Reviewerly/exHarmony huggingface dataset:
Research on Intelligent Network Operation Management System Based on Anomaly Detection and Time Series Forecasting Algorithms

The research try to implements an intelligent network operation management system for enterprise networks. First, based on Flask-state software architecture, the system adapt to Phytium CPU and Galaxy Kylin operating system successfully. Second, using the Isolation Forest algorithm, the system implements the anomaly detection of host data such as CPU usage. Third, using the Holt-winters seasonal prediction model, the system can predict time series data such as network I/O. The results show that the system can realizes anomaly detection and time series data prediction more precisely and intelligently.
Social Distancing Monitoring and Alerting System using YOLO Deep Learning Algorithm

The Covid-19 disease is caused by the severe acute respiratory (SAR) syndrome coronavirus-2 and becomes the reason for the Global Pandemic since 2019. Until July 2022, the total reported cases were 572 million and reported deaths were 6.38 million around the world. In many countries the infections caused severe damages. It not only took the precious lives but also caused few other national damages like economic crisis. The only solution to stop this pandemic is to increase the vaccination and reducing the spreads. The covid 19 virus is an airborne disease and spread when people breathe virus contaminated air. The WHO and all the nations were insisting to maintain social distance to control the virus spreading. But maintaining the social distance in public places is very hard. In this project we developed a method for detecting social distance. The system uses Raspberry Pi processor to detect the distance between two people from the live video stream. The YOLOv3 technique is used to detect the object from single frame of the video.
Mixed-Resolution HESP for More Efficient Fast Channel Switching and Packet-Loss Repair

Low-delay live streaming applications desire fast channel switching and packet-loss repair capabilities. However, existing methods that provide these capabilities have a negative impact on the stream of steady-state users. To minimize this impact, techniques such as the High Efficiency Streaming Protocol (HESP) utilize keyframe injection. Such techniques combine compression-efficient normal streams with corresponding companion streams that are used in case of random access or packet loss. Unfortunately, because a companion stream is needed for every normal stream, the distribution cost and encoding complexity are considerable costs. Additionally, injecting a companion keyframe into a normal stream causes a bitrate spike. Therefore, this paper evaluates the impact of utilizing mixed-resolution keyframe injection in the H.266/VVC standard. By providing a single companion stream for all normal streams of a bitrate ladder, the three mentioned downsides can be mitigated. We found that injecting a lower-resolution keyframe effectively reduces the bitrate spike, at the cost of only a modest quality loss. For dynamic video content, the quality impact reduces over time, and is less perceptible than traditional packet-loss repair using frame copy. In conclusion, mixed-resolution HESP can reduce the bitrate spike and computational/distribution overhead of HESP when enabling fast channel switching or packet-loss repair.
Elegant Fit-On – Virtual Fitting Room on Handheld Devices

Clothing has been one of the basic human needs since ancient times. It is a common thing to try on clothes and consider certain features when buying clothes. With the current pandemic situation, it is risky to wear and buy clothes by physical shopping. Consequently, people do online shopping. Those existing shopping websites are not user-friendly and less reliable as the customers will not have the privilege to purchase the exactly fitting outfit. Therefore, the customer satisfaction level is low with the clothes they have bought through online platforms. Therefore, the aim is to utilize technology to provide a virtual fitting room experience on handheld devices. The objective is to create a customized 3D avatar that represents the customer’s unique body shapes and features, which allows to try on clothes. This avatar is 360 degrees rotatable with pre-defined poses to check what the fit-on looks like. This solution shows whether the clothes are too fit or loose for the customer by showing live wrinkles. The text and voice feedback are generated at the end, which would be helpful for differently-abled people, especially those with vision issues.
Gaze-based Object Detection in the Wild

I human-robot collaboration, one challenging task is to teach a robot new yet unknown objects enabling it to interact with them. Thereby, gaze can contain valuable information. We investigate if it is possible to detect objects (object or no object) merely from gaze data and determine their bounding box parameters. For this purpose, we explore different sizes of temporal windows, which serve as a basis for the computation of heatmaps, i.e., the spatial distribution of the gaze data. Additionally, we analyze different grid sizes of these heatmaps, and demonstrate the functionality in a proof of concept using different machine learning techniques. Our method is characterized by its speed and resource efficiency compared to conventional object detectors. In order to generate the required data, we conducted a study with five subjects who could move freely and thus, turn towards arbitrary objects. This way, we chose a scenario for our data collection that is as realistic as possible. Since the subjects move while facing objects, the heatmaps also contain gaze data trajectories, complicating the detection and parameter regression. We make our data set publicly available to the research community for download.
Robust Photogrammetry-Based Online Pose Correction of Industrial Robots Employing Adaptive Integral Terminal Fractional-Order Super-Twisting Algorithm

In this paper, a novel adaptive robust control scheme is proposed for pose correction of eye-to-hand photogrammetry-based industrial robots subject to uncertainties. The proposed method uses two control loops: internal and external loops. The former is the dynamic controller designed for controlling the robot’s joints. The external loop is the kinematic controller to correct the pose error using the estimated end-effector’s pose acquired by the photogrammetry sensor (in this research C-track AMETEK). An adaptive integral terminal fractional-order super-twisting algorithm (AITFOSTA) is developed and employed for both control loops. AITFOSTA is an integral sliding mode controller (ISMC) whose nominal control law is a terminal one and its switching part is replaced with a fractional-order super-twisting algorithm (FOSTA), reducing the chattering to a great extent while rejecting the uncertainties. Additionally, an adaptive uncertainty and disturbance estimator based on radial basis function neural network (RBFNN) is designed and used as a compensator to reduce the uncertainty bounds, contributing to further chattering reduction. The stability analysis of the proposed controller is also presented. Experimental results on a PUMA200 industrial robot show superiority of the proposed method over other well-known approaches by reaching an unprecedented tracking accuracy, i.e., 0.06 mm and 0.18 deg for position and orientation, respectively.
Variability Analysis for Robot Operating System Applications

Robotic applications are often designed to be reusable and configurable. Sometimes, due to the different supported software and hardware components, as well as the different implemented robot capabilities, the total number of possible configurations for a single system can be extremely large. In these scenarios, understanding how different configurations coexist and which components and capabilities are compatible with each other is a significant time sink both for developers and end users alike. In this paper, we present a static analysis tool, specifically designed for robotic software developed for the Robot Operating System (ROS), that is capable of presenting a graphical and interactive overview of the system’s runtime variability, with the goal of simplifying the deployment of the desired robot configuration.
An Attack-Resilient and Energy-Adaptive Monitoring System for Smart Farms

In this work, we propose an energy-adaptive moni-toring system for a solar sensor-based smart animal farm (e.g., cattle). The proposed smart farm system aims to maintain high-quality monitoring services by solar sensors with limited and fluctuating energy against a full set of cyberattack behaviors including false data injection, message dropping, or protocol non-compliance. We leverage Subjective Logic (SL) as the belief model to consider different types of uncertainties in opinions about sensed data. We develop two Deep Reinforcement Learning (D RL) schemes leveraging the design concept of uncertainty maximization in SL for DRL agents running on gateways to collect high-quality sensed data with low uncertainty and high freshness. We assess the performance of the proposed energy-adaptive smart farm system in terms of accumulated reward, monitoring error, system overload, and battery maintenance level. We compare the performance of the two DRL schemes developed (i.e., multi-agent deep Q-Iearning, MADQN, and multi-agent proximal policy optimization, MAPPO) with greedy and random baseline schemes in choosing the set of sensed data to be updated to collect high-quality sensed data to achieve resilience against attacks. Our experiments demonstrate that MAPPO with the uncertainty maximization technique outperforms its counterparts.
Multi-criteria Service Function Chaining Orchestration for Multi-user Virtual Reality Services

Immersive entertainment based on Multi-User Virtual Reality (MUVR) is gaining popularity to enable in-game interactions with multiple users. However, computing and network-intensive utilization of resources require changes in cloud-based and traditional monolithic (single machine) deployments towards distributed edge computing architecture to enable high-quality interaction. Decomposing MUVR into elements with specialized Service Functions (SF) in order and organizing it into a Service Function Chaining (SFC) requests orchestration enables MUVR to reuse frame processing between users and improves MUVR scalability. However, orchestrating distributed edge computing resources and multiple destination SFCs while minimizing delay and meeting resource requirements is a challenging task. This article proposes a multi-criteria SFC orchestration scheme for MUVR services, called MuSFiCO. MuSFiCO maps edge computing resources and instantiates SFCs on distributed servers based on delays threshold, CPU and memory resources, and bandwidth. We developed a constrained-based heuristic to minimize delay and compare it with the baseline monolithic deployment and cloud-based SFC algorithms. Results demonstrate the efficiency of MuSFiCO compared to other approaches in terms of latency, CPU, memory, bandwidth utilization, as well as orchestration decision-time.
Hierarchical Federated Learning with Adaptive Clustering on Non-IID Data

Federated learning (FL) in a mobile edge network faces challenges from both communication and learning per-spectives. The typically non-i.i.d. data can lead to slow convergence and low accuracy. To ease these challenges, frequent communications between user equipments (UEs) and the cen-tral macro base station (MBS) are necessary, aggravating the communication burden. In this paper, a novel hierarchical FL framework is proposed to alleviate the biased convergence of the global model, achieving better communication and computation efficiency. Specifically, the UEs are adaptively clustered and allocated to specific small base stations (SBSs) according to channel conditions, geographic locations, and data distributions. The SBSs are further aggregated to the MBS, forming a hier-archical FL framework. The joint user clustering and wireless resource allocation optimization problem is formulated. To solve this problem, a cross entropy (CE) based method with low computational complexity is proposed. Simulation results validate that the proposed hierarchical FL system can save more than 87 percent training time under the EMNIST Letters dataset, achieving fast convergence and significantly improving the system efficiency.
Admission control and end-to-end slicing for video streaming in MEC-empowered cellular networks

Cache- and network-agnostic Dynamic Adaptive Streaming over HTTP creates oscillation dynamics upon delivering mobile video content over cache- and slice-enabled data networks. In this paper, we bring to light the problem of user admission control, end-to-end slicing and video bitrate selection in next-generation 5G and Beyond 5G mobile data networks, where some minimum throughput guarantees can be provided for both the user-to-base-station and base-station-to-content-delivery-network links. Accordingly, we propose an exact algorithm to identify feasible bitrates and their minimum throughput requirements when the multi-access edge computing (MEC) cellular base stations has cached a known subset of video segments, potentially encoded at different resolutions, for a tagged video file. Simulation results provide valuable insights for user admission control, end-to-end slicing, video bitrate selection and caching in slice-enabled MEC-empowered cellular networks.
Real-Time Jaywalking Detection and Notification System using Deep Learning and Multi-Object Tracking

Jaywalking refers to pedestrians walking or crossing in a roadway that is not dedicated to pedestrians. Due to illegal jaywalking, every year a lot of accidents happen worldwide that cause a significant amount of death and other physical injuries. Real-time jaywalking detection and notification systems can contribute to protecting vulnerable road users and increasing road safety. Many computer vision-based image processing techniques have been proposed to detect jaywalking including deep learning, motion path analysis, motion object segmentation, trajectory forecasting and position localization. However, these techniques are designed and evaluated for a single road area and have limited notification capability. In this paper, we propose a real-time multi-object tracking approach for jaywalking detection and notification that can be applied in multiple road areas simultaneously. We use the state-of-the-art deep learning model YOLOv4 and the multi-object tracking algorithm DeepSORT for real-time object detection and tracking, respectively. The notification component incorporates a novel vehicle-region pair matching algorithm based on the proximity of vehicles to the monitored region. Performance evaluation shows that our proposed approach can effectively detect jaywalking with 100% accuracy and provide push notifications to nearby vehicles in real-time.
Social Robot Detection Using RoBERTa Classifier and Random Forest Regressor with Similarity Analysis

Twitter has skyrocketed over the past few years and has become a major social media platform. At the same time, the number of social robots on Twitter has also increased significantly. These bot accounts imitate the speeches of normal users to manipulate public opinions, affect the normal communication of users. Therefore, bot account detection came into being. Despite extensive research efforts, bots on Twitter are still evolving to evade detection. Most of the current bot detection methods have a single structure and cannot detect and identify different types of bot accounts well. In this paper, we propose a new system for social robot detection that uses a RoBERTa (Robustly Optimized Bidirectional Encoder Representations from Transformers Pretraining Approach) classifier and a random forest regressor with similarity analysis. In particular, the system considers the similarity of tweets and uses a voting system in addition to a set of features extracted from the user profile information and the tweets themselves. We conduct experiments using the largest dataset of bots available and show that the accuracy of our system is up to 0.8588, which is higher than that of all the other baseline methods.
DearFSAC: A DRL-based Robust Design for Power Demand Forecasting in Federated Smart Grid

Power demand forecasting plays a significant role in the operation of power plants and utility companies. For data privacy, federated learning (FL) is widely adopted to aggregate local models of utility companies to a global model with very few data leaks. However, defects such as malicious updates, poisoning attacks, and low-quality data, may exist in multiple FL processes. As the general resistance to various defects is not considered by most FL approaches, a design with strong generalization is strongly needed. In this paper, we adopt DEfect-AwaRe federated soft actor-critic (DearFSAC), which dynamically assigns weights to FL's local models according to their quality. For fast and stable convergence, a deep neural network based on auto-encoder is designed for model quality evaluation and dimension reduction. Then, a deep reinforcement learning (DRL) algorithm soft actor-critic (SAC) is adopted to achieve the optimal weights assignment, considering SAC's near-optimum and sufficient exploration. We conduct simulations on power consumption data in real world. The results show that our approach performs well no matter if there exist defects or not.
CryptoFE: Practical and Privacy-Preserving Federated Learning via Functional Encryption

Cloud-based services for federated learning has received widespread attention for its ability to collaboratively train a model without collecting users' local data. Although there are existing methods such as homomorphic encryption and secure multi-party computation to address the privacy issues associated with the model parameter exchanging during aggregation, these methods will inevitably lead to huge communication overheads or slow down the training time. Functional encryption (FE) is considered as a new approach to address privacy-preserving federated learning probelms, but the only known FE solution has severe security issues such as leaking master private key, and is impractical. Thus, in this paper, we propose CryptoFE, a cloud-based privacy-preserving federated learning aggregation scheme based on FE. Compared with the only existing FE solution, CryptoFE is efficient in aggregation phase, especially when a high model precision is required, and provides formal privacy guarantees for users' gradients. The experiments with real-world data demonstrate the efficeint performance of our proposed scheme.
Quantum Discrimination of Noisy Photon-Subtracted Squeezed States

Quantum state discrimination (QSD) is crucial for various applications of quantum sensing, communications, and computing. This paper characterizes the binary QSD with photon-subtracted squeezed states (PSSSs) affected by thermal noise during state preparation. First, noisy PSSSs are charac-terized and their Fock representation is given. Then, the binary QSD problem with noisy PSSSs affected by phase diffusion is presented. Finally, it is shown the suitability of PSSSs for QSD-based applications, especially when employing quantum states with unbalanced prior probabilities.
Zero Touch Coordinated UAV Network Formation for $360^{\circ}$ Views of a Moving Ground Target in Remote VR Applications

Unmanned aerial vehicles (UAVs) with on-board cameras are widely used for remote surveillance and video capturing applications. In remote virtual reality (VR) applications, multiple UAVs can be used to capture different partially overlapping angles of the ground target, which can be stitched together to provide <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$360^{\circ}$</tex> views. This requires coordinated formation of UAVs that is adaptive to movements of the ground target. In this paper, we propose a joint UAV formation and tracking framework to capture <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$360^{\circ}$</tex> angles of the target. The proposed framework uses a zero touch approach for automated and adaptive reconfiguration of multiple UAVs in a coordinated manner without the need for human intervention. This is suited to both military and civilian applications. Simulation results demonstrate the convergence and configuration of the UAVs with arbitrary initial locations and orientations. The performance has been tested for various number of UAVs and different mobility patterns of the ground target.
Removing the RF Fingerprint: A Least Squares Approach to Compensate for a Device's Hardware Impairments

Radio Frequency (RF) Fingerprinting is proposed as a key method of authentication and physical layer security in 5G and beyond networks. The process could classify a device based on the impairments in the received signal that are caused by hardware imperfections in the transmitter. This paper proposes a method that allows a device to add a filter to the transmit chain that removes the RF fingerprint from the transmit signal. We assess the effectiveness of this technique by computing several divergence-based metrics on the RF fingerprints to determine if the fingerprint has been successfully “removed.” Our results indicate that this technique can remove a statistical fingerprint calculated on the received waveform's time domain responses.
Context-aware Collaborative Neuro-Symbolic Inference in IoBTs

IoBTs must feature collaborative, context-aware, multi-modal fusion for real-time, robust decision-making in adversarial environments. The integration of machine learning (ML) models into IoBTs has been successful at solving these problems at a small scale (e.g., AiTR), but state-of-the-art ML models grow exponentially with increasing temporal and spatial scale of modeled phenomena, and can thus become brittle, untrustworthy, and vulnerable when interpreting large-scale tactical edge data. To address this challenge, we need to develop principles and methodologies for uncertainty-quantified neuro-symbolic ML, where learning and inference exploit symbolic knowledge and reasoning, in addition to, multi-modal and multi-vantage sensor data. The approach features integrated neuro-symbolic inference, where symbolic context is used by deep learning, and deep learning models provide atomic concepts for symbolic reasoning. The incorporation of high-level symbolic reasoning improves data efficiency during training and makes inference more robust, interpretable, and resource-efficient. In this paper, we identify the key challenges in developing context-aware collaborative neuro-symbolic inference in IoBTs and review some recent progress in addressing these gaps.
A Distributed Approach for Robotic Coverage Path Planning Under Steep Slope Terrain Conditions

This article proposes a novel algorithm to determine the optimal coverage path of a mobile robot for uniform seed dispersion in any tract of agricultural land. The robot is required to operate under steep terrain conditions that are too risky for conventional, human operated equipment. Using data from an field experiment with the real robot, machine learning based function approximators are trained to estimate the minimum energy paths between adjacent points. Exemplar-based clustering is used to identify a suitable subset of way-points that ensure full coverage of a tract of land. An optimal cyclic tour is computed from the way points using a new asymmetric TSP algorithm proposed for this specific application. The clustering and the cyclic path planning algorithms can be implemented entirely through local message passing in a field-deployed sensor network. Simulations with synthetic as well as real topographic data establish the overall effectiveness of the proposed method.