WEBCAM-BASED LASER DOT DETECTION TECHNIQUE IN COMPUTER REMOTE CONTROL

ABSTRACTIn this paper, the authors propose a method to detect the laser dot in an interactive system using laser pointers. The method is designed for presenters who need to interact with the computer during the presentation by using the laserpointer. The detection technique is developed by using a camera to capture the presentation screen and processing every frames transferred to the ara computer. This paper focuses on the detection and tracking of laser dots, based on their characteristics to distinguish a laser dotfrom other areas on the captured frames. Experimental results showed that the proposed method could reduce the rate of misdetection by light noises of a factor of 10 and achieve an average accuracy of 82% of detection in normal presentation environments. The results point out that the better way to describe the laser dots’ features based on visual concept is to use the HSI color space instead of the normal RGB space.Keywords.  laser pointer; laser dot/spot; laser pointer interaction; control; mouse; computer screen/display

Towards an Improved Understanding of Software Vulnerability Assessment Using Data-Driven Approaches

Software Vulnerabilities (SVs) can expose software systems to cyber-attacks, potentially causing enormous financial and reputational damage for organizations. There have been significant research efforts to detect these SVs so that developers can promptly fix them. However, fixing SVs is complex and time-consuming in practice, and thus developers usually do not have sufficient time and resources to fix all SVs at once. As a result, developers often need SV information, such as exploitability, impact, and overall severity, to prioritize fixing more critical SVs. Such information required for fixing planning and prioritization is typically provided in the SV assessment step of the SV lifecycle. Recently, data-driven methods have been increasingly proposed to automate SV assessment tasks. However, there are still numerous shortcomings with the existing studies on data-driven SV assessment that would hinder their application in practice. This PhD thesis aims to contribute to the growing literature in data-driven SV assessment by investigating and addressing the constant changes in SV data as well as the lacking considerations of source code and developers’ needs for SV assessment that impede the practical applicability of the field. Particularly, we have made the following five contributions in this thesis. (1) We systematize the knowledge of data-driven SV assessment to reveal the best practices of the field and the main challenges affecting its application in practice. Subsequently, we propose various solutions to tackle these challenges to better support the real-world applications of data-driven SV assessment. (2) We first demonstrate the existence of the concept drift (changing data) issue in descriptions of SV reports that current studies have mostly used for predicting the Common Vulnerability Scoring System (CVSS) metrics. We augment report-level SV assessment models with subwords of terms extracted from SV descriptions to help the models more effectively capture the semantics of ever-increasing SVs. (3) We also identify that SV reports are usually released after SV fixing. Thus, we propose using vulnerable code to enable earlier SV assessment without waiting for SV reports. We are the first to use Machine Learning techniques to predict CVSS metrics on the function level leveraging vulnerable statements directly causing SVs and their context in code functions. The performance of our function-level SV assessment models is promising, opening up research opportunities in this new direction. (4) To facilitate continuous integration of software code nowadays, we present a novel deep multi-task learning model, DeepCVA, to simultaneously and efficiently predict multiple CVSS assessment metrics on the commit level, specifically using vulnerability-contributing commits. DeepCVA is the first work that enables practitioners to perform SV assessment as soon as vulnerable changes are added to a codebase, supporting just-in-time prioritization of SV fixing. (5) Besides code artifacts produced from a software project of interest, SV assessment tasks can also benefit from SV crowdsourcing information on developer Question and Answer (Q&A) websites. We automatically retrieve large-scale security/SVrelated posts from these Q&A websites. We then apply a topic modeling technique on these posts to distill developers’ real-world SV concerns that can be used for data-driven SV assessment. Overall, we believe that this thesis has provided evidence-based knowledge and useful guidelines for researchers and practitioners to automate SV assessment using data-driven approaches.Thesis (Ph.D.) -- University of Adelaide, School of Computer Science, 202

mmWave communication for 5G mobile networks

Fifth generation (5G) mobile networks are one of the huge developments in recent years. This thesis presents a study on the use of millimeter-wave (mmWave) communication in 5G mobile networks. It begins with an overview of 5G technology, including the various frequency bands utilized. The enabling technologies for mmWave, such as radio access network (RAN) architecture, antennas, and beamforming techniques, are examined, including beam acquisition and tracking. The characteristics and performance of mmWave communication, including signal transmission, system design implications, and physical limitations like blockage and multipath fading, are discussed. The potential applications and future prospects of mmWave technology, including commercial applications and the possibilities for hybrid sub 3GHz/mmWave networks with enhanced spectral, energy, and cost efficiency, are also examined. Overall, mmWave communication emerges as a promising technology for 5G mobile networks, offering faster data rates, improved capacity, and reduced latency. However, it also presents unique challenges, necessitating careful system design and the mitigation of physical limitations. As we move into the future, it is crucial to continue exploring the possibilities of mmWave technology and finding innovative solutions to overcome its challenges. This thesis contributes to our understanding of mmWave communication for 5G mobile networks, offering an overview of this important and rapidly evolving field

Hemoglobinopathies in mountainous region of Thua Thien Hue, Vietnam

The spectrum of ß-thalassemia mutations was determined in two Districts of the Central province of Thua Thien Hue (A Luoi and Nam Dong). A community-based assessment was conducted to estimate the prevalence of hemoglobinopathies and to assess their molecular basis. 1100 participants were enrolled including 83.73% of the minorities and 16.27% of the Kinh. The blood samples were firstly screened by complete blood count and osmotic fragility test. Hemoglobinopathies were diagnosed by electrophoresis and High Performance Liquid Chromatography. Mutations at the level of β and α globin genes were identified by DNA sequencing. Four mutations of the β0-thalassemia were observed in five subjects: - Two of these showed the AAG→TAG at codon 17. One was in combination with the βE gene (genotype β0/βE) and one was β0/β. - One showed the G→T at the IVS-I nt 1 (genotype β0/βE). Due to the presence of Hb F, the γ globin genes were also sequenced. The C→T in the promoter region, at position -158 of the Gγ gene (also known as the XmnI polymorphism) was found. - One was the carrier of a 4 bp deletion (-TTCT) involving codons 41/42 (genotype β0/β). - One was the carrier of the G insertion at codons 14/15 (genotype β0/β). Sequencing also confirmed that the G→A at codon 26 (GAG→AAG) of the β globin gene is responsible for Hb E. The prevalence of the hemoglobinopathies appears to be higher within the minorities than the Kinh population.</br

GUNNEL: Guided Mixup Augmentation and Multi-View Fusion for Aquatic Animal Segmentation

Recent years have witnessed great advances in object segmentation research. In addition to generic objects, aquatic animals have attracted research attention. Deep learning-based methods are widely used for aquatic animal segmentation and have achieved promising performance. However, there is a lack of challenging datasets for benchmarking. In this work, we build a new dataset dubbed "Aquatic Animal Species." We also devise a novel GUided mixup augmeNtatioN and multi-viEw fusion for aquatic animaL segmentation (GUNNEL) that leverages the advantages of multiple view segmentation models to effectively segment aquatic animals and improves the training performance by synthesizing hard samples. Extensive experiments demonstrated the superiority of our proposed framework over existing state-of-the-art instance segmentation methods

A Regularization of the Backward Problem for Nonlinear Parabolic Equation with Time-Dependent Coefficient

We study the backward problem with time-dependent coefficient which is a severely ill-posed problem. We regularize this problem by combining quasi-boundary value method and quasi-reversibility method and then obtain sharp error estimate between the exact solution and the regularized solution. A numerical experiment is given in order to illustrate our results

MaskDiff: Modeling Mask Distribution with Diffusion Probabilistic Model for Few-Shot Instance Segmentation

Few-shot instance segmentation extends the few-shot learning paradigm to the instance segmentation task, which tries to segment instance objects from a query image with a few annotated examples of novel categories. Conventional approaches have attempted to address the task via prototype learning, known as point estimation. However, this mechanism depends on prototypes (\eg mean of $K-$shot) for prediction, leading to performance instability. To overcome the disadvantage of the point estimation mechanism, we propose a novel approach, dubbed MaskDiff, which models the underlying conditional distribution of a binary mask, which is conditioned on an object region and $K-$shot information. Inspired by augmentation approaches that perturb data with Gaussian noise for populating low data density regions, we model the mask distribution with a diffusion probabilistic model. We also propose to utilize classifier-free guided mask sampling to integrate category information into the binary mask generation process. Without bells and whistles, our proposed method consistently outperforms state-of-the-art methods on both base and novel classes of the COCO dataset while simultaneously being more stable than existing methods. The source code is available at: https://github.com/minhquanlecs/MaskDiff.Comment: Accepted at AAAI 2024 (oral presentation