Reviewerly/exHarmony huggingface dataset:
Multimodal Sentiment Analysis Platform and Application

This chapter introduces the need for an integrated platform specifically designed for Multimodal Sentiment Analysis (MSA) tasks. To address this gap, the M-SENA platform is introduced as an open-source tool aimed at facilitating advanced research in MSA. The platform is built on the principles of flexibility, reliability, and intuitive usability, offering researchers and practitioners a range of toolkits, benchmarks, and demonstrations to drive innovation in the field. This chapter provides an overview of the M-SENA platform’s architecture and highlights the key features of its core modules. It also presents reliable baseline results of different modality features and MSA benchmarks to demonstrate the platform’s effectiveness. The evaluation and analysis tools provided by M-SENA are utilized to assess model performance, visualize intermediate representations, conduct instance tests, and evaluate model generalization ability. Furthermore, the preface acknowledges the importance of model robustness against modality noise in real-world applications. The introduction of Robust-MSA, an interactive platform, is described to help researchers visualize the impact of modality noise on models and explore defense methods to improve robustness. The goal of Robust-MSA is to provide researchers with a better understanding of model performance with imperfect real-world data and identify areas for improvement.
Overview

This chapter introduces the background and significance of sentiment analysis and multimodal machine learning. With the rapid development of online social media, various forms of information have covered the entire Internet, and people express their emotions in diverse ways. Therefore, the concept of multimodality emerges, providing different representations for each modality of information. At the same time, the rich evaluation information in multimodal forms enriches the evaluation of products, works, and other objects on various platforms, enabling businesses and users to access the desired information conveniently and accurately. This chapter also discusses the advancements in deep learning in fields such as natural language processing and computer vision, highlighting the importance of multitask learning in multimodal sentiment analysis. Multitask learning improves the robustness and learning performance of models by sharing model parameters and combining the characteristics of different tasks. This chapter aims to provide a systematic overview of recent research methods in multimodal machine learning, covering the perspectives of multimodal representation learning and multimodal fusion.
Implementation of Failover on Mikrotik Router Using Check Gateway and Distance Parameters

At the end of 2019 the world faced the Covid 19 Virus pandemic which also changed activities in daily life. All activities are carried out at home (Work From Home) including meetings and exchanging information via digital. Problems at this time in the office of PT. PLN often loses internet connection, at PT. PLN ULTG Tarahan also uses 2 ISP (Internet Service Provider) lines Indihome and Icon Plus in the server room, there is no system that can regulate the two ISP networks so that they can switch connection lines if there is a disturbance on the main ISP line so that there is a possibility of interruption during activities using the internet network. Failover is a technique for adding connections in Mikrotik, where if one of the internet connections dies (main connection), the other connection (backup connection) will replace the main connection line by defining a check gateway and differentiating the distance value on each routing rule. The results of this study will be able to guarantee the availability of internet at the PT. PLN ULTG Tarahan so as to increase employee productivity.
English Teaching Model Based on Mind Map and Cultivation of Innovative Thinking in the Age of Internet of Things

The current era is a multimedia network era. Under the current situation of the big data era, the use of the Internet of Things (IoT) in life is countless. Due to the existence of the IoT, the English Teaching Model is being updated more and more quickly. A series of teaching reforms have made the quality of English teaching more and more high. The purpose of English teaching is to cultivate students with comprehensive language application ability. The traditional English Teaching Model cannot adapt to the rapid development of the IoT era. The application of mind map can well eliminate the drawbacks of the traditional English Teaching Model, and provide students with an open and free imagination space. Based on the influence of English Teaching Model in the IoT era, the introduction of mind map teaching can improve students' logical thinking ability, and inspire imagination and innovative thinking. This paper studied the English Teaching Model based on mind mapping and the cultivation of innovative thinking in the IoT era, and explored the relationship between mind mapping input and academic performance. The experiment proved that the application of mind mapping in English Teaching Model would bring more efficient classroom for teachers and students. In view of the use of mind mapping technology, a questionnaire survey was conducted on 52 students. The survey results showed that 43 students believed that the introduction of mind mapping had a significant role in promoting their academic performance. At the same time, in order to cultivate students' innovative thinking in English, this paper set up a feasible plan to use mind mapping to promote innovative thinking.
Analysis on the Service Capacity of University Canteens under the Epidemic Prevention Based on Queue Theory

During the epidemic period, the good service capacity of university canteens is of great significance for formulating scientific epidemic prevention and control measures and solving the phenomenon of student dining congestion. In order to solve the congestion phenomenon, during the epidemic prevention and control period, our school implemented the staggered peak dining, that is, according to different grades and different teaching buildings, the class is set at different times to reduce the queuing phenomenon. Based on this background, questionnaire survey, theoretical research and mathematical statistics are used to conduct preliminary exploration and analysis of the data, and the service time, dining time and service window of the school canteen are investigated. Based on the queuing theory, MATLAB simulation is carried out, and the canteen service system under the two modes is evaluated and analyzed by using the simulation model. Some suggestions for reasonable window opening under two modes are given.
Analysis of The Limits of Criminal Acts of Gambling in The System of Acquiring Virtual Assets Through Loot Crates in Virtual Games

The development of globalization and technological advances influences the progress of the creative industry, particularly in the virtual gaming sector, commonly known as online games. As time progresses, the online gaming industry is rapidly evolving, both in terms of its audience and the entrepreneurs involved. This sector enables easy selling of various items related to online games. With the increasing number of enthusiasts due to the changing times, gaming companies are introducing innovations to captivate players' interest.One such innovation is the introduction of a new category of virtual assets in online games, commonly referred to as Loot Crates. These are virtual assets in online games that can be purchased using real currency or other payment methods, such as in-game currency. The Loot Crate system involves a mechanism that provides virtual assets randomly, incorporating an element of luck to obtain desired assets. However, the random nature of Loot Crates raises concerns about meeting the elements of gambling offenses.The Loot Crate system, implemented by various producers, operates as an electronic transaction and is subject to the Electronic Information and Transactions Law No. 11 of 2008, in conjunction with Law No. 19 of 2016. According to these laws, one of the prohibited actions, as stated in Article 27, Paragraph 2, is "Any person intentionally and without right distributes and/or transmits and/or makes electronically stored information and/or electronic documents that contain gambling content."Please note that the translation may not capture the full legal nuances, and legal advice may be necessary for a precise interpretation.
The Influence of the Audio-Lingual Method in Improving the Students' Vocabulary at SMA Negeri 21 Makassar

This study aims to find out the influence of the audio-lingual method (AML) in learning student vocabulary. The study uses a quantitative methodology to assess students' proficiency with the audio-lingual method (AML) in vocabulary learning. The audio-lingual Method (AML) arose after the aural-oral approach was devised in 1939 at the first English language Institute at the University of Michigan, United States by Charles Fries. The population of this study is first-grade students of SMA Negeri 21 Makassar 2023 There were five classes consisted 33 students per class. Moreover, The collecting data in this research are vocabulary tests and questionnaires. A vocabulary test is the instrument to find out whether dialogue can improve their vocabulary, while a questionnaire is an instrument for knowing the students' responses. The result of this study is Using the audio-lingual Method (ALM), students' pre-test and post-test scores on their vocabulary tests increased. The quantity of data, where 40% of the data showed that the student's scores improved as a result of using the audio-lingual method (ALM) in their English learning. In particular, with the growing influence of technology, vocabulary learning should be easier to be creative with, as there are many learning models that teachers can be inspired by to make their classes suit students' learning preferences, especially in this era.
METHODOLOGY OF HISTORICAL AND CULTURAL EDUCATION: PERSONAL AND ACTIVE APPROACHES

The article examines the features of the use of personal and activity approaches in the process of professional training of future teachers of history, art culture and art. The authors analyse the use of these approaches in the theory and practice of the educational process of the State Higher Educational Institution «Donbass State Pedagogical University». Methodological approaches, both personal and activity, have been implemented at the Faculty of Philology, Economics and Humanities and the Faculty of Primary, Technological and Vocational Education. The greatest attention is paid to the teaching of the such educational components as «History of Ukraine», «History of Ukrainian culture», «Professional Ethics and Aesthetics», «Electronic Content of Teaching Art Disciplines» with the use of personal and activity approaches.
Design of an Automated System to Optimize the Capture of Solar Energy in Photovoltaic Panels

This article presents the design of an automated solar tracker system to increase the efficiency of low-cost photovoltaic panels. The proposed meetings follow the sun's movement across the sky at any time of the day using two axes, which perform an azimuthal activity and estimate the solar height. The axes are controlled by a program that uses an astronomical model implemented within a Raspberry Pi module. The program receives the date and time data through the Wi-Fi network and the geographic coordinates through a GPS module for updating the position of the solar tracker in real time. The proposed design can be applied to most commercial photovoltaic panels with maximum measurements of 2m x 1.05m x 4 cm.
Implementation of Flipped Learning in Teaching Syntax Course from Digital Pedagogy Perspective: A Case Study of King Khalid University’s Students

This study investigated the implementation of technology-enhanced models of flipped learning in teaching syntax courses to Saudi students majoring in English language bachelor’s degree. The study used a quasi-experimental design, including a control group and an experimental group. Additionally, the researchers designed a questionnaire to evaluate the outcome of using flipped learning in accomplishing the course objectives and students’ comprehension of the course. The findings display that the implementation of flipped learning in teaching syntax is more effective in terms of comprehension of the KKU specified material course properly and broaden syntax knowledge through searching and discussion aspects in terms of class discussion and presentational activities. Moreover, students reveal positive mental models, collaboration, positive interaction, and enjoyment of flipped learning. Hence, the students become researchers, and they change the class to a discussion platform and debating room. The study paper concludes that the integration of technology in teaching English courses should be implemented at KKU, and English instructors should be trained to use flipped learning in teaching English courses from a digital transformative perspective, copying with KKU’s orientation.
Image-Based Hand Tools and Accessories Recognition by ResNet50

The objective of this research is to create a computer system which can recognize various kinds of hand-tools by using only a single image. The developed system is called “Hand-tool and accessory image recognition system or (HTAIRS)”. The system consists of 4 main modules, namely: 1) dataset training, 2) image acquisition, 3) image recognition, and 4) result presentation modules. The system employs the convolutional neural networks (CNN) called “ResNet50”, which is a toolbox in MATLAB software. The developed system creates its own dataset called “Hand Tools Dataset”, which consists of 165 different video clips in 110 hand tool categories and 600 images each. The HTAIRS separates 500 images for training dataset and 100 images for evaluating the system. The accuracy of the training system is 99.30% and the accuracy that of the evaluating is also 99.30%. The system’s average access time are is 0.8549 Seconds per image.
Blood Test Robot

Abstract: In the realm of medical diagnostics, blood tests play a vital part, serving as an abecedarian approach for complaint analysis. still, the reliance on mortal prosecution introduces an implicit source of error, arising from indeed the most minute miscalculations. In order to address this challenge and enhance the delicacy and responsibility of blood testing procedures, a new approach is being pursued. This action centers on the development of a sophisticated robotic arm acclimatized for the precise prosecution of blood tests. By integrating advanced robotization and robotics technologies, the ideal is to significantly dock mortal involvement in the testing process, thereby mollifying the essential pitfalls associated with mortal error. The envisaged robotic system is poised to bring about a transformative shift in blood testing practices, icing an advanced degree of perfection and trust ability in test issues. The proposed robotic arm aims to replicate the intricate movements and dexterity of mortal technicians while circumventing the implicit risks introduced by mortal fallibility. Through scrupulous engineering and slice- edge software, the robotic arm will seamlessly maneuver the necessary outfit, conduct blood tests, and induce accurate results. This invention not only minimizes the liability of crimes but also expedites the testing process, leading to bettered effectiveness in healthcare settings. By employing the eventuality of robotics, this design strives to usher in a new period of blood testing, characterized by enhanced delicacy, reduced mortal intervention, and heightened responsibility of results. Eventually, this pursuit stands to revise the geography of individual drug, reaffirming the commitment to delivering precise and dependable healthcare results
Analisis dan Pengembangan Quality of Experience Website E-Commerce Menggunakan GTMetrix

The website is something that is familiar to hear at this time, where the world of technology, especially the internet, is developing rapidly. The development of the internet, in this case the website, is not only felt and used by business people, but also by the wider community, especially small businesses. The analytical method in this study uses the Automated usability testing tools approach which is used to test website performance. The performance is based on several general parameters, namely: performance, no of requests. Speed load time, page size. Parameter testing is measured using several tools: Pingdom, GTMetrix, Website Grader, Page Speed Insight, Site Analyzer and other Web Page Analyzer tools, performance is at a percentage of 30-38% and structure is at a percentage of 65-76%. The lowest access speed is 6.3 s and the highest is 29.5 s. Based on performance tests it is known that the system runs slowly and on average takes a long time, because image optimization means optimizing image size, so that image file sizes can be reduced. Measuring quality of experience (QoE) parameters on websites is very important to increase user satisfaction and reduce failure rate in the use of the website. Several QoE parameters that can be measured include load time, response speed, display quality, ease of use, user interface responsiveness, availability, reliability, and social interaction.
An Augmented Learning Approach for Multiple Data Streams Under Concept Drift

Multiple data streams learning attracts more and more attention recently. Different from learning a single data stream, the uncertain and complex occurrence of concept drift in multiple data streams, bring challenges in real-time learning task. To address this issue, this paper proposed a method called time-warping-based concept drift learning method (TW-CDM) for dealing with multiple data streams. First, a time-warping-based drift identification process is given to recognize the drift region. Second, an augmented learning process is developed by crossly using the located region data. Finally, a selectively augmented learning process is given to reduce the influence of different drift severity. The proposed method is evaluated on both synthetic and real-world datasets, and compared with benchmark methods. The experiment results show the efficiency of the proposed method.
Superpixel Attack

Deep learning models are used in safety-critical tasks such as automated driving and face recognition. However, small perturbations in the model input can significantly change the predictions. Adversarial attacks are used to identify small perturbations that can lead to misclassifications. More powerful black-box adversarial attacks are required to develop more effective defenses. A promising approach to black-box adversarial attacks is to repeat the process of extracting a specific image area and changing the perturbations added to it. Existing attacks adopt simple rectangles as the areas where perturbations are changed in a single iteration. We propose applying superpixels instead, which achieve a good balance between color variance and compactness. We also propose a new search method, versatile search, and a novel attack method, Superpixel Attack, which applies superpixels and performs versatile search. Superpixel Attack improves attack success rates by an average of 2.10% compared with existing attacks. Most models used in this study are robust against adversarial attacks, and this improvement is significant for black-box adversarial attacks. The code is available at https://github.com/oe1307/SuperpixelAttack.git .
A Sampling Method for Performance Predictor Based on Contrastive Learning

Performance predictors are commonly dedicated to mitigating the substantial resource consumption of neural architecture search. Nevertheless, existing performance predictors are typically constructed based on the randomly sampled training data. Such a sampling method will not only lead to unnecessary computation budget caused by the sampled similar architectures, but also induce performance deterioration resulting from the poor spanning of search space. In this paper, we propose a contrastive learning-based sampling method to address the aforementioned issues. Specifically, we first encode the architectures as directed acyclic graphs, based on which a large number of architectures are augmented to learn invariant knowledge of architectures. After that, we maximize agreement based on augmented architectures to express similar architectures to analogous representations. Consequently, representative architectures are selected through clustering similar architectures to improve the spanning of the search space. We conduct extensive experiments on NAS-Bench-101 and NAS-Bench-201. The experimental results show that the proposed method can improve the predictive ability of performance predictors compared with the random sampling-based ones and can help search superior architectures when integrating with neural architecture search. In addition, an ablation study shows the effectiveness of contrastive learning and the clustering method used in the proposed sampling method.
Non-stationarity Detection in Model-Free Reinforcement Learning via Value Function Monitoring

The remarkable success achieved by Reinforcement learning (RL) in recent years is mostly confined to stationary environments. In realistic settings, RL agents can encounter non-stationarity when the environmental dynamics change over time. Detecting when this change occurs is crucial for activating adaptation mechanisms at the right time. Existing research on change detection mostly relies on model-based techniques which are challenging for tasks with large state and action spaces. In this paper, we propose a model-free, low-cost approach based on value functions (V or Q) for detecting non-stationarity. The proposed approach calculates the change in the value function ( $$\varDelta V$$ or $$\varDelta Q$$ ) and monitors the distribution of this change over time. Statistical hypothesis testing is used to detect if the distribution of $$\varDelta V$$ or $$\varDelta Q$$ changes significantly over time, reflecting non-stationarity. We evaluate the proposed approach in three benchmark RL environments and show that it can successfully detect non-stationarity when changes in the environmental dynamics are introduced at different magnitudes and speeds. Our experiments also show that changes in $$\varDelta V$$ or $$\varDelta Q$$ can be used for context identification leading to a classification accuracy of up to 88%.
LiDAR Inpainting of UAV Based 3D Point Cloud Using Supervised Learning

Unmanned Aerial Vehicles (UAV) can quickly scan unknown environments to support a wide range of operations from intelligence gathering to search and rescue. LiDAR point clouds can give a detailed and accurate 3D representation of such unknown environments. However, LiDAR point clouds are often sparse and miss important information due to occlusions and limited sensor resolution. Several studies used inpainting techniques on LiDAR point clouds to complete the missing regions. However, these studies have three main limitations that hinder their use in UAV-based environment 3D reconstruction. First, existing studies focused only on synthetic data. Second, while the point clouds obtained from a UAV flying at moderate to high speeds can be severely distorted, none of the existing studies applied inpainting to UAV-based LiDAR point clouds. Third, all existing techniques considered inpainting isolated objects and did not generalise to inpainting complete environments. This paper aims to address these gaps by proposing an algorithm for inpainting point clouds of complete 3D environments obtained from a UAV. We use a supervised learning encoder-decoder model for point cloud inpainting and environment reconstruction. We tested the proposed approach for different LiDAR parameters and different environmental settings. The results demonstrate the ability of the system to inpaint the objects with a minimum average Chamfer Distance (CD) loss of 0.028 at a UAV speed of 5 ms $$^{-1}$$ . We present the results of the 3D reconstruction for a few test environments.