Reviewerly/exHarmony huggingface dataset:
Dynamic Auxiliary Soft Labels for decoupled learning

The long-tailed distribution in the dataset is one of the major challenges of deep learning. Convolutional Neural Networks have poor performance in identifying classes with only a few samples. For this problem, it has been proved that separating the feature learning stage and the classifier learning stage improves the performance of models effectively, which is called decoupled learning. We use soft labels to improve the performance of the decoupled learning framework by proposing a Dynamic Auxiliary Soft Labels (DaSL) method. Specifically, we design a dedicated auxiliary network to generate auxiliary soft labels for the two different training stages. In the feature learning stage, it helps to learn features with smaller variance within the class, and in the classifier learning stage it helps to alleviate the overconfidence of the model prediction. We also introduce a feature-level distillation method for the feature learning, and improve the learning of general features through multi-scale feature fusion. We conduct extensive experiments on three long-tailed recognition benchmark datasets to demonstrate the effectiveness of our DaSL.
Gesture Recognition-based AI Virtual Mouse

Abstract: An amazing invention in Computer Technology is the mouse. Nowadays, A Bluetooth mouse or wireless mouse still has some limitations as it requires a battery for power and a dongle to connect to a PC. This issue may be solved in the proposed gesture-based AI virtual mouse by capturing hand motions and revealing hand tips with a webcam or integrated camera, as gestures are a powerful means of communication between people. Based on hand gestures, the computer can be almost controlled and can perform right- clicking, left-clicking, scrolling and computer cursor functions without using the physical mouse. Therefore, the proposed system will help avoid the dissemination of COVID19 by hindering the deadly intervention and confidence of bias in the control of the computer. Keywords: AI, Virtual Mouse, OpenCV, Computer Vision, Mediapipe
TdmTracker: Multi-Object Tracker Guided by Trajectory Distribution Map

With the great progress of object detection, some detection-based multiple object tracking (MOT) paradigms begin to emerge, including tracking-by-detection, joint detection and tracking, and attention mechanism-based MOT. Due to the separately executed detection, embedding, and data association, tracking-by-detection-based methods are much less efficient than other end-to-end MOT methods. Therefore, recent works are devoted to integrating these separate processes into an end-to-end paradigm. Some of the transformer-based end-to-end methods introducing track queries to detect targets have achieved good results. Self-attention and track query of these methods has given us some inspiration. Moreover, we adopt optimized class query instead of static learned object query to detect new-coming objects of target category. In this work, we present a novel anchor-free attention mechanism-based end-to-end model TdmTracker, where we propose a trajectory distribution map to guide position prediction, and introduce an adaptive query embedding set and query-key attention mechanism to detect tracked objects in the current frame. The experimental results on MOT17 dataset show that the TdmTracker achieves a good speed-accuracy trade-off compared with other state-of-the-arts.
Framework for Analyzing Netizen Opinions on BPJS Using Sentiment Analysis and Social Network Analysis (SNA)

The Social Security Administrative Body is a legal entity established to administer social security programs. News about BPJS policies is often found online and social media that has received responses from netizens as a form of public opinion on the policy. One of them is the opinion of netizens on social media Twitter. Ideas can be positive, neutral, or negative. These opinions are processed using the Support Vector Machine (SVM) method, in some SVM studies still getting unsatisfactory results, with rates below 60%. For this reason, it is necessary to have feature selection or a combination with the other methods to obtain higher accuracy. To see the actors who influence the opinion of netizens on the topic of BPJS, the Social Network Analysis (SNA) method is used. Based on the SVM Method's test results, the best accuracy results are obtained in combining the SVM Method with Adaboost, with an accuracy rate of 92%. Compared to the pure SVM method by 91%, the Combination of SVM Particle Swarm Optimization (PSO) by 87% and SVM using Feature Selection Genetic Algorithm (GA) by 86%.
Phase-Matching Continuous-Variable Measurement-Device-Independent Quantum Key Distribution

Continuous-variable measurement-device-independent quantum key distribution (CV-MDI-QKD) allows remote parties to share information-theoretical secure keys while defending all the side-channel attacks on measurement devices. However, the secure transmission distance and the secret key rate are quite limited due to the high untrusted equivalent excess noise in the Gaussian modulation. More particularly, extremely high-efficiency homodyne detections are required for even non-zero secure transmission distances, which directly restrict its practical realization. Here, we propose a CV-MDI-QKD protocol by encoding the key information into matched discrete phases of two groups of coherent states, which decreases the required detection efficiency for ideally asymmetric cases, and makes it possible to practically achieve secure key distribution with current low-efficiency homodyne detections. Besides, a proof-of-principle experiment with a locally generated oscillator is implemented, which, for the first time, demonstrates the realizability of CV-MDI-QKD using all fiber-based devices. The discrete-modulated phase-matching method provides an alternative direction of an applicable quantum key distribution with practical security.
Unsupervised Anomaly Detection and Segmentation on Dirty Datasets

Industrial quality control is an important task. Most of the existing vision-based unsupervised industrial anomaly detection and segmentation methods require that the training set only consists of normal samples, which is difficult to ensure in practice. This paper proposes an unsupervised framework to solve the industrial anomaly detection and segmentation problem when the training set contains anomaly samples. Our framework uses a model pretrained on ImageNet as a feature extractor to extract patch-level features. After that, we propose a trimming method to estimate a robust Gaussian distribution based on the patch features at each position. Then, with an iterative filtering process, we can iteratively filter out the anomaly samples in the training set and re-estimate the Gaussian distribution at each position. In the prediction phase, the Mahalanobis distance between a patch feature vector and the center of the Gaussian distribution at the corresponding position is used as the anomaly score of this patch. The subsequent anomaly region segmentation is performed based on the patch anomaly score. We tested the proposed method on three datasets containing the anomaly samples and obtained state-of-the-art performance.
Facial recognition as a tool to identify Roman emperors: towards a new methodology

Abstract Portraits of Roman emperors are traditionally recognised by their unique coiffure patterns, a method that runs the risk of ignoring portraits that do not cohere to the standardised image of the emperor. This article investigates whether it is possible to recognise and distinguish emperors using the facial features of their portraits. By using a technique called transfer learning, it utilises existing deep-learning facial recognition models, augmented with images of Roman imperial portraits, to provide a new empirical foothold in the debate of Roman emperor recognition. The results of the experiments demonstrate that by only a limited amount of training, such a so-called “pre-trained” model (i.e., InceptionResnet-V1) is able to correctly classify most images in the dataset of Roman emperors. As such, this article has made a first step towards applying facial recognition models to the study of ancient imperial portraiture.
The Effectiveness between Using CALL with Hot Potatoes and MALL with Quizizz for Vocabulary Enhancement at MAN 2 Gresik

This study has the purpose to know which are more effective used weather using Hot Potatoes for CALL(computer-assisted language learning) and Quizizz for MALL(mobile-assisted language learning) to improve vocabulary enhancement in a learning among 11 grade students in Gresik, Indonesia especially at MAN 2 Gresik. this study was used a quantitative research experiment with quasi experiment design. This research experiment was used a sample size of 49 students of 11 grade. The students were divided into two groups that were picked used purposive sampling technique or determine the sample with used a certain consideration. The two group were 11 MIPA 1 taught with CALL media and 11 MIPA 3 taught with MALL media. Both group did pre-test treatment and post-test in the same time, also the got same duration and material when did treatment. The finding shown revealed that there is a significant difference in vocabulary enhancement between students who were taught using CALL and MALL. Then, after the researcher calculate about the effect size, the result shown the data was 0.3067, which means that there is a medium effect.
Study of Video Conferencing Software Risks and Mitigation Strategies

Due to the recent pandemic, video conferencing platforms – once niche products aimed at limited communities have become a pervasive way of conducting business and sustaining social connections on a global scale. This project explored cybersecurity vulnerabilities and risks faced by these platforms – their data, hardware, and the information exchanged during virtual meetings – and explains some ways these issues can be mitigated. Published research was compiled and analyzed to uncover general risks, vulnerabilities, and security measures. Then, three popular platforms – Zoom, Skype and GoToMeeting were subjected to closer scrutiny. Findings show that platform vendors, business organizations, education institutions, and end users all bear responsibility to train themselves and their constituents on specific cybersecurity steps to enhance video conferencing security. Targeted recommendations are shared, along with some opportunities to build upon this research in the future.
Collection of 2429 constrained headshots of 277 volunteers for deep learning

Deep learning has rapidly been filtrating many aspects of human lives. In particular, image recognition by convolutional neural networks has inspired numerous studies in this area. Hardware and software technologies as well as large quantities of data have contributed to the drastic development of the field. However, the application of deep learning is often hindered by the need for big data and the laborious manual annotation thereof. To experience deep learning using the data compiled by us, we collected 2429 constrained headshot images of 277 volunteers. The collection of face photographs is challenging in terms of protecting personal information; we therefore established an online procedure in which both the informed consent and image data could be obtained. We did not collect personal information, but issued agreement numbers to deal with withdrawal requests. Gender and smile labels were manually and subjectively annotated only from the appearances, and final labels were determined by majority among our team members. Rotated, trimmed, resolution-reduced, decolorized, and matrix-formed data were allowed to be publicly released. Moreover, simplified feature vectors for data sciences were released. We performed gender and smile recognition by building convolutional neural networks based on the Inception V3 model with pre-trained ImageNet data to demonstrate the usefulness of our dataset.
Devising a competence method to build information spaces for executors of educational projects in a dynamic environment

This paper describes the conceptual apparatus for the formation of information spaces of executors of educational projects in a dynamic environment. A multiple model of identification of the executor of an educational project was developed, taking into consideration the competence approach and dynamics of the components of the information space. To assess the competencies of the executors of educational projects, three components are calculated: the grade of the executor’s knowledge, the grade of the executor’s performance, the grade of the executor’s personal qualities. Based on the obtained grades, generalized assessments of the level of competence of each executor are formed. These grades for each executor of a particular educational project are used to determine the level of performance and potential of this project. To find a generalized grade of the project potential, the least square method was used to construct a regression line, which indicates a tendency to develop the competencies of project executors. The stages of building information spaces of executors of educational projects in a dynamic environment were proposed. The hypothesis of the study that the progressive dynamic development of educational project executors has an impact on the potential of these projects was verified experimentally. To verify the described construction method, ten educational projects, which were implemented in the period from 2014 to 2020, were selected. The results indicate that the grade of the competencies of project executors recorded at the time of completion of a project almost completely coincides with the grade of evaluators after completion of the projects. The ranks obtained after the final evaluation of projects and the ranks according to calculated performance grades coincide by 40 %. Evaluation of projects is mainly focused on the reputation and performance of executors and does not sufficiently take into consideration the transformation of the competencies of executors during the implementation of projects.
Software Development Effort Estimation Techniques: A Survey

Software Effort Estimation (SEE) is used in accurately predicting the effort in terms of (person-hours or person-months).Although there are many models, Software Effort Estimation (SEE) is one of the most difficult tasks for successful software development.Several SEE models have been proposed.However, software effort overestimation or underestimation can lead to failure or cancellation of a project.Hence, the main target of this research is to find a performance model for estimating the software effort through conduction empirical comparisons using various Machine Learning (ML) algorithms.Various ML techniques have been used with seven datasets used for Effort Estimation.These datasets are China, Albrecht, Maxwell, Desharnais, Kemerer, Cocomo81, Kitchenham, to determine the best performance for Software Development Effort Estimation.Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and R-Squared were the evaluation metrics considered.Results and experiments with various ML algorithms for software effort estimation have shown that the LASSO algorithm with China dataset produced the best performance compared to the other algorithms.
English Process Assessment Based on Deep Learning and Cloud Computing

Summative assessment has long been the primary assessment method used in college English classes. Overemphasizing examinations has had a significant negative impact on foreign language instruction. We design and practice generative instructional objectives, open instructional activities, dynamic course resources, interactive instruction process, and developmental instructional assessment based on a cloud platform based on the basic idea of generative instruction and using the characteristics of a cloud computing platform. Measures to improve the generativeness of online classrooms have piqued students’ interest and enthusiasm for self-directed learning, allowing them to experience the joy of learning brought on by new technological advancements. We discovered that if you want to improve students’ language acquisition ability, you cannot just rely on assessment. The diversified process assessment can not only ensure that students complete their learning tasks efficiently and quickly but also ensure that students are assessed objectively and fairly. Using cloud computing, MATLAB algorithm, and artificial neural network theory, a mathematical model of an instruction quality assessment system is established in this paper. After the network has been trained, the quality of the instruction can be assessed, providing a useful reference point for future research into instruction quality assessment systems.
Fundamental Design of Flood Management Educational Games Using Virtual Reality Technology

The geographical location of Makassar, which is included in the lowlands and the increasingly uncertain climate change, makes this area included in the flood map in Indonesia. Almost every year, the city of Makassar is haunted by the potential for flooding in several vulnerable areas such as the sub-districts of Manggala, Wajo and Bontoala and the Antang National Housing, every disaster in the city of Makassar is handled by the Regional Office. Disaster Management Agency. So far, flood disaster preparedness is still not responsive to a disaster, most of them sit around waiting for help, and the community lacks understanding of what to do when a flood disaster hits them. This study aims to design a system that provides education to the public about flood disaster management using virtual reality technology. This research method uses Virtual Reality technology based on educational games. The result of this research is the existence of a flood control simulation system that can help the people of Makassar understand what to do in the event of a flood and with virtual reality-based, educational game technology providing a more attractive appearance. So that people are happy and happy to use educational games. Based on black-box testing, valid results were obtained for all functional tests on the flood prevention educational game simulation system, especially in Makassar. Then based on the results of a questionnaire to determine the system's benefits obtained from fifty respondents consisting of children, adolescents and adults, the outcomes received 91.3% stated very useful, 5.2% useful, 3.5% quite helpful and 0% useless.
Effective of Modern Techniques on Content-Based Medical Image Retrieval: A Survey

The advancement in medical imaging has resulted in a rapid and large increase in medical images inside repositories. These medical images contain very important information that can be used in many things, including diagnosing diseases. This implies that a precise, efficient way of indexing and retrieving biomedical images is necessary to obtain medical images from such repositories in real-time. CBMIR, therefore, played an important part, where the CBMIR’s area is very important in the field of image processing and involves low-level feature extraction and similarity measures for the comparison of medical images such as color histograms, edges, texture, shape. The majority of the methods already in use in CBMIR enhance the retrieval of a medical image and diseases diagnosis by reducing the issue of the semantic gap between low visual and high semantic levels. Also, secure access to the medical image of diverse cases, which are often kept on a network and are susceptible to malicious attacks is considered an important target for all medical practitioners. So, most CBMIRs try to cover this target for the purpose of privacy preservation. So, in this survey, the most advanced (CBMIR) frameworks that were used to reduce the issue of semantic gaps, high dimensionality feature maps were covered, disease diagnosis, and medical image security. Furthermore, the different publicly and standard databases used in measuring the performance of these frameworks also were covered.
A Decomposition and Dominance-Based Multiobjective Artificial Bee Colony Algorithm for Multiple Sequence Alignment

The multiple sequence alignment (MSA) problem is essential in biological research for finding a specific relationship between the biologic sequences and their function. This study proposes a decomposition and dominance-based multiobjective artificial bee colony optimization algorithm for MSA (MOABC/D-MSA). MOABC/D-MSA uses three kinds of searching strategies to obtain a group of nondominated solutions with high quality and diversity of an MSA problem. A decomposition-based employed bee strategy is proposed to search for high-performance solutions of the MSA, while insuring their diversity. A nondominated sorting-based onlooker strategy searches for the solutions near the Pareto front (PF) to guide the subsequent searching. The scout bee strategy facilitates the algorithm to get out of the local optimal. A comparative experiment is implemented on BAliBASE 3.0, a benchmark for MSA algorithms. Experimental results show that the proposed algorithm has competitive performance with state-of-the-art metaheuristic algorithms. Furthermore, nondominated solutions of MOABC/D-MSA have a more uniform distribution in the objective space.
A Lightweight Data Integrity Verification with Data Dynamics for Mobile Edge Computing

As a special scenario of mobile cloud computing, mobile edge computing can meet the requirements of low latency of data integrity verification and support of mobility in mobile scenarios. However, most existing data integrity verification methods have relatively large computational overhead and few considerations of data dynamic update. To address the above problems, we propose a lightweight data integrity verification method that can support data dynamics in mobile edge computing scenarios. The proposed method is based on an algebraic signature and data integrity verification framework, which ensures security and reduces the computational overhead to achieve the requirement of lightweight. On this basis, analysis and proof of the feasibility, security, and privacy are given. At the same time, in order to support the dynamic update of the data, an optimized strategy based on matrix index is designed with low overhead. In comparison with other baseline methods, simulation experiments show that our method is superior in terms of computational overhead and has good performance in supporting data dynamics.