Reviewerly/exHarmony huggingface dataset:
Photonic convolutional neural network with robustness against wavelength deviations

We experimentally explore the practicality of integrated multiwavelength laser arrays (MLAs) for photonic convolutional neural network (PCNN). MLAs represent excellent performance for PCNN, except for imperfect wavelength spacings due to fabrication variation. Therefore, the performance of PCNN with non-ideal wavelength spacing is investigated experimentally and numerically for the first time. The results show that there exists a certain tolerance for wavelength deviation on the degradation of the structural information of the extracted feature map, leading to the robustness of photonic recognition accuracy under non-ideal wavelength spacing. The results suggest that scalable MLAs could serve as an alternative source for the PCNN, to support low-cost optical computing scenarios. For a benchmark classification task of MNIST handwritten digits, the photonic prediction accuracy of 91.2% for stride 1 × 1 scheme using the testing dataset are experimentally obtained at speeds on the order of tera operations per second, compared to 94.14% on computer. The robust performance, flexible spectral control, low cost, large bandwidth and parallel processing capability of the PCNN driven by scalable MLAs may broaden the application possibilities of photonic neural networks in next generation data computing applications.
A Service Recommendation Algorithm Based on Self-Attention Mechanism and DeepFM

This article proposes a recommendation model based on self-attention mechanism and DeepFM service, the model is SelfA-DeepFM. The method firstly constructs the service network with DTc-LDA model to mine the potential relationship between Mashup and API, which not only fully considers the text attributes but also combines the network structure information to effectively mitigate the sparsity of the service data. Secondly, service clustering to obtain numerical feature similarities. Finally, the self-attention mechanism is used to capture the different importance of feature interactions, and the DeepFM model is used to mine the complex interaction information between multidimensional features to predict and rank the quality score of API services to recommend suitable APIs. To verify the performance of the model, the authors use the real data crawled from the ProgrammableWeb platform to conduct multiple groups of experiments. The experimental results show that the model significantly improves the accuracy of service recommendation.
Tunable-coupling architectures with capacitively connecting pads for large-scale superconducting multiqubit processors

We have proposed and experimentally verified a tunable interqubit coupling scheme for large-scale integration of superconducting qubits. The key feature of the scheme is the insertion of connecting pads between the qubit and tunable coupling element. In such a way, the distance between two qubits can be increased considerably to a few millimeters, leaving enough space for arranging control lines, readout resonators, and other necessary structures. The increased interqubit distance provides more wiring space for the flip-chip process and reduces crosstalk between qubits and from control lines to qubits. We use the term tunable coupler with capacitively connecting pad (TCCP) to name the tunable coupling part that consists of a transmon coupler and capacitively connecting pads. With the different placement of connecting pads, different TCCP architectures can be realized. We have designed and fabricated a few multiqubit devices in which TCCP is used for coupling. The measured results show that the performance of the qubits coupled by TCCP, such as ${T}_{1}$ and ${T}_{2}$, was similar to that of traditional transmon qubits without TCCP. Meanwhile, our TCCP also exhibited a wide tunable range of the effective coupling strength and a low residual $ZZ$ interaction between the qubits by properly tuning parameters on the design. Finally, we successfully implemented an adiabatic controlled-$Z$ gate with TCCP. Furthermore, by introducing TCCP, we also discuss the realization of the flip-chip process and tunable coupling qubits between different chips.
A recommendation model for college majors based on deep learning and clustering algorithms

Many colleges in China have adopted the policy of recruiting students by academic subject categories in order to optimize the talent training mode. To solve the problems in major selection after enrollment, this paper has designed an intelligent algorithm model for recommending college majors. Compared with existing methods for assigning college majors, the model uses deep neural networks and clustering algorithms to simulate complex calculations in the human brain. It uses historical learning data from senior students or graduates to predict the future grades of freshmen, judge their adaptability to various college majors, reduce human interference in the college major selection process, recommend the most suitable college major to students.
ORGANISATIONAL LEADERSHIP CHALLENGES IN ADOPTING DEVOPS IN NORTHERN NIGERIA SMES

The development and operation issue is rapidly expanding as some organisations in northern Nigeria seek to capitalise on the benefits it can provide to software development organisations and information technology projects. Adopting DevOps, on the other hand, requires significant organisational change, especially where tradition and established processes exist. This study presents the findings of a five-month qualitative diary study following the implementation of DevOps in a Nigerian SME with over 100 employees as part of a large-scale doctoral research project that investigates software development processes and leadership challenges. Based on the study's findings, the case study organisation employs the DevOps approach to develop new software for internal and external use. DevOps appears to be well-known in theory, but it is extremely difficult to put into practise. This difficulty is exacerbated by the need to maintain old systems, a lack of technical leadership, management systems, and resilience. This research also found evidence of job creation, particularly among software developers. Taken together, we argue that DevOps is an interdisciplinary topic that requires all stakeholders to participate in order to communicate and collaborate, and that it would benefit greatly from more leadership and possibly deeper psychological research.
AN EFFECTIVE LOST AND FOUND SYSTEM IN UNIVERSITY CAMPUS

Different organizations and researchers have carried out much work to highlight the problem of lost and misplaced items. In this work, a web and mobile-based lost and found service for The National University of Malaysia (UKM) is designed and developed. The primary goal of this research is to help the students, staff, and visitors on the UKM campus find their lost items more efficiently. The main features of this web and mobile application included a login system and a lost and found listing page where the user can add things they have discovered or items reported as lost. Users can add extra information about the items, such as where the items are found or lost, and even attach pictures. Besides, users must update their contact information and matric number to ensure authenticity. Due to security issues, users must add security questions for every post to protect users from any frauds and tricks. Once the users have held an item for over a month, the disposal system will automatically delete the item. The application is an online lost and found system created using programming languages such as PHP, HTML, CSS, and Java Script connected to the MySQL database. The software used in this project is Sublime Text, XAMPP, and Filezilla, whereas the software used to develop the mobile application is Android Studio, and the programming language is Java. In conclusion, the system has successfully helped the students and staff of UKM to find their lost belongings and items faster and easier. This research also played an essential role in creating an efficient digital campus environment and enhancing the university's sustainability.
DETERMINATION OF MALAYSIAN GOVERNMENT WEBSITE EVALUATION METRICS BASED ON THE ANALYTICAL HIERARCHY PROCESS

Telecommunications technology is constantly changing with more modern hardware, software and even networks, making it viable. Along with the sophistication, the government uses websites known as electronic government (E-Government) to deliver critical information, thus forcing the government to consider good website performance, compliance with the latest standards, user-friendly services, and digital services devices. In setting website evaluation criteria, the government lacks emphasis on the study of website evaluation metrics that can extend to website technology 5.0 and beyond. The website quality concept, consisting of various metrics and criteria, makes it difficult for stakeholders to analyse and set their priorities and importance in implementing web-based services. This study was carried out to develop the priority index of the metrics and criteria through the Analytical Hierarchy Process (AHP) method and confirm the priority of metrics and criteria based on comparing alternative positions obtained with the UN EGDI report for 2020 as a benchmark. The results of this study successfully classified five metrics out of 10 main metrics and 17 criteria out of 89 criteria metrics. The five main metrics for evaluating Government agency websites and their priorities identified in this study are Security, Performance, Content Structure, Technology and Accessibility. The discovery of these metrics and criteria gives exposure to all stakeholders in the website development process and its evaluation.
Implementation of Hybrid CNN-XGBoost Method for Leukemia Detection Problem

Leukemia is a blood cancer in which blood cells become malignant and uncontrolled. It can cause damage to the function of the body's organs. Several machine learning methods have been used to automatically detect biomedical images, including blood cell images. In this study, we utilized a hybrid machine learning method, called a hybrid Convolutional Neural Network-eXtreme Gradient Boosting (CNN-XGBoost) method to detect leukemia in blood cells. The hybrid method combines two machine learning methods. We use CNN as the basic classifier and XGBoost as the main classification method. The aim of this methodology was to assess whether incorporating the basic classification method would lead to an enhancement in the performance of the main classification model. The experimental findings demonstrated that the utilization of XGBoost as the main classifier led to a marginal increase in accuracy, elevating it from 85.32% to 85.43% compared to the basic CNN classification. This research highlights the potential of hybrid machine learning approaches in biomedical image analysis and their role in advancing the early diagnosis of leukemia and potentially other medical conditions.
Non-English Students' English Learning Motivation

English is very important to learn by students in all majors, but students in two non-English majors in Bima town rarely attend English meeting held in campus. Therefore, this research was conducted to find out how non-English students' motivation to learn English is. This research used a qualitative descriptive method using an instrument in the form of a motivation questionnaire. The research results showed that non-English students have high motivation in learning English.
Zero-shot voice conversion with HuBERT

This study introduces an innovative model for zero-shot voice conversion that utilizes the capabilities of HuBERT. Zero-shot voice conversion models can transform the speech of one speaker to mimic that of another, even when the model has not been exposed to the target speaker
Building robust Korean speech recognition model by fine-tuning large pretrained model*

Automatic speech recognition (ASR) has been revolutionized with deep learning-based approaches, among which self-supervised learning methods have proven to be particularly effective. In this study, we aim to enhance the performance of OpenAI's Whisper model, a multilingual ASR system on the Korean language. Whisper was pretrained on a large corpus (around 680,000 hours) of web speech data and has demonstrated strong recognition performance for major languages. However, it faces challenges in recognizing languages such as Korean, which is not major language while training. We address this issue by fine-tuning the Whisper model with an additional dataset comprising about 1,000 hours of Korean speech. We also compare its performance against a Transformer model that was trained from scratch using the same dataset. Our results indicate that fine-tuning the Whisper model significantly improved its Korean speech recognition capabilities in terms of character error rate (CER). Specifically, the performance improved with increasing model size. However, the Whisper model's performance on English deteriorated post fine-tuning, emphasizing the need for further research to develop robust multilingual models. Our study demonstrates the potential of utilizing a fine-tuned Whisper model for Korean ASR applications. Future work will focus on multilingual recognition and optimization for real-time inference.
E-Modules of dynamic fluid using Science, Technology, Engineering and Mathematics (STEM) approach android applications

Abstract In the digital era, students use smartphones as a platform to seek help and information, implement learning strategies, and online collaborative learning. Android is an operating system that is widely used in Indonesia. Therefore, this study aims to produce learning media in the form of dynamic fluid electronic module (E-module) with the STEM approach assisted by android applications. The research method used is Research and Development with the ADDIE development model. The results of this study have been tested for validation by material experts with an average percentage of all aspects of 80% (valid), by media experts with an average percentage of all aspects of 93.75% (very valid), by learning experts with an average the percentage of all aspects is 77.86% (valid) and by physics teachers with an average overall percentage of 99% (very valid). Then a product trial questionnaire was distributed to find out user responses to e-module involving high school students. The percentage of product trial questionnaire results by students is 95.74% (very valid). Based on the results of validation tests and product trials, the dynamic fluid e-module with the STEM approach assisted by the android application is declared valid so that it can be used as a instructional media for class XI high school students.
Penambahan Variabel Tingkat Kecerdasan dari Chatbot untuk Mempengaruhi Kepercayaan Pengguna dalam Aplikasi Telekonsultasi Kesehatan

The use of Artificial Intelligence (AI) technology is often encountered in everyday life, especially in the world of business marketing, namely chatbots which are part of the implementation of Natural Language Processing (NLP). However, in its application, it is still felt that it cannot meet the needs of consumers for the specific questions they often ask, especially for those who use health teleconsultation services. In this study, the intelligence variable is added as an additional variable to answer the level of user trust in the chatbot. The data used in this study are quantitative with the target respondents being users of health teleconsultation services with a minimum user experience of 1 year. A total of 178 respondents met the criteria from a total of 238 respondents. In this research, three variables are developed. For each dependent variable, empathy, friendliness, and intelligence are factors moderated by task complexity and chatbot disclosure to determine the results of the independent variable, which is trust towards the chatbot. Based on the results of the outer model and inner model analysis using SmartPLS, it can be concluded that the addition of intelligence variables has a positive effect on user trust in chatbots. In addition, the level of chatbot complexity is also able to mediate the relationship between intelligence and user trust in chatbots. However, chatbot disclosure has a negative effect as mediating the relationship between intelligence and user trust in chatbots. In the application of using chatbots, the level of intelligence may have an effect on user trust, but natural human attitudes such as friendliness from chatbots do not affect user trust.
Perbandingan Pre-Trained CNN: Klasifikasi Pengenalan Bahasa Isyarat Huruf Hijaiyah

The number of documented deaf people continues to increase. To communicate with each other, the deaf use sign language. The problem arises when Muslims with hearing impairment or deafness need to recite the Al-Quran. Muslims recite Al-Quran using their voice, but for the deaf, there are no available means to do the reciting. Thus, learning hijaiyah letters using finger gestures is considered important to develop. In this study, we use the recognition of hijaiyah letters based on pictures as the learning model. The real-time-based recognition then uses the learning model. This study uses 4 CNN pre-trained models, namely MnetV2, VGG16, ResNet50, and Xception. The learning process shows that MnetV2, VGG16, and Xception reach the accuracy limit of 99.85% in 2, 3, and 11 s, respectively. Meanwhile, ResNet50 cannot reach the accuracy limit after processing 100 s. ResNet50 achieves 82.12% accuracy. The testing process shows that MnetV2, VGG16, and ResNet50 achieve 100% precision, recall, f1-score, and accuracy. ResNet50 shows figures 81.55%, 86.04%, 82.04%, and 82.58%. The implementing process of the learning outcomes from MnetV2 shows good performance for recognizing finger shapes in real-time.
Implementasi Framework Laravel Dalam Pembuatan Sistem Pencatatan Notula Berbasis Website

Minutes of Meeting are notes that function to record the things discussed in a meeting agenda. Minutes of Meeting are important and must exist so that agreements and decisions can be saved, so that they can be discussed again on the agenda of the next meeting. With the development of current technology, it is hoped that it can be implemented in various ways, one of which is that Minutes which were previously only written in books can be presented on the website so that it can facilitate monitoring and evaluation quickly, precisely, anywhere, and anytime. This study aims to realize a Web-based E-Notulen system by utilizing the Laravel framework and the Waterfall method for design which includes Analysis, Data Requirements, Design, Development, Testing, and Implementation. Based on the method used, the E-Notulen system can be designed by utilizing the existing Laravel Framework library using the Waterfal method. The Waterfall method can be applied in system development, especially systems that require fast time from design to implementation such as the E-Minutes system.
Perbandingan Performa Cluster Model Algoritma K-Means Dalam Mengelompokkan Penerima Bantuan Program Keluarga Harapan

Poverty has so far played a role as a problem faced by residents of the Mentawa Baru sub-district, Ketapang. The inability of this community is related to the need to meet education and health needs in social welfare. In assisting the grouping of beneficiary data is carried out using the K-Means algorithm. Apart from that, to increase performance, those who have gone through the first grouping process are then continued using feature selection in the decision tree tool. The algorithm used aims to classify PKH beneficiary data to help the government find out about the handling of the aid program in Mentawa Baru Ketapang sub-district. As for the results obtained from this study, namely, the accuracy of the initial clustering obtained a DBI value of -0.994 at K=8 while the second clustering value that had gone through feature selection with K=3 obtained a DBI value of -0.865. It is known from the performance testing of the comparison of the two clustering that the best performance value is found in the second cluster after going through feature selection.
Welcome to AI Matters 9(2)

Welcome to the second issue of this year's AI Matters Newsletter. As usual, we will start the issue with a conference report by Louise A. Dennis. Following it, we are very excited to present three award-winning or candidacy papers by Wijmans et al. (ICLR 2023 Outstanding Paper), Ma et al. (SIGKDD 2022 Best Paper), and Li, Liu, and Wu (CVPR 2023, Award Candidate). The papers cover a range of intriguing topics, such as the formation of spatial memory in navigation agents, causal modeling with hypergraphs, and full-body motion estimation from egocentric videos. Finally, we end this issue by a collection of "blue sky" ideas presented in the AAAI/ACM SIGAI New and Future AI Educator Program at EAAI 2023, summarized by Guerzhoy et al.
Sentiment Analysis of User-Generated Data Using CNN-BiLSTM Model

With the availability of user-generated data, like reviews, it has become possible to gain valuable information that can help service providers to improve their services. There can be various methods to gain helpful information, including sentiment analysis of the reviews. Sentiment analysis is a popular method used to extract information from these reviews, but it can be challenging due to irregularities and ambiguities in the data. This chapter proposes a novel model architecture that enhances sentiment analysis and provides more accurate results than state-of-the-art technology. The proposed model takes advantage of the convolutional layer and Bidirectional long short-term layer (BiLSTM) coupled with max-pooling and a network of hidden layers. Regularizers are also incorporated to enhance the accuracy of the proposed model during training. The proposed model’s main advantage is its ability to provide more accurate sentiment analysis results compared to previous models. In our chapter, we observed that the model overcomes the limitations of earlier models by improving the handling of irregularities and ambiguities in unstructured data (user-generated reviews) and providing valuable insights to service providers by accurately classifying reviews into positive and negative categories, enabling them to take appropriate actions based on customer feedback.
Breast Cancer Detection by Using Decision Tree

A machine learning’s (ML) objective, the goal of this field of artificial intelligence technology is to improve the precision and effectiveness of medical work. Once skin the type of cancer that most frequently affects women is breast cancer. Unchecked breast cell proliferation is the root cause of breast cancer. The national cancer institute estimates that 287,850 new cases of cancer, or 15% of all new cases, will be diagnosed in 2022. Estimated death toll in the USA year 2022 will be 43,250. 15% of all newly diagnosed cancer cases in the United States are in women with breast cancer. 1, 62,468 new instances of breast cancer were discovered in 2018 in India, and there were 87,090 cases of the disease-related death. In order to determine which Machine Learning algorithms are optimal for breast cancer detection made possible by the use of machine learning, this study will examine the various phases of breast cancer, examine datasets, and assess supervised Machine Learning algorithms. The outcome demonstrates that the decision tree’s accuracy is superior to the K-nearest neighbor.