Reviewerly/exHarmony huggingface dataset:
Exploratory Inference Learning for Scribble Supervised Semantic Segmentation

Scribble supervised semantic segmentation has achieved great advances in pseudo label exploitation, yet suffers insufficient label exploration for the mass of unannotated regions. In this work, we propose a novel exploratory inference learning (EIL) framework, which facilitates efficient probing on unlabeled pixels and promotes selecting confident candidates for boosting the evolved segmentation. The exploration of unannotated regions is formulated as an iterative decision-making process, where a policy searcher learns to infer in the unknown space and the reward to the exploratory policy is based on a contrastive measurement of candidates. In particular, we devise the contrastive reward with the intra-class attraction and the inter-class repulsion in the feature space w.r.t the pseudo labels. The unlabeled exploration and the labeled exploitation are jointly balanced to improve the segmentation, and framed in a close-looping end-to-end network. Comprehensive evaluations on the benchmark datasets (PASCAL VOC 2012 and PASCAL Context) demonstrate the superiority of our proposed EIL when compared with other state-of-the-art methods for the scribble-supervised semantic segmentation problem.
Structure Flow-Guided Network for Real Depth Super-resolution

Real depth super-resolution (DSR), unlike synthetic settings, is a challenging task due to the structural distortion and the edge noise caused by the natural degradation in real-world low-resolution (LR) depth maps. These defeats result in significant structure inconsistency between the depth map and the RGB guidance, which potentially confuses the RGB-structure guidance and thereby degrades the DSR quality. In this paper, we propose a novel structure flow-guided DSR framework, where a cross-modality flow map is learned to guide the RGB-structure information transferring for precise depth upsampling. Specifically, our framework consists of a cross-modality flow-guided upsampling network (CFUNet) and a flow-enhanced pyramid edge attention network (PEANet). CFUNet contains a trilateral self-attention module combining both the geometric and semantic correlations for reliable cross-modality flow learning. Then, the learned flow maps are combined with the grid-sampling mechanism for coarse high-resolution (HR) depth prediction. PEANet targets at integrating the learned flow map as the edge attention into a pyramid network to hierarchically learn the edge-focused guidance feature for depth edge refinement. Extensive experiments on real and synthetic DSR datasets verify that our approach achieves excellent performance compared to state-of-the-art methods. Our code is available at: https://github.com/Yuanjiayii/DSR-SFG.
Progressive Bayesian Inference for Scribble-Supervised Semantic Segmentation

The scribble-supervised semantic segmentation is an important yet challenging task in the field of computer vision. To deal with the pixel-wise sparse annotation problem, we propose a Progressive Bayesian Inference (PBI) framework to boost the performance of the scribble-supervised semantic segmentation, which can effectively infer the semantic distribution of these unlabeled pixels to guide the optimization of the segmentation network. The PBI dynamically improves the model learning from two aspects: the Bayesian inference module (i.e., semantic distribution learning) and the pixel-wise segmenter (i.e., model updating). Specifically, we effectively infer the semantic probability distribution of these unlabeled pixels with our designed Bayesian inference module, where its guidance is estimated through the Bayesian expectation maximization under the situation of partially observed data. The segmenter can be progressively improved under the joint guidance of the original scribble information and the learned semantic distribution. The segmenter optimization and semantic distribution promotion are encapsulated into a unified architecture where they could improve each other with mutual evolution in a progressive fashion. Comprehensive evaluations of several benchmark datasets demonstrate the effectiveness and superiority of our proposed PBI when compared with other state-of-the-art methods applied to the scribble-supervised semantic segmentation task.
Just Noticeable Visual Redundancy Forecasting: A Deep Multimodal-Driven Approach

Just noticeable difference (JND) refers to the maximum visual change that human eyes cannot perceive, and it has a wide range of applications in multimedia systems. However, most existing JND approaches only focus on a single modality, and rarely consider the complementary effects of multimodal information. In this article, we investigate the JND modeling from an end-to-end homologous multimodal perspective, namely hmJND-Net. Specifically, we explore three important visually sensitive modalities, including saliency, depth, and segmentation. To better utilize homologous multimodal information, we establish an effective fusion method via summation enhancement and subtractive offset, and align homologous multimodal features based on a self-attention driven encoder-decoder paradigm. Extensive experimental results on eight different benchmark datasets validate the superiority of our hmJND-Net over eight representative methods.
Post hoc Interpretability: Review on New Frontiers of Interpretable AI

In order to make critical decisions, organizations are increasingly using sophisticated black box machine learning algorithms. Post hoc interpretability techniques are a well-liked solution to the issue of transparency in black box machine learning algorithms. These techniques try to explain the internal workings of the underlying machine learning models so that stakeholders can comprehend them. This paper suggests that post hoc interpretability approaches’ insights can be used to control black box machine learning. The veracity of these statements is investigated in this article. This paper discusses the post hoc interpretability methods on individual prediction. The paper provides detailed comparison of post hoc interpretability methods, software packages, and interpretability toolkits available in market.
Feature Selection and Support Vector Machine Classification method for Banknote Dirtiness Recognition Based on Marine Predator Algorithm with Mathematical Functions

Dealing with classification problems requires the crucial step of feature selection (FS), which helps to reduce data dimensions and shorten classification time. Feature selection and support vector machines (SVM) classification method for banknote dirtiness recognition based on marine predator algorithm (MPA) with mathematical functions was proposed. The mathematical functions were mainly used to improve the optimizatio of MPA for feature parameter selection, and the loss function and kernel function parameters of the SVM are optimized by slime mold optimization algorithm (SMA) and marine predator algorithm. According to the experimental results, the accuracy of identifying dirtiness on the entire surface of the banknote reaches 89.07%. At the same time, according to the image pattern distribution of the banknoteS, the white area image in the middle left of the collected banknote is selected by the same method to select the feature parameters and identify the dirtiness of the banknoteS. The accuracy of dirtiness recognition in the middle left white area reached 86.67%, this shows that the white area in the middle left can basically completely replace the entire banknote. To confirm the effectiveness of the feature selection method, the proposed optimization method has been compared with four other swarm intelligent optimization algorithms to verify its performance. The experiment results indicate that the enhanced strategy is successful in improving the performance of MPA. Moreover, the robustness analysis proves its effectiveness.
A Predication Analysis of the Factors Influencing Minimum Ignition Temperature of Coal Dust Cloud Based on Principal Component Analysis and Support Vector Machine

To investigate the effect of different proximate index on minimum ignition temperature(MIT) of coal dust cloud, 30 types of coal specimens with different characteristics were chosen.A two-furnace automatic coal proximate analyzer was employed to determine the indexes for moisture content, ash content, volatile matter, fixed carbon and MIT of different types of coal specimens.As the calculated results showed that these indexes exhibited high correlation, a principal component analysis (PCA) was adopted to extract principal components for multiple factors affecting MIT of coal dust, and then, the effect of the indexes for each type of coal on MIT of coal dust was analyzed.Based on experimental data, support vector machine (SVM) regression model was constructed to predicate the MIT of coal dust, having a predicating error below 10%.This method can be applied in the predication of the MIT for coal dust, which is beneficial to the assessment of the risk induced by coal dust explosion (CDE).
Patterns in Microservices-based Development: A Grey Literature Review

Microservices emerged due to the massive adoption of cloud computing and the need to integrate legacy systems. However, there still needs to be a greater understanding of adopting a microservice-based architectural style. Besides, there is a need for guidelines to operationalize those microservices. We conducted a grey literature review to identify commonly used architectural patterns and how they are implemented following design patterns. We present two key contributions. Firstly, we identified four architectural patterns and 23 design patterns. Secondly, we identified a catalog of tools for implementing the main patterns adopted when using the microservices style. The Proxy and the SAGA patterns are the most used in communicating and linking data for services. Additionally, tools such as Kubernetes, Docker, and Amazon WS are the most used for implementing microservices and deploying them into containers.
Using Software Architecture Descriptions to Detect Architectural Smells at Design Time

Architectural smells are decisions made at the software architecture level, whether intentional or not, that may negatively impact the quality of a software system. In the literature, architectural smells are identified mainly by relying on the source code or other implementation artifacts. However, architectural smells could be detected at design time, even before employing implementation efforts and preventing them from being reflected at the system implementation. This research investigates how software architecture descriptions realized through architecture description languages (ADLs) can be used to identify architectural smells at design time. This work focuses on how architectural smells manifest and can be detected in SysADL, an ADL that allows describing both structure and behavior of software architectures using standardized diagrams from the OMG’s SysML language.
A Health Assessment Method for Business Full-Link on Cloud Platform

The current business health assessment on the cloud platform is rather onesided and unable to achieve a full link from VM to network egress.Based on the physical link and logical link of the cloud platform, the activity path of business flow is found, and the whole link model of end-to-end business flow is established.Subsequently, according to the importance of the business VM and the health of each redundant node on the link, the health analysis model of the business end-to-end full-link is calculated.
A Study on the Competency of Youth worker - With a focus on youth workers and prospective youth workers -

The purpose of the present study is to analyze competency units and the level of performance – considered important by youth workers and prospective youth workers – with regards to competency unit elements in the field of youth activities based on the NCS. The study surveyed a total 331 subjects including 137 youth workers and 184 prospective youth workers to achieve such a purpose. The basic statistics of the subjects were analyzed, and an IPA analysis was conducted to figure out the general characteristics of the research subjects and the differences in importance and performance. The analysis results show, first, that the importance average by competency unit of youth activities for youth workers(incumbent) to be 4.19, while the performance average was at 3.56. Second, results indicate a 2nd quadrant(evaluating youth programs, operating youth self-government activities). Third, the importance average by competency unit of youth activities for prospective youth workers was 4.15, while the performance average was 2.92. Fourth, the results indicate a 2nd quadrant(planning youth projects, evaluating youth programs). Fifth, in the IPA analysis of youth workers and prospective youth workers, the results indicated a a 2nd quadrant(operating youth self-government activities, building and operating a collaboration system, supporting activities that promote youth rights)
Pengembangan Bahan Ajar Berbasis Android pada Materi Konsep Mol Di SMAN 4 Kota Bengkulu

The era of the industrial revolution 4.0 in education is characterized by the use of technology in the learning process. This study aims to test the feasibility, test student responses and see student learning outcomes on android-based teaching materials on the mole concept material at SMAN 4 Kota Bengkulu. The research was conducted using the Research &amp; Development (R&amp;D) method using a 4D development model which was limited to the 3D stage (Define, Design and Development). The feasibility test of android-based teaching materials is seen through the assessment of media and material experts, test student responses on small-scale and large-scale tests and student learning outcomes from the N-Gain score. The assessment of the feasibility of android-based teaching materials was carried out by two media and materials experts respectively. The results of the feasibility obtained are the results of media validation 91% and the results of material validation 88.33% with a very feasible category. Student response tests were carried out on a small and large scale. Determination of small-scale and large-scale samples with a normality test for all students of class X SMAN 4 Kota Bengkulu TA 2020/2021, obtained 9 students of class X IPA 1 as samples of the small-scale response test and 34 students of class X IPA 2 as samples of large-scale response test. The results of the small-scale student response test are 91.79% and the large-scale response test results are 85.9%, with a very good category. Learners on a large scale are also seen from their learning outcomes from the N-Gain score obtained, which is 0.62 or is in the medium category. The results obtained show that android-based teaching materials on the mole concept material are very feasible to be used in the learning process. Keywords: Development Research, Teaching Materials, Android, Mole Concept
Interactive Learning Media Development Based on Android Assisted by Google Sites on Building Static Calculation Element in 2nd Depok National Vocational High School

Background: This study aims to develop interactive learning media based on android assisted by Google Sites on building static calculation element class X SMK N 2 Depok and to determine the feasibility of the media in terms of material and media aspects.Methods: The research is Research and Development (RD) method using waterfall model development. The stage of waterfall model are analysis, design, coding, testing, and maintenance. Data is collected using interviews, questionnaires, and device usage. Feasibility data analysis using category range analysis.Results: The results of this research according to the stages (1) the analysis stage reveals problems with learning methods and models that are not yet sufficient for students to learn according to the independent learning curriculum. The software used is Adobe Illustrator and assisted by Google Sites with an android smartphone v4.1 has a minimum of 1GB of RAM and 1GB of free memory. (2) The design stage uses UML planning and storyboards. UML diagrams include use case, activity, class, and sequence. (3) The coding stage is assisted by Google Sites using Markup Language HTML and Website Appsgeyser. (4) The testing phase is carried out on material and media aspects according to ISO 25010. (5) The maintenance stage involves improving the appearance and explanation of the questions.Conclusion: Based on the test results it is concluded that the media developed is feasible to use.
IoT-assisted Smart English Language Translation and Grammar Learning Framework

Improvements in the skills of language interpreters are crucial to the growth of communicative communities.Information and communication technology (ICT)-enhanced media for language education and teaching across various subject areas have developed rapidly in recent years.With its unique characteristics, grammatical learning rates are among the most challenging places for learning the language.Still, even the most cutting-edge e-learning technologies have been poorly implemented and underexplored.This study introduces an internet-of-things-assisted smart english language translation and grammar learning (ISLTGL) platform.The study uses the internet of things (IoT) to perform linguistic modeling and reliability tests.On integrating interactive features to aid in designing an information-assisted translation system.The translator still risks introducing grammatical errors into the target language version of the source text when using the conventional translation approach.The offered translation approach facilitates students' reading of English to comprehend literature.Based on this principle, develop and implement an interactive English translation and grammar learning procedure using corpus and text data sets.This method makes the translation shorter and clearer and effectively addresses the problems of massive volumes of text and significant problems of expression.The system analyzes the translation outputs statistically to assess their accuracy and completeness, providing a benchmark for the ultimate function realization.To help students learn English grammar, the ISLTGL combines the IoT system with the current English language translator.The experimental results demonstrated that ISLTGL improves student participation (97.3%), increases fluency (98.4%), boosts efficiency (94.4%), increases satisfaction (90.7%), and predicts words and sentences (95.7%) with the maximum efficiency compared to current methods for teaching English.
Role Of Health Sector to Increase Public Awareness Through Educating the Community and Social Media Platforms: Case of Riyadh Saudi Arabia

Contemporary study aims to investigate the role of the health sector in educating the community and studying public awareness in Saudi Arabia.The country has a growing need for health education and awareness programs to address and prevalent health issues such as non-communicable diseases (NCDs), diabetes, hypertension, and obesity, among others.The paper will explore the current state of health education and awareness programs, their effectiveness, and potential for improvement.The research will also examine the perceptions of healthcare providers and the general public regarding the role of the health sector in promoting health education and awareness in the community.The study will use a convenience sampling technique to select a sample population of individuals residing in Saudi Arabia, who are 18 years of age or older, and have a basic understanding of health issues.The collected data analyzed using descriptive statistics, including frequency distribution, percentage analysis, mean, standard deviation, and correlation analysis.The findings of the study showed that there is a need to improve health education and awareness programs in Saudi Arabia to address these issues and promote healthier lifestyles among the population.The findings of this research can guide the development of effective and sustainable health education and awareness initiatives that address the prevalent health issues in Saudi Arabia.
Front-End Security Analysis for Cloud-Based Data Backup Application Using Cybersecurity Tools

In this challenging, demanding, daunting, and competitive business world, the rise, and growth of cybercrimes are very high. With the proliferation of Cloud Computing techniques, usually in industrial arenas, business information and important clients’ data are stored and managed using cloud platforms. Application programs are developed to handle such valuable information assets of the organizations. Cloud backups are provided for these client data where security is the most concerning aspect. There are many vulnerabilities in the current scenario where intruders can cause havoc. Destruction of the product can happen by exploiting vulnerabilities that can put the company and the product in jeopardy. It may create a bad impression about the organization among the customers, competitors, and the public world. This paper shows the work done by a cyber security team whose main objective is to run vulnerability analysis and mitigate threats on an application that backs up the client’s data to the cloud. Cyber Security is an important aspect in all types of businesses because it protects all categories of data such as fragile data, private information, intellectual property data, and other data including governmental and industrial information systems from theft and damage which concludes in huge financial loss and loss of client data.
Deep Fake Generation and Detection

Nowadays, a lot of fake videos and images are made with the help of various software and new AI technologies but leave few hints of manipulation. There are a lot of unethical ways the videos can be used to threaten, fight, or create panic among people. It is important these days people make sure that such ways are not used to make fake videos. An AI-based technique for the synthesis of human images is called Deep Fake. They are created by combining and superimposing existing videos onto source videos. We will develop a system that uses a convolutional neural network (CNN) and Long Short-Term Memory (LSTM) for the extraction of frame-level features. After this these features are used to train the dataset which learns to classify a fraud and original video. A dataset with thousand videos will be used to detect the originality. With the help of simple architecture, we can achieve this. The main intention of this paper is to detect deep fake videos and eradicate problems like fake news, financial fraud, and malicious hoaxes.
SincSquareNet: Deep Neural Network-Based Speaker Identification for Raw Speech

Biometrics, forensics, and access control systems such as speaker identification systems all benefit from advances in machine learning and deep learning. A distinctive convolutional neural network is SincNet architecture that performs speaker identification by feeding one-dimensional raw audio into the first convolutional layer, which is made up of parameterized sinc filters. We proposed a SincSquareNet for speaker identification in this paper, in which a CNN effectively learns tailored triangular bandpass filters using trainable sinc-squared functions. Only the low and high cutoff frequencies of bandpass filters are directly learned from data, unlike typical CNNs, which are able to learn the features of each filter. The proposed model is tested on the LibriSpeech dataset, and it outperforms SincNet in terms of performance. According to experimental results, identification accuracy is increased by 3.5%, while validation loss is reduced by 12% when compared to SincNet.