Reviewerly/exHarmony huggingface dataset:
State-of-the-art in IoT forensic challenges

Ubiquity is the trend of any successful technology. The deployment of billions of interrelated computing devices connected that can be accessed from anywhere is referred to as the Internet of Things (IoT). A thing in the internet of things can be a person with a heart monitor chip implant that sends a heart reading to the hospital, an automobile that has built-in sensors that report issues to the dealer, an animal with a biochip transponder for location, connected home appliances, and smart home security systems. Most, if not all, IoT devices can transfer data over their network without any human or computer interaction. This can lead to massive security issues in the IoT era, where intruders can take advantage of it. Where you find issues, crimes can be committed, so forensics investigation is needed. Since a vast amount of data can be collected from an IoT network and crimes can be committed, forensics is needed for collecting and analyzing this data as evidence to ensure the chain of custody in the court of law. The main purpose of this paper is to identify the major issues and challenges that can be faced during an IoT forensics investigation.
Indonesian Automatic Speech Recognition Based on End-to-end Deep Learning Model

The Indonesian language is different from English in phonetics. It is challenging to develop AI technology, machine learning, and deep learning with various algorithms to select the appropriate methods and algorithms for Indonesian speech recognition needs. Much research on speech recognition has been performed for high-resource languages, such as English. Unfortunately, those models cannot be directly used for the Indonesian language. To create an excellent speech recognition model, we need a high-quality and quantity dataset of the Indonesian language. But, such a dataset is not available at the moment. Hence, in this research, we start collecting such a dataset. Next, the developed dataset is used to train an end-to-end deep learning-based speech recognition model. The evaluation shows that the developed model achieves a word error rate of 14.172%, better than two previous models: Mozilla DeepSpeech (23.10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> ) and Kaituoxu Speech-Transformer (22.00 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> ).
Circle Detection System Using Image Moments

The growth of system detection has significant development. The circle detection system is widely used to help people based on their needs, and it also could be used as learning media in educational fields. Especially for students with special needs, applying a circle detection system in Augmented Reality (AR) media would help them a lot. In order to make the study activity more effective and suit the learning material purpose, a circle detection system that only detects perfect full circles is needed to minimalize misconceptions in circle learning material. From the previous method such as Circle Hough Transform (CHT), circle detection faces the complex transition from cartesian coordinate into Hough coordinate. The use of image moments would give a coordinate of centroid that could use to find the radius by using the circle equation. Two groups of datasets would test the newly proposed method of detecting circles. Based on the experiment, the accuracy of the new method was 96.7%. The average time consumption is 0. 405 s which is faster than the CHT method with 1.024 s. Circle detection using image moments is also more robust towards noise than the previous CHT method.
Modern No Code Software Development Android Inventory System for Micro, Small and Medium Enterprises

The use of android applications is substantial among the general public, making android applications one of the media used in the trade sector. Entrepreneurs compete in promoting products sold through the application to reach all circles of society. Although many have used applications in product sales, micro, small and medium entrepreneurs still have not used the Android inventory Information System due to the limited capabilities and information of micro, small and medium enterprises owners. In this study, there will be an application based on no code that can help small business owners to maintain their inventory products and buyers. Platform no code is a visual software development environment platform where users can drag and drop components such as buttons, drop-down boxes, etc. and connect them without a line of code or less. It is a quick way to develop a definite software or website. The platform based on no code used is AppSheet. AppSheet is an application development platform connected to the google sheet and google cloud designed for all users who want to create applications without having coding knowledge. This research will be made android application based on no code for micro, small and medium enterprises so that business owners can make applications without using coding in inventory issue. The owner can solve manual problem of administrative propose. After the application is made, the business owners can design the application as needed to obtain information from the products sold directly, maintain inventory data to accelerate digital transformation easily.
Day Ahead Solar Irradiation Forecasting Based on Extreme Learning Machine

Solar radiation data is very important for humans in meteorology, agriculture and energy. An Extreme Learning Machine (ELM) model is a data-based model developed from a single hidden layer feed-forward neural network (SLFN) which has the superiority in terms of training speed that is better than its predecessor generation. A model for predicting solar radiation in the Manokwari area and its surroundings was built with the ELM algorithm. The model has been used to predict daily solar radiation in the area. The ELM model has been trained using 8016 data solar irradiation and temperature from NASA. The test results show that the built has fairly high accuracy with MAE values of about 0.6392 in a training time of 4.4375 seconds. The ELM model has superiority in time consuming compared to a simple feedforward neural network.
Takhasosi: Career Specialization Guidance System on Permissioned Blockchain Infrastructure for Undergraduate Students

Identifying suitable career goals is one of the challenges that mostly faced by the students, especially in their undergraduate studies, in which there is a need to identify a specialization path that they need to pursue. In this paper, we present our proposed specialization guidance application known as Takhasosi. It is an online application platform that is accessible and made to serve students for the purpose of guiding and selecting a suitable specialization based on their academic track record and other related information that is stored within a permission-based blockchain for distributed data access and storage. Majority of students are struggling in choosing their major and continue to pursuit their career goals. This is a big issue that many students face even with the latest technologies and advanced learning that is available nowadays. The proposed distributed application enables different entities to access and evaluate students' academic performances, as well as the capability to analyze their interests on a particular field, based on their academic track records.
Multi-task Solution for Aspect Category Sentiment Analysis on Vietnamese Datasets

In this article, we solved two tasks in the Vietnamese Aspect-based Sentiment Analysis problem: Aspect Category Detection (ACD) and Sentiment Polarity Classification (SPC). Besides, we proposed end-to-end models to handle the above tasks simultaneously for two domains (Restaurant and Hotel) in the VLSP 2018 Aspect-based Sentiment Analysis dataset using PhoBERT as Pre-trained language models for Vietnamese in two ways: Multi-task and Multi-task with Multi-branch approach. Both models give very good results when applied preprocessing. Specifically, the Multi-task model achieves state-of-the-art (SOTA) results in the Hotel domain of the VLSP 2018 ABSA dataset, with the F1-score being 82.55% for ACD and 77.32% for ACD with SPC. For the Restaurant domain, our Multi-task model also achieved SOTA in the ACD with SPC task by an F1-score of 71.55% and 83.29% for the ACD.
High-fidelity rf/microwave-based universal control of trapped ion qubits

Universal control of multiple qubits-the ability to entangle qubits and to perform arbitrary individual qubit operations-is a fundamental resource for quantum computation, simulation, and networking. We implement a new scheme for universal control of trapped ion qubits based on microwave magnetic fields and rf magnetic field gradients instead of laser beams, and create entangled states whose fidelity is statistically indistinguishable from the highest-fidelity laser-beam-generated entangled states reported to date. We describe the rf and microwave electronics used, and discuss avenues for using this scheme in a large-scale trapped ion quantum processor.
A Multi-agent System Construction Method for Substation Digital Twin

At present, substation is not completely intelligent, this paper has done a further study on the intelligent substation. This paper presents a multi-agent construction method for substation digital twin system. In this method, the digital twin system provides the structure frame for substation management, and the multi-agent system realizes the intelligent function of substation. Firstly, the architecture of substation digital twin system is described in detail. Then, a multi-agent system framework for substation digital twin is proposed, and the function of the agent is introduced in detail. And, the operating process of multi-agent is further introduced from two aspects of environment perception and data sharing.
Blockchain Trust Impact in Agribusiness Supply Chain: A Survey, Challenges, and Directions

The use of data and information is becoming increasingly important for the businesses sector to increase productivity, sustainability, and traceability. Businesses involved with the supply chain face challenges due to the lack of trust in data sharing. The integration of Blockchain technology in supply chain management will have a high potential to increase efficiency, transparency, and trust throughout the supply chain process. This paper aims to present the evolution of Blockchain technology towards optimizing trust in agribusiness supply chain management to address the trust inefficiency issues. We consider the environment of agribusiness supply chain networks and issues concerning constructing Blockchain-based trust networks among participants in the process of data sharing throughout the supply chain information flow. Accordingly, we also deliberate the research challenges that arise during the designing and the directions of the Blockchain solution to optimize the trust in agribusiness supply chain management.
SignEnd: An Indian Sign Language Assistant

Communication is an essential day-to-day activity that human society thrives on. Indian Sign Language (ISL) is one form of oral communication among the deaf-mute community in India. As the general public usually tends to be unaware of this form of interaction, daily conversations are strenuous for a deaf-mute person. Previously created systems focus on detecting alpha-numeric signs pertaining to users with five fingers solely. This paper describes an ISL system that can recognize the alpha-numeric hand signs of its users with five and six fingers and translate them into their corresponding text equivalences. A custom dataset is created that explicitly tailors to these requirements. Additionally, this system can convert entered text (letter, number, word, sentence) into corresponding sign equivalences. Sign-to-Text conversion is achieved by using Mediapipe-Hands Machine Learning (ML) model to detect hand signs for deaf-mute people with five fingers. Similarly, an Object Detection Application Programming Interface (API) is implemented to detect hand signs for users with six fingers. Presently, the proposed system has an average accuracy of 90 percent.
Computer Vision based Visual Activity Classification through Deep Learning Approaches

Activity recognition is a challenging and practical research problem in computer vision. A good number of applications are approached by researchers in this area, like activity monitoring in homes, smart healthcare systems, surveillance & monitoring for security, and more. Literature around this problem provides some solutions where machine learning approaches have profoundly been utilized. This paper proposes a method based on deep neural networks to classify and recognize human activities for video surveillance applications. In order to achieve our objective, we ensemble the two different specialized neural network models, viz convolutional neural network (CNN) and recurrent neural network (RNN), to perform activity classification. Here, CNN extracts relevant spatial features from the video sequence and RNN creates a temporal relationship among those spatial features, which in turn leads to activity classification. To evaluate the effectiveness of the proposed method, we use precision, recall and accuracy metrics. On evaluation, the method is found to be more efficient in classifying video sequences, with an accuracy of up to 91.90%.
Exploring Primary School Students' Academic Emotions and Learning Achievement in an Immersive Virtual Reality Science Classroom

The advancement in immersive virtual reality (IVR) offers many opportunities for science education. This study investigated the effects of an integrated IVR-based classroom on primary school students' academic emotions and learning achievement. A total of 44 fifth-grade students from primary schools (one primary school in northeast China) were recruited as participants. We used an academic emotion questionnaire to survey students' class-related academic emotions and knowledge post-test so as to measure the students' understanding of the IVR science lesson. The results show that most of the primary school students experienced positive academic emotions (e.g., enjoyment and hope) before the class, and “enjoyment”, “hope”, and “pride” during the class, as well as “enjoyment” and “pride” after the class. Students experienced greater positive emotions than negative emotions. Moreover, they displayed a better understanding of the scientific material taught.
Mammogram image enhancement using a two-threshold model of shadowed set with gradual representation of cardinality

Breast cancer can cause severe side effects on women's health. According to some recent study it has become the second leading cause of woman's death by cancer. Screening is one of the most successful ways of early-stage detection of breast cancer even before it has started showing any symptom. Among other popular screening tests, mammography plays a significant role in early-stage breast cancer detection. Microcalcification is an important characteristic of a mammogram. However, the identification of calcification in the presence of dense breast tissues is a difficult task, contrast enhancement plays a significant role to improve the visual quality of mammograms, thereby helps to identify microcalcifications. In this regard to deal with the challenges of identifying microcalcification from low contrast mammogram images we have applied a two-threshold shadowed set where cardinality has a gradual representation. The overall process is done in two-steps. Separately, we perform the global processing and local processing on the image then finally we combine the effect of both. Furthermore, subjective and objective measures have also been provided along with a comparative study with the stand alone enhancement approaches as part of experimental analysis.
Evaluation of Cryptocurrency coins with Machine Learning algorithms and Blockchain Technology

Cryptocurrency is a new buzz in the financial sector. This study helps user to evaluate the price of cryptocurrency and user can decide and invest on the current crypto coin based on predicted price graphs. We are proposing a system to evaluate daily price of five coins i.e. Bitcoin, XRP, Ethereum, Dogecoin and SHIBA INU with the help of two Machine Learning algorithm models ARIMA (auto-regressive integrated moving average model) and LSTM (long-short-term memory networks) where the database is protected by Blockchain technology. User will be able to trade in the current financial cryptocurrency market by selecting the coin and prediction date, system will display data using Smart Contract coding and the visualized graph with Time Series Forecasting. The system will improve the crypto coin evaluation as it will get trained and dataset will get updated using Machine learning algorithm.
Research on the Design and Effect of Early Childhood Education Games Supported by AR Technology

The main content of this study is the research on the design and effect of early childhood educational games supported by augmented reality (hereinafter referred to as AR) technology. The research starts with the investigation and literature review of practical problems in early childhood education, aiming at the practical problems, combined with the characteristics of AR technology, and systematically proposes a preschool education communication process model supported by technology to solve the practical problems of early childhood education. Using the characteristics of technological multimedia integration, establish an all-media integrated resource package for early childhood education to solve practical problems such as scattered educational resources, duplication of construction, and resource waste technology.
A Fast Moving Object Detection Technique using QR Decomposition based TV Regularized RPCA Framework

Moving Object Detection (MOD) is one of the prominent application in the field of computer vision. This paper contributes towards designing a fast and accurate MOD technique for real time applications by addressing the issues like dynamic background, illumination changes, occlusion, camera jitter, moving shadows, etc. The brilliance of the proposed method is confined in the effective utilization of QR decomposition and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$l_{2,1}$</tex> -norm minimization in the well-known Robust Principal Component Analysis (RPCA) framework. The <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$l_{2,1}$</tex> norm is robust to outliers and exploits the low rankness of the background component. While the use of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$l_{1}$</tex> -norm enhances the sparsity in the foreground part. Moreover, Total Variation(TV) regularization technique helps in retaining the details about the edges. Experimental result shows the supremacy of the proposed method w.r.t. the state of the art methods in terms of computational complexity and F-measure score.
The Influence of AR Storybook on Preschool Children's Autonomous Reading Ability

At present, augmented reality (AR) technology is increasingly used in the field of education. At the same time, the input of early childhood education to infant language learning is also increasing. Therefore, more and more attention has been paid to the research on the combination of the two. And children&#x0027;s reading ability is a very important content in children&#x0027;s language learning. But there are few studies on augmented reality and young children&#x0027;s reading ability. This research designed and produced an AR story reading software, including original story content. Compared with traditional story reading, AR reading is more interesting and interactive. In the specific research on the relationship between augmented reality and children&#x0027;s autonomous reading ability. This research compared AR story reading with electronic story reading. The results show that by reading AR storybooks, children can have stronger learning motivation, better flow experience and better feedback on some aspects of academic performance.
Blockchain for EHR: an off-chain based approach

Electronic health records (EHR) are digital versions of the traditional paper-based medical records. Blockchain is a type of distributed database that can be updated and is shared across multiple computers. The existing EHRs suffer from data manipulation, delayed communication, and trustless cooperation in data collection, storage, and distribution, making it arduous to achieve the ACID principles- atomicity, consistency, integrity and durability. The project proposes a blockchain-based mobile app with an integrated wallet for performing transactions to store and retrieve data on the blockchain network. The project demonstrates the use of a secondary off-chain system using IPFS. Further, the project observes integration of role-based access control for the entities that interact with the contract having the hospital as the default admin. The project finds application at sectors requiring intense levels of data privacy and integrity like healthcare, finance, public records, etc.
iTutor: Promoting AI Guided Knowledge Interaction in Online Learning

Although online education can balance educational resources and promote educational equity, it lacks interaction between teachers and learners. AI-based tutor systems may ease the unbalanced distribution of teachers, yet studies rarely examine AI-guided knowledge interaction in online learning as a potential factor in online platforms. To address this research gap, this study examined the experience and critical requirements of knowledge interactive tutor systems. Based on survey experiences and findings, we construct an Intelligent Tutor system (iTutor), a conversational agent tool that uses a real-time stream to enhance the learning experience. Relying on the AI technology, iTutor can build dynamic interactive support for online users' autonomous learning, including guide support, resource support, conversational interactive support, evaluation support, and feedback support. We conduct experiments to evaluate the acceptance, factors, and impact of AI-driven assistant tools in online learning. Results demonstrate that iTutor can affect learners' satisfaction with promoting online interactive feedback. These results highlight the critical factors affecting learners' satisfaction and the design frameworks for improving AI-guided knowledge teaching interaction in online learning.
Video Scene Searching using Modified Bi-Modal Transformer

Most of the times, it is difficult to navigate through the videos having a long-running duration like movies to find a certain scene. If a user wants to start the video from a particular scene, it becomes a tedious task to manually search that scene. For finding any scene in a video, we propose a novel algorithm that takes the textual input of the scene description and finds the scene which best matches the given description. This algorithm utilizes the concept of video-captioning. A modified version of the Bi-modal Transformer is used in which the video gets pre-processed to identify various potential scenes and generate the scene descriptions from those respective scenes. These descriptions are stored in a list of dictionaries with their timestamps. Later when the user inputs the description of the scene which needs to be found, that user-given input is then compared with all the model-generated descriptions to find the best match and thus finding the scene. We introduce a new evaluation metric to check the performance of the model to identify various scenes present in the video. Experiments show that the modified Bi-modal transformer generates scene descriptions with less inference time without compromising on the accuracy of predicted scenes.