Reviewerly/exHarmony huggingface dataset:
Observer Recommended Maneuver for Bearing Only Tracking of an Underwater Target

It is widely acknowledged that when tracking a target with nearly constant velocity using bearing only measurements, the observer must execute a maneuver to achieve observability and estimate states accurately. In such a scenario, the precision of state estimation algorithms is notably influenced by the trajectory the ownship takes during maneuvering. This letter aims to enhance estimation accuracy by recommending an ownship maneuver. The recommended observer maneuver, determined by maximizing the Fisher information matrix, leads to a reduced Cramer–Rao lower bound (CRLB) and improved estimator accuracy, evaluated in terms of root mean square error and track loss percentage. Certain heuristics are taken into consideration to maintain a not more than two-legged observer trajectory, to prevent the target from entering the blind zone of the sonar, and to avoid a direct collision of the observer with the target. Thus, this letter recommends a safe maneuver for the ownship to track an enemy target more efficiently. As the Fisher information matrix requires the true states to be known, this work has been done using offline true data of the target. The recommended maneuver for every course of the target can be saved in an onboard computer, and during real life engagement, the ownship can follow such maneuver recommendation once the course of the target is estimated.
Enhancing Blockchain Interoperability Through Sidechain Integration and Valid-Time-Key Data Access Control

Nowadays, in the realm of blockchain technology, a pressing challenge lies in the current lack of interoperability, which significantly limits its potential for innovation and advancement. However, the attainment of cross-chain interoperability is undeniable of utmost importance, as it holds the key to maximizing the network’s computing performance, expanding storage capacity, and unlocking unparalleled scalability. Therefore, this research introduces a groundbreaking cross-chain architecture that tackles the challenge of interoperability and sharing capabilities, together with surpassing the limitations posed by previous interchain interaction systems. In the proposed system, interchain data transfer is revolutionized through the utilization of a Sidechain that incorporates a valid-time key (VTK) data access control scheme for the purpose of empowering the verification of blockchain data across multiple blockchain networks. In the realm of healthcare data exchange, our experiments unequivocally showcase the effectiveness of the proposed architecture in enabling seamless interchain communication and secure data transfer while ensuring the security and authenticity of the transferred information. Through the implementation of the VTK mechanism, our system establishes a robust defense against unauthorized access and fraudulent activities by tightly controlling access privileges, ensuring that only authorized parties are granted access within specified time limits.
Low-Light Image Enhancement Using Quaternion CNN

Images captured in low-light conditions suffer severely from degradations such as poor visibility or unexpected noise which cause challenges for computer vision systems. This paper presents an algorithm for low-light enhancement using Quaternion Convolutional Neural Network (QCNN). The proposed algorithm integrates 3-tuple (RGB) and one zero/gray channel into Unet model to extract informative detail of color and texture for image reconstruction. In addition, an attention module for QCNN has been proposed to help the model focus to learn important parts of Quaternion feature map. The experimental results show that the proposed method outperforms the conventional methods using deep learning algorithms.
An Efficient Method for Realizing Contractions of Access Structures in Cloud Storage

In single-cloud storage, ciphertext-policy attribute-based encryption (CP-ABE) allows one to encrypt any data under an access structure to a cloud server, specifying what attributes are required to decrypt. In multi-cloud storage, a secret sharing scheme (SSS) allows one to split any data into multiple shares, one to a single server, and specify which subset of the servers are able to recover the data. It is an interesting problem to remove some attributes/servers but still enable the remaining attributes/servers in every authorized set to recover the data. The problem is related to the contraction problem of access structures for SSSs. In this paper, we propose a method that can efficiently transform a given SSS for an access structure to SSSs for contractions of the access structure. We show its applications in solving the attribute removal problem in the CP-ABE based single-cloud storage and the data relocating problem in multi-cloud storage. Our method results in solutions that require either less server storage or even no additional server storage.
Robust Traffic Sign Detection and Classification Through the Integration of YOLO and Deep Learning Networks

This paper presents a comparative study on the integration of YOLOv5 (You Only Look Once) object detection model with four popular deep learning networks, namely MobileNetv2, ResNet50, and VGG19, for robust traffic sign detection and classification. The objective is to evaluate the performance of these networks in terms of accuracy and computational efficiency. The proposed methodology consists of two main stages: traffic sign detection and classification. YOLOv5 is employed for efficient traffic sign detection, utilizing a single convolutional neural network to predict bounding boxes and class probabilities directly. This approach allows for real-time performance while maintaining high accuracy. After detecting the traffic signs, the classification stage utilizes the four deep learning networks to classify them into predefined categories. Experimental evaluations are conducted on a benchmark traffic dataset, and the results show that the integrated approach outperforms individual networks in terms of overall accuracy. MobileNetv2 achieves the fastest processing time, followed by ResNet50, and VGG19. These findings assist in selecting the most suitable network based on specific application requirements.
Adaptive Conflict-Averse Multi-gradient Descent for Multi-objective Learning

One of the most significant challenges of multi-task learning is that different tasks may have conflicting gradients during joint optimization. Previous approaches have attempted to address this problem by modifying the gradients based on specific criteria. However, most of these methods do not guarantee convergence or may converge to any Pareto-stationary point. The researchers developed a recent method called Conflict-Averse Gradient Descent (CAGrad) to handle this issue, but they face a challenge in accurately estimating the necessary Lipschitz constant. Additionally, choosing an excessively small learning rate to avoid this problem can result in slow convergence. In response to these challenges, we introduce Adaptive Conflict-Averse Multi-Gradient Descent (AdaCAGrad) algorithm. Our approach has a unique feature in that it can adjust the learning rate adaptively based on a specific condition, which sets it apart from other existing methods.
CSLT: Contourlet-Based Siamese Learning Tracker for Dim and Small Targets in Satellite Videos

Most popular visual trackers for natural scenarios always adopt handcraft features or deep features to track the target in a video. However, they face with difficulties in discriminative feature representation and usually suffer from severe model drift for satellite videos, especially when encountering challenges of dim and small targets, low contrast or similar target interference. To overcome these difficulties, we propose a Contourlet-based Siamese Learning Tracker (CSLT), which mainly aims at tracking dim and small objects in satellite videos. In contrast to conventional methods, the contourlet transform enriches directional multi-resolution information which is crucial to discriminative feature representation for dim and small targets in satellite video frames that lack distinguishable appearance features. We jointly use multi-resolution features with deep features by spatial-attention fusion strategy and then track the targets by a Siamese structure network. To further improve the accuracy and robustness, a model drift alarm and calibration module, including translation drifting penalty and rotation drifting penalty, is employed during tracking. We conduct extensive comparisons with 16 popular state-of-the-art trackers on three satellite video datasets. The experimental results validate the effectiveness of the proposed tracker.
Solving Feature Selection Problem by Quantum Optimization Algorithm

This study is to propose a method for feature selection with Quadratic Unconstrained Binary Optimization (QUBO) formulation by applying Conditional Value at Risk (CVaR) hybrid with Quantum Approximate Optimization Algorithm (QAOA) using hybrid Differential evolution (DE) -Trotterized Quantum Annealing (TQA) initialization method to solve the QUBO formulation of feature selection. This is a new approach to feature selection, which is very important for machine learning research. This method is applied to 11 real-life datasets and the results have been improved significantly.
A Quantum Computer Trusted Execution Environment

We present the first architecture for a trusted execution environment for quantum computers. In the architecture, to protect the user's circuits, they are obfuscated with decoy control pulses added during circuit transpilation by the user. The decoy pulses are removed, i.e. attenuated, by the trusted hardware inside the superconducting quantum computer's fridge before they reach the qubits. This preliminary work demonstrates that protection from possibly malicious cloud providers is feasible with minimal hardware cost.
Named Entity Recognition Utilized to Enhance Text Classification While Preserving Privacy

Recent development in Natural Language Processing (NLP) techniques has encouraged NLP-based application in various field including business, legal and health. An important process for all NLP projects is text preprocessing which is a process that modifies text data before using them in a machine learning model. Usually text preprocessing process includes cleaning, filtering, removing and replacing some texts to increase model accuracy, robustness, reduce data size or preserve privacy. Named entities recognizer (NER) is an NLP tool which finds Named Entities in text such as: names, organization, addresses, numbers and date. In this work, we create a preproccessing approach that uses NER to find named entities and, then, use them to improve accuracy and preserve privacy instead of removing them or letting them became noise to our data. Experiments for text classification task using our approach have been conducted on several datasets some of which was collected in-house. Experiments indicate that using this approach enhances classifier accuracy and reduces dimensionality while, also, preserve privacy.
Video Classification Based on the Behaviors of Children in Pre-school Through Surveillance Cameras

In preschool, children are active and curious about the world around them. The tendency to engage in unusual or dangerous behaviors can pose a significant risk to their safety. Constantly monitoring surveillance camera footage and analyzing it to determine if any abnormal behavior is occurring requires considerable attention and effort from human observers. Therefore, applying technology to moni- tor the abnormal behavior of children is crucial in preschool education settings. With the development of technology, the problem of classifying video through surveillance cameras can be solved. However, there is still a shortage of datasets for this task with video data in preschool environments due to difficulties in collecting data and potential violations of children’s privacy and safety. Therefore, in this paper, we propose Behaviors of Children in Preschool (BCiPS), a new dataset for the above problem. BCiPS consists of 4268 videos with lengths ranging from 3–6 seconds. We evaluate some machine learning and deep learning models on BCiPS. The CNN-LSTM model achieves the highest performance, over 75%, with four performance metrics: accuracy, recall, precision, and $$F1_{score}$$ . Additionally, we will analyze cases where the models fail to identify abnormal behavior to determine the reasons for these fails- ures, identify weaknesses in the dataset, and improve the accuracy of the dataset to create a reliable tool for building an effective warning system in real-life situations.
Directed Acyclic Graphs With Prototypical Networks for Few-Shot Emotion Recognition in Conversation

Emotion recognition in conversation is the task of recognizing the emotion in each utterance of a conversation, and it is an active field of study with various applications. Many studies have been conducted in well-designed settings in which all of the emotion labels in the training set are available. However, a rich dataset with emotion labels for all utterances is rare. In this study, we address this problem by resorting to few-shot learning. Specifically, we propose Directed Acyclic Graph (DAG)-based prototypical networks, namely ProtoDAG, to better capture contextual information in conversations, which in turn leads to accurate emotion predictions. In our model, the DAG layers are tailored into prototypical networks, which is learned end-to-end. Our experiments with popular benchmark datasets demonstrate that our model achieves state-of-the-art results outperforming the existing few-shot learning model by a significant margin and is even competitive with fully supervised baseline models for emotion recognition in conversation.
Bio-Inspired Clustering: An Ensemble Method for User-Based Collaborative Filtering

Clustering techniques are used to group users to enhance the recommendations-generating process of collaborative filtering systems. Collaborative filtering-based approaches are commonly used for generating similarity-based recommendations. While traditional clustering methods are often used for user clustering, there is a need to explore bio-inspired clustering techniques to improve recommendation generation. This article proposes a bio-inspired clustering collaborative filtering (BICCF) approach as an ensemble method for recommendation systems. The process utilizes swarm intelligence to improve the accuracy of recommendations for user-based collaborative filtering. Real-world datasets from MovieLens are used to do experiments that evaluate the effectiveness of the proposed method. Results show significant improvements in accuracy and efficiency based on Recall, Precision, and MAE metrics compared to other baseline methods.
Scalable QKD Postprocessing System With Reconfigurable Hardware Accelerator

Key Distillation is an essential component of every quantum key distribution (QKD) system because it compensates for the inherent transmission errors of a quantum channel. However, the interoperability and throughput aspects of the post-processing components are often neglected. In this paper, we propose a high-throughput key distillation framework that supports multiple QKD protocols, implemented in a field-programmable gate array (FPGA). The proposed design adapts a MapReduce programming model to efficiently process large chunks of raw data across the limited computing resources of an FPGA. We present a novel hardware-efficient integrated post-processing architecture that offers dynamic error correction, mutual authentication with a physically unclonable function, and an inbuilt high-speed encryption application that utilizes the key for secure communication. Additionally, we have developed a semi-automated high-level synthesis (HLS) framework that is compatible with any discrete variable QKD system, showing promising speedup. Overall, the experimental results demonstrate a noteworthy enhancement in scalability achieved through the utilization of a single FPGA platform.
Comparison of Machine Learning Classification and Clustering Algorithms for TV Commercials Detection

One of the essential aspects of broadcast monitoring is to detect and consequently extract commercial blocks in telecast news videos. The research carried out until now have based their work almost entirely on preconceived characteristics that are associated with a channel. With the advertisers constantly looking to work around the existing policies, the reliance on the nature of channels during an advertisement does not suffice. The other approach towards identifying a commercial is by frequentist approach. However, it is often the case that sponsored programs and other programs share similar time in any specified hour, rendering the frequentist approach almost useless in the process. As such, this paper uses machine learning based approach which is more generic and can employ inherent differences that commercials have over their non-commercial counterparts for classifying and clustering commercials in the news videos. The datasets which contain 90 hours of recordings from five different news channels from US, England and India have been used to train and test nine different classifiers – K Neighbors, Support Vector Machine, Decision Tree, Random Forests, Ada Boost, Gradient Boost, Gaussian NB, Linear Discriminant Analysis, and Quadratic Discriminant Analysis – and five different clustering algorithms – K Means, Agglomerative, Birch, Mini-Batch K Means, and Gaussian Mixture. Our results show that the Random Forests outperforms all the other classifiers used with the highest F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> score of 97% and median time of 31 seconds to train and test on each of these datasets that consists of features of shots extracted from 18 hours of video. Similarly, Mini Batch K Means was found to perform the best for clustering with the highest Silhouette Score of 0.7 and median time of 30 seconds to form clusters of news and commercials.
A Lightweight Traffic Sign Recognition Model Based on Improved YOLOv5

Traffic sign recognition and detection is a key technology in automatic vehicle driving and driver assistance systems. However, existing traffic sign recognition algorithms suffer from problems such as large model size, complex computation, high computational cost, which make it difficult to achieve an effective balance between detection speed and detection accuracy. This paper proposed an improved lightweight recognition algorithm, which is based on YOLOv5. This algorithm replaces the convolutional structure in the original YOLOv5 neck network with Ghost Module and C3Ghost Module, thereby reducing redundant features in the feature fusion process, lowering computational cost and the number of parameters. The structure of the PAN network was improved and the hybrid attention mechanism module CBAM was introduced to capture key information in traffic signs. Cross-layer connections were added to shorten the path of information transfer in feature pyramid network, which fused more features and improved the network feature recognition accuracy. In addition, the EIoU_Loss function was adopted as the bounding box regression loss function to improve the localization accuracy of the algorithm. The performance of the improved algorithm was also verified on the Chinese traffic sign dataset. Experimental results showed that the improved algorithm’s detection accuracy was enhanced by 1.2%, while mAP@0.5 and mAP@0.5:0.95 were enhanced by 1.5% and 3.4% respectively over the existing YOLOv5 algorithm, and the overall parameter numbers and computational cost of the model were reduced by 14.5% and 16%. The proposed algorithm performs better than the current mainstream detection algorithms, has higher recognition accuracy in multiple environments, and meets the demand for real-time traffic sign recognition.
Applying the Distributed Ledger Technology for the Product Origin Traceability

Tracing the origin of products, particularly in the agricultural and food sectors, has gained significant attention to combat counterfeit goods and address trust and immutability concerns associated with multi-party processes in enterprises. This paper aims to propose the open-source distributed ledger technology (DLT) with Hyperledger Fabric platform and implement a system of product origin tracing with a simple product flow. This selection is based on considerations from existing studies such as performance, technical architecture, ledger structure, network configuration, peer node implementation, ordering service setup, consensus mechanisms, smart contract development, and market adoption of Hyperledger Fabric across different business domains. The proposed demonstration is designed to showcase the capabilities of Fabric as an enterprise-grade and production-ready permissioned blockchain platform for addressing the challenges associated with product origin traceability. Overall, the implementation of the proposed DLT solution with Fabric for product origin traceability demonstrates significant advantages, providing a secure, transparent, and efficient framework for multi-party processes.
Securing Digital Futures: Exploring Decentralised Systems and Blockchain for Enhanced Identity Protection

The Digital Identity Decentralized System on Blockchain (DIDSB), a decentralised system for digital identity management, leverages the features of blockchain technology to provide a novel solution to the challenges of identity management. The DIDSB framework classifies entities into three distinct categories, namely identity providers, service providers, and end-users. The efficacy of the system in identifying and extracting fields from identification cards is attributed to the integration of artificial intelligence (AI) algorithms. Subsequently, the system dissects the fields into smaller constituents to facilitate optical character recognition (OCR) and user authentication. The utilisation of the zero-knowledge proof concept by DIDSB enables secure authentication with service providers. This obviates the necessity of divulging any personal data while undergoing the authentication procedure. This advanced system empowers users to exercise authority over their confidential information while simultaneously safeguarding it with optimal efficacy and security. The experimental outcomes demonstrate that the system exhibits a proficient ability to combat identity fraud, while concurrently preserving the confidentiality and integrity of sensitive data.
Optimizing ECC Implementations Based on SoC-FPGA with Hardware Scheduling and Full Pipeline Multiplier for IoT Platforms

In the context of Industry 4.0 and the Internet of Things (IoT), it is crucial to ensure the safety and security of electronic devices. This paper proposes a design using the SoC-FPGA platform to enhance IoT device security. The design combines a powerful ARM processor with customizable IP cores on the FPGA, resulting in high processing speed. The co-processor performs asymmetric encryption using Elliptic Curve Cryptography (ECC) with the SECP256K1 curve. The main operation is point multiplication, with point addition and doubling as secondary operations. The results demonstrate high efficiency, with the ECC core operating at 30 MHz and point addition and doubling taking around 37 microseconds. The point multiplication operation can be processed in approximately 17 ms. This design provides a secure and efficient solution for enhancing IoT device and connection security.