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Federated Learning (FL) has gained attention across various industries for its capability to train machine learning models without centralizing sensitive data. While this approach offers significant benefits such as privacy preservation and decreased communication overhead, it presents several challenges, including deployment complexity and interoperability issues, particularly in heterogeneous scenarios or resource-constrained environments. Over-the-air (OTA) FL was introduced to tackle these challenges by disseminating model updates without necessitating direct device-to-device connections or centralized servers. However, OTA-FL brought forth limitations associated with heightened energy consumption and network latency. In this paper, we propose a multi-attribute client selection framework employing the grey wolf optimizer (GWO) to strategically control the number of participants in each round and optimize the OTA-FL process while considering accuracy, energy, delay, reliability, and fairness constraints of participating devices. We evaluate the performance of our multi-attribute client selection approach in terms of model loss minimization, convergence time reduction, and energy efficiency. In our experimental evaluation, we assessed and compared the performance of our approach against the existing state-of-the-art methods. Our results demonstrate that the proposed GWO-based client selection outperforms these baselines across various metrics. Specifically, our approach achieves a notable reduction in model loss, accelerates convergence time, and enhances energy efficiency while maintaining high fairness and reliability indicators.

Password managers encourage users to generate passwords to improve their security. However, research has shown that users avoid generating passwords, often giving the rationale that it is difficult to enter generated passwords on devices without a password manager. In this paper, we conduct a survey ($n=999$) of individuals from the US, UK, and Europe, exploring the range of devices on which they enter passwords and the challenges associated with password entry on those devices. We find that password entry on devices without password managers is a common occurrence and comes with significant usability challenges. These usability challenges lead users to weaken their passwords to increase the ease of entry. We conclude this paper with a discussion of how future research could address these challenges and encourage users to adopt generated passwords., Comment: 12 pages, 4 figures, 8 tables

Multi-agent Reinforcement Learning (MARL) is emerging as a key framework for various sequential decision-making and control tasks. Unlike their single-agent counterparts, multi-agent systems necessitate successful cooperation among the agents. The deployment of these systems in real-world scenarios often requires decentralized training, a diverse set of agents, and learning from infrequent environmental reward signals. These challenges become more pronounced under partial observability and the lack of prior knowledge about agent heterogeneity. While notable studies use intrinsic motivation (IM) to address reward sparsity or cooperation in decentralized settings, those dealing with heterogeneity typically assume centralized training, parameter sharing, and agent indexing. To overcome these limitations, we propose the CoHet algorithm, which utilizes a novel Graph Neural Network (GNN) based intrinsic motivation to facilitate the learning of heterogeneous agent policies in decentralized settings, under the challenges of partial observability and reward sparsity. Evaluation of CoHet in the Multi-agent Particle Environment (MPE) and Vectorized Multi-Agent Simulator (VMAS) benchmarks demonstrates superior performance compared to the state-of-the-art in a range of cooperative multi-agent scenarios. Our research is supplemented by an analysis of the impact of the agent dynamics model on the intrinsic motivation module, insights into the performance of different CoHet variants, and its robustness to an increasing number of heterogeneous agents., Comment: 8 pages, 4 figures

We make a general Killing spinor analysis of a particular $D=3, \, N=4$ gauged supergravity that comes from a consistent S$^3$ reduction of $D=6, \, N=(1,0)$ supergravity coupled to a single chiral tensor multiplet. We then focus on its supersymmetric solutions with a null Killing vector and find three new ones. Two of these, namely the null warped AdS$_3$ (also known as the Schr\"odinger spacetime) and the charged domain wall solutions, admit non-trivial gauge fields which give rise to rotating solutions in 6-dimensions. The uplift of the first one produces an interesting AdS$_3 \times$S$^3$ background with a non-trivial rotation in the $U(1)$ fiber direction of the S$^3$ which retains the Schr\"odinger scale invariance that the seed solution has. The second one leads to the well-known rotating dyonic string solution. Finally, the uplift of the third one, which is a domain wall solution with no gauge fields, results in a distribution of dyonic strings., Comment: We dedicate this paper to the memory of Prof. Rahmi G\"uven (1948-2019)

A System of Systems (SoS) comprises Constituent Systems (CSs) that interact to provide unique capabilities beyond any single CS. A key challenge in SoS is ad-hoc scalability, meaning the system size changes during operation by adding or removing CSs. This research focuses on an Unmanned Vehicle Fleet (UVF) as a practical SoS example, addressing uncertainties like mission changes, range extensions, and UV failures. The proposed solution involves a self-adaptive system that dynamically adjusts UVF architecture, allowing the Mission Control Center (MCC) to scale UVF size automatically based on performance criteria or manually by operator decision. A multi-agent environment and rule management engine were implemented to simulate and verify this approach., Comment: 2023 7th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence (ISMSI 2023)

Multi-Robot System (MRS) is a complex system that contains many different software and hardware components. This main problem addressed in this article is the MRS design complexity. The proposed solution provides a modular modeling and simulation technique that is based on formal system engineering method, therefore the MRS design complexity is decomposed and reduced. Modeling the MRS has been achieved via two formal Architecture Description Languages (ADLs), which are Systems Modeling Language (SysML) and Business Process Model and Notation (BPMN), to design the system blueprints. By using those abstract design ADLs, the implementation of the project becomes technology agnostic. This allows to transfer the design concept from on programming language to another. During the simulation phase, a multi-agent environment is used to simulate the MRS blueprints. The simulation has been implemented in Java Agent Development (JADE) middleware. Therefore, its results can be used to analysis and verify the proposed MRS model in form of performance evaluation matrix.

In order to ensure traffic safety through a reduction in fatalities and accidents, vehicle speed detection is essential. Relentless driving practices are discouraged by the enforcement of speed restrictions, which are made possible by accurate monitoring of vehicle speeds. Road accidents remain one of the leading causes of death in Bangladesh. The Bangladesh Passenger Welfare Association stated in 2023 that 7,902 individuals lost their lives in traffic accidents during the course of the year. Efficient vehicle speed detection is essential to maintaining traffic safety. Reliable speed detection can also help gather important traffic data, which makes it easier to optimize traffic flow and provide safer road infrastructure. The YOLOv8 model can recognize and track cars in videos with greater speed and accuracy when trained under close supervision. By providing insights into the application of supervised learning in object identification for vehicle speed estimation and concentrating on the particular traffic conditions and safety concerns in Bangladesh, this work represents a noteworthy contribution to the area. The MAE was 3.5 and RMSE was 4.22 between the predicted speed of our model and the actual speed or the ground truth measured by the speedometer Promising increased efficiency and wider applicability in a variety of traffic conditions, the suggested solution offers a financially viable substitute for conventional approaches.

In six dimensions there exists a unique one-parameter family of non-linear conformal electrodynamics for a chiral 2-form gauge field which includes (in a free-field limit) the linear chiral 2-form theory and is related, by dimensional reduction, to the four-dimensional ModMax electrodynamics. In this work, we present the first exact solutions of this theory in presence of gravity. In particular, we will consider plane-wave, Robinson-Trautman, and self-dual dyonic string configurations, generalizing well-known solutions of this type in six-dimensional supergravities with linear chiral 2-form multiplets., Comment: 37 pages, v2: typos corrected, references added

This paper presents an extensive characterization of the low-frequency noise (LFN) at room temperature (RT) and cryogenic temperature (4.2 K) of 40-nm bulk-CMOS transistors. The noise is measured over a wide range of bias conditions and geometries to generate a comprehensive overview of LFN in this technology. While the RT results are in-line with the literature and the foundry models, the cryogenic behavior diverges in many aspects. These deviations include changes with respect to RT in magnitude and bias dependence that are conditional on transistor type and geometry, and even an additional systematic Lorentzian feature that is common among individual devices. Furthermore, we find the scaling of the average LFN with the area and its variability to be similar between RT and 4.2 K, with the cryogenic scaling reported systematically for the first time. The findings suggest that, as no consistent decrease of LFN at lower temperatures is observed while the white noise is reduced, the impact of LFN for precision analog design at cryogenic temperatures gains a more predominant role.

In recent times, impedance side-channel analysis has emerged as a potent strategy for adversaries seeking to extract sensitive information from computing systems. It leverages variations in the intrinsic impedance of a chip's internal structure across different logic states. In this study, we conduct a comparative analysis between the newly explored impedance side channel and the well-established power side channel. Through experimental evaluation, we investigate the efficacy of these two side channels in extracting the cryptographic key from the Advanced Encryption Standard (AES) and analyze their performance. Our results indicate that impedance analysis demonstrates a higher potential for cryptographic key extraction compared to power side-channel analysis. Moreover, we identify scenarios where power side-channel analysis does not yield satisfactory results, whereas impedance analysis proves to be more robust and effective. This work not only underscores the significance of impedance side-channel analysis in enhancing cryptographic security but also emphasizes the necessity for a deeper understanding of its mechanisms and implications.

The complexity and dynamic nature of System of Systems (SoS) necessitate efficient communication mechanisms to ensure interoperability and collaborative functioning among constituent systems, termed holons. This paper proposes an innovative approach to enhance holon communication within SoS through the integration of Conversational Generative Intelligence (CGI) techniques. Our approach leverages advancements in CGI, specifically Large Language Models (LLMs), to enable holons to understand and act on natural language instructions. This fosters more intuitive human-holon interactions, improving social intelligence and ultimately leading to better coordination among diverse systems. This position paper outlines a conceptual framework for CGI-enhanced holon interaction, discusses the potential impact on SoS adaptability, usability and efficiency, and sets the stage for future exploration and prototype implementation., Comment: Preprint accepted in ICSOFT'24

The "MedBike" is an innovative project in the field of pediatric cardiac rehabilitation. It is a 2D interactive game created specifically for children under the age of 18 who have cardiac conditions. This game is part of the MedBike system, a novel rehabilitation tool combining physical exercise with the spirit of gaming. The MedBike game provides children with a safe, controlled, and engaging environment in which to exercise and recover. It has three distinct levels of increasing intensity, each with its own set of environments and challenges that are tailored to different stages of rehabilitation. This report dives into the details of the MedBike game, highlighting its unique features and gameplay., Comment: 5 pages, 11 figures

This study focuses on optimizing the design parameters of a Dual Active Bridge (DAB) converter for use in 350 kW DC fast chargers, emphasizing the balance between efficiency and cost. Addressing the observed gaps in existing high-power application research, it introduces an optimization framework to evaluate critical design parameters,number of converter modules, switching frequency, and transformer turns ratio,within a broad operational voltage range. The analysis identifies an optimal configuration that achieves over 95% efficiency at rated power across a wide output voltage range, comprising seven 50 kW DAB converters with a switching frequency of 30 kHz, and a transformer turns ratio of 0.9.

Computer vision, particularly vehicle and pedestrian identification is critical to the evolution of autonomous driving, artificial intelligence, and video surveillance. Current traffic monitoring systems confront major difficulty in recognizing small objects and pedestrians effectively in real-time, posing a serious risk to public safety and contributing to traffic inefficiency. Recognizing these difficulties, our project focuses on the creation and validation of an advanced deep-learning framework capable of processing complex visual input for precise, real-time recognition of cars and people in a variety of environmental situations. On a dataset representing complicated urban settings, we trained and evaluated different versions of the YOLOv8 and RT-DETR models. The YOLOv8 Large version proved to be the most effective, especially in pedestrian recognition, with great precision and robustness. The results, which include Mean Average Precision and recall rates, demonstrate the model's ability to dramatically improve traffic monitoring and safety. This study makes an important addition to real-time, reliable detection in computer vision, establishing new benchmarks for traffic management systems., Comment: 5 pages, 2 figures

The rise of fake news has made the need for effective detection methods, including in languages other than English, increasingly important. The study aims to address the challenges of Bangla which is considered a less important language. To this end, a complete dataset containing about 50,000 news items is proposed. Several deep learning models have been tested on this dataset, including the bidirectional gated recurrent unit (GRU), the long short-term memory (LSTM), the 1D convolutional neural network (CNN), and hybrid architectures. For this research, we assessed the efficacy of the model utilizing a range of useful measures, including recall, precision, F1 score, and accuracy. This was done by employing a big application. We carry out comprehensive trials to show the effectiveness of these models in identifying bogus news in Bangla, with the Bidirectional GRU model having a stunning accuracy of 99.16%. Our analysis highlights the importance of dataset balance and the need for continual improvement efforts to a substantial degree. This study makes a major contribution to the creation of Bangla fake news detecting systems with limited resources, thereby setting the stage for future improvements in the detection process., Comment: Accepted for publication in the 7th International Conference on Networking, Intelligent Systems & Security. The conference Proceedings will be published by ACM International Conference Proceeding Series (ICPS) ISBN N{\deg}: 979-8-4007-0019-4. 8 pages, 11 figures

Worker-Robot Cooperation is a new industrial trend, which aims to sum the advantages of both the human and the industrial robot to afford a new intelligent manufacturing techniques. The cooperative manufacturing between the worker and the robot contains other elements such as the product parts and the manufacturing tools. All these production elements must cooperate in one manufacturing workcell to fulfill the production requirements. The manufacturing control system is the mean to connect all these cooperative elements together in one body. This manufacturing control system is distributed and autonomous due to the nature of the cooperative workcell. Accordingly, this article proposes the holonic control architecture as the manufacturing concept of the cooperative workcell. Furthermore, the article focuses on the feasibility of this manufacturing concept, by applying it over a case study that involves the cooperation between a dual-arm robot and a worker. During this case study, the worker uses a variety of hand gestures to cooperate with the robot to achieve the highest production flexibility

Cooperative manufacturing is a new trend in industry, which depends on the existence of a collaborative robot. A collaborative robot is usually a light-weight robot which is capable of operating safely with a human co-worker in a shared work environment. During this cooperation, a vast amount of information is exchanged between the collaborative robot and the worker. This information constructs the cooperative manufacturing knowledge, which describes the production components and environment. In this research, we propose a holonic control solution, which uses the ontology concept to represent the cooperative manufacturing knowledge. The holonic control solution is implemented as an autonomous multi-agent system that exchanges the manufacturing knowledge based on an ontology model. Ultimately, the research illustrates and implements the proposed solution over a cooperative assembly scenario, which involves two workers and one collaborative robot, whom cooperate together to assemble a customized product.

It is known that the balance laws of hyperelasticity (Green elasticity), i.e., conservation of mass and balance of linear and angular momenta, can be derived using the first law of thermodynamics by postulating its invariance under rigid body motions of the Euclidean ambient space -- the Green-Naghdi-Rivlin theorem. In the case of a non-Euclidean ambient space, covariance of the energy balance -- its invariance under arbitrary diffeomorphisms of the ambient space -- gives all the balance laws and the Doyle-Ericksen formula -- the Marsden-Hughes theorem. In this note, we show that the constitutive equations as well as the balance laws of hyperelasticity can be derived using the first and second laws of thermodynamics without assuming any invariance.

In this work we focus on the study of RG flows of conformal field theories that are holographically dual to Poincar\'e domain wall solutions in $D=3$, $\mathcal{N}=(2,0)$ gauged supergravity coupled to a sigma model with target space $SU(1, 1)/U(1) = H^2$. This theory is truncated to a subsector where the vector field and phase of the scalar field vanish and we consider different boundary conditions for the remaining real scalar field. The RG flows, which are mostly non-superysymmetric, are analyzed by treating the supergravity field equations as a dynamical system for the scalar field and its derivative with respect to the scale factor. Phase diagrams are constructed for different values of the parameter $a^2$, which is related to the curvature of the scalar manifold. The behavior of solutions near the boundary is used to determine their type based on the expansion of the corresponding fake superpotential. By incorporating information on the boundary conditions, the obtained RG flows are interpreted using the holographic dictionary. Numerical solutions and plots of the fake superpotential are also provided., Comment: 36 pages plus Appendix, v2: updated references, tables, corrected misprints

In today's digital age, concerns about online security and privacy have become paramount. However, addressing these issues can be difficult, especially within the context of family relationships, wherein parents and children may have conflicting interests. In this environment, parents and children may turn to online security advice to determine how to proceed. In this paper, we examine the advice available to parents and children regarding content filtering and circumvention as found on YouTube and TikTok. In an analysis of 839 videos returned from queries on these topics, we found that half (n=399) provide relevant advice. Our results show that of these videos, roughly three-quarters are accurate, with the remaining one-fourth containing factually incorrect advice. We find that videos targeting children are both more likely to be incorrect and actionable than videos targeting parents, leaving children at increased risk of taking harmful action. Moreover, we find that while advice videos targeting parents will occasionally discuss the ethics of content filtering and device monitoring (including recommendations to respect children's autonomy) no such discussion of the ethics or risks of circumventing content filtering is given to children, leaving them unaware of any risks that may be involved with doing so. Ultimately, our research indicates that video-based social media sites are already effective sources of security advice propagation and that the public would benefit from security researchers and practitioners engaging more with these platforms, both for the creation of content and of tools designed to help with more effective filtering., Comment: 15 pages, 5 figures, 8 tables

$ $Large Language Models (LLMs) are being increasingly utilized in various applications, with code generations being a notable example. While previous research has shown that LLMs have the capability to generate both secure and insecure code, the literature does not take into account what factors help generate secure and effective code. Therefore in this paper we focus on identifying and understanding the conditions and contexts in which LLMs can be effectively and safely deployed in real-world scenarios to generate quality code. We conducted a comparative analysis of four advanced LLMs--GPT-3.5 and GPT-4 using ChatGPT and Bard and Gemini from Google--using 9 separate tasks to assess each model's code generation capabilities. We contextualized our study to represent the typical use cases of a real-life developer employing LLMs for everyday tasks as work. Additionally, we place an emphasis on security awareness which is represented through the use of two distinct versions of our developer persona. In total, we collected 61 code outputs and analyzed them across several aspects: functionality, security, performance, complexity, and reliability. These insights are crucial for understanding the models' capabilities and limitations, guiding future development and practical applications in the field of automated code generation., Comment: 12 pages, 2 figures

We present an action for minimal massive gravity (MMG) in three dimensions in terms of a dreibein and an independent spin connection. Furthermore, the construction provides an action principle for an infinite family of so-called third-way consistent generalizations of the three-dimensional Einstein field equations, including exotic massive gravity and new higher-order generalizations. It allows to systematically construct the matter couplings for these models, including the couplings to fermions, depending on the spin connection. In particular, we construct different supersymmetric extensions of MMG, and derive their second order fermionic field equations. This establishes a new class of three-dimensional supergravity theories and we discuss their limit to topological massive supergravity. Finally, we identify the landscape of (A)dS vacua of the supersymmetric models. We analyze the spectrum and the unitarity properties of these vacua. We recover the known AdS vacua of MMG which are bulk and boundary unitary., Comment: 41 pages, 2 figures

Reading right books contributes to children's imagination and brain development, enhances their language and emotional comprehension abilities, and strengthens their relationships with others. Building upon the critical role of reading books in individual development, this paper aims to develop an algorithm that determines the suitability of books for middle school students by analyzing their structural and semantic features. Using methods described, an algorithm will be created that can be utilized by institutions and individuals responsible for children's education, such as the Ministry of National Education officials and schools. This algorithm will facilitate the selection of books to be taught at the middle school level. With the algorithm, the book selection process for the middle school curriculum can be expedited, and it will serve as a preliminary reference source for those who evaluate books by reading them. In this paper, the Python programming language was employed, utilizing natural language processing methods. Additionally, an artificial neural network (ANN) was trained using the data which had been preprocessed to construct an original dataset. To train this network, suitable books for middle school students were provided by the MEB, Oxford and Cambridge and with content assessed based on the "R" criterion, and inappropriate books for middle school students in terms of content were included. This trained neural network achieved a 90.06% consistency rate in determining the appropriateness of the test-provided books. Considering the obtained findings, it can be concluded that the developed software has achieved the desired objective., Comment: 14 pages, 3 figures, 5 tables

Generating code from a natural language using Large Language Models (LLMs) such as ChatGPT, seems groundbreaking. Yet, with more extensive use, it's evident that this approach has its own limitations. The inherent ambiguity of natural language presents challenges for complex software designs. Accordingly, our research offers an Agile Model-Driven Development (MDD) approach that enhances code auto-generation using OpenAI's GPT-4. Our work emphasizes "Agility" as a significant contribution to the current MDD method, particularly when the model undergoes changes or needs deployment in a different programming language. Thus, we present a case-study showcasing a multi-agent simulation system of an Unmanned Vehicle Fleet. In the first and second layer of our approach, we constructed a textual representation of the case-study using Unified Model Language (UML) diagrams. In the next layer, we introduced two sets of constraints that minimize model ambiguity. Object Constraints Language (OCL) is applied to fine-tune the code constructions details, while FIPA ontology is used to shape communication semantics and protocols. Ultimately, leveraging GPT-4, our last layer auto-generates code in both Java and Python. The Java code is deployed within the JADE framework, while the Python code is deployed in PADE framework. Concluding our research, we engaged in a comprehensive evaluation of the generated code. From a behavioural standpoint, the auto-generated code aligned perfectly with the expected UML sequence diagram. Structurally, we compared the complexity of code derived from UML diagrams constrained solely by OCL to that influenced by both OCL and FIPA-ontology. Results indicate that ontology-constrained model produce inherently more intricate code, but it remains manageable and low-risk for further testing and maintenance.

Discovering new vulnerabilities and implementing security and privacy measures are important to protect systems and data against physical attacks. One such vulnerability is impedance, an inherent property of a device that can be exploited to leak information through an unintended side channel, thereby posing significant security and privacy risks. Unlike traditional vulnerabilities, impedance is often overlooked or narrowly explored, as it is typically treated as a fixed value at a specific frequency in research and design endeavors. Moreover, impedance has never been explored as a source of information leakage. This paper demonstrates that the impedance of an embedded device is not constant and directly relates to the programs executed on the device. We define this phenomenon as impedance leakage and use this as a side channel to extract software instructions from protected memory. Our experiment on the ATmega328P microcontroller and the Artix 7 FPGA indicates that the impedance side channel can detect software instructions with 96.1% and 92.6% accuracy, respectively. Furthermore, we explore the dual nature of the impedance side channel, highlighting the potential for beneficial purposes and the associated risk of intellectual property theft. Finally, potential countermeasures that specifically address impedance leakage are discussed.