Reviewerly/exHarmony huggingface dataset:
Investigation on Feasibility of Covert TT&amp;C Scheme Using Satellite Payload Channel

A covert TT&C scheme is proposed transmitting TT&C signal via satellite payload channel. The proposed covert TT&C scheme is fulfilled by multiplexing the spectrum spread signal of TT&C transponder and the signal of repeater in the same frequency bandwidth into one payload channel and transmitting to the earth station. The proposed TT&C scheme has been demonstrated that TT&C signal and the repeater signal can operate compatibly by theoretical analysis and numerical simulation. The proposed TT&C scheme is a kind of covert communication, which ensures the safety of TT&C information effectively. And also it might be a redundant means of on-board TT&C subsystem, which enhance the reliability and robustness of on-board TT&C subsystem and guarantee the safety and stability of in-orbit operation of satellite.
Lightweight Privacy Preserving Framework at Edge Layer in IoT

The Internet of Things is helping us to make our lives comfortable. IoT devices used here are resource-constrained in terms of memory and CPU as well as they lack basic security design. When everyone is using IoT, there are high chances of their personal data getting exposed to the outside world. For real-time application, data need to be processed at the edge layer directly instead of doing the entire processing and analysis at cloud. Privacy preserving at the edge layer in the Internet of Things is a challenging task. We proposed a lightweight privacy preserving framework that uses the concept of blockchain, zero knowledge proof algorithm, principal component analysis, and digital signatures. Here for the mining process in blockchain, Trust concept is being used instead of Proof of Work (POW) and Proof of Stake (POS) as it consumes fewer resources and our edge layer is resource constrained. This could be a feasible solution and can be deployed at the edge layer in the Internet of Things for preserving privacy.
Autonomous Traffic Sign Detection and Recognition in Real Time

Every day, over 400 traffic accidents occur in India, according to official statistics. Traffic sign recognition plays very important role in intelligent autonomous vehicles as well as in driver assistant system to disburden the driver. The main aim of this project is to reduce the cause of road accidents because of an inactive drivers on the road. We are developing a model which makes image classification and buzzer to avoid drivers distraction. In this model we are improving the performance to detect the Indian road signs to assist the drivers. And we will also reduce the over speed problems, they will follow traffic rules strictly by using this model. Our project will also help new learners to improve their driving experience. A skeleton for an Autonomous Traffic Sign Detection and Recognition (TSDR) in real time has been proposed. The proposed framework includes two parts: traffic sign detection and recognition of detected traffic signs. Future autonomous vehicles will benefit from this framework because it allows for the traffic sign detection and recognition in real time from the complex images that occur on the road. The proposed framework helps the drivers to improve road safety as well to help new learners to improve their driving experience also.
Exploding haystacks

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation John Adam; Exploding haystacks. The Physics Teacher 1 March 2023; 61 (3): 236. https://doi.org/10.1119/5.0141360 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAmerican Association of Physics TeachersThe Physics Teacher Search Advanced Search |Citation Search
A prospective analysis of immersive journalism from the perspective of experts 1 , 2

The aim of this text is to analyse the future of immersive journalism from the perspective of experts now that the fever of the initial experimental stage has passed. To do this, we conducted two focus groups with 15 international leaders in the sector, including both professional and academic points of view. We asked them about the main strengths, weaknesses, applications, best practices, challenges, how to deal with them and what future they foresee for immersive journalism in relation to the (imminent?) arrival of the metaverse. The results reveal few differences between the views of academics and practitioners, especially regarding the strengths and weaknesses of this new narrative, which is as versatile as it is complex.
Scattering Characteristic Extraction Method for Manmade Target Based on Target Null Theory

Scattering characteristic extraction is an essential part of manmade target recognition.However, if two scattering points are in adjacent pixels, scattering characteristic extraction may fail to acquire accurate polarimetric scattering matrices (PSMs) of the weak scattering points due to the contamination caused by the strong scattering points.Target null theory provides a way to solve this problem.By selecting the transmitting and receiving polarization states of radar antennas simultaneously, the echo power of a strong scattering point becomes zero and the contamination effect is avoided.In this paper, a method based on target null theory for scattering characteristic extraction is proposed.First, we optimize the transmitting and receiving polarization states of the radar antenna to suppress the intensities of the strong scattering points to highlight the positions of the weak scattering points in certain polarimetric channels.Second, to suppress the contamination effects of strong scattering points in other polarimetric channels, we establish perturbation correction equations to erase the error generated by the point spread function (PSF) among adjacent scattering points in the radar image.Finally, the solved polarimetric scattering matrices of corresponding positions are implemented for target retrieval.The electromagnetic simulation results demonstrate the effectiveness of the proposed method.
Click-through Rate Prediction and Uncertainty Quantification Based on Bayesian Deep Learning

Click-through rate (CTR) prediction is a research point for measuring recommendation systems and calculating AD traffic. Existing studies have proved that deep learning performs very well in prediction tasks, but most of the existing studies are based on deterministic models, and there is a big gap in capturing uncertainty. Modeling uncertainty is a major challenge when using machine learning solutions to solve real-world problems in various domains. In order to quantify the uncertainty of the model and achieve accurate and reliable prediction results. This paper designs a CTR prediction framework combining feature selection and feature interaction. In this framework, a CTR prediction model based on Bayesian deep learning is proposed to quantify the uncertainty in the prediction model. On the squeeze network and DNN parallel prediction model framework, the approximate posterior parameter distribution of the model is obtained using the Monte Carlo dropout, and obtains the integrated prediction results. Epistemic and aleatoric uncertainty are defined and adopt information entropy to calculate the sum of the two kinds of uncertainties. Epistemic uncertainty could be measured by mutual information. Experimental results show that the model proposed is superior to other models in terms of prediction performance and has the ability to quantify uncertainty.
NNTrak

In this work, we demonstrate a radically novel approach towards inertial-only tracking of wrist in real-time on a smartwatch for air-writing tasks. Deriving motion trajectories from commercial-grade Inertial Measurement Units (IMU) has always been a challenging task due to inherent sensor errors and associated trajectory drift. Computationally expensive solutions offered in literature cannot be used for a fully real-time tracking while also maintaining acceptable accuracy. This work presents 'NNTrak', marking our attempt to address these issues using a Convolutional Neural Network (CNN), which is trained to learn various strokes of the wrist and efficiently generates motion trajectory in real-time for air-writing. For this demonstration, we show computationally constrained Raspberry Pi 3B running our solution and a smartwatch worn while drawing a gesture in air with the trajectory being displayed in true real-time.
Legal, Library, and Accounting Barriers of e-Books for e-Learning during COVID-19 at Polytechnic in Pozega

The sudden and rapid appearance of COVID-19 surprised many, so the whole world switched to the digital environment at one point. Business meetings, classes, and social events took place via the Internet. In such an environment, e-books have played a major role in responding to distance learning. But the e-book is still a novelty in both the library and accounting businesses. In this paper, we will give an overview of legal, technical, library, and accounting issues that e-book brings and try to answer how to solve them and how to ensure the availability of e-books in the long term. We will also show how this affected students in the learning process.
Project Management Methodologies for Engineering KMS based on PMBOK Approach: A Systematic Literature Review

The process of creating, sharing, discovering, and capturing knowledge becomes an intangible asset for an organization. As a valuable asset, Knowledge needs to be managed. There is a tool called Knowledge Management System (KMS). The latest technology and social or structural mechanisms are components to develop KMS. But developing an IT project has a high risk of failure, so it should be managed using a project management approach. A project is a temporary endeavor to make a product or service. It means that a project has a start and an end project phase. Some methodologies which exist do not offer complete stages of development KMS. They do not cover feasibility study activities, lack of clear specification, and lack of closing project phase. The study gap is expected to be loaded up and well explained in this paper through a systematic review of the published literature over the last six years using the Kitchenham method. The results show that most researchers used a predictive approach compared to an iterative, incremental, or adaptive development approach to build KMS. The main result of this study we found 36 processes in the developing KMS. The novelty of this research, we generated a table to map the processes into the model, method, artifact and presented it by group process based on the Project Management Body of Knowledge (PMBOK). The table can help the organization develop the KMS project to improve its effectiveness and competitiveness.
Contents of Interrogative Speech Act of Indonesian Language Teachers in Teaching Processes at Senior High School of Muhammadiyah Wonosobo

This study aims to reveal the contents of interrogative speech act of Indonesian language teachers in teaching processes at Senior High School of Muhammadiyah Wonosobo. This study was qualitative research. The subjects were four Indonesian teachers at Senior High School of Muhammadiyah Wonosobo. The object was the speech of the four Indonesian teachers in their teaching processes. The data were collected by recording, interviews, and through observations, and was analyzed by using the content analysis. The results are as follows. First, the most frequently used is conceptual question, because in teaching the teachers ask more about the concept of a particular material. Second, empirical questions appear a lot in early learning activities. Third, the values questions does not appear much in learning.
Comparative Analysis of Naive Bayes, K-Nearest Neighbors (KNN), and Support Vector Machine (SVM) Algorithms for Classification of Heart Disease Patients

Heart disease is still the leading cause of death. In this study, we tried to test several factors that can identify patients with heart disease using 3 classification algorithms: Naive Bayes, K-Nearest Neighbors (KNN), and Support Vector Machine (SVM).Â The purpose of this study is to find out which algorithm can produce the highest accuracy in classifying, analyzing, and obtaining confusion matrix values along with the accuracy of predicting heart disease based on several factors or other comorbidities that the patient has, ranging from BMI to the patient's skin cancer status.Â From the results of trials conducted by the SVM algorithm, it has the highest accuracy value, which is 92% while the Naive Bayes algorithm is the lowest with an accuracy value of 88%.
A Framework for Intrinsic Evolvable Systems

Systems with hardware that can dynamically and autonomously change their architecture and behavior by interacting with their environment are becoming very valuable in modern applications. Therefore, research and development in intrinsically evolvable embedded systems are becoming increasingly attractive. Runtime reconfiguration and relocation are a promising approach for designing self-adaptive and self-optimizing autonomous embedded systems. The vision behind this PhD work is to provide an all-encompassing framework to automate all the challenging tasks required for designing self-adaptive systems. This paper presents our framework and preliminary results and highlights our next steps and future work.
FPL Demo: Logic Shrinkage: A Neural Architecture Search-Based Approach to FPGA Netlist Generation

Logic shrinkage is an open-source, state-of-the-art neural architecture search (NAS)-based approach to the automated design of DNN inference accelerators that ideally suit FPGA deployment [1], [2]. Where NAS traditionally sees candidate functions such as convolutions automatically evaluated and selected between to form a network, logic shrinkage operates at ultra-fine granularity, resulting in a netlist of LUTs as the topology. Our results for datasets of complexity ranging from MNIST to ImageNet show area and energy efficiency gains vs binary neural networks (BNNs) of up to ~6 x and ~ lOx.
The Who in VR/AR for Education: A Scoping Review from IEEE Publications

HEIs have continuously been the vanguard of novel technologies, pushing progress and establishing the afterward generation of scientists, businesspersons, and engineers. Virtual and augmented reality technologies are on the borderline of enlargement nowadays. To better understand who are essential actors in this new research area, we have extracted metadata of all published papers by IEEE, dating from 1990 to 2021, using keywords VR, AR, and Education. For each publication, we have extracted the list of all the authors, creating a co-authorship dataset. This dataset is then further processed using social network analysis metrics. The results are discussed to understand research publications' main actors driving VR/AR/Education trends.
Effectiveness of using ICT in higher education in Covid – 19

For the past two years, the whole world has been facing a pandemic caused by COVID-19.All segments of our everyday living are affected by this and so is the educational process.Although in the last decade, in our country there has been an increase in the integration of ICT in teaching, especially in the higher education, during this pandemic number of problems were detected in online courses: unpreparedness for online teaching, not sufficient knowledge of using ICT for teaching, infrastructure problems etc.This paper aims to present ways to increase the effectiveness of using ICT to improve online teaching during a pandemic.
Deep Learning Approach For Modelling The Spread of Covid-19

In March 2020, the Covid-19 outbreak in Indonesia, where the symptoms of the corona virus made the Indonesian people worry and experience depression. It has been almost two years that Covid-19 has not known what causes it, let alone a person's body condition is not good which can result in being attacked by the virus. Covid-19 first appeared in the city of Wuhan, part of China, where it spread very quickly and was deadly. Its spread through direct physical contact with humans is transmitted through the mouth, nose and eyes, therefore a model is needed for the spread of the corona virus. The spread of COVID-19 affects the pattern of interaction between susceptible (susceptible) and infected (infectious) individuals, where human social contact is very heterogeneous and in groups. To influence the impact of the spread of COVID-19 using deep learning approach that is modeled on the spread of COVID-19, individuals are exposed, infected, recover and die. The purpose of this research is to produce good predictions with a deep learning approach for modeling the spread of COVID-19. The results of the deep learning approach for the COVID-19 spread model carried out the 400 time iteration with an MSE achievement of 0.021112.
Enhancing the Transferability of Adversarial Examples Based on Nesterov Momentum for Recommendation Systems

The attacker's malicious behavior of injecting well-designed adversarial examples (i.e., fake users) into recommender systems will severely affect the security of systems. It's difficult to fully obtain details of victim recommendation models (i.e., black-box model) in practical recommendation scenarios, using the transferability of adversarial examples to achieve black-box attacks is still an effective way. At present, adversarial examples generated by existing gradient-based methods are prone to drop into local minima, making it impossible to achieve the expected attack effect and reducing the transferability. In this article, we propose an attack algorithm that enhances the transferability of adversarial examples based on the Nesterov Momentum for Recommendation Systems (ETANRS). With white-box recommendation surrogate models, we utilize Nesterov momentum to generate better adversarial examples, then inject them into black-box victim models to attack. We utilize the accumulated gradients and pre-determine the update direction of the gradients to keep the optimal value from being lost, thus enhancing the transferability of the adversarial examples. Experimental results demonstrate that our method is better than state-of-the-art gradient-based attack algorithms, which affect recommendation performance.
QDM-SSD: Quality-Aware Dynamic Masking for Separation-Based Speaker Diarization

We improve iterative separation-based speaker diarization (ISSD) with quality-aware dynamic masking (QDM). We call the proposed framework QDM-SSD. Compared with ISSD, QDM-SSD enhances the simulated data used for model adaptation through QDM to alleviate the influence of errors in speaker priors. In addition to data quality purification, QDM-SSD also makes the adaptation data sparse by automatically adjusting speaker overlap ratios according to data quality. Furthermore, using a sliding window over the adaptation data, clean regions in speech segments can be better localized. Experiments on the two-speaker conversational telephone speech (CTS) corpus show that the proposed QDM-SSD framework can reduce the diarization error rate (DER) by 18.56% relatively compared with ISSD. Moreover, QDM-SSD is shown to generalize to other two-speaker non-conversation telephone speech data sets where ISSD fails to work. Finally, we demonstrate that QDM-SSD can serve as a front-end to improve the performances of back-end automatic speech recognition.
Analysis of Research Trends Towards Types of Code Clone Detection Techniques

Objective: The key research objectives of this study are: (1.)To compare and contrast the research trend towards the tree, token, text, metric, and graph-based code clone detection techniques; (2.)To study the distribution of metric-based code clone detection techniques on various online repositories; (3.)To make a statistical analysis of the hybrid techniques available for clone detection.The overall objective is to investigate the research trends of code clone detection approaches.Methods: Various repositories like google scholar, IEEE, and ELSEVIER Digital Libraries were systematically examined to attain the results in terms of research articles published in various places like conferences, journals, etc. followed by the inclusion and exclusion criteria.Findings: (1.)The findings related to objective 1 depicted that 50% of total clone detection techniques are tree and graph-based Code Clone Detection techniques followed by 20% of text-based and 30% of token-based code-clone detection techniques (2.)The findings related to the second objective depicted that an equal percentage of 46% of research work related to metric-based code clone detection techniques has been published in journals and conferences.(3.)The findings related to the third objective showed that 43% of hybrid code clone detection techniques are based on machine learning techniques, 24% are based on neural networks, and 18% of techniques are data mining based followed by 15% nature inspired based algorithms.Novelty: The study conducted is novel in identifying and exploring those potential code clone detection techniques that are underutilized and least explored.The result of research questions will assist researchers to draw inferences regarding usage, application, research trends, future needs, and research directions.
A Two-Stage Trajectory Prediction Algorithm for Mobile Robots Combining the Bayesian and the DMOC Frameworks

In many real-world robotic applications, predicting an observed agent’s trajectory can significantly improve cooperation efficiency, avoid potential collisions, and alleviate the communication strain in multi-agent scenarios. Given a well predicted trajectory of the observed other agents, the observer can either establish a clear path to its destination or collaborate with the observed agent in a more energy-efficient manner. To address this challenge, in this paper, a two-stage trajectory prediction algorithm is proposed based on the observed agent’s previous trajectory data. First, the potential destination of the observed robot is guessed, and the future guessed path is then sampled using the Monte-Carlo sampling approach within a Bayesian framework. Then, an optimization problem based on a discrete mechanics and optimal control (DMOC) framework with complementarity constraints is proposed to forecast a more reasonable trajectory, while the previously predicted path is used as the reference. Finally, several experiments are undertaken to verify the performance of the proposed algorithm in simulations and real-world applications with our holonomic and nonholonomic mobile robots.