arXiv subjects

Ngoc-Thanh Nguyen

Publications and source records attributed to Ngoc-Thanh Nguyen.

9 recordsLinked to original sources

arXiv2026

Feedback-Efficient Beam-User Association for Near-Field mmWave Hybrid Beamforming Systems

Near-field multiuser hybrid beamforming (HBF) requires joint angle-distance codebooks whose size, and hence reporting overhead, grows with the array aperture. For the extremely large array considered here, reporting one quality metric per codeword already incurs more overhead than full channel state information (CSI) feedback. This letter develops a feedback-efficient beam--user equipment (UE) association framework. The focusing codebook is sampled at beam-depth spacing within an effective beamfocusing Rayleigh distance (EBRD)-aware focusing region, with one far-field codeword per angular direction beyond that region, so that its radial law and size follow from the array geometry. Each UE probes this codebook but reports only its M strongest candidates, and the base station associates UEs and beams with a proportional-fair metric that consumes the leakage terms carried by this report together with the codeword correlations it knows, thereby allowing co-angular UEs to be multiplexed by focal distance. Simulations show that M=3 suffices: the proposed scheme stays within 0.6% of an optimistic full-metric reporting reference while using 0.8% of its feedback, a 99.11% reduction with respect to full-CSI feedback, and the interference-aware metric contributes up to 18.7% of the sum spectral efficiency over its interference-blind counterpart.

eess.SP↗

arXiv2026

ClouDens: Operational Context-Aware Anomaly Detection for Large-scale Cloud System Monitoring

With the rapid growth of cloud computing infrastructures in scale and complexity, network monitoring for Large-scale Cloud Systems (LCSs) has become increasingly challenging, requiring automated and reliable anomaly detection to maintain service availability. Modern LCSs continuously generate telemetry logs from distributed cloud services, producing high-dimensional multivariate time series that capture system operations. Detecting anomalies in this context is difficult due to extreme dimensionality, complex dependencies among distributed components, and severe sparsity from intermittently active services. Taking these challenges into account, we first conduct an empirical study on telemetry logs from the IBM Cloud Console platform, and then propose ClouDens, an anomaly detection framework tailored to LCS monitoring that leverages operational-context attributes encoded in the telemetry log schema to improve detection accuracy and early identification of anomalies. ClouDens partitions high-dimensional telemetry logs into domain-guided subsets, constructs a context-aware graph modeling operational service dependencies, and employs Spatio-Temporal Graph Neural Networks for forecasting-based anomaly detection. We evaluate ClouDens on the recently released IBM Cloud Telemetry Dataset and provide practical insights into designing reliable anomaly detection solutions for LCS monitoring. Results show ClouDens achieves higher NAB scores in count-based telemetry features, indicating more accurate, earlier anomaly detection with broader coverage than a GRU-based model. Our study further reveals that telemetry feature subsets, operational-context modeling, scoring strategies, and sparsity imputation all substantially influence detection performance, offering practical guidance for designing and fairly benchmarking anomaly detection approaches for LCS monitoring.

cs.NI↗

arXiv2026

Pack-A-Mal: A Malware Analysis Framework for Open-Source Packages

The increasingly sophisticated environment in which attackers operate makes software security an even greater challenge in open-source projects, where malicious packages are prevalent. Static analysis tools, such as Malcontent, are highly useful but are often incapable of dealing with obfuscated malware. Such situations lead to an unreasonably high rate of false positives. This paper highlights that dynamic analysis, rather than static analysis, provides greater insight but is also more resource-intensive for understanding software behaviour during execution. In this study, we enhance a dynamic analysis tool, package-analysis, to capture key runtime behaviours, including commands executed, files accessed, and network communications. This modification enables the use of container sandboxing technologies, such as gVisor, to analyse potentially malicious packages without significantly compromising the host system.

cs.CR↗

arXiv2025

Towards Classifying Benign And Malicious Packages Using Machine Learning

Recently, the number of malicious open-source packages in package repositories has been increasing dramatically. While major security scanners focus on identifying known Common Vulnerabilities and Exposures (CVEs) in open-source packages, there are very few studies on detecting malicious packages. Malicious open-source package detection typically requires static, dynamic analysis, or both. Dynamic analysis is more effective as it can expose a package's behaviors at runtime. However, current dynamic analysis tools (e.g., ossf's package-analysis) lack an automatic method to differentiate malicious packages from benign packages. In this paper, we propose an approach to extract the features from dynamic analysis (e.g., executed commands) and leverage machine learning techniques to automatically classify packages as benign or malicious. Our evaluation of nearly 2000 packages on npm shows that the machine learning classifier achieves an AUC of 0.91 with a false positive rate of nearly 0%.

cs.CR↗

arXiv2024

A data-flow oriented software architecture for heterogeneous marine data streams

Marine in-situ data is collected by sensors mounted on fixed or mobile systems deployed into the ocean. This type of data is crucial both for the ocean industries and public authorities, e.g., for monitoring and forecasting the state of marine ecosystems and/or climate changes. Various public organizations have collected, managed, and openly shared in-situ marine data in the past decade. Recently, initiatives like the Ocean Decade Corporate Data Group have incentivized the sharing of marine data of public interest from private companies aiding in ocean management. However, there is no clear understanding of the impact of data quality in the engineering of systems, as well as on how to manage and exploit the collected data. In this paper, we propose main architectural decisions and a data flow-oriented component and connector view for marine in-situ data streams. Our results are based on a longitudinal empirical software engineering process, and driven by knowledge extracted from the experts in the marine domain from public and private organizations, and challenges identified in the literature. The proposed software architecture is instantiated and exemplified in a prototype implementation.

cs.SE↗

arXiv2024

A Road Less Travelled and Beyond: Towards a Roadmap for Integrating Sustainability into Computing Education

Education for sustainable development has evolved to include more constructive approaches and a better understanding of what is needed to align education with the cultural, societal, and pedagogical changes required to avoid the risks posed by an unsustainable society. This evolution aims to lead us toward viable, equitable, and sustainable futures. However, computing education, including software engineering, is not fully aligned with the current understanding of what is needed for transformational learning in light of our current challenges. This is partly because computing is primarily seen as a technical field, focused on industry needs. Until recently, sustainability was not a high priority for most businesses, including the digital sector, nor was it a prominent focus for higher education institutions and society. Given these challenges, we aim to propose a research roadmap to integrate sustainability principles and essential skills into the crowded computing curriculum, nurturing future software engineering professionals with a sustainability mindset. We conducted two extensive studies: a systematic review of academic literature on sustainability in computing education and a survey of industry professionals on their interest in sustainability and desired skills for graduates. Using insights from these studies, we identified key topics for teaching sustainability, including core sustainability principles, values and ethics, systems thinking, impact measurement, soft skills, business value, legal standards, and advocacy. Based on these findings, we will develop recommendations for future computing education programs that emphasise sustainability. The paper is accepted at the 2030 Software Engineering workshop, which is co-located with the FSE'24 conference.

cs.SE↗

arXiv2023

Wave Measurements using Open Source Ship Mounted Ultrasonic Altimeter and Motion Correction System during the One Ocean Expedition

This study reviews the design and signal processing of ship borne ultrasonic altimeter wave measurements. The system combines a downward facing ultrasonic altimeter to capture the sea surface elevation as a time series, and an inertial measurement unit to compensate for the ship's motion. The methodology is cost-effective, open source, and adaptable to various ships and platforms. The system was installed on the barque Statsraad Lehmkuhl and recorded data continuously during the 20-month One Ocean Expedition. Results from 1-month crossing of the Tropical Atlantic are presented here. The one-dimensional wave spectrum and associated wave parameters are obtained from the sea surface elevation time series. The observed significant wave height agrees well with satellite altimetry and a spectral wave model. The agreement between observations and the spectral wave model is better for the mean wave period than the peak period. We perform Doppler shift corrections to improve wave period estimates by accounting for the speed of the ship relative to the waves. This correction enhances the accuracy of the mean period, but not the peak period. We suggest that the Doppler correction could be improved by complementing the data sources with directional wave measurements from a marine X-band radar.

physics.ao-ph↗

arXiv2023

Sustainability Competencies and Skills in Software Engineering: An Industry Perspective

Achieving the UN Sustainable Development Goals (SDGs) demands adequate levels of awareness and actions to address sustainability challenges. Software systems will play an important role in moving towards these targets. Sustainability skills are necessary to support the development of software systems and to provide sustainable IT-supported services for citizens. While there is a growing number of academic bodies, including sustainability education in engineering and computer science curricula, there is not yet comprehensive research on the competencies and skills required by IT professionals to develop such systems. This study aims to identify the industrial sustainability needs for education and training from software engineers' perspective. We conducted interviews and focus groups with experts from twenty-eight organisations with an IT division from nine countries to understand their interests, goals and achievements related to sustainability, and the skills and competencies needed to achieve their goals. Our findings show that organisations are interested in sustainability, both idealistically and increasingly for core business reasons. They seek to improve the sustainability of processes and products but encounter difficulties, like the trade-off between short-term financial profitability and long-term sustainability goals. To fill the gaps, they have promoted in-house training courses, collaborated with universities, and sent employees to external training. The acquired competencies make sustainability an integral part of software development. We conclude that educational programs should include knowledge and skills on core sustainability concepts, system thinking, soft skills, technical sustainability, sustainability impact and measurements, values and ethics, standards and legal aspects, and advocacy and lobbying.

cs.SE↗

arXiv2021

Characterizing Crisp Simulations and Crisp Directed Simulations between Fuzzy Labeled Transition Systems by Using Fuzzy Modal Logics

We formulate and prove logical characterizations of crisp simulations and crisp directed simulations between fuzzy labeled transition systems with respect to fuzzy modal logics that use a general t-norm-based semantics. The considered logics are fragments of the fuzzy propositional dynamic logic with the Baaz projection operator. The logical characterizations concern preservation of positive existential (respectively, positive) modal formulas under crisp simulations (respectively, crisp directed simulations), as well as the Hennessy-Milner property of such simulations.

cs.LO↗