Reviewerly/exHarmony huggingface dataset:
Perception of Prospective Teachers to the Needs of ICT in Chemical Learning in the Age of Digital Transformation

The current era of digital transformation forces teachers to be skilled in learning activities, meaning that they must be able to collaborate between learning activities and the use of ICT devices.The purpose of this study is (1) to find out how the perception of prospective teachers to the understanding of digital literacy based on digital literacy index, (2) to find out the perception of prospective teachers to how the level of idealization of teacher ability in the use of ICT in chemical learning, and (3) to know the perception of prospective teachers to their own abilities in the use of ICT in chemical learning.The sample in this study is all the prospective students of chemical education study program which amounted to 88 zzrespondents.The method used in this study is a quantitative descriptive.Data collection is done through a questionnaire-shaped instrument.The results of the study for the assessment of the digital literacy index of prospective chemistry teachers are assessed from 8 dimensions, namely functional skill and beyond, creativity, collaboration, communication, the ability to find and select information, critical thinking and evaluation, cultural and social understanding, and e-safety.Entirety, the average percentage is 69,6%, with the category fair.Assessment of respondents' perception of how ideally the ability of teachers in the use of ICT in chemical learning is associated with the assessment of ICT capabilities owned by the respondents themselves.From the calculation of the questionnaire assessment, the ability that should be possessed by a teacher the majority of respondents answer that teachers must be proficient in using ICT devices in learning.While the perception of prospective teachers to their own ability to use ICT devices obtained results of 60,6% which fall into the category of average.
The Role of Ground Truth Annotation in Semantic Image Segmentation Performance for Autonomous Driving

The key to reaching full autonomous vehicles lies in the degree of robustness and generalization of the visual perception system for any prompt scenario during driving or under any weather condition. Regardless of all the advent in semantic segmentation and the numerous datasets existing, they still fail to perform well when the input images are unclear (fog, rain, snow, or nighttime), or small-scale. In this study, we will highlight the impact of choosing the adequate coarse ground truth annotation on achieving the best outcome possible out of the preparation and training of the dataset to increase the accuracy of the perception system in driverless cars. Our experiment has shown that indeed the accuracy values of semantic image segmentation using coarse ground truth annotation outperform the fine ground truth for the principal classes (car, road) which implies faster datasets preparation and tuning before pragmatic applications.
Fine-Tuning Pre-trained Vision Transformer Model for Anomaly Detection in Video Sequences

Detecting anomalous in video sequences is one of the most popular computer vision topics. It is considered a challenging task in video analysis due to its definition, which is subjective or context-dependent. Various deep learning models such as convolutional neural networks (CNNs) have been previously utilized for this purpose. This paper proposes a novel solution based on the state-of-the-art deep learning models called Vision Transformer, since it is a trendy topic nowadays and it is performance. We are going to fine-tune a pre-trained Vision Transformer model on the UCSD dataset, which enables the automatic classification of video frames (abnormal and normal objects). The evaluation of this model shows that it achieves a good Accuracy score.
MMM-GCN: Multi-Level Multi-Modal Graph Convolution Network for Video-Based Person Identification

Video-based multi-modal person identification has attracted rising research interest recently to address the inadequacies of single-modal identification in unconstrained scenes. Most existing methods model video-level and multi-modal-level information of target video respectively, which suffer from separation of different levels and insufficient information contained in a specific video. In this paper, we introduce extra neighbor-level information for the first time to enhance the informativeness of target video. Then a Multi-Level(neighbor-level, multi-modal-level, and video-level) and Multi-Modal GCN model is proposed, to capture correlation among different levels and achieve adaptive fusion in a unified model. Experiments on iQIYI-VID-2019 dataset show that MMM-GCN significantly outperforms current state-of-the-art methods, proving its superiority and effectiveness. Besides, we point out feature fusion is heavily polluted by noisy nodes that result in a suboptimal result. Further improvement could be explored on this basis to approach the performance upper bound of our paradigm.
Problems of ethical communication in computer-mediated communication

The problem associated with determining when interpersonal interaction of interlocutors in the format of computer-mediated communication leads to unethical verbal behavior and why this form of behavior is realized is analyzed. Based on the theoretical concept of B.N. Miroshnikov about the relationship between the Internet and society, it was not only found out why the interlocutors choose specific means of computer-mediated communication, but also the nature of the influence of this choice on the maintenance of interpersonal relationships and the ethical problems that could potentially arise in this case are traced. During the statistical analysis of the data obtained by the method of questionnaire survey, it was found that we can talk about external factors that influence the behavioral reactions of interlocutors in the course of communicative interaction. The choice of communication strategies aimed at maintaining interpersonal relationships with the addressee may be the result of the speaker’s increased awareness of his partner, the current communication environment, and the ability to adapt to this environment accordingly. Participants in virtual communication use a variety of methods to maintain communicative contact, depending on such factors as the psychological personality of the addressee. It is concluded that this factor is of particular importance in the course of maintaining fatal contact with an unfamiliar interlocutor in the format of computer-mediated communication.
Development of Integrated Blog-Based Teaching Materials of Dyestuffs Adsorption to Improve Students’ Literacy in Chemistry

Chemical literacy is an understanding of the nature of chemical particles, chemical reactions, chemical laws and theories, as well as general chemical applications in everyday life.In Indonesia, students' chemical literacy is still low that need further action to improve it.To boost the students' chemical literacy, an appropriate learning strategy is needed.The presentation of chemical theory with everyday life and current issues in the world of science can be the main attraction for students.The purpose of this research was to develop inquiry-based teaching materials that are made following the development of learning in the 4.0 industrial revolution era by utilizing blogs.This research was a research and development design.The subjects in this study were 30 students of class X of high school in Bengkulu City, Indonesia.The data on students' chemical literacy were obtained from the pre-test and post-test scores.Based on the data analysis conducted, students' chemical literacy increased with an average n-gain of 0.71 that is in the high category.Thus, the proposed chemical blog is suitable to improve the students' literacy that can be developed also for other subjects in the field of chemistry.
STN: Stochastic Triplet Neighboring Approach to Self-supervised Denoising from Limited Noisy Images

With the rapid development of artificial intelligence in recent years, deep learning has shown great potential in the field of image denoising. However, most of the work is based on supervised learning, and the lack of clean images in the real application will limit neural network training. For this reason, self-supervised learning in the absence of clean images is getting more and more attentions. Nevertheless, since both the source and target in self-supervised training are the limited noisy image itself, such denoising methods suffer from overfitting. To this end, a stochastic triplet neighboring approach, thereafter referred to as STN, is proposed in this paper. Given an input noisy image, the source fed to the STN is the downsized sub-image via 4-neighbor sampling, whereas the target in STN training is a stochastic combination from the two neighbored sub-images. Such a mechanism is actually the augmentation of training data, which leads to the relief of the overfitting problem. Extensive experimental results show that our proposed STN approach outperforms the state-of-the-art image denoising methods.
Visual Question Generation Under Multi-granularity Cross-Modal Interaction

Visual question generation (VQG) aims to ask human-like questions automatically from input images targeting on given answers. A key issue of VQG is performing effective cross-modal interaction, i.e., dynamically focus on answer-related regions during question. In this paper, we propose a novel framework based on multi-granularity cross-modal interaction for VQG containing both object-level and relation-level interaction. For object-level interaction, we leverage both semantic and visual features under a contrastive learning scenario. We further illustrate the importance of high-level relations (e.g., spatial, semantic) between regions and answers for generating deeper questions. Since such information were somewhat ignored by prior VQG studies, we propose relation-level interaction based on graph neural networks. We perform experiments on VQA2.0 and Visual7w datasets under automatic and human evaluations and our model outperforms all baseline models.
Rapid Artificial Diagnostic Test in Intelligent Teacher Assistant System (ITAS) to Identify Misconceptions on Kinematics

Errors in interpreting a concept can occur because students are still in the process of understanding.The experience gained through observation and reasoning has not been able to form complete knowledge, so students often experience errors in interpreting a concept.Therefore, errors in interpreting a concept that is not following the concept of science must be assessed as early as possible by the teacher.This research is a research development that produces a product using a four-level diagnostic test based on artificial intelligence.The purpose of this study was to see the development process and the diagnostic feasibility level of a four-level test using an Intelligent Teacher Assistant System (ITAS) based on the responsive website, the subject of developed Kinematics.This research was conducted at a senior high school in Tanjung Jabung Barat.Four validators of Physics Education lecturers carried out the feasibility test at Jambi University and one physics subject teacher at senior high school in Tanjung Jabung Barat.The result of the feasibility level of the media by media experts is 93.28% (category "very feasible").In contrast, the test results or user responses obtained a value of 80.43% (category "very feasible").Based on what was done, the development of a four-level test diagnostic using Matlab, Python Programming, Orange Data Mining, and a responsive website to identify high school student misconceptions on the subject of statistical fluids in total on the aspects of software engineering and visual communication can be categorized as"very feasible", so that it can be used to support the learning process of students in class.
Weakly-Supervised Temporal Action Localization with Regional Similarity Consistency

The weakly-supervised temporal action localization task aims to train a model that can accurately locate each action instance in the video using only video-level class labels. The existing methods take into account the information of different modalities (primarily RGB and Flow), and present numerous multi-modal complementary methods. RGB features are obtained by calculating appearance information, which are easy to be disrupted by the background. On the contrary, Flow features are obtained by calculating motion information, which are usually less disrupted by the background. Based on this phenomenon, we propose a Regional Similarity Consistency (RSC) constraint between these two modalities to suppress the disturbance of background in RGB features. Specifically, we calculate the regional similarity matrices of RGB and Flow features, and impose the consistency constraint through $$L_2$$ loss. To verify the effectiveness of our method, we integrate the proposed RSC constraint into three recent methods. The comprehensive experimental results show that the proposed RSC constraint can boost the performance of these methods, and achieve the state-of-the-art results on the widely-used THUMOS14 and ActivityNet1.2 datasets.
A Distributed Architecture for Visual Data Processing in Visual Internet of Things (V-IoT)

As wireless sensors and computer vision have advanced, Visual Internet of Things (V-IoT) has emerged as a form of IoT that can offer different levels of intelligence based on its application and algorithms. It is important to note, however, that V-IoT nodes collect a substantial amount of visual data that must be transmitted to the network for further analysis. The purpose of our paper is to present a new architecture that will reduce the information flow from the V-IoT nodes to the base station and balance the traffic load on the network. Our architecture is based on the Message Queue Telemetry Transport (MQTT) protocol, and it distributes the data processing across the network levels taking into account the limited resources of the V-IoT nodes. To evaluate our architecture, we built a V-IoT node prototype using a Raspberry Pi board and then used it as part of an experiment to recognize and track faces. The results of the prototype implementation indicate that our proposal is effective.
Voice Assisted System for Garbage Sorting

With garbage sorting throughout China, provinces and cities have put forward their waste separation systems and measures. Some provinces have introduced simple sorting and labeling of different bins, but places such as Shanghai have introduced more detailed garbage sorting, making it challenging to sort accurately. Some research institutions have designed bins that can be automatically sorted by scanning, but there are problems of high investment and low cost-effectiveness. In order to make effective use of the existing equipment and guide people to realize the precision of garbage sorting in a lower investment and more straightforward way, this paper implements a garbage sorting auxiliary system based on speech recognition. The system takes a WeChat applet as the application form, adopts the open speech processing function, completes the question and answers an auxiliary query to the standard garbage classification, reduces the difficulty which people carry out the garbage classification, enhances the garbage classification efficiency of the effect.
Analysis of Convolutional Neural Network Image Classifiers in a Rotationally Symmetric Model

Convolutional neural network image classifiers are defined and the rate of convergence of the misclassification risk of the estimates towards the optimal misclassification risk is analyzed. Here we consider images as random variables with values in some functional space, where we only observe discrete samples as function values on some finite grid. Under suitable structural and smoothness assumptions on the functional a posteriori probability, which includes some kind of symmetry against rotation of subparts of the input image, it is shown that least squares plug-in classifiers based on convolutional neural networks are able to circumvent the curse of dimensionality in binary image classification if we neglect a resolution-dependent error term. The finite sample size behavior of the classifier is analyzed by applying it to simulated and real data.
Multi-scale Object Detection in Remote Sensing Images Based on Dual Attention Mechanism

Aiming at the detection difficulties in optical remote sensing images, such as the large range of target scale variation and the diversification of target directions, a remote sensing image target detection algorithm based on dual attention mechanism and multi-scale feature fusion is proposed. On the basis of the lightweight network Mobilenet v3, a dual attention mechanism is added, which brings obvious performance gains to the detection model while adding a few parameters. Through multi-scale feature fusion, the receptive field is enlarged, the fusion features of targets of different scales are obtained, and the multi-scale target detection effect is effectively improved. Anchor box detection in any direction is performed for multi-directional targets by introducing a rotating frame. Experiments show that the proposed method has higher detection accuracy and faster detection speed than the current popular methods.
An Overview on Machine Learning Approach to Secure the Blockchain

As a part of artificial intelligence, machine learning has been widely used in recent research to solve problems such as classification, clustering, and regression, using different approaches. The fact that blockchain technology has made far-reaching changes in the world will be displayed in this article, which represents a benchmark for research on these technologies together. As a set of security issues that threaten blockchain technology (BT) have been listed, some of them have already been solved through machine learning with its different approaches, whether it is supervised learning, unsupervised learning, or reinforcement learning, and that gives us an idea of the different points covered by the enumerated technologies.
SNDAE: Self-Normalizing Deep AutoEncoder for Recommendation

Users and consumers on the web are inundated with massive marketing and information notices while they crave directly personalized and precise content from online businesses. Bearing in mind the circumstances of the pandemic’s impact that weighted in to help online sales growth, it is self-evident that companies should enhance employed recommendation engines. Engaging with relevant recommendations proves that users’ needs are understood, and their preferences are genuinely catered for, leading to a superlative and satisfactory experience. This paper proposes a novel Self-Normalizing Deep AutoEncoder (SNDAE) to overcome sparsity issues of recommender systems. The proposed SNDAE first leverages a greedy layer-by-layer training procedure to learn a deep generative model for efficient feature representation. Furthermore, SNDAE exploits self-normalization properties, yielding enhanced convergence and improved regularization performance. Experimental results demonstrate that the proposed SNDAE outperforms state-of-the-art approaches in sparse rating prediction.
Classification Model for Easily Confused Breast Ultrasound Image Based on Shape and Texture Features

With the development of computer vision, in addition to natural images, medical images can also carry out edge extraction, tissue segmentation, cancer location and classification diagnosis through deep learning model to improve the diagnosis efficiency. In this paper, for the easily confused and difficult to classify samples, a breast ultrasound image classification model based on shape and texture features and improved loss function is designed. A new loss function combining lesion features is proposed. The loss function realizes a wider decision boundary between different categories in the classification task. While expanding the distance between classes, the distance within classes is reduced, and distance between lesion feature vectors of samples is included in the calculation of loss function with a certain weight. On the basis of denoising the ultrasonic image and extracting the edge information by the improved Mask R-CNN neural network, the classification model is supervised by MT-LMCL loss function proposed to achieve good training and verification effect on large-scale multi-channel public data set, and evaluation indexes are improved by 6% - 13% compared with the unmodified model.
Leadership Practices among Private Higher Education Institutions: Basis for Transformational Leadership Model

Leadership is the foundation and basis of an organization's success. Its context has been melded in Private Higher Education Institutions by the shifts in technological modes of teaching and learning and its response to the demand of the new generation and competition. The purpose of this research is to identify leadership practices and their effectiveness that will help private higher education institutions achieve institutional goals and vision, manage their operations, maintain good relationships, and lead with a high-quality education. The study used a quantitative research design to determine and assess the effectiveness of leadership practices among private education institutions in the 2nd district of Albay, likewise using descriptive statistics to interpret the study findings. The analyzed data revealed that the respondents 'agree' that their institutional leaders have demonstrated their management skills in terms of academic leadership (3.43), human resources (3.29), financial resources (3. 15), and facilities and equipment (3.09); thus, their leadership skills along intrapersonal skills (3.34), interpersonal skills (3.3), and innovation skills (3.18). However, there are problems encountered by the respondents along with the leadership skills of their leaders. Therefore, developing a transformational model is necessary to address further development and ensure their long-term success and sustainability.
Super-Resolution Reconstruction of License Plate Image Based on ESRGAN

This paper presents an algorithm for super-resolution reconstruction of license plate images in an intelligent transportation system. It is based on ESRGAN. By replacing the discriminator used to evaluate the difference between the original image and the generated image, a more accurate reconstruction of the license plate details is realized. In this paper, we use mixture of MSE and minimized VGG as the loss function of the algorithm, which makes generator pay more attention to the details such as edges in the process of generating images. Compared with several mainstream SR algorithms, using PSNR and SSIM indexes to evaluate the results, the proposed algorithm has higher accuracy in the field of fuzzy license plate reconstruction, and the reconstruction time is obviously optimized.
Face Attribute Recognition Based on Multistage Adaptive Task Convolution Neural Network

The face attribute is a description of various features of a face image. Since there is more than one face attribute, it belonged to multi-task recognition, and the complexity of extracting attribute features varied, so the task of face attribute recognition was a challenge. The difficulties of face attribute recognition tasks would be summarized into two points: First, there were often many face attributes in the dataset, and the number of samples of positive and negative attributes often differed by several times, or even dozens of times, which would affect the recognition performance of the model for some attributes Second, in terms of a single attribute, the learning difficulty of face attributes varies, and creating deeper convolutional neural networks was often prone to overfitting. In response to the above two problems, we proposed a data augmentation method to alleviate the data imbalance, and proposed a multistage adaptive task convolutional neural network (MAT-CNN). Experiments show that the MAT-CNN achieves higher accuracy.