Distributed communication model-learning architecture for anomaly detection in multi-service shared M2M area networks

2016 ◽  
Author(s):  
Nobuhiro Azuma ◽  
Toshimitsu Tsubaki ◽  
Masao Aihara
Keyword(s):  
Anomaly Detection ◽  
Communication Model ◽  
Model Learning ◽  
Distributed Communication

scholarly journals A New Semantic and Statistical Distance-Based Anomaly Detection in Crowd Video Surveillance

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Fariba Rezaei ◽  
Mehran Yazdi
Keyword(s):  
Anomaly Detection ◽  
Topic Model ◽  
Nonnegative Matrix ◽  
Feature Representation ◽  
Test Time ◽  
Model Learning ◽  
Deep Model ◽  
Level Information ◽  
The Difference ◽  
High Level

Recently, attention toward autonomous surveillance has been intensified and anomaly detection in crowded scenes is one of those significant surveillance tasks. Traditional approaches include the extraction of handcrafted features that need the subsequent task of model learning. They are mostly used to extract low-level spatiotemporal features of videos, neglecting the effect of semantic information. Recently, deep learning (DL) methods have been emerged in various domains, especially CNN for visual problems, with the ability to extract high-level information at higher layers of their architectures. On the other side, topic modeling-based approaches like NMF can extract more semantic representations. Here, we investigate a new hybrid visual embedding method based on deep features and a topic model for anomaly detection. Features per frame are computed hierarchically through a pretrained deep model, and in parallel, topic distributions are learned through multilayer nonnegative matrix factorization entangling information from extracted deep features. Training is accomplished through normal samples. Thereafter, K -means is applied to find typical normal clusters. At test time, after achieving feature representation through deep model and topic distribution for test frames, a statistical earth mover distance (EMD) metric is evaluated to measure the difference between normal cluster centroids and test topic distributions. High difference versus a threshold is detected as an anomaly. Experimental results on the benchmark Ped1 and Ped2 UCSD datasets demonstrate the effectiveness of our proposed method in anomaly detection.

scholarly journals Anomaly Detection for Industrial Control System Based on Autoencoder Neural Network

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Chao Wang ◽  
Bailing Wang ◽  
Hongri Liu ◽  
Haikuo Qu
Keyword(s):  
Neural Network ◽  
Intrusion Detection ◽  
Anomaly Detection ◽  
Fog Computing ◽  
Water Supply Systems ◽  
Industrial Control ◽  
Model Learning ◽  
Industrial Control Systems ◽  
Key Factor ◽  
Improved Performance

As the Industrial Internet of Things (IIoT) develops rapidly, cloud computing and fog computing become effective measures to solve some problems, e.g., limited computing resources and increased network latency. The Industrial Control Systems (ICS) play a key factor within the development of IIoT, whose security affects the whole IIoT. ICS involves many aspects, like water supply systems and electric utilities, which are closely related to people’s lives. ICS is connected to the Internet and exposed in the cyberspace instead of isolating with the outside recent years. The risk of being attacked increases as a result. In order to protect these assets, intrusion detection systems (IDS) have drawn much attention. As one kind of intrusion detection, anomaly detection provides the ability to detect unknown attacks compared with signature-based techniques, which are another kind of IDS. In this paper, an anomaly detection method with a composite autoencoder model learning the normal pattern is proposed. Unlike the common autoencoder neural network that predicts or reconstructs data separately, our model makes prediction and reconstruction on input data at the same time, which overcomes the shortcoming of using each one alone. With the error obtained by the model, a change ratio is put forward to locate the most suspicious devices that may be under attack. In the last part, we verify the performance of our method by conducting experiments on the SWaT dataset. The results show that the proposed method exhibits improved performance with 88.5% recall and 87.0% F1-score.

scholarly journals ANALISIS KEMAMPUAN KONEKSI MATEMATIS : DAMPAK LASSWELL COMMUNICATION BERBASIS PEMECAHAN MASALAH DAN KEPRIBADIAN KEIRSEY

2021 ◽  
Vol 4 (1) ◽  
pp. 7-14
Author(s):  
Indah Resti Ayuni Suri ◽  
Ruhban Masykur ◽  
Ramayudha Dwi Aji G.
Keyword(s):  
Problem Solving ◽  
Statistical Tests ◽  
Personality Type ◽  
Homogeneity Test ◽  
Communication Model ◽  
Model Learning ◽  
Model Based ◽  
Mathematical Connections ◽  
Study Results ◽  
Mathematical Connection

This research has objectives, including analyzing the influence of the Lasswell Communication Model based on problem-solving and Keirsey's personality on the ability of mathematical connections. And to investigate the interaction between the Lasswell Communication Model based on problem-solving and Keirsey personality to the mathematical connection ability. Researchers will apply some action or treatment in this study using Quasi-experiments. The sampling technique is a technique used in selecting a sample "random" in research—the collection techniques in this study, namely: documentation techniques, interviews, essay tests. The data analysis technique used the normality test and homogeneity test using the Bartlett test. One of the parametric statistical tests is the Anava test. The study results concluded that: There is an influence between students who get the Lasswell Communication Model based on problem-solving learning on the ability of mathematical connections. There is no influence of the Keirsey personality type (guardian, artisan, rational, and idealist) on the mathematical connection abilities of students. There is no interaction between Lasswell's Communication Model learning based on problem-solving and Keirsey's personality type on students' mathematical connection abilities. Abstrak Penelitian ini memiliki tujuan, diantaranya : menganalisa pengaruh Lasswell Communication Model berbasis pemecahan masalah dan kepribadian keirsey terhadap kemampuan koneksi matematis. Serta Untuk menganalisa interaksi antara model pembelajaran Lasswell Communication Model berbasis pemecahan masalah dan kepribadian keirsey terhadap kemampuan koneksi matematis. Peneliti menggunakan Karena akan menerapkan sesuatu tindakan atau perlakuan maka dalam penelitian ini menggunakan Quasi eksperimen  Teknik sampling merupakan teknik yang digunakan dalam memilih suatu sampel secara “acak kelas” dalam penelitian. Teknik pengumpulan pada penelitian ini, yaitu: Teknik dokumentasi, Wawancara, tes uraian (essay).  Teknik Analisis Data dengan Uji normalitas dan Uji homogenitas menggunakan uji Bartlett,  Salah satu uji statistic parametik adalah uji Anava. hasil penelitian ditarik kesimpulan bahwa:  Terdapat pengaruh antara peserta didik yang memperoleh pembelajaran Lasswell Communication Model berbasis pemecahan masalah terhadap kemampuan koneksi matematis.   Tidak terdapat pengaruh tipe kepribadian keirsey (guardian, artisan, rational, dan idealist) terhadap kemampuan koneksi matematis peserta didik.  Tidak terdapat interaksi antara pembelajaran Lasswell Communication Model berbasis pemecahan masalah dan tipe kepribadian keirsey terhadap kemampuan koneksi matematis peseta didik.

Using Binding Communication to Promote Conservation Among Hotel Guests

2015 ◽  
Vol 74 (3) ◽  
pp. 169-175 ◽  
Author(s):  
Lohyd Terrier ◽  
Benedicte Marfaing
Keyword(s):  
Low Cost ◽  
Communication Strategies ◽  
Communication Strategy ◽  
Communication Model ◽  
Classical Communication ◽  
Persuasive Message ◽  
Sustainable Behavior ◽  
And Behavior ◽  
Attitudes And Behavior

This research applies the binding communication model to the sustainable communication strategies implemented in most hotels. The binding communication model links a persuasive message with the implementation of a low-cost commitment to strengthen the link between the attitudes and behavior of those receiving the message. We compared the effectiveness of a classical communication strategy (n = 86) with that of a binding communication strategy (n = 101) to encourage guests to choose sustainable behavior. Our results show that using the binding communication strategy generates significantly more sustainable behavior in guests than using the classical communication strategy. We discuss our results and suggest future avenues of research.

Anomaly Detection in Sensor Data

2018 ◽  
Vol 18 (1) ◽  
pp. 20-32 ◽  
Author(s):  
Jong-Min Kim ◽  
Jaiwook Baik
Keyword(s):  
Anomaly Detection ◽  
Sensor Data

Islamic Communication Model in Therapeutic Communication Practices at Adam Malik Hospital

SIASAT ◽  
2020 ◽  
Vol 4 (2) ◽  
pp. 84-93
Author(s):  
Syafruddin Ritonga ◽  
Zamri ◽  
Selamat Riadi ◽  
Zakaria Siregar
Keyword(s):  
Qualitative Approach ◽  
Communication Model ◽  
Medical Team ◽  
Therapeutic Communication ◽  
Implementation Model ◽  
Communication Practices ◽  
The Way ◽  
Doctors And Nurses

Studies on Therapeutic Communication, especially its relationship to Islamic communication, are still rarely found in the field. This study aims to see how the practice of Islamic communication can be done well by doctors and nurses. This research uses a qualitative approach. The values of Islamic communication in Therapeutic communication can be seen from the way communication is carried out by doctors and nurses with their patients through ethics and good language. The implementation model of Islamic communication in therapeutic communication produces a marker communication model, that is, communication carried out on the basis of the awareness of the medical team. This communication model is not formally implemented, but in substance has similarities with the value of Islamic communication.

An Anomaly Detection Method with Exemplar Subsequence for Time Series Data

2016 ◽  
Vol 136 (3) ◽  
pp. 363-372
Author(s):  
Takaaki Nakamura ◽  
Makoto Imamura ◽  
Masashi Tatedoko ◽  
Norio Hirai
Keyword(s):  
Time Series ◽  
Anomaly Detection ◽  
Time Series Data ◽  
Detection Method ◽  
Series Data
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