Explain what you see:argumentation-based learning and robotic vision

In this thesis, we have introduced new techniques for the problems of open-ended learning, online incremental learning, and explainable learning. These methods have applications in the classification of tabular data, 3D object category recognition, and 3D object parts segmentation. We have utilized argumentation theory and probability theory to develop these methods. The first proposed open-ended online incremental learning approach is Argumentation-Based online incremental Learning (ABL). ABL works with tabular data and can learn with a small number of learning instances using an abstract argumentation framework and bipolar argumentation framework. It has a higher learning speed than state-of-the-art online incremental techniques. However, it has high computational complexity. We have addressed this problem by introducing Accelerated Argumentation-Based Learning (AABL). AABL uses only an abstract argumentation framework and uses two strategies to accelerate the learning process and reduce the complexity. The second proposed open-ended online incremental learning approach is the Local Hierarchical Dirichlet Process (Local-HDP). Local-HDP aims at addressing two problems of open-ended category recognition of 3D objects and segmenting 3D object parts. We have utilized Local-HDP for the task of object part segmentation in combination with AABL to achieve an interpretable model to explain why a certain 3D object belongs to a certain category. The explanations of this model tell a user that a certain object has specific object parts that look like a set of the typical parts of certain categories. Moreover, integrating AABL and Local-HDP leads to a model that can handle a high degree of occlusion

Chemical Structure and Dynamics in Laminar Flame Propagation

The objective of this dissertation is to investigate fundamental aspects of premixed flame structures as well as flame dynamics that arise due to conjugate heat transfer in narrow channels. Laminar premixed combustion simulations in narrow 2D channels show that conjugate heat transfer allows for combustion of mixtures at small scales that are not flammable at normal conditions. To investigate the impact of conjugate heat transfer, preheated 1D cases with premixed H2/Air fuel are simulated for a wide range of operating conditions based on inlet temperature and equivalence ratio. For post-processing, Chemical Explosive Mode Analysis (CEMA, an eigen-analysis technique) is used as a computational diagnostic tool. Classical CEMA is refined to introduce directional information to track dominant promoting and counteracting chemical modes that are linked to specific species and reactions. A major result of this analysis is that flame structures are shown to follow the same trend if they have similar flame temperature, regardless of the inlet conditions. Laminar premixed combustion in narrow channels is known to produce a range of dynamic flame phenomena (stationary/non-stationary and symmetric/asymmetric flames) that depend on operating conditions. Mechanisms that lead to different dynamics are investigated by tracking flame fronts and related metrics for laminar premixed CH4/air and syngas/air flames. Flow re-directions because of local extinctions and corresponding flame edges are found to be the main causes for such dynamics. Synthesized gases (syngas) have been recently considered to be used at small-scale combustion systems because a) they can be produced from cheap heavy fuels such as glycerol and b) they have better combustion characteristics compared to the initial heavy fuel. Therefore, syngas production from glycerol, which is available in high volumes and low costs has been studied. By investigating glycerol reforming processes at a wide range of intermediate temperatures and stoichiometries, optimum operating conditions for producing syngas are explained

A novel motion-model-free UWB short-range positioning method

In recent years, the number of location-based services is increasing and consequently, the researchers’ attentions are captivated in designing accurate real-time positioning systems. Despite having a good performance in outdoor environment, Global Positioning System (GPS) is not capable of estimating an object’s position in an indoor environment precisely. In this paper, we present a novel tracking algorithm for indoor environment with a known floor plan. The object location is estimated by utilizing the information of the multipath components which are created by one physical and some virtual anchors. We will link this information to the floor plan by defining a channel model that has a combination of stochastic and deterministic traits. As we have used only one physical anchor in this paper, we would encounter several challenges such as lack of data association and existence of clutters amid real data. We dealt with these problems through random finite set methodology. Additionally, we will demonstrate that the proposed method is not restricted by the model of motion and is capable to precisely track the trajectory. It will be shown that it provides a better accuracy, particularly in non-linear trajectories, compared with two other relevant models which are adopting linear motion model

SpArX: Sparse Argumentative Explanations for Neural Networks

Neural networks (NNs) have various applications in AI, but explaining their decision process remains challenging. Existing approaches often focus on explaining how changing individual inputs affects NNs' outputs. However, an explanation that is consistent with the input-output behaviour of an NN is not necessarily faithful to the actual mechanics thereof. In this paper, we exploit relationships between multi-layer perceptrons (MLPs) and quantitative argumentation frameworks (QAFs) to create argumentative explanations for the mechanics of MLPs. Our SpArX method first sparsifies the MLP while maintaining as much of the original mechanics as possible. It then translates the sparse MLP into an equivalent QAF to shed light on the underlying decision process of the MLP, producing global and/or local explanations. We demonstrate experimentally that SpArX can give more faithful explanations than existing approaches, while simultaneously providing deeper insights into the actual reasoning process of MLPs

The Looming Threat of Fake and LLM-generated LinkedIn Profiles: Challenges and Opportunities for Detection and Prevention

In this paper, we present a novel method for detecting fake and Large Language Model (LLM)-generated profiles in the LinkedIn Online Social Network immediately upon registration and before establishing connections. Early fake profile identification is crucial to maintaining the platform's integrity since it prevents imposters from acquiring the private and sensitive information of legitimate users and from gaining an opportunity to increase their credibility for future phishing and scamming activities. This work uses textual information provided in LinkedIn profiles and introduces the Section and Subsection Tag Embedding (SSTE) method to enhance the discriminative characteristics of these data for distinguishing between legitimate profiles and those created by imposters manually or by using an LLM. Additionally, the dearth of a large publicly available LinkedIn dataset motivated us to collect 3600 LinkedIn profiles for our research. We will release our dataset publicly for research purposes. This is, to the best of our knowledge, the first large publicly available LinkedIn dataset for fake LinkedIn account detection. Within our paradigm, we assess static and contextualized word embeddings, including GloVe, Flair, BERT, and RoBERTa. We show that the suggested method can distinguish between legitimate and fake profiles with an accuracy of about 95% across all word embeddings. In addition, we show that SSTE has a promising accuracy for identifying LLM-generated profiles, despite the fact that no LLM-generated profiles were employed during the training phase, and can achieve an accuracy of approximately 90% when only 20 LLM-generated profiles are added to the training set. It is a significant finding since the proliferation of several LLMs in the near future makes it extremely challenging to design a single system that can identify profiles created with various LLMs.Comment: 33rd ACM Conference on Hypertext and Social Media (HT '23

An evaluation of architectural monuments in Afghanistan as in the capital city, Kabul

Afghanistan as a multi-cultural country witnesses a diversity of architectural styles influenced by many civilizations. Architecture in the Kabul city, the capital of Afghanistan, encompasses styles before emerging Islamic and after emerging Islamic religion. Considering the civilization influences, architecture styles in Afghanistan may be divided into three parts: Central Asia, Persian, and Indian. Kabul city is the meeting place of all these three styles.  After the establishment of current Afghanistan in 1747, for the first time the evolution of architectural style in Kabul city as the capital occurred in 1880 which has been influenced by western architecture styles, and it has become the most famous style in the city. Basically, the architecture styles in Kabul city in relation to the civilization influences are characterized by Central Asian, Persian, Indian, and Western styles which have been reflected in most of the architectural monuments in the country. In this paper it is aimed to study the architectural evolution of the Kabul city by considering the civilization impacts through history, particularly before emerging Islam and after emerging Islam religion. The associated architectural monuments of each historical period in the city were studied based on its architectural style and related civilization. Furthermore, these impacts on shaping the current architectural style of the Kabul city have also been reviewed. This study is carried out mostly by reviewing the literature to highlight the architectural styles developed over the periods in Kabul city and the impact of cultural influences on them. As a case from each historical period, the monuments according to their historical importance, architecture style, and construction method are evaluated. As a mapping technique, Arc GIS 10.5 is used to visualize the distribution of architectural monuments within Kabul city.

ProtoArgNet: Interpretable Image Classification with Super-Prototypes and Argumentation [Technical Report]

We propose ProtoArgNet, a novel interpretable deep neural architecture for image classification in the spirit of prototypical-part-learning as found, e.g. in ProtoPNet. While earlier approaches associate every class with multiple prototypical-parts, ProtoArgNet uses super-prototypes that combine prototypical-parts into single prototypical class representations. Furthermore, while earlier approaches use interpretable classification layers, e.g. logistic regression in ProtoPNet, ProtoArgNet improves accuracy with multi-layer perceptrons while relying upon an interpretable reading thereof based on a form of argumentation. ProtoArgNet is customisable to user cognitive requirements by a process of sparsification of the multi-layer perceptron/argumentation component. Also, as opposed to other prototypical-part-learning approaches, ProtoArgNet can recognise spatial relations between different prototypical-parts that are from different regions in images, similar to how CNNs capture relations between patterns recognized in earlier layers

Argue to Learn:Accelerated Argumentation-Based Learning

Human agents can acquire knowledge and learn through argumentation. Inspired by this fact, we propose a novel argumentation-based machine learning technique that can be used for online incremental learning scenarios. Existing methods for online incremental learning problems typically do not generalize well from just a few learning instances. Our previous argumentation-based online incremental learning method outperformed state-of-the-art methods in terms of accuracy and learning speed. However, it was neither memory-efficient nor computationally efficient since the algorithm used the power set of the feature values for updating the model. In this paper, we propose an accelerated version of the algorithm, with polynomial instead of exponential complexity, while achieving higher learning accuracy. The proposed method is at least 200 times faster than the original argumentation-based learning method and is more memory-efficient