Reviewerly/exHarmony huggingface dataset:
Can Language Really Understand Depth?

Vision-Language Pre-training (e.g., CLIP) bridges image and language. Despite its great success in a wide range of zero-shot downstream tasks, it still underperforms in some abstract or systematic tasks, such as classifying the distance to the nearest car. Recently, however, some researchers found that vision-language pre-trained models are able to estimate monocular depth, and their performance even approaches that of some earlier fully-supervised methods. Given these conflicting findings, in this paper, we focus on the question - Can vision-language pre-trained models really understand depth? If so, how well does it perform? To answer these two questions, we propose MonoCLIP, which attempts to fully exploit the potential of vision-language pre-trained models by introducing three basic depth estimators and global context-guided depth fusion. Results on two mainstream monocular depth estimation datasets demonstrate the ability of vision-language pre-trained model in understanding depth. Moreover, adequate ablation studies further shed light on why and how it works.
Document-Level Relation Extraction with Relation Correlation Enhancement

Document-level relation extraction (DocRE) is a task that focuses on identifying relations between entities within a document. However, existing DocRE models often overlook the correlation between relations and lack a quantitative analysis of relation correlations. To address this limitation and effectively capture relation correlations in DocRE, we propose a relation graph method, which aims to explicitly exploit the interdependency among relations. Firstly, we construct a relation graph that models relation correlations using statistical co-occurrence information derived from prior relation knowledge. Secondly, we employ a re-weighting scheme to create an effective relation correlation matrix to guide the propagation of relation information. Furthermore, we leverage graph attention networks to aggregate relation embeddings. Importantly, our method can be seamlessly integrated as a plug-and-play module into existing models. Experimental results demonstrate that our approach can enhance the performance of multi-relation extraction, highlighting the effectiveness of considering relation correlations in DocRE.
An Interpretable Vulnerability Detection Framework Based on Multi-task Learning

Vulnerability detection (VD) techniques are critical to software security and have been widely studied. Many recent research works have proposed VD approaches built with deep learning models and achieved state-of-the-art performance. However, due to the black-box characteristic of deep learning, these approaches typically have poor interpretability, making it challenging for analysts to understand the causes and mechanisms behind vulnerabilities. Although a few strategies have been presented to improve the interpretability of deep learning models, their outputs are still difficult to understand for those with little machine learning knowledge. In this study, we propose IVDM, an Interpretable Vulnerability Detection Framework Based on Multi-task Learning. IVDM integrates the VD and explanation generation tasks into a multi-task learning mechanism. It can generate explanations of the detected vulnerabilities in the form of natural language while performing the VD task. Compared with existing methods, the explanations outputted by IVDM are easier to understand. Moreover, IVDM is trained based on a large-scale pre-trained model, which brings it the cross-programming-language VD ability. Experimental results conducted on both a dataset collected by ourselves and public datasets have demonstrated the effectiveness and rationality of IVDM.
Graph Convolutional Network Based Feature Constraints Learning for Cross-Domain Adaptive Recommendation

The problem of data sparsity is a key challenge for recommendation systems. It motivates the research of cross-domain recommendation (CDR), which aims to use more user-item interaction information from source domains to improve the recommendation performance in the target domain. However, finding useful features to transfer is a challenge for CDR. Avoiding negative transfer while achieving domain adaptation further adds to this challenge. Based on the superiority of graph structural feature learning, this paper proposes a graph convolutional network based Cross-Domain Adaptive Recommendation model using Feature Constraints Learning (CDAR-FCL). To begin with, we construct a multi-graph network consisting of single-domain graphs and one cross-domain graph based on overlapping users. Next, we employ specific and common graph convolution on the graphs to learn domain-specific and domain-invariant features, respectively. Additionally, we design feature constraints on the features obtained in different graphs and mine the potential correlation for domain adaptation. To address the issue of shared parameter conflicts within the constraints, we develop a binary mask learning approach based on contrastive learning. Experiments on three pairs of real cross-domain datasets demonstrate the effectiveness.
KESIAPSIAGAAN MASYARAKAT TERHADAP MEGATHRUST DI KELURAHAN SIDOHARJO DAN DESA SEDENG KECAMATAN PACITAN KABUPATEN PACITAN

This research was carried out in Sidoharjo Village and Sedeng Village, Pacitan District, Pacitan Regency with the aim of knowing the community's preparedness for megatrhusts in Sidoharjo Village and Sedeng Village, Pacitan District, Pacitan Regency. The research method used in this research is descriptive qualitative with a spatial approach. The research was conducted on community leaders in Sidoharjo Village and Sedeng Village with research samples using regional samples based on topography and knowledge. Data collection techniques in this research are observation and interviews. Then, to test the validity using data triangulation techniques. The results of the research show the level of community preparedness in facing megatrusts in Sidoharjo Village and Sedeng Village, Pacitan District are included in the same classification, that both areas have individual community preparedness that is very ready to face megatrust disasters.
ADV-POST: Physically Realistic Adversarial Poster for Attacking Semantic Segmentation Models in Autonomous Driving

In recent years, deep neural networks have gained significant popularity in real-time semantic segmentation tasks, particularly in the domain of autonomous driving. However, these networks are susceptible to adversarial examples, which pose a serious threat to the safety of autonomous driving systems. Existing adversarial attacks on semantic segmentation models primarily focus on the digital space and lack validation in real-world scenarios, or they generate meaningless and visually unnatural examples. To address this gap, we propose a method called Adversarial Poster (ADV-POST), which generates physically plausible adversarial patches to preserve semantic information and visual naturalness by adding small-scale noise to posters. Specifically, we introduce a dynamic regularization method that balances the effectiveness and intensity of the generated patches. Moreover, we conduct comprehensive evaluations of the attack effectiveness in both digital and physical environments. Our experimental results demonstrate the successful misguidedness of real-time semantic segmentation models in the context of autonomous driving, resulting in inaccurate semantic segmentation results.
MDAM: Multi-Dimensional Attention Module for Anomalous Sound Detection

Unsupervised anomaly sound detection (ASD) is a challenging task that involves training a model to differentiate between normal and abnormal sounds in an unsupervised manner. The difficulty of the task increases when there are acoustic differences (domain shift) between the training and testing datasets. To address these issues, this paper proposes a state-of-the-art ASD model based on self-supervised learning. Firstly, we designed an effective attention module called the Multi-Dimensional Attention Module (MDAM). Given a shallow feature map of sound, this module infers attention along three independent dimensions: time, frequency, and channel. It focuses on specific frequency bands that contain discriminative information and time frames relevant to semantics, thereby enhancing the representation learning capability of the network model. MDAM is a lightweight and versatile module that can be seamlessly integrated into any CNN-based ASD model. Secondly, we propose a simple domain generalization method that increases domain diversity by blending the feature representations of different domain data, thereby mitigating domain shift. Finally, we validate the effectiveness of the proposed methods on DCASE 2022 Task 2 and DCASE 2023 Task 2.
Urgency in Urban Flood Disaster Mitigation: Response and Policy Initiation by Makassar City Government

This study examines the urgency and efforts to mitigate urban flood risk in Makassar City by identifying the factors that cause high flood risk and analyzing the steps taken by the government. This research method uses a qualitative approach. The data collection stage involves interviews, observation, and document review. The data obtained is then transferred into an analysis tool for data coding using Nvivo 12 Plus. The research findings indicate that Makassar City faces a significant risk of flooding caused by high rainfall, increasingly extreme climate change, rapid population growth, poor land use change, and ineffective water management. Makassar City Government has responded by increasing budget capacity, improving drainage systems, developing early warning systems, conducting public awareness campaigns, and adopting cross-sectoral cooperation. This study can become policy recommendations in the future with a focus on expanding drainage infrastructure, more coordinated spatial planning, monitoring climate change, increasing public awareness, and strengthening inter-agency collaboration.
The web-based Accounting Information System at KUD Giri Tani Bogor uses the Prototype Method

KUD Giri Tani Bogor is the largest dairy cooperative in Bogor. In carrying out financial records, it is done using Ms. Excel. The weakness of the financial recording process like this is when there are scattered paper invoices and late entries to Ms. Excel. If this happens, making financial statements are delayed and takes time in decision-making. Based on these problems, KUD Giri Tani needs system information to assist them in accelerating the financial recording process that is easily accessible and monitored by KUD management. SIGITA (Giri Tani Accounting Information System) is a business solution to overcome financial determination problems, such as generating profit and losses. SIGITA is a web-based information system that has several features, such as recording income from the sale of milk, dairy products, and animal feed, routine expenses, viewing and downloading income and expenditure data, see profit or loss, validating in each period, enter milk prices according to with milk quality, enter feedstock, view income, and expenditure journals. SIGITA was developed using the Prototype method. The design and writing of program code uses an OOP approach and uses CodeIgniter with a database using MySQL.
Aero-Engine Remaining Useful Life Prediction Based on Bi-Discrepancy Network

Most unsupervised domain adaptation (UDA) methods align feature distributions across different domains through adversarial learning. However, many of them require introducing an auxiliary domain alignment model, which incurs additional computational costs. In addition, they generally focus on the global distribution alignment and ignore the fine-grained domain discrepancy, so target samples with significant domain shifts cannot be detected or processed for specific tasks. To solve these problems, a bi-discrepancy network is proposed for the cross-domain prediction task. Firstly, target samples with significant domain shifts are detected by maximizing the discrepancy between the outputs of the dual regressor. Secondly, the adversarial training mechanism is adopted between the feature generator and the dual regressor for global domain adaptation. Finally, the local maximum mean discrepancy is used to locally align the fine-grained features of different degradation stages. In 12 cross@-domain prediction tasks generated on the C-MAPSS dataset, the root-mean-square error (RMSE) was reduced by 77.24%, 61.72%, 38.97%, and 3.35% on average, compared with the four mainstream UDA methods, which proved the effectiveness of the proposed method.
IV-PSNR: Software for immersive video objective quality evaluation

In this article, we present the IV-PSNR software, which allows for objective quality assessment of immersive video. The software outputs the IV-PSNR metric calculated for a pair of raw video files. The IV-PSNR metric is designed to mimic the subjective perception of the quality of rendered virtual views, which are characterized by artifacts induced by reprojection of pixels between different views. Since 2019, the IV-PSNR software is a reference software for immersive video quality assessment of the ISO/IEC MPEG VC standardization group and is commonly used both in standardization activities towards the development of the ISO/IEC 23090–12: MPEG immersive video (MIV) standard and scientific research on immersive video.
Deep Learning Based CAPTCHA Recognition Network with Grouping Strategy

Websites can improve their security and protect against harmful Internet attacks by incorporating CAPTCHA verification, which assists in distinguishing between human users and robots. Among the various types of CAPTCHA, the most prevalent variant involves text-based challenges that are intentionally designed to be easily understandable by humans while presenting a difficulty for machines or robots in recognizing them. Nevertheless, due to significant advancements in deep learning, constructing convolutional neural network (CNN)-based models that possess the capability of effectively recognizing text-based CAPTCHAs has become considerably simpler. In this regard, we present a CAPTCHA recognition method that entails creating multiple duplicates of the original CAPTCHA images and generating separate binary images that encode the exact locations of each group of CAPTCHA characters. These replicated images are subsequently fed into a well-trained CNN, one after another, for obtaining the final output characters. The model possesses a straightforward architecture with a relatively small storage in system, eliminating the need for CAPTCHA segmentation into individual characters. Following the training and testing of the suggested CNN model for CAPTCHA recognition, the experimental results demonstrate the model's effectiveness in accurately recognizing CAPTCHA characters.
Implementation of Synology NAS-Based Storage System Type DS220+ at LSP-SV IPB

As the digital world evolves, safeguarding digital assets becomes crucial for organizations, particularly educational institutions. LSP SV-IPB, as a vocational certification body, manages student data, academic records, and other valuable information. Therefore, it is essential to implement an effective protection strategy. The research aims to develop and implement digital asset protection strategies at the LSP SV-IPB. The protection focuses on data storage management so that passive data is properly stored as the main backup and transactional data runs efficiently. The Synology Network Attached Storage (NAS) was proposed as the development model. NAS has proven efficient in managing data and providing reliable backups. Through NAS, passive data can be properly archived and protected, while transactional data can be accessed and processed at optimal speed. Hence, literature studies, software analysis, and experiments on the network environment were conducted. Model development was conducted using the design and implementation of NAS infrastructure at the LSP SV-IPB. Data collection covered the effective and efficient use of NAS in maintaining data integrity and mitigating the risk of losing important information. The results of this study are expected to contribute to the development of an efficient and reliable digital asset protection strategy within the LSP SV-IPB environment.
Trends in research publication topics related to eco enzymes with bibliometric analysis

Ecoenzyme is a potential sustainable waste management solution that could make waste management more beneficial. This study seeks to analyze the evolution of ecoenzyme-related research publication topic trends over the past decade, then visualize them and identify potential ecoenzyme-related research topics for future study. Using Publish or Perish software with Google Scholar database sources and VOSviewer software, this research method employs a literature review and bibliometric analysis. The investigation and search produced a collection of published document metadata regarding ecoenzymes that included up to 666 documents from 2013 to 2023. During that time, ecoenzyme-related research publications covered a variety of topics, including composition, training, and product development. N. Ginting and T. Wikaningrum are the authors who have contributed the most to the publication of the ecoenzyme topic, with a total of 12 publication documents. Further research can be conducted on the development of ecoenzymes, such as the investigation of the efficacy and effectiveness of using ecoenzymes and the investigation of other ecoenzyme research involving various other scientific disciplines. This study has limitations due to its reliance on data obtained through Google Scholar and its lack of a rigorous screening process. Therefore, it is recommended that future research take additional measures to filter more collected data.
Research on User Experience and Needs of Virtual Reality Learning Home Appliances

The surge in e-commerce and the COVID-19 pandemic has dramatically driven the demand for home appliances. However, this surge has also resulted in a proportional increase in electrical accidents. Furthermore, the current product manual does not provide users with intuitive and timely information. Virtual reality (VR) can deliver effective learning information with its exceptional simulation capabilities and immersive content. Thus, we explored the application of VR to enhance home appliances’ display and user experience for user demand to deliver the manuals on the appliances and promote safe uses. A purposive sampling technique was employed, and 12 target users participated in the in-depth interviews. In the study, the VR application of the home appliance product experience and learning showed positive outcomes. The results can be used as a reference for future design. According to the users’ feedback, we summarized and proposed eight future design directions for enhancing the virtual home appliance experience.
Common Errors in Grammar and Mechanics in Academic Writing by Senior High School Students

In the realm of language learning, learners' errors have emerged as a significant concern for analysis. Despite academic writing's formal nature, students continue to commit errors. In this qualitative study focusing on Grade 12 students' written academic texts, specifically application letters, a detailed analysis of common errors in grammar and mechanics was conducted. The research utilized content analysis on 186 application letters, employing a researcher-made rubric and involving three raters, including the researcher and two fellow English teachers. Thirty letters with the lowest scores were subjected to in-depth analysis using criterion sampling. The findings revealed prevalent grammatical errors, such as comma splice, subject-verb agreement issues, misused prepositions, and fragments. Mechanical inaccuracies included spelling mistakes, misused punctuation, and capitalization errors. Notably, a significant proportion of errors were identified in mechanics, indicating students' limited grasp of these aspects. The study underscores the crucial need for students to enhance their mastery of grammar, sentence structure, and mechanics in writing. To address these challenges, teachers are encouraged to employ engaging writing scenarios that involve grammatical exercises. Additionally, students should be advised to read extensively and understand the importance of thorough proofreading in refining their written communication skills. This study emphasizes the vital role of educators in guiding students toward proficiency in academic writing, ensuring their competence in grammar and mechanics for effective communication.
Abmas-Td Pengolahan Nilai Mahasiswa Dan Pelaporan Keuangan Dengan Microsoft Excel

Universitas Veteran Bangun Nusantara Sukoharjo consists of 20 study programs spread across 6 faculties with 162 lecturers. At the end of each semester, all lecturers have the task of reporting the grades obtained by students to the study program. In addition to assessing each lecturer in carrying out activities and having to report the use of funds. To assist lecturers in reporting assessments and compiling reports on the use of funds or financial reports, this computer training activity is important to be carried out to help improve the skills of lecturers and employees in operating the Microsoft Ecxel program. The method used in this community service is to provide training material and directly practice in a computer laboratory. From the provision of material, it was then carried out training on questions and evaluation. Evaluation is carried out to determine the increase in knowledge and skills of the Microsoft Ecxel program. Microsoft ecxel is a program that can be used to process data quickly, precisely and accurately according to user requests. The result of this service activity is an increase in the skills of lecturers and employees in computer operation and certificates for training participants. Based on the results of the pre test and post test, there was an increase of 48.94% after computer training
Cybercrime in India: Insight into the Extra-territoriality of the Information Technology Act

This article covers the ins-and-outs of cybercrime, its categorization and the space it occupies in India’s ever-increasing cyberspace. It further explores the IT Act which till date serves as India’s primary statute regulating the cyberspace and the crime therein. The article discusses the challenges posed by the advancement of technology that creates inefficiencies in the IT Act and hopes to provide substantial solutions to these issues.
A Deep CNN Framework for Oral Cancer Detection Using Histopathology Dataset

One of the most common oncological types is oral cancer. Although medical technology has advanced at a phenomenal rate, high fatality has been observed in developing countries due to the lack of early stage diagnosis of the disease. The most fundamental symptom being the prolonged inflammation in the mouth areas. Cancers in the tongue, lips, cheeks, the floor of the mouth, hard and soft palates, sinuses, and pharynx (throat) are all considered oral cancers. This study focuses on early stage diagnosis of the disease using deep learning frameworks. It will offer a more thorough understanding of the disease and help experts make judgments about diagnostic and treatment options that are well-versed. We have used a deep learning framework based on the modified Convolutional Neural Network (CNN) that uses different sizes of hidden layers. The dataset comprised histopathology images. Histopathology datasets have the potential to transform the field of medical research. By feeding a histopathology dataset into a deep-learning framework, researchers can rapidly and precisely classify patterns in the data that would otherwise be difficult or impossible to detect. It could lead to faster diagnosis of diseases and more effective treatments. A total of 8000 images (4000 for each category of the cancers) are used for result analysis. Per epoch, the testing loss likewise diminishes gradually. As a final result, at 30 epochs, it has reached the highest accuracy of 97.6%. The convolutional neural network exhibits result which fare better than peer proposals in literature.