Miracle-dz/UniFAR-FTUs huggingface dataset:
Outage Performance of Satellite-UAV Network Framework based on NOMA

A unified framework for UAV-aided satellite communication network based on non-orthogonal multiple access (NOMA) is proposed. The satellite serves as base station with the aid of a decode-forward (DF) UAV relay to communicate with two ground users using NOMA protocol. Assuming that there is no direct link between the satellite and users due to severe fading. The link of satellite-to-UAV is assumed to experience Rician fading and UAV-to-users are assumed to experience Nakagami-m fading. In order to evaluate the performance of the system model more directly, the expressions for the exact and asymptomatic outage probability at high SNR are derived. On the basis of these results, the system throughput under delay-limited transmission mode is obtained. The correctness of theoretical deduction is verified by Monte-Carlo simulation. The simulation results show that: (1) NOMA performs better than traditional orthogonal multiple access (OMA) in terms of outage probability and system throughput. (2) The system performance of NOMA network is affected by the distribution fading parameter $\mu$ of Nakagami-m.
On Short-Term Load Forecasting Using Machine Learning Techniques and a Novel Parallel Deep LSTM-CNN Approach

Since electricity plays a crucial role in countries’ industrial infrastructures, power companies are trying to monitor and control infrastructures to improve energy management and scheduling. Accurate forecasting is a critical task for a stable and efficient energy supply, where load and supply are matched. This article discusses various algorithms and a new hybrid deep learning model which combines long short-term memory networks (LSTM) and convolutional neural network (CNN) model to analyze their performance for short-term load forecasting. The proposed model is called parallel LSTM-CNN Network or PLCNet. Two real-world data sets, namely “hourly load consumption of Malaysia ” as well as “daily power electric consumption of Germany”, are used to test and compare the presented models. To evaluate the tested models’ performance, root mean squared error (RMSE), mean absolute percentage error (MAPE), and R-squared were used. In total, this article is divided into two parts. In the first part, different machine learning models, including the PLCNet, predict the next time step load. In the second part, the model’s performance, which has shown the most accurate results in the first part, is discussed in different time horizons. The results show that deep neural networks models, especially PLCNet, are good candidates for being used as short-term prediction tools. PLCNet improved the accuracy from 83.17% to 91.18% for the German data and achieved 98.23% accuracy in Malaysian data, which is an excellent result in load forecasting.
How Fast Can We Insert? An Empirical Performance Evaluation of Apache Kafka

Message brokers see widespread adoption in modern IT landscapes, with Apache Kafka being one of the most employed platforms. These systems feature well-defined APIs for use and configuration and present flexible solutions for various data storage scenarios. Their ability to scale horizontally enables users to adapt to growing data volumes and changing environments. However, one of the main challenges concerning message brokers is the danger of them becoming a bottleneck within an IT architecture. To prevent this, knowledge about the amount of data a message broker using a specific configuration can handle needs to be available. In this paper, we propose a monitoring architecture for message brokers and similar Java Virtual Machine-based systems. We present a comprehensive performance analysis of the popular Apache Kafka platform using our approach. As part of the benchmark, we study selected data ingestion scenarios with respect to their maximum data ingestion rates. The results show that we can achieve an ingestion rate of about 420,000 messages/second on the used commodity hardware and with the developed data sender tool.
Effect of Sexism and Self-Esteem Toward Self-Objection

Physical appearance is important for women, because women will feel successful as an ideal woman when she can present a beautiful body. A beautiful word can be an insult to women because they don’t appreciate women’s actual abilities, but merely appreciating the beauty she has not because of her accomplishments, occupation, or thoughts. This research examines the influence of sexism and self-esteem against the occurrence of self-objection in the sorority. The study spreads samples amounting to 299 sorority. The sampling techniques in this study used purposive sampling with female criteria, aged 17-23 years, and had a good-looking appearance. Data retrieval uses three research instruments namely the objectified body consciousness scale, the ambivalent sexism inventory, and the multidimensional self-esteem inventory which are then analyzed using multiple linear regression analyses. Results showed that partially, hostile sexism had an influence of 2.7%, benevolent sexism had an influence of 4.6%, and self-esteem did not give a 0% influence on the occurrence of self-objection. These results suggest that there is a injury that sexism will further influence the coed focus on its physique, while self-esteem does not impact the student’s focus on physique or self-objectification.
Proactive Content Caching for Internet-of-Vehicles based on Peer-to-Peer Federated Learning

To cope with the increasing content requests from emerging vehicular applications, caching contents at edge nodes is imperative to reduce service latency and network traffic on the Internet-of-Vehicles (IoV). However, the inherent characteristics of IoV, including the high mobility of vehicles and restricted storage capability of edge nodes, cause many difficulties in the design of caching schemes. Driven by the recent advancements in machine learning, learning-based proactive caching schemes are able to accurately predict content popularity and improve cache efficiency, but they need gather and analyse users' content retrieval history and personal data, leading to privacy concerns. To address the above challenge, we propose a new proactive caching scheme based on peer-to-peer federated deep learning, where the global prediction model is trained from data scattered at vehicles to mitigate the privacy risks. In our proposed scheme, a vehicle acts as a parameter server to aggregate the updated global model from peers, instead of an edge node. A dual-weighted aggregation scheme is designed to achieve high global model accuracy. Moreover, to enhance the caching performance, a Collaborative Filtering based Variational AutoEncoder model is developed to predict the content popularity. The experimental results demonstrate that our proposed caching scheme largely outperforms typical baselines, such as Greedy and Most Recently Used caching.
T-CREo: A Twitter Credibility Analysis Framework

Social media and other platforms on Internet are commonly used to communicate and generate information. In many cases, this information is not validated, which makes it difficult to use and analyze. Although there exist studies focused on information validation, most of them are limited to specific scenarios. Thus, a more general and flexible architecture is needed, that can be adapted to user/developer requirements and be independent of the social media platform. We propose a framework to automatically and in real-time perform credibility analysis of posts on social media, based on three levels of credibility: Text, User, and Social. The general architecture of our framework is composed of a front-end, a light client proposed as a web plug-in for any browser; a back-end that implements the logic of the credibility model; and a third-party services module. We develop a first version of the proposed system, called T-CREo (Twitter CREdibility analysis framework) and evaluate its performance and scalability. In summary, the main contributions of this work are: the general framework design; a credibility model adaptable to various social networks, integrated into the framework; and T-CREo as a proof of concept that demonstrates the framework applicability and allows evaluating its performance for unstructured information sources; results show that T-CREo qualifies as a highly scalable real-time service. The future work includes the improvement of T-CREo implementation, to provide a robust architecture for the development of third-party applications, as well as the extension of the credibility model for considering bots detection, semantic analysis and multimedia analysis.
The Role of Energy Levels in Semiconductor‐Electrolyte Solar Cells

The transfer of minority carriers across the semiconductor-electrolyte interface during illumination has been studied. In particular the parameters which determine the competition between pure redox processes and hydrogen evolution at p-type or anodic dissolution at n-type material have been investigated. In the first case the electron transfer between p-type electrodes and the redox system can simply be interpreted on the basis of an energy scheme. In the case of hole transfer from n-type electrodes across the interface the competition of a pure redox process and anodic dissolution is more complex. It is mainly determined by kinetic parameters. The results obtained with CdS and various III-V compounds are discussed in view of application of n- and p-type semiconductors in solar energy conversion systems.
Transfer Learning Approach for Diabetic Retinopathy Detection using Residual Network

Diabetic retinopathy is a disease caused due to Diabetes Mellitus in the diabetic patients. It is the major cause of blindness for several countries. The longer the diabetic retinopathy affected the high chance that the person can go blind. 90% of cases reported that the cause of blindness due to the diabetic retinopathy occur due to the lack of proper treatment and monitoring the retinopathy before severe stage [2]. The doctors can't find a cure to the severe stage of diabetic retinopathy but can be detected in early stage and prevent it. Hence automated computer diagnosis will assist the doctors in finding the Diabetic Retinopathy at early stage with less cost and time. Deep learning is playing a major role in health informatics. Residual Network, a deep learning approach is used to detect the DR and its stages. With the transfer learning approach, Residual Network model detects the stages of diabetic retinopathy with good accuracy.
Study on The Prediction of Electricity Stealing Based on Improved SMOTE Algorithm and Ensemble Learning

The smart grid system accumulates a large number of user electricity data, but the distribution of positive and negative samples is extremely uneven. Data cleaning, feature selection and data normalization are used to preprocess the collected user power consumption data and an improved SMOTE algorithm is proposed to balance the sample data. An ensemble learning classifier based on Adam BP neural network is constructed to identify the electricity stealing behavior of users, and the model is evaluated. The experimental results show that the proposed model can accurately predict the electricity stealing behavior of users, and has a good application prospect in the electric-power industry.
Trust Evaluation Algorithm based on Association Rule Extraction

The current trust network evaluation models are mainly based on nodes' behavior, multi-attribute decision-making method or nodes' reputation. Due to the ambiguity and roughness of trust, these methods cannot express the trust of nodes well. Rule set matching algorithm can effectively deal with the ambiguity and roughness of trust. As the scale of the network expands and time increases, the scale of the trusted rule set will continue to grow, and at the same time, some of the rules collected may be invalid or even wrong. This paper proposes a rule extraction algorithm based on inclusion to improve the effectiveness and accuracy of rule set matching. Experimental results show that the algorithm is effective.
A Data-Driven Interpretable Method Based on Reversely Built FIS and Its Applications in Measurement

With the rapid development of machine learning, model interpretability has become a major topic in recent studies, especially in the field of measurement. However, there has always been a dilemma in interpreting machine learning models: those intrinsically interpretable methods cannot achieve desirable results in complex applications, whereas the methods with good performances are not interpretable. To resolve this dilemma, this article proposes an interpretable method based on fuzzy inference system (FIS), an intrinsically interpretable method capable of handling complicated applications. First, a method of reversely building FIS is proposed, and each part of the FIS is obtained by driving data, so that the reversely built FIS is enabled to interpret machine learning models. Second, three optimization methods are presented, and the complexities of the antecedents, the consequent, and the rules in the built FIS are greatly reduced. Then, based on fuzzy logic, a multilevel knowledge extracting method is put forward to acquire both the global knowledge and the local knowledge, so that the target machine learning model can be comprehensively interpreted. Finally, the proposed method is evaluated by real applications in industrial fields. The evaluation results show that the proposed method is effective in interpreting machine learning models.
Survey on Detection of Credit Card Frauds using Hmm and various Clustering Approaches

This paper focuses on detecting the credit card frauds using hmm and various clustering. This research has been worked in such a way that the state of transaction is to be determined based upon on the past transactions and the amount he used in those transactions. This prediction is done by using HMM (Hidden Markov Model). This model takes behavior pattern of the card holder and then the model is trained so that it can be used for prediction. The incoming transaction will be accepted only when it satisfies the probability. Firstly, it has been required to work on the historical transactions and try to analyze the hidden pattern of the cardholder profile. Secondly, using many clustering algorithms and the characteristic pattern of all the fraud and normal samples are required to be classified into clusters that share the common characteristics. Finally, this model helps us to decides the state of the present transaction based upon the historical transaction using Hmm to predict the probability whether a transaction is genuine or fraud transaction. This experiment helps us to know which clustering algorithm in integration with Hmm helps us to predict the transaction state with a good accuracy score such that a fraud transaction will never be accepted. And so genuine transactions will never be rejected in a bunch of transaction for being a fraud one
LBNN: Perceiving the State Changes of a Core Telecommunications Network via Linear Bayesian Neural Network

The core network is the most basic facility in the entire telecommunications network, which is consists of large number of routers, switches and firewalls. Network management like re-planning routes or adjusting policies is very important to avoid failures. However, the timing of intervention is very challenging. Too early intervention will incur unnecessary overheads, and too late intervention will cause serious disaster. In this paper, we analyzed a large data set from a real-world core telecommunications network and proposed Linear Bayesian Neural Networks (LBNN)11Code available at https://github.com/YanyingLin/Lbnn to perceive the core network state changes and make decisions about network intervention. In particular, we considered three aspects of complexity, including the weight of the mutual effect between devices, the dependence on the time dimension of the network states, and the randomness of the network state changes. The entire model is extended to a probability model based on the Bayesian framework to better capture the randomness and variability of the data. Experimental results on real-world data set show that LBNN achieves very high detection accuracy, with an average of 92.1%.
An Accurate and Fast Global Map-Matching Approach for Low-Sampling-Rate GPS Trajectories

Current global matching algorithms for low-sampling rate data still face the challenges of large computational costs and significant declines in accuracy with increases in the sampling interval time. Therefore, this paper proposes a novel global map-matching algorithm based on the hidden Markov model to match the data accurately and quickly. First, we utilize the road accessibility instead of the gridding or confidence intervals to obtain the candidate information. Then, this algorithm synthetically considers the trajectory location at the previous and next moments of the current trajectory point to establish the candidate paths. Finally, this algorithm calculates the transition probability through a similarity calculation between the spatial-temporal position of the candidate paths and the GPS trajectory. The accuracy, calculation time and stability of the proposed algorithm are verified systematically. For different sampling intervals, compared with the testing algorithms, the proposed algorithm improves the mean accuracy by a minimum of 3.3% and a maximum of 32.4% for different sampling intervals. The computational efficiency of the proposed algorithm is significantly improved by 3–391 times. It provides a new method for the development and analysis of low-sampling rate data.
Biometric Jammer: A Security Enhancement Scheme using SVM Classifier

A new privilege of biometrics help to reduce the stress of user, Which comes along with the traditional access methods of passwords and token. Using the biometrics limitations and weaknesses can be knocked out. However, biometrics has raise privacy risks and new security since they cannot be easily revoked. Due to the spoofing attack on biometrics. Thus, to protect biometric traits against spoofing attack a multimodal biometric jammer scheme for the security enhancement have been developed and suggested in this paper. Firstly, we analyze why the multimodal biometric system have attracted attention for high security-demanding schemes. Secondly, security of biometric system is increasing and prevented it from spoofing attack developing a machine learning system model. We show that these machine learning algorithms perform pre-processing of biometric traits images. Further we analyze user identification with the increase precision and reliability using biometric features. Where feature extraction of each one trait of biometric is done and then all features are concatenation to get a single feature. With the aid of machine learning classifier using extracted features the algorithm predict the result of the system.
Mechanistic studies on the bromate-1,4-cyclohexanedione-ferroin oscillatory system

A chemical mechanism including 22 reactions and 15 species is suggested for describing the dynamical behavior of the ferroin controlled BrO3−–1,4-cyclohexanedione (CHD) batch oscillator. The model represents an extended version of the mechanism developed to explain the oscillations in the ferroin-free BrO3−–CHD system. The kinetics and rate constants for some additional reactions such as ones between ferriin and CHD, BrCHD (2-bromo-1,4-cyclohexanedione) and H2Q (1,4-hydroquinone) and between ferroin and bromate were determined and incorporated in the mechanism of the BrO3−–CHD reaction. The model proposed here simulates satisfactorily the behavior of the BrO3−–CHD–ferroin oscillator at increasing concentration of ferroin and at the condition of [ferroin] = 0 mol dm−3.
Enhanced Audio Source Separation and Musical Component Analysis

Audio source separation is a cornerstone problem for researchers engaged in Digital Signal Processing and Artificial Intelligence. Music unmixing is the task of decomposing music into its constitutive components, like yielding separated stems for the vocals, bass, drums, accompaniment, jazz, and others from a mastered song track. Due to recent progress in the field of Deep Learning, researchers have been able to devise Neural Networks that can perform this task with considerable precision. However, these models lack performance when dealing with generic musical audio despite having decent utility over specific music genres. The proposed system aims to develop a universal platform-independent software for accurate domain-specific implementation of music source separation for acute subsets of stereo audio using the Bidirectional Long Short Term Memory (BLSTM) architecture of Recurrent Neural Networks. The Deep Neural Network helps demix audio mixtures into the jazz solo and its accompaniment. In cohesion, these two models extract five independent audio stems from the original audio with reasonable accuracy. Further, the extracted accompaniment stem is processed using ConvNet Model to estimate the instrumental components. In synchronization, these three models can break down audio to its fundamental elements.
Task Offloading in Trusted Execution Environment empowered Edge Computing

To tackle the computation resource poorness on the end devices, task offloading is developed to reduce the task completion time and improve the Quality-of-Service (QoS). Edge computing facilitates such offloading by provisioning resources at the proximity of the end devices. Nowadays, many tasks on end devices have an urgent demand for the security of execution environment. To address this problem, we introduce trusted execution environment (TEE) to empower edge computing for secure task offloading. To explore TEE, the offloading process should be redesigned with the introduction of data encryption and decryption. This makes traditional offloading optimization policy fail to be applied directly. To address this issue, we are motivated to take the data encryption and decryption into the offloading scheduling algorithm. In particular, we propose a Customized List Scheduling based Offloading (CLSO) algorithm, aiming at minimizing the total completion time with the consideration of energy budget limitations on the end devices. The experiment results show that our approximation algorithm can effectively reduce the total completion time and significantly outperforms existing state-of-the-art offloading strategy.
Design of Novel Time Monitored Touchless Operation using 555 Timer for Automatic Dispenser

This paper proposes a practical, low-cost circuit for the operation and control of a final control element (valve). The circuit presented in the paper uses an Infrared (IR) proximity sensor for object detection for triggering and 555 timer IC for precise time monitoring. The proposed system is intended to be used in soap and sanitizer dispensers installed at public places in this case in the university premises to assess the effectiveness. It may be proved very effective in mitigating the risk of the spread of Covid-19 due to the soap and sanitizer dispensers used at public places and the time monitoring capability of the proposed design allows the end-user to ensure the World Health Organization (WHO) guidelines for hand hygiene. Finally, theoretical concepts are validated using experimental results presented in the paper.