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Robust intrusion detection for network communication on the Internet of Things: a hybrid machine learning approach

Cluster Computing 2024年第7期27卷 9975-9991页

作者： Soltani, Nasim Rahmani, Amir Masoud Bohlouli, Mahdi Hosseinzadeh, Mehdi Department of Computer Engineering Science and Research Branch Islamic Azad University Tehran Iran Future Technology Research Center National Yunlin University of Science and Technology Yunlin Douliu Taiwan Research and Innovation Department Petanux GmbH Bonn Germany Institute of Research and Development Duy Tan University Da Nang Viet Nam Department of Computer Science University of Human Development Sulaymaniyah Iraq

The importance and growth of the Internet of Things (IoT) in computer networks and applications have been increasing. Additionally, many of these applications generate large volumes of data, which are critical and require protection against attacks. Various techniques have been proposed to identify and counteract these threats. In this paper, we offer a hybrid machine learning approach (using the k-nearest neighbors and random forests as supervised classifiers) to enhance the accuracy of intrusion detection systems and minimize the risk of potential attacks. Also, we employ backward elimination and linear discriminant analysis algorithms for feature reduction and to lower computational costs. Following the training phase, when discrepancies arose between the decisions of the classifiers, the ultimate determination was supported by ISO/IEC 27001 regulations. The performance of the proposed model was assessed within a Python programming framework, utilizing the CICIDS 2017, NSL-KDD, and TON-IoT datasets. The outcomes illustrated that the proposed approach attained a noteworthy accuracy of 99.96% in the multi-class classification of CICIDS 2017, 99.37% in the binary classification of the NSL-KDD dataset, and 99.96% in the multi-class classification of TON-IoT dataset. Furthermore, the attack success rate for each dataset stands at 0.05%, 0.24%, and 0% respectively, demonstrating a significant reduction compared to other methods. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Intrusion detection

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Do I enjoy my friends? Friendship and enjoyment during recess in autistic and non-autistic children

Research in Autism

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Research in Autism 2025年 124卷

作者： Jiayin Zhao Boya Li Yung-Ting Tsou Mitra Baratchi Els Blijd-Hoogewys Özgür Gülen Carolien Rieffe Unit of Developmental and Educational Psychology Institute of Psychology Leiden University Leiden the Netherlands Department of Computer Science Faculty of Science Vrije Universiteit Amsterdam Amsterdam the Netherlands Leiden Institute of Advanced Computer Science (LIACS) Leiden University Leiden the Netherlands Developmental Psychology Faculty of Behavioral and Social Sciences. University of Groningen Groningen the Netherlands Psychiatrie Noord Groningen the Netherlands Department of Psychology Faculty of Economics Administrative and Social Sciences Bahçeşehir University İstanbul Turkey Department of Human Media Interaction Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Enschede the Netherlands Department of Psychology and Human Development Institute of Education University College London London UK

Friendships play a crucial role in children’s well-being and school experiences. This study aimed to gain a better understanding of how autistic and non-autistic children's friendships are related to their enjoyment of the school time spent with peers, i.e. at recess time (school break time). A multi-method approach, including self-reports, peer nominations, and objective measures based on sensor data was used. Forty-five autistic children and 45 non-autistic children from two special education schools participated, aged between 8 to 14 years. Outcomes showed that autistic and non-autistic children did not differ regarding the number of reciprocal and non-reciprocated friends. Yet, autistic children spent less time in contact with their reciprocal friends during recess at the schoolyard compared to their non-autistic peers. Also, while non-autistic pupils spent more time with reciprocal friends than with non-reciprocated ones, this difference was not found among autistic pupils. Notably, spending more time with non-reciprocated friends during recess was related to lower levels of enjoyment in both autistic and non-autistic children. Our findings suggest that autistic children may approach friendships with different priorities. Furthermore, this study underscores the need to consider broader factors beyond reciprocity when assessing children’s social experience at school.

关键词： Peer contact Social Functioning School environment RFID data Schoolyard

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How Do Children Combine Pointing and language in the Earliest Stages of development? A Case Study of Russian and Chintang 44

How Do Children Combine Pointing and Language in the Earlies...

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44th Annual Meeting of the Cognitive science Society: Cognitive Diversity, CogSci 2022

作者： Mazara, Jekaterina Lieven, Elena V.M. Stoll, Sabine Department of Comparative Language Science Center for the Interdisciplinary Study of Language Evolution University of Zurich Switzerland Division of Human Communication Development and Hearing University of Manchester United Kingdom

Learning to establish joint reference is one of the main milestones of communicative and linguistic development. Pointing is one of the first entry points into this process, since gestures often precede verbal communication (Iverson & Goldin-Meadow, 2005). During early development, as well as in later language use, pointing and linguistic utterances interact in many ways, complementing each other's information. However, little is known about the development of this relationship during language acquisition. In this paper, we focus on the development of the co-occurrence of finger pointing and its accompanying utterances in infants growing up in two different cultures: Russia and Chintang (Sino-Tibetan, Eastern Nepal). We show that despite the differences in environment, the development of finger pointing and accompanying language use show substantial similarities. During the early phases of development, a larger proportion of points is not accompanied by language. As the children's linguistic abilities develop, children first use language to specify what is being pointed at, and later elaborate on some aspect of the referent. © 2022 The Author(s). This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY)

关键词： cross-cultural cross-linguistic finger points gestural development language development longitudinal corpus study pointing

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Semantic roles for SMT: A hybrid two-pass model

Semantic roles for SMT: A hybrid two-pass model

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2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics: human language Technologies, NAACL-HLT 2009

作者： Wu, Dekai Fung, Pascale Human Language Technology Center HKUST Department of Computer Science and Engineering University of Science and Technology Clear Water Bay Hong Kong Hong Kong Human Language Technology Center HKUST Department of Electronic and Computer Engineering University of Science and Technology Clear Water Bay Hong Kong Hong Kong

ISBN: (纸本)9781932432428

We present results on a novel hybrid semantic SMT model that incorporates the strengths of both semantic role labeling and phrase-based statistical machine translation. The approach avoids major complexity limitations via a two-pass architecture. The first pass is performed using a conventional phrase-based SMT model. The second pass is performed by a re-ordering strategy guided by shallow semantic parsers that produce both semantic frame and role labels. Evaluation on a Wall Street Journal newswire genre test set showed the hybrid model to yield an improvement of roughly half a point in BLEU score over a strong pure phrase-based SMT baseline – to our knowledge, the first successful application of semantic role labeling to SMT. © 2009 Association for Computational Linguistics

关键词： Semantics

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Investigation of mixture splitting concept for training linear bottlenecks of deep neural network acoustic models 40

Investigation of mixture splitting concept for training line...

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40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015

作者： Tahir, Muhammad Ali Wiesler, Simon Schluter, Ralf Ney, Hermann Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Germany Spoken Language Processing Group LIMSI CNRS Paris France

ISBN: (纸本)9781467369978

A Gaussian or log-linear mixture model trained by maximum likelihood may be trained further using discriminative training. It is desirable that the mixture splitting is also done during the discriminative training, to achieve better mixture density distribution. In previous work such a discriminative splitting approach was presented. Similarly, the resolution of a deep neural network may also be increased by splitting. In this paper, discriminative splitting is applied as a way of initializing a linear bottleneck between two layers of a DNN. Experiments for a single hidden layer and six hidden layer cases show the potential of this approach as an alternative method of pre-training for linear bottlenecks for MLP hidden layers. © 2015 IEEE.

关键词： deep neural network linear bottleneck mixture splitting

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Can semantic role labeling improve SMT?

Can semantic role labeling improve SMT?

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13th Annual Conference of the European Association for Machine Translation, EAMT 2009

We present a series of empirical studies aimed at illuminating more precisely the likely contribution of semantic roles in improving statistical machine translation accuracy. The experiments reported study several aspects key to success: (1) the frequencies of types of SMT errors where semantic parsing and role labeling could help, and (2) if and where semantic roles offer more accurate guidance to SMT than merely syntactic annotation, and (3) the potential quantitative impact of realistic semantic role guidance to SMT systems, in terms of BLEU and METEOR scores. © 2009 European Association for Machine Translation.

关键词： computer aided language translation

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Correction: Predicting substance use behaviors among students taking university entrance test: a cross-sectional study with machine learning techniques

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Current Psychology 2025年 1-1页

作者： Hasan, Md Emran Mamun, Mohammed A. Roy, Nitai ALmerab, Moneerah Mohammad Hossain, Md. Shakhaoat Al-Mamun, Firoj CHINTA Research Bangladesh Savar Bangladesh School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China Department of Public Health and Informatics Jahangirnagar University Savar Bangladesh Department of Public Health University of South Asia Dhaka Bangladesh Department of Biochemistry and Food Analysis Patuakhali Science and Technology University Patuakhali Bangladesh Department of Psychology College of Education and Human Development Princess Nourah bint Abdulrahman University Riyadh Saudi Arabia

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SMT versus AI redux: How semantic fames evaluate MT more accurately

SMT versus AI redux: How semantic fames evaluate MT more acc...

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22nd International Joint Conference on Artificial Intelligence, IJCAI 2011

作者： Lo, Chi-Kiu Wu, Dekai Human Language Technology Center Department of Computer Science and Engineering University of Science and Technology Clear Water Bay Hong Kong Hong Kong

ISBN: (纸本)9781577355120

We argue for an alternative paradigm in evaluating machine translation quality that is strongly empirical but more accurately reflects the utility of translations, by returning to a representational foundation based on AI oriented lexical semantics, rather than the superficial flat n-gram and string representations recently dominating the field. Driven by such metrics as BLEU and WER, current SMT frequently produces unusable translations where the semantic event structure is mistranslated: who did what to whom, when, where, why, and how? We argue that it is time for a new generation of more intelligent" automatic and semi-automatic metrics, based clearly on getting the structure right at the lexical semantics level. We show empirically that it is possible to use simple PropBank style semantic frame representations to surpass all currently widespread metrics' correlation to human adequacy judgments, including even HTER. We also show that replacing human annotators with automatic semantic role labeling still yields much of the advantage of the approach. We combine the best of both worlds: from an SMT perspective, we provide superior yet low-cost quantitative objective functions for translation quality;and yet from an AI perspective, we regain the representational transparency and clear reflection of semantic utility of structural frame-based knowledge representations.

关键词： Semantics

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Investigation on estimation of sentence probability by combining forward, backward and Bi-directional LSTM-RNNs 19

Investigation on estimation of sentence probability by combi...

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19th Annual Conference of the International Speech Communication, INTERSPEECH 2018

作者： Irie, Kazuki Lei, Zhihong Deng, Liuhui Schlüter, Ralf Ney, Hermann Human Language Technology and Pattern Recognition Group Computer Science Department RWTH Aachen University AachenD-52056 Germany

A combination of forward and backward long short-term memory (LSTM) recurrent neural network (RNN) language models is a popular model combination approach to improve the estimation of the sequence probability in the second pass N-best list rescoring in automatic speech recognition (ASR). In this work, we further push such an idea by proposing a combination of three models: a forward LSTM language model, a backward LSTM language model and a bi-directional LSTM based gap completion model. We derive such a combination method from a forward backward decomposition of the sequence probability. We carry out experiments on the Switchboard speech recognition task. While we empirically find that such a combination gives slight improvements in perplexity over the combination of forward and backward models, we finally show that a combination of the same number of forward models gives the best perplexity and word error rate (WER) overall. © 2018 International Speech Communication Association. All rights reserved.

关键词： Long short-term memory

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The RWTH Aachen Machine Translation System for WMT 2013 8

The RWTH Aachen Machine Translation System for WMT 2013

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8th Workshop on Statistical Machine Translation, WMT 2013

作者： Peitz, Stephan Mansour, Saab Peter, Jan-Thorsten Schmidt, Christoph Wuebker, Joern Huck, Matthias Freitag, Markus Ney, Hermann Human Language Technology and Pattern Recognition Group Computer Science Department RWTH Aachen University AachenD-52056 Germany

ISBN: (纸本)9781937284572

This paper describes the statistical machine translation (SMT) systems developed at RWTH Aachen University for the translation task of the ACL 2013 Eighth Workshop on Statistical Machine Translation (WMT 2013). We participated in the evaluation campaign for the French-English and German-English language pairs in both translation directions. Both hierarchical and phrase-based SMT systems are applied. A number of different techniques are evaluated, including hierarchical phrase reordering, translation model interpolation, domain adaptation techniques, weighted phrase extraction, word class language model, continuous space language model and system combination. By application of these methods we achieve considerable improvements over the respective baseline systems. © 2013 Association for Computational Linguistics

关键词： Machine translation

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