Digital Commons Network^™

Targeted Content-Sharing In A Multi-Group Dtn Application Using Attribute-Based Encryption, Xiaofei Cao, Shudip Datta, Ram Charan Bolla, Sanjay Kumar Madria

Computer Science Faculty Research & Creative Works

In a battlefield, multiple groups operate with different missions, but their missions and groups can dynamically change based on the evolving situation. Due to the unavailability of network infrastructure after deployment, group members form a Delay Tolerant Network (DTN) which is prone to security attacks. Hence, based on the mission attributes, group memberships, nodes' interests, and data tags determination, targeted contents need to be distributed in a secure fashion to different users. Though existing Attributes Based Encryption (ABE) can provide security of information, revoking a member from a group is always an issue in DTN as the Attribute Authority (AA) …

Active Learning Augmented Folded Gaussian Model For Anomaly Detection In Smart Transportation, Venkata Praveen Kumar Madhavarapu, Prithwiraj Roy, Shameek Bhattacharjee, Sajal K. Das

Computer Science Faculty Research & Creative Works

Smart transportation networks have become instrumental in smart city applications with the potential to enhance road safety, improve the traffic management system and driving experience. A Traffic Message Channel (TMC) is an IoT device that records the data collected from the vehicles and forwards it to the Roadside Units (RSUs). This data is further processed and shared with the vehicles to inquire the fastest route and incidents that can cause significant delays. The failure of the TMC sensors can have adverse effects on the transportation network. In this paper, we propose a Gaussian distribution-based trust scoring model to identify anomalous …

Improving Age Of Information With Interference Problem In Long-Range Wide Area Networks, Preti Kumari, Hari Prabhat Gupta, Tanima Dutta, Sajal K. Das

Computer Science Faculty Research & Creative Works

Low Power Wide Area Networks (LPWAN) offer a promising wireless communications technology for Internet of Things (IoT) applications. Among various existing LPWAN technologies, Long-Range WAN (LoRaWAN) consumes minimal power and provides virtual channels for communication through spreading factors. However, LoRaWAN suffers from the interference problem among nodes connected to a gateway that uses the same spreading factor. Such interference increases data communication time, thus reducing data freshness and suitability of LoRaWAN for delay-sensitive applications. To minimize the interference problem, an optimal allocation of the spreading factor is requisite for determining the time duration of data transmission. This paper proposes a …

Mobility Management In Industrial Iot Environments, Marco Pettorali, Francesca Righetti, Carlo Vallati, Sajal K. Das, Giuseppe Anastasi

Computer Science Faculty Research & Creative Works

The Internet Engineering Task Force (IETF) has defined the 6TiSCH architecture to enable the Industrial Inter-net of Things (IIoT). Unfortunately, 6TiSCH does not provide mechanisms to manage node mobility, while many industrial applications involve mobile devices (e.g., mobile robots or wearable devices carried by workers). In this paper, we consider the Synchronized Single-hop Multiple Gateway framework to manage mobility in 6TiSCH networks. For this framework, we address the problem of positioning Border Routers in a deployment area, which is similar to the "Art Gallery"problem, proposing an efficient deployment policy for Border Routers based on geometrical rules. Moreover, we define a …

Mdz: An Efficient Error-Bounded Lossy Compressor For Molecular Dynamics, Kai Zhao, Sheng Di, Danny Perez, Xin Liang, Zizhong Chen, Franck Cappello

Computer Science Faculty Research & Creative Works

Molecular dynamics (MD) has been widely used in today's scientific research across multiple domains including materials science, biochemistry, biophysics, and structural biology. MD simulations can produce extremely large amounts of data in that each simulation could involve a large number of atoms (up to trillions) for a large number of timesteps (up to hundreds of millions). In this paper, we perform an in-depth analysis of a number of MD simulation datasets and then develop an efficient error-bounded lossy compressor that can significantly improve the compression ratios. The contributions are fourfold. (1) We characterize a number of MD datasets and summarize …

Spade: Multi-Stage Spam Account Detection For Online Social Networks, Federico Concone, Giuseppe Lo Re, Marco Morana, Sajal K. Das

Computer Science Faculty Research & Creative Works

In recent years, Online Social Networks (OSNs) have radically changed the way people communicate. The most widely used platforms, such as Facebook, Youtube, and Instagram, claim more than one billion monthly active users each. Beyond these, news-oriented micro-blogging services, e.g., Twitter, are daily accessed by more than 120 million users sharing contents from all over the world. Unfortunately, legitimate users of the OSNs are mixed with malicious ones, which are interested in spreading unwanted, misleading, harmful, or discriminatory content. Spam detection in OSNs is generally approached by considering the characteristics of the account under analysis, its connection with the rest …

Locality-Aware Qubit Routing For The Grid Architecture, Avah Banerjee, Xin Liang, R. Tohid

Computer Science Faculty Research & Creative Works

Due to the short decohorence time of qubits available in the NISQ-era, it is essential to pack (minimize the size and or the depth of) a logical quantum circuit as efficiently as possible given a sparsely coupled physical architecture. In this work we introduce a locality-aware qubit routing algorithm based on a graph theoretic framework. Our algorithm is designed for the grid and certain 'grid-like' architectures. We experimentally show the competitiveness of algorithm by comparing it against the approximate token swapping algorithm, which is used as a primitive in many state-of-the-art quantum trans pilers. Our algorithm produces circuits of comparable …

Privacy-Preserving Data Falsification Detection In Smart Grids Using Elliptic Curve Cryptography And Homomorphic Encryption, Sanskruti Joshi, Ruixiao Li, Shameek Bhattacharjee, Sajal K. Das, Hayato Yamana

Computer Science Faculty Research & Creative Works

In an advanced metering infrastructure (AMI), the electric utility collects power consumption data from smart meters to improve energy optimization and provides detailed information on power consumption to electric utility customers. However, AMI is vulnerable to data falsification attacks, which organized adversaries can launch. Such attacks can be detected by analyzing customers' fine-grained power consumption data; however, analyzing customers' private data violates the customers' privacy. Although homomorphic encryption-based schemes have been proposed to tackle the problem, the disadvantage is a long execution time. This paper proposes a new privacy-preserving data falsification detection scheme to shorten the execution time. We adopt …

Look-Up Table Based Fhe System For Privacy Preserving Anomaly Detection In Smart Grids, Ruixiao Li, Shameek Bhattacharjee, Sajal K. Das, Hayato Yamana

Computer Science Faculty Research & Creative Works

In advanced metering infrastructure (AMI), the customers' power consumption data is considered private but needs to be revealed to data-driven attack detection frameworks. In this paper, we present a system for privacy-preserving anomaly-based data falsification attack detection over fully homomorphic encrypted (FHE) data, which enables computations required for the attack detection over encrypted individual customer smart meter's data. Specifically, we propose a homomorphic look-up table (LUT) based FHE approach that supports privacy preserving anomaly detection between the utility, customer, and multiple partied providing security services. In the LUTs, the data pairs of input and output values for each function required …

Message From The Bits 2022 Workshop Co-Chairs, Sajal K. Das, Hayato Yamana, Keiichi Yasumoto, Shameek Bhattacharjee

Computer Science Faculty Research & Creative Works

No abstract provided.

Anomaly Based Incident Detection In Large Scale Smart Transportation Systems, Jaminur Islam, Jose Paolo Talusan, Shameek Bhattacharjee, Francis Tiausas, Sayyed Mohsen Vazirizade, Abhishek Dubey, Keiichi Yasumoto, Sajal K. Das

Computer Science Faculty Research & Creative Works

Modern smart cities are focusing on smart transportation solutions to detect and mitigate the effects of various traffic incidents in the city. To materialize this, roadside units and ambient trans-portation sensors are being deployed to collect vehicular data that provides real-time traffic monitoring. In this paper, we first propose a real-time data-driven anomaly-based traffic incident detection framework for a city-scale smart transportation system. Specifically, we propose an incremental region growing approximation algorithm for optimal Spatio-temporal clustering of road segments and their data; such that road segments are strategically divided into highly correlated clusters. The highly correlated clusters enable identifying a …

Sum-Rate Optimization For Visible-Light-Band Uav Networks Based On Particle Swarm Optimization, Yuwei Long, Nan Cen

Computer Science Faculty Research & Creative Works

The mobility nature of unmanned aerial vehicles (UAVs) takes them into high consideration in military, public, and civilian applications in recent years. However, scaling out millions of UAVs in the air will inevitably lead to a more crowded radio frequency (RF) spectrum. Therefore, researchers have been focused on new technologies such as millimeter-wave, Terahertz, and visible light communications (VLCs) to alleviate the spectrum crunch problem. VLC has shown its great potential for UAV networking because of its high data rate, interference-free to legacy RF spectrum, and low-complex frontends. While the physical layer design of the VLC system has been extensively …

Leveraging Spanning Tree To Detect Colluding Attackers In Federated Learning, Priyesh Ranjan, Federico Coro, Ashish Gupta, Sajal K. Das

Computer Science Faculty Research & Creative Works

Federated learning distributes model training among multiple clients who, driven by privacy concerns, perform training using their local data and only share model weights for iterative aggregation on the server. In this work, we explore the threat of collusion attacks from multiple malicious clients who pose targeted attacks (e.g., label flipping) in a federated learning configuration. By leveraging client weights and the correlation among them, we develop a graph-based algorithm to detect malicious clients. Finally, we validate the effectiveness of our algorithm in presence of varying number of attackers on a classification task using a well-known Fashion-MNIST dataset.

More To Less (M2l): Enhanced Health Recognition In The Wild With Reduced Modality Of Wearable Sensors, Huiyuan Yang, Han Yu, Kusha Sridhar, Thomas Vaessen, Inez Myin-Germeys, Akane Sano

Computer Science Faculty Research & Creative Works

Accurately recognizing health-related conditions from wearable data is crucial for improved healthcare outcomes. To improve the recognition accuracy, various approaches have focused on how to effectively fuse information from multiple sensors. Fusing multiple sensors is a common choice in many applications but may not always be feasible in real-world scenarios. For example, although combining bio signals from multiple sensors (i.e., a chest pad sensor and a wrist wearable sensor) has been proved effective for improved performance, wearing multiple devices might be impractical in the free-living context. To solve the challenges, we propose an effective more to less (M2L) learning framework …

Asymmetrical Trusted Technology Networks In Developing Economies: A Case Study On Critical Infrastructure In Bhutan, Pratima Pradhan, Bal Subba, Thinley Jamtsho, Ganga Ram Ghimiray, David M. Cook

Research outputs 2022 to 2026

Developing Nations are subject to amplified challenges in terms of the integration of technology, and the exposure to non-domestic opportunism from larger neighboring economies. These challenges are recognizable as asymmetrical differences between what is seen as the normative list of critical infrastructures, and the specialisms that can dominate an emerging economy with early maturity technology networks. This paper discusses the case of Bhutan and demonstrates the need for strengthened approaches to trusted networks to ensure the reliability and continuity of the Nation's critical infrastructures. The paper also links the importance of trusted information sharing networks as part of an overarching …

Covid-19 And Csr Disclosure: Evidence From New Zealand, Stephen Bahadar, Rashid Zaman

Research outputs 2022 to 2026

Purpose – Stakeholders’ uncertainty about firms’ value drives their urge to get information, as well as managerial disclosure choices. In this study, the authors examine whether and how an important source of uncertainty – the recent COVID-19 pandemic’s effect on corporate social responsibility (CSR) disclosure – is beyond managerial and stakeholders’ control. Design/methodology/approach – The authors develop a novel construct for daily CSR disclosure by employing computer-aided text analysis (CATA) on the press releases issued by 125 New Zealand Stock Exchange (NZX) listed from 28 February 2020 to 31 December 2020. To capture COVID-19 intensity, the authors use the growth …

Identifying Key Elements To Assess Patient’S Acceptability Of Neurorehabilitation In Stroke Survivors–A Delphi Method, Manonita Ghosh, Kaoru Nosaka, Lisa Whitehead, Kazunori Nosaka

Research outputs 2014 to 2021

Purpose: Assessing patient acceptability of treatment is a clinical concern. No guidance exists to determine the best way to measure acceptability in stroke neurorehabilitation. This study identifies key elements to measure patient’s acceptance of stroke neurorehabilitation by establishing expert consensus.

>Materials and methods: A four-phase Delphi method with a three-round electronic-based survey was conducted. Experts were considered as stroke survivors or their caregivers and professionals in stroke neurorehabilitation. A twenty-five-item list was sourced from a literature review and discussion with a consumer panel (n = 22). In Round-1 (n = 118) and Round-2 (n = 80), experts …

How Can We Address The Wicked Problem Of University Student Attrition? A Bed (Early Childhood) Case Study, Gillian Kirk

Research outputs 2022 to 2026

Student retention in education courses is a political imperative. This research examined the wicked problem of attrition in a Bachelor of Education (Early Childhood Studies) course, drawing on the experiences of three different groups of students studying early childhood. The participants from each study represented different student groups within the course, which included all female (n = 20), all male (n = 6) and all mid-year entry students (n = 10). A qualitative methodology was employed to capture students’ university experiences and their perceptions of events at university. The research generated a unique theoretical framework examining the intersection between student …

Anatomy Of An Internet Hijack And Interception Attack: A Global And Educational Perspective, Ben A. Scott, Michael N. Johnstone, Patryk Szewczyk

Research outputs 2022 to 2026

The Internet’s underlying vulnerable protocol infrastructure is a rich target for cyber crime, cyber espionage and cyber warfare operations. The stability and security of the Internet infrastructure are important to the function of global matters of state, critical infrastructure, global e-commerce and election systems. There are global approaches to tackle Internet security challenges that include governance, law, educational and technical perspectives. This paper reviews a number of approaches to these challenges, the increasingly surgical attacks that target the underlying vulnerable protocol infrastructure of the Internet, and the extant cyber security education curricula; we find the majority of predominant cyber security …

More Amazon Than Mafia: Analysing A Ddos Stresser Service As Organised Cybercrime, Roberto Musotto, David S. Wall

Research outputs 2014 to 2021

© 2020, The Author(s). The internet mafia trope has shaped our knowledge about organised crime groups online, yet the evidence is largely speculative and the logic often flawed. This paper adds to current knowledge by exploring the development, operation and demise of an online criminal group as a case study. In this article we analyse a DDoS (Distributed Denial of Service) stresser (also known as booter) which sells its services online to enable offenders to launch attacks. Using Social Network Analysis to explore the service operations and payment systems, our findings show a central business model that is similar to …

‘The Participation Group Means That I'M Low Ability’: Students’ Perspectives On The Enactment Of ‘Mixed-Ability’ Grouping In Secondary School Physical Education, Shaun D. Wilkinson, Dawn Penney

Research outputs 2022 to 2026

Mixed-ability grouping is widespread in primary schools and in several subject areas in secondary schools in England. Notwithstanding, there is scant research on mixed-ability grouping in the education literature, particularly in terms of its impact on students’ experiences. The research reported in this paper employs enactment theory to provide original insights into the diverse practices and complex contextual factors that shape students’ perceptions and experiences of mixed-ability grouping in physical education (PE). Enactment theory acknowledges that school decisions about grouping policy are impacted by wider education policy and other contextual influences, and that the expression of grouping policies in specific …

Myth-Busting In An Aboriginal Pre-University Bridging Program: Embedding Transformative Learning Pedagogy, Rebecca Bennett, Karin Strehlow, Braden Hill

Research outputs 2022 to 2026

Pre-university bridging programs can address the significant under-representation of Indigenous students in Australian universities by providing culturally supported alternative pathways into undergraduate study. However, successful completion of bridging programs does not always correlate with university enrolment for Indigenous students. This paper offers a pedagogical rationale for an Indigenous bridging program that aims to address this discrepancy. The program curriculum challenges deficit myths about Aboriginal and Torres Strait Islander Australians and education, while developing foundational academic skills for university study. Leveraging Transformative Learning and Cultural Interface theories, the program aims to empower students with the opportunity to develop their own narratives …

Deep Learning Inspired Feature Engineering For Classifying Tremor Severity, Ahmed Al Taee, Seyedehmarzieh Hosseini, Rami N. Khushaba, Tanveer Zia, Chin-Teng Lin, Adel Al-Jumaily

Research outputs 2022 to 2026

Bio-signals pattern recognition systems can be impacted by several factors with a potential to limit their associated performance and clinical translation. Among these factors, selecting the optimum feature extraction method, that can effectively exploit the interaction between the temporal and spatial information, is the most prominent. Despite the potential of deep learning (DL) models for extracting temporal, spatial, or temporal-spatial information, they are typically restricted by their need for a large amount of training data. The deep wavelet scattering transform (WST) is a relatively recent advancement within the DL literature to replace expensive convolution neural networks models with computationally less …

Shapley Idioms: Analysing Bert Sentence Embeddings For General Idiom Token Identification, Vasudevan Nedumpozhimana, Filip Klubicka, John Kelleher

Articles

This article examines the basis of Natural Language Understanding of transformer based language models, such as BERT. It does this through a case study on idiom token classification. We use idiom token identification as a basis for our analysis because of the variety of information types that have previously been explored in the literature for this task, including: topic, lexical, and syntactic features. This variety of relevant information types means that the task of idiom token identification enables us to explore the forms of linguistic information that a BERT language model captures and encodes in its representations. The core of …

Instrumental Analysis, David T. Harvey

Chemistry & Biochemistry Faculty Publications

Instrumental analysis is a field of analytical chemistry that investigates analytes using scientific instruments.

Interannual Variability And Seasonal Dynamics Of Evapotranspiration Of Arundo Donax L. And Populations Of Its Biological Control Agent (Tetramesa Romana), Alexis Racelis, Pradeep Wagle, Jose R. Escamilla Jr., John A. Goolsby, Prasanna Gowda

School of Earth, Environmental, & Marine Sciences Faculty Publications

Giant reed (Arundo donax L.), a woody grass native to the Mediterranean, has become a cause of concern for national water security in its invaded range of the arid southwestern United States, Australia, New Zealand, and South Africa. The main objective of this study was to provide the first, landscape-level estimates of water use by giant reed. The study utilized the eddy covariance method to quantify evapotranspiration (ET) throughout the 2014 and 2015 growing seasons along the Rio Grande River in Eagle Pass, Texas. We monitored ET concurrently with the implementation of a biological control program targeting giant reed. …

Saturating Relationship Between Phytoplankton Growth Rate And Nutrient Concentration Explained By Macromolecular Allocation, Jongsun Kim, Gabrielle Armin, Keisuke Inomura

School of Earth, Environmental, & Marine Sciences Faculty Publications

Phytoplankton account for about a half of photosynthesis in the world, making them a key player in the ecological and biogeochemical systems. One of the key traits of phytoplankton is their growth rate because it indicates their productivity and affects their competitive capability. The saturating relationship between phytoplankton growth rate and environmental nutrient concentration has been widely observed yet the mechanisms behind the relationship remain elusive. Here we use a mechanistic model and metadata of phytoplankton to show that the saturating relationship between growth rate and nitrate concentration can be interpreted by intracellular macromolecular allocation. At low nitrate levels, the …

Native And Non-Native Plant Species Differentially Affect Arthropod Community Dynamics With Consequences For Crop Production In Lower Rio Grande Valley, Kaitlynn Lavallee, Pushpa G. Soti, Alexis Racelis, Rupesh R. Kariyat

School of Earth, Environmental, & Marine Sciences Faculty Publications

In agricultural ecosystems, arthropods play critical roles- including biocontrol, pollination services, and as herbivores. While herbivory negatively affects crop production, the recent decline in beneficial insect numbers have created a global concern, and consequently have led into multiple lines of conservation strategies. Agroecological practices that can provide sustenance, nesting, and refuge for beneficial organisms are considered as some of them, except we lack a better understanding of how seasonal and crop specific variation can affect their community dynamics. In this study, we examined this by investigating how native and non-native plants, when incorporated into a vegetable agroecosystem in Lower Rio …

An Argument For Incorporating Sociological Approaches Into Phenomenological Analyses In Engineering Education Research, S. I. Cruz Moreno, S. Chance, B. Bowe

Research Outputs: 2025-Present

Despite numerous research studies that have examined why women are underrepresented in engineering education programmes, the phenomenon is still not fully understood, and no effective general solutions have been found. In this context, analysing women's experiences in engineering education can provide insights regarding the evolution of the students' learning strategies and socialization processes as well as contextual factors that influence their choice to persist in or leave their courses. This paper explores the pertinence of enhancing phenomenological analyses conducted in engineering education research by incorporating sociological perspectives, drawing on sociological studies that explore the relationship between gender, STEM education and …

Innovations In The Design Of An Architectural Engineering Curriculum, Shannon Chance, Emanuela Tilley

Research Outputs: 2025-Present

This paper introduces a new curriculum, launched in October 2021, in Architectural Engineering, designed out of London, UK for implementation in Giza, Egypt. The developers of this newly formed higher education institution, Newgiza University, sought to introduce more contemporary approaches as well as cutting-edge curricular innovations to the education landscape in Egypt. To achieve this, they enlisted curriculum developers in architecture and engineering from University College London who have expertise in education research, curricular innovation, and the delivery of engineering and architecture modules and degree programs. The team worked in collaboration with experts and educators from Egypt to create a …