bluuebunny/crossref_metadata_2025_split huggingface dataset:
Analisis Kandungan Kimia Biji Kopi Arabika Solok berdasarkan Tingkat Kematangan yang Berbeda
Authors: Yuwita Fitri , Anis Ulfah , Uker Damres 
Published: June 2023

ABSTRACT
The rate of increase in coffee production in Solok, especially Arabica coffee, must be accompanied by a good post-harvest period. Now the rate of increase in the area of ​​Arabica coffee production in the Solok area has not been matched by the uniformity of the quality produced. The first thing to pay attention to in maintaining quality is the maturity level the coffee beans harvest. The purpose of this study was to determine the characteristics of the chemical content of Solok Arabica coffee beans with two different maturity levels. The material used in the study was Arabica Solok coffee beans harvested directly from the farmers' gardens. Coffee beans are picked at two different maturity levels, namely ripe and overripe Arabica coffee beans. The method used is descriptive analytics to determine the chemical properties of coffee with two maturity levels. The results showed that the level of maturity affected chemical compounds such as caffeine, protein, and fat in Solok Arabica coffee beans. Caffeine content at ripe maturity is 1.56% and increases at overripe to 2.38%. Protein content at ripe maturity is 13.35%, which increases at overripe to 13.54%. The ripe fat content, which was 13.06%, decreased to 11.70%.
Keywords: Arabica Coffee, Chemical Content, Level of Maturity

ABSTRAK
Laju peningkatan produksi kopi di Solok, khususnya kopi arabika harus diiringi dengan pasca panen yang baik. Saat ini laju peningkatan area produksi kopi arabika di daerah Solok belum diimbangi dengan keseragaman mutu yang dihasilkan. Hal pertama yang harus diperhatikan untuk menjaga mutu adalah tingkat kematangan dari panen biji kopi. Penelitian ini bertujuan mengetahui karakteristik kandungan kimia dari biji kopi arabika Solok dengan dua tingkat kematangan yang berbeda. Bahan yang digunakan pada penelitian adalah biji kopi Arabika Solok yang dipanen langsung dari kebun petani. Biji kopi dipetik dengan dua tingkat kematangan yang berbeda yaitu biji kopi arabika masak sempurna (ripe) dan kelewat matang (overripe). Metode yang digunakan yaitu deskriptif analitik untuk mengetahui sifat kimia kopi dengan dua tingkat kematangan. Hasil penelitian menunjukkan tingkat kematangan mempengaruhi kandungan kimia seperti kafein, protein, dan lemak pada biji kopi arabika Solok. Kadar kafein pada kematangan ripe 1,56%, meningkat pada overripe menjadi 2,38%. Kadar protein pada kematangan ripe 13.35 %, meningkat pada overripe menjadi 13.54 %. Kadar lemak ripe yaitu 13.06% mengalami penurunan menjadi 11.70 %.
Kata kunci : Kopi Arabika, Kandungan Kimia, Tingkat Kematangan
EVALUASI ARSITEKTUR JARINGAN KOMPUTER PEMERINTAH DAERAH DALAM PENYELENGGARAAN SISTEM PEMERINTAH BERBASIS EKEKTRONIK (SPBE)
Authors: Sa`di Ahmad , Andriani Ria , Ghozali Bahrun 
Published: June 2023

Penyelengggaraan jaringan komputer di pemerintah daerah belum memenuhi standar ketersediaan. Permasalahan yang sering dijumpai yaitu perluasan jaringan dilakukan asal terhubung, sehingga berkibat bencana. Ketika terjadi masalah pada jalur jaringan atau perangkat sehingga berdampak besar pada layanan atau aplikasi di jaringan, protokol yang mendukung redundansi juga tidak digunakan, dan beberapa gedung pemerintahan tidak terhubung ke jaringan. Kondisi tersebut belum mendukung terwujudnya Sistem Pemerintahan Berbasis Elektronik.
Peneliti mengusulkan, Pemerintah yang memiliki wewenang menyediakan jaringan privat pemerintah menggunakan konsep connected, scalability, high availability, security, dan integrated. Metode yang digunakan yaitu pendekatan audit yaitu pre-audit, perform audit dan pasca-audit. Hasilnya yaitu berupa rekomendasi arsitektur jaringan intra pemerintah untuk mewujudkan sistem pemerintah berbasis elektronik yang mumpuni.
Untuk menaikkan level ke skalability, dengan menggunakan pendekatan jaringan hierarki. Kuncinya yaitu dengan memperhatikan konsep desain jaringan, kualitas dan kapabilitas perangkat di layer edge, core, distribusi dan access. Pada level handal yaitu dengan mempertimbangkan layer fisik dan layer logis. Memastikan jalur redundant dan protokol yang digunakan harus mendukung failover. Protocol yang digunakan pada layer edge, core distribusi menggunakan protorokol mendukung failover. Di tingkatan selanjutnya yaitu terintegrasi. Jaringan dapat mendukung integrasi data di pusat data dengan data di DRC
A Conceptual Insight Into Achieving Interoperability Between Heterogeneous Blockchain Enabled Interconnected Smart Microgrids
Authors: Dinesha Disha Lagadamane , Patil Balachandra 
Published: June 2023

<p>Blockchain technology (BCT) has emerged as a game-changer for many industries since its inception in 2008. Its application in the energy industry as blockchain enabled interconnected smart microgrids (BSMG) is on the rise as it can execute energy trading, automate the market operations, manage the grid, and facilitate real-time payments. With the increase in usage of BSMGs, different types of BCT will emerge, making the system heterogeneous in nature. BCT is also limited currently due to its scalability and low transaction rate. Interoperability between heterogeneous BSMGs can counteract the drawbacks and improve the functionality and, thereby, adoption of BCT in energy. This paper recognises the need for interoperability and thoroughly reviews the different methods of interoperability that currently exist (i.e., notary mechanism, relay or side chains, hashed time-locked contracts, and blockchain routers). Two relay mechanisms - Polkadot and Cosmos with Inter Blockchain Communication Protocol (IBC) are reviewed further to determine the usability of the protocols. Finally, a conceptual architecture of interconnection of heterogeneous BSMGs is proposed. Conceptual solution to connecting Ethereum and Hyperledger Fabric to the Cosmos Hub through IBC is explored. While interoperability between heterogeneous users is addressed in Decentralized Finance (DeFi), it has not yet been addressed in energy. A conceptual solution is provided for this research gap for the first time in energy domain. </p>
Analysis of Time-to-Lane-Change-Initiation Using Realistic Driving Data
Authors: Jokhio Sarang , Olleja Pierluigi , Bärgman Jonas , Yan Fei , Baumann Martin 
Published: June 2023

<p>Lane changing is complex, yet it is one of the most common manoeuvres a driver performs in everyday driving. Before initiating a lane change, drivers should indicate their intention using a turn signal. When drivers are changing lanes, their use of a turn signal could provide meaningful insights about their behaviour. Therefore, we investigated the behaviour of drivers initiating a lane change using realistic data. In particular, time-to-lane-change-initiation (TTLCI) was investigated. TTCLI is the time from when a driver signals to when the driver initiates the lane change. We used the Kaplan-Meier method and mixed effect Cox Proportional Hazard (CPH) model to analyze the lane change data obtained from a large dataset collected as part of the L3Pilot project. The results indicate that most lane changes were initiated within 2 seconds of signalling. Furthermore, the results of the mixed-effect CPH model revealed that the speed of the lane-changing vehicle, the type and the direction of the lane change, and the presence of either a lead or lag vehicle and the lag gap significantly affected the TTLCI. The findings of this paper provide detailed insights into pre-lane change behaviour and pave the way for future studies. The findings of this study have important implications for the development of regulations concerning the usage of turn signals by human drivers. Additionally, they have implications for the development of advanced algorithms for autonomous vehicles, particularly in improving the detection of lane changes of surrounding vehicles and estimation of the remaining time to it.</p>
The Expression of Happiness in Social Media of Individuals Diagnosed With Depression
Authors: Bucur Ana-Maria , Chulvi Berta , Cosma Adrian , Rosso Paolo 
Published: June 2023

<p>Depression has long been studied in the NLP field, with most works focusing on individuals' negative emotions. People with depression also experience happiness, but this was not extensively studied.Previous works have shown that approaches relying on sentiment or emotion classification are unsuitable for extracting the expressions of feelings that bring happiness to an individual because they may not be expressed in positive words only. In this work, we conduct a large-scale study of happy moments from social media texts of depressed and non-depressed individuals. We develop an extensive deep learning-based framework to extract happy moments from text, and annotate them with semantic topics, gender labels, and agency and sociality measures. We analyze over 400,000 happy moments and show significant differences in topics, agency, and sociality of depressed and not-depressed users, varying by gender. Both male and female users with depression expressed less sociality in their happy moments than control users. Male users' agency was not impaired in depression, while female users with depression expressed fewer happy moments with agency than the control group. Our research can inform psychology interventions, which can foster feelings of longer-lasting happiness and represent a promising path of collaboration between computational linguistics and psychology.</p>
Graphical Consensus-based Sharding for Efficient and Secure Sharings in Blockchain-enabled Internet of Vehicles
Authors: Li Wenqi , Zhao Zheng , Ma Pingchuan , Xie Zeqiang , Palade Vasile , Liu Hongbo 
Published: June 2023

<p>Blockchain-enabled Internet of Vehicles provides a reliable collaboration environment for traffic entities and promotes road safety and traffic efficiency through real-time sharings between vehicles and infrastructure. However, this is usually confronted by a bottleneck as the Internet of Vehicles scales up. In this paper, we propose a novel Graphical Consensus-based Sharding (GCS) framework, which is underpinned by four important strategies: 1) A graphical consensus is adopted as its intra-shard consensus, where the consensus group is set up according to its maximal connected subgraph, and the leader is elected by its reliability weight. The consensus group is refreshed intermittently by alternating the role of leader. 2) Within GCS, the intra-shard data are stored in the local chain, while a block-based directed acyclic graph, rather than a chained structure, is employed as the main chain. The local chain is used to respond to requests within each shard, and the main chain supports the cross-shard sharings. GCS will parallelly optimize the throughput of the blockchain-enabled Internet of Vehicles. 3) GCS further introduces the shard backup and node scheduling to handle shard failure and overheating by using new backup strategies and temporal-spatial graph convolutional network prediction model, respectively. 4) An off-chain transmission algorithm is presented for secure sharings between the infrastructure and the vehicles. Simulation results show that the number of Transactions Per Second is 1.69 times higher than that of the non-sharding blockchain, and the pending time is dramatically reduced compared to the mainstreaming sharding approach, which is 1.02s. </p>
A Decoupling Paradigm with Prompt Learning for Remote Sensing Image Change Captioning
Authors: Liu Chenyang , Zhao Rui , Chen Jianqi , Qi Zipeng , Zou Zhengxia , Shi Zhenwei 
Published: June 2023

<p>Remote sensing image change captioning (RSICC) is a novel task that aims to describe the differences between bi-temporal images by natural language. Previous methods ignore a significant specificity of the task: the difficulty of RSICC is different for unchanged and changed image pairs. They process the unchanged and changed image pairs in a coupled way, which usually causes confusion for change captioning. In this paper, we decouple the task into two issues to ease it: whether and what changes have occurred. An image-level classifier performs binary classification to address the first issue. A feature-level encoder contributes to extracting discriminative features to help the caption generation module address the second issue. For caption generation, we utilize prompt learning to introduce pre-trained large language models (LLMs) into the RSICC task. A multi-prompt learning strategy is proposed to generate a set of unified prompts and a class-specific prompt conditioned on the image-level classifier's results. It can prompt a pre-trained LLM to know whether changes exist and generate captions. Finally, the multiple prompts and the features of the feature-level encoder are fed into a frozen LLM for captioning. Compared with previous methods, our method can leverage the powerful abilities of the pre-trained LLM in language to generate plausible captions, which is free of training. Extensive experiments show that our method is effective and achieves state-of-the-art performance. Besides, an additional experiment demonstrates that our decoupling paradigm is more promising than the previous coupled paradigm for the RSICC task. We will make our codebase publicly available to facilitate future research at https://github.com/Chen-Yang-Liu/PromptCC</p>
A Reference Architecture for Quantum Computing as a Service
Authors: Ahmad Aakash , Altamimi Ahmed B. , Aqib Jamal 
Published: June 2023

<p>Quantum computers (QCs) aim to disrupt the status-quo of computing – replacing traditional systems and platforms that are driven by digital circuits and modular software – with hardware and software that operates on the principle of quantum mechanics. QCs that rely on quantum mechanics can exploit quantum circuits (i.e., quantum bits for manipulating quantum gates) to achieve ‘quantum computational supremacy’ over traditional, i.e., digital computing systems. This research contributes by (i) developing a reference architecture for enabling quantum computing as a service, (ii) implementing microservices with the quantum-classic split pattern as an architectural use-case, and (iii) evaluating the reference architecture based on feedback by 22 practitioners. In the QSE context, the research focuses on unifying architectural methods and service-orientation patterns to promote reuse knowledge and best practices to tackle emerging and futuristic challenges of architecting and implementing Quantum Computing as a Service (QCaaS). </p>
Inspection in high-mix and high-throughput handling with skeptical and incremental learning
Authors: Schimanek Robert , Bilge Pinar , Dietrich Franz 
Published: June 2023

<p>This preprint is based on an accepted paper for the Annals of Scientific Society for Assembly, Handling, and Industrial Robotics 2023, and may undergo further revisions before the final publication. This preprint presents a groundbreaking research study that focuses on the application of incremental learning processes to enhance high-throughput product inspection and minimize false labeling in industrial environments.</p>
<p>The paper tackles a critical challenge faced by industries today, which is the accurate identification and labeling of products during the inspection process. Traditional methods often fall short in maintaining high accuracy rates due to evolving production lines and ever-changing product variations. However, this preprint introduces an innovative approach that leverages incremental learning techniques to adapt and improve the inspection process continuously.</p>
<p>By employing incremental learning, the proposed system can autonomously learn from new data and make adjustments to its inspection algorithms in real-time. This adaptive capability allows the system to keep up with changing product characteristics, ensuring accurate and reliable labeling throughout high-throughput operations. The result is a significant reduction in false labeling, leading to enhanced quality control and improved efficiency in industrial environments.</p>
<p>We invite researchers, industry professionals, and enthusiasts in the field of assembly, handling, and industrial robotics to explore this preprint. The findings presented here have the potential to revolutionize the way product inspection is conducted in industrial settings, addressing a critical aspect of quality control. We encourage readers to engage in discussions, share feedback, and contribute to the advancement of this exciting research area.</p>
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<p><strong>Abstract:</strong> In the circular economy, remanufacturing success relies heavily on the accurate identification and classification of used products. Processes, which rely on worker experience, lack objective validation, leading to the potential for mislabeling and inaccurate damage assessment. This, in turn, results in additional manual evaluations and unproductive costs, which run counter to the principles of sustainability. To address these issues, machine learning and artificial intelligence have been applied with promising results. However, producing reliable large amounts of labeled data remains a challenge, as workers are susceptible to human error. This paper addresses process design in production. It proposes a new design to ensure that only valid labels enter the prediction models, reducing the potential for false labels in the dataset. Through this, the aim is to improve the accuracy and reliability of remanufacturing, ultimately reducing costs and mitigating the carbon footprint in the manufacturing, repair, and maintenance industries. </p>
AI for Anomaly Detection in IoT
Authors: Ma Maode , Harsh Piyush 
Published: June 2023

<p>The Internet of Things (IoT) is today’s rapidly growing technology that consists of a massive number of smart devices. The advent of IoT has brought about huge prospects of innovation and opportunities on the domestic as well as industry levels. However, there are vulnerabilities and weaknesses of IoT that inflame concerns about security and privacy issues. Enhancing the security of IoT networks is a critical issue and Intrusion Detection Systems (IDS) become the forefront solution in safeguarding the network from different malicious attacks. In this work, we tackle the emerging anomaly detection problem in IoT using Artificial Intelligence, more precisely a deep learning scheme called Convolutional Neural Network (CNN). The CSE-CIC-IDS 2018 dataset provided by the Canadian Institute of Cybersecurity was used for the experimental analysis. According to the study, the suggested approach outperforms the existing machine learning-based algorithms, with a low false alarm rate and better detection rate in the detection of attacks. </p>
Optimal Hub Placement and Deadlock-Free Routing for Payment Channel Network Scalability
Authors: Yang Lingxiao , Dong Xuewen , Gao Sheng , Qu Qiang , Zhang Xiaodong , Tian Wensheng , Shen Yulong 
Published: June 2023

<p>As a promising implementation model of payment channel network (PCN), payment channel hub (PCH) could achieve high throughput by providing stable off-chain transactions through powerful hubs. However, existing PCH schemes assume hubs preplaced in advance, not considering payment requests' distribution and may affect network scalability, especially network load balancing. In addition, current source routing protocols with PCH allow each sender to make routing decision on his/her own request, which may have a bad effect on performance scalability (e.g., deadlock) for not considering other senders' requests. This paper proposes a novel multi-PCHs solution with high scalability. First, we are the first to study the PCH placement problem and propose optimal/approximation solutions with load balancing for small-scale and large-scale scenarios, by trading off communication costs among participants and turning the original NP-hard problem into a mixed-integer linear programming (MILP) problem solving by supermodular techniques. Then, on global network states and local directly connected clients' requests, a routing protocol is designed for each PCH with a dynamic adjustment strategy on request processing rates, enabling high-performance deadlock-free routing. Extensive experiments show that our work can effectively balance the network load, and improve the performance on throughput by 29.3% on average compared with state-of-the-arts. </p>
Common-Mode Frequency of Power Systems Affected by Voltage Dynamics
Authors: Gao Huisheng , Xin Huanhai , Hu Guang , Cao Bin , Yuan Hui , Huang Linbin 
Published: June 2023

<p>The high penetration of converter-interfaced generation (CIGs) in power systems has raised concerns regarding the risk of frequency collapse. However, most existing theoretical analysis methods for system frequency assume constant bus voltages, which can lead to inaccurate results and potentially incorrect conclusions. The main challenge lies in the absence of analytical models that consider voltage dynamics when representing the system-level frequency. To address this issue, this article defines the voltage-influenced common-mode frequency (VCMF), which captures the dynamics of the system frequency in the scenario of non-constant bus voltages. The derivation process is implicitly based on the strong relationship between the consistent part of the bus frequencies (i.e., system frequency) and the rotational invariance of power flow. The expression of VCMF demonstrates that non-constant voltages affect the system frequency through a global term that involves the dynamics of all the devices, making its analysis challenging. Therefore, a network partitioning method based on the algebraic graph theory is proposed, which effectively divides the globally coupling term into several locally coupling terms, enabling a more tractable analysis of VCMF. Finally, simulations are used to validate the proposed method and confirm its validity. </p>
Prompt Sapper: LLM-Empowered Software Engineering Infrastructure for AI-Native Services
Authors: Xing Zhenchang , Huang Qing , Cheng Yu , Zhu Liming , Qinghua Lu, , Xiwei Xu, 
Published: June 2023

<p>Foundation models, such as GPT-4, DALL-E have brought unprecedented AI "operating system" effect and new forms of human-AI interaction, sparking a wave of innovation in AI-native services, where natural language prompts serve as executable "code" directly (prompt as executable code), eliminating the need for programming language as an intermediary and opening up the door to personal AI. Prompt Sapper has emerged in response, committed to support the development of AI-native services by AI chain engineering. It creates a large language model (LLM) empowered software engineering infrastructure for authoring AI chains through human-AI collaborative intelligence, unleashing the AI innovation potential of every individual, and forging a future where everyone can be a master of AI innovation. This article will introduce the R\&D motivation behind Prompt Sapper, along with its corresponding AI chain engineering methodology and technical practices. </p>