Reviewerly/exHarmony huggingface dataset:
SIX-Trust for 6G: Toward a Secure and Trustworthy Future Network

Recent years have witnessed a digital explosion in the deployment of 5G and the proliferation of 5G-enabled innovations. Compared with 5G, 6G is envisioned to achieve a much higher performance and experience a number of paradigm shifts, such as exploiting new spectrum, applying ubiquitous Machine Learning and Artificial Intelligence (ML/AI) technologies and building a space-air-ground-sea integrated network. However, these paradigm shifts may lead to numerous new security and privacy issues, that traditional security measures may not be able to address. Moreover, the expected high performance of 6G also challenges network reliability and energy efficiency. To tackle these issues and build a trustworthy 6G network, we introduce a novel trust framework called SIX-Trust. This framework is composed of three layers with an emphasis on distinct aspects: sustainable trust (S-Trust), with a particular focus on trust in applications of AI, novel trust evaluation methods and modeling of trust relationships; infrastructure trust (I-Trust), which is more focused on the trustworthiness of network infrastructure; and xenogenesis trust (X-Trust) paying special attention to the core technologies which form the backbone of 6G trust. Besides, the importance of each layer varies under different application scenarios of 6G. For each layer, we briefly introduce its related enabling technologies, and demonstrate how these technologies can be applied to enhance the trust and security of the 6G network. Finally, a use case is illustrated and analyzed. In general, SIX-Trust provides a holistic framework for defining and modeling trust in 6G, which can facilitate establishment of a trustworthy 6G network.
A Deep Learning Framework for the Classification of Brazilian Coins

In this quickly developing world, automatic currency identification and recognition are crucial tasks. Several financial institutions, such as banks and hardware-based devices such as vending machines and slot machines, play an essential role in all monetary unification fields. Accurate coin recognition is essential in various contexts, including vending machines, currency exchange, and archaeological research. However, the distinctive visual characteristics of Brazilian coins, including variations in size, color, and design, pose significant challenges for automated classification. Most of the existing currency recognition systems are based on the physical properties of the currencies, such as length, breadth, and mass. At the same time, image-based methods rely on other properties like color, shape, and edge. This paper presents a novel deep-learning framework tailored to classify Brazilian coins. Our proposed deep learning framework leverages state-of-the-art convolutional neural networks (CNNs) to address these challenges. We introduce a Repetitive Feature Extractor Convolution Neural Network (RFE-CNN) model to recognize the currency faster and accurately. Our framework employs a multi-stage approach for coin classification. First, a pre-processing module handles coin localization and image enhancement to mitigate variations in lighting and background. Next, an RFE-CNN-based feature extractor extracts discriminative features from the coin images. We explore transfer learning from pre-trained models to enhance the model’s generalization capability, given limited data availability. We used a comprehensive dataset of Brazilian coins, comprising various denominations, minting years, and conditions, to facilitate model training and evaluation. The dataset includes high-resolution images captured under diverse lighting and environmental conditions, ensuring robust model performance in real-world scenarios. In conclusion, our proposed deep learning framework offers a powerful and efficient solution for classifying Brazilian coins. The framework’s adaptability makes it a valuable tool for recognizing coins from other regions with similar visual diversity and variability challenges. The proposed model has achieved a classification accuracy of 98.34% for the classification of Brazilian coins.
DF-YOLO: Highly Accurate Transmission Line Foreign Object Detection Algorithm

Ensuring the promptly removal of foreign objects from transmission lines is crucial for electricity safety. However, the existing object detection algorithms exhibit low precision and recall due to factors such as uncertainty in the type of foreign objects, imbalance of positive and negative samples, and the complexity of aerial photography backgrounds. Therefore, in this paper, an algorithm called DF-YOLO (Deformable Faster-You Only Look Once) for transmission line foreign object detection is proposed to resolve these problems. The algorithm is based on YOLOv7-Tiny and tailored to the dataset’s characteristics to achieve high precision along with excellent recall. The Focal-DIoU loss function is utilized to balance positive and negative sample proportions during training. Additionally, the algorithm incorporates the deformable convolution (DCN) module and the SimAM attention mechanism to enhance model performance, particularly in terms of foreign object recall and detection accuracy. Moreover, we optimized the network inference speed with the improved SPPCSPC_S-F module. Experimental results demonstrate that the improved DF-YOLO network achieves a 2.04% increase in mAP@.5 compared to the original YOLOv7-Tiny network. The recall rate also improves from 89% to 91.51%. Additionally, the inference speed of the network rises from 130 to 140 FPS, which enhances detection effectiveness and reduces the frequency of transmission line leaks triggered by foreign object incursion.
Joint Estimation of Unresolved Leader–Follower in the Presence of Dense False Signals Using Monopulse Radar

Monopulse radar accurately determines the target direction. However, multiple target echoes and dense false signals lead to unresolved targets and false detections. This makes target tracking challenging. The aircraft is equipped with a towed radar active decoy at a fixed distance, forming the leader-follower configuration. This study treats the aircraft and the decoy as a single target in the Bayesian state model. Subsequently, constraints from the configuration are employed to jointly estimate their angles. Monopulse measurements from the leader-follower configuration are a mixture of potentially unresolved measurement, dense false signals and background clutter, and they are modeled as target area measurements, state-dependent clutter and state-independent clutter, respectively. Subsequently, these different measurement types are combined using a random finite set approach, enabling a holistic tracking solution. The proposed method is implemented by particle filtering. In the simulations, the proposed method was compared with other methods and found to be efficient in tracking unresolved aircraft and towed decoy. The results indicate that the performance of the method improves as the follower-to-leader ratio increases within a certain range, highlighting its suitability for practical applications.
A Novel Attention-Based Model for Semantic Segmentation of Prostate Glands Using Histopathological Images

A foremost cause of death in males worldwide is prostate cancer. Its early identification, detection and diagnosis is crucial in saving lives. In this paper, we present an efficient gland segmentation model using digital histopathology and deep learning. These methods have the potential to revolutionize the approach by identifying hidden patterns within the image. The recent improvements in data acquisition, processing and analysis of Deep Learning Models has made Artificial Intelligence driven Healthcare a topic of intensive investigation, in terms of inferring from the data and delivering meaningful insights. This study presents an automated method for segmenting histopathological images of human prostate glands. It focuses on developing new methods for segmenting these histopathological images using a multi-channel algorithm with an attention mechanism to focus on important areas. We compare our results with a host of contemporary techniques, and show that our method performs better at the segmentation task for histopathological imagery. Our method is able to delineate gland and background parts with an average Dice-coefficient of 0.9168. In this attention-based model, we have thereby demonstrated an accurate segmentation of glands regions, which could have significant positive implications for medical screening applications.
Digital Forensic Investigation of Web-Based Virtual Reality Worlds: Decentraland as a Case Study

The last decade witnessed increased usage of web browsers to access virtual reality (VR) worlds such as Decentraland, Somnium Space and Cryptovoxels. As these VR worlds become more popular, it is no surprise for them to be used as avenues for adverse and criminal purposes, where unknowing users could be targets for malicious activities. To the best of our knowledge, there is no literature providing insight into the kinds of artefacts that could be left behind on users' systems after the use of such web-based VR worlds. To close this gap, we provide foundations for digital forensic investigations on web-based VR worlds using Decentraland as a case study. We performed experiments on Google Chrome and Microsoft Edge web browsers. Findings revealed that details of the real-world user as well as their avatar activities within the VR world could be recovered from the web browser data files on disk, in memory, and in network traffic which could be used to reconstruct the chain of activities.
Fast and Robust Mid-Air Gesture Typing for AR Headsets using 3D Trajectory Decoding

We present a fast mid-air gesture keyboard for head-mounted optical see-through augmented reality (OST AR) that supports users in articulating word patterns by merely moving their own physical index finger in relation to a virtual keyboard plane without a need to indirectly control a visual 2D cursor on a keyboard plane. To realize this, we introduce a novel decoding method that directly translates users' three-dimensional fingertip gestural trajectories into their intended text. We evaluate the efficacy of the system in three studies that investigate various design aspects, such as immediate efficacy, accelerated learning, and whether it is possible to maintain performance without providing visual feedback. We find that the new 3D trajectory decoding design results in significant improvements in entry rates while maintaining low error rates. In addition, we demonstrate that users can maintain their performance even without fingertip and gesture trace visualization.
Exploring Users Pointing Performance on Large Displays with Different Curvatures in Virtual Reality

Large curved displays inside Virtual Reality environments are becoming popular for visualizing high-resolution content during analytical tasks, gaming or entertainment. Prior research showed that such displays provide a wide field of view and offer users a high level of immersion. However, little is known about users' performance (e.g., pointing speed and accuracy) on them. We explore users' pointing performance on large virtual curved displays. We investigate standard pointing factors (e.g., target width and amplitude) in combination with relevant curve-related factors, namely display curvature and both linear and angular measures. Our results show that the less curved the display, the higher the performance, i.e., faster movement time. This result holds for pointing tasks controlled via their visual properties (linear widths and amplitudes) or their motor properties (angular widths and amplitudes). Additionally, display curvatures significantly affect the error rate for both linear and angular conditions. Furthermore, we observe that curved displays perform better or similar to flat displays based on throughput analysis. Finally, we discuss our results and provide suggestions regarding pointing tasks on large curved displays in VR.
Quaternion Neural Network Architectures in Various Application Domains

On real-world applications like image processing, speech reorganization, and signal processing, QNN have significantly outperformed real-valued neural networks. This survey contains an overview of the most recent and ongoing research on quaternion neural networks and their applications in diverse disciplines. For each QVNN that has been proposed, the methods, algorithms, and applications described in the paper.
A Comparative Study of Hybrid Deep Sentimental Analysis Learning Techniques with CNN and SVM

People are becoming more and more engaged with digital touchpoints and social platforms as a result of technological advancements. Each interaction is generating a lot of data in form of text, images, audio files, and videos. However, the most comprehensive form of user-generated data is still text. One of the text mining applications that is gaining prominence is sentiment analysis. By analysing the tone of online product reviews, any business can learn more about the opinions and thoughts of its customers. To learn more about the ideas and sentiments of customers, a variety of unique approaches and procedures are applied. Deep Learning models have been developed by numerous researchers in the past and many of the research did focus on standalone models like CNN and LSTM. In this article, we suggest a method to further increase the classification's overall accuracy by combining deep convolutional neural networks (CNN) and support vector machines (SVM). The CNN, SVM, 1DCNN, and LSTM networks were used to analyse the sentiments in this study. The dataset comes from Kaggle and is called Amazon Product Review Dataset.
ImLiDAR: Cross-Sensor Dynamic Message Propagation Network for 3-D Object Detection

LiDAR and camera, as two different sensors, supply geometric (point clouds) and semantic (RGB images) information of 3D scenes. However, it is still challenging for existing methods to fuse data from the two cross sensors, making them complementary for quality 3D object detection (3OD). We propose ImLiDAR, a new 3OD paradigm to narrow the cross-sensor discrepancies by progressively fusing the multi-scale features of camera Images and LiDAR point clouds. ImLiDAR enables to provide the detection head with cross-sensor yet robustly fused features. To achieve this, two core designs exist in ImLiDAR. First, we propose a cross-sensor dynamic message propagation module to combine the best of the multi-scale image and point features. Second, we raise a direct set prediction problem that allows designing an effective set-based detector to tackle the inconsistency of the classification and localization confidences, and the sensitivity of hand-tuned hyperparameters. Besides, the novel set-based detector can be detachable and easily integrated into various detection networks. Comparisons on the KITTI, nuScenes, and SUN-RGBD datasets all show clear visual and numerical improvements of our ImLiDAR over forty-five state-of-the-art 3OD methods.
A Theoretical Framework for AI Models Explainability with Application in Biomedicine

EXplainable Artificial Intelligence (XAI) is a vibrant research topic in the artificial intelligence community. It is raising growing interest across methods and domains, especially those involving high stake decision-making, such as the biomedical sector. Much has been written about the subject, yet XAI still lacks shared terminology and a framework capable of providing structural soundness to explanations. In our work, we address these issues by proposing a novel definition of explanation that synthesizes what can be found in the literature. We recognize that explanations are not atomic but the combination of evidence stemming from the model and its input-output mapping, and the human interpretation of this evidence. Furthermore, we fit explanations into the properties of faithfulness (i.e., the explanation is an accurate description of the model’s inner workings and decision-making process) and plausibility (i.e., how much the explanation seems convincing to the user). Our theoretical framework simplifies how these properties are operationalized, and it provides new insights into common explanation methods that we analyze as case studies. We also discuss the impact that our framework could have in biomedicine, a very sensitive application domain where XAI can have a central role in generating trust.
Reservoir Computing Model For Multi-Electrode Electrophysiological Data Analysis

In this paper we present a computational model which decodes the spatio-temporal data from electro-physiological measurements of neuronal networks and reconstructs the network structure on a macroscopic domain, representing the connectivity between neuronal units.The model is based on reservoir computing network (RCN) approach, where experimental data is used as training and validation data.Consequently, the model can be used to study the functionality of different neuronal cultures and simulate the network response to external stimuli.
Meta-Analysis of Artificial Intelligence Solution for Prevention of Violence Against Women and Girls

It is a widespread international concern that violence against women and girls over the internet using AI-based technologies. In this paper, researchers studied different concepts of AI, ML & DL, Big Data, IoT and mHealth, Law-U chatbots, and other heuristic strategies & models that can be used to fight & predict violent behavior on a large-scale using algorithms. Also, 7 INSPIRE strategies were developed by the WHO, utilizing a comprehensive model grounded on public healthcare values, since it aims to combat crime through a sector-wide reaction and multidimensional set of initiatives. In recent years, computer technology-related AI technologies have been utilized to aid in the reduction of this societal issue. Solution strategies for detecting and preventing cruelty against women and girls have been developed using innovations to combat the widespread issue of violence and cruelty to women and girls over the internet. This study provides a thorough analysis of some of the current approaches being taken to this complex issue. As a result, there are promising new AI technologies, models, algorithms & apps which provide victims & their families with legal and health advice as well as predicts AI-based crime over the internet.
Symmetric and Supersymmetric Polynomials and Their Applications in the Blockchain Technology and Neural Networks

We consider properties of symmetric and supersymmetric polynomials on rings of multisets. Using supersymmetric polynomials we found some conditions of the existence and uniqueness of solutions of algebraic equations on the ring of complex multisets. Obtained results can be applied to hash functions in blockchain technology, for comparing big sets of data and to neural networks with symmetric input data.
Visual Sentiment Analysis Using Machine Learning for Entertainment Applications

The expression of emotions through images is said to be much more effective than via text. Therefore, it is important to develop an image-based model for sentiment analysis. The objective of visual sentiment analysis is to determine how various types of images affect viewers' emotions. Despite the fact that this topic is still relatively young, numerous strategies have been created for a variety of data sources and issues, leading to a substantial body of study. During sentiment analysis, feelings are sorted into optimistic, unpleasant, and unbiased categories. With the aid of machine learning, this work provides an analysis of the most recent advancement, theoretical and practical ideas defining an improvement in sentiment analysis for entertainment applications, specifically on social media. This work aims to focus at latest studies in emoticon-based sentiment analysis and machine learning for review images.
Automatic Person Detection in Search and Rescue Operations Using Deep Based MultiModel CNN Detectors

Search and rescue operations are critical tasks where quick and accurate detection of humans can save lives. Due to growing number of people doing adventure tourism and going to other inaccessible places it is very important to organize a search and rescue operations to aid and health care to the needy. It's very difficult to find people who are relatively very small considering the area where they are. The detection of persons and objects in images and videos captured by drones during operations is of great importance, and thus, the ability to do so automatically is highly significant. Due to their ability to quickly capture a large, controlled area in a short amount of time, UAVs or drones are becoming increasingly involved in search operations. Despite the high success rate in detecting objects in normal ground captured images, detecting humans or other objects from UAVs (Unmanned Aerial Vehicles) still remains a challenging task in captured images or aerial images due to a few challenges like weather conditions, varying attitude, pose and scale variations, artefacts like people wearing hats and camouflaged environment. This project use CNN detectors for human detection in search and rescue operations.
Real Time Sign Language Recognition Using Yolov5

This paper proposes real-time sign language detection models for deaf and mute individuals created using the You Only Look Once version 5 (YOLOv5) algorithm. There were 3 different models created. The first model detects American Sign Language, the Second model detects Hindi/ Marathi Sign Language and the third model detects static gestures. The YOLOv5 models were trained on the custom data set, achieving high accuracy. The model's real-time performance was evaluated by capturing live sign language gestures using a webcam, achieving an average detection time of 0.05 seconds per frame. This demonstrates the model's potential for real-time sign language interpretation and communication for deaf and mute individuals. To implement the models, our custom data set includes a total of 1892 images, consisting of 452 training images of static gestures, 611 training images of Hindi/Marathi sign language, and 829 training images for American sign language representing different sign language gestures. The proposed models performed better than other deep learning models, including Faster R-CNN and Mask R-CNN, showing superior accuracy and real-time performance. The results demonstrate the potential of using YOLOv5-based models for real-time sign language detection and interpretation, which can significantly improve the quality of life for deaf and mute individuals.
FMM-X3D: FPGA-Based Modeling and Mapping of X3D for Human Action Recognition

3D Convolutional Neural Networks are gaining increasing attention from researchers and practitioners and have found applications in many domains, such as surveillance systems, autonomous vehicles, human monitoring systems, and video retrieval. However, their widespread adoption is hindered by their high computational and memory requirements, especially when resource-constrained systems are targeted. This paper addresses the problem of mapping X3D, a state-of-the-art model in Human Action Recognition that achieves accuracy of 95.5% in the UCF101 benchmark, onto any FPGA device. The proposed toolflow generates an optimised stream-based hardware system, taking into account the available resources and off-chip memory characteristics of the FPGA device. The generated designs push further the current performance-accuracy pareto front, and enable for the first time the targeting of such complex model architectures for the Human Action Recognition task.