Reviewerly/exHarmony huggingface dataset:
Machine Learning Model for Joint Semantic and Channel Coding/Decoding in Wireless Systems

AbstractWith a significant increase in the data and demand for large bandwidth, the wireless research community has identified the need to pay attention to transmitting the context rather than focusing on only the manner of transmission. Inspired by this, in the current article a semantic wireless system enabled by deep learning is proposed which aims to maximize the system capacity. As opposed to evaluating only the bit/symbol errors, the proposed technique is able to recover the meaning of sentences and is hence able to minimize the semantic errors. Furthermore, transfer learning is implemented to accelerate the process of re-training. Extensive simulations to validate the performance of the proposed technique demonstrate that it is able to (i) maintain enhanced robustness to channel fluctuations and (ii) achieve higher performance. Overall, the current study makes it evident that the proposed technique is a good candidate for implementation in semantic wireless systems.KEYWORDS: Deep learningdeep neural networkssemantic communicationtransfer learningwireless systems Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsSridhar IyerSridhar Iyer (Senior Member IEEE) received the MS degree in electrical engineering from NMSU, U.S.A in 2008, and the PhD degree from Delhi University, India in 2017. He received the Young Scientist Award from DST/SERB, Govt. of India in 2013, and Young Researcher Award from the Institute of Scholars in 2021. He is the recipient of the “Protsahan Award” from IEEE ComSoc, Bangalore Chapter in 2021 and 2022. His current research focus includes semantic communications and spectrum enhancement techniques for 6G wireless networks. Currently, he serves as a professor in the dept. of CSE(AI), KLE Technological University, Dr MSSCET, Belagavi, Karnataka, India. Corresponding author. Email: sridhariyer1983@klescet.ac.in.
<scp>HECC‐ABE</scp>: A novel blockchain‐based <scp>IoT</scp> healthcare data storage using hybrid cryptography schemes with key optimization by hybrid <scp>meta‐heuristic</scp> algorithm

Abstract The most contemporary development in information technology is the Internet of Things (IoT), which integrates the digital world with the real world. It makes it possible for things and people to communicate via the Internet. Blockchain is one of the popular topics of interest right now, and it can be used in most IoT applications. The blockchain's salient characteristics, like decentralization, data integrity, confidentiality, protection, and openness, are the main reasons for implementing it in healthcare services. Hence, this paper introduces an effective approach for storing IoT‐based healthcare data storage in the blockchain. The procedures in this suggested paradigm are as follows: user authentication, user trust verification, and optimal key storage in the blockchain. Wearable sensors are inserted in or adhered to a patient's body and used by IoT networks to gather healthcare data. These details are supplied into the verification structure constructed by adaptive dilated long short term memory with attention network (AD‐LSTM‐AN). Once the user's authentication is confirmed, then the user's trust level is computed. The same AD‐LSTM‐AN‐based verification structure is used to verify the historical data that has been stored and retrieved by the user, together with their previous transactions, in this phase. The healthcare data are then sent to the hybrid elliptic curve cryptography with attribute‐based encryption (HECC‐ABE) cryptography schemes for data encryption once the authentication of the person who attempts to store the data is confirmed. An adaptive final position‐based golden eagle‐Harris hawks optimization (AFP‐GEHHO) algorithm is used for optimizing the keys of the encrypted data. The blockchain platform stores the encrypted data of the authorized user with a high level of trust. The same process is done in reverse order whenever a user needs to recover information that is saved in the blockchain to retrieve the initially stored healthcare data. The effectiveness of secure data storage in blockchain is tested through experimental simulations. Throughout the analysis, the designed model achieves 96% in terms of accuracy. Thus, the developed model shows more secure and effective, has less error and reduces the time and memory requirements compared to existing approaches.
Interactive training in the organization of independent work of students in universities of Kazakhstan

The purpose of the article is to improve the methods and forms of teaching students, the formation of a scientifically based unified system. The training of qualified specialists at the university requires the use of new forms and methods of teaching. It is necessary to identify and improve the ways of education based on innovative pedagogical technologies. One of the new tasks of state strategies remains to increase the requirements for training specialists in the international education market and the active introduction of modern innovative technologies into the educational process, support for the promotion of scientific technology in higher education and medicine. The introduction of interactive learning technologies into the educational process, including the use of remote technologies with video content, the strengthening of the material and technical base for medical and technical specialties, the increase in the number of electronic reading rooms, the organization of research work, continues widely. It should be noted that modern innovative technologies are widely used not only in the educational process, but also in the training of competitive young professionals who are in high demand on the international market. Effective ways of education are determined at the level of knowledge using various methods. Today, along with the basic teaching methods formed in the learning process, new methods are often used. The assessment of the level of knowledge of students is determined by various teaching methods.
Sarcastic emojis: why don’t you understand?

Purpose Technology-enabled communication used in workplace settings includes nuanced tools such as emojis, that are interpreted differently by different populations of people. This paper aims to evaluate the use of emojis in work environments, particularly when they are used sarcastically. Design/methodology/approach This research uses a survey method administered on MTurk. Overall, 200 participants were included in the analysis. Items were contextualized from prior research and offered on a seven-point Likert scale. Findings Females are better able to understand if an emoji is used sarcastically. Additionally, older employees are more capable of interpreting sarcasm than younger employees. Finally, understanding of emojis has a negative relationship with frustration, indicating that when users understand emojis are being used sarcastically, frustration is reduced. Research limitations/implications This research is primarily limited by the survey methodology. Despite this, it provides implications for theory of mind and practical understanding of emoji use in professional settings. This research indicates emojis are often misinterpreted in professional settings. Originality/value The use of emojis is becoming commonplace. The authors show the use of emojis in a professional setting creates confusion, and in some instances can lead to frustration. This work can help businesses understand how best to manage employees with changing communication tools.
Arbitrary‐oriented traffic participant detection and axis prediction for complex crossing road in aerial view

Abstract Different from object detection in natural images, aerial‐view object detection faces special challenges with large changes in object orientation and wide multi‐scale distribution. Many methods based on Oriented Bounding Boxes (OBB) can reach accurate results, yet may face the problem of parameter mutation and axis prediction deviation. To address these problems, a two‐stage detection framework is proposed for arbitrary‐oriented traffic participant detection and axis prediction, named Axis Prediction Network. First, a Deformable Convolution Fusion Module (DCF‐Module) is proposed to enhance the ability of FPN to extract multi‐scale semantic features, for dealing with the multi‐scale change of objects. Then, the axis heat‐map prediction head network is proposed to fit the long axis of oriented objects labeled with Gaussian model, which uses pixel‐by‐pixel prediction heatmap to calculate the long axis of the object, avoiding the angle mutation of an OBB. Last, the long and short side prediction head network is proposed to predict the shape of an oriented object, avoiding the mutation of the width and height of an OBB. The experiments are conducted on the new built Crossroad dataset and the public DOTA dataset. Experiment results show that the proposed method achieve good performance in arbitrary‐oriented traffic participant detection and axis prediction in aerial view.
A continuous time model for Karnatic flute music synthesis

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Strong and Weak Supervision Combined with CLIP for Water Surface Garbage Detection

Water surface garbage has a significant impact on the protection of water environments and ecological balance, making water surface garbage object detection a critical task. Traditional supervised object detection methods require a large amount of annotated data. To address this issue, we propose a method that combines strong and weak supervision with CLIP (Contrastive Language–Image Pretraining) for water surface garbage object detection. First, we train on a dataset annotated with strong supervision, using traditional object detection algorithms to learn the location information of water surface garbage. Then, we input the water surface garbage images into CLIP’s visual encoder to obtain visual feature representations. Simultaneously, we train CLIP’s text encoder using textual description annotations to obtain textual feature representations of the images. By fusing the visual and textual features, we obtain comprehensive feature representations. In the weak supervision training phase, we input the comprehensive feature representations into the object detection model and employ a training strategy that combines strong and weak supervision to detect and localize water surface garbage. To further improve the model’s performance, we introduce attention mechanisms and data augmentation techniques to enhance the model’s focus and robustness towards water surface garbage. By conducting experiments on two water surface garbage datasets, we validate the effectiveness of the proposed method based on the combination of strong and weak supervision with CLIP for water surface garbage object detection tasks.
Attention Mechanism Used in Monocular Depth Estimation: An Overview

Monocular depth estimation (MDE), as one of the fundamental tasks of computer vision, plays important roles in downstream applications such as virtual reality, 3D reconstruction, and robotic navigation. Convolutional neural networks (CNN)-based methods gained remarkable progress compared with traditional methods using visual cues. However, recent researches reveal that the performance of MDE using CNN could be degraded due to the local receptive field of CNN. To bridge the gap, various attention mechanisms were proposed to model the long-range dependency. Although reviews of MDE algorithms based on CNN were reported, a comprehensive outline of how attention boosts MDE performance is not explored yet. In this paper, we firstly categorize recent attention-related works into CNN-based, Transformer-based, and hybrid (CNN–Transformer-based) approaches in the light of how the attention mechanism impacts the extraction of global features. Secondly, we discuss the details and contributions of attention-based MDE methods published from 2020 to 2022. Then, we compare the performance of the typical attention-based methods. Finally, the challenges and trends of the attention mechanism used in MDE are discussed.
A Local Information Perception Enhancement–Based Method for Chinese NER

Integrating lexical information into Chinese character embedding is a valid method to figure out the Chinese named entity recognition (NER) issue. However, most existing methods focus only on the discovery of named entity boundaries, considering only the words matched by the Chinese characters. They ignore the association between Chinese characters and their left and right matching words. They ignore the local semantic information of the character’s neighborhood, which is crucial for Chinese NER. The Chinese language incorporates a significant number of polysemous words, meaning that a single word can possess multiple meanings. Consequently, in the absence of sufficient contextual information, individuals may encounter difficulties in comprehending the intended meaning of a text, leading to the emergence of ambiguity. We consider how to handle the issue of entity ambiguity because of polysemous words in Chinese texts in different contexts more simply and effectively. We propose in this paper the use of graph attention networks to construct relatives among matching words and neighboring characters as well as matching words and adding left- and right-matching words directly using semantic information provided by the local lexicon. Moreover, this paper proposes a short-sequence convolutional neural network (SSCNN). It utilizes the generated shorter subsequence encoded with the sliding window module to enhance the perception of local information about the character. Compared with the widely used Chinese NER models, our approach achieves 1.18%, 0.29%, 0.18%, and 1.1% improvement on the four benchmark datasets Weibo, Resume, OntoNotes, and E-commerce, respectively, and proves the effectiveness of the model.
Ship Detection via Multi-Scale Deformation Modeling and Fine Region Highlight-Based Loss Function

Ship detection in optical remote sensing images plays a vital role in numerous civil and military applications, encompassing maritime rescue, port management and sea area surveillance. However, the multi-scale and deformation characteristics of ships in remote sensing images, as well as complex scene interferences such as varying degrees of clouds, obvious shadows, and complex port facilities, pose challenges for ship detection performance. To address these problems, we propose a novel ship detection method by combining multi-scale deformation modeling and fine region highlight-based loss function. First, a visual saliency extraction network based on multiple receptive field and deformable convolution is proposed, which employs multiple receptive fields to mine the difference between the target and the background, and accurately extracts the complete features of the target through deformable convolution, thus improving the ability to distinguish the target from the complex background. Then, a customized loss function for the fine target region highlight is employed, which comprehensively considers the brightness, contrast and structural characteristics of ship targets, thus improving the classification performance in complex scenes with interferences. The experimental results on a high-quality ship dataset indicate that our method realizes state-of-the-art performance compared to eleven considered detection models.
Improving Dimensionality Reduction Projections for Data Visualization

In data science and visualization, dimensionality reduction techniques have been extensively employed for exploring large datasets. These techniques involve the transformation of high-dimensional data into reduced versions, typically in 2D, with the aim of preserving significant properties from the original data. Many dimensionality reduction algorithms exist, and nonlinear approaches such as the t-SNE (t-Distributed Stochastic Neighbor Embedding) and UMAP (Uniform Manifold Approximation and Projection) have gained popularity in the field of information visualization. In this paper, we introduce a simple yet powerful manipulation for vector datasets that modifies their values based on weight frequencies. This technique significantly improves the results of the dimensionality reduction algorithms across various scenarios. To demonstrate the efficacy of our methodology, we conduct an analysis on a collection of well-known labeled datasets. The results demonstrate improved clustering performance when attempting to classify the data in the reduced space. Our proposal presents a comprehensive and adaptable approach to enhance the outcomes of dimensionality reduction for visual data exploration.
Sistemnye problemy obrazovaniia v Rossii i napravleniia ikh resheniia

Based on the application of the methodology of the system approach, the chapter identifies such problems of the education sphere as insufficient quality of goal-setting, finding consensus among stakeholders, perception of participants of relations in education and other subjects of influence on its development as systemic phenomena, the need to specify models (portraits) of graduates as the ultimate goals of life activity of the education system, measuring their qualities and others.
Adversarial Attack Defense Method for a Continuous-Variable Quantum Key Distribution System Based on Kernel Robust Manifold Non-Negative Matrix Factorization

Machine learning has been applied in continuous-variable quantum key distribution (CVQKD) systems to address the growing threat of quantum hacking attacks. However, the use of machine learning algorithms for detecting these attacks has uncovered a vulnerability to adversarial disturbances that can compromise security. By subtly perturbing the detection networks used in CVQKD, significant misclassifications can occur. To address this issue, we utilize an adversarial sample defense method based on non-negative matrix factorization (NMF), considering the nonlinearity and high-dimensional nature of CVQKD data. Specifically, we employ the Kernel Robust Manifold Non-negative Matrix Factorization (KRMNMF) algorithm to reconstruct input samples, reducing the impact of adversarial perturbations. Firstly, we extract attack features against CVQKD by considering the adversary known as Eve. Then, we design an Artificial Neural Network (ANN) detection model to identify these attacks. Next, we introduce adversarial perturbations into the data generated by Eve. Finally, we use the KRMNMF decomposition to extract features from CVQKD data and mitigate the influence of adversarial perturbations through reconstruction. Experimental results demonstrate that the application of KRMNMF can effectively defend against adversarial attacks to a certain extent. The accuracy of KRMNMF surpasses the commonly used Comdefend method by 32.2% and the JPEG method by 30.8%. Moreover, it exhibits an improvement of 20.8% compared to NMF and outperforms other NMF-related algorithms in terms of classification accuracy. Moreover, it can complement other defense strategies, thus enhancing the overall defensive capabilities of CVQKD systems.
Assessing Alzheimer’s Therapeutic Environment Digitally through a People with Alzheimer’s’ Disease Perspective: A Computation-Based Approach Framework

People with Alzheimer’s disease (PWAD) are impacted by their surroundings, and their performance improves in therapeutic environments designed to meet their specific individual needs, are adjustable in terms of their health status, and are created to accommodate their abilities. A literature review of the field revealed scarce knowledge in using a combination of building information modeling (BIM) and the Internet of Things (IoT) for the purpose of understanding the daily needs and self-orientation ability of PWAD, as well as the architectural barriers they face in their rooms in long-term healthcare centers. In this context, this paper proposes a framework based on computational design approaches to assess the existing therapeutic environment for PWAD using BIM–IoT sensors-based monitoring. The proposed framework used the user experience design concept (UX) and the design thinking framework to evaluate the resident rooms of PWAD. The UX design concept and the design thinking framework core allow for the adoption of user-centered methods to provide a comprehensive image of the issues that affect PWAD in their therapeutic environment. The proposed framework-structured approach will enable healthcare architects/designers to (1) digitalize old building architecture plans using BIM; (2) strategize IoT sensor selection; (3) recognize the activities performed by PWAD and detect any anomaly; and (4) integrate IoT real-time data into the BIM system. The proposed framework supports three types of professionals: (1) architects in decision-making processes, (2) researchers in collecting/analyzing accurate data for shadow observations, and (3) neurologists in following up the health statuses of PWAD.
Sp2PS: Pruning Score by Spectral and Spatial Evaluation of CAM Images

CNN models can have millions of parameters, which makes them unattractive for some applications that require fast inference times or small memory footprints. To overcome this problem, one alternative is to identify and remove weights that have a small impact on the loss function of the algorithm, which is known as pruning. Typically, pruning methods are compared in terms of performance (e.g., accuracy), model size and inference speed. However, it is unusual to evaluate whether a pruned model preserves regions of importance in an image when performing inference. Consequently, we propose a metric to assess the impact of a pruning method based on images obtained by model interpretation (specifically, class activation maps). These images are spatially and spectrally compared and integrated by the harmonic mean for all samples in the test dataset. The results show that although the accuracy in a pruned model may remain relatively constant, the areas of attention for decision making are not necessarily preserved. Furthermore, the performance of pruning methods can be easily compared as a function of the proposed metric.
A Denoising Scheme for Scanned Wood Grain Images via Adaptive Color Substitution

Real-world image denoising is a challenging problem in low-level vision. In order to reduce the luminance noise on scanned wood grain images randomly generated by the Microtek Phantom 9900XL scanner, the images were classified and sorted according to the noise size. The proposed denoising scheme reduces noise by substituting dissimilar pixels within a certain window size. The No.1 to No. 6 wood images with noise size of approximately (or no greater than) 3 pixels × 3 pixels were processed using coarse denoising with a 7 × 7 window (α = 100, β = 30), fine denoising with a 5 × 5 window (α = 90, β = 40), and the Dust &amp; Scratches filter at settings of 1 (pixels) and 35 (levels). The No.7 to No. 16 wood images with noise size of approximately (or no greater than) 1 pixel × 1 pixel were processed using fine denoising with a 5 × 5 window (α = 100, β = 30), and the Dust &amp; Scratches filter at settings of 1 (pixel) and 35 (levels). The proposed Scheme I and II was then compared with Wiener filtering, Gaussian filtering, median filtering, and the Dust &amp; Scratches filter under designated settings. The results of subjective and objective evaluations demonstrated that the proposed Scheme outperformed the above denoising methods on reducing the luminance noise. When using the median values of R (red), G (green), and B (blue) channels within a certain window to substitute the R, G, and B values of the luminance noise, the denoising ranges of α≥100 and β≤30 were suitable for the No.1 to No.16 wood images.