Reviewerly/exHarmony huggingface dataset:
Heterogeneous Secure Coded Matrix Multiplication: Straggler Problem versus Information Leakage

Distributed computing has made it possible to satisfy the demands for large-scale matrix multiplication. A distributed computing system suffers from both straggler problem and information leakage. In a heterogeneous network consisting of worker nodes with different capacities, the straggler effect is more likely to occur on weaker nodes. While stronger nodes are supposed to compute more tasks to provide robustness, more information is leaked to them. In this paper, we propose a heterogeneous secure coded matrix multiplication (HSCMM) scheme to solve the two problems in a heterogeneous network. By taking the heterogeneity of worker nodes into consideration, the corresponding recovery threshold and security constraint are obtained. To improve the performance of such a network under the security constraint, an optimal task allocation policy is given. Numerical simulation results verify the effectiveness of our proposed scheme.
Joint Bitrate Transcoding and Parallel Cooperative Transmission Optimization for Adaptive Video Streaming in Edge Assisted Cellular Networks

The advent of online video services has resulted in a remarkable surge in Internet traffic, prompting the need for mobile edge computing (MEC) as a crucial element in augmenting the quality of adaptive streaming media services amidst the time-varying wireless channels. MEC reduces network backhaul traffic by providing video transcoding and adaptive streaming services closer to users. Nonetheless, the process of video transcoding introduces additional latency and energy consumption. In order to effectively tackle this challenge and uphold the optimal quality of experience (QoE), we propose the Joint Bitrate Transcoding and Parallel Cooperative Transmission (JBTPCT) model, which operates at the edge of mobile networks and handles multiple video chunks simultaneously. Within the JBTPCT model, the Asynchronous Advantage Actor-Critic (A3C) algorithm framework is employed to jointly account for radio access network conditions and MEC resources, leveraging a parallel execution strategy for transmission and transcoding. This integrated approach aims to minimize both latency and energy consumption while enhancing the QoE of video streaming. We evaluate the average QoE of JBTPCT in different network scenarios, and the experimental results demonstrate that JBTPCT consistently achieves higher average QoE compared to competing algorithms.
A Modified Dempster Shafer Approach to Classification in Surgical Skill Assessment

Artificial intelligence systems are usually implemented either using machine learning or expert systems. Machine learning methods are usually more accurate and applicable to a broader range of applications. Expert systems, on the other hand, require much less data for training and generate more comprehensible results. These characteristics are typically desired in the fields of surgery and medicine because there isn't much data available. In order to give a machine's decisions a deeper level of semantics, it is also advantageous to incorporate a doctor's expertise into it. Furthermore, it is safer to understand the reasoning behind a machine's choices. In this paper, a Dempster-Shafer Theory (DST) based expert system is suggested for the task of surgical training skill assessment. An interval-based probabilistic feature analysis was applied to the data to assign values to the mass functions. Zhang's rule of combination was applied to handle the conflicting evidence in the prediction phase. The performance of the proposed method was compared to another DST classifier, SVM, and XGBoost. Our method outperforms SVM and other DST classifiers, but it is not as precise as XGBoost. By reducing the size of the dataset, the added benefit of using an expert system as opposed to a machine learning method was explored further. The performance of the suggested method is not adversely affected by the size of the dataset, whereas the XGBoost classifier is.
Kalman Filter Fusion Based on Interactive Multiple Model for Target Tracking in Wireless Sensor Networks

Target tracking in wireless sensor networks has become one of the most challenging and popular topics in the last decade. Target tracking includes different algorithms, one of the most effective of which is the interactive multiple model algorithm based on the Kalman filter (IMM-KF). This algorithm is used when the target is maneuverable and the equations of motion are linear. In this paper, in order to have a better tracking performance and obtain extensive information from the target in wireless sensor networks, a fusion algorithm based on Kalman filter and interactive multiple model is proposed. Finally, the Monte Carlo simulation results of the proposed fusion algorithm are compared with single-sensor algorithms.
An Improved Real-Time Implementation of Adaptive Neuro-Fuzzy Controller

An adaptive neural network-based control method is highly effective for controlling complex systems with various operating situations since it can adapt and train its control strategy to compensate for changes in the system's behavior. The backpropagation algorithm is utilized to optimize the neural controller values and reach the desired system reference. Compared to other neural control methods, the proposed neuro-fuzzy controller's learning algorithm has a quick response time, making it suitable for a wide range of real-world applications that require ANNs. Fuzzy rules can be established to improve the adaptability and performance of a neural controller by optimizing its decisions and taking into account various factors such as data and desired outcomes. Furthermore, these fuzzy rules can help determine the optimal momentum and learning rate values for a given problem. The proposed method has been executed using LabViewprogram and validated in practical through a research lab actuation control structure, demonstrating that optimizing the neuro-fuzzy controller with optimal parameters of the neural network model leads to improved control and faster response times in nonlinear controller systems.
Optimal Path Planning of Mobile Robots Using IsoCost-Based Dynamic Programming

This paper proposes an optimal local path planning algorithm for mobile robots by means of the so-called IsoCost-based dynamic programming. This algorithm explores and finds the optimal paths to the goal point from different start points in the environment of the robot while avoiding arbitrary numbers and forms of obstacles. The concept of IsoCost HyperSurface (ICHS) is first explained and it is shown that with a certain cost value, the ICHS corresponding to optimal path planning surrounds all other ICHSs corresponding to non-optimal path planning algorithms. Based on this geometric property, agents are initiated and planned to explore and reveal the optimal ICHSs using dynamic programming. The optimality of the proposed method is proved and the results of this method are compared with those of Modified Potential Field and Bug2 algorithms, indicating that the proposed method surpasses the other two algorithms in terms of the optimality of path length and execution time.
Prediction of Interest in Advanced Studies Using the K-Nearest Neighbor

Prediction systems play an important role today. Prediction systems are widely used today to provide recommendations to users based on user preferences. One of them is to make predictions related to interest in further studies. There are still many who continue their studies not according to their learning interests, because of coercion or even they still do not know for sure their study interests. Therefore, the purpose of this studyis to provide recommendations for study interests based on user preferences. This study uses the CRISP-DM development methodology as well as the K-Nearest Neighbor Algorithm with a k-value of 24, resulting in a model accuracy of 73% with the Manhattan distance metric using training data of 250 training data samples with each testing data of 300 samples of label data. (Academics), 100 label data samples (Arts), 155 label data samples (Sports). The model is wrapped in a pickle to be integrated with the system interface via the Flask API, so that users can use the application easily.
Deep Learning Enabled Semantic Communication Systems for Video Transmission

Semantic communication has emerged as a promising approach for improving efficient transmission in the next generation of wireless networks. Inspired by the success of semantic communication in different areas, we aim to provide a new semantic communication scheme from the semantic level. In this paper, we propose a novel DL-based semantic communication system for video transmission, which compacts semantic-related information to improve transmission efficiency. In particular, we utilize the Bi-optical flow to estimate residual information of inter-frame details. We also propose a feature choice module and a feature fusion module to drop semantically redundant features while paying more attention to the important semantic-related content. We employ a frame prediction module to reconstruct semantic features of the prediction frame from the received signal at the receiver. To enhance the system’s robustness, we propose a noise attention module that assigns different importance weights to the extracted features. Simulation results indicate that our proposed method outperforms existing approaches in terms of transmission efficiency, achieving about 33.3% reduction in the number of transmitted symbols while improving the peak signal-to-noise ratio (PSNR) performance by an average of 0.56dB.
Sentiment Analysis on the Issue of the Palestine-Israel Conflict on Twitter Using the Convolutional Neural Network Algorithm

The Palestinian-Israeli conflict is a territorial conflict that has been going on for a long time. The conflict has captured many people's attention, so many are pro and contra in responding to the Palestinian-Israeli conflict. Various opinions were conveyed through social media, one of which was on Twitter. This research was conducted to analyse the responses and statements from the public in the form of positive, negative, and neutral. To maximise the classification process, a Convolutional Neural Network algorithm is used. The data used are Indonesian tweets with a total dataset of 3632 data. The results of the tests that have been carried out are that the classification process using the Convolutional Neural Network algorithm produces an accuracy of 68%. The percentage of text classification shows more neutral sentiments than positive and negative sentiments. Sentiment results get 34 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> positive, 19% negative, and 47% for neutral emotions.
Mach-Zehnder Interferometer Cell for Realization of Quantum Computer; A Feasibility Study

In this study, the basic hardware for manipulating photons' quantum states, the Mach-Zehnder Interferometer (MZI), is theoretically studied. An array of these MZI cells make up Programmable Nanophotonic Processor. This paper focuses on MZI quantum information processing capabilities. Simulation results of these cells will be reported and the possible use of squeezed state of light is discussed.
Metaverse-Based Learning Media to Increase Wireless Security Awareness

Wireless technology has become an indispensable part of our lives, but it also poses security risks. A study found that most users are unaware of these risks and prioritize learning how to use wireless networks over considering their security. This paper proposes the development of a metaverse-based learning media using Mozilla Hubs platform to increase awareness about wireless security. A total of 30 participants from IT and non-IT backgrounds will undertake a pre-test before the treatment and a post-test after they have finished learning the media in order to gauge their level of wireless security awareness. Then, the pre-test and post-test scores will be compared using the One Group Pretest-Posttest Design as a quantitative approach.
Joint Computing Resource and Bandwidth Allocation for Semantic Communication Networks

As a new communication paradigm, neural network-driven semantic communication (SemCom) has demonstrated considerable promise in enhancing resource efficiency by transmitting the semantics rather than all bits of source information. Using a large semantic coding model can accurately distil semantics, and significantly save the required bandwidth. However, this consumes a large amount of computing resources, which are also precious in the network. In this paper, we investigate the joint computing resources and bandwidth allocation for SemCom networks. We first introduce the computing latency model in SemCom, and formulate the joint computing resources and bandwidth allocation optimization problem with the objective of maximizing semantic accuracy. Then, we transform this problem into a deep reinforcement learning framework and exploit a multi-agent proximal policy optimization to solve it. Numerical results show that the proposed method significantly improves the average semantic accuracy in the resource-constrained cases, compared with the two baselines.
The Combination of Contrast Stretching and Histogram Equalization Methods to Optimizing Radiology Image Quality

Several difficulties frequently arise when acquiring radiological pictures, which can impair the clarity of visual images. The doctor's capacity to precisely interpret images can be hampered by several issues, including noise (interference), low contrast, and uneven intensity distribution. The combination method of Contrast Stretching and Histogram Equalization was created to enhance the visual quality of radiological pictures by improving contrast and balancing pixel intensity. The technique can help expose essential information in radiological images previously obscured by or difficult to discern in uneven intensity distributions. As a result, the variance and standard deviation values increased without the average value changing, indicating that the contrast in the combined image had increased. Using the contrast stretching approach first, followed by the histogram equalization method, is preferable in the image processing technique.
Gaussian Kernel Naïve Bayes Classifier on Hadith Corpus

The development of Arabic texts from electronic documents used in many applications, resulting in a fast and accurate method of text classification is very important. Classification is a method of grouping data by building a model. Hadith data is the one of importance information for Muslim. Therefore, we need to get a precise and accurate classification model to classify hadith data in the form of text. Naïve Bayes is a method of calculating probabilities and Kernel Naïve Bayes is a Naïve Bayes method by giving kernel functions to these methods by giving a higher probability value for each small probability value. From this research result, a better method used for the document classification process is the Naïve Bayes kernel method.
Design and Fabrication of Low Cost, Time Saving, Automatic Dishwasher with Water Recycling Feature

The design and manufacture of automatic conveyor-based dishwashers is a complex process that involves the integration of mechanical, electrical, and electronic components. The main objective of this project is to create a low cost, highly efficient, water and detergent saving based dishwasher that can handle a bulk number of dishes in a very short time. Dishwashers are designed to transport dishes automatically using a conveyor belt system. The system includes the flow of water and detergents to ensure optimal cleaning and hygiene. The manufacturing process includes material selection, creation of detailed computer aided design (CAD) models, and machine construction using precision manufacturing techniques. The result is a reliable and efficient dishwasher that can meet the needs of commercial kitchens and other high-volume food service operations. The main goal of semi-automatic dishwashers is to reduce human effort and time with an innovative simple design. The dishwasher design has been made environment friendly as well by introducing reusing of water and less consumption of detergent. The proposed dishwasher in this work is a new concept that does all the work of a traditional dishwashing i.e.; plate placing, detergent spraying, sparkling water in one wash cycle. In addition to that the dishwasher model is composed of AC gear Motors, Universal Motor, Conveyor chain and a central controller for the control of process and time delaying. This work also presents the benefits of water, time and detergent saving by using the proposed dishwasher as compared to conventional dishwashing.
Smart Greenhouse Application With MERN and AES-128 CBC Data Encryption Based on IoT

The greenhouse is a building construction used for growing crops. Many farmers still use traditional methods for the greenhouse data monitoring process, making it inefficient in terms of time and energy spent. Farmers need an attractive, fast, and secure application for an efficient greenhouse data monitoring process. Farmers can use a website-based application connected to the greenhouse's IoT system. This research discusses a website-based smart greenhouse application for the greenhouse data monitoring process. The application was developed using the Scrum Agile Development method, where each stage has an iterative and structured process. The technology combines MongoDB, Express JS, React, and Node JS, known as the MERN framework, the MQTT protocol, and data encryption techniques with AES-128 CBC. Testing this application uses the black box testing method by testing 14 types of REST API services and 9 application services. The duration of testing results varies depending on the number of services to be tested. In addition to being tested with a black box, the application was tested with GTMetrix, which gave an “A” grade with a score of 99%, and the total page size was 519kb. This test proves that MERN has good enough application performance to be implemented as an IoT-based smart greenhouse application. The encryption results of sensor data stored in the MongoDB database have a level of randomness that is very difficult to break, so the data cannot be processed or used to create machine learning models if the developer's key and Initialization Vector (IV) are not known.
Object Detection Enhancement Based on Super-Resolution Mapping

Object detection, while being a key step in many applications, has remained challenging, mainly due to the different resolutions of objects in an image. On the other hand, Super-Resolution (SR) approaches have recently been developed to improve the resolution of an image. In this paper, we utilize SR to enhance the quality of images before applying them to a detection model and propose a structure where the detection performance is improved. Furthermore, we remove the image noises by SR methods. Comparisons depict the superior visual performance of the proposed method compared to the conventional detection models. We evaluate our proposed method for detection of solar panels in two countries. To this aim, we created a dataset of satellite images needed for implementing detection models. As a result, we can simply evaluate the usage of solar panels in countries.
Predicting Career Trajectories of Information System Graduates Using AdaBoost Algorithm

The undergraduate unemployment rate in Indonesia is increasing, necessitating universities to produce quality graduates. In alignment with global technological advancements, universities are committed to enhancing the quality of their graduates through curriculum evaluation. In this study, we investigated the Department of Information Systems at the University of Riau and developed an alum career analysis system using the AdaBoost algorithm. The data used in the study was collected via an online survey from graduates. Relevant features were selected from features to preprocess the data. The experiments conducted using the SAMME.R boosting method, with a learning rate of 0.1, 100 estimators, and cross-validation on the five-fold sampling method, resulted in the highest accuracy of 83.3%. The decision tree of the AdaBoost algorithm explains the factors influencing alums to pursue careers in their field, with English proficiency, learning methods, alum cohorts, and alum's activeness in job searching being the main determinants of success in their careers. The alum career analysis system proved reliable for evaluating the curriculum and producing quality graduates in line with technological advancements. Future work includes expanding the system to include graduates from other departments and implementing findings to improve the curriculum.
Big Five Personality Type Prediction on Twitter Users with the Long Short-Term Memory Algorithm

Every individual has a different personality. By knowing your personal personality, you can develop your potential to the fullest, by knowing the personalities of others you can position yourself socially. One of the individual personalities that is internationally recognized and recognized by psychology is the big five personality. This study aims to predict the big five personalities of individual Twitter social media users. Research activities follow the stages of the Cross Industry Standard Process for Data Mining (CRISP-DM) method, while the algorithm used to predict the big five personalities is Long Short-Term Memory (LSTM). The data used in this study is a public dataset published in previous research which contains 269 Twitter account user data with 240,000 tweets data. This study uses a variety of training data and test data as well as the Convolutional Neural Network (CNN) algorithm as a comparison of the research results. The results of this study are that the LSTM algorithm produces the best accuracy of 77.72% in the comparison of training data and test data of 80:20. These results are better than using the CNN algorithm and the random forest algorithm in previous studies with the same data splitting scenario dataset.
Cost-Effective Automation: Cloud-Based Monitoring Combining HPA with VPA for Scalable Startups

This paper proposes to automate server usage monitoring for resource metrics using Prometheus and Grafana. The main contribution of this work is to create a cost-effective web-based application monitoring system combining Horizontal Pod Autoscaler (HPA) and Vertical Pod Autoscaler (VPA), called Anbu, suitable for scalable startups. The web-based application monitoring system is achieved by comparing the monthly fee with the existing monitoring system. To validate the accuracy of Anbu, services in the Kubernetes cluster are tested in two cases using a load test. The first case is to scale the number of replicas set for the service according to CPU and memory. The second is to provide recommendations for the ideal number of CPUs and memory according to the history of their actual use. The results show that scalable startups can do automation monitoring with an Anbu usage fee for 24 hours of uptime of around $200 per month. It will save $139.04 less per month than the existing monitoring system. This price is suitable for scalable startups who still manually automate server usage with an annual salary of less than $100.000.