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2022

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Articles 871 - 900 of 3613

Full-Text Articles in Computer Sciences

Diacritics Correction In Turkish With Context-Aware Sequence To Sequence Modeling, Asi̇ye Tuba Özge, Özge Bozal, Umut Özge Sep 2022

Diacritics Correction In Turkish With Context-Aware Sequence To Sequence Modeling, Asi̇ye Tuba Özge, Özge Bozal, Umut Özge

Turkish Journal of Electrical Engineering and Computer Sciences

Digital texts in many languages have examples of missing or misused diacritics which makes it hard for natural language processing applications to disambiguate the meaning of words. Therefore, diacritics restoration is a crucial step in natural language processing applications for many languages. In this study we approach this problem as bidirectional transformation of diacritical letters and their ASCII counterparts, rather than unidirectional diacritic restoration. We propose a context-aware character-level sequence to sequence model for this transformation. The model is language independent in the sense that no language-specific feature extraction is necessary other than the utilization of word embeddings and is …

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A Carbon-Aware Planning Framework For Production Scheduling In Mining, Nurual Asyikeen Azhar, Aldy Gunawan, Shih-Fen Cheng, Erwin Leonardi Sep 2022

A Carbon-Aware Planning Framework For Production Scheduling In Mining, Nurual Asyikeen Azhar, Aldy Gunawan, Shih-Fen Cheng, Erwin Leonardi

Research Collection School Of Computing and Information Systems

Managing the flow of excavated materials from a mine pit and the subsequent processing steps is the logistical challenge in mining. Mine planning needs to consider various geometric and resource constraints while maximizing the net present value (NPV) of profits over a long horizon. This mine planning problem has been modelled and solved as a precedence constrained production scheduling problem (PCPSP) using heuristics, due to its NP-hardness. However, the recent push for sustainable and carbon-aware mining practices calls for new planning approaches. In this paper, we propose an efficient temporally decomposed greedy Lagrangian relaxation (TDGLR) approach to maximize profits while …

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Exploiting Reuse For Gpu Subgraph Enumeration, Wentiao Guo, Yuchen Li, Kian-Lee Tan Sep 2022

Exploiting Reuse For Gpu Subgraph Enumeration, Wentiao Guo, Yuchen Li, Kian-Lee Tan

Research Collection School Of Computing and Information Systems

Subgraph enumeration is important for many applications such as network motif discovery, community detection, and frequent subgraph mining. To accelerate the execution, recent works utilize graphics processing units (GPUs) to parallelize subgraph enumeration. The performances of these parallel schemes are dominated by the set intersection operations which account for up to $95\%$ of the total processing time. (Un)surprisingly, a significant portion (as high as $99\%$) of these operations is actually redundant, i.e., the same set of vertices is repeatedly encountered and evaluated. Therefore, in this paper, we seek to salvage and recycle the results of such operations to avoid repeated …

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Risk-Aware Procurement Optimization In A Global Technology Supply Chain, Jonathan Chase, Jingfeng Yang, Hoong Chuin Lau Sep 2022

Risk-Aware Procurement Optimization In A Global Technology Supply Chain, Jonathan Chase, Jingfeng Yang, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

Supply chain disruption, from ‘Black Swan’ events like the COVID-19 pandemic or the Russian invasion of Ukraine, to more ordinary issues such as labour disputes and adverse weather conditions, can result in delays, missed orders, and financial loss for companies that deliver products globally. Developing a risk-tolerant procurement strategy that anticipates the logistical problems incurred by disruption involves both accurate quantification of risk and cost-effective decision-making. We develop a supplier-focused risk evaluation metric that constrains a procurement optimization model for a global technology company. Our solution offers practical risk tolerance and cost-effectiveness, accounting for a range of constraints that realistically …

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Using Deep Learning To Detect Social Media ‘Trolls’, Áine Macdermott, Michal Motylinski, Farkhund Iqbal, Kellyann Stamp, Mohammed Hussain, Andrew Marrington Sep 2022

Using Deep Learning To Detect Social Media ‘Trolls’, Áine Macdermott, Michal Motylinski, Farkhund Iqbal, Kellyann Stamp, Mohammed Hussain, Andrew Marrington

All Works

Detecting criminal activity online is not a new concept but how it can occur is changing. Technology and the influx of social media applications and platforms has a vital part to play in this changing landscape. As such, we observe an increasing problem with cyber abuse and ‘trolling’/toxicity amongst social media platforms sharing stories, posts, memes sharing content. In this paper we present our work into the application of deep learning techniques for the detection of ‘trolls’ and toxic content shared on social media platforms. We propose a machine learning solution for the detection of toxic images based on embedded …

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Distinctive Features Of Nonverbal Behavior And Mimicry In Application Interviews Through Data Analysis And Machine Learning, Sanne Rogiers, Elias Corneillie, Filip Lievens, Frederik Anseel, Peter Veelaert, Wilfried Philips Sep 2022

Distinctive Features Of Nonverbal Behavior And Mimicry In Application Interviews Through Data Analysis And Machine Learning, Sanne Rogiers, Elias Corneillie, Filip Lievens, Frederik Anseel, Peter Veelaert, Wilfried Philips

Research Collection Lee Kong Chian School Of Business

This paper reveals the characteristics and effects of nonverbal behavior and human mimicry in the context of application interviews. It discloses a novel analyzation method for psychological research by utilizing machine learning. In comparison to traditional manual data analysis, machine learning proves to be able to analyze the data more deeply and to discover connections in the data invisible to the human eye. The paper describes an experiment to measure and analyze the reactions of evaluators to job applicants who adopt specific behaviors: mimicry, suppress, immediacy and natural behavior. First, evaluation of the applicant qualifications by the interviewer reveals …

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Analyzing The Impact Of Covid-19 Control Policies On Campus Occupancy And Mobility Via Wifi Sensing, Camellia Zakaria, Amee Trivedi, Emmanuel Cecchet, Michael Chee, Prashant Shenoy, Rajesh Krishna Balan Sep 2022

Analyzing The Impact Of Covid-19 Control Policies On Campus Occupancy And Mobility Via Wifi Sensing, Camellia Zakaria, Amee Trivedi, Emmanuel Cecchet, Michael Chee, Prashant Shenoy, Rajesh Krishna Balan

Research Collection School Of Computing and Information Systems

Mobile sensing has played a key role in providing digital solutions to aid with COVID-19 containment policies, primarily to automate contact tracing and social distancing measures. As more and more countries reopen from lockdowns, there remains a pressing need to minimize crowd movements and interactions, particularly in enclosed spaces. Many COVID-19 technology solutions leverage positioning systems, generally using Bluetooth and GPS, and can theoretically be adapted to monitor safety compliance within dedicated environments. However, they may not be the ideal modalities for indoor positioning. This article conjectures that analyzing user occupancy and mobility via deployed WiFi infrastructure can help institutions …

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Contrastive Transformer-Based Multiple Instance Learning For Weakly Supervised Polyp Frame Detection, Tian Yu, Guansong Pang, Fengbei Liu, Yuyuan Liu, Chong Wang, Yuanhong Chen, Johan Verjans, Gustavo Carneiro Sep 2022

Contrastive Transformer-Based Multiple Instance Learning For Weakly Supervised Polyp Frame Detection, Tian Yu, Guansong Pang, Fengbei Liu, Yuyuan Liu, Chong Wang, Yuanhong Chen, Johan Verjans, Gustavo Carneiro

Research Collection School Of Computing and Information Systems

Current polyp detection methods from colonoscopy videos use exclusively normal (i.e., healthy) training images, which i) ignore the importance of temporal information in consecutive video frames, and ii) lack knowledge about the polyps. Consequently, they often have high detection errors, especially on challenging polyp cases (e.g., small, flat, or partially visible polyps). In this work, we formulate polyp detection as a weakly-supervised anomaly detection task that uses video-level labelled training data to detect frame-level polyps. In particular, we propose a novel convolutional transformer-based multiple instance learning method designed to identify abnormal frames (i.e., frames with polyps) from anomalous videos (i.e., …

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Performance Evaluation Of Aggregation-Based Group Recommender Systems For Ephemeral Groups, Edgar Ceh-Varela, Huiping Cao, Hady Wirawan Lauw Sep 2022

Performance Evaluation Of Aggregation-Based Group Recommender Systems For Ephemeral Groups, Edgar Ceh-Varela, Huiping Cao, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

Recommender Systems (RecSys) provide suggestions in many decision-making processes. Given that groups of people can perform many real-world activities (e.g., a group of people attending a conference looking for a place to dine), the need for recommendations for groups has increased. A wide range of Group Recommender Systems (GRecSys) has been developed to aggregate individual preferences to group preferences. We analyze 175 studies related to GRecSys. Previous works evaluate their systems using different types of groups (sizes and cohesiveness), and most of such works focus on testing their systems using only one type of item, called Experience Goods (EG). As …

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Towards An Optimal Bus Frequency Scheduling: When The Waiting Time Matters, Songsong Mo, Zhifeng Bao, Baihua Zheng, Zhiyong Peng Sep 2022

Towards An Optimal Bus Frequency Scheduling: When The Waiting Time Matters, Songsong Mo, Zhifeng Bao, Baihua Zheng, Zhiyong Peng

Research Collection School Of Computing and Information Systems

Reorganizing bus frequencies to cater for actual travel demands can significantly save the cost of the public transport system. This paper studies the bus frequency optimization problem considering the user satisfaction. Specifically, for the first time to our best knowledge, we study how to schedule the buses such that the total number of passengers who could receive their bus services within the waiting time threshold can be maximized. We propose two variants of the problem, FAST and FASTCO, to cater for different application needs and prove that both are NP-hard. To solve FAST effectively and efficiently, we first present an …

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Two-Phase Matheuristic For The Vehicle Routing Problem With Reverse Cross-Docking, Aldy Gunawan, Audrey Tedja Widjaja, Pieter Vansteenwegen, Vincent F. Yu Sep 2022

Two-Phase Matheuristic For The Vehicle Routing Problem With Reverse Cross-Docking, Aldy Gunawan, Audrey Tedja Widjaja, Pieter Vansteenwegen, Vincent F. Yu

Research Collection School Of Computing and Information Systems

Cross-dockingis a useful concept used by many companies to control the product flow. It enables the transshipment process of products from suppliers to customers. This research thus extends the benefit of cross-docking with reverse logistics, since return process management has become an important field in various businesses. The vehicle routing problem in a distribution network is considered to be an integrated model, namely the vehicle routing problem with reverse cross-docking (VRP-RCD). This study develops a mathematical model to minimize the costs of moving products in a four-level supply chain network that involves suppliers, cross-dock, customers, and outlets. A matheuristic based …

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A Traitor-Resistant And Dynamic Anonymous Communication Service For Cloud-Based Vanets, Huiying Hou, Jianting Ning, Yunlei Zhao, Robert H. Deng Sep 2022

A Traitor-Resistant And Dynamic Anonymous Communication Service For Cloud-Based Vanets, Huiying Hou, Jianting Ning, Yunlei Zhao, Robert H. Deng

Research Collection School Of Computing and Information Systems

Cloud-based VANETs are designed to enable communication between high-speed vehicles. In such a highly dynamic environment, how to provide secure and anonymous communication service is a challenge. In this article, we affirmatively address the challenge by proposing a traitor-resistant and dynamic anonymous communication framework (TD-ACF) for cloud-based VANETs, which supports several advantageous features. In TD-ACF, each vehicle is represented by a set of attributes instead of its real identity, and the driving data is transmitted in encrypted form. Therefore, the anonymous authentication and the confidentiality of driving data are achieved in this way. Meanwhile, TD-ACF supports two practical requirements in …

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Fine-Grained And Controllably Editable Data Sharing With Accountability In Cloud Storage, Huiying Hou, Jianting Ning, Yunlei Zhao, Robert H. Deng Sep 2022

Fine-Grained And Controllably Editable Data Sharing With Accountability In Cloud Storage, Huiying Hou, Jianting Ning, Yunlei Zhao, Robert H. Deng

Research Collection School Of Computing and Information Systems

With the increasing cloud storage service, users can enjoy non-interactive data sharing. Nonetheless, the data owner cannot timely update the shared data all the while. To ensure the timeliness and the authoritative source of the data, some users should be allowed to update the data on behalf of an authoritative data owner without changing data source. However, this allows harmful information to be injected into the data unnoticeably. How to efficiently realize editable cloud-based data sharing supporting malicious user tracing has not been fully explored. To address the problem, we propose a fine-grained and controllably editable cloud-based data sharing scheme …

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Learning To Solve Multiple-Tsp With Time Window And Rejections Via Deep Reinforcement Learning, Rongkai Zhang, Cong Zhang, Zhiguang Cao, Wen Song, Puay Siew Tan, Jie Zhang, Bihan Wen, Justin Dauwels Sep 2022

Learning To Solve Multiple-Tsp With Time Window And Rejections Via Deep Reinforcement Learning, Rongkai Zhang, Cong Zhang, Zhiguang Cao, Wen Song, Puay Siew Tan, Jie Zhang, Bihan Wen, Justin Dauwels

Research Collection School Of Computing and Information Systems

We propose a manager-worker framework (the implementation of our model is publically available at: https://github.com/zcaicaros/manager-worker-mtsptwr) based on deep reinforcement learning to tackle a hard yet nontrivial variant of Travelling Salesman Problem (TSP), i.e. multiple-vehicle TSP with time window and rejections (mTSPTWR), where customers who cannot be served before the deadline are subject to rejections. Particularly, in the proposed framework, a manager agent learns to divide mTSPTWR into sub-routing tasks by assigning customers to each vehicle via a Graph Isomorphism Network (GIN) based policy network. A worker agent learns to solve sub-routing tasks by minimizing the cost in terms of both …

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Secure Deterministic Wallet And Stealth Address: Key-Insulated And Privacy-Preserving Signature Scheme With Publicly Derived Public Key, Zhen Liu, Guomin Yang, Duncan S. Wong, Khoa Nguyen, Huaxiong Wang, Xiaorong Ke, Yining Liu Sep 2022

Secure Deterministic Wallet And Stealth Address: Key-Insulated And Privacy-Preserving Signature Scheme With Publicly Derived Public Key, Zhen Liu, Guomin Yang, Duncan S. Wong, Khoa Nguyen, Huaxiong Wang, Xiaorong Ke, Yining Liu

Research Collection School Of Computing and Information Systems

Deterministic Wallet (DW) and Stealth Address (SA) mechanisms have been widely adopted in the cryptocurrency community, due to their virtues on functionality and privacy protection, which come from a key derivation mechanism that allows an arbitrary number of derived keys to be generated from a master key. However, these algorithms suffer a vulnerability that, when one derived key is compromised somehow, the damage is not limited to the leaked derived key only, but to the master key and in consequence all derived keys are compromised. In this article, we introduce and formalize a new signature variant, called Key-Insulated and Privacy-Preserving …

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Deep Learning-Based Text Recognition Of Agricultural Regulatory Document, Hua Leong Fwa, Farn Haur Chan Sep 2022

Deep Learning-Based Text Recognition Of Agricultural Regulatory Document, Hua Leong Fwa, Farn Haur Chan

Research Collection School Of Computing and Information Systems

In this study, an OCR system based on deep learning techniques was deployed to digitize scanned agricultural regulatory documents comprising of certificates and labels. Recognition of the certificates and labels is challenging as they are scanned images of the hard copy form and the layout and size of the text as well as the languages vary between the various countries (due to diverse regulatory requirements). We evaluated and compared between various state-of-the-art deep learningbased text detection and recognition model as well as a packaged OCR library – Tesseract. We then adopted a two-stage approach comprising of text detection using Character …

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Choice-Based Crowdshipping: A Dynamic Task Display Problem, Alp Arslan, Firat Kilci, Shih-Fen Cheng, Archan Misra Sep 2022

Choice-Based Crowdshipping: A Dynamic Task Display Problem, Alp Arslan, Firat Kilci, Shih-Fen Cheng, Archan Misra

Research Collection School Of Computing and Information Systems

This paper studies the integration of the crowd workforce into a generic last-mile delivery setting in which a set of known delivery requests should be fulfilled at a minimum cost. In this setting, the crowd drivers are able to choose to perform a parcel delivery among the available and displayed requests. We specifically investigate the question: what tasks should be displayed to an individual driver, so as to minimize the overall delivery expenses? In contrast to past approaches, where drivers are either (a) given the choice of a single task chosen so as to optimize the platform’s profit, or (b) …

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Deep Learning For Coverage-Guided Fuzzing: How Far Are We?, Siqi Li, Xiaofei Xie, Yun Lin, Yuekang Li, Ruitao Feng, Xiaohong Li, Weimin Ge, Jin Song Dong Sep 2022

Deep Learning For Coverage-Guided Fuzzing: How Far Are We?, Siqi Li, Xiaofei Xie, Yun Lin, Yuekang Li, Ruitao Feng, Xiaohong Li, Weimin Ge, Jin Song Dong

Research Collection School Of Computing and Information Systems

Fuzzing is a widely-used software vulnerability discovery technology, many of which are optimized using coverage-feedback. Recently, some techniques propose to train deep learning (DL) models to predict the branch coverage of an arbitrary input owing to its always-available gradients etc. as a guide. Those techniques have proved their success in improving coverage and discovering bugs under different experimental settings. However, DL models, usually as a magic black-box, are notoriously lack of explanation. Moreover, their performance can be sensitive to the collected runtime coverage information for training, indicating potentially unstable performance. In this work, we conduct a systematic empirical study on …

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Joint Hyperbolic And Euclidean Geometry Contrastive Graph Neural Networks, Xiaoyu Xu, Guansong Pang, Di Wu, Mingsheng Shang Sep 2022

Joint Hyperbolic And Euclidean Geometry Contrastive Graph Neural Networks, Xiaoyu Xu, Guansong Pang, Di Wu, Mingsheng Shang

Research Collection School Of Computing and Information Systems

Graph Neural Networks (GNNs) have demonstrated state-of-the-art performance in a wide variety of analytical tasks. Current GNN approaches focus on learning representations in a Euclidean space, which are effective in capturing non-tree-like structural relations, but they fail to model complex relations in many real-world graphs, such as tree-like hierarchical graph structure. This paper instead proposes to learn representations in both Euclidean and hyperbolic spaces to model these two types of graph geometries. To this end, we introduce a novel approach - Joint hyperbolic and Euclidean geometry contrastive graph neural networks (JointGMC). JointGMC is enforced to learn multiple layer-wise optimal combinations …

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Eeg-Video Emotion-Based Summarization: Learning With Eeg Auxiliary Signals, Wai-Cheong L. Lew, Di Wang, Kai-Keng Ang, Joo-Hwee Lim, Chai Quek, Ah-Hwee Tan Sep 2022

Eeg-Video Emotion-Based Summarization: Learning With Eeg Auxiliary Signals, Wai-Cheong L. Lew, Di Wang, Kai-Keng Ang, Joo-Hwee Lim, Chai Quek, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

Video summarization is the process of selecting a subset of informative keyframes to expedite storytelling with limited loss of information. In this paper, we propose an EEG-Video Emotion-based Summarization (EVES) model based on a multimodal deep reinforcement learning (DRL) architecture that leverages neural signals to learn visual interestingness to produce quantitatively and qualitatively better video summaries. As such, EVES does not learn from the expensive human annotations but the multimodal signals. Furthermore, to ensure the temporal alignment and minimize the modality gap between the visual and EEG modalities, we introduce a Time Synchronization Module (TSM) that uses an attention mechanism …

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Constrained Multiagent Reinforcement Learning For Large Agent Population, Jiajing Ling, Arambam James Singh, Duc Thien Nguyen, Akshat Kumar Sep 2022

Constrained Multiagent Reinforcement Learning For Large Agent Population, Jiajing Ling, Arambam James Singh, Duc Thien Nguyen, Akshat Kumar

Research Collection School Of Computing and Information Systems

Learning control policies for a large number of agents in a decentralized setting is challenging due to partial observability, uncertainty in the environment, and scalability challenges. While several scalable multiagent RL (MARL) methods have been proposed, relatively few approaches exist for large scale constrained MARL settings. To address this, we first formulate the constrained MARL problem in a collective multiagent setting where interactions among agents are governed by the aggregate count and types of agents, and do not depend on agents’ specific identities. Second, we show that standard Lagrangian relaxation methods, which are popular for single agent RL, do not …

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Toward Intention Discovery For Early Malice Detection In Bitcoin, Ling Cheng, Feida Zhu, Yong Wang, Huiwen Liu Sep 2022

Toward Intention Discovery For Early Malice Detection In Bitcoin, Ling Cheng, Feida Zhu, Yong Wang, Huiwen Liu

Research Collection School Of Computing and Information Systems

Bitcoin has been subject to illicit activities more often than probably any other financial assets, due to the pseudo-anonymous nature of its transacting entities. An ideal detection model is expected to achieve all the three properties of (I) early detection, (II) good interpretability, and (III) versatility for various illicit activities. However, existing solutions cannot meet all these requirements, as most of them heavily rely on deep learning without satisfying interpretability and are only available for retrospective analysis of a specific illicit type.First, we present asset transfer paths, which aim to describe addresses' early characteristics. Next, with a decision tree based …

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Automatic Fairness Testing Of Neural Classifiers Through Adversarial Sampling, Peixin Zhang, Jingyi Wang, Jun Sun, Xinyu Wang, Guoliang Dong, Xinggen Wang, Ting Dai, Jinsong Dong Sep 2022

Automatic Fairness Testing Of Neural Classifiers Through Adversarial Sampling, Peixin Zhang, Jingyi Wang, Jun Sun, Xinyu Wang, Guoliang Dong, Xinggen Wang, Ting Dai, Jinsong Dong

Research Collection School Of Computing and Information Systems

Although deep learning has demonstrated astonishing performance in many applications, there are still concerns about its dependability. One desirable property of deep learning applications with societal impact is fairness (i.e., non-discrimination). Unfortunately, discrimination might be intrinsically embedded into the models due to the discrimination in the training data. As a countermeasure, fairness testing systemically identifies discriminatory samples, which can be used to retrain the model and improve the model’s fairness. Existing fairness testing approaches however have two major limitations. Firstly, they only work well on traditional machine learning models and have poor performance (e.g., effectiveness and efficiency) on deep learning …

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Does Social Media Accelerate Product Recalls? Evidence From The Pharmaceutical Industry, Yang Gao, Wenjing Duan, Huaxia Rui Sep 2022

Does Social Media Accelerate Product Recalls? Evidence From The Pharmaceutical Industry, Yang Gao, Wenjing Duan, Huaxia Rui

Research Collection School Of Computing and Information Systems

Social media has become a vital platform for voicing product-related experiences that may not only reveal product defects but also impose pressure on firms to act more promptly than before. This study scrutinizes the rarely-studied relationship between these voices and the speed of product recalls in the context of the pharmaceutical industry where social media pharmacovigilance is becoming increasingly important for the detection of drug safety signals. Using Federal Drug Administration (FDA) drug enforcement reports and social media data crawled from online forums and Twitter, we investigate whether social media can accelerate the product recall process in the context of …

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Evaluation Of Artificial Neural Network Methods To Forecast Short-Term Solar Power Generation: A Case Study In Eastern Mediterranean Region, Heli̇n Bozkurt, Ramazan Maci̇t, Özgür Çeli̇k, Ahmet Teke Sep 2022

Evaluation Of Artificial Neural Network Methods To Forecast Short-Term Solar Power Generation: A Case Study In Eastern Mediterranean Region, Heli̇n Bozkurt, Ramazan Maci̇t, Özgür Çeli̇k, Ahmet Teke

Turkish Journal of Electrical Engineering and Computer Sciences

Solar power forecasting is substantial for the utilization, planning, and designing of solar power plants. Global solar irradiation (GSI) and meteorological variables have a crucial role in solar power generation. The ever-changing meteorological variables and imprecise measurement of GSI raise difficulties for forecasting photovoltaic (PV) output power. In this context, a major motivation appears for the accurate forecast of GSI to perform effective forecasting of the short-term output power of a PV plant. The presented study comprises of four artificial neural network (ANN) methods; recurrent neural network (RNN) method, feedforward backpropagation neural network (FFBPNN) method, support vector regression (SVR) method, …

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Error Analysis Of Siso And Dual-Branch Communications With Generalized Gaussian Noise Over Ftr Fading Channels, Mehmet Bi̇li̇m Sep 2022

Error Analysis Of Siso And Dual-Branch Communications With Generalized Gaussian Noise Over Ftr Fading Channels, Mehmet Bi̇li̇m

Turkish Journal of Electrical Engineering and Computer Sciences

In this paper, we present error probability analysis for single-input single-output (SISO) and asymmetric dual-branch networks with additive white generalized Gaussian noise (AWGGN) over millimeter-wave (mmW) fluctuating two-ray (FTR) fading channels. Then, we examine the error probability evaluation of a SISO system with imperfect phase errors over mmW FTR fading channels. The probability density function (PDF) approach is employed for the error probability performance evaluation and the novel PDF of the asymmetric dual-branch system over Nakagami-m/mmW FTR fading channels is obtained. Specifically, closed-form expressions are derived for the error probability of the SISO and asymmetric dual-branch networks. The derived error …

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A Quantitative Evaluation Of Explainable Ai Methods Using The Depth Of Decision Tree, Nizar Abdulaziz Mahyoub Ahmed, Adi̇l Alpkoçak Sep 2022

A Quantitative Evaluation Of Explainable Ai Methods Using The Depth Of Decision Tree, Nizar Abdulaziz Mahyoub Ahmed, Adi̇l Alpkoçak

Turkish Journal of Electrical Engineering and Computer Sciences

It is necessary to develop an explainable model to clarify how and why a medical model makes a particular decision. Local posthoc explainable AI (XAI) techniques, such as SHAP and LIME, interpret classification system predictions by displaying the most important features and rules underlying any prediction locally. Therefore, in order to compare two or more XAI methods, they must first be evaluated qualitatively or quantitatively. This paper proposes quantitative XAI evaluation metrics that are not based on biased and subjective human judgment. On the other hand, it is dependent on the depth of the decision tree (DT) to automatically and …

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Track Density Imaging Using Diffusion Tensor Imaging Data From 1.5 T Mri Scanner, Fatma Betül Köşker, Kazim Zi̇ya Gümüş, Mahmut Tokmakçi, Ni̇yazi̇ Acer, Serkan Şenol, Mehmet Bi̇lgen Sep 2022

Track Density Imaging Using Diffusion Tensor Imaging Data From 1.5 T Mri Scanner, Fatma Betül Köşker, Kazim Zi̇ya Gümüş, Mahmut Tokmakçi, Ni̇yazi̇ Acer, Serkan Şenol, Mehmet Bi̇lgen

Turkish Journal of Electrical Engineering and Computer Sciences

Superresolution track density imaging (TDI) has recently been developed for achieving high resolution track density maps from low-resolution diffusion images acquired at 3 T. But, the utility of the approach is still unclear when applied to diffusion tensor imaging (DTI) data acquired at lower 1.5 T magnetic field strength and thus its advantages or disadvantages awaits for exploration. We implemented an algorithm to generate track density maps of human white matter using streamline tracking and tested its performance with data acquired from two healthy volunteers at 1.5 Tesla. The effects of number of diffusion directions and seed selections on the …

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Comparison Of Ml Algorithms To Distinguish Between Human Or Human-Like Targets Using The Hog Features Of Range-Time And Range-Doppler Images In Through-The-Wall Applications, Yunus Emre Acar, İsmai̇l Saritaş, Ercan Yaldiz Sep 2022

Comparison Of Ml Algorithms To Distinguish Between Human Or Human-Like Targets Using The Hog Features Of Range-Time And Range-Doppler Images In Through-The-Wall Applications, Yunus Emre Acar, İsmai̇l Saritaş, Ercan Yaldiz

Turkish Journal of Electrical Engineering and Computer Sciences

When detecting the human targets behind walls, false detections occur for many systematic and environmental reasons. Identifying and eliminating these false detections is of great importance for many applications. This study investigates the potential of machine learning (ML) algorithms to distinguish between the human and human-like targets behind walls. For this purpose, a stepped-frequency continuous-wave (SFCW) radar has been set up. Experiments have been carried out with real human targets and moving plates imitating a regular breath of a healthy human. Unlike conventional methods, human and human-like returns are classified using range-Doppler images containing range and Doppler information. Then, the …

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Building A Surrogate Model Of A Perfect Electric Conductor Using Polynomial Chaos Expansion And The Characteristic Mode Analysis, Adem Yilmaz, Hulusi̇ Açikgöz, Alaaldeen Barakat Ahmed Elrouby Sep 2022

Building A Surrogate Model Of A Perfect Electric Conductor Using Polynomial Chaos Expansion And The Characteristic Mode Analysis, Adem Yilmaz, Hulusi̇ Açikgöz, Alaaldeen Barakat Ahmed Elrouby

Turkish Journal of Electrical Engineering and Computer Sciences

A surrogate model for a perfect electric conductor plate is built by using the polynomial chaos expansion method. The plate is excited via four capacitive coupling elements for which the locations are determined by the analysis of the current distribution for each mode provided by the characteristic mode analysis. A numerical model based on the method of moments is then created to generate a database needed to build the surrogate model. Radiation patterns calculated by the surrogate model are compared with those given by the numerical model. The results show that the surrogate model can mimic the numerical model and …

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