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Is Green Energy the Pathway to Sustainability? – An Explanation From the Perspective of Degrowth Theory

Strategic Planning for Energy and the Environment 2024年第4期43卷 761-790页

作者： Yuan, Yiming Li, Li Zhang, Rui School of Economics and Management Beijing Information Science & Technology University Beijing100192 China Laboratory of Big Data Decision Making for Green Development Beijing Information Science & Technology University Beijing China Beijing International Science and Technology Cooperation Base of Intelligent Decision Making and Big Data Application Beijing Information Science & Technology University Beijing China

The discourse surrounding energy transition is intensifying due to insufficient global energy resources. Nonetheless, given that the capitalist economy prioritizes expansion and growth, it remains uncertain whether green energy solutions can genuinely pave the way towards sustainability and address the challenges of climate change, energy shortages, and food security concerns. Degrowth calls for a radical reorganization of politics and economics to reduce resource, energy consumption. This paper discusses the crisis of capitalism from multiple dimensions. It uses the Analytic Hierarchy Process (AHP) as an approach to explain degrowth and green energy. It also explores green energy in terms of three aspects of sustainable development: the profit-making industry, the high-efficiency paradox, and the re-challenge to the environment. Finally, it discusses the possible pathways for society to solve the global environmental crisis in the context of degrowth. These movements encourage critical thinking about the transition to green energy and offer a feasible pathway to sustainability without relying on growth ideology. This highlights the viability of the degrowth framework as a means to address a range of problems. © 2024 River Publishers.

关键词： Green development

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Safe and Sparse Newton Method for Entropic-Regularized Optimal Transport 38

Safe and Sparse Newton Method for Entropic-Regularized Optim...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Tang, Zihao Qiu, Yixuan School of Statistics and Data Science Shanghai University of Finance and Economics China

Computational optimal transport (OT) has received massive interests in the machine learning community, and great advances have been gained in the direction of entropic-regularized OT. The Sinkhorn algorithm, as well as its many improved versions, has become the de facto solution to large-scale OT problems. However, most of the existing methods behave like first-order methods, which typically require a large number of iterations to converge. More recently, Newton-type methods using sparsified Hessian matrices have demonstrated promising results on OT computation, but there still remain a lot of unresolved open questions. In this article, we make major new progresses towards this direction: first, we propose a novel Hessian sparsification scheme that promises a strict control of the approximation error;second, based on this sparsification scheme, we develop a safe Newton-type method that is guaranteed to avoid singularity in computing the search directions;third, the developed algorithm has a clear implementation for practical use, avoiding most hyperparameter tuning;and remarkably, we provide rigorous global and local convergence analysis of the proposed algorithm, which is lacking in the prior literature. Various numerical experiments are conducted to demonstrate the effectiveness of the proposed algorithm in solving large-scale OT problems. © 2024 Neural information processing systems foundation. All rights reserved.

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Web Application Development using Flask - Faculty Directory Management 14

Web Application Development using Flask - Faculty Directory ...

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14th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2023

作者： Tarunika, R. Rohit Ram, S.S. Sujithra, M. Sudha Devi, D. Department of Computing-Data Science Coimbatore Institute of Technology India Department of Computing-Data Science Coimbatore Institute of Technology India

The main goal of web development is to create, build and maintain websites. It is what allows the user to experience seamless performance when accessing a website. The web applications landscape has evolved tremendously over the years using various web development frameworks. A web development framework is a set of resources and tools for software developers to build and manage web applications, web services and websites. One such framework is Flask written in Python. The objective is to create a faculty directory using Flask with a goal to provide a centralized resource that can be used by students, staff, and administrators to easily find and connect with faculty members. By improving access to information about faculty members, the directory project aims to promote collaboration and facilitate the sharing of knowledge within the academic community. © Grenze Scientific Society, 2023.

关键词： Python

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DBRepo: A data Repository System for Research data in databases

DBRepo: A Data Repository System for Research Data in Databa...

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2024 IEEE International Conference on Big data, Bigdata 2024

作者： Weise, Martin Rauber, Andreas Ise Tu Wien Data Science Research Unit Vienna Austria

ISBN: (纸本)9798350362480

In the era of big data, research has become increasingly data-driven, with vast amounts of information being generated and analyzed to produce new insights and discoveries. This data deluge requires a combination of methods and technologies to store, process, share and preserve research data. With many of the world's most valuable data being stored in relational databases where it evolves over time as new knowledge is gained and old knowledge invalidated, current repository systems fail to provide researchers with interfaces to conveniently work with this kind of data within their research environments. For this reason, we have developed DBRepo, an institutional data repository for research data in databases (DBRepo) supporting guidelines of the Working Group on data Citation of the Research data Alliance. The system has been in use at TU Wien for almost three years now and provides a variety of data science-related interfaces and can be integrated into many workflows and tools. Further, it assists researchers in depositing their datasets by suggesting the table schema (column names, data types, primary key constraints) and it addresses data interoperability issues by suggesting semantic concepts for dataset columns and units of measurements, where applicable. DBRepo is currently in use by six universities globally who use it as data store for hot and cold research data sets. In the paper, we describe their use-cases and provide lessons learned from the various deployments and workflows. Finally, we show how depositing research data into DBRepo increases the data's visibility. © 2024 IEEE.

关键词： relational databases research data management research data repositories

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A Comparative Study of Generative Pre-trained Transformer-based Models for Chinese Slogan Generation of Crowdfunding 35

A Comparative Study of Generative Pre-trained Transformer-ba...

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35th Conference on Computational Linguistics and Speech Processing, ROCLING 2023

作者： Liang, Yu-Cheng Zheng, Meng-Heng Wu, Jheng-Long Department of Data Science Soochow University Taiwan

ISBN: (纸本)9789869576963

In recent years, language generation models have made significant progress and garnered extensive attention, aiming to generate diverse sentences across domains. However, effectively conveying deep semantics within constrained word limits and expressive formats remains a challenging endeavor. Therefore, we utilize GPT-2, GPT-3.5, and Bloom to generate slogan. Incorporating product descriptions, we have experimented, using metrics like ROUGE, BLEU, and semantic relevance for model evaluation. Overall, compared to product descriptions, GPT-3 demonstrates the best similarity in terms of vocabulary and meaning. In terms of human evaluation results, Bloom better captures the uniqueness of the slogan, while GPT-3 is more closely related to the description, and its sentences are the most fluent. © 2023 ROCLING 2023 - Proceedings of the 35th Conference on Computational Linguistics and Speech Processing. All rights reserved.

关键词： Conveying

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Resume Ranking and Shortlisting with DistilBERT and XLM 2

Resume Ranking and Shortlisting with DistilBERT and XLM

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2nd IEEE International Conference for Women in Innovation, Technology and Entrepreneurship, ICWITE 2024

作者： Mukherjee, Anuska Umme Salma, M. Christ Deemed to Be University Department of Statistics and Data Science Karnataka India

ISBN: (纸本)9798350383287

The research presented in this paper offers a solution to the time-consuming task of manual recruitment process in the field of human resources (HR). Screening resumes is a challenging and crucial responsibility for HR personnel. A single job opening can attract hundreds of applications. HR employees invest additional time in the candidate selection process to identify the most suitable candidate for the position. Shortlisting the best candidates and selecting the appropriate individual for the job can be difficult and time-consuming. The proposed study aims to streamline the process by identifying candidates who closely match the job requirements based on the skills listed in their resumes. Since it is an automated process, the candidate's individual preferences and soft skills remain unaffected by the hiring process. We leverage advanced Natural Language Processing (NLP) models to improve the recruitment process. Specifically, our emphasis lies in the utilization of the distilBERT model and the XLM (Crosslingual Language Model). This paper explores the application of these two models in taking hundreds of resumes for the job as input and providing the ranked resumes fit for the job as output. To refine our approach further, two types of metrics for resume ranking, such as Cosine similarity score and Spatial Euclidean distance, are used, and the results are compared. Intriguingly, distilBERT and XLM result in different sets of top ten ranked resumes, highlighting the nuanced variations in their ranking approaches. © 2024 IEEE.

关键词： Personnel

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Bayesian Optimized Random Forest Classifier for Improved Credit Card Fraud Detection: Overcoming Challenges and Limitations 8th

Bayesian Optimized Random Forest Classifier for Improved Cre...

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8th International Conference on Computers, Management and Mathematical sciences, ICCM 2022

作者： Rajesh, P.K. Shreyanth, S. Sarveshwaran, R. Nithin Chary, V. MTech in Data Science and Engineering Birla Institute of Technology and Science Rajasthan Pilani India

ISBN: (纸本)9783031511660

In the financial sector, credit card fraud is a common issue that costs both people and organizations a lot of money. The ability of machine learning algorithms to automatically identify patterns and anomalies from big datasets has made them a common method for fraud detection. In order to increase the detection of credit card fraud, we suggest an improved Bayesian random forest classifier in this article. By using Bayesian optimization to choose the ideal hyperparameters for the model, we handle the difficulties and restrictions of conventional random forest classifiers. Using an openly accessible dataset on credit card fraud, the proposed method is assessed and contrasted with other cutting-edge approaches. By obtaining an accuracy of 99.6% and an area under the curve (AUC) of 0.99, our findings demonstrate that the suggested optimized Bayesian random forest classifier outperforms conventional random forest and other benchmark methods. We also examine the significance of features in fraud identification in order to show the model’s interpretability. Finally, we discuss the suggested strategy’s drawbacks and potential future developments. Our study aids in the creation of reliable and effective fraud detection tools for the finance sector. The suggested approach can be used as a decision support tool for fraud analysts and detectives and can be applied to a variety of fraud detection issues beyond credit card fraud. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： data mining

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LLM-based methods for the creation of unit tests in game development 28th

LLM-based methods for the creation of unit tests in game dev...

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28th International Conference on Knowledge Based and Intelligent information and Engineering Systems, KES 2024

作者： Paduraru, Ciprian Staicu, Adelina Stefanescu, Alin University of Bucharest Department of Computer Science Romania Institute of Logic and Data Science Romania

Problems related to the quality of games, whether on the initial release or after updates, can lead to player dissatisfaction, media attention, and potential financial setbacks. These issues can stem from software bugs, performance bottlenecks, or security vulnerabilities. Despite these challenges, game developers often rely on manual playtesting, highlighting the need for more robust and automated processes in game development. This research explores the application of Large Language Models (LLMs) to automate the creation of unit tests in game development, focusing on strongly typed programming languages such as C++ and C#, which are widely used in the industry. The study focuses on fine-tuning Code Llama, an advanced code generation model, to address common scenarios in game development, including game engines and specific APIs or backends. Although the prototyping and evaluations primarily took place within the Unity game engine, the proposed methods can be adapted to other internal or publicly available solutions. The evaluation results demonstrate these methods' effectiveness in improving existing unit test suites or automatically generating new tests based on natural language descriptions of class contexts and targeted methods. © 2024 The Authors.

关键词： C++ (programming language)

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Are Multiple Instance Learning Algorithms Learnable for Instances? 38

Are Multiple Instance Learning Algorithms Learnable for Inst...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Jang, Jaeseok Kwon, Hyuk-Yoon Graduate School of Data Science Seoul National University of Science and Technology Korea Republic of

Multiple Instance Learning (MIL) has been increasingly adopted to mitigate the high costs and complexity associated with labeling individual instances, learning instead from bags of instances labeled at the bag level and enabling instance-level labeling. While existing research has primarily focused on the learnability of MIL at the bag level, there is an absence of theoretical exploration to check if a given MIL algorithm is learnable at the instance level. This paper proposes a theoretical framework based on probably approximately correct (PAC) learning theory to assess the instance-level learnability of deep multiple instance learning (Deep MIL) algorithms. Our analysis exposes significant gaps between current Deep MIL algorithms, highlighting the theoretical conditions that must be satisfied by MIL algorithms to ensure instance-level learnability. With these conditions, we interpret the learnability of the representative Deep MIL algorithms and validate them through empirical studies. © 2024 Neural information processing systems foundation. All rights reserved.

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Scalable and High-Performance Large-Scale Dynamic Graph Storage and Processing System

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ACM Transactions on Storage 2025年第2期21卷 1-34页

作者： Wang, Rui Zong, Weixu He, Shuibing Li, Yongkun Xu, Yinlong Zhejiang University Hangzhou China Institute of Blockchain and Data Security Hangzhou China University of Science and Technology of China Hefei China Computer Science University of Science and Technology of China Heifei China

Existing in-memory graph storage systems that rely on DRAM have scalability issues because of the limited capacity and volatile nature of DRAM. The emerging persistent memory (PMEM) offers us a chance to solve these issues through its larger capacity and non-volatile characteristics. However, simply adapting existing DRAM-based graph storage systems to PMEM would result in inefficient PMEM stores and accesses, including high read and write amplification to PMEM, imbalanced work division for PMEM accesses, and costly remote PMEM access across NUMA nodes. These issues severely limit the performance of large graph processing. In this article, we aim at achieving scalable and high-performance graph processing in PMEM. We first propose an XPLine-friendly graph storage model that uses vertex-centric graph buffering, hierarchical vertex buffer managing, and in-place vertex block merging to optimize PMEM graph storage. Furthermore, we develop a scalable graph processing model that leverages multi-threaded work dividing and NUMA-friendly graph accessing to optimize PMEM graph accesses. Based on these techniques, we implement XPGraph, a PMEM-based graph storage system for large-scale evolving graphs, and several variants for different system settings. Our experiments demonstrate that XPGraph surpasses the state-of-the-art in-memory graph storage system on a PMEM-based system by 3.07× to 4.99× in update performance and up to 5.87× in query performance, and performs much better in highly parallel multi-threaded scenarios. © 2025 Copyright held by the owner/author(s).

关键词： Dynamic random access storage

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