Reviewerly/exHarmony huggingface dataset:
An End-to-End Communication System with Environmental Adaptability

Existing E2E systems would suffer performance degradation when applied in the environments where the channel conditions deviate from the trained ones. To address this problem, we propose a comprehensive method to develop an E2E communication system that possesses strong adaptability under dynamically varying unknown environments. From the system structure prospective, our system consists of the impulse generative adversarial network (IGAN) and transceiver. Within the E2E system, a novel transceiver is proposed, which maps information bit sequences to symbol vectors to introduce the inter-symbols correlation such that the signals can be detected without pilots. Hence, the transceiver is more flexible in a changing fading environment while achieving higher spectrum efficiency. On top of that, the model-agnostic meta-learning (MAML) is utilized to meta-train the IGAN to enable fast adaptation to practical noise samples. The transceiver is then general-trained based on the designed fading channel set that includes various time and frequency selectivity and the simulated noise set. Simulation results show that our proposed E2E system can outperform conventional approaches both in terms of BER, overheads and adaptability.
Validating Requirement Specification Using Text Mining Analysis

Requirement elicitation is the first step of requirement engineering where software developers focus on obtaining the users' needs and produce a requirement specification. This activity can be accomplished through conducting interviews. However, the requirement specified might not conform to the actual interview transcripts from the elicitation process due to perspective differences between the clients and developers. This study aims to propose a method to validate the requirement specification using text mining analysis. The method involves text preprocessing and text similarity analysis between the specification and elicitation transcripts. The proposed method is validated using interview elicitation transcripts and the software requirement specification (SRS) from the Baker's Corner application. The results demonstrate that the proposed method can effectively validate requirement specifications, producing comparable results to the manual validation process. This finding highlights the potential of automating the requirement validation process using text mining techniques. Additionally, the paper offers recommendations for future studies and developers to enhance the generation of software requirements.
FPGA Implementation Mix Column for BCF Algorithm

Block Cipher Four Algorithm is used to protect data. BCF Architecture has been developed, but MixColoumn operation in that architecture become bottleneck. In this paper, we proposed fast MixColoumn architecture using parallel computing technique. Fast MixColoumn architecture can reduce 128 bit Encryption opereation clock from 182 to 122 clock. As a trade off, the number of Logic Elements and Registers required by the Fast MixColumn Architecture is 1.16 and 1.68 more. The Fast MixColoumn architecture offers a maximum speed of 1.15 times faster.
Car Body Damage Detection System Using YOLOv7

Car damage inspection is an important step when submitting car insurance claims. Currently, insurance companies manually gather car damage assessment reports. However, this approach takes a lot of time and is susceptible to fraud. Advances in data science and artificial intelligence offer solutions for automated systems for car damage detection. This system can detect and classify different types of car damage. Implementation of this system can reduce operational costs, save time, and minimize leakage. In this study, we proposed an object detection algorithm that is YOLOv7 to automate car damage detection. Three variations from the YOLOv7 algorithm will be used in this paper. There are YOLOv7-tiny, YOLOv7, and YOLOv7x. Our experiment shows that YOLOv7x achieved the greatest performance with a validation F1-score of 0.949 for car damage detection.
Fusion of Multi-Level CNN With LBP Features For Facial Emotion Recognition

A facial emotion recognition framework is proposed in this work. The convolutional neural network (CNN) has high ability in extraction of hierarchical spatial features from low level texture characteristics to high level contextual features. A simple CNN model with three layers is suggested in this paper where the features extracted in all layers containing multi-level features are activated and used for classification. In addition, the local binary pattern (LBP) descriptor is used to extract discriminative features from the spatial structure of the input image. Therefore, four feature sources are provided by multi levels of CNN and the LBP descriptor. Each feature source is used for facial emotion recognition by applying to the support vector machine (SVM) classier. Finally, the majority voting rule is used for decision fusion to provide the final emotional label of each given face image. The proposed method with 84% overall accuracy, 83% weighted F1-score and 81% kappa coefficient provides the best performance compared to LBP, multi-level CNN and two-dimensional principal component analysis (2DPCA) methods.
Fragility Impact of RL Based Advanced Air Mobility under Gradient Attacks and Packet Drop Constraints

The increasing utilization of unmanned aerial vehicles (UAVs) in advanced air mobility (AAM) necessitates highly automated conflict resolution and collision avoidance strategies. Consequently, reinforcement learning (RL) algorithms have gained popularity in addressing conflict resolution strategies among UAVs. However, increasing digitization introduces challenges related to packet drop constraints and various adversarial cyber threats, rendering AAM fragile. Adversaries can introduce perturbations into the system states, reducing the efficacy of learning algorithms. Therefore, it is crucial to systematically investigate the impact of increased digitization, including adversarial cyber-threats and packet drop constraints to study the fragile characteristics of AAM infrastructure. This study examines the performance of artificial intelligence(AI) based path planning and conflict resolution strategies under different adversarial and stochastic packet drop constraints in UAV systems. The fragility analysis focuses on the number of conflicts, collisions and fuel consumption of the UAVs with respect to its mission, considering various adversarial attacks and packet drop constraint scenarios. The safe deep q-networks (DQN) architecture is utilized to navigate the UAVs, mitigating the adversarial threats and is benchmarked with vanilla DQN using the necessary metrics. The findings are a foundation for investigating the necessary modification of learning paradigms to develop antifragile strategies against emerging adversarial threats.
Knowledge-Driven Multi-Agent Reinforcement Learning for Computation Offloading in Cybertwin-Enabled Internet of Vehicles

By offloading computation-intensive tasks of vehicles to roadside units (RSUs), mobile edge computing (MEC) in the Internet of Vehicles (IoV) can relieve the onboard computation burden. However, existing model-based task offloading methods suffer from heavy computational complexity with the increase of vehicles and data-driven methods lack interpretability. To address these challenges, in this paper, we propose a knowledge-driven multi-agent reinforcement learning (KMARL) approach to reduce the latency of task offloading in cybertwin-enabled IoV. Specifically, in the considered scenario, the cybertwin serves as a communication agent for each vehicle to exchange information and make offloading decisions in the virtual space. To reduce the latency of task offloading, a KMARL approach is proposed to select the optimal offloading option for each vehicle, where graph neural networks are employed by leveraging domain knowledge concerning graph-structure communication topology and permutation invariance into neural networks. Numerical results show that our proposed KMARL yields higher rewards and demonstrates improved scalability compared with other methods, benefitting from the integration of domain knowledge.
A Fair and Efficient Federated Learning Algorithm for Autonomous Driving

With the dispersed and privacy-preserving features, federated learning (FL) enables connected and autonomous vehicles (CAVs) to achieve cooperative perception, decision-making, and planning by utilizing the learning capabilities and sharing model parameters. However, the discrepancies in local training cost and model upload durations between various CAVs make the energy and time costs caused by traditional FL algorithms unfair. In this paper, a fair and efficient FL algorithm is proposed with to address the challenges arising from imbalanced data distribution and fluctuating channel conditions. Specifically, to achieve uniformity in total time and energy cost among CAVs, a personalized approach is employed for the local training rounds of each CAV. This approach ensures fairness and training effectiveness while reducing the local training time in each round of global iteration. Furthermore, it enhances the convergence speed of the global model. Extensive simulations demonstrate that the proposed algorithm achieves fairness in energy cost while reducing the duration of each round of global iteration.
Curiosity-Driven Rainbow Agent Exploratory Learning

Reinforcement learning (RL) agents rely on well-designed external rewards. However, due to the lack of external rewards, the learning is slow or even unable to carry out normally in the actual training process. It is difficult to set rewards artificially for each step, hence sparse and lagging rewards issues will occur by using global rewards. Adding intrinsic curiosity to agents on condition of sparse rewards can solve this problem effectively. The paper introduces the DQN algorithm and six extended integration agents-Rainbow with corresponding applications in Super Mario Bros. The purpose of the paper is to master the extent to which curiosity can attract agents to explore continuously as an intrinsic reward mechanism, as well as how curiosity mechanism is combined with Rainbow algorithm in deep RL. Experimental results show that the curiosity module in sparse reward environment is more obvious and efficient than that in intensive reward environment.
A LiDAR Semantic Segmentation Framework for the Cooperative Vehicle-Infrastructure System

LiDAR semantic segmentation plays an important role in 3D scene understanding for autonomous driving. However, the performance based on the LiDAR equipped on a vehicle may be limited due to small perception perspective, object occlusion, and sparsity of point clouds in the distance. To address these challenges, we propose a vehicle-infrastructure cooperative semantic segmentation (VICSS) framework to enhance the vehicle-side perception capability. An infrastructure feature extraction (IFE) module is employed to extract features from the roadside LiDAR point cloud. A local feature extraction (LFE) module and a global feature extraction (GFE) module are applied to the vehicle point cloud to capture the local and global features, respectively. The features from both point clouds are fused through a feature aggregation (FA) module, which applies the cross-attention mechanism to learn beneficial information from the infrastructure features. Using a dataset generated by CARLA we show that the proposed VICSS framework achieves good performance in terms of semantic segmentation accuracy.
Adaptive Transceiver Design for Wireless Hierarchical Federated Learning

Deploying federated learning (FL) in wireless networks faces the critical challenge of communication bottlenecks. To address this issue, in this paper, we consider an over-the-air computation (AirComp) assisted hierarchical FL (HFL) framework, where a cloud-edge-device-based three-tier network architecture is constructed to train a global model. We first theoretically characterize the convergence of the AirComp-assisted HFL framework and formulate a combinatorial optimization problem that jointly optimizes the edge interval control and local device transceiver design to minimize the convergence upper bound to boost the overall learning performance and reduce communication cost. We show that the formulated optimization problem can be decoupled into an edge interval control problem and a transceiver design problem, which can be tackled by developing a relaxation and rounding algorithm and an alternating Lyapunov drift-based algorithm, respectively. Extensive simulations demonstrate that our proposed algorithm significantly outperforms the baseline schemes.
Metaverse Tweet Sentiment Text Classification Using Bert Algorithm and Tunning Hyperparameter

The current technological advancements have brought forth new experiences for society, one of which is the emergence of the metaverse. The metaverse provides a platform where people can socialize virtually but with a sense of reality. Up to this point, the metaverse has been utilized in various fields such as business, healthcare, tourism, and gaming. Users can move and interact as if they are in the real world. However, there are several risks and challenges associated with the metaverse, such as changes in behavioral patterns and cultures towards increased individualism and a decrease in real-life interactions. Additionally, data within the metaverse is susceptible to cyberattacks. Hence, this research aims to analyze Twitter users' sentiments regarding the evolving concept of the metaverse to assess society's readiness to adopt it. In this study, the researcher combines the intelligence of the BERT algorithm (Bidirectional Encoder Representations from Transformers) with a hyperparameter tuning approach to analyze Twitter users' tweets related to the metaverse. Data was collected from Twitter, totaling 8000 data points, using web scraping techniques. The analysis results provide a deeper understanding of Twitter users' responses to the metaverse concept. This study reveals that the majority of tweets express positive sentiments toward the metaverse concept, focusing on its innovative potential and opportunities. The addition of hyperparameter tuning has also been proven to enhance accuracy in sentiment classification, with a 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> increase in model performance accuracy. This research has broad implications for understanding public perspectives on technological trends like the metaverse. By combining the strengths of the BERT algorithm and hyperparameter tuning, this study offers valuable insights for the technology industry and product development.
Enhancing Food Security: A Blockchain-Enabled Traceability Framework to Mitigate Stockpiling of Food Commodities

Stockpiling or hoarding of food supplies has become a major threat to global food security in recent years. To mitigate this problem, this study proposes a blockchain-enabled tracing system to reduce food commodity stockpiling and improve global food security. The framework uses the immutability, transparency, and decentralized consensus of blockchain technology to create a reliable and auditable food supply chain tracking system. The proposed system uses blockchain technology to permanently record all transactions along the entire supply chain, from manufacturing to retail. The framework uses smart contracts to automate and enforce standards for the handling, transportation, and supply of food products. The system also features a distributed network of nodes responsible for validating and verifying the data stored on the blockchain to ensure its accuracy and authenticity. The system’s security is improved by the distributed architecture, which also makes it more resistant to fraud and tampering. Furthermore, producers, suppliers, wholesalers, and regulators can all utilize the blockchain to get real-time visibility of the flow of food commodities, allowing for prompt action to be taken in the event any stockpiling tendencies are observed. These findings underline the efficacy of the blockchain-enabled tracking framework in reducing hoarding behaviors and fostering a more uniform distribution of food commodities, both of which contribute to higher levels of food safety. This study concludes with a creative solution to better ensure food security for all, The proposed traceability architecture improves supply chain transparency, efficiency, and accountability.
Accelerators in Embedded Systems for Machine Learning: A RISCV View

Embedded systems, which are mobile systems with IoT and constrained processing capability, have mostly functioned as a depiction of ubiquitous computing throughout the previous ten years. On the other hand, innovative applications are expanding, consuming more data, and employing ever-complicated algorithms. To run complex algorithms, accelerators have been added into the normal architecture of embedded systems made for convolutional neural networks (CNN), a machine learning technique. Energy economy, adaptability, versatility, and heterogeneity are some characteristics that the new devices that handle ML in embedded applications must have. However, ML algorithms cannot be executed on embedded systems because to a lack of computational capacity. To compute CNN model inference, accelerators are recommended and integrated into the embedded system utilizing the same energy and resource-saving methods. The appeal of accelerators based on the RISC-V architecture is the possibility to deliberately alter the host processor to improve communication with the accelerator. This work seeks a balanced design between acceleration and flexibility to deploy several CNN model configurations.
Spoofing Detection Performance of Snapshot OSNMA Under Time and Symbol Errors

In the recent years there have been intense research efforts to protect users of Global Navigation Satellite Systems (GNSS) from spoofing attacks that aim to mislead the user’s navigation solution by means of counterfeit signals. Galileo, the European GNSS, has been at the forefront of such efforts and implemented already its so-called Open Service Navigation Message Authentication (OSNMA) protocol, intended to authenticate the navigation message and thus provide a protection layer not existing before in any other GNSS open signal. Rather than message authentication, this paper focuses on a novel technique for detecting the presence of potential spoofers by taking advantage of the unpredictability of some of the OSNMA data. Such opportunistic method is based on processing short sequences of received unpredictable symbols, sending them to a remote trusted server where access to the authentic unpredictable symbols is available, and then comparing both the received and the expected sequences for detecting potential mismatches. This approach is well-suited for receivers operating in snapshot mode, which can only gather and process a short piece of received signal due to their limited wireless connectivity, as in many Internet-of-Things (IoT) devices with low-power constraints. Interestingly, the problem addressed herein has some similarities with frame synchronization in digital networks. At the same time, though, it poses new challenges such as the presence of propagation errors, time uncertainty and different propagation times among different satellites, thus deserving a dedicated study.
Rank-Based Adaptive Brooding in a Mimetic Coral Reefs Search for Feature Selection

Feature selection (FS) is an essential preprocessing step in machine learning and data mining. FS algorithms eliminate the irrelevant and redundant features from a feature vector, thereby increasing the accuracy and robustness of the data mining process while offering higher transparency and lower computation. Here, we suggest using the coral reefs optimization algorithms combined with a betahill climbing algorithm as part of a mimetic search strategy. The Coral Reefs is a population-based global search technique that combines well with local search techniques, as suggested here. However, such a combination is also computationally expensive. They could also converge prematurely with few iterations or insufficient population diversity. Accordingly, we propose here a more efficient search in the problem space by transforming the brooding operator into a function of the ranks of coral larvae. In this way, the chance of disturbing a larva with a higher rank is reduced, and at the same time, the exploratory role of larvae with a lower rank is used best. The performance of the proposed method has been evaluated using three KNN, Decision Tree, and SVM classifiers on eight UCI standard datasets using Precision, Recall and f1 score criteria. Results show that the rank-based adaptive brooding approach is superior to the fixed brooding approach in the high-dimensional problem space.
Efficient Resource Allocation and Semantic Extraction for Federated Learning Empowered Vehicular Semantic Communication

Semantic communication provides a new paradigm that aims at serving upcoming intelligent transportation applications including autonomous driving and real-time video monitoring. However, the problem of computing efficiency and data privacy during semantic extraction and transmission remains unsolved that need to be further investigated. In this paper, an efficient federated learning-empowered vehicular semantic communication(FVSCom) framework is proposed by jointly considering computing efficiency and data privacy, where federated learning is used to perform semantic extraction. To measure the performance of FVSCom, a metric of semantic utility that jointly considers semantic timeliness and semantic fidelity is proposed. We further analyze the end-to-end delay of the FVSCom network and formulate the semantic utility maximization problem. A DRL-driven dynamic semantic-aware algorithm for semantic utility optimization in FVSCom is proposed. The proposed algorithm can guide the agent to approach the suitable policy of semantic extraction and resource allocation, and dynamically respond to the leave or exit of vehicles. Experimental results showcase the potential of the proposed method for achieving substantial advantages over comparison algorithms and demonstrate strong robustness concerning the departure or exit of vehicles.
Latency Minimization for Split Federated Learning

The development of artificial intelligence (AI) provides opportunities for the promotion of deep neural network (DNN)-based applications. However, the large amount of parameters and computational complexity of DNN makes it difficult to deploy it on edge devices which are resource-constrained. An efficient method to address this challenge is model partition/splitting, in which DNN is divided into two parts which are deployed on device and server respectively for co-training or co-inference. In this paper, we consider a split federated learning (SFL) framework that combines the parallel model training mechanism of federated learning (FL) and the model splitting structure of split learning (SL). We consider a practical scenario of heterogeneous devices with individual split points of DNN. We formulate a joint problem of split point selection and bandwidth allocation to minimize the system latency. By using alternating optimization, we decompose the problem into two sub-problems and solve them optimally. Experiment results demonstrate the superiority of our work in latency reduction and accuracy improvement.