Reviewerly/exHarmony huggingface dataset:
3D Path Optimisation of Unmanned Aerial Vehicles Using Q Learning-Controlled GWO-AOA

Unmanned Aerial Vehicles (UAVs) or drones introduced for military applications are gaining popularity in several other fields as well such as security and surveillance, due to their ability to perform repetitive and tedious tasks in hazardous environments.Their increased demand created the requirement for enabling the UAVs to traverse independently through the Three Dimensional (3D) flight environment consisting of various obstacles which have been efficiently addressed by metaheuristics in past literature.However, not a single optimization algorithms can solve all kind of optimization problem effectively.Therefore, there is dire need to integrate metaheuristic for general acceptability.To address this issue, in this paper, a novel reinforcement learning controlled Grey Wolf Optimisation-Archimedes Optimisation Algorithm (QGA) has been exhaustively introduced and exhaustively validated firstly on 22 benchmark functions and then, utilized to obtain the optimum flyable path without collision for UAVs in three dimensional environment.The performance of the developed QGA has been compared against the various metaheuristics.The simulation experimental results reveal that the QGA algorithm acquire a feasible and effective flyable path more efficiently in complicated environment.
Analysis of Dual-Tasking Effect on Gait Variability While Interacting with Mobile Devices

Cognitive deficits are very difficult to diagnose during the initial stages; tests typically consist of a patient performing punctual dual-task activities, which are subjectively analyzed to determine the cognitive decline impact on gait. This work supports novel and objective diagnosis methods by stating a baseline on how neurotypical aging affects dual tasks while using a smartphone on the move. With this aim, we propose a twofold research question: Which mobile device tasks performed on the move (dual tasking) have characteristic changes in gait parameters, and which are especially characteristic at older ages? An experiment was conducted with 30 healthy participants where they performed 15 activities (1 single task, 2 traditional dual-tasks and 12 mobile-based dual-tasks) while walking about 50 m. Participants wore a wireless motion tracker (15 sensors) that made the concise analysis of gait possible. The results obtained characterized the gait parameters affected by mobile-based dual-tasking and the impact of normal cognitive decline due to aging. The statistical analysis shows that using smartphone-based dual-tasking produces more significant results than traditional dual-tasking. In the study, 3 out of 10 gait parameters were very significantly affected (p &lt; 0.001) when using the traditional dual tasks, while 5 out of 10 parameters were very significantly affected (p &lt; 0.001) in mobile-based dual-tasking. Moreover, the most characteristic tasks and gait parameters were identified through the obtained results. Future work will focus on applying this knowledge to improve the early diagnosis of MCI.
K-mer-Based Human Gesture Recognition (KHGR) Using Curved Piezoelectric Sensor

Recently, human activity recognition (HAR) techniques have made remarkable developments in the field of machine learning. In this paper, we classify human gestures using data collected from a curved piezoelectric sensor, including elbow movement, wrist turning, wrist bending, coughing, and neck bending. The classification process relies on data collected from a sensor. Machine learning algorithms enabled with K-mer are developed and optimized to perform human gesture recognition (HGR) from the acquired data to achieve the best results. Three machine learning algorithms, namely support vector machine (SVM), random forest (RF), and k-nearest neighbor (k-NN), are performed and analyzed with K-mer. The input parameters such as subsequence length (K), number of cuts, penalty parameter (C), number of trees (n_estimators), maximum depth of the tree (max_depth), and nearest neighbors (k) for the three machine learning algorithms are modified and analyzed for classification accuracy. The proposed model was evaluated using its accuracy percentage, recall score, precision score, and F-score value. We achieve promising results with accuracy of 94.11 ± 0.3%, 97.18 ± 0.4%, and 96.90 ± 0.5% for SVM, RF, and k-NN, respectively. The execution time to run the program with optimal parameters is 19.395 ± 1 s, 5.941 ± 1 s, and 3.832 ± 1 s for SVM, RF, and k-NN, respectively.
Rule-Based Embedded HMMs Phoneme Classification to Improve Qur’anic Recitation Recognition

Phoneme classification performance is a critical factor for the successful implementation of a speech recognition system. A mispronunciation of Arabic short vowels or long vowels can change the meaning of a complete sentence. However, correctly distinguishing phonemes with vowels in Quranic recitation (the Holy book of Muslims) is still a challenging problem even for state-of-the-art classification methods, where the duration of the phonemes is considered one of the important features in Quranic recitation, which is called Medd, which means that the phoneme lengthening is governed by strict rules. These features of recitation call for an additional classification of phonemes in Qur’anic recitation due to that the phonemes classification based on Arabic language characteristics is insufficient to recognize Tajweed rules, including the rules of Medd. This paper introduces a Rule-Based Phoneme Duration Algorithm to improve phoneme classification in Qur’anic recitation. The phonemes of the Qur’anic dataset contain 21 Ayats collected from 30 reciters and are carefully analyzed from a baseline HMM-based speech recognition model. Using the Hidden Markov Model with tied-state triphones, a set of phoneme classification models optimized based on duration is constructed and integrated into a Quranic phoneme classification method. The proposed algorithm achieved outstanding accuracy, ranging from 99.87% to 100% according to the Medd type. The obtained results of the proposed algorithm will contribute significantly to Qur’anic recitation recognition models.
A VVC Video Steganography Based on Coding Units in Chroma Components with a Deep Learning Network

Versatile Video Coding (VVC) is the latest video coding standard, but currently, most steganographic algorithms are based on High-Efficiency Video Coding (HEVC). The concept of symmetry is often adopted in deep neural networks. With the rapid rise of new multimedia, video steganography shows great research potential. This paper proposes a VVC steganographic algorithm based on Coding Units (CUs). Considering the novel techniques in VVC, the proposed steganography only uses chroma CUs to embed secret information. Based on modifying the partition modes of chroma CUs, we propose four different embedding levels to satisfy the different needs of visual quality, capacity and video bitrate. In order to reduce the bitrate of stego-videos and improve the distortion caused by modifying them, we propose a novel convolutional neural network (CNN) as an additional in-loop filter in the VVC codec to achieve better restoration. Furthermore, the proposed steganography algorithm based on chroma components has an advantage in resisting most of the video steganalysis algorithms, since few VVC steganalysis algorithms have been proposed thus far and most HEVC steganalysis algorithms are based on the luminance component. Experimental results show that the proposed VVC steganography algorithm achieves excellent performance on visual quality, bitrate cost and capacity.
Centralized vs. Decentralized: Performance Comparison between BigchainDB and Amazon QLDB

Decentralized databases have gained popularity in the last few years in different areas, such as: traceability, supply chains or finance. Leveraging this type of emerging technology will improve knowledge sharing, as well as the transparency and traceability of the data for digital systems. In a similar way, the characteristics are advertised by the centralized ledger technologies, which are manufactured by large cloud service providers such as Amazon. The present study analyzes the performance of two ledger technologies: BigchainDB (i.e., the decentralized blockchain database) and Amazon QLDB (i.e., the centralized ledger database with transparent and immutable characteristics). For the purposes of comparison, we have integrated these technologies into our traceability platform, which is called the Smart Tracking Platform (STP), and performed a series of experiments enabling us to acquire data for different metrics, such as CPU or memory usage for both the reading and writing operations. The findings of the present study show that QLDB has an overall better performance compared to BigchainDB, based on the metrics that have been considered. From the perspective of database ledger implementation, Amazon QLDB proved to be an integrated solution, easier to use, while BigchainDB comprises a more complex system to be implemented and developed, but is more flexible. Although both systems are almost ready to use solutions for local environments, when it comes to configuration and setting up the communication between nodes within a production environment, BigchainDB adds a layer of complexity from a DevOps perspective, while Amazon QLDB completely overcomes it. Depending on the area considered and the identified needs, both BigchainDB and Amazon QLDB can be considered as suitable solutions for a ledger database.
Blockchain Traceability in Trading Biomasses Obtained with an Integrated Multi-Trophic Aquaculture

This study explores the application and critical issues related to the implementation of blockchain technology (BT) to the aquaculture sector, in order to understand the possibilities of improving the relationship with the supply chain and the end consumer, with a view to a sustainability for the marine environment and circular economy. Starting from considerations of commercial and political challenges related to credibility and fairness for all parties involved—from producers, to retailers, to end consumers—the procedure adopted was applied to the case of an Integrated Multi-Trophic Aquaculture project, developed in the sea of Taranto (Italy). Furthermore, it considered two different end markets: the food market for farmed fish and the ornamental fish market for marine aquariums. The results of the study confirm that although the implementation of BT by industries and producers of marine species has the potential to lead to successful sustainability solutions, such adoption is feasible over time only if all actors in the supply chain, from aquaculture companies, to retailers, to consumers, are actively and consciously involved and can access common benefits.
Cognitive Adaptive Systems for Industrial Internet of Things Using Reinforcement Algorithm

Agile product development cycles and re-configurable Industrial Internet of Things (IIoT) allow more flexible and resilient industrial production systems that can handle a broader range of challenges and improve their productivity. Reinforcement Learning (RL) was shown to be able to support industrial production systems to be flexible and resilient to respond to changes in real time. This study examines the use of RL in a wide range of adaptive cognitive systems with IIoT-edges in manufacturing processes. We propose a cognitive adaptive system using IIoT with RL (CAS-IIoT-RL) and our experimental analysis showed that the proposed model showed improvements with adaptive and dynamic decision controls in challenging industrial environments.
Blind Image Quality Assessment with Deep Learning: A Replicability Study and Its Reproducibility in Lifelogging

The wide availability and small size of different types of sensors have allowed for the acquisition of a huge amount of data about a person’s life in real time. With these data, usually denoted as lifelog data, we can analyze and understand personal experiences and behaviors. Most of the lifelog research has explored the use of visual data. However, a considerable amount of these images or videos are affected by different types of degradation or noise due to the non-controlled acquisition process. Image Quality Assessment can plays an essential role in lifelog research to deal with these data. We present in this paper a twofold study on the topic of blind image quality assessment. On the one hand, we explore the replication of the training process of a state-of-the-art deep learning model for blind image quality assessment in the wild. On the other hand, we present evidence that blind image quality assessment is an important pre-processing step to be further explored in the context of information retrieval in lifelogging applications. We consider that our efforts have been successful in the replication of the model training process, achieving similar results of inference when compared to the original version, while acknowledging a fair number of assumptions that we had to consider. Moreover, these assumptions motivated an extensive additional analysis that led to significant insights on the influence of both batch size and loss functions when training deep learning models in this context. We include preliminary results of the replicated model on a lifelogging dataset, as a potential reproducibility aspect to be considered.
Preference-aware Graph Attention Networks for Cross-Domain Recommendations with Collaborative Knowledge Graph

Knowledge graphs (KGs) can provide users with semantic information and relations among numerous entities and nodes, which can greatly facilitate the performance of recommender systems. However, existing KG-based approaches still suffer from severe data sparsity and may not be effective in capturing the preference features of similar entities across domains. Therefore, in this article, we propose a P reference-aware G raph A ttention network model with C ollaborative K nowledge G raph ( PGACKG ) for cross-domain recommendations. Preference-aware entity embeddings with some collaborative signals are first obtained by exploiting the graph-embedding model, which can transform entities and items in the collaborative knowledge graph into semantic preference spaces. To better learn user preference features, we devise a preference-aware graph attention network framework that aggregates the preference features of similar entities within domains and across domains. In this framework, multi-hop reasoning is employed to assist in the generation of preference features within domains, and the node random walk based on frequency visits is proposed to gather similar preferences across domains for target entities. Then, the final preference features of entities are fused, while a novel C ross-domain B ayesian P ersonalized R anking ( CBPR ) is proposed to improve cross-domain recommendation accuracy. Extensive empirical experiments on four real-world datasets demonstrate that our proposed approach consistently outperforms state-of-the-art baselines. Furthermore, our PGACKG achieves strong performance in different ablation scenarios, and the interaction sparsity experiments also demonstrate that our proposed approach can significantly alleviate the data sparsity issue.
A Novel Lightweight Audio-visual Saliency Model for Videos

Audio information has not been considered an important factor in visual attention models regardless of many psychological studies that have shown the importance of audio information in the human visual perception system. Since existing visual attention models only utilize visual information, their performance is limited but also requires high-computational complexity due to the limited information available. To overcome these problems, we propose a lightweight audio-visual saliency (LAVS) model for video sequences. To the best of our knowledge, this article is the first trial to utilize audio cues for an efficient deep-learning model for the video saliency estimation. First, spatial-temporal visual features are extracted by the lightweight receptive field block (RFB) with the bidirectional ConvLSTM units. Then, audio features are extracted by using an improved lightweight environment sound classification model. Subsequently, deep canonical correlation analysis (DCCA) aims at capturing the correspondence between audio and spatial-temporal visual features, thus obtaining a spatial-temporal auditory saliency. Lastly, the spatial-temporal visual and auditory saliency are fused to obtain the audio-visual saliency map. Extensive comparative experiments and ablation studies validate the performance of the LAVS model in terms of effectiveness and complexity.
Neuron-Based Spiking Transmission and Reasoning Network for Robust Image-Text Retrieval

Most of the image-text retrieval methods carry out accurate results using fine-grained features for feature alignment. However, extracting the robustness features while maintaining the retrieval accuracy in wireless communication is still a challenge, especially with channel noises and limited transmission bandwidth. Inspired by spike signals of neurons in the human brain, we propose the neuron-based spiking transmission and reasoning network (NSTRN). In this way, the features are compressed into compacted efficient representations. In NSTRN, we construct the feature sender based on spiking activation function to selectively encode only important information in images and sentences into binary codes, and reduce the transmission cost. Moreover, the feature receiver is designed as a recurrent architecture and applies both temporal attention and global attention blocks to memorize long-term information. Finally, to compensate for the loss of visual concepts in transmission, we use the global textual features as coefficients to guide the formation of visual features in the training stage. The traditional CNN-based joint source-channel coding model outputs float-point encoded features, which requires additional quantization steps to convert features into binary bitstreams in the practical wireless communication system. Instead, the spiking neural networks (SNNs) directly use binary spike trains to reduce the computation complexity caused by the quantization steps. More importantly, SNNs can naturally encode the asynchronous event streams and inhibit the discrete noisy events to extract robust information. Even with binary bitstreams, NSTRN shows effectiveness compared with the state-of-the-art image-text retrieval methods. In the wireless communication scenario, NSTRN not only reduces the transmission bandwidth but also alleviates the “cliff effect” to a certain extent in the traditional separate encoding methods. To the best of our knowledge, this is the first work using SNNs on robust image-text retrieval.
A Privacy-Preserving JPEG Image Retrieval Scheme Using the Local Markov Feature and Bag-of-Words Model in Cloud Computing

The development of cloud computing attracts a great deal of image owners to upload their images to the cloud server to save the local storage. But privacy becomes a great concern to the owner. A forthright way is to encrypt the images before uploading, which, however, would obstruct the efficient usage of image, such as the Content-Based Image Retrieval (CBIR). In this paper, we propose a privacy-preserving JPEG image retrieval scheme. The image content is protected by a specially-designed image encryption method, which is compatible to JPEG compression and makes no expansion to the final JPEG files. Then, the encrypted JPEG files are uploaded to the cloud, and the cloud can directly extract the features from the encrypted JPEG files for searching similar images. Specifically, big-blocks are first assembled with adjacent 8×8 discrete cosine transform (DCT) coefficient blocks. Then, the big-blocks are permuted and the binary code of DCT coefficients are substituted, so as to disturb the content of image. After receiving the encrypted images, local Markov features are extracted from the encrypted big-blocks, and then the Bag-Of-Words (BOW) model is applied to construct a feature vector with these local features to represent the image, so as to provide the CBIR service to image owner. Experimental results and security analysis demonstrate the retrieval performance and security of our scheme.
Graph Neural Networks With Triple Attention for Few-Shot Learning

Recent advances in Graph Neural Networks (GNNs) have achieved superior results in many challenging tasks, such as few-shot learning.Despite its capacity to learn and generalize a model from only a few annotated samples, GNN is limited in scalability, as deep GNN models usually suffer from severe over-fitting and over-smoothing.In this work, we propose a novel GNN framework with a triple-attention mechanism, i.e., node self-attention, neighbor attention, and layer memory attention, to tackle these challenges.We provide both theoretical analysis and illustrations to explain why the proposed attentive modules can improve GNN scalability for few-shot learning tasks.Our experiments show that the proposed Attentive GNN model outperforms the state-of-the-art few-shot learning methods using both GNN and non-GNN approaches.The improvement is consistent over the mini-ImageNet, tiered-ImageNet, CUB-200-2011, and Flowers-102 benchmarks, using both ConvNet-4 and ResNet-12 backbones, and under both the inductive and transductive settings.Furthermore, we demonstrate the superiority of our method for few-shot fine-grained and semi-supervised classification tasks with extensive experiments.The code for this work is publicly available at https://github.com/chenghao-ch94/AGNN.
A Multi-Scale Spatio-Temporal Network for Violence Behavior Detection

Violence behavior detection has played an important role in computer vision, its widely used in unmanned security monitoring systems, Internet video filtration, etc. However, automatically detecting violence behavior from surveillance cameras has long been a challenging issue due to the real-time and detection accuracy. In this brief, a novel multi-scale spatio-temporal network termed as MSTN is proposed to detect violence behavior from video stream. To begin with, the spatio-temporal feature extraction module (STM) is developed to extract the key features between foreground and background of the original video. Then, temporal pooling and cross channel pooling are designed to obtain short frame rate and long frame rate from STM, respectively. Furthermore, short-time building (STB) branch and long-time building (LTB) branch are presented to extract the violence features from different spatio-temporal scales, where STB module is used to capture the spatial feature and LTB module is used to extract useful temporal feature for video recognition. Finally, a Trans module is presented to fuse the features of STB and LTB through lateral connection operation, where LTB feature is compressed into STB to improve the accuracy. Experimental results show the effectiveness and superiority of the proposed method on computational efficiency and detection accuracy.