As businesses move toward cloud computing environments, security and privacy concerns become increasingly important. This research report systematically investigates various challenges and vulnerabilities in cloud computing, focusing on security and privacy issues. This study comprehensively examines potential threats, from data breaches to unauthorized access, and assesses the impact of these challenges on user trust and data integrity in cloud infrastructure. Additionally, effective strategies and solutions to reduce security risks and protect user privacy in cloud computing are proposed and discussed. This paper contributes to the ongoing debate on cloud security and privacy and provides both practitioners and researchers with a valuable resource for navigating the evolving landscape of cloud-based services.

The foundations of nonlinear optics are revisited, and the formalism is applied to waveguide modes. The effect of loss and dispersion are included rigorously along with the vectorial nature of the modes, and a full derivation of a new version of the nonlinear Schrödinger (NLS) equation is presented. This leads to more general expressions for the group index, for the group-index dispersion (GVD), and for the Kerr coefficient. These quantities are essential for the design of waveguides suitable for e.g. the generation of optical frequency combs and all-optical switches. Examples are given using the silicon nitride material platform. Specifically, values are extracted for the coefficients of the chi-3 tensor based on measurements of Kerr coefficients and mode simulations.

To better understand the upper-limb needs and challenges survivors of neurological events face, and the issues associated with existing technology, customer discovery conversations were conducted with 153 people in the ecosystem (60 patients, 30 caregivers, and 63 medical providers). Patients with upper-limb effects fell into two populations: spastic (stiff, clenched hands) and flaccid (limp hands). Focusing on the first category, a set of design constraints was developed based on the information collected from the customer discovery. With these in mind, the OrthoHand Extend was designed and prototyped as a powered wrist-hand stretching orthosis (exoskeleton) to aid in recovery. The orthosis was tested on two patients, one survivor of stroke and one of traumatic brain injury. Finally, a mathematical modeling technique was developed to characterize joint stiffness based on experimental testing. The prototype met most of the target design constraints and was able to consistently open both patients' hands. Donning and doffing times averaged 76 and 12.5 s, respectively, for each subject unassisted. These are approximately twice as fast than the next closest times shown in literature. This device has the potential as effective therapy devices accessible to patients in the home, and lays the foundation for clinical trials and further device development. The study was approved by the Vanderbilt University Institutional Review Board, study number 221203.

The field of blockchain interoperability plays a pivotal role in blockchain adoption. Despite these advances, a notorious problem persists: the high number and success rate of attacks on blockchain bridges. We propose Harmonia, a framework for building robust, secure, efficient, and decentralized cross-chain applications. A main component of Harmonia is DendrETH, a decentralized and efficient zero-knowledge proof-based light client. DendrETH mitigates security problems by lowering the attack surface by relying on the properties of zero-knowledge proofs. The DendrETH instance of this paper is an improvement of Ethereum’s light client sync protocol that fixes critical security flaws. This light client protocol is implemented as a smart contract, allowing blockchains to read the state of the source blockchain in a trust-minimized way. Harmonia and DendrETH support several cross-chain use cases, such as secure cross-blockchain bridges (asset transfers) and smart contract migrations (data transfers), without a trusted operator. We implemented Harmonia in 9K lines of code. Our implementation is compatible with the Ethereum Virtual Machine (EVM) based chains and some non-EVM chains. Our experimental evaluation shows that Harmonia can generate light client updates with reasonable latency, costs (a dozen to a few thousand US dollars per year), and minimal storage requirements (around 4.5 MB per year). We also carried out experiments to evaluate the security of DendrETH. We provide an open-source implementation and reproducible environment for researchers and practitioners to replicate our results.

Automotive driving assistant systems (ADAS) and Automated driving (AD) technologies are widely used in unmanned vehicle control along a trajectory, and just like many other technologies, they present some risks such as the risk of misdirection and collision of the vehicle with an obstacle along its trajectory. To avoid these, many tracking systems such as the radar tracking system are used to detect and track targets and trajectories of ADAS/AD vehicles. However, the performance of the tracking operation is an important factor in determining the reliability of the tracking systems and ADAS/AD technologies, especially at the edge computing level. In this study, we proposed a tracking technique using a collaborative predictive model in time series, called CoPreMo, to improve the reliability of the tracking operation of a radar system. We carried out three experiments with the model on a simulated radar system to track the range of a target with different speeds in three ADAS/AD scenarios and achieve a range tracking error of 0.21m, 0.26m, and 0.32m, better than those of the baseline models.

The advent of computerized medical recording systems in healthcare facilities has made data retrieval tasks easier, compared with manual recording. Nevertheless, the potential of the information contained within medical records remains largely untapped, mostly due to the time and effort required to extract data from unstructured documents. Natural Language Processing (NLP) represents a promising solution to this challenge, as it enables the use of automated text-mining tools for clinical practitioners. In this work, we present the architecture of the Virtual Dementia Institute (IVD), a consortium of sixteen Italian hospitals, using the NLP Extraction and Management Tool (NEMT), a (semi-)automated end-to-end pipeline that extracts relevant information from clinical documents and stores it in a centralized database. NEMT core is a Question Answering Bot (QABot) based on a modern NLP model, fine-tuned using thousands of examples produced from IVD centers. Detailed descriptions of the process for defining a common minimum dataset, the Inter-Annotator Agreement calculated on clinical documents, and NEMT results, are provided. The best QABot performance in terms of Exact Match (EM) and F1-score (78.1% and 84.7%) outperforms ChatGPTv3.5 (68.9% and 52.5%). NEMT represents an efficient tool that paves the way for medical information extraction and exploitation for new research studies.

In this paper, the nonlinear extended state observer (NESO) is proposed to estimate the state-of-charge (SOC) for the Lithium-ion batteries (LIBs). The NESO is designed based on the simple first-order equivalent circuit model (ECM) to estimate the total disturbance of the battery model and compensate them as the new extended state, which can achieve the active disturbance rejection to estimate the battery state accurately. Furthermore, the observability and stability of the NESO is proven, and estimation performance of the observer is verified under different ambient temperatures, testing driving and different noise levels. The SOC estimation results indicate that the NESO can obtain accurate estimation values and strong robustness against different disturbances, even though all validation conditions use constant battery value of the first-order ECM. In addition, compared with other common estimation methods, the NESO exhibits a faster convergence time and more accurate estimation results.  

Driving while drowsy may lead to car accidents and other dangerous situations. Since yawning is an obvious sign of drowsiness, it is important to construct a high-accuracy real-time approach to yawning detection. However, existing research on facial keypoint-based segment-level models is still relatively scarce, and not yet comprehensive. Therefore, this paper proposes an approach where the facial keypoints in video clips are first recognized by OpenPose and standardized, and then yawning and other mouth behaviors are detected by our graphtemporal convolutional network (GTCN) model. Extensive experimental results on the public yawning detection dataset YawDD not only reflect the superiority of OpenPose as the facial keypoint extractor and graph convolutional network (GCN) as the spatial feature extractor, but also indicate that the GTCN model achieved a state-of-the-art performance, with 91.73% accuracy on three classifications of normal, talking, and yawning, and 99.25% accuracy on the binary classification problem of yawning detection on the testing set. Experiments also reveal that the GTCN model has good real-time performance in practice.

Federated Learning (FL) has established itself as a widely accepted distributed paradigm. Without sharing data, it may seem like a privacy-preserving paradigm, but recent studies have revealed vulnerabilities in weight sharing which results in information disclosure. Hence, privacy-preserving approaches must be incorporated during aggregation to avoid disclosures. In the literature of FL, not much focus has been given on generating generalized models which can be generated by multiple sets of datasets thus avoiding identity disclosure. Integrally private models are the models which recur frequently from different datasets. So, in this paper we focus on generating the integrally private global models proposing k-Anonymous Integrally Private Federated Averaging (k-IPfedAvg), a novel aggregation algorithm which clusters similar user weights to compute a global model which can be generated by multiple sets of users. Convergence analysis of k-IPfedAvg reveals a rate of O(1 T) over training epochs. Furthermore, the experimental analysis shows that k-IPfedAvg maintains a consistent level of utility across various privacy parameters in contrast to existing noise based privacypreserving mechanisms. We have compared k-IPfedAvg with classical fedAvg and its differentially private counterpart. Our results shows that k-IPfedAvg has comparable accuracy score with baseline fedAvg and outperforms DP-fedAvg on iid and non-iid distributions of MNIST, FashionMNIST and CIFAR10 datasets.

A new approach is developed for the joint queue-length distribution of the two-level non-preemptive Markovian priority queue that allows explicit and exact results to be obtained. Marginal distributions are derived for the general multi-level problem. The results are based on a representation of the joint queue-length probability mass function as a single-variable complex contour integral, that reduces to a real integral on a finite interval arising from a cut on the real axis. Both numerical quadrature rules and exact finite sums, involving Legendre polynomials and their generalization, are presented for the joint and marginal distributions. A high level of accuracy is demonstrated across the entire ergodic region. Relationships are established with the waiting-time distributions. Asymptotic behaviour in the large queue-length regime is extracted.

In this work, we perform a comparative analysis of discrete-and continuous-time estimators of information-theoretic measures quantifying the concept of memory utilization in short-term heart rate variability (HRV). Specifically, considering heartbeat intervals in discrete time we compute the measure of information storage (IS) and decompose it into immediate memory utilization (IMU) and longer memory utilization (MU) terms; considering the timings of heartbeats in continuous time we compute the measure of MU rate (MUR). All measures are computed through model-free approaches based on nearest neighbor entropy estimators applied to the HRV series of a group of 15 healthy subjects measured at rest and during postural stress. We find, moving from rest to stress, statistically significant increases of the IS and the IMU, as well as of the MUR. Our results suggest that both discrete-time and continuous-time approaches can detect the higher predictive capacity of HRV occurring with postural stress, and that such increased memory utilization is due to fast mechanisms likely related to sympathetic activation.

The vision of biocomputing is to develop computing paradigms using biological systems, ranging from micron-level components to collections of cells, such as organoids. This paradigm shift exploits hidden natural computing properties, developing miniaturized wet computing devices deployable in harsh environments, and exploring designing novel energyefficient systems. Parallelly, we witness the emergence of AI hardware including neuromorphic processors aiming to improve computational capacity. This study brings together the concept of bio-computing and neuromorphic systems by focusing on the Bacterial gene regulatory networks and their transformation into Gene Regulatory Neural Networks (GRNNs) that can be used for biocomputing. We explore the intrinsic properties of gene regulations, map this to a gene-perceptron function, and propose an application-specific sub-GRNN search algorithm that maps the network structure to match a problem. Focusing on the model organism Escherichia coli (E. coli), the base-GRNN is initially extracted and validated for accuracy. Subsequently, a comprehensive feasibility analysis of the derived GRNN confirms its computational prowess in classification and regression tasks. Furthermore, we discuss the possibility of performing a wellknown digit classification task as a use case. Our analysis and simulation experiments show promising results in offloading computation to GRNN in bacterial cells, advancing wet-neuromorphic computing using natural cells.

This study proposes a decentralized collision avoidance method for multiple ships and utilizes information exchange between vessels. The method is formulated with a hierarchical algorithm architecture and communicates with other ships using the route exchange concept to calculate collision-free trajectories. The hierarchical architecture consists of mid-level and reactive trajectory planners and considers navigational rules, e.g. the COLREG, and both static and dynamic obstacles. Dynamic obstacles represent the ships in the scenarios and the algorithm accounts for both cooperative and non-cooperative vessels. The mid-level planner is used for finding effective control actions by combining the A* algorithm with a proactive collision avoidance optimizer based on the MPC. The reactive planner uses the Informed SB-MPC algorithm and serves as a final barrier for finding solutions to problematic scenarios where the mid-level planner solutions are infeasible. The main motivations of the study include 1) to utilize ship-to-ship information exchange to convey intentions and to solve collision avoidance problems and 2) to prevent autonomous ships to initiate aggressive maneuvers in close-range encounters by calculating and applying proactive control actions in advance. The performance of the method is validated via simulations of 22 Imazu problem cases and a case scenario defined in the Trondheim Fjord. The results support that the algorithm can generate collision-free trajectories considering the COLREG rules 8, 13-18 and maintains a minimum safety distance. Finally, the hierarchical algorithm demonstrates computationally efficient run times to be used in real-time applications.

IQ mixers and complex mixers are both used to cancel the image band signal. In this paper, we analyse a complex IF sub-THz receiver architecture aimed for splitting a multiGHz baseband signal into narrower sub-bands for easier A/D conversion. The architecture consists of a normal IQ direct downconversion, followed by complex mixers to center the chosen sub-carriers to DC. Image rejection versus mismatch errors is calculated both analytically and on system and circuit-level simulations. It was seen that the image rejection is dominated by the IQ downconversion stage. It is demonstrated that the mismatches in the IQ stage can be corrected digitally by weighted summing of the digital sub-bands.

Earth Explorer 10 mission Harmony will consist of two satellites that fly in formation with Sentinel-1. It will operate as a multistatic radar in which Sentinel-1 transmits signals and all three satellites receive signals from different lines-of-sight. To prepare for Harmony and other possible future bistatic missions, transforms are derived to map the ocean-wave spectrum into bistatic synthetic aperture radar (SAR) spectra. The SAR mapping follows the standard derivation using the multi-dimensional characteristic function, but with adjustments for the modulation transfer functions compared to the monostatic case. This paper focuses on the SAR modulations caused by velocity bunching as it is the dominant distortion mechanism. We argue that a multistatic system, such as Harmony, leads to an inversion that constrains the real aperture radar (RAR) response on a scene-by-scene basis. A benefit of having additional receivers for wave-spectra estimation is that the three lines-of-sight enables to capture a larger fraction of the wave spectrum. Improvements are especially expected in high wind speed conditions such as tropical cyclones, where large energetic surface motions strongly deteriorate the (azimuth) resolution of the SAR data. Enhanced directional wave spectral characteristics will further help to improve the interpretation of the new bistatic Harmony high-resolution scatter and Doppler combined directional measurements .

Over the past few years YouTube has became a popular site for video broadcasting and earning money by publishing various different skills in the form of videos. For some people it has become a main source to earn money. Getting the videos trending among the viewers is one of the major tasks which each and every content creator wants. Popularity of any video and its reach to the audience is completely based on YouTube's Recommendation algorithm. This document is a dataset descriptor for the dataset collected over the time span of about 45 days during the Israel-Hamas War.

Affective patterns based on physiological signals reflect bodily changes linked to specific emotional states. Previous studies on the cardiac electrical signal, a key peripheral physiological signal, were limited by the measurement density of single-lead ECG signal, focusing solely on temporal pattern analysis but ignoring topographic pattern analysis that can reflect the body's emotional response. Our research advances affective peripheral pattern studies by innovatively using body surface potentials to comprehensively monitor cardiac electrical activity with increased measurement density. To tackle the challenge of extracting spatial and temporal features from multi-channel body surface potentials, we establish a dynamic correlation among these diverse channel signals through covariance matrices. Our hypothesis is that the dynamic inter-channel relationship provides a valuable source of insights into emotional clues. Experimental results demonstrate that the extracted spatial and temporal features effectively capture topographic and temporal patterns from cardiac electrical signals, and achieve excellent performance in classification tasks simultaneously. Our finding reveals affective patterns based on body surface potentials for the first time, offering novel insights into affective peripheral patterns analysis.

This paper presents the design of an open-API-based explainable AI (XAI) service to provide feature-contribution explanations for cloud AI services. Cloud AI services have broad usage in developing domain-specific applications with learning precision metrics. However, the underlying AI models remain opaque on how the model produces the prediction. We argue that XAI operations are accessible as open APIs to enable the consolidation of the XAI operations into the cloud AI services assessment. We propose a design using a microservice architecture that offers feature contribution explanations for cloud AI services without unfolding the network structure of the cloud AI services. We can also utilize this architecture to evaluate the performance and XAI consistency metrics showing cloud AI services' trustworthiness. We collect provenance data from XAI operations to enable traceability within the XAI service. Furthermore, we present the discovery scenarios for the experimental tests regarding performance and XAI consistency metrics for the leading cloud AI services for computer vision. The results confirm the open-API-based architecture cloud-agnostic. Additionally, data augmentation has a marked improvement in XAI consistency metrics for the cloud AI services.