In the context of mobile and Internet of Things (IoT) networks, data naturally originates at the edge, making crowdsourcing a convenient and inherent approach to data collection. However, crowdsourcing presents challenges related to privacy, sampling bias, statistical sufficiency, and the need for time-consuming post-processing. To this end, generating synthetic data using Deep Learning techniques emerges as a promising solution to overcome such limitations. In this study, we propose an innovative framework that transcends applications and data types, enabling the conditional generation of crowdsourced datasets with location information in mobile and IoT networks. A crucial aspect of our methodology is its ability to assess uncertainty in newly generated samples and produce calibrated predictions through approximate Bayesian methods. Without loss of generality, we ascertain the validity of our method on the task of Minimization of Drive Test (MDT) data generation, presenting for the first time a comparison of synthetically generated data with an original large-scale MDT set collected from a Mobile Network Operator’s network infrastructure. By offering a versatile solution to data generation, our framework contributes to overcoming challenges associated with crowdsourced data, opening up possibilities for advanced analytics and experimentation in mobile and IoT networks.

In industrial settings, robots are typically employed to accurately track a reference force to exert on the surrounding environment to complete interaction tasks. Interaction controllers are typically used to achieve this goal. Still, they either require manual tuning, which demands a significant amount of time, or exact modeling of the environment the robot will interact with, thus possibly failing during the actual application. A significant advancement in this area would be a high-performance force controller that does not need operator calibration and is quick to be deployed in any scenario. With this aim, this paper proposes an Actor-Critic Model Predictive Force Controller (ACMPFC), which outputs the optimal setpoint to follow in order to guarantee force tracking, computed by continuously trained neural networks. This strategy is an extension of a reinforcement learning-based one, born in the context of human-robot collaboration, suitably adapted to robot-environment interaction. We validate the ACMPFC in a real-case scenario featuring a Franka Emika Panda robot. Compared with a base force controller and a learning-based approach, the proposed controller yields a reduction of the force tracking MSE, attaining fast convergence: with respect to the base force controller, ACMPFC reduces the MSE by a factor of 4.35.

The COVID-19 pandemic is an unparalleled global crisis that has led to a surge in innovative responses. Central to this innovation landscape was the emergence of open-source projects that offered rapid, collaborative solutions, complementing scientifically led endeavors. This paper undertakes a comprehensive exploration of open-source innovations during the pandemic, chronicling their instigation, impact, and broader implications for research and development (R&D) paradigms. Utilizing a meticulously curated database of open-source COVID-19 innovations, we compared these projects with scientifically led initiatives and derived insights into their respective strengths, challenges, and contributions. Our findings underscore the transformative potential of collaborative research models, emphasizing the synergies between open source and scientific approaches. Furthermore, we delineate the broader implications of these findings for future R&D landscapes, highlighting the pivotal roles of community engagement, adaptability, and interdisciplinary collaboration. This study offers a holistic understanding of innovation dynamics during global crises, serving as a guide for future research endeavors and policy formulations.

The utilization and engineering of thermo-optic effects have found broad applications in integrated photonic devices, facilitating efficient light manipulation to achieve various functionalities. Here, we perform both an experimental characterization and theoretical analysis of these effects in integrated micro-ring resonators made from high index doped silica (HIDS), which have had many applications in integrated photonics and nonlinear optics. By fitting the experimental results with theory, we obtain fundamental parameters that characterize their thermo-optic performance, including the thermo-optic coefficient, the efficiency for the optically induced thermo-optic process, and the thermal conductivity. The characteristics of these parameters are compared to those of other materials commonly used for integrated photonic platforms, such as silicon, silicon nitride, and silica. These results offer a comprehensive insight into the thermo-optic properties of HIDS based devices. Understanding these properties is essential for efficiently controlling and engineering them in many practical applications.

A noise eigenspace projection method is described to improve accuracy and parsimony for pattern classification.  A set of 11 classifiers was employed with 10-fold cross validation on 13 publicly available datasets.  For each dataset, a “baseline”’ classification analysis was first performed using interclass principal component analysis (PCA) scores based on the signal eigenspace of the correlation matrix.  Next, intraclass eigendecomposition was run and percentiles of feature values were projected onto noise eigenvectors from each class to develop an m-tuple of information-to-noise ratio (INR) estimators.  The top 5 INR estimators were then used for classification analysis.  By projecting percentiles of feature values on the class-specific noise eigenvectors, it was found that INR estimators resulted in a 14 percentage point increase in mean ensemble classifier fusion accuracy over datasets from 84% to 98% when compared with use of signal PCs for class prediction.  Unlike feature selection, which identifies features whose values differ greatly across class labels, the proposed approach exploits the fact that the noise eigenspace from all input features differs across classes.  We also only used the top 5 INR estimators, which is typically lower than the number of input features selected through filtering or wrapping.  Lastly, for operational employment, we recommend use of the k-nearest neighbor (KNN) classifier because of its proximity to ensemble classifier fusion results, high performance, and low computational cost.   In conclusion, use of INR estimators based on class-specific noise can greatly improve classification performance and model parsimony without use of input feature selection.

The paper “Large Language Models Bias Issues Solving Through SDRT” discusses the challenges and ethical concerns posed by large language models (LLMs) such as GPT-3 and GPT-4 in the realm of natural language processing (NLP) and artificial intelligence research. It proposes a solution in the form of Segmented Discourse Representation Theory (SDRT) to address these challenges. By integrating SDRT into existing transformer models and incorporating it into both encoders and decoders, the paper aims to reduce bias, enhance semantic understanding, and foster more meaningful and transparent conversations. This approach recognizes the importance of responsible LLM development and the need for solutions to mitigate issues like misinformation, biased content, and lack of contextual understanding. Through its technical details and architectural improvements, the paper contributes to the ongoing discourse on enhancing the capabilities and ethical use of large language models in complex NLP environments.

Federated Learning (FL) is a distributed machine learning (ML) approach that allows multiple devices to train a model while keeping training data private on edge devices. However, existing FL libraries are incapable of: (i) supporting a wide range of existing FL algorithms, (ii) handling diverse public as well as custom datasets, (ii) discarding unreliable updates from malicious edge devices, and (iv) facilitating edge-device training. With the rapid progression in AI, there is an increment in the computation power while training the models on edge devices which results in more carbon emission (CE). However, in the existing FL frameworks, there is no module for estimating CE. In this paper, we present \textbf{FedERA}, a modular and fully customizable open-source FL framework aiming to address these issues especially also by offering extensive support on heterogeneous edge devices and by incorporating both standalone and distributed training approaches. The integration of new software modules unforeseen in existing FL frameworks not only amplifies the scope of its usability but also fosters environment-friendly FL. We believe that FedERA will be a valuable tool for researchers and practitioners working in FL. Source codes of this library along with documentation and tutorials are available at: https://github.com/anupamkliv/FedERA.

A document by Nicasio Canino . Click on the document to view its contents.

In this paper, we explore the application of nonlinear networked predictive control (NNPC) to the unmanned ground vehicle (UGV) that experiences skid-slip, static and moving obstacles, and communication delays in both sensor-to-controller (SC) and controller-to-actuator (CA) channels. Our approach utilizes adaptive dynamic programming (ADP) based model predictive control (MPC) and introduces a delay compensation strategy, effectively mitigating the impact of communication delays on control performance. Specifically, our proposed method generates a control sequence that accounts for delayed control inputs and transmits it to the actuator when the controller node receives a new state measurement. To ensure robustness, our method explicitly considers the presence of collision avoidance and skid-slip and employs an ADP process to develop the control law, which receives state estimation from the Extended Kalman filter (EKF). By integrating these elements, our approach provides an effective and reliable solution for the path-tracking control of the UGV in the presence of communication delays and external disturbances. Finally, comprehensive simulations are performed on a large map and traditional circular trajectory to test and validate the performance of the proposed control scheme.

This research delineates the transformative potential of blockchain technology within the realm of European governments. By conducting a meticulous analysis of existing literature and case studies, the study seeks to identify and evaluate the opportunities and challenges associated with implementing blockchain technology in governmental operations across Europe. The methodology involves a comprehensive review of peer-reviewed articles, white papers, and governmental reports, aiming to construct a nuanced perspective on the current state of blockchain applications in European governmental structures. Key findings indicate that blockchain technology can significantly enhance transparency, efficiency, and security in governmental processes within the European context, albeit with certain challenges and barriers to implementation. This study serves as a pivotal reference point, fostering a deeper understanding of the intricate dynamics of blockchain technology in the European governmental sphere and paving the way for future research and policy developments.

An analytical method is proposed to synthesize the angle-dependent surface susceptibilities, χ of spatially dispersive or non-local zero-thickness metasurfaces. The proposed method is based on the extended Generalized Sheet Transition Conditions (GSTCs), whereby spatially dispersive metasurfaces are modeled using angle-dependent surface susceptibilities that take the form of rational polynomial functions of the transverse wave-vector, k∥. The suggested method derives the rational polynomial form of χ(k∥), which can then be expressed in the space-domain using spatial derivatives of the fields, resulting in a corresponding higher-order spatial boundary condition to achieve the desired field operation. The proposed synthesis method is illustrated using variety of examples such as, a space-plate, spatial filters and field absorbers, which are then validated using an Integral Equation (IE) solver, in which the corresponding higher-order boundary conditions are integrated to predict the scattered fields. The proposed method thus not only represents a simple way to synthesize ideal zero-thickness metasurfaces, but helps establishes a way to define fundamental operational limits of spatially dis- persive metasurfaces. This is illustrated by considering the space- plate example, and deriving the fundamental trade-off between operation bandwidth and the achievable space-compression.

Time-frequency analysis plays a crucial role in various fields, including signal processing and feature extraction. In this article, we propose an alternative and innovative method for time-frequency analysis using a biologically inspired spiking neural network (SNN), encompassing both specific spike-continuous-time-neuron (SCTN) based neural architecture and an adaptive learning rule. We aim to efficiently detect frequencies embedded in a given signal for the purpose of feature extraction. To achieve this, we suggest using an SN-based network functioning as a resonator for the detection of specific frequencies. We developed a modified supervised Spike-Timing-Dependent Plasticity (STDP) learning rule to effectively adjust the network parameters. Unlike traditional methods for time-frequency analysis, our approach obviates the need for segmenting the signal into several frames, resulting in a streamlined and more effective frequency analysis process. Simulation results demonstrate the efficiency of the proposed method, showcasing its ability to detect frequencies and  generate a Spikegram akin to the Fast Fourier Transform (FFT) based spectrogram. The proposed approach is applied to analyzing EEG signals demonstrating an accurate correlation to the equivalent FFT transform.

The research is built upon a comprehensive compilation of user stories and their associated test cases, which have been carefully extracted from existing software projects in real?world settings. Each user story and test case are represented as a collection of unigrams by capturing the essential features and requirements of the respective functional components. By emphasizing the Three C’s - Card, conversation, and confirmation, user stories are crafted to be precise, comprehen?sible through teamwork, and are subjected to thorough testing. This strategy improves communication, reduces the risk of misinterpretation, and yields software that strictly meets user requirements

The digital transformation of power distribution system has increased the demand for Distribution System State Estimation (DSSE) techniques that are robust to adversarial perturbations in addition to noise. We design a versatile method for detecting stealthy data manipulation attacks on DSSE, drawing on a model-agnostic attribution method that quantifies the contribution of each input feature to the state estimation result. The key intuition for this work is that data manipulation attacks, including adversarial and false data injection attacks, generally have a discernible effect on this feature saliency measure. Through extensive numerical simulation, we corroborate that the proposed method reliably detects various data manipulation attacks, outperforming the most prominent detection methods from the previous work.

A document by Mohaimenul Azam Khan Raiaan. Click on the document to view its contents.

In cell-free (CF) networks, access points (APs) jointly serve the user equipments (UEs). In user-centric (UC) variant, only a handful of APs serve a particular UE, namely the ones with the best channels. In this paper, we propose a hybrid beamforming uplink receiver scheme for highly loaded UC massive MIMO (mMIMO) networks, operating in millimeter-wave(mmWave) frequencies, employing single carrier modulation under frequency selective channels. Every AP employs its own analog beamformer (AB) called the modified eigen-beamformer (MEB), which promotes a trend of fairness. In the digital domain, two detectors are proposed; Cell-Free Iterative Detector (CFI-D) and User-Centric Iterative Subset-Detector (UCIS-D). CFI-D introduces the iterative block decision feedback equalization (IB-DFE) method for CF networks. UCIS-D is an alternative to CFI-D. UCIS-D is UC, has low computational complexity (CCP) and is scalable. UCIS-D relies on parallel interference cancellation (PIC) aided IB-DFE. Interfering UEs are divided into three sets. Weak interferers are processed with PIC, strong interferers with IB-DFE which considers the residual interference from PIC. Weakest interferers are ignored. Both PIC and IB-DFE utilize the soft decisions from previous iterations. Simulation results show that these receivers can handle ultra high loads in terms of bit-error-rate (BER) and achievable information rate (AIR).

This paper presents a novel method for using Graph Neural Networks in combination with reinforcement learning for power reliability studies. Monte Carlo methods are the backbone of such probabilistic power system reliability analyses. Recent efforts from the authors have shown that it is possible to replace Optimal Power Flow solvers with the policies of deep reinforcement learning agents and obtain significant speedups of Monte Carlo simulations while retaining close to optimal accuracies. However, a limitation of this reinforcement learning approach is that the training of the agent is tightly connected to the specific case being analyzed, and the agent cannot be used as is in new, unseen cases. In this paper, we seek to overcome these issues. First, we represent the state and actions in the power reliability environment by features in a graph, where the adjacency matrix can vary from time step to time step. Second, we train the agent by applying a message passing graph neural network architecture to an integrated variant of an actor-critic algorithm. This implies that the agent can solve the problem independently of the power system grid structure. Third, we show that the agent can solve small extensions of a test case without having seen the new parts of the power system during training. In all of our reliability Monte Carlo simulations using this  graph neural network agent, the simulation time is competitive with that based on optimal power flow, while still retaining close to optimal accuracy.

This paper introduces a novel adaptive neural network-based sliding mode controller for trajectory tracking of a quadrotor UAV under unknown parameters such as inertia and mass. Unknown external disturbances and nonlinear aerodynamic forces are also considered. Due to its complex dynamics, the quadrotors need a dual controller for outer and inner loop control. For the commercial UAV, the inner loop controller, which ensures the attitude control, is usually assumed to be well developed and unmodifiable. Under this assumption, this paper focuses on the position control of a quadrotor UAV. A new position controller will be then designed and used to compute the appropriate input commands for the attitude controller to achieve the trajectory tracking task. The proposed method combines both Back-Propagation Neural Network (BPNN) scheme and sliding mode control to eliminate the effects of exogenous disturbances and model uncertainties. To illustrate the efficiency of the proposed controller, a comparative analysis is performed with different methods through experimental results.