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arXiv 2023年

作者： Anand, Konrad Göbel, Andreas Pappik, Marcus Perkins, Will School of Informatics University of Edinburgh United Kingdom Hasso Plattner Institute University of Potsdam United States School of Computer Science Georgia Institute of Technology United States

We provide a perfect sampling algorithm for the hard-sphere model on subsets of d with expected running time linear in the volume under the assumption of strong spatial mixing. A large number of perfect and approximate sampling algorithms have been devised to sample from the hard-sphere model, and our perfect sampling algorithm is efficient for a range of parameters for which only efficient approximate samplers were previously known and is faster than these known approximate approaches. Our methods also extend to the more general setting of Gibbs point processes interacting via finite-range, repulsive potentials. © 2023, CC BY.

关键词： Spheres

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Spatio-temporal Network Analysis for Detecting Traffic Accident Clusters 14

Spatio-temporal Network Analysis for Detecting Traffic Accid...

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14th IIAI International Congress on Advanced Applied informatics, IIAI-AAI 2023

作者： Ando, Keisuke Kuniyoshi, Yusuke Shimizu, Kazuhiko Uchitane, Takeshi Mukai, Naoto Iwata, Kazunori Ito, Nobuhiro Jiang, Yong Graduate School of Business Administration and Computer Science Aichi Institute of Technology Toyota Japan Graduate School of Informatics and Engineering The University of Electro-Communications Tokyo Japan Faculty of Information Science Aichi Institute of Technology Toyota Japan Sugiyama Jogakuen University Faculty of Culture-Information Studies Nagoya Japan Aichi University Faculty of Business Administration Nagoya Japan Aichi University Faculty of Regional Policy Toyohashi Japan

ISBN: (纸本)9798350324228

Various methods have been proposed for analyzing traffic accident hotspots. One of these methods is to detect traffic accident hotspots on the road network using a hypothesis testing method. However, this method does not consider the time of occurrence of a traffic accident. In other words, this method provides information on high-risk locations for traffic accidents but not on the corresponding time. This paper proposes a new method for detecting traffic accident hotspots considering not only location but also time. We therefore extended the previous hypothesis testing method to consider the time of occurrence of a traffic accident. First, we check for changes in spatial properties over time in the target area using local indicators of spatial autocorrelation (LISA) cluster maps. Next, we estimate the probability density function of traffic accidents in the spatio-temporal network using the spatio-temporal network kernel density estimation (STNKDE) method. Finally, we detect clusters where the probability density of traffic accidents is significantly higher through hypothesis testing based on the estimation results. We detect clusters under four conditions while testing different hypotheses and parameters. The results show that the proposed method can detect traffic accident clusters. Therefore, when considering traffic safety measures, the method can be adopted to detect the location and time that require attention and allocate the necessary resources. © 2023 IEEE.

关键词： Roads and streets

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Bridging the Domain Gaps in Context Representations for k-Nearest Neighbor Neural Machine Translation

arXiv

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arXiv 2023年

作者： Cao, Zhiwei Yang, Baosong Lin, Huan Wu, Suhang Wei, Xiangpeng Liu, Dayiheng Xie, Jun Zhang, Min Su, Jinsong School of Informatics Xiamen University China Language Technology Lab Alibaba DAMO Academy China Institute of Artificial Intelligence Xiamen University China Institute of Computer Science and Technology Soochow University China

k-Nearest neighbor machine translation (kNNMT) has attracted increasing attention due to its ability to non-parametrically adapt to new translation domains. By using an upstream NMT model to traverse the downstream training corpus, it is equipped with a datastore containing vectorized key-value pairs, which are retrieved during inference to benefit translation. However, there often exists a significant gap between upstream and downstream domains, which hurts the retrieval accuracy and the final translation quality. To deal with this issue, we propose a novel approach to boost the datastore retrieval of kNN-MT by reconstructing the original datastore. Concretely, we design a reviser to revise the key representations, making them better fit for the downstream domain. The reviser is trained using the collected semantically-related key-queries pairs, and optimized by two proposed losses: one is the key-queries semantic distance ensuring each revised key representation is semantically related to its corresponding queries, and the other is an L2-norm loss encouraging revised key representations to effectively retain the knowledge learned by the upstream NMT model. Extensive experiments on domain adaptation tasks demonstrate that our method can effectively boost the datastore retrieval and translation quality of kNN-MT. © 2023, CC BY.

关键词： Nearest neighbor search

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A comparative study of word embedding techniques for SMS spam detection 14

A comparative study of word embedding techniques for SMS spa...

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14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022

作者： Joseph, Prashob Yerima, Suleiman Y. School Computer Science and Informatics De Montfort University Faculty of Computing Engineering and Media Leicester United Kingdom Cyber Technology Institute De Montfort University Faculty of Computing Engineering and Media Leicester United Kingdom

ISBN: (纸本)9781665487719

E-mail and SMS are the most popular communication tools used by businesses, organizations and educational institutions. Every day, people receive hundreds of messages which could be either spam or ham. Spam is any form of unsolicited, unwanted digital communication, usually sent out in bulk. Spam emails and SMS waste resources by unnecessarily flooding network lines and consuming storage space. Therefore, it is important to develop high accuracy spam detection models to effectively block spam messages, so as to optimize resources and protect users. Various word-embedding techniques such as Bag of Words (BOW), N-grams, TF-IDF, Word2Vec and Doc2Vec have been widely applied to NLP problems, however a comparative study of these techniques for SMS spam detection is currently lacking. Hence, in this paper, we provide a comparative analysis of these popular word embedding techniques for SMS spam detection by evaluating their performance on a publicly available ham and spam dataset. We investigate the performance of the word embedding techniques using 5 different machine learning classifiers i.e. Multinomial Naive Bayes (MNB), KNN, SVM, Random Forest and Extra Trees. Based on the dataset employed in the study, N-gram, BOW and TF-IDF with oversampling recorded the highest F1 scores of 0.99 for ham and 0.94 for spam. © 2022 IEEE.

关键词： Embeddings

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PR-FCNN: a data-driven hybrid approach for predicting PM2.5 concentration

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Discover Artificial Intelligence 2024年第1期4卷 1-22页

作者： Inam, Syed Azeem Khan, Abdullah Ayub Mazhar, Tehseen Ahmed, Noor Shahzad, Tariq Khan, Muhammad Amir Saeed, Mamoon M. Hamam, Habib Department of Artificial Intelligence and Mathematical Sciences Sindh Madressatul Islam University Karachi74000 Pakistan Department of Computer Science Bahria University Karachi Campus Sindh Karachi75000 Pakistan Department of Computer Science and information Technology School Education Department Government of Punjab Layyah 31200 Pakistan Department of Computer Science Shaheed Zulfiqar Ali Bhutto Institute of Science and Technology Karachi Pakistan Department of Computer Science COMSATS University Islamabad Sahiwal Campus Sahiwal57000 Pakistan School of Computing Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Selangor Shah Alam40450 Malaysia Sana’a Yemen Faculty of Engineering Uni de Moncton MonctonNBE1A3E9 Canada School of Electrical Engineering University of Johannesburg Johannesburg 2006 South Africa Av. Grandes Ecoles Libreville BP 1989 Gabon Bridges for Academic Excellence Spectrum Tunis Tunisia

The atmosphere’s fine articulate Matter (PM2.5) poses various health-related risks. Even though multiple efforts have been made to lower the emissions of these substances, the mortality rate is continuously increasing, requiring immediate inclination of the scientific community towards the design and development of advanced predictive models. Conventional statistical approaches have become dormant due to their limitations in capturing the innate relationships between the pollutants, particularly for predicting PM2.5 concentrations. In contrast, machine and deep learning techniques have shown great potential for forecasting air quality, providing more accuracy than its predecessor techniques. The present study investigates the utilization of hybrid approaches by integrating machine learning models with deep learning models to improve the prediction capabilities of PM2.5 concentration. It uses datasets from the World Air Quality Index (WAQI) and the State of Global Air (SOGA) to analyze the performance of the models on both the daily and annual data, respectively. This ensures the model’s effectiveness on a diversified dataset. The present study implements Random Forest (RF), Polynomial Regression (PR), XGBoost, and Extra Tree Regressor (ETR) coupled with Fully Connected Neural Network (FCNN), Long Short-Term Memory (LSTM), and Bi-directional LSTM (Bi-LSTM) for obtaining optimized results. Finally, after a thorough investigation, the hybrid PR model coupled with FCNN (PR-FCNN) is found to be the best model with improved R-squared (R2) values, portraying its potential for predicting PM2.5 concentration accurately. Based on the experimentation, the preset study recommends implementing hybrid approaches, offering better predictive accuracy in forecasting air pollutants, especially PM2.5. © The Author(s) 2024.

关键词： Health risks

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Relip: Reliable in-Band Parallel Communication for Magnetic MIMO Wireless Power Transfer System

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IEEE Transactions on Mobile Computing 2025年

作者： Wang, Xinyu Zhou, Wangqiu Zhou, Hao Jiang, Shenyao Liu, Zhi Wang, Xiaoyan Ji, Yusheng Song, Qi City University of Hong Kong Department of Computer Science Hong Kong University of Science and Technology of China School of Computer Science and Technology Hefei230027 China The University of Electro-Communications Graduate School of Informatics and Engineering Tokyo182-8585 Japan Ibaraki University Graduate School of Science and Engineering Ibaraki310-8512 Japan National Institute of Informatics Information Systems Architecture Science Research Division Tokyo101-0003 Japan

In magnetic resonant coupling (MRC) based wireless power transfer (WPT) systems, receiver (RX) feedback communication is promising to enhance the capability and efficiency of the system. Although some studies have explored in-band implementations with low overhead costs, it has not been comprehensively investigated. In this paper, we propose Relip, a Reliable layer-level in-band parallel feedback communication mechanism for MIMO MRC-WPT systems, which addresses the impact of RX-RX couplings (i.e., non-negligible interference from strong couplings and positive effects of relay phenomenon), and provides a theoretical analysis of communication reliability. Technically, we first devise an On-Off based two-phase modulation mechanism to achieve RX identification and dependency detection under relay phenomenon. Then, we utilize observed channel decomposability to collect group-level power transfer channel conditions for eliminating the interference caused by strong RX-RX couplings. Furthermore, we perform RX selection to optimize the trade-off between communication reliability and time overhead. We design and implement the Relip prototype and conduct extensive experiments. The results validate the effectiveness of our mechanism, i.e., Relip can provide ≥99% average decoding accuracy for concurrent feedback communication of 14 devices, achieving an 18.31% improvement compared to the state-of-the-art solution. © 2002-2012 IEEE.

关键词： Inductive power transmission

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Cardiac Arrhythmia Disease Classifier Model Based on a Fuzzy Fusion Approach

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computers, Materials & Continua 2023年第5期75卷 4485-4499页

作者： Fatma Taher Hamoud Alshammari Lobna Osman Mohamed Elhoseny Abdulaziz Shehab Eman Elayat College of Technological Innovation Zayed UniversityDubaiUAE Department of Information Systems College of Computer and Information SciencesJouf UniversitySakakaSaudi Arabia Department of Electronics and Communications Engineering Delta Higher Institute for Engineering&TechnologyMansouraEgypt College of Computing and Informatics University of SharjahSharjahUnited Arab Emirates Department of Information Systems Mansoura UniversityMansoura35516Egypt Department of Teacher Preparation Faculty of Specific EducationMansoura UniversityMansouraEgypt

Cardiac diseases are one of the greatest global health *** to the high annual mortality rates,cardiac diseases have attracted the attention of numerous researchers in recent *** article proposes a hybrid fuzzy fusion classification model for cardiac arrhythmia *** fusion model is utilized to optimally select the highest-ranked features generated by a variety of well-known feature-selection *** ensemble of classifiers is then applied to the fusion’s *** proposed model classifies the arrhythmia dataset from the University of California,Irvine into normal/abnormal classes as well as 16 classes of ***,at the preprocessing steps,for the miss-valued attributes,we used the average value in the linear attributes group by the same class and the most frequent value for nominal ***,in order to ensure the model optimality,we eliminated all attributes which have zero or constant values that might bias the results of utilized *** preprocessing step led to 161 out of 279 attributes(features).Thereafter,a fuzzy-based feature-selection fusion method is applied to fuse high-ranked features obtained from different heuristic feature-selection *** short,our study comprises three main blocks:(1)sensing data and preprocessing;(2)feature queuing,selection,and extraction;and(3)the predictive *** proposed method improves classification performance in terms of accuracy,F1measure,recall,and precision when compared to state-of-the-art *** achieves 98.5%accuracy for binary class mode and 98.9%accuracy for categorized class mode.

关键词： Cardiac arrhythmia preprocessing missing values classification model fusion

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REMOVE SYMMETRIES TO CONTROL MODEL EXPRESSIVITY AND IMPROVE OPTIMIZATION

arXiv

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arXiv 2024年

作者： Ziyin, Liu Xu, Yizhou Chuang, Isaac Research Laboratory of Electronics Massachusetts Institute of Technology United States Physics & Informatics Laboratories NTT Research Computer and Communication Sciences École Polytechnique Fédérale de Lausanne Switzerland Department of Physics Massachusetts Institute of Technology United States

When symmetry is present in the loss function, the model is likely to be trapped in a low-capacity state that is sometimes known as a "collapse." Being trapped in these low-capacity states can be a major obstacle to training across many scenarios where deep learning technology is applied. We first prove two concrete mechanisms through which symmetries lead to reduced capacities and ignored features during training and inference. We then propose a simple and theoretically justified algorithm, syre, to remove almost all symmetry-induced low-capacity states in neural networks. When this type of entrapment is especially a concern, removing symmetries with the proposed method is shown to correlate well with improved optimization or performance. A remarkable merit of the proposed method is that it is model-agnostic and does not require any knowledge of the symmetry. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep learning

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Implementation of PID Control as a One Axis Balancing Control System on the Bicopter Vehicle

Implementation of PID Control as a One Axis Balancing Contro...

引用

Converging technology in Electrical and Information Engineering (ICCTEIE), International Conference on

作者： Akhmad Jayadi Faisal Dharma Adhinata Jaka Persada Sembiring Novia Utami Putri Elka Pranita Arif Setiawan Faculty of Engineering and Computer Science Universitas Teknokrat Indonesian Lampung Indonesia Faculty of Informatics Institute Technology Telkom Purwokerto Central Java Indonesia

This Bicopter is an unmanned aircraft with two brushless motor drive systems connected to propellers in a horizontal parallel position. The problem that often occurs in bicopters is stability or flight balance. When the vehicle cannot control or maintain its balance angle, it is most likely that the bicopter vehicle will fall while flying. Thus, a research design is carried out that can develop and overcome problems in Bicopter stability through PID Control Implementation as a One Axis Balancing Control System on the Bicopter Vehicle. This study aims to find out how to apply the PID control system as a balance controller on the bicopter and to see the results of the dynamic response generated by the PID control system using testing methods using different load weights and load positions. The research results show that the prototype of the bicopter vehicle with PID controller has been successfully made and can work well in carrying out balance control. By providing a value of Kp = 2.4329, Ki = 0.000821, Kd = 603.98901, and a Throttle of 1600 rpm, it produces a dynamic response in the form of the smallest Steady State Error value of 0.16 and the largest value of 9.29, the smallest Rise Time is 0.22s and the largest is 1.92s, The biggest overshoot is 6.68 and the smallest is -0.17. The biggest settling time is 3.36s, and the smallest is 0.92s

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The Goofus & Gallant Story Corpus for Practical Value Alignment

arXiv

引用

arXiv 2025年

作者： Nahian, Md Sultan Al Tasrin, Tasmia Frazier, Spencer Riedl, Mark Harrison, Brent Institute for Biomedical Informatics University of Kentucky LexingtonKY United States Department of Computer Science University of Kentucky LexingtonKY United States School of Interactive Computing Georgia Institute of Technology AtlantaGA United States

Values or principles are key elements of human society that influence people to behave and function according to an accepted standard set of social rules to maintain social order. As AI systems are becoming ubiquitous in human society, it is a major concern that they could violate these norms or values and potentially cause harm. Thus, to prevent intentional or unintentional harm, AI systems are expected to take actions that align with these principles. Training systems to exhibit this type of behavior is difficult and often requires a specialized dataset. This work presents a multi-modal dataset illustrating normative and non-normative behavior in real-life situations described through natural language and artistic images. This training set contains curated sets of images that are designed to teach young children about social principles. We argue that this is an ideal dataset to use for training socially normative agents given this fact. © 2025, CC BY.

关键词： Adversarial machine learning

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