Reviewerly/exHarmony huggingface dataset:
CGAN BeiDou Satellite Short-Message-Encryption Scheme Using Ship PVT

The transmission standard and format of short-message communication in the BeiDou satellite-navigation-system are open, the civilian channel adopts a direct-transmission plaintext mechanism, and the content of short messages is at risk of data leaks. Aiming at addressing the problem of safely transmitting BeiDou short messages, this study proposes a CGAN BeiDou satellite short-message-encryption scheme, using ship PVT (position, speed, and time). Various BeiDou commands with ship position, speed, or time are employed as the input. The two communicating parties use the CGAN (conditional generative adversarial network) confrontation mechanism to encrypt and decrypt the ship-PVT information and generate a symmetrically encrypted key, while the receiver parses only the sender data within the specified PVT-range. Additionally, because the BeiDou system has a positioning error and transmission delay, and considering the ship mobility, the concept of a dynamic tolerance region is introduced at the receiver, to improve the scheme’s decryption success-rate. Finally, the proposed scheme is verified, using simulation and experiments. The proposed algorithm achieves good security, with acceptable efficiency. Furthermore, the experimental platform built by this study is used to prove the feasibility of the scheme applied to BeiDou short-message encryption and decryption.
A Framework to Analyze Function Domains of Autonomous Transportation Systems Based on Text Analysis

With the development of information and communication technologies, the current intelligent transportation systems (ITSs) will gradually become automated and connected, and can be treated as autonomous transportation systems (ATSs). Function, which unites cutting-edge technology with ATS services as a fundamental component of ATS operation, should be categorized into function domains to more clearly show how ATS operates. Existing ITS function domains are classified mostly based on the experience of experts or the needs of practitioners, using vague classification criteria. To ensure tractability, we aim to categorize ATS functions into function domains based on text analysis, minimizing the reliance on subjective experience. First, we introduce the Latent Dirichlet Allocation (LDA) topic model to extract text features of functions into distribution weights, reflecting the semantics of the text data. Second, based on the LDA model, we categorize ATS functions into twelve function domains by the k-means method. The comparison between the proposed function domains and the existing counterparts of other ITS framework demonstrates the effectiveness of the LDA-based classification method. This study provides a reference for text processing and function classification of ATS architecture. The proposed functions and function domains reveal the objectives in future transportation systems, which could guide urban planners or engineers to better design control strategies when facing new technologies.
Research on Federated Learning Data Sharing Scheme Based on燚ifferential燩rivacy

To realize data sharing, and to fully use the data value, breaking the data island between institutions to realize data collaboration has become a new sharing mode.This paper proposed a distributed data security sharing scheme based on C/S communication mode, and constructed a federated learning architecture that uses differential privacy technology to protect training parameters.Clients do not need to share local data, and they only need to upload the trained model parameters to achieve data sharing.In the process of training, a distributed parameter update mechanism is introduced.The server is mainly responsible for issuing training commands and parameters, and aggregating the local model parameters uploaded by the clients.The client mainly uses the stochastic gradient descent algorithm for gradient trimming, updates, and transmits the trained model parameters back to the server after differential processing.To test the performance of the scheme, in the application scenario where many medical institutions jointly train the disease detection system, the model is tested from multiple perspectives by taking medical data as an example.From the testing results, we can know that for this specific test dataset, when the parameters are properly configured, the lowest prediction accuracy rate is 90.261% and the highest accuracy rate is up to 94.352.It shows that the performance of the model is good.The results also show that this scheme realizes data sharing while protecting data privacy, completes accurate prediction of diseases, and has a good effect.
Contrastive Learning via Local Activity

Contrastive learning (CL) helps deep networks discriminate between positive and negative pairs in learning. As a powerful unsupervised pretraining method, CL has greatly reduced the performance gap with supervised training. However, current CL approaches mainly rely on sophisticated augmentations, a large number of negative pairs and chained gradient calculations, which are complex to use. To address these issues, in this paper, we propose the local activity contrast (LAC) algorithm, which is an unsupervised method based on two forward passes and locally defined loss to learn meaningful representations. The learning target of each layer is to minimize the activation value difference between two forward passes, effectively overcoming the limitations of applying CL above mentioned. We demonstrated that LAC could be a very useful pretraining method using reconstruction as the pretext task. Moreover, through pretraining with LAC, the networks exhibited competitive performance in various downstream tasks compared with other unsupervised learning methods.
On the Convergence Properties of a Stochastic Trust-Region Method with Inexact Restoration

We study the convergence properties of SIRTR, a stochastic inexact restoration trust-region method suited for the minimization of a finite sum of continuously differentiable functions. This method combines the trust-region methodology with random function and gradient estimates formed by subsampling. Unlike other existing schemes, it forces the decrease of a merit function by combining the function approximation with an infeasibility term, the latter of which measures the distance of the current sample size from its maximum value. In a previous work, the expected iteration complexity to satisfy an approximate first-order optimality condition was given. Here, we elaborate on the convergence analysis of SIRTR and prove its convergence in probability under suitable accuracy requirements on random function and gradient estimates. Furthermore, we report the numerical results obtained on some nonconvex classification test problems, discussing the impact of the probabilistic requirements on the selection of the sample sizes.
Squirrel Search Optimization with Deep Convolutional Neural Network for Human Pose Estimation

Human pose estimation (HPE) is a procedure for determining the structure of the body pose and it is considered a challenging issue in the computer vision (CV) communities.HPE finds its applications in several fields namely activity recognition and human-computer interface.Despite the benefits of HPE, it is still a challenging process due to the variations in visual appearances, lighting, occlusions, dimensionality, etc.To resolve these issues, this paper presents a squirrel search optimization with a deep convolutional neural network for HPE (SSDCNN-HPE) technique.The major intention of the SSDCNN-HPE technique is to identify the human pose accurately and efficiently.Primarily, the video frame conversion process is performed and pre-processing takes place via bilateral filtering-based noise removal process.Then, the EfficientNet model is applied to identify the body points of a person with no problem constraints.Besides, the hyperparameter tuning of the EfficientNet model takes place by the use of the squirrel search algorithm (SSA).In the final stage, the multiclass support vector machine (M-SVM) technique was utilized for the identification and classification of human poses.The design of bilateral filtering followed by SSA based EfficientNet model for HPE depicts the novelty of the work.To demonstrate the enhanced outcomes of the SSDCNN-HPE approach, a series of simulations are executed.The experimental results reported the betterment of the SSDCNN-HPE system over the recent existing techniques in terms of different measures.
Transfer Learning-Based Semi-Supervised Generative Adversarial Network for Malaria Classification

Malaria is a lethal disease responsible for thousands of deaths worldwide every year.Manual methods of malaria diagnosis are timeconsuming that require a great deal of human expertise and efforts.Computerbased automated diagnosis of diseases is progressively becoming popular.Although deep learning models show high performance in the medical field, it demands a large volume of data for training which is hard to acquire for medical problems.Similarly, labeling of medical images can be done with the help of medical experts only.Several recent studies have utilized deep learning models to develop efficient malaria diagnostic system, which showed promising results.However, the most common problem with these models is that they need a large amount of data for training.This paper presents a computer-aided malaria diagnosis system that combines a semi-supervised generative adversarial network and transfer learning.The proposed model is trained in a semi-supervised manner and requires less training data than conventional deep learning models.Performance of the proposed model is evaluated on a publicly available dataset of blood smear images (with malariainfected and normal class) and achieved a classification accuracy of 96.6%.
Exploiting Human Pose and Scene Information for Interaction Detection

Identifying human actions and interactions finds its use in many areas, such as security, surveillance, assisted living, patient monitoring, rehabilitation, sports, and e-learning.This wide range of applications has attracted many researchers to this field.Inspired by the existing recognition systems, this paper proposes a new and efficient human-object interaction recognition (HOIR) model which is based on modeling human pose and scene feature information.There are different aspects involved in an interaction, including the humans, the objects, the various body parts of the human, and the background scene.The main objectives of this research include critically examining the importance of all these elements in determining the interaction, estimating human pose through image foresting transform (IFT), and detecting the performed interactions based on an optimized multi-feature vector.The proposed methodology has six main phases.The first phase involves preprocessing the images.During preprocessing stages, the videos are converted into image frames.Then their contrast is adjusted, and noise is removed.In the second phase, the human-object pair is detected and extracted from each image frame.The third phase involves the identification of key body parts of the detected humans using IFT.The fourth phase relates to three different kinds of feature extraction techniques.Then these features are combined and optimized during the fifth phase.The optimized vector is used to classify the interactions in the last phase.The MSR Daily Activity 3D dataset has been used to test this model and to prove its efficiency.The proposed system obtains an average accuracy of 91.7% on this dataset.
Aspect Extraction Approach for Sentiment Analysis Using Keywords

Sentiment Analysis deals with consumer reviews available on blogs, discussion forums, E-commerce websites, and App Store.These online reviews about products are also becoming essential for consumers and companies as well.Consumers rely on these reviews to make their decisions about products and companies are also very interested in these reviews to judge their products and services.These reviews are also a very precious source of information for requirement engineers.But companies and consumers are not very satisfied with the overall sentiment; they like fine-grained knowledge about consumer reviews.Owing to this, many researchers have developed approaches for aspect-based sentiment analysis.Most existing approaches concentrate on explicit aspects to analyze the sentiment, and only a few studies rely on capturing implicit aspects.This paper proposes a Keywords-Based Aspect Extraction method, which captures both explicit and implicit aspects.It also captures opinion words and classifies the sentiment about each aspect.We applied semantic similarity-based WordNet and SentiWordNet lexicon to improve aspect extraction.We used different collections of customer reviews for experiment purposes, consisting of eight datasets over seven domains.We compared our approach with other state-of-the-art approaches, including Rule Selection using Greedy Algorithm (RSG), Conditional Random Fields (CRF), Rule-based Extraction (RubE), and Double Propagation (DP).Our results have shown better performance than all of these approaches.
Intelligent Deep Learning Based Cybersecurity Phishing Email Detection and Classification

Phishing is a type of cybercrime in which cyber-attackers pose themselves as authorized persons or entities and hack the victims' sensitive data.E-mails, instant messages and phone calls are some of the common modes used in cyberattacks.Though the security models are continuously upgraded to prevent cyberattacks, hackers find innovative ways to target the victims.In this background, there is a drastic increase observed in the number of phishing emails sent to potential targets.This scenario necessitates the importance of designing an effective classification model.Though numerous conventional models are available in the literature for proficient classification of phishing emails, the Machine Learning (ML) techniques and the Deep Learning (DL) models have been employed in the literature.The current study presents an Intelligent Cuckoo Search (CS) Optimization Algorithm with a Deep Learning-based Phishing Email Detection and Classification (ICSOA-DLPEC) model.The aim of the proposed ICSOA-DLPEC model is to effectually distinguish the emails as either legitimate or phishing ones.At the initial stage, the pre-processing is performed through three stages such as email cleaning, tokenization and stop-word elimination.Then, the N-gram approach is; moreover, the CS algorithm is applied to extract the useful feature vectors.Moreover, the CS algorithm is employed with the Gated Recurrent Unit (GRU) model to detect and classify phishing emails.Furthermore, the CS algorithm is used to fine-tune the parameters involved in the GRU model.The performance of the proposed ICSOA-DLPEC model was experimentally validated using a benchmark dataset, and the results were assessed under several dimensions.Extensive comparative studies were conducted, and the results confirmed the superior performance of the proposed ICSOA-DLPEC model over other existing approaches.The proposed model achieved a maximum accuracy of 99.72%.5902
A Credit Card Fraud Model Prediction Method Based on Penalty Factor Optimization AWTadaboost

With the popularity of online payment, how to perform credit card fraud detection more accurately has also become a hot issue.And with the emergence of the adaptive boosting algorithm (Adaboost), credit card fraud detection has started to use this method in large numbers, but the traditional Adaboost is prone to overfitting in the presence of noisy samples.Therefore, in order to alleviate this phenomenon, this paper proposes a new idea: using the number of consecutive sample misclassifications to determine the noisy samples, while constructing a penalty factor to reconstruct the sample weight assignment.Firstly, the theoretical analysis shows that the traditional Adaboost method is overfitting in a noisy training set, which leads to the degradation of classification accuracy.To this end, the penalty factor constructed by the number of consecutive misclassifications of samples is used to reconstruct the sample weight assignment to prevent the classifier from over-focusing on noisy samples, and its reasonableness is demonstrated.Then, by comparing the penalty strength of the three different penalty factors proposed in this paper, a more reasonable penalty factor is selected.Meanwhile, in order to make the constructed model more in line with the actual requirements on training time consumption, the Adaboost algorithm with adaptive weight trimming (AWTAdaboost) is used in this paper, so the penalty factor-based AWTAdaboost (PF_AWTAdaboost) is finally obtained.Finally, PF_AWTAdaboost is experimentally validated against other traditional machine learning algorithms on credit card fraud datasets and other datasets.The results show that the PF_AWTAdaboost method has better performance, including detection accuracy, model recall and robustness, than other methods on the credit card fraud dataset.And the PF_AWTAdaboost method also shows excellent generalization performance on other datasets.From the experimental results, it is shown that the PF_AWTAdaboost algorithm has better classification performance.
Quantum Multi-Round Resonant Transition Algorithm

Solving the eigenproblems of Hermitian matrices is a significant problem in many fields. The quantum resonant transition (QRT) algorithm has been proposed and demonstrated to solve this problem using quantum devices. To better realize the capabilities of the QRT with recent quantum devices, we improve this algorithm and develop a new procedure to reduce the time complexity. Compared with the original algorithm, it saves one qubit and reduces the complexity with error ϵ from O(1/ϵ2) to O(1/ϵ). Thanks to these optimizations, we can obtain the energy spectrum and ground state of the effective Hamiltonian of the water molecule more accurately and in only 20 percent of the time in a four-qubit processor compared to previous work. More generally, for non-Hermitian matrices, a singular-value decomposition has essential applications in more areas, such as recommendation systems and principal component analysis. The QRT has also been used to prepare singular vectors corresponding to the largest singular values, demonstrating its potential for applications in quantum machine learning.
Billiards Optimization Algorithm: A New Game-Based Metaheuristic Approach

Metaheuristic algorithms are one of the most widely used stochastic approaches in solving optimization problems.In this paper, a new metaheuristic algorithm entitled Billiards Optimization Algorithm (BOA) is proposed and designed to be used in optimization applications.The fundamental inspiration in BOA design is the behavior of the players and the rules of the billiards game.Various steps of BOA are described and then its mathematical model is thoroughly explained.The efficiency of BOA in dealing with optimization problems is evaluated through optimizing twenty-three standard benchmark functions of different types including unimodal, high-dimensional multimodal, and fixed-dimensional multimodal functions.In order to analyze the quality of the results obtained by BOA, the performance of the proposed approach is compared with ten well-known algorithms.The simulation results show that BOA, with its high exploration and exploitation abilities, achieves an impressive performance in providing solutions to objective functions and is superior and far more competitive compared to the ten competitor algorithms.
Adaptive Resource Planning for AI Workloads with Variable Real-Time Tasks

AI (Artificial Intelligence) workloads are proliferating in modern real-time systems.As the tasks of AI workloads fluctuate over time, resource planning policies used for traditional fixed real-time tasks should be reexamined.In particular, it is difficult to immediately handle changes in real-time tasks without violating the deadline constraints.To cope with this situation, this paper analyzes the task situations of AI workloads and finds the following two observations.First, resource planning for AI workloads is a complicated search problem that requires much time for optimization.Second, although the task set of an AI workload may change over time, the possible combinations of the task sets are known in advance.Based on these observations, this paper proposes a new resource planning scheme for AI workloads that supports the re-planning of resources.Instead of generating resource plans on the fly, the proposed scheme pre-determines resource plans for various combinations of tasks.Thus, in any case, the workload is immediately executed according to the resource plan maintained.Specifically, the proposed scheme maintains an optimized CPU (Central Processing Unit) and memory resource plan using genetic algorithms and applies it as soon as the workload changes.The proposed scheme is implemented in the opensource simulator SimRTS for the validation of its effectiveness.Simulation experiments show that the proposed scheme reduces the energy consumption of CPU and memory by 45.5% on average without deadline misses.
Two-Stream Deep Learning Architecture-Based Human Action Recognition

Human action recognition (HAR) based on Artificial intelligence reasoning is the most important research area in computer vision.Big breakthroughs in this field have been observed in the last few years; additionally, the interest in research in this field is evolving, such as understanding of actions and scenes, studying human joints, and human posture recognition.Many HAR techniques are introduced in the literature.Nonetheless, the challenge of redundant and irrelevant features reduces recognition accuracy.They also faced a few other challenges, such as differing perspectives, environmental conditions, and temporal variations, among others.In this work, a deep learning and improved whale optimization algorithm based framework is proposed for HAR.The proposed framework consists of a few core stages i.e., frames initial preprocessing, fine-tuned pre-trained deep learning models through transfer learning (TL), features fusion using modified serial based approach, and improved whale optimization based best features selection for final classification.Two pre-trained deep learning models such as InceptionV3 and Resnet101 are fine-tuned and TL is employed to train on action recognition datasets.The fusion process increases the length of feature vectors; therefore, improved whale optimization algorithm is proposed and selects the best features.The best selected features are finally classified using machine learning (ML) classifiers.Four publicly accessible datasets such as Ut-interaction, Hollywood, Free Viewpoint Action Recognition using Motion History Volumes (IXMAS), and centre of computer vision (UCF) Sports, are employed and achieved the testing accuracy of 100%, 99.9%, 99.1%, and 100% respectively.Comparison with state of the art techniques (SOTA), the proposed method showed the improved accuracy.5932