Showing items 21 - 40 of 2095

Your selections:

Deep outdated fact detection in knowledge graphs

- Tu, Huiling, Yu, Shuo, Saikrishna, Vidya, Xia, Feng, Verspoor, Karin

  • Authors: Tu, Huiling , Yu, Shuo , Saikrishna, Vidya , Xia, Feng , Verspoor, Karin
  • Date: 2023
  • Type: Text , Conference paper
  • Relation: 23rd IEEE International Conference on Data Mining Workshops, ICDMW 2023, Shanghai, China, 1-4 December 2023, 23rd IEEE International Conference on Data Mining Workshops Proceedings p. 1443-1452
  • Full Text:
  • Reviewed:
  • Description: Knowledge graphs (KGs) have garnered significant attention for their vast potential across diverse domains. However, the issue of outdated facts poses a challenge to KGs, affecting their overall quality as real-world information evolves. Existing solutions for outdated fact detection often rely on manual recognition. In response, this paper presents DEAN (Deep outdatEd fAct detectioN), a novel deep learning-based framework designed to identify outdated facts within KGs. DEAN distinguishes itself by capturing implicit structural information among facts through comprehensive modeling of both entities and relations. To effectively uncover latent out-of-date information, DEAN employs a contrastive approach based on a pre-defined Relations-to-Nodes (R2N) graph, weighted by the number of entities. Experimental results demonstrate the effectiveness and superiority of DEAN over state-of-the-art baseline methods. © 2023 IEEE.

Distributed formation trajectory planning for multi-vehicle systems

- Nguyen, Binh, Nghiem, Truong, Nguyen, Linh, Nguyen, Tung, La, Hung, Sookhak, Mehdi, Nguyen, Thang

  • Authors: Nguyen, Binh , Nghiem, Truong , Nguyen, Linh , Nguyen, Tung , La, Hung , Sookhak, Mehdi , Nguyen, Thang
  • Date: 2023
  • Type: Text , Conference paper
  • Relation: 2023 American Control Conference, ACC 2023 Vol. 2023-May, p. 1325-1330
  • Full Text: false
  • Reviewed:
  • Description: This paper addresses the problem of distributed formation trajectory planning for multi-vehicle systems with collision avoidance among vehicles. Unlike some previous distributed formation trajectory planning methods, our proposed approach offers great flexibility in handling computational tasks for each vehicle when the global formation of all the vehicles changes. It affords the system the ability to adapt to the computational capabilities of the vehicles. Furthermore, global formation constraints can be handled at any selected vehicles. Thus, any formation change can be effectively updated without recomputing all local formations at all the vehicles. To guarantee the above features, we first formulate a dynamic consensus-based optimization problem to achieve desired formations while guaranteeing collision avoidance among vehicles. Then, the optimization problem is effectively solved by ADMM-based or alternating projection-based algorithms, which are also presented. Theoretical analysis is provided not only to ensure the convergence of our method but also to show that the proposed algorithm can surely be implemented in a fully distributed manner. The effectiveness of the proposed method is illustrated by a numerical example of a 9-vehicle system. © 2023 American Automatic Control Council.

Dynamic trust boundary identification for the secure communications of the entities via 6G

- Basri, Rabeya, Karmakar, Gour, Kamruzzaman, Joarder, Newaz, S. H. Shah, Nguyen, Linh, Usman, Muhammad

  • Authors: Basri, Rabeya , Karmakar, Gour , Kamruzzaman, Joarder , Newaz, S. H. Shah , Nguyen, Linh , Usman, Muhammad
  • Date: 2023
  • Type: Text , Conference paper
  • Relation: 18th International Conference on Information Security Practice and Experience (ISPEC), 24-25 August 2023, Copenhagen, Denmark, International Conference on Information Security Practice and Experience: 18th International Conference, ISPEC 2023, Copenhagen, Denmark, August 24–25, 2023, Proceedings Vol. 14341, p. 194-208
  • Full Text: false
  • Reviewed:
  • Description: 6G is more likely prone to a range of known and unknown cyber-attacks because of its highly distributive nature. Current literature and research prove that a trust boundary can be used as a security door (e.g., gateway/firewall) to validate entities and applications attempting to access 6G networks. Trust boundaries allow these entities to connect or work with entities of other trust boundaries via 6G by dynamically monitoring their interactions, behaviors, and data transmissions. The importance of trust boundaries in security protection mechanisms demands a dynamic multi-trust boundary identification. There exists an automatic trust boundary identification for IoT data. However, it is a binary trust boundary classification and the dataset used in the approach is not suitable for dynamic trust boundary identification. Motivated by these facts, to provide automatic security protection for entities in 6G, in this paper, we propose a mechanism to identify dynamic and multiple trust boundaries based on trust values and geographical location coordinates of 6G communication entities. Our proposed mechanism uses unsupervised clustering and splitting and merging techniques. The experimental results show that entities can dynamically change their boundary location if their trust values and locations change over time. We also analyze the trust boundary identification accuracy in terms of our defined two performance metrics, i.e., trust consistency and the degree of gateway coverage. The proposed scheme allows us to distinguish between entities and control their access to the 6G network based on their trust levels to ensure secure and resilient communication.

Effect of wood/binder ratio, slag/binder ratio, and alkaline dosage on the compressive strength of wood-geopolymer composites

- Gigar, Firensenay, Khennane, Amar, Liow, Jong-leng, Tekle, Biruk

  • Authors: Gigar, Firensenay , Khennane, Amar , Liow, Jong-leng , Tekle, Biruk
  • Date: 2023
  • Type: Text , Conference paper
  • Relation: International Symposium of the International Federation for Structural Concrete, fib Symposium 2023, Istanbul, 5-7 June 2023, Building for the Future: Durable, Sustainable, Resilient: Vol. 349 LNCE, p. 658-667
  • Full Text: false
  • Reviewed:
  • Description: The impact of building construction on the environment is significant. Occupying large land areas (urban footprint), buildings are one of the most important consumers of resources and raw materials. They are responsible for 38% of greenhouse gas (GHG) emissions in both developed and developing countries. Therefore, incorporating sustainability and resilience into all aspects of urban infrastructure has become necessary. To curb emissions, part of the answer lies in the use of construction and building materials made from recycled materials. Bio-sourced materials, like wood chips, combined with a cementitious matrix, offer an alternative to conventional materials. They are sustainable, lightweight, and have good thermal insulation. However, because of their inferior mechanical strength, they have limited use as load-bearing structural parts. Furthermore, the use of Portland cement as a binder still poses some challenges due to its high carbon footprint. This study investigates the potential of wood-geopolymer composites for better mechanical performance and environmental sustainability. A 6x2x2x2 fractional factorial-based experimental design was used to simultaneously study the effect of slag content, wood binder ratio, and alkaline on the compressive strength of the wood-geopolymer composite. The experiments showed encouraging results for developing ambient cured wood geopolymer composites. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Efficient graph learning for anomaly detection systems

- Febrinanto, Falih

Elastic step DDPG : multi-step reinforcement learning for improved sample efficiency

- Ly, Adrian, Dazeley, Richard, Vamplew, Peter, Cruz, Francisco, Aryal, Sunil

  • Authors: Ly, Adrian , Dazeley, Richard , Vamplew, Peter , Cruz, Francisco , Aryal, Sunil
  • Date: 2023
  • Type: Text , Conference paper
  • Relation: 2023 International Joint Conference on Neural Networks, IJCNN 2023 Vol. 2023-June
  • Full Text: false
  • Reviewed:
  • Description: A major challenge in deep reinforcement learning is that it requires more data to converge to an policy for complex problems. One way to improve sample efficiency is to use n-step updates to reduce the number of samples required to converge to a good policy. However n-step updates are known to be brittle and difficult to tune. Elastic Step DQN has shown that it is possible to automate the value of n in DQN to solve problems involving discrete action spaces, however the efficacy of the technique when applied on more complex problems and against problems with continuous action spaces is yet to be shown. In this paper we adapt the innovations proposed by Elastic Step DQN onto the DDPG algorithm and show empirically that Elastic Step DDPG is able to achieve a much stronger final training policy and is more sample efficient than DDPG. © 2023 IEEE.

Embodied carbon footprint analysis of signage industry : insights from two case studies

- Paresi, Prudvireddy, Javidan, Fatemeh, Sparks, Paul

  • Authors: Paresi, Prudvireddy , Javidan, Fatemeh , Sparks, Paul
  • Date: 2023
  • Type: Text , Conference paper
  • Relation: International Conference on Green Building, ICoGB 2023, Malmo, Sweden, 19-21 May 2023, Proceedings of 2023 International Conference on Green Building p. 69-76
  • Full Text: false
  • Reviewed:
  • Description: Embodied carbon has recently become a hot topic among environmentalists and designers, especially after the Paris Agreement on climate change. Embodied carbon refers to the carbon emissions associated with the manufacturing and transportation of building materials and the process of construction. The “Global Status Report for Buildings and Construction” report estimated that the building and construction sector alone contributed nearly 37–39% of global carbon emissions in 2017–2020. To tackle embodied carbon, the World Green Building Council (WorldGBC) has set a bold vision to reduce it by at least 40% by 2030 and achieve net-zero operating carbon in all new buildings. The signage industry plays a significant role in the building industry, as signages are a key component of buildings. Signages serve multiple purposes, such as providing information, enhancing brand identity, and promoting safety. Therefore, it is essential to understand the embodied carbon emissions associated with signage materials used to minimise the overall carbon emissions of construction projects. The present paper aims to study the embodied carbon footprint of the signage industry with the help of two case studies. The embodied carbon factors required while estimating the overall footprint of the signages are taken from Environmental Performance in Construction (EPiC) database. The study identifies the aluminum as the major contributor of the embodied emissions in the signage projects. This study provides insight into the other sources of embodied carbon and makes more informed decisions while selecting signage materials used in designs to create sustainable and economic projects. This information helps to increase sustainability and reduce the carbon footprint of signage projects in the early decision-making stages. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Exploring the relationship between testosterone and diabetes within the UK Biobank data

- Oatley, Giles

Federated learning based trajectory optimization for UAV enabled MEC

- Nehra, Anushka, Consul, Prakhar, Budhiraja, Ishan, Kaur, Gagandeep, Nasser, Nidal, Imran, Muhammad

  • Authors: Nehra, Anushka , Consul, Prakhar , Budhiraja, Ishan , Kaur, Gagandeep , Nasser, Nidal , Imran, Muhammad
  • Date: 2023
  • Type: Text , Conference paper
  • Relation: 2023 IEEE International Conference on Communications, ICC 2023 Vol. 2023-May, p. 1640-1645
  • Full Text: false
  • Reviewed:
  • Description: We present a moving mobile edge computing architecture in which unmanned aerial vehicles (UAV) serve as an equipment, providing computational power and allowing task offloading from mobile devices (MD). By improving user association, resource allocation, and UAV trajectory, we optimizing the energy consumption of all MDs. Towards that purpose, we provide a Trajectory optimization technique for making real-time choices while considering all the situation of the environment, followed by a DRL-based Trajectory control approach (RLCT). The RLCT approach may be adapted to any UAV takeoff point and can find the solution faster. The FL is introduced to address the Optimization problem in a Semi-distributed DRL technique to deal with UAV trajectory constraints. The proposed FRL approach enables devices to rapidly train the models locally while communicating with a local server to construct a network globally. The simulation results in the result section shows that the proposed technique RLCT and FRL in the paper outperforms the existing methods' while the FRL performs best among all. © 2023 IEEE.

Fine-grained image classification based on knowledge distillation

- Liu, Ying, Feng, Hao, Zhang, Weidong, Fang, Jie, Xiao, Peng, Zhang, Dengsheng

  • Authors: Liu, Ying , Feng, Hao , Zhang, Weidong , Fang, Jie , Xiao, Peng , Zhang, Dengsheng
  • Date: 2023
  • Type: Text , Conference paper
  • Relation: 19th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2023, Harbin, China, 29-31 July 2023, 2023 19th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)
  • Full Text: false
  • Reviewed:
  • Description: Despite the outstanding performance of deep learning-based fine-grained image classification methods, the commonly used models still suffer from high cost of computation and memory Therefore, this paper proposes a mobile-based CNN network that focuses on discriminative features of fine-grained images by embedding a hybrid-domain attention module to achieve higher accuracy in recognition. Specifically, under the premise of reducing network parameters, this paper presents a classification method that combines transfer learning and knowledge distillation to enhance the model's generalization performance and resistance to overfitting. Different knowledge transfer strategies are validated through the experiments in the knowledge distillation process. Mobile models such as SqueezeNet, MobileNetV2, and CBAM MobileNetV2 all demonstrate enhanced performance the knowledge distillation optimization. The proposed method in this paper can be used to develop a lightweight mobile-based CNN model with comparable performance to complex models making it more advantageous in real-life scenarios with limited storage resources and low hardware computation levels. Additionally, the model compression process utilizes only the intermediate features of the original dataset, meeting the confidentiality requirements of the original data in the field of public security. © 2023 IEEE.

Identification of fake news : a semantic driven technique for transfer domain

- Ferdush, Jannatul, Kamruzzaman, Joarder, Karmakar, Gour, Gondal, Iqbal, Das, Raj

Impact of MMC-HVDC control on power system dynamics : various concepts and parameterization

- Hasan, Mehedi, Shah, Rakibuzzaman, Amjady, Nima, Hossain, Md Jahangir, Islam, Syed

  • Authors: Hasan, Mehedi , Shah, Rakibuzzaman , Amjady, Nima , Hossain, Md Jahangir , Islam, Syed
  • Date: 2023
  • Type: Text , Conference paper
  • Relation: 2023 IEEE International Conference on Energy Technologies for Future Grids, ETFG 2023, Wollongong, 3-6 December 2023, 2023 IEEE International Conference on Energy Technologies for Future Grids, ETFG 2023
  • Full Text: false
  • Reviewed:
  • Description: Recently, the modular multi-level converter (MMC) based HVDC has become a popular method for integrating offshore wind and other renewable sources into the AC grid. Several studies have reported the possible use of MMC-HVDC to enhance grid strength. However, the control concept, parameterization, and the non-linearity related to fault-ride through behavior of MMC-HVDC greatly affects the dynamics of the host AC system. With the increasing number of HVDC and the stochastic behavior of current power systems, it is essential to comprehensively assess the impact of various control concepts of HVDC and control parametrization on the AC system. An equivalent and modified model of the Great British system has been used for this analysis with the enhanced reactive power and voltage control model. The various tuning and parameterization of the phase-locked loop control system are considered. The simulations conducted using the DIgSILENT PowerFactory show the relations of the control parameterization with the rotor angle and the short-term voltage stability of the system. © 2023 IEEE.

Improved model predictive torque control with reduced active prediction vectors for voltage source inverter driven induction motor drives

- Nahin, Nahin, Biswas, Shuvra, Hosain, Md Kamal, Bin Islam, Md Anas, Islam, Md Rabiul, Shah, Rakibuzzaman

Improved switching scheme to reduce the junction temperature and power loss of CHB inverters

- Afrin, Sadia, Biswas, Suvra, Bin Islam, Md Sabbir, Islam, Md Rabiul, Shah, Rakibuzzaman

Improved voltage balancing discontinuous PWM scheme for solar PV Fed grid-tied NPC inverters

- Hossain, Shahriar, Biswas, Shuvra, Islam, Md Rabiul, Shah, Rakibuzzaman

Machine learning driven digital twin for industrial control black box system : a novel framework and case study

- Siddiqui, Mustafa, Kahandawa, Gayan, Hewawasam, Hasitha, Rehman Siddiqi, Muftooh

Missing health data pattern matching technique for continuous remote patient monitoring

- Arora, Teena, Balasubramanian, Venki, Stranieri, Andrew

Model predictive control of master-slave inverters operating with fixed switching frequency

- Carnielutti, Fernanda, Aly, Mokhtar, Norambuena, Margarita, Hu, Jiefeng, Guerrero, Josep, Rodriguez, Jose

Modeling and analysis of finite-scale clustered backscatter communication networks

- Wang, Qiu, Zhou, Yong, Dai, Hong-Ning, Zhang, Guopeng, Imran, Muhammad, Nasser, Nidal

  • Authors: Wang, Qiu , Zhou, Yong , Dai, Hong-Ning , Zhang, Guopeng , Imran, Muhammad , Nasser, Nidal
  • Date: 2023
  • Type: Text , Conference paper
  • Relation: 2023 IEEE International Conference on Communications, ICC 2023, Rome, 28 May-1 June 2023, ICC 2023 - IEEE International Conference on Communications Vol. 2023-May, p. 1456-1461
  • Full Text: false
  • Reviewed:
  • Description: Backscatter communication (BackCom) is an intriguing technology that enables devices to transmit information by reflecting environmental radio frequency signals while consuming ultra-low energy. Applying BackCom in the Internet of things (IoT) networks can effectively address the power-unsustainability issue of energy-constraint devices. Considering many practical IoT applications, networks are finite-scale and devices are needed to be deployed at hotspot regions organized in clusters to cooperate for specific tasks. This paper considers finite-scale clustered backscatter communication networks (F-CBackCom Nets). To ensure communications, this paper establishes a theoretic model to analyze the communication connectivity of F-CBackCom Nets. Different from prior studies analyzing the connectivity with a focus on the transmission pair located at the center of the network, this paper analyzes the connectivity of a transmission pair located in an arbitrary location, because the performance of transmission pairs potentially varies with their network location. Extensive simulations validate the accuracy of our analytical model. Our results show that the connectivity of a transmission pair can be affected by its network location. Our analytical model and results can offer beneficial implications for constructing F-CBackCom Nets. © 2023 IEEE.

Novel few-shot learning based fuzzy feature detection algorithms

- Luo, Yun, Lu, Liangfu, Cui, Xudong, Du, Yan, Bi, Yingying, Zhu, Limin, Liang, Christy

  • Authors: Luo, Yun , Lu, Liangfu , Cui, Xudong , Du, Yan , Bi, Yingying , Zhu, Limin , Liang, Christy
  • Date: 2023
  • Type: Text , Conference paper
  • Relation: 10th IEEE International Conference on Data Science and Advanced Analytics, DSAA 2023, Thessaloniki, Greece, 9-12 October 2023, 2023 IEEE 10th International Conference on Data Science and Advanced Analytics, DSAA 2023 - Proceedings
  • Full Text: false
  • Reviewed:
  • Description: The Internet of Things (IoT) has significantly enhanced various aspects of our daily lives, including security, health, education, and energy efficiency, among others. Within the realm of IoT, image classification stands as a pivotal technique that has achieved notable success in domains such as facial recognition within security and scene recognition in transportation for traffic analysis. Nonetheless, the challenge emerges when tackling classification tasks with only limited labeled samples available for each category. Conventional machine learning techniques often struggle to attain satisfactory classification results under such circumstances. To address this issue, the concept of few-shot learning has emerged, aiming to achieve effective classification using only a small number of labeled samples. State-of-the-art few-shot learning models have introduced novel frameworks to tackle this problem. However, the inherent ambiguity and uncertainty within data often hinder the performance of classification methods. To overcome this limitation, this paper proposes the integration of fuzzy learning with few-shot learning in the context of feature extraction. The objective is to mitigate data fuzziness and enhance model performance. Leveraging a fuzzy extraction algorithm, we introduce fuzzy prototype networks and a fuzzy graph neural network with fuzzy reasoning. These frameworks are designed to analyze noisy and uncertain data, utilizing convolutional neural networks for feature extraction and applying fuzzy reasoning to capture ambiguity representations for features within each fuzzy set. The SoftMax function is then normalized to serve as a feature weight, effectively constraining the original feature vector. The effectiveness and efficiency of our proposed model are demonstrated through experimental evaluations conducted on various public datasets. The results showcase the model's capability in addressing the challenges posed by limited labeled data and data uncertainty, thus reaffirming its potential in enhancing the performance of image classification tasks within the IoT context. © 2023 IEEE.