Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Learning Models for Air Pollution Forecasting in Seoul South Korea

Air pollution has been a major cause of health problems in several countries such as South Korea which is a country with rapid industrial and population growth, it urges the government to pay more attention to this issue. Due to the harmful effects of air pollution, many researchers conduct studies to predict the air quality index as an effort to prevent more severe health issues. In this paper, we propose three deep learning models, namely: Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), and combined CNN- LSTM to do air pollution forecasting. We mainly focus on the performance of the models applied in the time-series forecasting task as a supervised learning problem. We use the data from Seoul Metropolitan Government collected hourly from 2017 to 2019 at some stations. The experiment was carried out on sulfur dioxide (SO2), carbon monoxide (CO), nitrogen oxide (NO2), ozone (O3), and two particulate matter (PM) concentrations. We evaluate our model using root mean squared error (RMSE) to compare the models’ performance. The result shows that with normalization CNN model gives the lowest RMSE value, however without normalization the combined CNN-LSTM gives the lowest RMSE value. It proves that the model can predict the air quality index in Seoul South Korea.
Collision Risk Analysis to Ensure the Safety of Autonomous Vehicle Motion Control

This paper discusses the problem of ensuring the safe (accident-free) autonomous vehicle (AV) movement along a trajectory. The task of ensuring the safety of autonomous vehicle movement along the trajectory is formalized. The article describes a model that quantitatively assesses the level of autonomous vehicle movement safety based on the analysis of the environment map using a risk-based approach. A method for finding the optimal moment of time for an emergency stop of an AV is presented on the basis of minimizing possible damage (losses) by analyzing the change in collision risk as a safety indicator. The author has simulated a collision between an AV and an obstacle in Matlab Simulink. The probability densities of the collision risk values in the event of an AV hitting an obstacle and in the absence of an accident were constructed based on the simulation results. The author has shown the possibility of using the results of accident modeling to solve the problem of ensuring safe autonomous vehicle movement.
Applying Bayesian probability for Android malware detection using permission features

The tremendous rise of mobile technology has boosted malware and has raised the threat of malware. The proliferation of malware has given a great concern among mobile users. Various approaches have been applied to prevent malware spread, including firewalls, antivirus software and many more methods. Google has provided permission features as the main security to filter out the possibility of malware-infected Android mobile. Nevertheless, some permissions immediately granted by Android without user confirmation. This paper proposes a malware detection system based on permission features using Bayesian probability to battle the malware issue. This study used 96,074 samples retrieved from Androzoo and Drebin. By using static analysis, this study focuses on permission features that are significant in Android applications. The experiments conducted using chi-square as an algorithm and Naïve Bayes as a classifier. The accuracy of the detection is 85%. In conclusion, the detection of Android malware using the dataset has produced a good performance.
Senti-COVID19: An Interactive Visual Analytics System for Detecting Public Sentiment and Insights Regarding COVID-19 From Social Media

As governments take measures against COVID-19, the epidemic situation is expected to improve, but public sentiment is likely to fluctuate during this process, potentially influencing the best course of action. Social media has become a prevalent way for the public to express emotions and opinions in recent times. So that, the sentiment analysis on top of it may detect and provide valuable evidence of public attitude and help governments subsequent formulation of measures and policies. We present Senti-COVID19, an interactive visual analytic system for reflecting and analyzing public sentiment and detecting sentiment fluctuation triggers on social media. Senti-COVID19 adopts lexicon-based sentiment analysis to divulge the public opinion to COVID-19 events, employing libraries to extract keywords and statistics for providing detailed information. In addition, it offers visualizations for presenting the analysis, allowing users to quickly discover relevant information. Our results show that Senti-COVID19 can be used effectively to analyze sentiment from social media text, allowing users to explore derived data and identify insights from the collected tweets.
Review of VCSELs for Complex Data-Format Transmission Beyond 100-Gbit/s

Persistent efforts on developing advanced and complex data algorithms to enable the vertical-cavity surface-emitting lasers (VCSELs) for data beyond 100-Gbit/s has been reviewed in this paper. This endeavor not only elevates the bit rate of the IEEE802.3 standards for data center applications but also to initiate the unification of data format for wired and wireless network coverage. To date, the highest allowable data rates of the single-VCSEL-based optical link are 80 Gbit/s for the NRZ-OOK format under back-to-back (BtB) transmission 168 Gbit/s for the PAM-4 format over 150-m OM5 MMF, and the 224 Gbit/s for the QAM-OFDM format at BtB case. For the high-level complex data-format transmission with rigorous demand on the signal-to-noise ratio (SNR), this work demonstrates the most up-to-date 32-ray quadrature amplitude modulation generalized frequency division multiplexing (32-QAM GFDM) data algorithm with optimized amplitude and K value for encoding the multimode (MM) VCSEL. To achieve the higher spectral-usage efficiency based on the SNR spectrum for maximal transmission data rate, the bit-loading discrete multi-tone (DMT) technique is implemented by rearranging each subcarrier to the appropriate QAM levels. By utilizing 32-QAM GFDM and bit-loading DMT, the maximal data rate in the BtB case could respectively achieve 119.5 Gbit/s and 130 Gbit/s when operating the MM VCSEL at 55 °C. The transmission capacity of the VCSEL operated at 55 °C would decrease to 93.8 Gbit/s for 32-QAM GFDM and 83 Gbit/s for bit-loading DMT after propagating through OM5 MMF with modal dispersion.
Deep Learning for Sign Language Recognition: Current Techniques, Benchmarks, and Open Issues

People with hearing impairments are found worldwide; therefore, the development of effective local level sign language recognition (SLR) tools is essential. We conducted a comprehensive review of automated sign language recognition based on machine/deep learning methods and techniques published between 2014 and 2021 and concluded that the current methods require conceptual classification to interpret all available data correctly. Thus, we turned our attention to elements that are common to almost all sign language recognition methodologies. This paper discusses their relative strengths and weaknesses, and we propose a general framework for researchers. This study also indicates that input modalities bear great significance in this field; it appears that recognition based on a combination of data sources, including vision-based and sensor-based channels, is superior to a unimodal analysis. In addition, recent advances have allowed researchers to move from simple recognition of sign language characters and words towards the capacity to translate continuous sign language communication with minimal delay. Many of the presented models are relatively effective for a range of tasks, but none currently possess the necessary generalization potential for commercial deployment. However, the pace of research is encouraging, and further progress is expected if specific difficulties are resolved.
Optimal Allocation/Sizing of DGs/Capacitors in Reconfigured Radial Distribution System Using Quasi-Reflected Slime Mould Algorithm

Increased load demands worsen distribution system problems such as greater line losses, voltage deviation and a plethora of other concerns. This current work presents an approach stressing simultaneous optimal allocation and sizing of capacitor banks and distributed generations, as well as optimal radial distribution system (RDS) reconfiguration, to address these difficulties. The above objectives are accomplished through the maiden application of the proposed quasi-reflection-based slime mould algorithm (QRSMA). The efficacy of QRSMA is established by testing it on different benchmark functions. A new modified backward forward load flow approach is also proposed and validated by comparing its results to those obtained using MATPOWER software for IEEE 69, 85, and 118 bus RDSs. The proposed load flow technique is independent of the sequential bus numbering scheme and may be applied to any RDS network topology. The proposed QRSMA is tested on 118 bus RDS and to prove its effectiveness; its results are compared to those of other studied algorithms. The study takes into account both fixed and variable loading scenarios. A cost-benefit analysis of the strategy is also performed in order to make the methodology more realistic.
SGAME: An Authoring Tool to Easily Create Educational Video Games by Integrating SCORM-Compliant Learning Objects

Previous research on game-based learning has provided firm empirical evidence that playing educational video games can positively impact students’ motivation and learning outcomes at all levels of education and disciplines. However, the use and uptake of game-based learning in educational settings is being hampered by the lack of authoring tools capable of allowing teachers to easily create educational video games and adapt them according to their needs and contexts of use. Moreover, in order to determine to which extent this kind of tools are useful for teachers to conduct effective game-based learning experiences, further research is required to evaluate these tools from the teachers’ perspective, as well as to evaluate the instructional effectiveness of the educational video games that teachers can create by using them. This article contributes to fill this research gap by presenting a teacher-oriented authoring tool for educational games called SGAME, as well as by examining the teachers’ perceptions toward this tool and the effect of the educational video games it allows to create on students’ perceptions and learning outcomes. A total of three evaluation instruments were used: a questionnaire to collect teachers’ perceptions toward the SGAME authoring tool, another questionnaire to collect students’ perceptions toward a game created with SGAME, and a pre-test and a post-test to measure the students’ learning gains achieved by playing this game. A total of 201 teachers and 79 students participated in the evaluation reported in this article. The results of this article show that SGAME is an easy-to-use authoring tool that allows teachers to create motivating and effective educational video games.
A Comprehensive Survey on Auction Mechanism Design for Cloud/Edge Resource Management and Pricing

Driven by the vision of cloud and edge computing as the 5th utility, a major challenge is the efficient resource management and pricing taking into account incentives and preferences of cloud/edge providers, users, and the system. In recent years, auction mechanism design has gained wide attention as a tool for addressing this challenge. Here existing surveys lack detailed studies in this area, hence this work presents a comprehensive survey on the state-of-the-art auction-based mechanisms in the field of cloud/edge computing. First, the problem statements and background are presented for cloud/edge computing and auction theory. This is followed by a collaboration model on the benefits of auction mechanism designs to cloud/edge computing. Next, various auction mechanisms in cloud computing are reviewed based upon the following aspects: (1) direction of bids—forward, reverse, and two-sided auctions, (2) the heterogeneity of resources—combinatorial auctions, (3) the number and types of adopted attributes in winner determination—multi-attribute auctions, (4) the setting of auction—offline, online, or sequential auctions, and (5) the interoperability among cloud providers—inter-clouds. For each aspect, existing studies are summarized along with their saliencies and drawbacks. Moreover, existing auction mechanisms in the context of edge computing are also presented. Our comprehensive survey shows that although significant progress has been achieved in this field, there still exist key challenges that need to be investigated, as discussed here to provide future research directions.
Analysis of Mosquito Genetic with PCR-RAPD Approach

The wide distribution of mosquitoes in the territory of Indonesia with differences in geographical location causes mosquitoes to have the ability to adapt to the environment. The ability to adapt to the environment results in high variations phenotypically and genotypically so that Mutation, recombination or migration of genes is the cause of genetic diversity in a population. The purpose of this research is to study the characteristics and inventory of distribution areas, as well as to analyze genetic variations using morphological and molecular approaches. Genetic typology data through the RAPD approach provides information about the genetic variation of mosquitoes in an effort to control them as disease vectors. Genetic characterization was carried out by PCR-RAPD using three primers, namely OPA 11, OPA 12, OPA 15 and OPL12. Data were analyzed using UPGMA with a similarity coefficient using the Jaccard Coefficient of Similarity presented as a dendrogram. The results showed that the variation of the mosquito polymorphy band from the Kelurahan Kemelak Bindung Langit, Komering Ulu Regency was high. With the DNA band profile of RAPD results as many as 37 bands with a polymorphy DNA band presentation of (86.65%).
An MDP Model for Integrated Operational Decision Making Under Uncertainty in a Gas and Oil Separation Plant

Oil and gas supply chain (SC) plays key role in the global economy. Gas and oil separation plants (GOSP) belong to the upper stream of the oil and gas SC. In this article we study a GOSP that pumps oil to a stabilizing plant through a 600 KM pipeline. The purpose of this research is to develop an integrated mathematical model to support operational decision making regarding the optimal pumps scheduling and the dosage level of a drag reduction chemical, which is injected in the oil pipeline to stabilize the pressure and achieve higher oil flow rate. To the best of our knowledge, there is no MDP model in the literature that jointly considers pumps scheduling and oil flow control. A novel Markov decision process (MDP) model and two intuitive heuristic polices are proposed and simulated based on historical data. The heuristic policies are to operate the oil pumps in a cyclic weekly or biweekly patterns and to use a maximum likelihood rule to select the dosage level. Compared to the heuristic policies, our results demonstrated that MDP can lead to a substantial amount of savings in terms of the total system operating and maintenance costs. It also provides some practical insights on the interaction ways between the frequency of operating the pumps, the maintenance costs and the costs of a chemical that is used to control the pressure and flow rate of oil in a 600 KM oil pipeline.
Improving the Reliability of Real-Time Hardware and Software Systems

The article aims to optimize the reliability of multiprocessor hardware and software systems with n-version software designed for real-time control systems. The reliability of real-time systems is critical. The reliability of the hardware and software complex of the control system can be ensured by means of hardware redundancy and the use of an n-version approach in software development. To find the composition of the hardware and software system that provides the specified reliability, it is proposed to use a mathematical model that takes into account the failures and recovery of hardware and software components of the computing system. At the same time, with such an approach to ensuring reliability, the cost of developing and operating a control system may increase unreasonably. Further, an optimization problem is formulated in which maximization of reliability is chosen as the optimality criterion, and the performance and cost criteria are translated into constraints. There is the conclusion that this problem can be effectively solved with the help of evolutionary optimization methods.
Vehicle Classification Application on Video Using Yolov5 Architecture

This paper describes an application that uses a neural network to classify vehicular traffic on video. With the increase in the number of vehicles, the need to regulate traffic on the roads to solve the problems of traffic congestion and high accident rate has arisen. Collecting data from video of vehicles on the road will help to create statistics, the use of which can be used to effectively think about the regulation of traffic on the roads. An advanced real-time object detection system YOLOv5 was used to solve the problem of vehicle classification on video. To train the neural network the data set was used consisting of 750 images from the outdoor surveillance cameras. As a result of testing the system, the recognition accuracy was 89%. The data set can be used by other researchers in their research.
Speech Emotion Recognition Using 2D-CNN with Data Augmentation

Speech emotion recognition becomes a challenging issue, especially interaction between human-machine. Each person expressed their emotions in various ways and the features of speech are still unclear to distinguish between existing emotions. The results of speech were caused by mental and psychological states where it is directly influenced by emotions. This research proposed speech emotion recognition based on the 2D-CNN model with Log-Mel spectrogram as the input. The proposed method of the 2D-CNN model with Log-Mel spectrogram was able to capture the significant speech signals. The experiments used the EMODB database to evaluate the proposed model with augmented data. The proposed model was able to produce higher accuracy of speech emotion than the existing models, which also used deep learning methods for recognizing emotions. The experimental results showed that the proposed model achieved an accuracy of 0.88.
Methods for Determining a Quantitative Indicator of Threats to Information Security in Telecommunications and Industrial Automation Systems

The paper considers the issue of assessing threats to information security in industrial automation and telecommunication systems in order to improve the efficiency of their security systems. A method for determining a quantitative indicator of threats is proposed, taking into account the probabilistic nature of the process of implementing negative impacts on objects of both industrial and telecommunications systems. The factors that contribute and (or) initiate them are also determined, the dependences of the formal definition of the quantitative indicator of threats are obtained. Methods for a quantitative threat assessment as well as the degree of this threat are presented in the form of a mathematical model in order to substantiate and describe the method for determining a threat to industrial automation systems. Recommendations necessary for obtaining expert assessments of negative impacts on the informatisation objects and information security systems counteracting are formulated to facilitate making decisions on the protection of industrial and telecommunication systems.
Analysis of a Wind Turbine with Wound Rotor Asynchronous Machine

Currently, wind energy occupies an important position in the world, which leads to a significant increase in the demand for wind turbines. Inexpensive power converters of energy, fast microprocessors, and automatic control of technological processes have made it possible to produce a high-tech wind turbine that can work efficiently in the power system, as well as in isolated remote areas. This article discusses the analysis of an asynchronous generator connected to wind turbines, which has improved the performance of the turbine through mathematical modeling and confirmation of the results by experimental work. Also, a method has been eveloped to control the rotational speed of a generator with a wound rotor, based on the additional resistance in the rotor circuit and using a voltage rectifier and a chopper. The torque-speed characteristics of the induction generator were obtained for different rotor resistance values and similarly, characteristics of a turbine with a power of 1500 watts were obtained by pitch angle control.
Neural Network Techniques for Detecting Intra-Domestic Water Leaks of Different Magnitude

Nowadays, water leak control at different levels is a necessary tool for sustainable water resource management. Research shows that more than one third of the world’s drinking water is lost during its transfer to users, and that leakages on users’ properties vary between 2 and 13% of total residential water demand, are very frequent and difficult to detect. Thanks to the advances in Internet of Things solutions for smart metering devices, it is possible to gather household water consumption information with high spatial and temporal resolution and to analyse them. This article applies several supervised Machine Learning (ML) techniques for the automatic detection of leakages of different magnitudes - even smaller than the meter sensitivity – in pipes within the dwelling, by using data collected by smart meters installed at the connection of users to the distribution network in an Italian town. The results obtained are compared with the performance of an “empirical algorithm” previously presented by the authors, able to automatically identify leakages by checking if the hourly flow rate is never zero during the whole day, but not able to distinguish the size of the leakages. Experimental results over about 40,500 records show that ML techniques significantly improve the detection performance both in discriminating between presence and absence of leakages and in discriminating different-size leakages.