List of Titles

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A nethack learning environment language wrapper for autonomous agents

- Goodger, Nikolaj, Vamplew, Peter, Foale, Cameron, Dazeley, Richard

Authors: Goodger, Nikolaj , Vamplew, Peter , Foale, Cameron , Dazeley, Richard
Date: 2023
Type: Text , Journal article
Relation: Journal of Open Research Software Vol. 11, no. (2023), p.
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Description: This paper describes a language wrapper for the NetHack Learning Environment (NLE) [1]. The wrapper replaces the non-language observations and actions with comparable language versions. The NLE offers a grand challenge for AI research while MiniHack [2] extends this potential to more specific and configurable tasks. By providing a language interface, we can enable further research on language agents and directly connect language models to a versatile environment. © 2023 The Author(s). This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License (CC-BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. See http://creativecommons.org/licenses/by/4.0/.

Heat transfer in directly-irradiated high-temperature solid–gas flows laden with polydisperse particles

- Chen, Jingling, Kumar, Apurv, Coventry, Joe, Lipiński, Wojciech

Authors: Chen, Jingling , Kumar, Apurv , Coventry, Joe , Lipiński, Wojciech
Date: 2022
Type: Text , Journal article
Relation: Applied Mathematical Modelling Vol. 110, no. (2022), p. 698-722
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Description: Heat transfer in directly-irradiated high-temperature solid–gas flows laden with polydisperse particles is investigated using a novel transient three-dimensional computational fluid dynamics model. The model couples particle–gas hydrodynamics of solid–gas flows laden with polydisperse particles, radiative heat transfer in non-grey absorbing, emitting and anisotropically-scattering multi-component participating media, conduction heat transfer in the gas phase, and interfacial convection heat transfer. The multiphase particle-in-cell method is used to predict high-fidelity solid–gas flow characteristics, such as the local discrete particle size distribution, with increased computational efficiency by combining the advantages of both Eulerian and Lagrangian methods. The multi-component radiative transfer model is implemented using an advanced collision-based Monte Carlo ray-tracing method. The number of the prescribed discrete particle components is found to be the key parameter affecting the computational accuracy and efficiency, which primarily depends on the size distribution of the particles. For the model particle–gas flow featuring free-falling Gamma-distributed ceramic particles exposed to concentrated solar irradiation, the particle volume fraction, radiative, fluid flow and thermal characteristics appear to converge with the increasing number of the discrete particle components. Five particle components are sufficient to obtain physically meaningful results. A further increase in the number of the particle components only slightly increases the accuracy of the numerical predictions at the expense of a rapidly increasing computational time. For five particle components, the particle vertical velocity at the receiver exit for particles with the diameter of 43.4

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Software-defined networks for resource allocation in cloud computing : a survey

- Mohamed, Arwa, Hamdan, Mosab, Khan, Suleman, Abdelaziz, Abdelaziz, Babiker, Sharief, Imran, Muhammad, Marsono, M.

Authors: Mohamed, Arwa , Hamdan, Mosab , Khan, Suleman , Abdelaziz, Abdelaziz , Babiker, Sharief , Imran, Muhammad , Marsono, M.
Date: 2021
Type: Text , Journal article
Relation: Computer Networks Vol. 195, no. (2021), p.
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Description: Cloud computing has a shared set of resources, including physical servers, networks, storage, and user applications. Resource allocation is a critical issue for cloud computing, especially in Infrastructure-as-a-Service (IaaS). The decision-making process in the cloud computing network is non-trivial as it is handled by switches and routers. Moreover, the network concept drifts resulting from changing user demands are among the problems affecting cloud computing. The cloud data center needs agile and elastic network control functions with control of computing resources to ensure proper virtual machine (VM) operations, traffic performance, and energy conservation. Software-Defined Network (SDN) proffers new opportunities to blueprint resource management to handle cloud services allocation while dynamically updating traffic requirements of running VMs. The inclusion of an SDN for managing the infrastructure in a cloud data center better empowers cloud computing, making it easier to allocate resources. In this survey, we discuss and survey resource allocation in cloud computing based on SDN. It is noted that various related studies did not contain all the required requirements. This study is intended to enhance resource allocation mechanisms that involve both cloud computing and SDN domains. Consequently, we analyze resource allocation mechanisms utilized by various researchers; we categorize and evaluate them based on the measured parameters and the problems presented. This survey also contributes to a better understanding of the core of current research that will allow researchers to obtain further information about the possible cloud computing strategies relevant to IaaS resource allocation. © 2021

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Wearable obstacle avoidance electronic travel aids for blind and visually impaired individuals : a systematic review

- Xu, Peijie, Kennedy, Gerard, Zhao, Fei-Yi, Zhang, Wen-Jing, Van Schyndel, Ron

Authors: Xu, Peijie , Kennedy, Gerard , Zhao, Fei-Yi , Zhang, Wen-Jing , Van Schyndel, Ron
Date: 2023
Type: Text , Journal article
Relation: IEEE Access Vol. 11, no. (2023), p. 66587-66613
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Description: Background Wearable obstacle avoidance electronic travel aids (ETAs) have been developed to assist the safe displacement of blind and visually impaired individuals (BVIs) in indoor/outdoor spaces. This systematic review aimed to understand the strengths and weaknesses of existing ETAs in terms of hardware functionality, cost, and user experience. These elements may influence the usability of the ETAs and are valuable in guiding the development of superior ETAs in the future. Methods Formally published studies designing and developing the wearable obstacle avoidance ETAs were searched for from six databases from their inception to April 2023. The PRISMA 2020 and APISSER guidelines were followed. Results Eighty-nine studies were included for analysis, 41 of which were judged to be of moderate to high quality. Most wearable obstacle avoidance ETAs mainly depend on camera- and ultrasonic-based techniques to achieve perception of the environment. Acoustic feedback was the most common human-computer feedback form used by the ETAs. According to user experience, the efficacy and safety of the device was usually their primary concern. Conclusions Although many conceptualised ETAs have been designed to facilitate BVIs' independent navigation, most of these devices suffer from shortcomings. This is due to the nature and limitations of the various processors, environment detection techniques and human-computer feedback those ETAs are equipped with. Integrating multiple techniques and hardware into one ETA is a way to improve performance, but there is still a need to address the discomfort of wearing the device and the high-cost. Developing an applicable systematic review guideline along with a credible quality assessment tool for these types of studies is also required. © 2013 IEEE.

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Collaborative filtering with network representation learning for citation recommendation

- Wang, Wei, Tang, Tao, Xia, Feng, Gong, Zhiguo, Chen, Zhikui, Liu, Huan

Authors: Wang, Wei , Tang, Tao , Xia, Feng , Gong, Zhiguo , Chen, Zhikui , Liu, Huan
Date: 2022
Type: Text , Journal article
Relation: IEEE Transactions on Big Data Vol. 8, no. 5 (2022), p. 1233-1246
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Description: Citation recommendation plays an important role in the context of scholarly big data, where finding relevant papers has become more difficult because of information overload. Applying traditional collaborative filtering (CF) to citation recommendation is challenging due to the cold start problem and the lack of paper ratings. To address these challenges, in this article, we propose a collaborative filtering with network representation learning framework for citation recommendation, namely CNCRec, which is a hybrid user-based CF considering both paper content and network topology. It aims at recommending citations in heterogeneous academic information networks. CNCRec creates the paper rating matrix based on attributed citation network representation learning, where the attributes are topics extracted from the paper text information. Meanwhile, the learned representations of attributed collaboration network is utilized to improve the selection of nearest neighbors. By harnessing the power of network representation learning, CNCRec is able to make full use of the whole citation network topology compared with previous context-aware network-based models. Extensive experiments on both DBLP and APS datasets show that the proposed method outperforms state-of-the-art methods in terms of precision, recall, and MRR (Mean Reciprocal Rank). Moreover, CNCRec can better solve the data sparsity problem compared with other CF-based baselines. © 2015 IEEE.

Enhancing dynamic ECG heartbeat classification with lightweight transformer model

- Meng, Lingxiao, Tan, Wenjun, Ma, Jiangang, Wang, Ruofei, Yin, Xiaoxia, Zhang, Yanchun

Authors: Meng, Lingxiao , Tan, Wenjun , Ma, Jiangang , Wang, Ruofei , Yin, Xiaoxia , Zhang, Yanchun
Date: 2022
Type: Text , Journal article
Relation: Artificial Intelligence in Medicine Vol. 124, no. (2022), p.
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Description: Arrhythmia is a common class of Cardiovascular disease which is the cause for over 31% of all death over the world, according to WHOs' report. Automatic detection and classification of arrhythmia, as an effective tool of early warning, has recently been received more and more attention, especially in the applications of wearable devices for data capturing. However, different from traditional application scenarios, wearable electrocardiogram (ECG) devices have some drawbacks, such as being subject to multiple abnormal interferences, thus making accurate ventricular contraction (PVC) and supraventricular premature beat (SPB) detection to be more challenging. The traditional models for heartbeat classification suffer from the problem of large-scale parameters and the performance in dynamic ECG heartbeat classification is not satisfactory. In this paper, we propose a novel light model Lightweight Fussing Transformer to address these problems. We developed a more lightweight structure named LightConv Attention (LCA) to replace the self-attention of Fussing Transformer. LCA has reached remarkable performance level equal to or higher than self-attention with fewer parameters. In particular, we designed a stronger embedding structure (Convolutional Neural Network with attention mechanism) to enhance the weight of features of internal morphology of the heartbeat. Furthermore, we have implemented the proposed methods on real datasets and experimental results have demonstrated outstanding accuracy of detecting PVC and SPB. © 2022 Elsevier B.V.

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Multistep predictions for adaptive sampling in mobile robotic sensor networks using proximal ADMM

- Le, Viet-Anh, Nguyen, Linh, Nghiem, Truong

Authors: Le, Viet-Anh , Nguyen, Linh , Nghiem, Truong
Date: 2022
Type: Text , Journal article
Relation: IEEE Access Vol. 10, no. (2022), p. 64850-64861
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Description: This paper presents a novel approach, using multi-step predictions, to the adaptive sampling problem for efficient monitoring of environmental spatial phenomena in a mobile sensor network. We employ a Gaussian process to represent the spatial field of interest, which is then used to predict the field at unmeasured locations. The adaptive sampling problem aims to drive the mobile sensors to optimally navigate the environment while the sensors adaptively take measurements of the spatial phenomena at each sampling step. To this end, an optimal sampling criterion based on conditional entropy is proposed, which minimizes the prediction uncertainty of the Gaussian process model. By predicting the measurements the mobile sensors potentially take in a finite horizon of multiple future sampling steps and exploiting the chain rule of the conditional entropy, a multi-step-ahead adaptive sampling optimization problem is formulated. Its objective is to find the optimal sampling paths for the mobile sensors in multiple sampling steps ahead. Robot-robot and robot-obstacle collision avoidance is formulated as mixed-integer constraints. Compared with the single-step-ahead approach typically adopted in the literature, our approach provides better navigation, deployment, and data collection with more informative sensor readings. However, the resulting mixed-integer nonlinear program is highly complex and intractable. We propose to employ the proximal alternating direction method of multipliers to efficiently solve this problem. More importantly, the solution obtained by the proposed algorithm is theoretically guaranteed to converge to a stationary value. The effectiveness of our proposed approach was extensively validated by simulation using a real-world dataset, which showed highly promising results. © 2013 IEEE.

Novel approach to evaluate rock mass fragmentation in block caving using unascertained measurement model and information entropy with flexible credible identification criterion

- Zhou, Jian, Chen, Chao, Khandelwal, Manoj, Tao, Ming, Li, Chuanqi

Authors: Zhou, Jian , Chen, Chao , Khandelwal, Manoj , Tao, Ming , Li, Chuanqi
Date: 2022
Type: Text , Journal article
Relation: Engineering with computers Vol. 38, no. Suppl 5 (2022), p. 3789-3809
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Description: In recent years, block caving has drawn the attention of many mine enterprises due to the admired extraction rate and lower cost, which can exploit the materials via gravity inflow. At the same time, the limitation of this advanced method cannot be underestimated easily, such as surface subsidence and boulder, usually, the latter leads to the frequent secondary blast and damage of bottom structure. Thus, it is significant and crucial to evaluate the fragmentation before the implement of this method. But, traditional fragmentation assessment model suffers from the complex process of modeling and simulation. In this study, a hybrid model consists of unascertained measurement theory and information entropy was constructed to meet the requirements of this prospective mining method. Considering the influence of various parameters on rock fragmentation at the same time, twenty-three factors (i.e., uniaxial compressive strength, modulus ratio, fracture frequency, aperture, persistence, joint orientation, roughness, infilling, weathering, in situ stresses, stress orientation, stress ratio, underground water, fine ratio, hydraulic radius, undercut height, draw column height, draw points geometry, draw rate, multiple draw interaction, air gap height, broken ore density and undercut direction) were chosen to extract the main characteristics of rock mass samples from the two different mines, namely Reserve North ( Chile ), Diablo Regimiento ( Chile ) and Kemess mine ( Canada ). A new membership function (logarithm curve) was added to eliminate uncertainty results from the low level of knowledge about rock mass properties. Then, information entropy was performed to quantify the impacts of individual index. A credible degree identification criterion ( R η ) was also applied to review the sample attributes qualitatively. Ultimately, degree of fragmentation of the three samples was judged easily on the basis of a composite measurement vectors and R η . The evaluation results showed that the fragmentation grades of Reserve North , Diablo Regimiento and Kemess mine , separately, were “Good”, “Medium” and “Good”. With regard to the excellent performance of this hybrid model, it can be seen as a reliable approach to describe the fragmentation potential during the ore extraction using block caving mining method.

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Metaphor research in the 21st century : a bibliographic analysis

- Zhang, Dongyu, Zhang, Minghao, Peng, Ciyuan, Jung, Jason, Xia, Feng

Authors: Zhang, Dongyu , Zhang, Minghao , Peng, Ciyuan , Jung, Jason , Xia, Feng
Date: 2020
Type: Text , Journal article
Relation: Computer Science and Information Systems Vol. 18, no. 1 (2020), p. 303-322
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Description: Metaphor is widely used in human communication. The cohort of scholars studying metaphor in various fields is continuously growing, but very few work has been done in bibliographical analysis of metaphor research. This paper examines the advancements in metaphor research from 2000 to 2017. Using data retrieved from Microsoft Academic Graph and Web of Science, this paper makes a macro analysis of metaphor research, and expounds the underlying patterns of its development. Taking into consideration sub-fields of metaphor research, the internal analysis of metaphor research is carried out from a micro perspective to reveal the evolution of research topics and the inherent relationships among them. This paper provides novel insights into the current state of the art of metaphor research as well as future trends in this field, which may spark new research interests in metaphor from both linguistic and interdisciplinary perspectives. © 2020, ComSIS Consortium. All rights reserved.

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Deep learning-based approach for detecting trajectory modifications of cassini-huygens spacecraft

- Aldabbas, Ashraf, Gal, Zoltan, Ghori, Khawaja, Imran, Muhammad, Shoaib, Muhammad

Authors: Aldabbas, Ashraf , Gal, Zoltan , Ghori, Khawaja , Imran, Muhammad , Shoaib, Muhammad
Date: 2021
Type: Text , Journal article
Relation: IEEE Access Vol. 9, no. (2021), p. 39111-39125
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Description: There were necessary trajectory modifications of Cassini spacecraft during its last 14 years movement cycle of the interplanetary research project. In the scale 1.3 hour of signal propagation time and 1.4-billion-kilometer size of Earth-Cassini channel, complex event detection in the orbit modifications requires special investigation and analysis of the collected big data. The technologies for space exploration warrant a high standard of nuanced and detailed research. The Cassini mission has accumulated quite huge volumes of science records. This generated a curiosity derives mainly from a need to use machine learning to analyze deep space missions. For energy saving considerations, the communication between the Earth and Cassini was executed in non-periodic mode. This paper provides a sophisticated in-depth learning approach for detecting Cassini spacecraft trajectory modifications in post-processing mode. The proposed model utilizes the ability of Long Short Term Memory (LSTM) neural networks for drawing out useful data and learning the time series inner data pattern, along with the forcefulness of LSTM layers for distinguishing dependencies among the long-short term. Our research study exploited the statistical rates, Matthews correlation coefficient, and F1 score to evaluate our models. We carried out multiple tests and evaluated the provided approach against several advanced models. The preparatory analysis showed that exploiting the LSTM layer provides a notable boost in rising the detection process performance. The proposed model achieved a number of 232 trajectory modification detections with 99.98% accuracy among the last 13.35 years of the Cassini spacecraft life. © 2013 IEEE.

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Energy efficiency perspectives of femtocells in internet of things : recent advances and challenges

- Al-Turjman, Fadi, Imran, Muhammad, Bakhsh, Sheikh

Authors: Al-Turjman, Fadi , Imran, Muhammad , Bakhsh, Sheikh
Date: 2017
Type: Text , Journal article
Relation: IEEE Access Vol. 5, no. (2017), p. 26808-26818
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Description: Energy efficiency is a growing concern in every aspect of the technology. Apart from maintaining profitability, energy efficiency means a decrease in the overall environmental effects, which is a serious concern in today's world. Using a femtocell in Internet of Things (IoT) can boost energy efficiency. To illustrate, femtocells can be used in smart homes, which is a subpart of the smart grid, as a communication mechanism in order to manage energy efficiency. Moreover, femtocells can be used in many IoT applications in order to provide communication. However, it is important to evaluate the energy efficiency of femtocells. This paper investigates recent advances and challenges in the energy efficiency of the femtocell in IoT. First, we introduce the idea of femtocells in the context of IoT and their role in IoT applications. Next, we describe prominent performance metrics in order to understand how the energy efficiency is evaluated. Then, we elucidate how energy can be modeled in terms of femtocell and provide some models from the literature. Since femtocells are used in heterogeneous networks to manage energy efficiency, we also express some energy efficiency schemes for deployment. The factors that affect the energy usage of a femtocell base station are discussed and then the power consumption of user equipment under femtocell coverage is mentioned. Finally, we highlight prominent open research issues and challenges. © 2013 IEEE.

A robust local texture descriptor in the parametric space of the weibull distribution

- Tania, Sheikh, Karmakar, Gour, Teng, Shyh, Murshed, Manzur

Authors: Tania, Sheikh , Karmakar, Gour , Teng, Shyh , Murshed, Manzur
Date: 2023
Type: Text , Journal article
Relation: IEEE Transactions on Multimedia Vol. 25, no. (2023), p. 6053-6066
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Description: Research in texture feature approximation is still in the embryonic stage because of difficulties in developing a sound theoretical model to express the unique pattern in the intensity-variation of pixels in the neighbourhood of the pixel-of-interest so that it can sufficiently discriminate different textures. Local texture descriptors are widely used in image segmentation as they comprise pixel-wise features. The Weber local descriptor (WLD) with differential excitation and gradient orientation components, inspired by Weber's Law, has been leveraged in the state-of-the-art iterative contraction and merging (ICM) image segmentation technique. However, WLD has inherent drawbacks in the formulation of the components that limit its discriminatory capability. This paper introduces a novel texture descriptor by directly modelling the distribution of intensity-variation in the parametric space of the Weibull distribution using its shape and scale parameters. A unified 'joint scale' texture property is introduced, which can discriminate textures better than the individual parameters while keeping the length of the descriptor shorter. Additionally, the accuracy of WLD's gradient orientation component is improved by using an extended Sobel operator and expressing gradients in -

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Coupled attention networks for multivariate time series anomaly detection

- Xia, Feng, Chen, Xin, Yu, Shuo, Hou, Mingliang, Liu, Mujie, You, Linlin

Authors: Xia, Feng , Chen, Xin , Yu, Shuo , Hou, Mingliang , Liu, Mujie , You, Linlin
Date: 2024
Type: Text , Journal article
Relation: IEEE Transactions on Emerging Topics in Computing Vol. 12, no. 1 (2024), p. 240-253
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Description: Multivariate time series anomaly detection (MTAD) plays a vital role in a wide variety of real-world application domains. Over the past few years, MTAD has attracted rapidly increasing attention from both academia and industry. Many deep learning and graph learning models have been developed for effective anomaly detection in multivariate time series data, which enable advanced applications such as smart surveillance and risk management with unprecedented capabilities. Nevertheless, MTAD is facing critical challenges deriving from the dependencies among sensors and variables, which often change over time. To address this issue, we propose a coupled attention-based neural network framework (CAN) for anomaly detection in multivariate time series data featuring dynamic variable relationships. We combine adaptive graph learning methods with graph attention to generate a global-local graph that can represent both global correlations and dynamic local correlations among sensors. To capture inter-sensor relationships and temporal dependencies, a convolutional neural network based on the global-local graph is integrated with a temporal self-attention module to construct a coupled attention module. In addition, we develop a multilevel encoder-decoder architecture that accommodates reconstruction and prediction tasks to better characterize multivariate time series data. Extensive experiments on real-world datasets have been conducted to evaluate the performance of the proposed CAN approach, and the results show that CAN significantly outperforms state-of-the-art baselines. © 2013 IEEE.

A survey of blockchain-based IoT eHealthcare : applications, research issues, and challenges

- Rahman, Md Shafiur, Islam, Md Amirul, Uddin, Md Ashraf, Stea, Giovanni

Authors: Rahman, Md Shafiur , Islam, Md Amirul , Uddin, Md Ashraf , Stea, Giovanni
Date: 2022
Type: Text , Journal article , Review
Relation: Internet of Things (Netherlands) Vol. 19, no. (2022), p.
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Description: Blockchain (BC) technology has recently emerged as an essential component for different applications, including healthcare and IoT, because of its decentralized ledger, source provenance, and tamper-proof nature. The Internet of Things (IoT) and BC have enabled health systems to expand their scalability and maintain consistency on a decentralized platform. As a result, many researchers have developed BC-enabled IoT eHealth systems and explored the application of BC technology in diverse fields of eHealthcare. This paper conducts a comprehensive survey on the emerging applications of BC technology in healthcare. We summarize applications, research issues, security threats, research challenges, opportunities, and the future scope of BC technologies in the IoT-enabled healthcare system when BC is adopted to handle the privacy and storage of current and future medical records. Furthermore, we analyze the state-of-the-art BC works in the medical area, assessing their benefits-drawbacks, and guiding future researchers to overcome the limitations of the existing articles. © 2022 Elsevier B.V.

A lightweight federated learning based privacy preserving B5G pandemic response network using unmanned aerial vehicles: A proof-of-concept

- Nasser, Nasser, Fadlullah, Zubair, Fouda, Mostafa, Ali, Asmaa, Imran, Muhammad

Authors: Nasser, Nasser , Fadlullah, Zubair , Fouda, Mostafa , Ali, Asmaa , Imran, Muhammad
Date: 2022
Type: Text , Journal article
Relation: Computer Networks Vol. 205, no. (2022), p.
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Description: The concept of an intelligent pandemic response network is gaining momentum during the current novel coronavirus disease (COVID-19) era. A heterogeneous communication architecture is essential to facilitate collaborative and intelligent medical analytics in the fifth generation and beyond (B5G) networks to intelligently learn and disseminate pandemic-related information and diagnostic results. However, such a technique raises privacy issues pertaining to the health data of the patients. In this paper, we envision a privacy-preserving pandemic response network using a proof-of-concept, aerial–terrestrial network system serving mobile user entities/equipment (UEs). By leveraging the unmanned aerial vehicles (UAVs), a lightweight federated learning model is proposed to collaboratively yet privately learn medical (e.g., COVID-19) symptoms with high accuracy using the data collected by individual UEs using ambient sensors and wearable devices. An asynchronous weight updating technique is introduced in federated learning to avoid redundant learning and save precious networking as well as computing resources of the UAVs/UEs. A use-case where an Artificial Intelligence (AI)-based model is employed for COVID-19 detection from radiograph images is presented to demonstrate the effectiveness of our proposed approach. © 2021 Elsevier B.V.

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Treating class imbalance in non-technical loss detection : an exploratory analysis of a real dataset

- Ghori, Khawaja, Awais, Muhammad, Khattak, Akmal, Imran, Muhammad, Amin, Fazal, Szathmary, Laszlo

Authors: Ghori, Khawaja , Awais, Muhammad , Khattak, Akmal , Imran, Muhammad , Amin, Fazal , Szathmary, Laszlo
Date: 2021
Type: Text , Journal article
Relation: IEEE Access Vol. 9, no. (2021), p. 98928-98938
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Description: Non-Technical Loss (NTL) is a significant concern for many electric supply companies due to the financial impact caused as a result of suspect consumption activities. A range of machine learning classifiers have been tested across multiple synthesized and real datasets to combat NTL. An important characteristic that exists in these datasets is the imbalance distribution of the classes. When the focus is on predicting the minority class of suspect activities, the classifiers' sensitivity to the class imbalance becomes more important. In this paper, we evaluate the performance of a range of classifiers with under-sampling and over-sampling techniques. The results are compared with the untreated imbalanced dataset. In addition, we compare the performance of the classifiers using penalized classification model. Lastly, the paper presents an exploratory analysis of using different sampling techniques on NTL detection in a real dataset and identify the best performing classifiers. We conclude that logistic regression is the most sensitive to the sampling techniques as the change of its recall is measured around 50% for all sampling techniques. While the random forest is the least sensitive to the sampling technique, the difference in its precision is observed between 1% - 6% for all sampling techniques. © 2013 IEEE.

A novel real-time deterministic scheduling mechanism in industrial cyber-physical systems for energy internet

- Peng, Yuhuai, Jolfaei, Alireza, Yu, Keping

Authors: Peng, Yuhuai , Jolfaei, Alireza , Yu, Keping
Date: 2022
Type: Text , Journal article
Relation: IEEE Transactions on Industrial Informatics Vol. 18, no. 8 (2022), p. 5670-5680
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Description: As an effective distributed renewable energy utilization paradigm, a microgrid is expected to realize the high integration of the industrial cyber-physical systems (CPS), which has attracted extensive attention from academia and industry. However, the real-time interaction and feedback loop between physical systems and cyber systems have posed severe challenges to the reliability, determinacy, and energy efficiency of the multiway flow of information and communication transmission. In order to solve the problem of slot scheduling and data transmission (SSDT) in the microgrid, a novel real-time deterministic scheduling (RTDS) scheme for industrial CPS is proposed in this article. First, the SSDT is formulated as a multiway flow scheduling problem, and it is theoretically proved that the SSDT problem is NP-hard. Then, the RTDS scheme designs two heuristic algorithms: scheduling request preprocessing and greedy-based multichannel time slot allocation for an optimal scheduling solution. Practical experimental results demonstrate that the proposed RTDS scheme has significant advantages in packet loss rate, deadline guarantee rate, and energy consumption compared with the traditional schemes, and thus, is more suitable for deployment in microgrid systems. © 2005-2012 IEEE.

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Multimodal educational data fusion for students' mental health detection

- Guo, Teng, Zhao, Wenhong, Alrashoud, Mubarak, Tolba, Amr, Firmin, Sally, Xia, Feng

Authors: Guo, Teng , Zhao, Wenhong , Alrashoud, Mubarak , Tolba, Amr , Firmin, Sally , Xia, Feng
Date: 2022
Type: Text , Journal article
Relation: IEEE Access Vol. 10, no. (2022), p. 70370-70382
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Description: Mental health issues can lead to serious consequences like depression, self-mutilation, and worse, especially for university students who are not physically and mentally mature. Not all students with poor mental health are aware of their situation and actively seek help. Proactive detection of mental problems is a critical step in addressing this issue. However, accurate detections are hard to achieve due to the inherent complexity and heterogeneity of unstructured multi-modal data generated by campus life. Against this background, we propose a detection framework for detecting students' mental health, named CASTLE (educational data fusion for mental health detection). Three parts are involved in this framework. First, we utilize representation learning to fuse data on social life, academic performance, and physical appearance. An algorithm, named MOON (multi-view social network embedding), is proposed to represent students' social life in a comprehensive way by fusing students' heterogeneous social relations effectively. Second, a synthetic minority oversampling technique algorithm (SMOTE) is applied to the label imbalance issue. Finally, a DNN (deep neural network) model is utilized for the final detection. The extensive results demonstrate the promising performance of the proposed methods in comparison to an extensive range of state-of-the-art baselines. © 2013 IEEE.

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False data detection in a clustered smart grid using unscented Kalman filter

- Rashed, Muhammad, Kamruzzaman, Joarder, Gondal, Iqbal, Islam, Syed

Authors: Rashed, Muhammad , Kamruzzaman, Joarder , Gondal, Iqbal , Islam, Syed
Date: 2022
Type: Text , Journal article
Relation: IEEE Access Vol. 10, no. (2022), p. 78548-78556
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Description: The smart grid accessibility over the Internet of Things (IoT) is becoming attractive to electrical grid operators as it brings considerable operational and cost efficiencies. However, this in return creates significant cyber security challenges, such as fortification of state estimation data such as state variables against false data injection attacks (FDIAs). In this paper, a clustered partitioning state estimation (CPSE) technique is proposed to detect FDIA by using static state estimation, namely, weighted least square (WLS) method in conjunction with dynamic state estimation using minimum variance unscented Kalman filter (MV-UKF) which improves the accuracy of state estimation. The estimates acquired from the MV-UKF do not deviate like WLS as these are purely based on the previous iteration saved in the transition matrix. The deviation between the corresponding estimations of WLS and MV-UKF are utilised to partition the smart grid into smaller sub-systems to detect FDIA and then identify its location. To validate the proposed detection technique, FIDAs are injected into IEEE 14-bus, IEEE 30-bus, IEEE 118-bus, and IEEE 300-bus distribution feeder using MATPOWER simulation platform. Our results clearly demonstrate that the proposed technique can locate the attack area efficiently compared to other techniques such as chi square. © 2013 IEEE.

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DQN approach for adaptive self-healing of VNFs in cloud-native network

- Arulappan, Arunkumar, Mahanti, Aniket, Passi, Kalpdrum, Srinivasan, Thiruvenkadam, Naha, Ranesh, Raja, Gunasekaran

Authors: Arulappan, Arunkumar , Mahanti, Aniket , Passi, Kalpdrum , Srinivasan, Thiruvenkadam , Naha, Ranesh , Raja, Gunasekaran
Date: 2024
Type: Text , Journal article
Relation: IEEE Access Vol. 12, no. (2024), p. 34489-34504
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Description: The transformation from physical network function to Virtual Network Function (VNF) requires a fundamental design change in how applications and services are tested and assured in a hybrid virtual network. Once the VNFs are onboarded in a cloud network infrastructure, operators need to test VNFs in real-time at the time of instantiation automatically. This paper explicitly analyses the problem of adaptive self-healing of a Virtual Machine (VM) allocated by the VNF with the Deep Reinforcement Learning (DRL) approach. The DRL-based big data collection and analytics engine performs aggregation to probe and analyze data for troubleshooting and performance management. This engine helps to determine corrective actions (self-healing), such as scaling or migrating VNFs. Hence, we proposed a Deep Queue Learning (DQL) based Deep Queue Networks (DQN) mechanism for self-healing VNFs in the virtualized infrastructure manager. Virtual network probes of closed-loop orchestration perform the automation of the VNF and provide analytics for real-time, policy-driven orchestration in an open networking automation platform through the stochastic gradient descent method for VNF service assurance and network reliability. The proposed DQN/DDQN mechanism optimizes the price and lowers the cost by 18% for resource usage without disrupting the Quality of Service (QoS) provided by the VNF. The outcome of adaptive self-healing of the VNFs enhances the computational performance by 27% compared to other state-of-the-art algorithms. © 2013 IEEE.

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