systems, contributing significantly to improved traffic scheduling and reduced net-zero emissions. It holds immense potential for advancing carbon neutrality efforts. By accurately recognizing and tracking vehicles, this technology optimizes traffic flow, enhances energy efficiency, reduces carbon emissions, and fosters sustainable transportation development. However, prevailing CNN-based feature extraction methods often lack the capability to associate global features, while Transformer-based approaches tend to overlook crucial local feature differences. Effectively mining local features and integrating them with global features are critical aspects of successful vehicle re-identification techniques. To tackle this challenge, we present a novel approach, the Locally Significant Feature Rearrangement (LSFR) module, based on the Swin Transformer model. This module enhances the learning of local features by initially focusing on specific regions using a single-shot multi-box detector (SSD) and employing an adaptive attention learning mechanism to highlight fine-grained local features. Subsequently, the salient local features are rearranged to prioritize their importance, and their embedding layers are fused with global features in a recombined manner. Our experimental results on the VeRi-776 and VehicleID benchmark datasets validate the effectiveness of our proposed method as a reliable technique to support intelligent transportation systems and drive progress towards traffic carbon neutralization.
Induced permutations for approximate metric search

Permutation-based Indexing (PBI) approaches have been proven to be particularly effective for conducting large-scale approximate metric searching. These methods rely on the idea of transforming the original metric objects into permutation representations, which can be efficiently indexed using data structures such as inverted files. The standard conceptualization of permutation associated with a metric object involves only the use of object distances and their relative orders from a set of anchors called pivots. In this paper, we generalized this definition in order to enlarge the class of permutation representations that can be used by PBI approaches. In particular, we introduced the concept of permutation induced by a space transformation and a sorting function, and we investigated which properties these transformations should possess to produce permutations that are effective for metric search. Furthermore, as a practical outcome, we defined a new type of permutation representation that is calculated using distances from pairs of pivots. This proposed technique allowed us to produce longer permutations than traditional ones for the same number of object-pivot distance calculations. The advantage lies in the fact that when longer permutations are employed, the use of inverted files built on permutation prefixes leads to greater efficiency in the search phase.
A modified federated Student’s t-based variational adaptive Kalman filter for multi-sensor information fusion

To overcome the problem of poor performance of multi-sensor fusion algorithm in linear systems with time-varying noise and outliers, a modified federated Student’s t-based variational robust adaptive Kalman filter (MFSTVRAKF) is proposed in this article. In this algorithm, an improved Student’s t-based variational robust adaptive Kalman filter (ISTVRAKF) is developed for adaptively mitigating the effects of time-varying noise and outliers of each local sensor, which improves the estimation accuracy of the algorithm. It reorganizes and derives a simplified variational filter to approximate the prior distribution of the measurement noise covariance via the Student’s t distribution to eliminate outliers. Subsequently, an adaptive factor is introduced into ISTVRAKF for adjusting the weights of prediction and measurement to further exclude outliers. Moreover, a modified adaptive information sharing factor is devised to adjust the multi-sensor fusion weights in accordance with the performance of local filter, which improves the fusion accuracy of the MFSTVRAKF algorithm. Simulations exhibit that the proposed algorithm is capable of resisting the effects of time-varying noise and outliers and achieving higher accuracy compared to other filtering algorithms and fusion algorithms.
Data centers with quantum random access memory and quantum networks

In this paper we propose the Quantum Data Center (QDC), an architecture combining Quantum Random Access Memory (QRAM) and quantum networks. We give a precise definition of QDC and discuss its possible realizations and extensions. We discuss applications of QDC in quantum computation, quantum communication, and quantum sensing, with a primary focus on QDC for $T$-gate resources, QDC for multiparty private quantum communication, and QDC for distributed sensing through data compression. We show that QDC will provide efficient, private, and fast services as a future version of data centers.
Evading noise in multiparameter quantum metrology with indefinite causal order

Quantum theory allows the traversing of multiple channels in a superposition of different orders. When the order in which the channels are traversed is controlled by an auxiliary quantum system, various unknown parameters of the channels can be estimated by measuring only the control system, even when the state of the probe alone would be insensitive. Moreover, increasing the dimension of the control system increases the number of simultaneously estimable parameters, which has important metrological ramifications. We demonstrate this capability for simultaneously estimating both unitary and noise parameters, including multiple parameters from the same unitary such as rotation angles and axes and from noise channels such as depolarization, dephasing, and amplitude damping in arbitrary dimensions. We identify regimes of unlimited advantages, taking the form of ${p}^{2}$ smaller variances in estimation when the noise probability is $1\ensuremath{-}p$, for both single and multiparameter estimations when using our schemes relative to any comparable scheme whose causal order is definite.
Multimodal Neurosymbolic Approach for Explainable Deepfake Detection

Deepfake detection has become increasingly important in recent years owing to the widespread availability of deepfake generation technologies. Existing deepfake detection methods present two primary limitations i.e., trained on a specific type of deepfake dataset, which renders them vulnerable to unseen deepfakes; and they regard deepfakes as a “black-box” with limited explainability, making it difficult for non-AI experts to understand and trust the decisions. Hence, this paper proposes a novel neurosymbolic deepfake detection framework that exploits the fact that human emotions cannot be imitated easily owing to their complex nature. We argue that deep fakes typically exhibit inter- or intra- modality inconsistencies in the emotional expressions of the person being manipulated. Thus, the proposed framework performs inter- and intra- modality reasoning on emotions extracted from audio and visual modalities using a psychological and arousal-valence model for deepfake detection. In addition to fake detection, the proposed framework provides textual explanations for its decisions. The results obtained using Presidential Deepfakes Dataset and World Leaders Dataset of real and manipulated videos demonstrate the effectiveness of our approach in detecting deepfakes and highlight the potential of neurosymbolic approach for expandability.
ENSURING THE CONFIDENTIALITY OF PERSONAL DATA AND SUPPORTING CYBER SECURITY WITH THE HELP OF BLOCKCHAIN

The recent increase in security breaches and digital surveillance highlights the need to improve privacy and security, especially of users' personal data. Advances in cybersecurity and new legislation promise to improve the protection of personal data. Blockchain and distributed ledger (DTL) technologies provide new opportunities to protect user data through decentralized identification and other privacy mechanisms. These systems can give users greater sovereignty through tools that allow them to own and control their own data. The purpose of the article is to research blockchain technology and mechanisms for achieving reliability in blockchain for the protection and security of personal data. Decentralized and federated identity systems give users control over what, when and how much of their personal information can be shared and with whom. These systems can also reduce cybersecurity threats. Through various consensus algorithms, blockchain-based privacy solutions allow users to better manage their data and ensure that the data and models derived from it are more accurate, honest and reliable.
Underwater Image Quality Assessment from Synthetic to Real-world: Dataset and Objective Method

The complicated underwater environment and lighting conditions lead to severe influence on the quality of underwater imaging, which tends to impair underwater exploration and research. To effectively evaluate the quality of underwater images, an underwater image quality assessment dataset is constructed from synthetic to real-world, and then a new objective underwater image assessment method based on the characteristics of the underwater imaging is proposed (UICQA). Specifically, to address the lack of a publicly available datasets and more accurately quantify the quality of underwater images, a subjective underwater image quality assessment dataset from synthetic to real-world underwater images, named USRD, is constructed. Considering that the transmission map can effectively reflect the characteristics of the underwater imaging, statistical features are effectively extracted from the transmission map for distinguishing underwater images of different quality. Further, considering that the transmission map negatively correlates with scene depth, a local-to-global transmission map weighted contrast feature is constructed. Additionally, the color features of human perception and texture features based on fractal dimensions are proposed. Finally, the experimental results show that the proposed UICQA method exhibits the highest correlation with ground truth scores compared to state-of-the-art UIQA methods.
Comparing the #StopTheSteal Movement across Multiple Platforms: Differentiating Discourse on Facebook, Twitter, and Parler

Given that political groups are dispersed across platforms, resulting in different discourses, there is a need for more studies comparing communication across platforms. In this study, we compared posts about #StopTheSteal from three social media platforms after the 2020 US Presidential election and preceding the January 6 Capitol Riot. To do so, we utilized Snow and Benford’s typology of social movement frames—diagnostic, prognostic, and motivational frames—in the context of far-right movements and an additional frame device: violence cues. This study focused on the following three social media platforms: Facebook, Twitter, and Parler. We built three corpora of social media data: 26,093 Facebook posts, 248,643 tweets, and 400,600 Parler posts. Using Bidirectional Encoder Representations from Transformers (BERT) classifiers, dictionary methods, and qualitative text analysis, we find that the use of these frames varies by platform, with users on the alt-tech platform Parler using violence cues such as “smash” and “combat,” suggesting a greater call to action relative to the mainstream platforms.
Application of Machine Learning and Deep Learning Models in Prostate Cancer Diagnosis Using Medical Images: A Systematic Review

Introduction: Prostate cancer (PCa) is one of the deadliest and most common causes of malignancy and death in men worldwide, with a higher prevalence and mortality in developing countries specifically. Factors such as age, family history, race and certain genetic mutations are some of the factors contributing to the occurrence of PCa in men. Recent advances in technology and algorithms gave rise to the computer-aided diagnosis (CAD) of PCa. With the availability of medical image datasets and emerging trends in state-of-the-art machine and deep learning techniques, there has been a growth in recent related publications. Materials and Methods: In this study, we present a systematic review of PCa diagnosis with medical images using machine learning and deep learning techniques. We conducted a thorough review of the relevant studies indexed in four databases (IEEE, PubMed, Springer and ScienceDirect) using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. With well-defined search terms, a total of 608 articles were identified, and 77 met the final inclusion criteria. The key elements in the included papers are presented and conclusions are drawn from them. Results: The findings show that the United States has the most research in PCa diagnosis with machine learning, Magnetic Resonance Images are the most used datasets and transfer learning is the most used method of diagnosing PCa in recent times. In addition, some available PCa datasets and some key considerations for the choice of loss function in the deep learning models are presented. The limitations and lessons learnt are discussed, and some key recommendations are made. Conclusion: The discoveries and the conclusions of this work are organized so as to enable researchers in the same domain to use this work and make crucial implementation decisions.
The Algorithm for Assessing the Effects of Distance Education in General Education on Students’ Academic Results

Distance education has gained great interest during the COVID-19 pandemic, when schools all over the world faced the challenge of transferring the traditional education processes to digital platforms. In this context, school communities not only discovered the new opportunities, but also encountered a number of problems when trying to provide high quality distance education and minimise learning losses for students. The rapid transition to distance education has had a negative impact on the academic results and daily routines of the students from all social groups, especially socially vulnerable families. Thus, it has become relevant to research various aspects of students’ learning losses. The purpose of this article is to theoretically substantiate the algorithm intended for assessing the effects of distance education on students’ academic results. The research was based on the methods of scientific literature analysis, secondary data analysis, theoretical modelling, inductive content analysis, and expert evaluation. The algorithm is intended for general education institutions. Results: After conducting scientific literature and secondary data analysis, the theoretical model for assessing the effects of distance education on students’ academic results was developed and substantiated. The model consists of four structural parts, represented by 11 criteria, which reveal the effects of distance education in general education following the appropriate algorithm.
Image feature based quality assessment of speckle patterns for digital image correlation measurement

A novel method is proposed to assess the quality of speckle patterns for deformation measurement using the digital image correlation (DIC) technique. Different from existing methods that focused on the characteristics of individual speckle or frequency of grayscale values, our approach explores the usage of the image features essential for image registration, which is the basis of DIC. An indicator called density and evenness of features (DEF) is defined, combining the distribution density and evenness of image features. Numerical and real experiments demonstrate that the DEF is sensitive to the quality of various speckle patterns. It can detect the subtle difference between good speckle images leading to small gap in measurement accuracy. The DEF can also provide reliable guidelines to design high-quality transferable speckle patterns, as it shows clear relation to the main controllable parameters in generation of speckle patterns, including speckle duty ratio, image contrast, speckle radius and its dispersion, as well as speckle edge sharpness.
PseudoDepth-SLR: Generating Depth Data for Sign Language Recognition

In this paper, we investigate the significance of depth data in Sign Language Recognition (SLR) and propose a novel approach for generating pseudo depth information from RGB data to boost performance and enable generalizability in scenarios where depth data is not available. For the depth generation, we rely on an approach that utilizes vision transformers as a backbone for depth prediction. We examine the effect of pseudo depth data on the performance of automatic SLR systems and conduct a comparative analysis between the generated pseudo depth data and actual depth data to evaluate their effectiveness and demonstrate the value of depth data in accurately recognizing sign language gestures. Our experiments show that our proposed generative depth architecture outperforms an RGB-only counterpart.
Postdigital Citizenship Education

Postdigital citizens are defined by their capacity to critically reflect and act upon political, digital, social, moral, ecological, and other issues. Postdigital citizenship education is continuous and empowered by citizens' actions and interactions in the postdigital society. Some of the challenges refer to the adoption of a critical stance towards the relationships between the digital and the analog; a conception of civic engagement and participation with a broader view of agency that includes non-human agents; and the raising of awareness towards the risks associated with digitization.
Extracting hydrothermally altered information using WorldView-3 data: a case study of Huitongshan, NW Gansu, China

Introduction: The Huitongshan skarn-type deposits, in which ore bodies primarily occur in the outer contact zone between K-feldspar granite and marble in the Beishan area, are evidently related to hydrothermal alteration of the surrounding rock. Key mineral alteration processes include serpentinization, epidotization, chloritization, carbonatization, jarosite, ferritization, and hematitization. Methods: WorldView-3 (WV-3), a satellite-recorded high-spatial resolution multispectral image, has been widely used in the exploration and prediction of different types of deposits around the world. In this study, WV-3 multispectral images were used to extract the spatial distribution data of the main altered minerals in the Huitongshan area. Dedicated radiometric calibration, atmospheric correction, and image fusion were used to pre-process the extracted spectral information related to hydrothermal alteration. In addition, directed principal component analysis (PCA) and a unique mineral index were designed based on the effective use of the WV-3 data band corresponding to the spectral absorption characteristics of altered minerals. Results: The findings of this study show that the PCA model and mineral index pro-posed herein are reliable both in theory and for practically obtaining extraction information. Additionally, the WV-3 data are well suited for identifying hydroxy-bearing alterations with rich short-wave infrared bands that distinguish Fe-OH–bearing alterations from Mg-OH–bearing alterations. The results obtained were applied to identify potential targets for skarn-type copper deposits and the implementation of prospecting practices. Discussion: This study provides a basis for the application of WV-3 data as an important and effective tool for alteration information extraction and determination of prospecting practice, thereby proving the validity of multispectral remote sensing images in mineral resource exploration.
GestureTeach: A gesture guided online teaching interactive model

Abstract Online education has become more popular and effective due to the availability of high‐speed internet and technological innovations, which allow people from different locations to access educational resources and opportunities. However, online classes often face challenges such as limited interactivity and display options, which can affect the quality and effectiveness of the online learning experience. In this article, we propose GestureTeach, a new pedagogical paradigm that enables free handwriting interaction and animation generation for online teaching. GestureTeach uses gestures as a natural and intuitive way of interaction, which enhances the teacher's intention expression and the student's engagement. GestureTeach also generates animations from handwritten sketches, which improves the display effects of the interaction and the student's knowledge comprehension. We conducted a two‐stage study with 15 teachers and 90 students to evaluate the effectiveness of GestureTeach in facilitating classroom interaction. The results show that GestureTeach is preferred by both teachers and students over traditional online teaching methods and has the potential to transform the online teaching landscape by providing a seamless and interactive experience.
Dynamic Dark Channel Prior Dehazing with Polarization

For traditional dark channel prior (DCP) imaging through haze environments, intensity information acts as the carrier to acquire the reflective character of the dehazed target image. We introduce polarization as auxiliary information into the traditional DCP dehazed process for achieving better imaging performance that can improve target detection or target tracking. In this paper, a polarization imaging system with a split-amplitude structure is designed to enable real-time polarization acquisition of transient scenes. The experimental results show that besides descattering, the proposed method can also simultaneously obtain both light intensity and polarization information from the target. Compared with the traditional DCP method, which only utilizes intensity information as a carrier, a combination of intensity and polarization in the proposed method can effectively detect the target hidden in the background with similar reflectivity. Our investigation has potential application value for optical imaging in scattering environments.