This paper introduces a cyber-physical test bench for metropolitan area digital substations, which serves as a platform for studying advanced cyber-attacks and evaluating intrusion detection systems. The test bench provides a hardwarein-the-loop environment with a high Technology Readiness Level (TRL). Additionally, the paper demonstrates the testbed's capabilities by testing and validating an AI-based intrusion detection system developed by an industrial company. The demonstration includes testing the cybersecurity tool against a wide range of cyber-attacks, such as false data injection, packet replay, and time desynchronization, on relevant substation automation protocols commonly used in EU power grid infrastructures like IEC-60870-5-104 and IEC61850. The results of the demonstration indicate that the test bench effectively simulates realistic impacts on substation operation when subjected to attacks, thus providing the opportunity to validate the anomaly detection solution.

During the design of electrical machines, multiple performance objectives need to considered. Although stochastic optimization algorithms are extensively employed for this purpose, a primary drawback is the time-consuming and substantial number of design evaluations. Bayesian optimization (BO) presents an alternative that can address multi-objective optimization in particular for objective functions which are expensive to evaluate. Probabilistic surrogate models based on Gaussian process regression (GPR) form its basis. The high accuracy of Gaussian processes and their uncertainty estimation render Bayesian optimization extremely efficient and effective. Nevertheless, Bayesian optimization is hardly used in the field of electric machine design. Consequently, this study explores, analyses and evaluates the application of global multi-objective Bayesian optimization for machine design. Employing four distinct Bayesian acquisition functions, the study conducts a three-objective design optimization of a reluctance synchronous machine characterized by 14 design variables. A comprehensive comparison with the most common NSGA-II reveals that significantly superior outcomes are achievable within a considerably shorter time.

Spam, defined as unwanted, malicious and improper content that led to various challenge in digital realm. This paper provides a comprehensive overview and analysis of the different type of spam such as social media spam, SMS spam, call spam and forum/blog spam. Each of these spams has its own characteristics and challenges. This paper analyzes the methods used by spammers such as obfuscation, social engineering, polymorphism and machine learning integration to make the spam message pass through the detection system. The analysis of the consequences of spam resulting financial losses, security breaches and social consequences. In address these challenges, the study proposes a comprehensive method in handling spam such as advanced filtering system, robust email authentication and enhanced the user awareness education about spam.

Modeling of high-density interconnect channels requires time-consuming, compute-intensive physics-based electromagnetic simulations. In this work, we use a supervised ensemble machine learning technique for this purpose to significantly reduce the design time and effort of such channels. A randomized dataset selection methodology has been proposed to significantly reduce the dataset required to train such models. The methodology uses the characteristic impedance profile and S-parameters to derive the reduced random dataset that follows a skewed distribution. The supervised ensemble model combining, K-nearest neighbor and decision tree regressor, is trained on this reduced dataset to predict S-parameters for given input physical parameters and material properties. The model is trained and tested on a single-ended stripline channel, and its accuracy is validated by comparing the predicted S-parameters to those obtained from the electromagnetic simulator. Training the model with a small dataset comprising merely 85 samples results in an accuracy of 97% in predicting the S-parameters, which improves significantly over the results obtained in the prior art.

Conventional Fourier modal methods (FMMs) remain computationally intensive for simulating and inversely designing free-form meta-atoms. This work introduces a convolutional neural network (CNN)-based method for generating normal vector (NV) fields to improve FMMs’ convergence. Our proposed models predict NV fields from free-form shapes with high accuracy and reduced computational demand. Theoretical analysis and experimental validation using the rigorous diffraction interface theory (R-DIT) demonstrate equivalent accuracy with significant speed improvement, particularly up to 3x speedup on GPU platforms, utilizing only minimal convolutional layers. This integration of machine learning with traditional simulation techniques illustrates the potential for pioneering advanced tools in photonics de

In wireless communications, trustworthiness computation has emerged as a crucial aspect of safeguarding modern systems against cybersecurity threats, ensuring reliable data transmission and upholding user trust. However, there is no unified definition of trustworthiness computation in the literature, and it is often presented as a specifically tailored adaptation of attack detection mechanisms. In contrast, this work introduces a general method for trustworthiness computation in wireless networks. It leverages key system characteristics, such as the channel, timing, and packet information to identify measurable Quality of Service (QoS) features with sufficient sensitivity across varying operational conditions.  Building on these features, a novel three-step approach is applied. It employs changepoint detection to identify potential trustworthiness issues, calculates indicators based on the observed features, and finally combines them into a quantitative representation of trustworthiness. This systematic method effectively distinguishes between regular statistical variations in QoS features and actual trustworthiness issues. The applicability of the presented approach is demonstrated using a typical IEEE 802.11 wireless link, where different QoS features and scenarios are defined. These scenarios include network attacks, system malfunctions, and typical operational conditions.  Our trustworthiness computation method correctly alerts the system to all trustworthiness issues that we challenge it with.

The classic implementation of dispatchable virtual oscillator control (dVOC) in the stationary αβ frame is counterintuitive and complex for small-signal modeling of the controller. Additionally, it makes integrating advanced control loops into the dVOC controller challenging and cannot achieve zero reactive power tracking error. To address these issues, for the first time, a new implementation of the dVOC in the dq frame is proposed by transforming the dynamics of the instantaneous angular frequency and voltage magnitude of the dVOC-controlled voltage vector into the local synchronous rotating frame of the converter. This new implementation resolves the inconvenience associated with small-signal modeling, enhances the flexibility and compatibility of the controller, and eliminates reactive power tracking errors, without compromising the operational characteristics of the dVOC. These improvements are achieved through the control of DC variables and the direct regulation of the converter phase angle and voltage magnitude. The effectiveness of the new dVOC controller is validated through EMT simulations.

Due to the high complexity of geometry-deterministic wireless channel modeling and the difficulty in its implementation, geometry-based stochastic channel modeling (GBSM) approaches have been used to evaluate system performance of wireless communications. This paper introduces a new method to model a GBSM by training a generative neural network using images formed by channel parameters. Toward this end, we process the data of channel parameters in the form of images and train the generative neural networks where the convolutional layers are mainly employed. Through a case study, we confirm that the use of channel images facilitates the training of the generative model and ensures that the model learns the correlations among multipath components. We show that the outputs of the generative model faithfully represent the distributions of the original data. Furthermore, to corroborate applicability of the trained model, we run simple system-level simulations and show the results from the trained model and ray-tracing data are well matched. Therefore, we argue that the proposed model trained by channel images will ease the burden of GBSM implementations with general wireless conditions and capture the statistical joint distributions of the original channel data.

This study presents a comprehensive Design-Technology Co-Optimization (DTCO) approach to evaluate and enhance power and performance in Gate-All-Around Nanosheet (GAA-Nsh) and Forksheet (Fsh) architectures. The analysis focuses on the impact of active widths and power delivery methods on the effective resistance (Reff) and capacitance (Ceff) of these devices. The research employs simulations of five-stage INVD1 ring oscillators (RO) at various metal pitches (Mx) to extract frequency and power data. Notably, a novel Gate-All-Around Forksheet (GAA-Fsh) structure is introduced, offering enhanced gate control while retaining the advantages of Fsh. The study also explores asymmetric N/PFETs within the Fsh technology, and innovative contacting approaches such as Buried Power Rail (BPR) and Backside Power Rail (BS-PR) with Backside Contact (BSC) to reduce access resistance. Results indicate that GAA-Fsh outperforms traditional GAA-Nsh and Fsh due to reduced Reff and Ceff, although it is process feasible only at larger Mx. At smaller Mx, GAA-Nsh demonstrates higher performance than Fsh at a given sheet width (WNS), but Fsh, with the advantage of additional WNS, can match GAA-Nsh performance at larger WNS. Furthermore, the BPR and BS-PR contacting schemes are found to provide similar performance. This research provides valuable insights into future semiconductor device designs, emphasizing higher performance and efficient scaling.

This paper develops a model that best predicts the diagnosis of a patient as to whether they have liver disease as well as figure out which features of the nine listed contribute the most to a positive liver disease diagnosis. The dataset used contains many different biological factors that play a role in influencing the liver. Creating a model that can accurately predict the diagnosis of a patient can be a helpful tool in the medical field as Machine Learning models with high predicting accuracy can detect intricate patterns and help to reduce the number of False Negative diagnoses that occur. In addition, knowing which particular feature contributes the most to liver disease can help doctors prioritize the normalization of certain feature levels to reduce the severity of the Liver Disease.

Short-packet communication (SPC) and unmanned aerial vehicles (UAVs) are anticipated to play crucial roles in the development of 5G-and-beyond wireless networks and the Internet of Things (IoT). In this paper, we propose a secure SPC system, where a UAV serves as a mobile decode-and-forward (DF) relay, periodically receiving and relaying small data packets from a remote IoT device to its receiver in two hops with strict latency requirements, in the presence of an eavesdropper. This system requires careful optimization of important design parameters, such as the coding blocklengths of both hops, transmit powers, and the UAV’s trajectory. While the overall optimization problem is nonconvex, we tackle it by applying a block successive convex approximation (BSCA) approach to divide the original problem into three subproblems and solve them separately. Then, an overall iterative algorithm is proposed to obtain the final design with guaranteed convergence. Our proposed low-complexity algorithm incorporates robust trajectory design and resource management to optimize the effective average secrecy throughput of the communication system over the course of the UAV-relay’s mission. Simulation results demonstrate significant performance improvements compared to various benchmark schemes and provide useful design insights on the coding blocklengths and transmit powers along the trajectory of the UAV.

This research introduces a hybrid feature extraction approach that combines Linear Discriminant Analysis (LDA) and Multilayer Perceptron (MLP) methods to address the challenges of reducing feature vector dimensionality and accurately classifying smartphone-based human activities. Moreover, to refine activity classification accuracy, Support Vector Machine (SVM) optimization with Stochastic Gradient Descent (SGD) is employed. LDA, a statistical tool, is leveraged to derive a new feature space for data projection, enhancing class separation and test feature label prediction. The proposed approach, named LMSS, was evaluated using the UCI-HAR dataset and compared with state-of-the-art models. The results demonstrate that the proposed approach outperformed the best-performing method over this dataset. It achieved an accuracy rate of 99.52%, precision of 99.55%, recall of 99.53%, and an F1-score of 99.54%, highlighting the effectiveness of the proposed method in accurately classifying the data.

Data mining is an evolutionary process, and data and information are also manifestations of knowledge, but people see concepts, rules, patterns, laws and constraints more as knowledge. Based on data mining, an expert system works by reasoning using expert knowledge, and it consists of knowledge acquisition, knowledge base, reasoning machine, and interpretation part. The knowledge base is used to store experts' experience, knowledge and existing knowledge in the field, and whether it is perfect or not determines the performance of the expert system. Data mining is getting more and more attention, while the existing expert systems or decision support systems still mainly rely on manual collection and manual input of required knowledge and information, which is not only time-consuming and expensive, but also lags behind the time when making time-sensitive decisions and reduces the quality of decision-making.

This paper introduces a novel adversarial attack method targeting text classification models, termed the Modified Word Saliency-based Adversarial Attack (MWSAA). The technique builds upon the concept of word saliency to strategically perturb input texts, aiming to mislead classification models while preserving semantic coherence. By refining the traditional adversarial attack approach, MWSAA significantly enhances its efficacy in evading detection by classification systems. The methodology involves first identifying salient words in the input text through a saliency estimation process, which prioritizes words most influential to the model's decision-making process. Subsequently, these salient words are subjected to carefully crafted modifications, guided by semantic similarity metrics to ensure that the altered text remains coherent and retains its original meaning. Empirical evaluations conducted on diverse text classification datasets demonstrate the effectiveness of the proposed method in generating adversarial examples capable of successfully deceiving state-of-the-art classification models. Comparative analyses with existing adversarial attack techniques further indicate the superiority of the proposed approach in terms of both attack success rate and preservation of text coherence.

On-chip integration of 2D materials with exceptional optical properties provides an attractive solution for next-generation photonic integrated circuits to address the limitations of conventional bulk integrated platforms. Over the past two decades, significant advancements have been made in the interdisciplinary field of 2D material integrated photonics, greatly narrowing the gap between laboratory research and industrial applications. In this paper, we provide a perspective on the developments of this field towards industrial manufacturing and commercialization. First, we review recent progress towards commercialization. Next, we provide an overview of cuttingedge fabrication techniques, which are categorized into large-scale integration, precise patterning, dynamic tuning, and device packaging. Both the advantages and limitations of these techniques are discussed in relation to industrial manufacturing. Finally, we highlight some important issues related to commercialization, including fabrication standards, recycling, service life, and environmental implications.

The increasing demand for intelligent services coupled with privacy protection of mobile and Internet of Things (IoT) devices motivates the widespread adoption of Federated Edge Learning (FEL), which enables devices to collaboratively train on-device Machine Learning (ML) models coordinated by the edge server without sharing their private data. However, the intrinsic constraints of device hardware, user behaviors' variability, and the network infrastructure pose significant challenges to FEL, requiring its algorithm design to take into account resource constraints, the need for personalization models, and diverse network conditions. Fortunately, Knowledge Distillation (KD) emerges as an important strategy to address these impediments within FEL. In this paper, we investigate the works that KD applies to FEL, discuss the limitations and open problems of existing KD-based FEL approaches, and provide guidance for their real deployment.

Many researchers have usually harnessed perturbation techniques to generate adversarial malware evading the deep learning-based malware detection systems. To fight against adversarial malware created by perturbation techniques, most defense schemes focus on reshaping deep learning-based malware detection systems. However, this defense approach can be paralyzed if the internal structures of deep learning-based malware detection systems are not revealed. As a result, it is imperative to devise a new defense scheme without requiring the prior knowledge of deep learning-based malware detection systems. To come up to this need, we propose a zone perturbation scheme which enhances the detection capability of deep learningbased malware detection system. Our proposed scheme adapts the Sequential Probability Ratio Test (SPRT) to zone-based perturbation on text or itext or code sections of Windows PE files. In the sense that our proposed defense scheme harnesses perturbation technique, it breaks the conventional adaptations of perturbation weakening the detection capability of deep learningbased malware detection system. To the best of our knowledge, our zone perturbation is the first perturbation technique to be leveraged for the purpose of defending against malware. We evaluate the performance of our proposed zone perturbation against original malware as well as adversarial malware. For our evaluation, we adopt MalConv, which is widely used in the literature, as deep learning-based malware detection system. Moreover, our zone perturbation is broken into NopPerturbation and RandomPerturbation in accordance with the perturbed content of NOP (No Operation) codes and randomly generated codes, respectively. Our key evaluation results show that NopPerturbation (resp. RandomPerturbation) fixes 65.7% (resp. 28.6%) of benign decisions of MalConv on original malware and adversarial malware to malicious decisions on an average. This substantial enhancement in malware detection capability of MalConv is done at the cost of reasonable false positive error rate of 0.16 (resp. 0.055) and low false negative error rate of 0.007 (resp. 0.024) in case of applying NopPerturbation (resp. RandomPerturbation) to MalConv on an average.

Existing methods for simulating missile dynamics and training often rely on three degrees of freedom (3DOF) motions, limiting their ability to represent the complex maneuvers and targeting strategies of modern missiles. This paper proposes integrating virtual reality (VR) technology with six degrees of freedom (6DOF) dynamics to enhance the accuracy and efficiency of missile simulation and training. By incorporating 6DOF dynamics, simulations can capture the full range of missile motion, providing a comprehensive understanding of their capabilities and limitations. Through VR, users can immerse themselves in realistic missile scenarios, interact with dynamic behaviors in real-time, and refine their targeting and interception skills. The integration of VR with 6DOF dynamics also facilitates realtime feedback and evaluation, enhancing training efficiency and proficiency in missile operations. This approach aims to revolutionize missile training by bridging the gap between traditional simulation methods and real-world dynamics.