SDMIS 2022 Abstracts

Area 1 - SDMIS
Full Papers Short Papers

Area 1 - SDMIS

Full Papers
Paper Nr: 1
Title:

Intelligent Human-input-based Blockchain Oracle (IHiBO)
Authors:

Liuwen Yu, Mirko Zichichi, Réka Markovich and Amro Najjar
Abstract: The advent of Distributed Ledger Technologies (DLTs) has paved the way for a new paradigm of traceability in all information systems areas. In the context of decision-making processes, however, DLTs are generally used only to trace the end results. In this work we argue that a reasoning system can be put in place for making these decisions, in order to enhance auditability, transparency, and finally to provide explainability. We propose the Intelligent Human-input-based Blockchain Oracle (IHiBO), a cross-chain oracle that enables the execution and traceability of formal argumentation and negotiation processes, involving the intervention of human experts. We take as reference the decision-making processes of fund managements, as trust is of crucial importance in such “trust services”. The architecture and implementation of IHiBO are based on leveraging two-layer DLTs, smart contracts, argumentation and negotiation in a multi-agent setup. Finally, we provide some experimental results that support our discussion, namely that in the use-case we have considered our methodology can increase trust from principals to trusted services.
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Paper Nr: 4
Title:

Comparison of Online Exploration and Coverage Algorithms in Continuous Space
Authors:

Malte Z. Andreasen, Philip I. Holler, Magnus K. Jensen and Michele Albano
Abstract: We propose a framework to compare algorithms for multi agent exploration in an unknown continuous 2d environment. To analyze trade offs we compare algorithms with varying robot hardware requirements. We showcase our approach on Random Ballistic Walk (RBW), frontier-based exploration (The Next Frontier, TNF), Spiraling and Selective Backtracking (SSB), and Local Voronoi Decomposition (LVD). Algorithms that operate in a discrete grid-based space, such as LVD and SSB, are mapped to a continuous space for comparison with other algorithms. To our knowledge, no other extensive comparison of these exploration algorithms operating under the same testing environment has been conducted. The algorithms are tested in a custom 2D physics-driven simulation (Multi Agent Exploration Simulator, MAES), with two types of maps, namely the Cave map (C-Map) and the Building map (B-Map). The performance of each algorithm is evaluated in terms of coverage and exploration of the map. Results show that SSB performed the best in terms of coverage in all tested scenarios. TNF performed the best in terms of exploration, especially on bigger maps. RBW achieved good results in terms of both coverage and exploration in C-Maps, but not in B-Maps. LVD performed similarly to RBW in C-Maps, but better in the B-Maps.
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Short Papers
Paper Nr: 2
Title:

CrossSiam: k-Fold Cross Representation Learning
Authors:

Kaiyu Suzuki, Yasushi Kambayashi and Tomofumi Matsuzawa
Abstract: One of the most important tasks for multi-agents such as drones is to automatically make decisions based on images captured by on-board cameras. These agents must be highly accurate and reliable. For this purpose, we applied k-fold cross validation to the task of classifying images using deep learning, which is a method that compares and evaluates models appropriately model of a given problem; this technique is easy to understand and easy to implement, and it produces results in lower bias estimates. However, k-fold cross validation reduces the amount of data per neural network, which reduces the accuracy. In order to address this problem, we propose CrossSiam. CrossSiam is a one of the representation learning methods to train feature encoders to mimic the embedding space of the validation data of each neural network. We show that the proposed method has a higher classification accuracy than the ParaSiam (baseline). This approach can be very important in the field where reliability is required, such as automated vehicles and drones in disaster situations.
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Paper Nr: 5
Title:

Skin Cancer Classification using Deep Learning Models
Authors:

Marwa Kahia, Amira Echtioui, Fathi Kallel and Ahmed Ben Hamida
Abstract: In recent years, researches proved that Melanoma is the deadliest form of skin cancer. In the early stages, it can be treated successfully with surgery alone and survival rates are high. A large number of methods for Melanoma classification has been proposed to deal with this problem, but although they did not find better ways to create the final solution. Thus, our aim is to go further and explore the classic models in order to handle the Melanoma classification problem based on modified VGG16 and modified InceptionV3. The conducted experiments revealed the effectiveness of our proposed method based on modified VGG16 with 73.33% of accuracy, when compared to other state-of-the-art methods on the same data sets, in terms of finding optimal and effective solutions and improving the objective function.
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Paper Nr: 3
Title:

EEG Motor Imagery Classification using Fusion Convolutional Neural Network
Authors:

Wassim Zouch and Amira Echtioui
Abstract: Brain-Computer Interfaces (BCIs) are systems that can help people with limited motor skills interact with their environment without the need for outside help. Therefore, the signal is representative of a motor area in the active brain system. It is used to recognize MI-EEG tasks via a deep learning techniques such as Convolutional Neural Network (CNN), which poses a potential problem in maintaining the integrity of frequency-time-space information and then the need for exploring the CNNs fusion. In this work, we propose a method based on the fusion of three CNN (3CNNs). Our proposed method achieves an interesting precision, recall, F1-score, and accuracy of 61.88%, 62.50%, 61.47%, 64.75% respectively when tested on the 9 subjects from the BCI Competition IV 2a dataset. The 3CNNs model achieved higher results compared to the state-of-the-art.
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