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Participatory Forest Management and Gender Inclusiveness within the Community Forest Management Groups of Bhutan

Journal of Geoscience and Environment Protection 2024年第4期12卷 12-30页

作者： Norbu Zangmo Takuya Hiroshima Spencer Sibanda Jigme Dorji Department of Global Agricultural Sciences Graduate School of Agricultural and Life Sciences The University of Tokyo Tokyo Japan Forest Monitoring and Evaluation Section Forest Monitoring and Information Division Department of Forests and Park Services Ministry of Energy and Natural Resources Thimphu Bhutan Department of Information and Computer Sciences Graduate School of Science and Engineering Saitama University Saitama Japan Project Management Unit Department of Tourism Thimphu Bhutan

Community forest management groups (CFMGs) in Bhutan exhibit participatory forest management practices that recognize the importance of community’s collective participation in the management of natural forest resources. This approach involves the community in the stewardship of designated forest areas and resources to ensure sustainable livelihoods and realization of forest conservation objectives. The increase of CFMGs in the country has been successful. However, research on the extent of gender-inclusive participation in CFMGs is either insufficient or missing vis-à-vis the allocation of decision-making power. Therefore, this study analyzes the factors influencing gender participation in CFMGs and their integration into decision-making processes. Primary data were collected from 12 study sites spanning 4 regions, complemented by secondary data from the Forest Department. Regression models were used to identify factors significantly influencing CFMG member participation in decision-making. The empirical results of this study reveal that gender is a significant factor influencing participation in CFMG decision-making. The study concludes that there is insufficient participation of women members in decision-making processes. Therefore, consideration of gender should be included in the development phase of the CFMG policy in addition to promoting awareness of inequity between gender and the promotion of leadership roles for women in CFMGs.

关键词： Decision-Making Face-to-Face Interview Regression Analysis Women

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Stabilizing Visual Reinforcement Learning via Asymmetric Interactive Cooperation

Stabilizing Visual Reinforcement Learning via Asymmetric Int...

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International Conference on computer Vision (ICCV)

作者： Yunpeng Zhai Peixi Peng Yifan Zhao Yangru Huang Yonghong Tian National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University Beijing China School of Electronic and Computer Engineering Peking University Shenzhen Graduate School Shenzhen China Peng Cheng Laboratory Shenzhen China

Vision-based reinforcement learning (RL) depends on discriminative representation encoders to abstract the observation states. Despite the great success of increasing CNN parameters for many supervised computer vision tasks, reinforcement learning with temporal-difference (TD) losses cannot benefit from it in most complex environments. In this paper, we analyze that the training instability arises from the oscillating self-overfitting of the heavy-optimizable encoder. We argue that serious oscillation will occur to the parameters when enforced to fit the sensitive TD targets, causing uncertain drifting of the latent state space and thus transmitting these perturbations to the policy learning. To alleviate this phenomenon, we propose a novel asymmetric interactive cooperation approach with the interaction between a heavy-optimizable encoder and a supportive light-optimizable encoder, in which both their advantages are integrated including the highly discriminative capability as well as the training stability. We also present a greedy bootstrapping optimization to isolate the visual perturbations from policy learning, where representation and policy are trained sufficiently by turns. Finally, we demonstrate the effectiveness of our method in utilizing larger visual models by first-person highway driving task CARLA and Vizdoom environments.

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Automatic Smart Crawling on Twitter for Weather information in Indonesia

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Procedia computer science 2023年 227卷 795-804页

作者： Kartika Purwandari Reza Bayu Perdana Join W.C. Sigalingging Reza Rahutomo Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta 11480 Indonesia Database Center Division of BMKG Meteorological Climatological and Geophysical Agency Jakarta 10720 Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program – Master of Computer Science Bina Nusantara University Jakarta 11480 Indonesia

Although Twitter is a popular platform for social interaction analysis and text data mining, it faces challenges with geolocation automation. To address this problem, the researchers propose the utilization of a Support Vector Machine (SVM) model to develop an automated Twitter crawling system. The system aims to collect data related to weather in Indonesia by employing Twin, a Python-based Twitter scraping software. To overcome null geolocations, the study incorporates aliases created based on the common practice of Indonesian users mentioning the country's location in tweets. The results demonstrate that the SVM model, combined with automated smart crawling, achieves an 85% accuracy rate.

关键词： Social media Twitter SVM smart crawling weather

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E-Business Startup: "universia" As University Collaborator System 8

E-Business Startup: "universia" As University Collaborator S...

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8th International Conference on ICT for Smart Society, ICISS 2021

作者： Oktavia, Tanty Kaslie Gaol, Ford Lumban Hosoda, Takaaki Bina Nusantara University School of Information Systems Information Systems Department Jakarta11530 Indonesia Bina Nusantara University Binus Graduate Program-Computer Science Computer Science Department Jakarta11530 Indonesia Advanced Institute of Industrial Technology Decision Science Department Tokyo Japan

ISBN: (纸本)9781665416979

According to Covid 19 pandemic every process has to shifting to collaborate with digital technology. One of the field that pushes to shift is Education field. Undergraduate education level, is one of many strategies to improve people competency. In Indonesia, the number of people has steadily increased. However, they have to improve their level of competency to compete with their competitor. According to this condition, many prospective student difficult to find information that their need to decide a place to study. There are many consideration factors that will involved in this decision. Therefore, in this research will build university collaborator system that can support analysis from prospective student perspective to choose education institution. To build this business design this study involve some analysis such as industrial competition will be using Porter Five Force's and supported by Business Model Canvas to define Universia's business model, as well as financial projections for the next seven years. According to the Porter Five Forces model, the information services market around universities still has a lot of room for new entrants. The results of the business model canvas analysis show that, despite being a newcomer to the sector, Universia has a number of value propositions that its competitors do not. Universia's product will be generated in the web based application. The application will be designed using Object Oriented Analysis and Design (OOAD) in conjunction with the Extreme programming approach, which includes steps such as planning, designing, coding, and testing. The result of this study is a system collaborator that can be bridge between prospective student and university, so the prospective student can easily find the information that their need to choose university. © 2021 IEEE.

关键词： Students

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Business Start-up: 'CLEVA' application design for employee collaboration training based on android 6

Business Start-up: 'CLEVA' application design for employee c...

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6th International Conference on information Management and Technology, ICIMTech 2021

作者： Oktavia, Tanty Erin Widjaya, Christhoper Arya Hansel Lee, Eugenius Gaol, Ford Lumban Hosoda, Takaaki Bina Nusantara University School of Information Systems Information Systems Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program - Computer Science Computer Science Department Jakarta11480 Indonesia Advanced Institute of Industrial Technology Decision Science Department Tokyo Japan

ISBN: (纸本)9781665449373

E-learning is used as one of the innovations in online learning, but in its implementation e-learning has limitations, especially in terms of flexibility, so that new innovations emerge, namely learning using m-learning because it is more flexible and can be accessed anywhere and anytime. The purpose of writing this study is to analyse problems and business opportunities related to the design of a mobile app-based start-up business in an e-learning system in a company and to design an information system in the form of an Android-based application to answer the analysis of existing problems and opportunities. The methodology used in this study is Rational Unified Process (RUP) with an Object-Oriented Analysis and Design (OOAD) approach which is described by the notation of the Unified Modelling Language (UML). The business analysis used in the writing of this study is the Business Model Canvas (BMC), Marketing Mix, Porter's Five Forces which is complemented by Financial Analysis using the Payback Period (PP), Break Even Point (BEP), and Return on Investment (ROI). In this case, Cleva creates an application system that helps companies carry out training for their employees and helps employees to learn about technical skills and soft skills through mobile-based applications © 2021 IEEE.

关键词： Android (operating system)

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Mobile Collaborator System Application for E-commerce Drugstore 8

Mobile Collaborator System Application for E-commerce Drugst...

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8th International Conference on ICT for Smart Society, ICISS 2021

作者： Oktavia, Tanty Michael Gaol, Ford Lumban Hosoda, Takaaki Bina Nusantara University School of Information Systems Information Systems Department Jakarta11530 Indonesia Bina Nusantara University Binus Graduate Program-Computer Science Computer Science Department Jakarta11530 Indonesia Advanced Institute of Industrial Technology Decision Science Department Tokyo Japan

ISBN: (纸本)9781665416979

Mostly Drugstores do not have any system that exclusively connect to access E-commerce system because the marketing concept of drugstores still use conventional store to sell their products. According to this situation, this study will propose e-commerce that accomodate drugstore service. For this study, the analysis methodology consists of seeing the most recent business processes in the e-commerce system system and the current situation in drugstore, interviewing the drugstore owner, and doing a literature research. Then, for the drugstore mobile application, create the Unified Modeling Language (UML). The system design is accomplished through the creation of Object-Oriented Detailed Designs in the form of diagrams, databases, and user interfaces. The drugstore mobile application, which includes features such as recommendation product menus, best-selling product menus, promo product menus, transaction cart menus, order tracking menus, order letter menus, and item receive confirmation menus, is the result of this study. © 2021 IEEE.

关键词： User interfaces

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Real-Time Tiny Object Detection and Motion Parameter Extraction of Water Obstacles for Unmanned Surface Vehicle

SSRN

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SSRN 2023年

作者： Zhang, Bin Dong, Wenyong Yan, Shankai Luo, Meizhen Zhao, Chao Wu, Liangshun Peng, Ling School of Remote Sensing and Information Engineering Wuhan University China Department of Computer Science City University of Hong Kong Hong Kong School of Information Network Security Xinjiang University Of Political Science And Law China School of Computer Science Technology Hainan University China Graduate School Binary University of Managment & Entrepreneurship Malaysia School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University China Huanggang Normal University China

The autonomous routing of unmanned surface vehicles relies heavily on the detection of obstacles on the water surface by the sensory module. We know that obstacles on the water have low resolution, blurred pictures, little information and a lot of noise. The detection and recognition of obstacles on the water surface in the autonomous movement of unmanned surface vehicles is always a practical and common difficulty. For the purpose of real-time detection and extraction of movement parameters of obstacles on the water surface, we have made a study to improve the functionality performance of previous perception module by using Kalman filtering algorithms to predicting and updating of motion parameters of obstacle point clouds. We introduced a local relation detector with small target detection algorithm based on attention model at the same *** show that this study can effectively improve the performance of target detection and reduce the *** has made an important contribution to autonomous navigation of unmanned surface vehicles. © 2023, The Authors. All rights reserved.

关键词： Extraction

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LProtector: An LLM-driven Vulnerability Detection System

arXiv

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arXiv 2024年

作者： Sheng, Ze Wu, Fenghua Zuo, Xiangwu Li, Chao Qiao, Yuxin Hang, Lei Computer and Information Science University of Pennsylvania Philadelphia United States Integrated Circuit Science & Engineering University of Electronic Science & Technology of China Chengdu China Computer Science & Engineering Texas A&M University College Station United States Graduate School of Arts and Sciences Georgetown University WashingtonDC United States Computer Information Technology Northern Arizona University Flagstaff United States Co. Ltd. Nanjing China

The security issues of large-scale software systems and frameworks have become increasingly severe with the development of technology. As complexity of software grows, vulnerabilities are becoming more challenging to detect. Although traditional machine learning methods have been applied in cybersecurity for a long time, there has been no significant breakthrough until now. With the recent rise of large language models (LLMs), a turning point seems to have arrived. The powerful code comprehension and generation capabilities of LLMs make fully automated vulnerability detection systems a possibility. This paper presents LProtector, an automated vulnerability detection system for C/C++ codebases based on GPT-4o and Retrieval-Augmented Generation (RAG). LProtector performs binary classification to identify vulnerabilities in target codebases. To evaluate its effectiveness, we conducted experiments on the Big-Vul dataset. Results show that LProtector outperforms two state-of-the-art baselines in terms of F1 score, demonstrating the potential of integrating LLMs with vulnerability detection. Copyright © 2024, The Authors. All rights reserved.

关键词： C++ (programming language)

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A Hybrid Model of Deep Learning Features and Clinical Features for Severe Cases Predication of COVID-19

A Hybrid Model of Deep Learning Features and Clinical Featur...

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Medical Imaging 2023: computer-Aided Diagnosis

作者： Kitrungrotsakul, Titinunt Chen, Qingqing Wu, Huitao Srichola, Preeyanuch Hu, Hongjie Zhu, Wenchao Chen, Chao Xu, Fangyi Zhou, Yong Lin, Lanfen Tong, Ruofeng Li, Jingsong Chen, Yen-Wei Research Center for Healthcare Data Science Zhejiang Lab Hangzhou China Sir Run Run Shaw Hospital Zhejiang University School of Medicine Hangzhou China Intelligent Healthcare Team Baidu Inc. Beijing China Kasetsart Agricultural and Agro-Industrial Product Improvement Institute Kasetsart University Bangkok Thailand College of Computer Science and Technology Zhejiang University Hangzhou China Graduate School of Information Science and Engineering Ritsumeikan University Shiga Japan

ISBN: (纸本)9781510660359

COVID-19 has spread around the world since 2019. Approximately 6.5% of COVID-19 a risk of developing severe disease with high mortality rate. To reduce the mortality rate and provide appropriate treatment, this research established an integrated models with to predict the clinical outcome of COVID-19 patients with clinical, deep learning and radiomics features. To obtain the optimal feature combination for prediction, 9 clinical features combination was selected from all available clinical factors after using LASSO, 18 deep learning features from U-Net architecture, and 9 radiomics features from segmentation result. A total of 213 COVID-19 patients and 335 non-COVID-19 patients from 5 hospitals were enrolled and used as training and test sample in this research. The proposed model obtained an accuracy, precision, recall, specificity, F1-score and ROC curve of 0.971, 0.943, 0.937, 0.974, 0.941 and 0.979, respectively, which exceeds the related work using only clinical, deep learning or radiomics factors. © 2023 SPIE.

关键词： COVID-19

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The effects of mobile marketing collaborator on consumers' impulse buying behavior in online marketplace during pandemic 6

The effects of mobile marketing collaborator on consumers' i...

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6th International Conference on information Management and Technology, ICIMTech 2021

作者： Kusuma, Alvita Vania Daniel, Juan Hasri, Raja Alan Oktavia, Tanty Gaol, Ford Lumban Hosoda, Takaaki Bina Nusantara University School of Information Systems Information Systems Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program - Computer Science Computer Science Department Jakarta11480 Indonesia Advanced Institute of Industrial Technology Decision Science Department Tokyo Japan

ISBN: (纸本)9781665449373

This study analyzes the influence of the COVID-19 pandemic on impulse buying on the marketplace platform in Jakarta. This research is motivated because the COVID-19 pandemic period lasted quite a long time. Therefore, this research was conducted so that it can be useful for entrepreneurs or marketplace sellers during the COVID-19 pandemic. The research method used is descriptive quantitative method. Data collection techniques using questionnaires and literature study. The techniques used in statistical testing include validity testing, reliability testing, and hypothesis testing. When the research was conducted from September 2020 to January 2021. The results obtained were that there was a relationship between variables, such as mobile marketing, discounts, and lifestyle that could affect impulse buying on the marketplace even though they were moderately related. © 2021 IEEE.

关键词： COVID-19

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