Reviewerly/exHarmony huggingface dataset:
AI in 5G: The Case of Online Distributed Transfer Learning over Edge Networks

Transfer learning does not train from scratch but leverages existing models to help train the new model of better accuracy. Unfortunately, realizing transfer learning in distributed cloud-edge networks faces critical challenges such as online training, uncertain network environments, time-coupled control decisions, and the balance between resource consumption and model accuracy. We formulate distributed transfer learning as a non-linear mixed-integer program of long-term cost optimization. We design polynomial-time online algorithms by exploiting the real-time trade-off between preserving previous decisions and applying new decisions, based on primal-dual one-shot solutions for each single time slot. While orchestrating model placement, data dispatching, and inference aggregation, our approach produces new models via combining the existing offline models and the online models being trained using weights adaptively updated based on inference upon data samples that dynamically arrive. Our approach provably incurs the number of inference mistakes no greater than a constant times that of the single best model in hindsight, and achieves a constant competitive ratio for the total cost. Evaluations have confirmed the superior performance of our approach compared to alternatives on real-world traces.
Use Machine Learning Technologies in E-learning

In this article, we generated some data as a result of new technologies, the internet, and linked items. Putting these facts into context and structuring them so that they may be perceived, understood, and reflected is critical. Humans had traditionally studied data. As the availability of data grows larger, humans are progressively turning to computerized technologies that can replicate them. Machine learning refers to technologies that can resolve issues by learning from both data and data modifications. Artificial intelligence (AI) has a significant influence on e-learning studies, and machine learning-based methodologies may be used to improve Technology Enhanced Learning Environments (TELEs). This publication provides an outline of relevant study outcomes in this domain. Initially, we'll go over some basic machine learning ideas. Then, we'll go through the significant latest research in the domain of e-learning that uses machine learning.
An Approach to design Keyboard and Mouse assisting device for handicap users

The main idea is to develop an intelligent computing system for disabled people, especially handicapped people who are willing to use a computer for their usage purpose. Still, due to their disability, they can't use it. Nowadays, engineers try to make some equipment to solve this problem because this century is the computer century. The usage of the computer nowadays is very important. A person needs to use computers for their office purpose, education, and communication. A normal person can do this easily, but a disabled person, especially handicapped people, can't do this. This is a hard situation for a handy cap person because He also needs to communicate with the world. The proposed design can help handicapped peoples use computers for their usage purpose. Intelligent Computing System (ICS) is based on two units one is a head unit which contains a LASER which is used as a remote control, the other unit is ground unit which contains LDR sensor-based keyboard, controlling circuits may assets in performing the whole functionality of the ICS system by providing the instructions through the coding directed by PIC Controller which sending the data to the computer through MAX233 IC. The proposed LDR-based-matrix keyboard system sensor was sensitive enough to detect laser light which was tested on ten different users and shown promising accuracy; for future enhancement in order to use this device as a commercial device, instead of LDR matrix, we can, or the fabrication industry can fabricate LDR matrix during the fabrication process, to minimize the size of the system in order to save the power consumption of the system and may also allow a handicap people to use a computer and communicate with the world and gain knowledge and information just like a normal people.
Radio Frequency Fingerprint Identification Method Based on Ensemble Learning

At the physical layer, the combination of radio frequency (RF) fingerprint and deep learning has been widely used to identify wireless communication devices. Considering that the traditional convolutional neural network (CNN) is applied to RF fingerprint, the classification performance is poor in the low signal to noise ratio (SRN) scenario. Considering the significant effect of ensemble learning on improving the classification accuracy of base classifier and the wide application of ensemble learning in other fields, we propose an RF fingerprint classification method based on ensemble learning, which improves the classification accuracy on the basis of traditional CNN. Firstly, the RF signals of four power amplifiers are collected by acquisition equipment. These signals are composed of in-phase and quadrature signals, the sampling points are 200,000. After slicing the data samples and artificially introducing different SRN noises, it is then input into an improved CNN for training. Bagging and Boosting algorithms in ensemble learning are combined with the improved CNN to integrate multiple base classifiers and output the final classification results. Finally, the simulation results prove the proposed method. Its classification accuracy is better than traditional CNN in low SNR environment.
AI Based Object Recognition Performance between General Camera and Omnidirectional Camera Images

In this paper, we present a comparison of the accuracies of AI-based object recognition using a general camera and an omnidirectional camera. Recently, with the improvement in machine learning technology, there has been significant research related to the detection and classification of objects from images and videos. In this field, it is common to use horizontal images and videos. However, omnidirectional cameras, which can acquire information from the entire surrounding area, are becoming popular in addition to general cameras. Although there are some studies on object recognition using these cameras, almost no studies have focused on comparisons between object recognition using general and omnidirectional cameras. Therefore, in this study, we compared the recognition rate of object recognition using the YOLO algorithm on both general and omnidirectional images taken in the same environment.
Branching Time Axiomatized With the Use of Change Operators

Abstract We present a temporal logic of branching time with four primitive operators: $\exists {\mathcal {C}}$ – it may change whether; $\forall {\mathcal {C}} $ – it must change whether; $\exists \Box $ – it may be endlessly unchangeable that; and $\forall \Box $ – it must be endlessly unchangeable that. Semantically, operator $\forall {\mathcal {C}}$ expresses a change in the logical value of the given formula in every state that may be an immediate successor of the one considered, while $\exists {\mathcal {C}}$ expresses a change in the logical value of the given formula in some state that is a possible immediate successor. $\forall {\mathcal {C}} $ and $\exists {\mathcal {C}}$ are not normal operators, they are not mutually definable and have unusual properties: $\forall {\mathcal {C}} A\leftrightarrow \forall {\mathcal {C}} \neg A$ and $\exists {\mathcal {C}} A \leftrightarrow \exists {\mathcal {C}} \neg A$ are axioms. Operators $\exists \Box $ and $\forall \Box $ express endless unchangeability in some and all paths, respectively. Our axiomatization contains two infinitary rules that allow one to obtain $\exists \Box A$ from infinitely many combinations of formulas with $A, \neg , \forall {\mathcal {C}}, \land $, and to get $\forall \Box A$ from infinitely many combinations of formulas with $A, \neg , \exists {\mathcal {C}}, \land $. We show that our formalism is complete by presenting a bridge between our logic and logic $\textsf {UB}$, which is a fragment of $\textsf {CTL}$ in which until operators does not occur.
Learn Dynamic Facial Motion Representations Using Transformer Encoder

Human face analysis is an essential topic in visual computing. Many of our daily applications, such as face-priority auto focus in camera, face-based identity verification, and TikTok stickers, are unattainable without face analysis techniques. In the past ten years, face-related visual computing tasks like face detection, face recognition, and facial expression classification have improved drastically in performance, benefiting from the rapid development of deep learning theory. This work explores how to model dynamic facial motion using a learning-based method. Our proposed model takes video clips containing customized facial motion as input and generates a uni-size vector (the embedding) as the output. We have inspected two different encoders–recurrent neural networks and transformers to extract the temporal features from the video clip. We collected our own facial motion analysis dataset because there is no suitable datasets for our facial motion analysis task. Although our domain-specific dataset is small compared to the well-known public datasets for ordinary face-related tasks, we adopt a transfer learning approach, and a data augmentation method (random trimming) to help the model converge. The experimental results show that the transformer-based encoder performs better than the RNN baseline, and the best F <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> -score with our validation data is 0.889.
Evaluating a Recurrent Neural Network Model for Predicting Readmission to Cardiovascular ICUs Based on Clinical Time Series Data

Unexpected readmission to intensive care units (ICUs) endangers patients' lives due to premature patient transfers or prolonged stays at the care units. This can be mitigated by stratification of the readmission risk at discharge times using state-of-the-art machine learning (ML) methods. We fitted two alternative recurrent neural network (RNN) models based on long short-term memory (LSTM) on the Medical Information Mart for Intensive Care (MIMIC-III) dataset and evaluated them with an independent cohort from our hospital's ICU (UKD). The first model processed all the available time series data from each patient's ICU stay, whereas the second model focused on the data from the last 48 hours of the ICU stay prior to transfer. Our readmission prediction on MIMIC data reached an area under the curve of receiver operating characteristic (AUC-ROC) of 0.82. Furthermore, the model with the 48 h time frame outperformed the other model, as both models were applied to the independent test cohort. The results suggest that the RNN model for time series forecasting holds promise for future use as a clinical decision support tool, although follow-up studies with larger cohorts as well as user studies should be conducted to assess the generalizability and usability of the methods, respectively.
Random Forest Algorithm for Solar Forecasting in Jamshedpur – India

Machine learning has created a wide collection of solar forecasting works because of its recent advancement. For machine learning models, choosing the appropriate hyper-parameter configuration directly affects the model’s performance. It frequently causes a thorough understanding of machine learning methods as well as proper hyper-parameter optimization strategies. Hence, this paper provides a Random Forest model and two other optimization techniques for solar radiation forecasting: Grid Search and Random Search, and the performance of these two are compared. Experiments are conducted on datasets of Diffuse Horizontal Irradiance (DHI), and Direct Normal Irradiance (DNI) including 9 other parameters, for Jamshedpur (Jharkhand). Widespread data analysis is done using a correlation plot, box plot, and histogram. These models predict using the similar day approach, which assumes that the sun and earth are in the same location on a comparable day in prior years. The results suggest that Random Search outperforms (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> = 0.93063 for DHI and 0.85956 for DNI) the Grid Search (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> = 0.93056 for DHI and 0.85727 for DNI) in terms of accuracy.
Big Brother is Listening: An Evaluation Framework on Ultrasonic Microphone Jammers

Covert eavesdropping via microphones has always been a major threat to user privacy. Benefiting from the acoustic non-linearity property, the ultrasonic microphone jammer (UMJ) is effective in resisting this long-standing attack. However, prior UMJ researches underestimate adversary’s attacking capability in reality and miss critical metrics for a thorough evaluation. The strong assumptions of adversary unable to retrieve information under low word recognition rate, and adversary’s weak denoising abilities in the threat model make these works overlook the vulnerability of existing UMJs. As a result, their UMJs’ resilience is overestimated. In this paper, we refine the adversary model and completely investigate potential eavesdropping threats. Correspondingly, we define a total of 12 metrics that are necessary for evaluating UMJs’ resilience. Using these metrics, we propose a comprehensive framework to quantify UMJs’ practical resilience. It fully covers three perspectives that prior works ignored in some degree, i.e., ambient information, semantic comprehension, and collaborative recognition. Guided by this framework, we can thoroughly and quantitatively evaluate the resilience of existing UMJs towards eavesdroppers. Our extensive assessment results reveal that most existing UMJs are vulnerable to sophisticated adverse approaches. We further outline the key factors influencing jammers’ performance and present constructive suggestions for UMJs’ future designs.
Fréchet Distances as a Similarity Metric for Store Trajectory Paths

Store trajectory similarity comparisons are typically based on subjective reasoning. In these situations, subjective reasoning refers to eyeballing how similar trajectories appear or behave. This type of comparison is subjective and is, many times, localized to a single aspect of the trajectories. A more suitable measure of trajectory similarity is Fréchet distance. Fréchet distance compares two paths irrespective of traversal speed. This metric is commonly known as the dog leash distance. It is analogous to a person walking a dog on a leash, where each takes a different path. To this end, this work focuses on the Fast Fréchet Distance Algorithm, an efficient algorithm for calculating discrete Fréchet distances, and on the applications of discrete Fréchet distance calculations in the USAF Store Separation Enterprise. The metric has proven useful in objectively comparing trajectory simulations, comparing trajectories to reference, and analyzing sensitivity studies.
Tri-Modal Dense Video Captioning Based on Fine-Grained Aligned Text and Anchor-Free Event Proposals Generator

Multi-modal dense video captioning is a task using multiple information to detect all meaningful events and generate a textual description for each event. The existing works mainly rely on single visual or dual audio-visual modals in dense video captioning, while completely ignoring the text modal (subtitle). The text modal has a similar data structure as the video captions, which provides immediate semantic information to the content description for a video. In this paper, we propose a novel framework, called Two-Stage Cross-Modal Encoding Transformer Network (TS-CMETN), to realize the multi-modal dense video captioning task by fusing multiple features, including audio, visual, and text. First, we design a two-stage feature fusion encoder that hierarchically achieves the intra- and inter-modal information interaction. Second, we propose an anchor-free temporal event proposal module, which efficiently generates event proposals at each time step without the complex anchor calculation. Extensive experiments on the ActivityNet Captions dataset show that our proposed framework achieves high performance. Moreover, our approach can adaptively handle cases of the missing text modal. Our code and data are available at https://github.com/xieyulai/TM-CMETN .
Online Node Cooperation Strategy Design for Hierarchical Federated Learning

With the rapid development of wireless communi-cation technology, a large number of data is generated in the network edge. The combination of mobile edge computing (MEC) and federated learning has become a key technology to reduce various cost and protect users’ privacy data in mobile networks. High cost may be caused by the nodes processing parameters in Hierarchical Federated Learning (HFL). In this paper, we investigate the problem of node cooperation for cost minimization in HFL. In order to achieve the stability, we design an online algorithm for node cooperation based on Lyapunov optimization theory(ONCA). In ONCA, the cooperation among nodes can be adjusted adaptively according to the dynamics of system state. D2D is adopted for node cooperation. Nodes prefer to select neighbors with high computing and transmission capabilities to cooperate, so that their capabilities can be fully utilized. In different time slots, nodes can cooperate with different peers in order to reduce the cost. Through extensive simulations, it is verified the performance of ONCA. We also observe the average cost is reduced by 13.86% and 18.04% respectively compared with HierFAVG and FedAvg.
Landweber Iteration-Inspired Network for Image Super-Resolution

Image super-resolution (SR) is one of the classical ill-posed image processing issues to generate high-resolution (HR) images from given low-resolution (LR) instances. Recent SR works aim to find an elaborate convolutional neural network (CNN) design and regard it as an end-to-end filter to map the image from LR space to HR space. However, seldom of them concentrate on the mathematical proof of network design or consider the problem from an optimization perspective. In this paper, we investigate the image SR based on the Landweber iteration method, which is an effective optimization method to find a feasible solution for the ill-posed problem. By considering the issue from the optimization perspective, we design a corresponding Landweber iteration-inspired network to adaptively learn the parameters and find the HR results. Experimental results show the proposed network achieves competitive or better subjective and objective performance than other state-of-the-art methods with fewer parameters and computational costs.
The end of Eavesdropping Attacks through the Use of Advanced End to End Encryption Mechanisms

In this article we present our novel Secure Node End-2-End Encryption (SNE2EE) mechanism that is under implementation. This mechanism offers both a software and hardware solution by extending encryption technologies and techniques to the end nodes to increase privacy. The SNE2EE mechanism can address the issues concerning spyware and stalkerware at both the individual and community level.
VLSI Implementation of a Real-time Modified Decision-based Algorithm for Impulse Noise Removal

In this paper, a Real-Time Impulse Noise Removal (RTINR) algorithm and its hardware architecture are proposed for denoising images corrupted with fixed valued impulse noise. A decision-based algorithm is modified in the proposed RTINR algorithm where the corrupted pixel is first detected & is restored with median or previous pixel value depending on the number of corrupted pixels in the image. The proposed RTINR architecture has been designed to reduce the hardware complexity as it requires 21 comparators, 4 adders, and 2 line buffers which in turn improve the execution time. The proposed architecture results better in qualitative and quantitative performance in comparison to different denoising schemes while evaluated based on PSNR, IEF, MSE, EKI, SSIM,& FOM. The proposed architecture has been simulated using the VIRTEX7 FPGA device and the reported maximum post place& route frequency is 360.88 MHz. The proposed RTINR architecture is capable of denoising images of size 512 × 512 at a frame rate of 686. The architecture has also been synthesized using UMC 90 nm technology where 103 mW power is consumed at a clock frequency of 100 MHz with a gate count of 2.3K (NAND2) including two memory buffers.
Land-Mobile Robots for Rescue and Search: A Technological and Systematic Review

Due to the large number of emergencies and disasters happening around the globe, the technology development to face these difficulties has risen in different areas, one of them being the robotics field: rescue robots. A great amount of incentives have been presented over the years to increase the numbers of studies in the creation of rescue robots, like competitions and funding opportunities. The objective of writing this paper is to document and review different rescue robot works to provide, to interested individuals in the research area, a basis to start in this field. Twenty-six articles on rescue robotics development, ranging from 2017 to 2021, were found and selected using academic search engines.
Adversarial robust image steganography against lossy JPEG compression

• This is the first work that introduces adversarial mechanism into robust steganography to improve the security of steganography. Adversarial dither modulation algorithm is proposed to guide the dither directions via the signs and magnitudes of cover gradients calculated from pre-trained steganalyzer. • According to “Matching Robust” or “Upward Robust” scene, we select proper embedding elements to balance the robustness against JPEG compression and steganography security. • Extensive experiments show that the proposed adversarial robust steganography could significantly improve the steganography security and maintain strong robustness. Traditional image steganography assumes that the transmission channel is lossless, thus it can not assure the correct extraction of hidden secret messages in real social networks. Recently, some robust image steganographic methods have been proposed to resist lossy JPEG compression. In this paper, we first introduce adversarial mechanism into robust image steganography to enhance steganography security. Instead of modifying embedding costs in traditional adversarial embedding steganography, we propose adversarial dither modulation algorithm according to the cover gradients calculated from a pre-trained CNN (Convolutional Neural Networks)-based steganalyzer. This algorithm determines the dither directions via the signs and magnitudes of the cover gradients. In addition, we carefully select proper embedding elements according to the prior information of the transmission channel to obtain a good tradeoff between “robustness” and “security” for robust steganography. Experimental results show that the proposed method can achieve significant improvements on security while preserving very good robustness compared with related work.
Demo Abstract: Environment-adaptive 3D Human Pose Tracking with RFID

RF-based human pose estimation has attracted increasing interest in recent years. Compared with vision-based approaches, RF-based techniques can better protect user’s privacy and are robust to lighting and non-line-of-sight conditions. However, due to complicated indoor propagation environments, most of the RF-based sensing approaches are sensitive to the deployment environment and hard to adapt to new environments. In this demo, we present a meta-learning-based approach to address the environment adaptation problem and design an environment-adaptive Radio-Frequency Identification (RFID) based 3D human pose tracking system. The system utilizes commodity RFID tags to estimate 3D human pose and leverage meta-learning algorithms to improve the environment adaptability. Experiments conducted in various environments demonstrate the high pose estimation performance and adaptability to environments.