Reviewerly/exHarmony huggingface dataset:
A defogging method for infrared camera based on polarization imaging

Due to the great influence of fog on life and production, this paper uses Stokes principle in polarization imaging combined with the physical model of fog imaging to design and implement an infrared camera fog removal method based on polarization imaging. In this paper, the defogging method of infrared camera based on polarization imaging is introduced in detail, and the sensor is verified by outdoor experiments. The experimental results show that the overall defogging effect of the proposed defogging method is good, with more details of the target scene preserved and strong contrast, which makes the restoration degree of the target scene higher than that of several classical defogging methods.
Feature-concatenated transformer for 3D object tracking in point clouds

Feature fusion is a key problem in 3D object tracking, especially in sparse and disordered point clouds scenes. The purpose of feature fusion is to achieve the communication and integration of template features and search features, so as to obtain the fusion features with object-specific information. However, most pervious Transformer-based methods use the SelfAttention Module(SAM) and Cross-Attention Module(CAM) to conduct attention operations progressively in two steps, which is not conducive to focus on the discriminative features from the beginning. Benefiting from the flexibility of attention operations, we propose a Feature-Concatenated Attention Module (FCAM) for ego-feature enhancement and cross-feature augment at the same time. Based on FCAM, we propose a Feature-Concatenated Transformer (FCT) framework to explore more effective 3D object tracking method. This scheme is more useful to achieve deeper integration and extensive communication between template and search features, which makes feature fusion more efficient. In order to verify the performance of the proposed framework, we carried out experimental verification on KITTI datasets. The results of the experiment indicate that our method is superior to the existing schemes in tracking success and accuracy for different object categories.
Temporal-Based Action Clustering for Motion Tendencies

Video-based action recognition encompasses the recognition of appearance and the classification of action types. This work proposes a discrete-temporal-sequence-based motion tendency clustering framework to implement motion clustering by extracting motion tendencies and self-supervised learning. A published traffic intersection dataset (inD) and a self-produced gesture video set are used for evaluation and to validate the motion tendency action recognition hypothesis.
In-vehicle LAN Data Utilization System Using Mutual Authentication Method Considering Key Management

To realize connected and/or self-driving vehicles for a mobility environment, it requires an authentication mechanism to prevent spoofing of on-board units of the vehicle and a mechanism to prevent eavesdropping and tampering of communication data between an on-board unit and servers in the cloud. Considering the huge number of vehicles in the system, an authentication method is required to mitigate the complexity of operation and management for the number of vehicles. In this paper, we propose a data utilization system that securely stores in-vehicle LAN data, which consist of control data flowing inside a vehicle, in the cloud in real time, and provides this information securely to third parties such as vehicle users, car dealers, and non-life insurance companies. The system uses ID-based cryptography as a mutual authentication method between on-board units and the data collection servers, and uses a key generated from the ID of an on-board unit to ease the key management. We integrate the management information of the vehicle and the authentication key of the vehicle in the system, and thus reduces the complexity of the operation and the key management.
Application of Machine Learning In Renewable Energy: A Bibliometric Analysis of a Decade

Machine learning studies in the field of renewable energy are analysed here (REML). So, from 2012 to 2021, we looked at the publication tendencies (PT) and bibliometric analysis (BA) of REML research that was indexed by Elsevier Scopus. Key insights into the research landscape, scientific discoveries, and technological advancement were revealed by BA, while PT highlighted REML’s important players, top cited papers, and financing organisations. In total, the PT discovered 1,218 works, 397 of which were conference papers and 106 were reviews. Because it spans the disciplines of science, technology, engineering, and mathematics, REML research is exhaustive, varied, and consequential. The most productive researchers, countries, and sponsors include Ravinesh C. Deo, the United States’ National Renewable Energy Laboratory, and China’s National Natural Science Foundation. Journal prestige and open access are valued by contributors, as seen by the success of Applied Energy and Energies. Productivity among REML’s key stakeholders is boosted by collaborations and research funding. Keyword co-occurrence analysis was used to categorise REML research into four broad topic areas: systems, technologies, tools/technologies, and socio-technical dynamics. According to the results, ML plays a crucial role in the prediction, operation, and optimisation of RET as well as the design and development of RE-related materials.
Exploiting Deep Neural Networks as Covert Channels

With the increasing development of deep learning models, the security of these models has become more important. In this work, for the first time, we have investigated the possibility of abusing the deep model as a covert channel. The concept of a covert channel is to use a channel that is not designed for information exchange for transmitting a covert message. This work studies how a deep model can be used by an adversary as a covert channel. The proposed approach is using an end-to-end training deep model called the covert model to produce artificial data which includes some covert messages. This artificial data is the input of the deep model, which is aimed at being exploited as a covert channel, in such a way that the signal will be covered in the output of this model. To achieve indistinguishability of concealment, generative adversarial networks are used. The results show that it is possible to have a covert channel with an acceptable message transmission power in well-known deep models such as the ResNet and InceptionV3 models. Results of case studies indicate the signal-to-noise ratio (SNR) of 12.67, the bit error rate (BER) of 0.08, and the accuracy of the deep model used to hide the signal reaches 92%.
Improved Exploration With Demonstrations in Procedurally-Generated Environments

Exploring sparse reward environments remains a major challenge in model-free deep reinforcement learning. State-of-the-art exploration methods address this challenge by utilizing intrinsic rewards to guide exploration in uncertain environment dynamics or novel states. However, these methods fall short in procedurally-generated environments, where the agent is unlikely to visit a state more than once due to the different environments generated in each episode. Recently, imitation learning-based exploration methods have been proposed to guide exploration in different kinds of procedurally-generated environments by imitating high-quality exploration episodes. However, these methods have weaker exploration capabilities and lower sample efficiency in complex procedurally-generated environments. Motivated by the fact that demonstrations can guide exploration in sparse reward environments, we propose Improved Exploration with Demonstrations (IEWD), an improved imitation learning-based exploration method in procedurally-generated environments, which utilizes demonstrations from these environments. IEWD assigns different episode-level exploration scores to each demonstration episode and generated episode. IEWD then ranks these episodes based on their scores and stores highly-scored episodes into a small ranking buffer. IEWD treats these highly-scored episodes as good exploration episodes, and makes the deep RL agent imitate exploration behaviors from the ranking buffer to reproduce exploration behaviors from good exploration episodes. Additionally, IEWD adopts the experience replay buffer to store generated positive episodes and demonstrations, and employs self-imitating learning to utilize experiences from the experience replay buffer to optimize the policy of the deep RL agent. We evaluate our method IEWD on several procedurally-generated MiniGrid environments and 3D maze environments from MiniWorld. The results show that IEWD significantly outperforms existing learning from demonstration methods and exploration methods, including state-of-the-art imitation learning-based exploration methods, in terms of sample efficiency and final performance in complex procedurally-generated environments.
A3D: Adaptive, Accurate, and Autonomous Navigation for Edge-Assisted Drones

Accurate navigation is of paramount importance to ensure flight safety and efficiency for autonomous drones. Recent research starts to use Deep Neural Networks (DNN) to enhance drone navigation given their remarkable predictive capability for visual perception. However, existing solutions either run DNN inference tasks on drones in situ, impeded by the limited onboard resource, or offload the computation to external servers which may incur large network latency. Few works consider jointly optimizing the offloading decisions along with image transmission configurations and adapting them on the fly. In this paper, we propose A3D, an edge server assisted drone navigation framework that can dynamically adjust task execution location, input resolution, and image compression ratio in order to achieve low inference latency, high prediction accuracy, and long flight distances. Specifically, we first augment state-of-the-art convolutional neural networks for drone navigation and define a novel metric called Quality of Navigation as our optimization objective which can effectively capture the above goals. We then design a deep reinforcement learning (DRL) based neural scheduler at the drone side for which an information encoder is devised to reshape the state features and thus improve its learning ability. To further support simultaneous multi-drone serving, we extend the edge server design by developing a network-aware resource allocation algorithm, which allows provisioning containerized resources aligned with drones’ demand. We finally implement a proof-of-concept prototype with realistic devices and validate its performance in a real-world campus scene, as well as a simulation environment for thorough evaluation upon AirSim. Extensive experimental results show that A3D can reduce end-to-end latency by 28.06% and extend the flight distance by up to 27.28% compared with non-adaptive solutions.
ELM-MFO: A New Nature-Inspired Predictive Model for Financial Contagion Modeling of Indian Currency Market

This paper proposes an enhanced hybridized machine learning approach to forecast future price exchange rates such as US Dollar (USD), Great Britain Pound (GBP) and Australian Dollar (AUD) to INR. This hybridized machine learning approach mainly consists of popularly used Extreme Learning Machine (ELM) and different optimization techniques used to optimize ELM parameters using Particle Swarm Optimization (PSO), Gray Wolf Optimization (GWO) and Moth Flame Optimization (MFO). Out of three optimization techniques, it has been observed that ELM with MFO (ELM-MFO) provides best accuracy in the process of forecasting as compared to rest two optimization techniques. The datasets used for experiments have been collected from public platform having different time delay formats such as one day, seven days, fifteen days and thirty days. Also, several technical indicators as well as statistical measures have been used to augment the original currency pair dataset to get a deeper insight of the datasets. From the experimentation, comparison and validation, it has been proved that the proposed ELM-MFO outperforms all the networks used for the comparison and achieves higher accuracy of 97% and 95% considering the overall and average accuracy, respectively, and additionally, the statistical validation through Kappa statistics shows the strong-level agreement with 73.25% of this ELM-MFO for the augmented currency pair datasets with combination of original attribute, TIs and SMs.
Performance and Analysis of Propagation Delay in the Bitcoin Network

Bitcoin denotes virtual money that relies on a P2P network to spread and validate transactions. Because it is decentralized, Bitcoin is different from conventional currencies. In this study, we provide an event-based simulated model of the Bitcoin P2P network. To allow accurate parameterization of the provided simulation model, extensive evaluation of the actual Bitcoin network is made. Additionally, validation findings show that the simulation model performs very identically to the actual Bitcoin network. Here the suggested Bitcoin Clustering Based Super Node (BCBSN) protocol is evaluated using the built simulation model as a means of accelerating information transmission in the Bitcoin network. The results of the evaluation demonstrate that the proposed clustering methodology may minimize the transaction propagation latency by a respectable amount.
Image Tagging Using Deep Learning

Deep learning is the process of training computers to think critically by implementing brain-inspired architectural design. An explanation in written form is given for each image in a collection through the process of image captioning. In the area of deep learning, it has been a significant and imperative effort. Image captioning has many applications. A application was made using picture captioning technology to help those with little or no vision. Image captioning may be considered as an end-to-end Sequence-to-Sequence problem since it converts photos, which are conceptualized as a series of pixels, into a sequence of words. This requires processing of both the words or assertions and the pictures. Recurrent neural networks (RNNs) are used to build the feature vectors for the linguistic component, whereas convolutional neural networks (CNNs) are used to create the feature vectors for the image component. We are working with information that is both semantically and visually based. In particular, we require a linguistic Long Short-Term Memory (LSTM) model to construct a word sequence. This prompts the query of whether to add vectors of image data to the language model. By searching for patterns in the pictures, convolution neural networks (CNNs) are highly useful for identifying objects, people, and settings in photographs. Considering datasets given during model development, Natural Language Processing (NLP) allows for the tagging of images with English keywords.
Medical Image Watermarking Using Slantlet Transform and Particle Swarm Optimization

Image quality can be increased to achieve the best imperceptibility without losing its robustness which is one of the benefits of using optimal schema as the process of embedding secret messages originating from PSO. The proposed technology uses improved SLT transient localization as an improved version of discrete wavelet transform (DWT). In addition, SLT is a multi-line component, and SLT uses zero moment, which is a better signal amplifier than DWT and discrete cosine transform (DCT). PSO is a popular optimization technique and is used to balance failure with success, because PSO increases the weight in the embedding process. As we know that SLT and PSO have been used by researchers for watermarks, this paper is used to examine the potential of SLT–PSO to be understood without compromising its power. In this paper, a technique designed to improve SLT performance during experiments on medical images is demonstrated.
Robust Scene Text Detection Under Occlusion via Multi-scale Adaptive Deep Network

Detecting text under occlusion in natural images is a challenge in scene text detection, which is severely sensitive and dramatically affects the performance of this field. Despite several research papers discussing the issue of occluded text in natural images, they still struggle to accurately identify word regions that are split by occlusion phenomena. In this paper, we first exploit the salient attention maps from Gradient Class Activation Maps Plus Plus (Grad-CAM++) to obtain knowledge of the important regions in the images. Furthermore, to effectively capture the different scales of text instances and enhance feature representations, we create a MulTi-scale adaptive Deep network (MTD). In addition, from ICDAR 2015 benchmark, we build occluded text, namely Realistic Occluded Text Detection dataset (ROTD), and then combine a part of ROTD dataset with the ICDAR 2015 dataset for the training stage to gain occluded text perception. Throughout these works, our model significantly improves the accuracy of text detection containing partially occluded text in natural scenes. Our proposed method achieves state-of-the-art results on partial occlusion text detection with F1-score of 69.6% on ISTD-OC, 78.7% on our ROTD, and validates competitive performance F1-score of 82.4% on ICDAR 2015 benchmark.
Customized CNN for Traffic Sign Recognition Using Keras Pre-Trained Models

Machine learning (ML) is the process of teaching a machine to understand and make decisions on real-world problems using an efficient set of algorithms. With almost every single task being automated, one such big leap in the fields of artificial intelligence and machine learning is the development of advanced driver assistance systems (ADAS). With the increasing demand for the intelligence of vehicles and the advent of self-driving cars, it is extremely necessary to detect and recognize traffic signs automatically through computer technology. Among all the components that come together to make an efficient and highly accurate ADAS, one of them is traffic sign recognition. Amidst all the publicly available datasets, this paper uses the GTSRB dataset. Our proposed model for traffic sign recognition is better than the other state-of-the-art models as we harness the power of 1 * 1 convolution blocks. In this paper, we propose a customized convolutional neural network (CNN), which extends the architecture of the existing pre-trained Keras models. This paper also tries to analyze the effect of different hyperparameters like batch size, number of hidden layers and hidden neurons, and different activation functions, on the accuracy with which it classifies the different traffic signs.
Predicting Corresponding Ratings from Goodreads Book Reviews

With the enhancement of the online world, people tend to trust and mould their opinions with respect to the text provided on commercial and social websites. The product or service reviews provided over the internet enable a new customer to form an opinion regarding the quality, and at the same time, the numeric rating provides a foundation for a quick decision on whether to accept the product or not. In this paper, we have proposed the state-of-the-art classification and Recurrent Neural Network (RNN) models along with the TF-IDF vectorizer, that predicts the numeric rating on a scale of 0–5 associated with a text review of a book on the Goodreads website. We have used Decision Tree, K-nearest neighbours, Logistic Regression, Gradient Boosting and Random Forest classifier and analysed their performance through accuracy, F1-score, recall and precision. The accuracies for the models were obtained as 37.29% for Decision Tree, 47.72% for Gradient Boosting, 46.05% for Random Forest, 23.41% for KNN with the best accuracy obtained as 49.50% from the Logistic Regression model. A more better result is obtained by implementation of the Recurrent Neural Network model on the set with the LSTM layer with an accuracy of 53.61%. The implementation result is helpful for book readers to determine their opinion about essence of a book.
Fusion Unleashed: A Comprehensive Analysis of Egypt's Digital Potential, the Growing Digital Economy, and its Socio-Economic Implications

This research paper explores the fusion-driven dynamics of Egypt's digital economy and its socio-economic implications, with a specific focus on the growth of e-commerce, FinTech, startups, and digital skills development. It explores the transformative effects of the digital economy on entrepreneurship, job creation, and inclusive participation in Egypt's evolving digital landscape. The study examines the barriers hindering inclusive access to digital technologies, digital skills, and digital financial services, aiming to propose strategies for promoting digital inclusion. Additionally, the research explores the role of social entrepreneurship in bridging the digital divide and fostering sustainable development in Egypt's digital economy. By analyzing the impact of social entrepreneurship initiatives, the study sheds light on innovative models that empower marginalized communities and contribute to the inclusive growth of the digital economy. The findings of this research contribute to the existing body of knowledge by providing insights into the unique dynamics of Egypt's digital economy and the potential of social entrepreneurship in driving digital inclusion and sustainable development. The study concludes with recommendations for policymakers, businesses, and stakeholders to foster an enabling environment that supports equitable and inclusive participation in Egypt's digital economy.
Multi-attributed Face Synthesis for One-Shot Deep Face Recognition

Nothing is more unique and crucial to an individual’s identity than their face. With the rapid improvement in computational power and memory space and recent specializations in deep learning models, images are becoming more essential than ever for pattern recognition. Several deep face recognition models have recently been proposed to train deep networks on enormously big public datasets like MSCeleb-1M [8] and VG-GFace2 [5], successfully achieving sophisticated performance on mainstream applications. It is particularly challenging to gather an adequate dataset that allows strict command over the desired properties, such as hair color, skin tone, makeup, age alteration, etc. As a solution, we devised a one-shot face recognition system that utilizes synthetic data to recognize a face even if the facial attributes are altered. This work proposes and investigates the feasibility of creating a multi-attributed artificial face dataset from a one-shot image to train the deep face recognition model. This research seeks to demonstrate how the image synthesis capability of the deep learning methods can construct a face dataset with multiple critical attributes for a recognition process to enable and enhance efficient face recognition. In this study, the ideal deep learning features will be combined with a conventional one-shot learning framework. We did experiments for our proposed model on the LFW and multi-attributed synthetic data; these experiments highlighted some insights that can be helpful in the future for one-shot face recognition.
Teaching Principles and Methods of Secondary School Mathematics

Teaching principles and teaching methods are the product of a certain social ideological system, while the social ideological system is a reflection of a certain social structure. The teaching principles and methods of secondary school mathematics have always been two important and closely related topics in mathematics teaching. The research on them is theoretically and practically significant.