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2024 International Conference on Information Technology and Computing, ICITCOM 2024

作者： Rajagukguk, Indra Marihot Lukito, Benedictus Kenneth Santoso Gunawan, Alexander Agung Syahputra, Muhammad Edo School of Computer Science University Bina Nusantara Computer Science Program Computer Science Departement Jakarta11480 Indonesia

ISBN: (纸本)9798350379839

An earthquake happens when the energy from the fracture of rock strata within the crust causes the earth to vibrate. Earth's surface experiences seismic waves because of this energy, which is produced by tectonic plate movement and radiates outward in all directions. Indonesia is especially vulnerable to earthquakes because of its location straddling three tectonic plates: the Pacific, Eurasian, and Indo-Australian. This project aims to mitigate this risk by using the clustering approach to evaluate large seismic datasets from the Meteorology, Climatology, and Geophysics Agency (BMKG) through 2022. Using the Clustering approach to forecast earthquake events requires a few crucial steps. First, a variety of sources provide seismic data that includes information about earthquake locations, magnitudes, depths, and timestamps. Based on the commonalities found in these attributes, the Clustering Method is then utilized to categorize seismic occurrences. The study concludes that K-Means is a better method for assessing seismic event clusters than DBSCAN. Compared to DBSCAN (0.009), K-Means produced more balanced and well-organized clusters with a silhouette score of 0.568. According to the K-means clustering results, there have been 3,756 occurrences (34.62%) and 3,932 incidents (36.24%) in the bright green and dark blue zones, respectively, which are places with high seismic intensity. This implies that to lessen earthquake damage in the future, these zones need to increase their mitigation efforts. © 2024 IEEE.

关键词： Seismic waves

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KnowLog: Knowledge Enhanced Pre-trained Language Model for Log Understanding 24

KnowLog: Knowledge Enhanced Pre-trained Language Model for L...

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46th IEEE/ACM International Conference on Software Engineering, ICSE 2024

作者： Ma, Lipeng Jiang, Sihang Yang, Weidong Fei, Ben Xu, Bo Liang, Jiaqing Zhou, Mingjie Xiao, Yanghua Shanghai Key Laboratory of Data Science School of Computer Science Fudan University Shanghai China School of Computer Science and Technology Donghua University Shanghai China School of Data Science Fudan University Shanghai China

ISBN: (纸本)9798400702174

Logs as semi-structured text are rich in semantic information, making their comprehensive understanding crucial for automated log analysis. With the recent success of pre-trained language models in natural language processing, many studies have leveraged these models to understand logs. Despite their successes, existing pre-trained language models still suffer from three weaknesses. Firstly, these models fail to understand domain-specific terminology, especially abbreviations. Secondly, these models struggle to adequately capture the complete log context information. Thirdly, these models have difficulty in obtaining universal representations of different styles of the same logs. To address these challenges, we introduce KnowLog, a knowledge-enhanced pre-trained language model for log understanding. Specifically, to solve the previous two challenges, we exploit abbreviations and natural language descriptions of logs from public documentation as local and global knowledge, respectively, and leverage this knowledge by designing novel pre-training tasks for enhancing the model. To solve the last challenge, we design a contrastive learning-based pre-training task to obtain universal representations. We evaluate KnowLog by fine-tuning it on six different log understanding tasks. Extensive experiments demonstrate that KnowLog significantly enhances log understanding and achieves state-of-the-art results compared to existing pre-trained language models without knowledge enhancement. Moreover, we conduct additional experiments in transfer learning and low-resource scenarios, showcasing the substantial advantages of KnowLog. Our source code and detailed experimental data are available at https://***/LeaperOvO/KnowLog. © 2024 IEEE computer Society. All rights reserved.

关键词： Computational linguistics

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Influence of different form of multimedia on motivation and topic perception in augmented reality learning experiences

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Multimedia Tools and Applications 2025年 1-28页

作者： Yap, Shook Chin Rahmat, Rahmita Wirza Ali, Siti Khadijah School of Computing and Data Science Xiamen University Malaysia Selangor Sepang Malaysia Faculty of Computer Science and Information Technology Universiti Putra Malaysia Selangor Serdang Malaysia

It has been widely proven that Augmented Reality (AR) brings numerous benefits in learning experiences, including enhancing learning outcomes and motivation. However, not many studies investigate how different forms of multimedia help in motivating students in AR learning experiences. Instead of examining whether AR can be used in education to motivate students, our research focused on investigating how and when AR learning experiences influence motivation when integrated with multimedia learning. A between-subjects 2 × 3 factorial design was employed in this study. The first factor was the type of AR (static-AR or animated-AR), and the second factor was the modality of the description (concurrent narration-text, text only, or narration only). Six experimental conditions were designed, each equipped with different versions of AR learning applications with various combinations of multimedia formats. One hundred and forty-four computer science and information technology undergraduate students (70 males and 74 females) were randomly assigned to one of the six experimental conditions. data collection was conducted with questionnaires, and data were analyzed using univariate analysis of variance (ANOVA). Results show that attention and satisfaction subscales of the ARCS motivational model can be enhanced with 3D animation implemented in AR. Descriptions delivered with concurrent narration and written text in AR are effective in enhancing motivation as well. Besides, participants’ perceived understanding and interest in learning the topic increased after AR learning experiences. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Contrastive Learning

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A hybrid memory architecture supporting fine-grained data migration

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Frontiers of computer science 2024年第2期18卷 31-41页

作者： Ye CHI Jianhui YUE Xiaofei LIAO Haikun LIU Hai JIN National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabCluster and Grid Computing LabSchool of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China Department of Computer Science Michigan Technological UniversityMichigan 49931USA

Hybrid memory systems composed of dynamic random access memory(DRAM)and Non-volatile memory(NVM)often exploit page migration technologies to fully take the advantages of different memory *** previous proposals usually migrate data at a granularity of 4 KB pages,and thus waste memory bandwidth and DRAM *** this paper,we propose Mocha,a non-hierarchical architecture that organizes DRAM and NVM in a flat address space physically,but manages them in a cache/memory *** the commercial NVM device-Intel Optane DC Persistent Memory Modules(DCPMM)actually access the physical media at a granularity of 256 bytes(an Optane block),we manage the DRAM cache at the 256-byte size to adapt to this feature of *** design not only enables fine-grained data migration and management for the DRAM cache,but also avoids write amplification for Intel Optane *** also create an Indirect Address Cache(IAC)in Hybrid Memory Controller(HMC)and propose a reverse address mapping table in the DRAM to speed up address translation and cache ***,we exploit a utility-based caching mechanism to filter cold blocks in the NVM,and further improve the efficiency of the DRAM *** implement Mocha in an architectural *** results show that Mocha can improve application performance by 8.2%on average(up to 24.6%),reduce 6.9%energy consumption and 25.9%data migration traffic on average,compared with a typical hybrid memory architecture-HSCC.

关键词： non-volatile memory hybrid memory system data migration fine-grained caching

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Predictive Analytics for Future Air Pollution Levels Based on Population Growth 9th

Predictive Analytics for Future Air Pollution Levels Based o...

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9th International Conference on computer science and Computational Intelligence, ICCSCI 2024

作者： Yeshe, Darel Wairooy, Irma Kartika Makalew, Brilly Andro Computer Science Department School of Computer Science Jakarta11480 Indonesia Mobile Application & Technology Program Computer Science Department School of Computer Science Jakarta11480 Indonesia

Air is very beneficial and crucial for every living creature on earth, hence it is very important to protect the air quality in order to avoid diseases. However, due to the increase in population, some human activities have been causing detrimental damage to the air quality. Some people have realized the potential impact of this problem and have done studies on predicting future air pollution. Some implemented machine learning models such as random forest regression, support vector machine, etc, while others have utilized deep learning models in their research.. This study will implement five different models, specifically linear regression, ridge regression, random forest regression, and multilayer perceptron regression. There will be two datasets used in this paper which will be merged and processed. There will be three key evaluation metrics being used in this paper namely root mean squared error, mean absolute error, and r-squared. The results from all of the models have concluded that more optimization and factors are needed in order to boost the final result of this study. © 2024 The Authors.

关键词： Adversarial machine learning

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Modeling the Percentage of NEET in Indonesia with Spatial Cauchy Regression through the Bayesian Analysis Approach

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IAENG International Journal of Applied Mathematics 2024年第7期54卷 1288-1295页

作者： Rantini, Dwi Fakhruzzaman, Muhammad Noor Ningrum, Ratih Ardiati Othman, Fazidah Choir, Achmad Syahrul Ramadan, Arip Alya, Najma Attaqiya Putri, Elfira Rahma Pratama, Muhammad Alfian Engineering Department Data Science Technology Study Program Faculty of Advanced Technology and Multidiscipline Universitas Airlangga Indonesia Engineering Department Data Science Technology Study Program Faculty of Advanced Technology andMultidiscipline Universitas Airlangga Indonesia Department of Computer System and Technology Faculty of Computer Science and Information Technology University of Malaya Malaysia Statistical Computing StudyProgram Politeknik Statistika STIS Indonesia Information System Study Program School of Industrial and System Engineering Telkom University Indonesia

Indonesia has entered a period of demographic bonus. Human resources must be optimized. The number of children who do not in employment, education or training (NEET) in each province needs attention. Several factors that could contribute to the decline in the NEET percentage are literacy rates and the number of adolescents with computer skills. Increasing these two factors is believed to be able to reduce the percentage of NEET in every province in Indonesia. To find out the relationship between these two factors and how much influence they have on the percentage of NEET, this research is modeled by Cauchy regression and includes spatial effects. The results of the analysis show that the best model is when the spatial effect is modeled by Fernandez Steel Skew Normal conditionally autoregressive (FSSN CAR). This result is seen from the smallest value of the Watanabe Akaike Information Criterion (WAIC) in this model, which is 190.5. The parameter estimated shows that the higher the literacy rate and the number of adolescents with computer skills, the lower the percentage of NEET in each province in Indonesia. The results of this research can be useful for the Indonesian government to increase the number of educational facilities related to these two factors. © (2024), (International Association of Engineers). All rights reserved.

关键词： Bayesian CAR Cauchy Education NEET

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Enhancing Field Tracking and Interprocedural Analysis to Find More Null Pointer Exceptions 31

Enhancing Field Tracking and Interprocedural Analysis to Fin...

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31st IEEE International Conference on Software Analysis, Evolution and Reengineering, SANER 2024

作者： Xie, Dongfang Chen, Bihuan Huang, Kaifeng Wang, Yu Pan, Linghao Chen, Zhicheng Peng, Xin Fudan University School of Computer Science Shanghai Key Laboratory of Data Science China

ISBN: (纸本)9798350330663

Null pointer dereference raises Null Pointer Exceptions (NPEs). There are two groups of approaches to detect NPEs. Type-based approaches carry out strict type-based null safety checking. They heavily rely on annotations, and thus produce many false positives. dataflow-based approaches leverage static forward and/or backward dataflow analysis. They mostly have a limited capability in tracking fields and interprocedural analysis, and introduce false positives and false negatives. To address these drawbacks, we propose Wheeljack to detect NPEs for Java. It does not rely on annotations, and hence can work effectively under a lack of annotations. It leverages our novel abstraction of nullness status to enhance field tracking, and our novel invocation analysis (capturing change to return value and side effect of an invocation) to enhance interprocedural analysis. Our evaluation on 28 Java projects has demonstrated that Wheeljack can mostly outperform the four state-of-the-art NPE detectors in recall without sacrificing precision. 5 and 2 new NPEs have been confirmed and fixed by developers after we submit 8 issues. © 2024 IEEE.

关键词： data flow analysis

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Fast and scalable off-target assessment for CRISPR guide RNAs using partial matches

Fast and scalable off-target assessment for CRISPR guide RNA...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Schmitz, Carl Bradford, Jacob Salomone, Robert Perrin, Dimitri Queensland University of Technology School of Computer Science Centre for Data Science Brisbane Australia

ISBN: (纸本)9798350386226

The design of CRISPR-Cas9 guide RNAs requires the analysis of all potential target sites to determine the risk of unintentional edits for any given guide. A number of methods have been developed to evaluate this risk but the size of the target genome is a challenging factor. Many existing methods are limited by their need for an unfeasible amount of time or memory. In this paper, we introduce Crackling++, an update to our previous Crackling method, in which we have reduced processing time by imposing stricter matching criteria. We conducted a benchmark to compare our performance to other tools and show that Crackling++ is the fastest off-targeting scoring method and that it can scale to the largest genomes. © 2024 IEEE.

关键词： Genome

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Multi-Dimensional Training Optimization for Efficient Federated Synergy Learning

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IEEE Transactions on Mobile Computing 2025年第7期24卷 6243-6258页

作者： Fu, Shucun Dong, Fang Chen, Runze Shen, Dian Zhang, Jinghui He, Qiang Southeast University School of Computer Science and Engineering Nanjing China Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Wuhan China

Edge learning (EL) is an end-to-edge collaborative learning paradigm enabling devices to participate in model training and data analysis, opening countless opportunities for edge intelligence. As a promising EL framework, federated synergy learning (FSyL) mitigates the computation and communication overhead on resource-constrained devices by offloading partial model layers to the edge server for synergistic training. Nevertheless, due to the system and statistical heterogeneity, naively using existing FSyL methods is significantly time-consuming and causes accuracy degradation. Motivated by this issue, this paper introduces a novel FSyL framework that integrates multi-dimensional training optimization and formulates the edge learning cost minimization (ELCM) problem. To tackle the ELCM efficiently, we design OL-MG, an OnLine Model Splitting and Resource Provisioning Game. Specifically, we first reformulate and decompose the original ELCM based on data quality evaluation. Then, given a model splitting decision, we determine the optimal resource provisioning in Sub-problem1, based on which optimal model splitting in Sub-problem2 is modeled as a potential game. Subsequently, we introduce a decentralized algorithm to find a Nash equilibrium (NE) solution. Furthermore, we further extend OL-MG to support a budget-aware multi-edge scenario. Extensive experiments demonstrate that the proposed mechanism significantly outperforms state-of-the-art methods in cost-saving and accuracy improvement. © 2025 IEEE.

关键词： Federated learning

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Energy Efficiency in Sustainable Software Development: Clean Code Approaches 4

Energy Efficiency in Sustainable Software Development: Clean...

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4th International Conference on Electrical Engineering and computer science, ICECOS 2024

作者： Inayatullah Widhiarso, Wijang Stiawan, Deris Universitas Multi Data Palembang Faculty of Computer Science Department Program in Engineering Science Palembang Indonesia Universitas Sriwijaya Faculty Af Computer Science Department Program in Engineering Science Palembang Indonesia Universitas Multi Data Palembang Faculty of Computer Science Palembang Indonesia Universitas Sriwijaya Faculty Af Computer Science Palembang Indonesia

ISBN: (纸本)9798350368253

The use of technology and information devices contributes to global warming. This issue has also become a concern for UN institutions, as stated in international environmental agreements, which aim to stabilize greenhouse gas emissions in the atmosphere at a level that prevents anthropogenic and dangerous disturbances to the environmental system. This research uses a line of code derived from the tanjak dataset, a student project from 2003 to 2020, numbering 500. This research aims to apply energy consumption efficiency to reduce CO2 emissions produced using computer devices, especially in developing software. The approach used in this research is clean code, which can provide benefits in the form of a line of code that is easy to read, understand, test, and maintain so that it can create savings in energy use. In the test, the line of code used was 1,072,089, producing an energy of 82,152.18 Joules, minus the line of code comments, which was 76,750, producing an energy of 2,971.25 Joules. Initial research results show that the clean code approach can be used to save energy usage, with the result of the line of code used being 995,339 and producing 79,180 Joules of energy, so it can affect reducing CO2 emissions when implementing software development. © 2024 IEEE.

关键词： Greenhouse gas emissions

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