Reviewerly/exHarmony huggingface dataset:
Higher Classification Accuracy of Income Class Using Decision Tree Algorithm over Naive Bayes Algorithm

Developing two machine learning classifiers with higher accuracy for classifying income classes for people earning less and a higher salary scale between 50,000. Decision Tree Algorithm (DTA) and Naive Bayes Algorithm (NBA) are the two classifier mechanisms employed. On a dataset of 32516 records, the methods were implemented and tested. Implemented each algorithm through programs and performed ten rounds on both methods to determine distinct scales of income class for who earns lesser and higher salary scale between 50,000. The G-power test is around 80% accurate. The findings of the programming experiment showed that the Decision Tree Algorithm had a mean accuracy of 84.3790 and the Naive Bayes Algorithm had a mean accuracy of 79.3170 for classifying income categories. The variation in accuracy between the two classifiers is statistically significant (p=0.53), which is insignificant when employing the unpaired samples t-test. The primary purpose of this work is to apply a novel technique to modern Machine Learning Classifiers to forecast income class classification. When the Decision Tree Algorithm is compared to the Naive Bayes Algorithm, the results show that the DTA outperforms the NBA.
Diagnosis of Asthma Disease and The Levels using Forward Chaining and Certainty Factor

Asthma disease is a major global health issue that affects at least 300 million people worldwide. Even for clinicians working in emergency rooms, predicting the severity of asthma is difficult. Predicting the intensity of an asthma attack is much more challenging because it is dependent on several factors, including the person's illness's features and severity. Forward Chaining and Certainty Factor algorithms can be implemented to diagnose the degree of asthma control, so the consultation process through the system becomes more detailed. The expert system can be used as an initial reference for the diagnosis process. The forward Chaining algorithm is useful for reasoning, starting from a fact to a solution. On the other hand, the Certainty Factor algorithm is used to provide a level of confidence in the conclusions by generating from the Forward Chaining algorithm. The research implemented several phases as follows analysis, data preparation, modeling, and evaluation. On evaluation, this research conduct three stages and tested using 80 medical record data. The result of the study has produced an expert system and generated an accuracy level of 65%, a precision value of 58.3%, and a recall also produced 57.13%. Therefore, the Chaining and Certainty Factor performs reasonably well in the diagnosis of asthma disease.
Efficient Cloud Environment to Improve User Authentication to Prevent Cyber Attacks Using DSA over AES

To provide an efficient cloud environment for secured cloud based users to improve user authentication and storage security to prevent cyber attacks based on Digital Signature Algorithm (DSA) over Novel Advanced Encryption Standard (AES). Sample dataset considered for both algorithms were N=10 and iterated by G power 80%, threshold 0.05%, CI 95% mean and standard deviation. Split size of training and test dataset is 70% and 30% respectively. The sigmoid function used in DSA maps value between 0 and 1 which helps to improve prediction of accuracy percentage. DSA has significantly better accuracy (80.7%) compared to AES accuracy (59%) and attained significance value of 0.004 (p&lt;0.05). DSA algorithm helps in predicting with better accuracy percentage in cyber attacks over AES.
Deep Learning-Based Solar Activity Prediction Using Sunspot Number and Solar Radio Flux

Nowadays, solar spectral irradiances are modelled by solar activity indices, which are used to identify the solar energy absorbed in the environment. This paper devises the Deep LSTM model for predicting solar activity using Sunspot Number (SSN) and Solar Radio Flux (SRF). The processing steps involved in the solar activity prediction are technical indicator extraction and solar activity prediction. In this paper, the solar indices are considered as an input of solar activity prediction, which is acquired from the solar cycle progression dataset. The technical indicators, like Simple Moving Average (SMA), Average True Range (ATR), Relative Strength Index (RSI), William’s %R, Stochastic %D, and Commodity Channel Index (CCI)are extracted for attaining a better prediction performance. In addition, the solar activity prediction is carried out using Deep LSTM based on SSN and SRF. The Deep LSTM is an effective deep learning technique that is widely utilized for prediction purposes since it has a better prediction ability. Moreover, the experimental result demonstrates that the devised Deep LSTM attained the minimum Mean Absolute Error (MAE), Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) of 1.186, 2.869 and 1.693, correspondingly.
Penerapan Logika ANFIS Sistem Penilaian Kinerja Dosen Pada Tri Dharma dan Perilaku Kerja

This research is motivated by several problems related to the not-yet optimal implementation of the performance assessment of tri dharma lecturers and work behavior on the ISB Atma Luhur campus. The importance of this research is because the tri dharma lecturer performance appraisal system and work behavior are useful for study program accreditation and university accreditation. The purpose of this study is to make lecturers responsible for the tri dharma and work behavior. The method used in this study is the ANFIS logic method. The results of this study are in the form of a lecturer's report card or a lecturer's performance assessment for one semester issued by SPMI. The variables used are education and teaching 40%, research 20%, dedication 10%, and work behavior 30%. The main result is the report card for each lecturer. The main finding from the lecturers' report cards, a decision can be made for SPMI to reprimand lecturers whose lecturers report cards to fall into the category of "BAD" lecturer performance. The average teaching value obtained is 3,624 plus the research value is 2,920 plus the service value is 2,800 plus work behavior, the value is 2,908. Lecturer performance value = 0.4 teaching plus 0.2 research plus 0.1 community service plus 0.3 work behavior. Equals 1.450 plus 0.584 plus 0.280 plus 0.872 equals 3.186. So the performance value of this lecturer is included in the category of "GOOD" lecturer performance.
Irreducible Magic Sets for <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mi>n</mml:mi></mml:math> -Qubit Systems

Magic sets of observables are minimal structures that capture quantum state-independent advantage for systems of n≥2 qubits and are, therefore, fundamental tools for investigating the interface between classical and quantum physics. A theorem by Arkhipov (arXiv:1209.3819) states that n-qubit magic sets in which each observable is in exactly two subsets of compatible observables can be reduced either to the two-qubit magic square or the three-qubit magic pentagram [N. D. Mermin, Phys. Rev. Lett. 65, 3373 (1990)PRLTAO0031-900710.1103/PhysRevLett.65.3373]. An open question is whether there are magic sets that cannot be reduced to the square or the pentagram. If they exist, a second key question is whether they require n>3 qubits, since, if this is the case, these magic sets would capture minimal state-independent quantum advantage that is specific for n-qubit systems with specific values of n. Here, we answer both questions affirmatively. We identify magic sets that cannot be reduced to the square or the pentagram and require n=3, 4, 5, or 6 qubits. In addition, we prove a generalized version of Arkhipov's theorem providing an efficient algorithm for, given a hypergraph, deciding whether or not it can accommodate a magic set, and solve another open problem, namely, given a magic set, obtaining the tight bound of its associated noncontextuality inequality.
The Effect of Transformational Leadership on Individual Readiness to Change: The Mediating Roles of Work Engagement and Organizational Justice (A Study of UAE Construction Industry)

This study aims to investigate the relationship between transformational leadership and individual readiness to change along with the mediators of organizational justice and work engagement in the construction industry of the UAE.The research applies quantitative analysis via PLS-SEM to determine the structural relationships among study constructs.The sample size consisted of 813 respondents from seven emirates.The proportion of respondents from each emirate in the sample is based on their relative representation in the UAE construction sector.The findings of the structural model indicate a statistically significant effect of transformational leadership on individual readiness to change.Moreover, the two mediating effects of work engagement and organizational justice are statistically significant.Further research may incorporate the role of moderators like culture to investigate the interaction effect of culture and transformational leadership on individual readiness to change.This study will help construction managers and policymakers shift to transformational leadership to make change eminent in the organizational structure.
Mathematical Fuzzy Logic in the Emerging Fields of Engineering, Finance, and Computer Sciences

With more than 50 years of literature, fuzzy logic has gradually progressed from an emerging field to a developed research domain, incorporating the sub-domain of mathematical fuzzy logic (MFL) [...]
Rethinking Privacy Risks from Wireless Surveillance Camera

Wireless home surveillance cameras are gaining popularity in elderly/baby care and burglary detection in an effort to make our life safer than ever before. However, even though the camera’s traffic is encrypted, attackers can still infer what the residents are doing at home. Although this security loophole has been reported, it does not attract much attention from the public, as it requires the attacker to be in close proximity to the camera and have some prior knowledge about the victims. Due to these requirements, the attacker has a low chance of success in the real world. In this article, we argue that the capability of attackers has been greatly underestimated. First, the attacker can leverage the characteristics of video transport protocols to recover the metadata of missing packets. Second, the attacker can build the inference model using the public datasets and adapt the model to the real traffic. Thus, the attacker can launch the attack at a distance from the camera, without prior knowledge about the victim. We also implement this attack scenario and verify that the attacker can infer the victims’ activities at a distance as large as 40 m without any knowledge about the victim, neither personal nor environmental.
Multi-Channel Blind Restoration of Mixed Noise Images under Atmospheric Turbulence

The imaging quality of astronomical or space objects is significantly degraded by atmospheric turbulence, photon noise, image sensor noise, and other factors. A multi-channel alternating minimization (MCAM) method is proposed to restore degraded images, in which multiple blurred images at different times are selected, and the imaging object and the point spread function are reconstructed alternately. Results show that the restoration index can converge rapidly after two iterations of the MCAM method when six different images are adopted. According to the analysis of the structure similarity index, the stronger the influence of turbulence and mixed noise, the higher the degree of image improvement. The above results can provide a reference for blind restoration of images degraded by atmospheric turbulence and mixed noises.
ENGLISH BORROWING TRANSLATION FROM ENGLISH INTO INDONESIAN SUBTITLE IN THE REDEEMING LOVE FILM

&lt;p&gt;Borrowing is one the various techniques to utilize the word to keeping the source language’s original words and phrases or sentence in the target language’s version. The researcher focus to find the types borrowing and dominant of borrowing in the especially in learning English. The researcher used Molina and Albir theories about borrowing the divided into two types there is pure borrowing and naturalized borrowing. The data sheets used as secondary instruments that during time there is collecting, classifying, analyzing, discussing and result the data This study was conducted using a qualitative method. The aim of the resaarch found many borrowing words in two types borrowing pure and naturalized borrowing in the film. And from the result that the dominant types is naturalized borrowing in 61 data, 36 or 59,16% is naturalized and 25 or 40,99% the pure borrowing. Based on the dominant types borrowing founded in the redeeming love film.&lt;/p&gt;
Detecting Spoofed Speeches via Segment-Based Word CQCC and Average ZCR for Embedded Systems

Intelligent speech recognition is increasingly used in embedded systems, which is also seriously threatened by malicious speech spoofing attacks. Different from the conventional methods, this article proposes a segment-based anti-spoofing detection (SASD) method for the quick detection of spoofed speeches against embedded speech recognition, which focuses on the anti-spoofing features rather than the contexts of speeches and the voiceprints of speakers. The speeches are divided into word segments and silent segments. Based on constant <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> cepstral coefficients (CQCCs), a word CQCC (WCQCC) extraction is first designed for the word segments of speeches. Then, based on short-term zero crossing rate (ZCR), an average ZCR (AZCR) extraction is devised for the silent segments. Combining the WCQCC of word segments and AZCR of silent segments, a biased decision strategy is proposed to quickly determine whether a speech is spoofed. Based on ASVspoof 2021 datasets, extensive experiments are conducted to evaluate the effectiveness of the proposed method. Specifically, our SASD can improve the accuracy of anti-spoofing detection by up to 33.47% and save up to 69.10% of time overhead on embedded devices compared with the existing methods.
Sixth IEEE IoT Vertical and Topical Summit at RWW 2023: “Quantum Information Technologies for the IoT” [RWW 2023]

Presents information on the above named conference.
Application of the Kerthi Bali Economy in a Web-Based Geospatial Visualization Information System

Traditional village is a unit of customary law community in Bali which has territory, position, original structure, traditional rights, own assets, traditions, and social manners of community life from generation to generation. One of the values ​​of local wisdom that can be implemented in economic activities is Sad Kerthi, namely the six main sources of welfare/happiness of human life. The Kerthi Bali Economy is an economy to realize an Independent Bali in the Economic Sector, built and developed based on the values ​​of Sad Kerthi's philosophy. Because the principle of Kerthi Bali Economics is a new concept, the researcher analyzes the contents and phenomena described in the Kerthi Bali Economics book and is associated with other economics references. At this stage will produce an indicator in measuring the economic principles of Kerthi Bali. There are 11 economic principles of Kerthi Bali that are harmonious with nature, culture and people. The development model used by the researcher uses the Waterfall Model. In this study, researchers used several stages in the waterfall model, including requirements, design, implementation, verification, and maintenance. The system design uses MySQL as the database as well as PHP and HTML for basic programming. This research succeeded in developing the Application of the Kerthi Bali Economy in a Web-Based Geospatial Visualization Information System. Testing the system using the Black Box Testing method, which produces all the pages tested in accordance with the expected results.
Cake-Hut: A Novel E-Commerce Platform for Booking and Purchasing Cakes Online

This research paper proposes an e-commerce cake shopping system: ‘Cake-Hut’ which will enable customers and cake bakery owners to handle their regular online selling and buying activities. Cake Hut is a Web Application where people can purchase customized cakes online from their favorite cake bakery according to their preferred designs or else, they can choose pre-defined cake designs from set of designs which were uploaded by sellers. The system is developed to replace the existing cake purchasing and order management methods, making it more effective and efficient while also addressing user needs. Agile software development methodology approach has been used to develop and test the system in a systematic manner, which includes the development phases of planning, design, and testing and implementation. This system is developed using React JS for frontend development and Express JS for backend development, MongoDB database, and runs on Node JS runtime environment.
Rancang Bangun Mesin Pelangsiran Material Konstruksi Tower Transmisi Sistem Crane Pulley dengan Kapasitas Maksimal 250 kg

Material shredders are used in construction projects to move materials from the starting point to the intended point. This trimming machine uses a type or system of crane pulley. In the world of construction, it can rarely be used in various terrains, whether flat terrain, valley terrain, or hilly terrain. Therefore, this paper aims to ensure that the process of upgrading / transferring the construction material of the 150 kV transmission tower can run well on flat terrain to difficult terrain such as valleys and hills based on several consideration factors. The design process of the material shredder consists of three stages, namely design, tool making, and tool testing. The design process consists of several stages, namely field review and the availability of supporting systems and raw materials. The fabrication process, carries out the assembly of materials that are already available by the predetermined design. The process of testing the tool, using the test method directly in the field so that the data presented is real loading data. The material drill engine can be used well in the field with a maximum carrying capacity of 250 kg with the highest effective tool capacity of 12,424kg/hour and fuel sed as much as 0.91 liters/hourKeywords : Design and Construction, Solidworks, Material Transfer Tool, Shifting Machine, Material Mover
Multi-Currency Integrated Serial Number Recognition Model of Images Acquired by Banknote Counters

The objective of this study was to establish an automated system for the recognition of banknote serial numbers by developing a deep learning (DL)-based optical character recognition framework. An integrated serial number recognition model for the banknotes of four countries (South Korea (KRW), the United States (USD), India (INR), and Japan (JPY)) was developed. One-channel image data obtained from banknote counters were used in this study. The dataset used for the multi-currency integrated serial number recognition contains about 150,000 images. The class imbalance problem and model accuracy were improved through data augmentation based on geometric transforms that consider the range of errors that occur when a bill is inserted into the counter. In addition, by fine-tuning the recognition network, it was confirmed that the performance was improved when the serial numbers of the banknotes of four countries were recognized instead of the serial number of a banknote from each country from a single-currency dataset, and the generalization performance was improved by training the model to recognize the diverse serial numbers of multiple currencies. Therefore, the proposed method shows that real-time processing of less than 30 ms per image and character recognition with 99.99% accuracy are possible, even though there is a tradeoff between inference speed and serial number recognition accuracy when data augmentation based on the characteristics of banknote counters and a 1-stage object detector for banknote serial number recognition is used.
Preface

2022 4th International Conference on Artificial Intelligence and Computer Science (AICS2022) has been held successfully on July 30-31, 2022. Due to the COVID-19 pandemic around the world, and with the strict travelling rules in China, it is still difficult to take international travel for our attendees abroad. Therefore, AICS2022 was held both in physical (Beijing, China) and online (Zoom). About 80 attendees have participated in the conference. The conference was divided into three parts: five keynote speeches in the first day, each with 45min, and 18 oral presentations followed in the first afternoon and the second day, each with 15min. Besides, 20 e-posters have been presented in the conference website. The session chairs and keynote speakers have worked together to selected the best oral presentation and poster presentation. This year, AICS2022 has attracted about 90 submissions, and 38 of them are accepted to be included in this collection. The process of evaluation and peer-review has last for about 6 months, with over 50 TPC members and reviewers involved in. We would like to express our sincere gratitude to all experts for their professional and detailed review comments, which have contributed to the high standard of this collection. This collection includes three major parts: (1) Algorithms and Programming, (2) Machine Learning and Computer Vision; (3) Artificial Intelligence and Innovations, with most up-to-date findings and technologies being discussed. We believe participants of AICS2022 have gained knowledge as well as met new friends in the same field. And we hope this collection can benefit the readers in the same way. List of General Chair, Program Chair, Publication Chair, Organizing Chair are available in this pdf.
Architecture Design for Analyzing Hyperspectral Images

Denoising images is a key part of the process of images. The hyperspectral image (HSI) has three dimensions in addition to the natural 2D image to display spectral and spatial information. In forestry [2, 2], agriculture, and urban planning, HSIs are commonly used. However, there are impacts with the multi-detector utilized to create the HSIs because of the harsh space environment, resulting in the noise of HSIs. The accuracy of consequent work, such as classification tasks [3], will be affected by the corrupted hyperspectral data with noise. As a result, in recent years have been an increase in interest in HSI denoising [4, 5]. Numerous methods, for example the Tenser-SVD [6] as well as the K-singular value decomposition [7], have been proposed. In general, the HSI denoising techniques may be categorized into three major groups.
Deep Neural Architecture Pruning

The majority of NAS algorithms are intended to identify the optimal CNN architectures for a specific task. CNNs utilized for image classification and recognition problems demand strong hardware, such as data centers as well as GPU workstations, for training and inference. As a consequence, many models are incompatible with consumer hardware like portable appliances and smartphones. Nowadays, if a developer wants to execute computer vision problems on these devices, they must have a constant internet connection, which may not always be available or dependable enough to perform such tasks effortlessly for users. As a result, it is extremely desired to develop algorithms and techniques that lower the complexity of computing in CNN models.