This study presents a novel application of a Probabilistic Bayesian Neural Network (PBNN) for estimating vocal function variables and enhancing non-invasive ambulatory voice monitoring by addressing aleatoric and epistemic uncertainties in regression tasks. The proposed PBNN allows for estimating key physiological parameters including subglottal pressure, vocal fold contact pressure, thyroaritenoid and cricothyroid muscle activations, from seven aerodynamic and acoustic features. The PBNN is trained on the Triangular Body-Cover Model (TBCM) of the vocal folds to produce a non-linear inverse mapping between its inputs and outputs. Furthermore, the selected aerodynamic and acoustic features can be obtained in ambulatory settings, thus enhancing the practical applicability of the proposed method. Transfer Learning is then applied to integrate real voice data into the initially synthetic-trained network to refine subglottal pressure estimations. The confidence intervals generated by the PBNN illustrate its ability to identify uncertain estimations, as the results show correlations between prediction errors and the estimated aleatoric and epistemic uncertainties. This correlation is advantageous because it shows that the network can effectively predict potential inaccuracies in its estimations. Increased uncertainty is mainly observed at operating points where the TBCM is likely to exhibit non-linear behaviors, at higher subglottal pressures. This suggests that the selected input features may not be robust enough for capturing the nonlinear effects in the TBCM. These results highlight the potential for future research to assess the viability of incorporating new features and additional measurements that could better capture non-linear responses.

Data availability can limit machine learning model generalizability, while privacy concerns arise when sharing data for collaborative learning. Additionally, limited device capabilities and increased communication power can hinder real-world deployment. Differential-Private-Federated-Learning (DPFL) addresses these challenges by sharing encrypted gradients to enhance privacy and minimize data vulnerability while supporting collaborative model training in privacy-sensitive environments. The integration of DPFL with low-power FP-GAs is beneficial in overcoming edge-device limitations. FP-GAs' parallel processing, reconfigurability, quantization support, and hardware-software co-design, enhance DPFL's efficiency and adaptability. This combined approach optimizes training, ensuring secure and efficient collaboration across applications. Importantly, as ML algorithms evolve, FPGA implementations promote sustainability by reducing energy consumption, extending the hardware lifespan, and reducing electronic waste. Moreover, this framework can be used for emulation on FPGAs to explore and develop novel ASIC architectures for DPFL machine learning accelerators. We propose a novel open-source framework-"DPFL-FPGA-Accel" for carrying out design-space exploration of FPGA-based DPFL, source code is made available on https://github.com/shakeelakram00/DPFL-FPGA-Accel-Framework.git for wider adoption. To the best of our knowledge, this marks the first implementation of DPFL with FPGAs that enables users to design optimized FPGA accelerators for ML tasks implementing privacy-preserving federated learning with adjustable epsilon values. We illustrate the application of this framework in processing healthcare data, designing a DPFLenabled FPGA system for cardiac arrhythmia classification within a typical hospital setting, where electrocardiograms are not shared among hospitals. The FPGA-Accel achieves significant inference speedups, providing up to 605.2x and 107.2x faster performance compared to Arm Cortex-A53 and Intel Core i7 processors, respectively.

 Reconfigurable Intelligent Surfaces (RIS) represent a transformative technology for achieving the ambitious performance requirements of 6G wireless networks. This comprehensive review examines advanced beamforming techniques incorporating RIS technology, presenting a systematic analysis of theoretical foundations, implementation challenges, and future directions. We develop a rigorous analytical framework that characterizes the fundamental performance limits of RIS-assisted beamforming, demonstrating that system capacity scales as O(log N) with the number of reflecting elements while maintaining energy efficiency that follows O(log N/N). Our analysis reveals that hybrid approaches combining traditional optimization with machine learning techniques can achieve 85-90% of theoretical performance bounds while reducing computational complexity by approximately 80%. We present a detailed examination of practical implementation challenges, including hardware impairments, channel estimation overhead, and real-time optimization requirements. The study establishes that RIS-assisted beamforming can enable a 300% increase in spectral efficiency and 60-70% reduction in power consumption compared to conventional massive MIMO systems. Additionally, we identify critical research directions, including distributed RIS optimization, multi-user scheduling, and integration with artificial intelligence frameworks. This review provides a comprehensive foundation for researchers and practitioners working toward the realization of RIS-enabled 6G networks, offering both theoretical insights and practical implementation guidelines.

This paper addresses the problem of achieving any desired group consensus in multi-agent systems (MAS) operating in arbitrary signed networks. By focusing on arbitrary network topologies, we develop a novel control strategy that drives the agents' states toward any desired state, independent of initial conditions or network topology. A key contribution is the introduction of a switching control mechanism that expands the range of reachable states, ensuring that any targeted outcome is attained for a given initial condition without any constraints on the network structure. Our approach not only guarantees the formation of desired clusters but also ensures that each agent converges to the specified consensus value within its cluster. Mathematical analysis establishes conditions for achieving system stability and reachability to any desired state, based on which the control input is designed. Finally, the theoretical results are validated through extensive simulation results.

Inner-product functional encryption (IPFE) is an exciting paradigm for non-interactively computing on encrypted data where a master secret key can be used to derive decryption keys associated with certain inner-product functions in such a way that the decryption operation reveals an inner-product result, and nothing else. Over the decades, all efforts regarding existing IPFE schemes have been dedicated to extending them to multi-input/multi-client scenarios, understanding the security concepts that can be achieved, and identifying a multitude of practical use cases. However, in the event of key compromise, a common issue in real-world applications, the existing IPFE schemes are unable to guarantee the confidentiality of past encryptions. This paper introduces a novel variant of IPFE that supports key updates, called updatable IPFE, potentially opening up a new avenue of research to address the common key exposure problem. In this setting, any sender can initiate the transition to the next period by computing a new public key and a special update. The special update can be processed by the system administrator and the receiver to compute the next-period master secret key and the next-period decryption key, respectively. If done honestly, future (regular) ciphertexts generated with the new public key can be decrypted with the new decryption key, but ciphertexts from the past cannot be decrypted with the new decryption key. To formally illustrate this, we formalize the notion of the indistinguishability-based security under chosen randomness chosen plaintext attacks (IND-CR-CPA). We further propose the first efficient construction of updatable IPFE in the standard model, based on Learning with Errors (LWE) assumption with polynomial modulus-to-noise rate. Finally, we conduct a comprehensive performance evaluation of our updatable IPFE, and the experimental results demonstrate its computational efficiency, making it a viable solution for real-world applications.

There is a lot of pressures from organizations to continually redesign the business processes and the technology infrastructure to support the organization, but to also help organizations manage competing stakeholder needs, changing objectives and behaviour, and so on. A key part of this is making sure that all their activities in the different components of the business are interconnected and that they are moving in the same direction. Enterprise Architecture (EA) is an effective way of dealing with the challenge of designing business activities and supporting IT. This paper will summarize the organization of EA and look more closely at some major components of EA: governance architecture, business architecture, information architecture and technical architecture and consider the overlap and constructive interaction involved. Enterprise Architecture can be best understood through business process redesign. So, it is useful to briefly review the leading frameworks for business process redesign, including COBIT from ISACA, LEAN from the Toyota Group, TOGAF from the Object-Managemen t Group, and the Capgemini Integrated Architecture Framework (IAF). All these frameworks allow us to more successful transformation, to return to the central idea of this paper.

The release of the Citizens Broadband Radio Service (CBRS) spectrum has enabled private network deployments, but signal strength in this band is highly sensitive to weather conditions. While traditional signal propagation models are often constrained by their assumptions and lack adaptability to varying environments, Machine Learning (ML) models offer a flexible alternative for predicting signal strength under complex conditions. In this work, we develop a Neural Network (NN) based predictive model for CBRS signal strength, leveraging a comprehensive set of weather parameters, collected over a 24month period from a live CBRS network in Buffalo, NY. Our model demonstrates a Mean Absolute Error (MAE) of 1.83 dB in predicting hourly signal strength, and we further evaluate its generalization capability by applying a transfer learning approach to another CBRS network in Elmira, NY, achieving a MAE of 2.13 dB. This study highlights the potential of ML methods in modeling weather-induced signal variability and their applicability across different network environments.

The crucial element for ensuring secure optical communication lies in the consistent recovery of high-quality messages and validation needs to occur periodically. Semiconductor lasers have proven to be outstanding devices in the role of transceivers. This study delves into optical communication utilizing unidirectionally coupled multimode semiconductor lasers, specifically 3-mode lasers, as both transmitter and receiver. Two types of coupling are examined: global coupling, where the output of transmitter modes connects to corresponding receiver modes, and cross-modal coupling, which introduces a detuning equivalent to the frequency spacing of modes. The research is focused on investigating the impact of the transmission coefficient and bias current on the quality of the transmitted message. In the first coupling scenario, a high-quality message is successfully recovered, whereas the second coupling scheme demonstrates a neutral effect on message recovery quality. Message recovery is shown with an analog signal and a periodic bit sequence.

In this work, a high-bandwidth very-low input capacitance original topology buffer was designed. The proposed buffer circuitry is conceived as part of a novel non-invasive non-immersed real-time dielectric spectroscopy biomass sensor. Both a circuital-based simulation model and an experimental PCB prototype were implemented and successfully tested. The buffer effective parasitic input capacitance was lowered to a 2% of its original value by means of power supply bootstrapping. This improvement will allow performing differential voltage measurements with the required accuracy and without compromising the necessary bandwidth.

Ransomware attacks have become increasingly sophisticated, targeting critical systems and encrypting vast amounts of data, often causing significant financial and operational damage. To address this escalating threat, a novel approach has been developed that leverages feature optimization techniques to enhance the performance of machine learning models for ransomware detection. Through the application of Recursive Feature Elimination (RFE) and Principal Component Analysis (PCA), the study demonstrates that reducing the feature space not only improves detection accuracy but also reduces false positives and computational overhead. The experiments conducted show that optimized models generalize effectively across a variety of ransomware strains, improving both efficiency and scalability. The findings demonstrate the critical role of feature selection in building adaptive and resilient ransomware detection systems, with potential applications in real-time cybersecurity infrastructure.

Task replication is a common technique for achieving fault tolerance. However, its effectiveness is limited by the accuracy of the fault detection mechanism; imperfect detection imposes a ceiling on achievable reliability. While perfect fault detection mechanisms offer higher reliability, they introduce significant overhead. To address this, we introduce Dynamic Task Replication, a fault tolerance technique that dynamically determines the number of replicas at runtime to overcome the limitations of imperfect fault detection. Our primary contribution, Reliability-Aware Replica-Efficient Dynamic Task Replication, optimizes this approach by minimizing the expected number of replicas while achieving the desired reliability target. We incorporate actual execution times into the reliability assessment. Additionally, we propose the Energy-Aware Reliability-Guaranteeing scheduling technique, which integrates our optimized replication method into hard real-time systems and leverages Dynamic Voltage and Frequency Scaling to minimize energy consumption while ensuring reliability and system schedulability. Experimental results demonstrate that our method requires 24% fewer replicas on average than the N-Modular Redundancy technique, with the advantage increasing to 58% for tasks with low base reliabilities. Furthermore, our scheduling technique significantly conserves energy and enhances feasibility compared to existing methods across diverse system workloads.

In artificial intelligence, the challenge of maintaining knowledge stability across sequentially evolving tasks and contexts has become increasingly apparent, as language models are deployed in applications requiring prolonged semantic coherence. While traditional training paradigms emphasize rapid adaptation and generalization, they often overlook the essential need for sustained knowledge retention, resulting in semantic drift and inconsistencies over time. Addressing this gap, the novel Residual Knowledge Stability (RKS) framework presented in this study introduces a robust approach for quantifying longterm knowledge retention, offering a systematic evaluation of the model's internal consistency in handling complex and varied task sequences. The framework's design emphasizes autonomous task sequences, employing metrics that detect semantic drift and measure retention accuracy, without requiring human intervention, thereby allowing for scalable, unbiased assessment. Results indicate that RKS reveals key retention patterns, specifically in scenarios with increased task complexity, high domain diversity, and frequent task assignments, with implications for advancing model design strategies to support stable retention across extensive temporal applications. By prioritizing long-term retention in model evaluation, the RKS framework offers valuable insights for enhancing language model resilience, aligning architectural development more closely with the demands of real-world, continuously evolving information ecosystems.

This study conducts a systematic literature review to explore the current state of the art of evolvable KGs in manufacturing, which have an important role in the era of Industry 4.0 and 5.0. These KGs leverage both machine learning algorithms and human expertise to enhance decision-making, operational efficiency, and predictive maintenance capabilities. Despite the advancements, challenges persist in quality assurance, process planning, and the integration of human expertise. Our findings advocate for the necessity of addressing these issues to foster wider adoption and optimization of KG technologies in manufacturing. Evolvable KGs address the changing nature of knowledge. We map existing literature based on the KGC process stages. By deepening the understanding of how KGs can evolve, this review sets the base for future research aimed at developing more dynamic and intelligent systems tailored to the emerging demands of Industry 5.0.

Integrated Gradients is a well-known technique for explaining deep learning models. It calculates feature importance scores by employing a gradient based approach computing gradients of the model output with respect to input features and accumulating them along a linear path. While this works well for continuous features spaces, it may not be the most optimal way to deal with discrete spaces like word embeddings. For interpreting LLMs (Large Language Models), there exists a need for a non-linear path where intermediate points, whose gradients are to be computed, lie close to actual words in the embedding space. In this paper, we propose a method called Uniform Discretized Integrated Gradients (UDIG) based on a new interpolation strategy where we choose a favorable nonlinear path for computing attribution scores suitable for predictive language models. We evaluate our method on two types of NLP tasks-Sentiment Classification and Question Answering and show that our method yields better results than existing approaches.

This paper presents an optimization methodology for the design of square and rectangular substation grounding grids with equal conductor spacing, ensuring compliance with IEEE Std. 80-2013 safety criteria while minimizing construction costs to minimum possible limits. The proposed algorithm systematically performed parameters sweeps on all the design variables as stipulated in the standard, including grid burial depth, surface layer thickness, conductor material, conductor size, conductor spacing, and the number and length of ground rods. For optimization, two different cost functions were used: one based solely on the material costs of conductors, rods, while the other incorporated additional factors such as installation, and excavation costs. The algorithm’s performance is evaluated using eight different test grid systems published in the literature, and results were validated with the optimization results obtained from ETAP software. The results demonstrate significant cost reductions while maintaining safe step and touch potentials, offering a practical tool for designing safe, reliable, and cost-effective grounding grids for electrical substations.

PCB-based Inductive Position Sensors (IPS) are advantageous with respect to resistive, capacitive or Hall effect based alternatives since they allow for contactless sensors that are immune to stray fields and robust to temperature changes, all while being easy to manifacture by using conventional PCB fabrication techniques. In this work we present an IPS design for measuring the angle of a target conductor with respect to the sensing coils, and the distance between them. Simulations are used to build a dataset for training a neural network to solve the inverse problem of estimating angle and airgap from the measured voltages. The trained network is tested against experimental data measured from a physical prototype.

Latent feature transformation introduces a novel framework for enhancing linguistic performance through the direct manipulation of latent space dynamics within pre-trained language models. By focusing on recalibrating representational hierarchies, the methodology achieves improved generalization and task-specific adaptability without requiring extensive retraining or architectural redesigns. Experimental results highlight substantial performance improvements across sentiment analysis, machine translation, and summarization tasks, showcasing the model's ability to align with diverse linguistic challenges. The framework leverages differentiable mappings to systematically adjust latent feature distributions, preserving semantic integrity while optimizing task relevance. Quantitative analysis reveals enhanced semantic coherence and error reduction across complex linguistic categories, affirming the robustness of the transformation. Computational efficiency gains, reflected in reduced inference latency and memory usage, demonstrate the practicality of the approach for deployment in resource-constrained environments. Comparative evaluations position latent feature manipulation as a scalable alternative to traditional methods reliant on parameter scaling or extensive fine-tuning. The integration of this framework into pre-trained architectures demonstrates its capacity to enhance interpretability and precision across emergent tasks. Comprehensive experimental validation, supported through rigorous evaluation metrics and diverse datasets, substantiates the technical and operational feasibility of the methodology. Analysis of attention patterns reveals improved alignment with task-critical tokens, further highlighting the framework's potential to refine linguistic outputs. These findings establish latent transformations as a critical avenue for advancing the adaptability and efficiency of large-scale language models in both theoretical and applied domains.

The emergence of millimeter-wave-based 5G/Beyond 5G and 6G communication technologies has led to advancements in high data rates and low latency. However, there is a massive challenge in implementing the hardware platform including the millimeter-wave antenna. Over-the-air (OTA) testing is crucial for evaluating these antennas, often requiring large facilities to obtain far-field (FF) characteristics. In addition, the measurement accuracy is often limited mainly due to the path loss of the millimeter waves in the far field. Therefore, it is necessary to test the antenna in the near-field (NF) region. In this paper, we present a practical method for obtaining an FF pattern of the antenna-under-test by using an NF-to-FF transformation method with a probe correction technique. The feasibility of the process is validated by the OTA measurement results with a dedicated NF scanning system, which accurately reconstructs the FF patterns from the NF measurement results. Additionally, we have demonstrated that a custom-designed multi-probe can be employed with the transformation method to save measurement time.