Reviewerly/exHarmony huggingface dataset:
An Accuracy Enhanced Vision Language Grounding Method Fused with Gaze Intention

Visual grounding aims to recognize and locate the target in the image according to human intention, which provides a new intelligent interaction idea and method for augmented reality (AR) and virtual reality (VR) devices. However, existing vision language grounding adopts language modals for visual grounding, but it performs ineffectively for images containing multiple similar objects. Gaze interaction is an important interaction mode in AR/VR devices, and it provides an advanced solution to the inaccurate vision language grounding cases. Based on the above questions and analysis, a vision language grounding framework fused with gaze intention is proposed. Firstly, we collect the manual gaze annotations using the AR device and construct a novel multi-modal dataset, RefCOCOg-Gaze, combining it with the proposed data augmentation methods. Secondly, an attention-based multi-modal feature fusion model is designed, providing a baseline framework for vision language grounding with gaze intention (VLG-Gaze). Through a series of precisely designed experiments, we analyze the proposed dataset and framework qualitatively and quantitatively. Comparing with the state-of-the-art vision language grounding model, our proposed scheme improves the accuracy by 5.3%, which indicates the significance of gaze fusion in multi-modal grounding tasks.
Neuromorphic Photonics Circuits: Contemporary Review

Neuromorphic photonics is a cutting-edge fusion of neuroscience-inspired computing and photonics technology to overcome the constraints of conventional computing architectures. Its significance lies in the potential to transform information processing by mimicking the parallelism and efficiency of the human brain. Using optics and photonics principles, neuromorphic devices can execute intricate computations swiftly and with impressive energy efficiency. This innovation holds promise for advancing artificial intelligence and machine learning while addressing the limitations of traditional silicon-based computing. Neuromorphic photonics could herald a new era of computing that is more potent and draws inspiration from cognitive processes, leading to advancements in robotics, pattern recognition, and advanced data processing. This paper reviews the recent developments in neuromorphic photonic integrated circuits, applications, and current challenges.
The impact of ISO/TC 37 standards on technical communication

Abstract Rather than just profiting from digitalization efforts such as the implementation and improvement of automated content delivery processes, technical communication itself currently has the opportunity of becoming a major driver in digitalization. The essence of technical communicators’ work is to create representations of products under the aspect of product use. In order to convey how a product functions and how it shall be used, they build concepts that reflect its structure and the structure of the domain to which it belongs. These concepts and their interrelations represent the underlying architecture of information that is rendered into ‘technical documentation’ in the course of the information development process which parallels the product development process. To harmonize concepts for information on products to be launched and in its proper domain, as well as to ensure interoperability between digital resources, technical communication relies on a range of standards created within and published by ISO Technical Committee 37, Language and Terminology ( ISO/TC 37 ).
DGW‐YOLOv8: A small insulator target detection algorithm based on deformable attention backbone and WIoU loss function

Abstract The YOLO series of algorithms have made substantial contributions to the detection of insulator defects in power transmission line operations. However, existing target detection algorithms for the small target detection and low‐quality insulator images encounter difficulties in effectively capturing relevant features, resulting in a higher probability of target loss. To identify and classify defects in the operational state of insulators, an improved YOLOv8 target identification algorithm called DGW‐YOLOv8 is proposed in this paper. The deformable attention backbone of the DGW‐YOLOv8 target identification algorithm is designed by adding the deformable ConvNets v2 module and the global attention mechanism. This addition reduces the feature loss caused by the network feature processing, enhances the sensitivity of the algorithm to small‐scale targets, and reduces the impact caused by the different global positions of the targets. Additionally, to address the problem of low quality of captured images, WIoU v3 is used to replace CIoU in the original YOLOv8 target identification algorithm to optimize the loss function, reduce the degrees of freedom, and improve the network robustness. Experimental results demonstrate that the enhanced YOLOv8 algorithm can achieve an improvement of 2.4% and 5.5% in mAP and mAP50‐95, respectively, compared with the original algorithm.
Unsupervised image blind super resolution via real degradation feature learning

Abstract In recent years, many methods for image super‐resolution (SR) have relied on pairs of low‐resolution (LR) and high‐resolution (HR) images for training, where the degradation process is predefined by bicubic downsampling. While such approaches perform well in standard benchmark tests, they often fail to accurately replicate the complexity of real‐world image degradation. To address this challenge, researchers have proposed the use of unpaired image training to implicitly model the degradation process. However, there is a significant domain gap between the real‐world LR and the synthetic LR images from HR, which severely degrades the SR performance. A novel unsupervised image‐blind super‐resolution method that exploits degradation feature‐based learning for real‐image super‐resolution reconstruction (RDFL) is proposed. Their approach learns the degradation process from HR to LR using a generative adversarial network (GAN) and constrains the data distribution of the synthetic LR with real degraded images. The authors then encode the degraded features into a Transformer‐based SR network for image super‐resolution reconstruction through degradation representation learning. Extensive experiments on both synthetic and real datasets demonstrate the effectiveness and superiority of the RDFL method, which achieves visually pleasing reconstruction results.
Prototypical Network with Residual Attention for Modulation Classification of Wireless Communication Signals

Automatic modulation classification (AMC) based on data-driven deep learning (DL) can achieve excellent classification performance. However, in the field of electronic countermeasures, it is difficult to extract salient features from wireless communication signals under scarce samples. Aiming at the problem of modulation classification under scarce samples, this paper proposes a few-shot learning method using prototypical network (PN) with residual attention (RA), namely PNRA, to achieve the AMC. Firstly, the RA is utilized to extract the feature vector of wireless communication signals. Subsequently, the feature vector is mapped to a new feature space. Finally, the PN is utilized to measure the Euclidean distance between the feature vector of the query point and each prototype in this space, determining the type of the signals. In comparison to mainstream few-shot learning (FSL) methods, the proposed PNRA can achieve effective and robust AMC under the data-hungry condition.
A Secure and Efficient Dynamic Analysis Scheme for Genome Data within SGX-Assisted Servers

With the rapid development of the Internet of Things (IoT), more and more user devices access the network and generate large amounts of genome data. These genome data possess significant medical value when researched. However, traditional genome analysis confronts security and efficiency challenges, including access pattern leakage, low efficiency, and single analysis methods. Thus, we propose a secure and efficient dynamic analysis scheme for genome data within a Software Guard Extension (SGX)-assisted server, called SEDASGX. Our approach involves designing a secure analysis framework based on SGXs and implementing various analysis methods within the enclave. The access pattern of genome data is always obfuscated during the analysis and update process, ensuring privacy and security. Furthermore, our scheme not only achieves higher analysis efficiency but also enables dynamic updating of genome data. Our results indicate that the SEDASGX analysis method is nearly 2.5 times more efficient than non-SGX methods, significantly enhancing the analysis speed of large-scale genome data.
Cross-Chain Asset Transaction Method Based on Ring Signature for Identity Privacy Protection

In recent years, the rapid development of blockchain technology has facilitated the transfer of value and asset exchange between different blockchains. However, achieving interoperability among various blockchains necessitates the exploration of cross-chain technology. While cross-chain technology enables asset flow between different blockchains, it also introduces the risk of identity privacy leakage, thus posing a significant threat to user security. To tackle this issue, this article proposes a cross-chain privacy protection scheme that leverages ring signature and relay chain technology. Specifically, this scheme utilizes RCROSS contracts based on ring signatures to handle cross-chain transactions, thereby ensuring the privacy of both parties involved in the transaction. This cross-chain solution demonstrates practicality and efficiency in facilitating cross-chain asset trading. Furthermore, it effectively combats reuse attacks and man-in-the-middle attacks at the application layer while also providing resistance against denial-of-service attacks at the network layer. To validate the proposed cross-chain solution, we conducted tests by constructing a specific cross-chain scenario and by focusing on the natural gas consumption values generated by the RCROSS contract function used in the application chain. The findings indicate that our proposed solution is highly practical in safeguarding the identity privacy of transaction participants. This article’s framework guarantees reliability, security, and efficiency in cross-chain asset transactions. By incorporating ring-based signatures and relay chain technology, users can confidently protect their identity privacy, thus ensuring secure and smooth cross-chain transactions.
Tweeting fallacies

Abstract The fallacy approach to argument pedagogy has been criticized as being overtly critical, theoretically defective and encouraging an adversarial attitude. In order to solve some of those issues, the effects of fallacy teaching on the arguer’s behavior should be studied empirically. Here I present an exploratory study in which I take a look at how accusations of fallacies are made on Twitter. 865 accusations were analyzed according to seven criteria: (1) whether the fallacy is identified, (2) whether it is mis identified, (3) whether the accusation was substantiated, (4) whether the substantiation makes reference to the context, (5) whether the accuser relies on the “taxonomic technique”, (6) whether the accuser relies on a problematic theory, and (7) whether the accuser is willing to discuss the accusation. Both the findings of the study and the reliability of the criteria are discussed.
Enhanced edge detection model for low resolution images

The technique of determining the borders between several objects or regions in an image is known as edge detection. The edges of an object in an image serve as the object’s limits and can reveal crucial details about the object’s size, shape, and position. The pre-processing stage of edge detection is crucial because it can increase the precision and effectiveness of edge detection algorithms. As low-density or low-pixel values muddy the image, detecting edges in low-resolution images is difficult. This paper aims to introduce LRED, an improved edge detection model for low-resolution images based on Gaussian smoothing. Also used for image pre-processing and smoothing is the Gaussian filter. The Gaussian smoothing method works well for spotting edges in images. Additionally, we have presented a comprehensive comparison of our proposed approach with three modern, cutting-edge detection approaches and algorithms. Investigations have been conducted on several images in addition to low-quality images to discover edges. RMSE and PSNR are two different evaluation metrics used to measure proposed methods. LRED achieved 90.25% MSE, which is slightly better than the other three approaches which show more reliable outcomes.
A secure blockchain framework for the internet of medical things

Abstract Technological advancements in the Internet of Medical Things (IoMT) enable medical services to provide incredibly challenging real‐time functions. The proliferation of health sensors has improved patient monitoring, both for patient care and medical purposes, giving professionals more data for early diagnosis and treatment recommendations. Privacy and security are critical considerations for collecting and transferring data from these heterogeneous sensors across the database and the IoT network. This study leverages blockchain technology to improve Electronic Health Record (EHR) privacy and security. This article proposes a novel decentralized architecture using IoT and Blockchain technology for storing EHR and monitoring real‐time patient data. The proposed patient‐centric framework and architecture offer a straightforward solution that fulfills data integrity and anonymity and meets the data interoperability demands. Self‐contained encryption–decryption and primitive cryptographic techniques enhance the system's security. The proposed study models IoT health sensors such as body temperature, pulse rate, spO2, and Electromyogram (EMG) in a blockchain‐based IoT framework. The proposed system has been evaluated to determine that it can process and store Remote Patient Monitoring (RPM) data with encouraging results. The article provides performance, security, and threat analysis to contribute toward a robust, secure, and smart healthcare system using metrics such as latency, throughput, IoT sensor activity, and resource usage. Experimental results present that the proposed method beats the standard healthcare system regarding service quality and patient data monitoring.
A machine learning approach for quantifying age-related histological changes in the mouse kidney

Abstract The ability to quantify aging-related changes in histological samples is important, as it allows for evaluation of interventions intended to effect health span. We used a machine learning architecture that can be trained to detect and quantify these changes in the mouse kidney. Using additional held out data, we show validation of our model, correlation with scores given by pathologists using the Geropathology Research Network aging grading scheme, and its application in providing reproducible and quantifiable age scores for histological samples. Aging quantification also provides the insights into possible changes in image appearance that are independent of specific geropathology-specified lesions. Furthermore, we provide trained classifiers for H&amp;E-stained slides, as well as tutorials on how to use these and how to create additional classifiers for other histological stains and tissues using our architecture. This architecture and combined resources allow for the high throughput quantification of mouse aging studies in general and specifically applicable to kidney tissues.
Aspects of Forming Spiritual and Moral Values in the Process of Socializing Children and Youth

This research delves into the intricate exploration of shaping spiritual and moral values during the socialization process of children and youth. The study meticulously examines the multifaceted factors influencing the development of these values, including family dynamics, educational influences, media impact, religious exposure, and societal institutions. Beyond analysis, the paper extends practical recommendations tailored for parents, educators, and community leaders, emphasizing the cultivation of spiritual and moral aspects in the context of family values, ethical upbringing, and active participation in spiritual and communal activities. The findings carry practical implications for the establishment of a harmonious and ethically conscious society. Educators and students can leverage the insights for the development and study of developmental and age psychology courses, while practical psychologists can incorporate them into programs for the psycho-pedagogical support of adolescents.
Studying the Level of Components of Professional and Pedagogical Preparation of Primary Education Teachers

As part of the experimental work, a plan-program was developed for increasing the level of professional and pedagogical preparedness of primary education teachers and a plan-program for a continuous study-seminar based on a systematic and practical approach. The plan-program is designed to solve a number of problems: the adoption by primary education teachers of the ideology of the new State educational system of school general education; mastering more effective methods of digitalization technology, pedagogical technology, methods of educational and cognitive activities aimed at achieving the intended results for students; teachers’ mastery of the modern system of requirements for objective assessment of students’ activities. After organizing a continuously ongoing study-seminar, planning and conducting the educational process using a set of digitalization and software tools, reflection was carried out on the professional and pedagogical activities of primary education teachers. The educational and cognitive system of experimental work provided for the creation of pedagogical prerequisites, which are: the reality of the needs of primary education teachers in restructuring their professional and pedagogical activities by increasing the level of preparedness using an accessible information and educational environment; educational and cognitive support of pedagogical activities, carried out through continuous study-seminars; providing direct assistance to teachers in planning and conducting the educational and cognitive process; active participation in activities for objective self-assessment based on the activation of one’s reflection.
The Formation of Ethnopedagogical Local History as a Means of Professional Training of Future Teachers

In the presented article, based on the works of scientific teachers, the socio-cultural and scientific-pedagogical prerequisites for introducing future teachers to the living sources of folk pedagogy are identified. Separate historical approaches to the organization of ethnocultural local history are described. The author highlights the theoretical, methodological and practice-oriented functions of ethnopedagogical local history. The main directions, contents and methods of organizing search, research, and self-educational activities of students in the process of ethnopedagogical local history in villages are described. The experience and positive results of organizing ethnopedagogical local history at the Pedagogical Faculty of Osh State University are summarized. Specific examples show how students expanded and deepened the knowledge of folk pedagogy acquired in the process of studying pedagogical disciplines. The multifunctionality of ethnopedagogical local history reflects the integrative nature of ethnopedagogical and local history knowledge. In conclusion, the Author comes to the conclusion that ethnopedagogical local history in the future can function as a full-fledged type of pedagogical practice.
Sewing-Driving Training Optimization-Enabled Feature Selection and Hand Gesture Recognition with Deep Residual Network using Semg Signal

An innovation of an accurate model along with low latency for hand gesture identification is still a huge challenge especially for rehabilitation. This research provides a unified solution for such limitations by constructing an efficient system for hand gesture recognition by employing designed Sewing Driving Training based Optimization (SDTO)_Deep Residual Network (DRN). In this work, pre-processing is done using Gaussian filtering, whereas feature selection is carried out based on SDTO. In order to attain efficient results, appropriate features are extracted at the feature extraction phase. The last step is the hand gesture recognition phase, which is accomplished effectively using DRN and the network is efficiently trained using designed SDTO and it is achieved by the integration of STBO and DTBO. The introduced SDTO_DRN model for hand gesture recognition has attained high accuracy of 0.943, TPR of 0.929, TNR of 0.919, PPV of 0.924, and NPV of 0.924.
Scalable Implementation of Temporal and Phase Encoding QKD with Phase‐Randomized States

Abstract Quantum key distribution (QKD), that is, exchanging cryptographic keys encoded in quantum particles exploiting the laws of quantum physics, is already a reality in our society. Current implementations are based on attenuated laser technique, a practical replacement of single photons which requires a random phase for each quantum state in order to achieve the highest level of security. In particular, the time‐bin and phase encoding techniques are mainly exploiting laser in gain‐switching modes combined with asymmetric interferometers or multiple laser sources in a master–slave configuration, which present limitations in terms of stability and scalability. In this work, a novel scheme for implementing a reconfigurable and scalable QKD transmitter based on the time‐bin encoding protocol with a decoy‐state method employing phase‐randomized weak coherent states is proposed and demonstrated. The scheme is tested and validated up to 26 dB‐attenuation channel using standard single‐photon detectors working in the telecom wavelength range.
Optimisation of deep neural network model using Reptile meta learning approach

Abstract The artificial intelligence (AI) within the last decade has experienced a rapid development and has attained power to simulate human‐thinking in various situations. When the deep neural networks (DNNs) are trained with huge dataset and high computational resources it can bring out great outcomes. But the learning process of DNN is very much complicated and time‐consuming. In various circumstances, where there is a data‐scarcity, the algorithms are not capable of learning tasks at a faster rate and perform nearer to that of human intelligence. With advancements in deep meta‐learning in several research studies, this problem has been dealt. Meta‐learning has outspread range of applications where the meta‐data (data about data) of the either tasks, data or the models which were previously trained can be employed to optimise the learning. So in order to get an insight of all existing meta‐learning approaches for DNN model optimisation, the authors performed survey introducing different meta‐learning techniques and also the current optimisation‐based approaches, their merits and open challenges. In this research, the Reptile meta‐learning algorithm was chosen for the experiment. As Reptile uses first‐order derivatives during optimisation process, hence making it feasible to solve optimisation problems. The authors achieved a 5% increase in accuracy with the proposed version of Reptile meta‐learning algorithm.