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Mining fuzzy local periodic activity pattern for Smart home applications

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Knowledge-Based Systems 2024年 293卷

作者： Iqbal, Mohammad Prayitno, Adila Sekarrati Dwi Pao, Hsing-Kuo Mukhlash, Imam Department of Mathematics Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember Sukolilo East Java Surabaya60111 Indonesia Department of Computer Science and Information Engineering National Taiwan University of Science and Technology Taipei106 Taiwan

Perceiving resident movements from ubiquitous sensor devices can aid health and safety management in smart home environment. One can find resident movement patterns to get notification about what should be aware of to prepare for possible accidents. The goal of this work is to propose a method to search for periodic residents’ activity patterns given smart home applications. Specifically, the proposed method is aimed to capture fuzzy local periodic activity patterns (FLPAP) from the smart home data. A FLPAP is an activity pattern that periodically occurs for a particular duration in fuzzy time intervals. We construct an algorithm to help to find the FLPAP called Fuzzy Local Periodic Activity Pattern Miner (FLoPMiner). On the side, a parameter called k-scale period is well-chosen to reorganize the data to make it suitable for the need of periodic activity pattern mining. The proposed method was demonstrated for its effectiveness on a public smart home dataset. After all, we successfully retrieved periodic resident activity patterns, which can be applied to self-health management systems, especially when focusing on a precise scale period. Overall, the proposed method shows descent pattern representation against the state-of-the-art methods of the same category. © 2024 Elsevier B.V.

关键词： Automation

Private Synthetic Text Generation with Diffusion Models

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arXiv 2024年

作者： Ochs, Sebastian Habernal, Ivan Trustworthy Human Language Technologies 1 Department of Computer Science Technical University of Darmstadt Germany Research Center Trustworthy Data Science and Security of the University Alliance Ruhr Faculty of Computer Science Ruhr University Bochum Germany

How capable are diffusion models of generating synthetics texts? Recent research shows their strengths, with performance reaching that of auto-regressive LLMs. But are they also good in generating synthetic data if the training was under differential privacy? Here the evidence is missing, yet the promises from private image generation look strong. In this paper we address this open question by extensive experiments. At the same time, we critically assess (and reimplement) previous works on synthetic private text generation with LLMs and reveal some unmet assumptions that might have led to violating the differential privacy guarantees. Our results partly contradict previous non-private findings and show that fully open-source LLMs outperform diffusion models in the privacy regime. Our complete source codes, datasets, and experimental setup are publicly available to foster future research1 © 2024, CC BY-SA.

关键词： Differential privacy

DCNN and LSTM based Plant Leaf Disease Detection Model For Edge IoT Device

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DCNN and LSTM based Plant Leaf Disease Detection Model For E...

Computing, Power and Communication Technologies (IC2PCT), IEEE International Conference on

作者： L. Selvam S. Roseline Mary A. Manimuthu M. Pandian Department of Computer Applications Faculty of Science and Humanities SRMIST Kattankulathur chengalpattu Department of Mathematics Loyola College Chennai Department of Data Science Loyola College Chennai

Edge Computing is an incredibly practical and affordable alternative IoT device for cloud computing that uses a revolutionary methodology. Not only does edge processing reduce the cost of systems administration frameworks, but it also improves edge-cloud latency, which is essential for all critical applications. Plant diseases have a catastrophic impact on food production security and can lead to a sharp decline in the quantity and quality of agricultural products. In severe cases, plant diseases may continue to be the cause of partial grain failure. As a result, an automated method for diagnosing and detecting plant diseases is vitally needed for all agricultural data. In this paper, we have proposed a plant disease detection method based on transfer learning and tested the model’s performance on IoT edge device. Long-Short Term Memory (LSTM) is used in the proposed system to classify plant leaf diseases based on data extracted by CNN. An internal memory within the LSTM network allows it to learn from critical experiences with long-term conditions. Therefore, the combination of CNN and LSTM layout features significantly enhances classification. It is suggested to use a Deep Convolutional Neural Network based on a pre-trained MobileNetV1 feature extractor and an LSTM classifier. The proposed model’s performance is contrasted with that of other pre-trained models, including NASNetMobile and MobileNetV2. The proposed model outperformed other models, achieving a validation accuracy of 0.9892.

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AWI-BS: An adaptive weight incentive for blockchain sharding

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Journal of Information and Intelligence 2023年第2期1卷 87-103页

作者： Zuobin Ying Laican Song Deng Chen Wusong Lan Ximeng Liu Faculty of Data Science City University of MacaoMacao 999078China National Institute of Education Nanyang Technological UniversitySingapore 999002Singapore College of Computer and Data Science Fuzhou UniversityFuzhou 350000China

The sharding technique enables blockchain to process transactions in parallel by dividing blockchain nodes into small groups,each of which handles a subset of all *** of the issues with blockchain sharding is generating a large number of cross-shard transactions that need to be checked on the output shard as well as the destination *** analysis suggests that the processing efficiency of cross-shard transactions is consistent with the barrel effect,i.e.,that efficiency is more dependent on slower processing *** of the existing studies focus on how to deal with cross-shard transactions,but neglecting the fact that the relative independence between sharding results in different incentive costs between *** perform a sharding analysis on 100,000 real transactions data on Ethereum,and the results show that there is a large difference in gas prices between different shards *** this paper,we propose an Adaptive Weight Incentive(AWI)for Blockchain Sharding,which uses adaptive weight in place of traditional incentive,to address the problem of differing incentive costs for each *** Ethereum as an example,AWI-BS computes the weight of a transaction as a function of a combination of the underlying gas price,the latency of the transaction,and the urgency of the *** the node chooses which transaction to pack based on the ***,we also perform an in-depth analysis of AWI-BS's security and *** evaluation indicates that AWI-BS outperforms the other alternatives in terms of transaction confirmation latency,transaction hit rate,and system throughput.

关键词： Sharding Blockchain Incentive mechanism

Multi Label Classification Based on Ensemble Learning: Investigative Study

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Multi Label Classification Based on Ensemble Learning: Inves...

Information Technology (ACIT)

作者： Ola Nasir Raed Alazaidah Wael Hadi Ghassan Samara Essam Al Daoud Suhaila Abu Owaida Ali Elrashidi Esraa Abu Elsoud Haneen Alzoubi Department of Computer Science Faculty of Information Technology Zarqa University Zarqa Jordan Department of Data Science and AI Faculty of Information Technology Zarqa University Zarqa Jordan Department of Cyber Security Faculty of Information Technology Petra University Amman Jordan Department of Computer Science Faculty of Information Technolog Zarqa University Zarqa Jordan Department of Computer Science Faculty of Information Technology Al-al-Bayt University Mafraq Jordan Department of Electrical Engineering University of Business and Technology Jeddah Saudi Arabia Department of Computer Science Faculty of IT Zarqa University Department of Computer Science Faculty of IT Applied Science Private University Amman Jordan

ISBN: (数字)9798331540012

ISBN: (纸本)9798331540029

In the big data technology nowadays, activities associated with Multi-Label Classification (MLC) pose big and complex challenges, receiving terrific interest in diverse fields. Existing MLC algorithms suffer from low overall performance. This paper considers four well-known Problem Transformation Methods (PTMs). Two of these PTMs are based on the frequency of the labels (MFL and LFL) while the last two PTMs are based on the positive correlations among labels. These PTMs have been implemented and compared against four multi label datasets of various sizes to identify the best one that fits the nature of MLC datasets. Moreover, six popular ensemble learning models have been carried out and compared on the four datasets with respect to several evaluation metrics such as Accuracy, Hamming loss, and Exact Match and using SMO, DecisionStump, Random Forest, and Random Commission classifiers. The results revealed that MFL accomplished the quality performance amongst the four PTMs, and Stacking SVM is the best ensemble learning technique.

关键词： Support vector machines Measurement Correlation Accuracy Stacking Multi label classification Big data Ensemble learning Information technology Random forests

A comparative study of augmented inverse propensity weighted estimators using outcome-oriented covariate selection via penalization with outcome-adaptive lasso

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arXiv 2024年

作者： Hongo, Wataru Ando, Shuji Tsuchida, Jun Sozu, Takashi Department of Information and Computer Technology Graduate School of Engineering Tokyo University of Science Tokyo Japan Advanced Quantitative Sciences Development Novartis Pharma K.K. Tokyo Japan Department of Information Sciences Faculty of Science and Technology Tokyo University of Science Chiba Japan Department of Data Science Faculty of Data Science Kyoto Women’s University Kyoto Japan Department of Information and Computer Technology Faculty of Engineering Tokyo University of Science Tokyo Japan

Background: When estimating causal effects from observational data with numerous covariates, employing penalized covariate selection can improve the estimation efficiency. Outcome-oriented covariate selection, which involves selecting covariates related to the outcome, can enhance efficiency, even for propensity score (PS) methods. For outcome-oriented covariate selection in PS models, outcome-adaptive lasso can be used for penalization with the oracle property. The performance of inverse propensity weighted (IPW) estimators using outcome-adaptive lasso was shown to be superior to that of the IPW estimators using other covariate selection methods for parametric models. However, the augmented IPW (AIPW) estimator is typically employed as a doubly robust estimator for the average treatment effect, which requires both PS and outcome models. Despite this, which covariate selection method for outcome models should be combined with the outcome-adaptive lasso to form the AIPW estimator remains unclear. We evaluated the performance of the AIPW estimators using the outcome-adaptive lasso for PS models and various outcome-oriented covariate selection via penalization for outcome models. Methods: We conducted numerical experiments to evaluate the performance of AIPW estimators using various covariate selection via penalization. In numerical experiments, we assessed bias, standard error, and root-mean-squared error. We applied the AIPW estimators to a clinical trial dataset for reference, comparing their point estimates and standard errors obtained via bootstrap. Results: In the numerical experiments, the performance of the AIPW estimators using outcome-oriented covariate selection via penalization with the oracle property for both PS and outcome models was superior to that of the other estimators and similar to that of the AIPW estimator, which relies on true confounders and outcome predictors. In contrast, the bias of the AIPW estimators not relying on the oracle property was

关键词： Mean square error

Fast Hybrid Transactional Index with Scalable Phantom Avoidance

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Fast Hybrid Transactional Index with Scalable Phantom Avoida...

International Symposium on Computing and Networking Workshops (CANDARW)

作者： Yutaro Bessho Hideyuki Kawashima Computer and Data Science Laboratories NTT Tokyo Japan Faculty of Environment and Information Studies Keio University Fujisawa Japan

ISBN: (数字)9798331505349

ISBN: (纸本)9798331505356

Griffin is a hybrid transactional index structure that combines a hash table and B+-tree. The key benefit of Griffin is that it processes single-key operations fast using the hash table. For serializable transactions, Griffin avoids phantoms with precision locking, a technique that protects updates and predicate reads with a lock. Although Griffin performs well under infrequent updates, under update-heavy workloads, the lists of locks kept by precision locking significantly grow in size, causing severe performance degradation. We identify this is because locks are released by only one thread called sync manager, which is also responsible for the heavy task of synchronizing the hash table and B+-tree. As a result, sync manager often fails to release locks as fast as they are acquired, causing the lock lists to grow. We propose a new design for Griffin, Griffin-PPL (Parallel Precision Locking), which prevents the lock lists from growing. Griffin-PPL removes sync manager, the bottlenecking thread, and parallelizes the work of releasing locks and synchronization among all transaction worker threads. To allow for parallelization, we adopt a decentralized data structure for the lock lists. Our evaluation in a microbenchmark shows that under an update-heavy workload (50% inserts, 50% scans), Griffin-PPL exhibits a throughput 6.5x the conventional Griffin architecture, at 64 threads.

关键词： Degradation databases Conferences Phantoms Parallel processing data structures Throughput Synchronization Indexes

Griffin: Fast Transactional database Index with Hash and B+-Tree

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arXiv 2024年

作者： Nakazono, Sho Bessho, Yutaro Kawashima, Hideyuki Nakamori, Tatsuhiro Computer and Data Science Laboratories NTT Tokyo Japan Faculty of Environment and Information Studies Keio University Kanagawa Japan

Index access is one of the dominant performance factors in transactional database systems. Many systems use a B+tree or one of its variants to handle point and range operations. This access pattern has room for performance improvement. Firstly, point operations can potentially be processed in O(1) with a hash table. Secondly, to ensure serializability of transactions, range operations incur overhead from phantom avoidance techniques that involve additional processing or synchronization, such as an extra traversal of the B+-tree. To address these issues, we propose a hybrid index architecture, Griffin. For point operations, Griffin has a hash table that provides access paths in O(1) time, along with a B+-tree. For phantom avoidance, Griffin employs a precision locking method, which does not involve additional traversal of the B+-tree. Despite its hybrid architecture, Griffin transparently provides linearizable operations and an interface of a single database index. We built a Griffin index combining a hash table and BwTree. Compared to a baseline index that is composed of a BwTree only, it achieves up to 3.1x higher throughput in a point operation dominant workload, and up to 5.4x higher throughput in a range operation dominant workload. © 2024, CC BY.

关键词： database systems

Settlement Mapping for Population Density Modelling in Disease Risk Spatial Analysis

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arXiv 2024年

作者： Mutasodirin, Mirza Alim Rizqullah, Rafi Dwi Setiyoko, Andie Arymurthy, Aniati Murni Faculty of Computer Science Universitas Indonesia Depok Indonesia Remote Sensing Technology and Data Center LAPAN Jakarta Indonesia

In disease risk spatial analysis, many researchers especially in Indonesia are still modelling population density as the ratio of total population to administrative area extent. This model oversimplifies the problem, because it covers large uninhabited areas, while the model should focus on inhabited areas. This study uses settlement mapping against satellite imagery to focus the model and calculate settlement area extent. As far as our search goes, we did not find any specific studies comparing the use of settlement mapping with administrative area to model population density in computing its correlation to a disease case rate. This study investigates the comparison of both models using data on Tuberculosis (TB) case rate in Central and East Java Indonesia. Our study shows that using administrative area density the Spearman’s ρ was considered as "Fair" (0.566, p Copyright © 2024, The Authors. All rights reserved.

关键词： Population statistics