Digital Commons Network^™

Information Systems Analysis And Design: Past Revolutions, Present Challenges, And Future Research Directions, Keng Siau, Carson Woo, Veda C. Storey, Roger H.L. Chiang, Cecil Eng Huang Chua, Jon W. Beard

Business and Information Technology Faculty Research & Creative Works

Systems Analysis and Design (SAND) is Undoubtedly a Pillar in the Field of Information Systems (IS). Some Researchers Have Even Claimed that SAND is the Field that Defines the Information Systems Discipline and is the Core of Information Systems. the Past Decades Have Seen the Development of Structured SAND Methodologies and Object-Oriented Methodologies. in the Early 1990s, Key Players in the Field Collaborated to Develop the Unified Modeling Language and the Unified Process. Agile Approaches Followed, as Did Other Dynamic Methods. These Approaches Remain Heavily Employed in the Development of Contemporary Information Systems. at the Same Time, New Approaches Such …

A Novel Echo State Network Autoencoder For Anomaly Detection In Industrial Iot Systems, Fabrizio De Vita, Giorgio Nocera, Dario Bruneo, Sajal K. Das

Computer Science Faculty Research & Creative Works

The Industrial Internet of Things (IIoT) technology had a very strong impact on the realization of smart frameworks for detecting anomalous behaviors that could be potentially dangerous to a system. In this regard, most of the existing solutions involve the use of Artificial Intelligence (AI) models running on Edge devices, such as Intelligent Cyber Physical Systems (ICPS) typically equipped with sensing and actuating capabilities. However, the hardware restrictions of these devices make the implementation of an effective anomaly detection algorithm quite challenging. Considering an industrial scenario, where signals in the form of multivariate time-series should be analyzed to perform a …

Noise Resilient Learning For Attack Detection In Smart Grid Pmu Infrastructure, Prithwiraj Roy, Shameek Bhattacharjee, Sahar Abedzadeh, Sajal K. Das

Computer Science Faculty Research & Creative Works

Falsified data from compromised Phasor Measurement Units (PMUs) in a smart grid induce Energy Management Systems (EMS) to have an inaccurate estimation of the state of the grid, disrupting various operations of the power grid. Moreover, the PMUs deployed at the distribution layer of a smart grid show dynamic fluctuations in their data streams, which make it extremely challenging to design effective learning frameworks for anomaly-based attack detection. In this paper, we propose a noise resilient learning framework for anomaly-based attack detection specifically for distribution layer PMU infrastructure, that show real time indicators of data falsifications attacks while offsetting the …

Cansafe: An Mtd Based Approach For Providing Resiliency Against Dos Attack Within In-Vehicle Networks, Ayan Roy, Sanjay Kumar Madria

Computer Science Faculty Research & Creative Works

Trending towards autonomous transportation systems, modern vehicles are equipped with hundreds of sensors and actuators that increase the intelligence of the vehicles with a higher level of autonomy, as well as facilitate increased communication with entities outside the in-vehicle network. However, increase in a contact point with the outside world has exposed the controller area network (CAN) of a vehicle to remote security vulnerabilities. In particular, an attacker can inject fake high priority messages within the CAN through the contact points, while preventing legitimate messages from controlling the CAN (Denial-of-Service (DoS) attack). In this paper, we propose a Moving Target …

Distributed Decision Making For V2v Charge Sharing In Intelligent Transportation Systems, Punyasha Chatterjee, Pratham Majumder, Arpita Debnath, Sajal K. Das

Computer Science Faculty Research & Creative Works

Electric vehicles (EVs) have emerged in the intelligent transportation system (ITS) to meet the increasing environmental concerns. To facilitate on-demand requirement of EV charging, vehicle-to-vehicle (V2V) charge transfer can be employed. However, most of the existing approaches to V2V charge sharing are centralized or semi-centralized, incurring huge message overhead, long waiting time, and infrastructural cost. In this paper, we propose novel distributed heuristic algorithms for V2V charge sharing based on the multi-criteria decision-making policy. The problem is mapped to an alias classical problem (i.e., optimum matching in weighted bipartite graphs), where the goal is to maximize the matching cardinality while …

Rssafe: Personalized Driver Behavior Prediction For Safe Driving, Bhumika, Debasis Das, Sajal K. Das

Computer Science Faculty Research & Creative Works

While the increased demand for taxi services like Uber, Lyft, Hailo, Ola, Grab, Cabify etc. provides livelihood to many drivers, the desire to raise income forces the drivers to work very hard without rest. However, continuous journeys not only affect their health, but also lead to abnormal driving behavior such as rash driving, swerving, sideslipping, sudden brakes, or weaving, leading to accidents in the worst cases. Motivated by the severity of rising accidents and health issues among drivers, this paper proposes a recommendation system, called RsSafe, for the safety of drivers. Aiming to improve the driving quality and the driver's …

An Icn-Based Secure Task Cooperation Scheme In Challenging Wireless Edge Networks, Ningchun Liu, Shuai Gao, Teng Liang, Xindi Hou, Sajal K. Das

Computer Science Faculty Research & Creative Works

Task cooperation is an effective way to execute a complex task in challenging wireless edge networks. Existing TCP/IP-based solutions encounter the problem of low network resource utilization and the heavy dependency of infrastructure connections. Information-centric networking (ICN) is a promising architecture to address these issues. In existing ICN-based task cooperation schemes, the data reuse feature of ICN improves the utilization of network resources, which also brings potential security threats to the reused data. To guarantee the security of data reuse in task cooperation without affecting the data reuse feature, we propose an ICN-based secure task cooperation scheme. In our scheme, …

Toward Feature-Preserving Vector Field Compression, Xin Liang, Sheng Di, Franck Cappello, Mukund Raj, Chunhui Liu, Kenji Ono, Zizhong Chen, Tom Peterka, Hanqi Guo

Computer Science Faculty Research & Creative Works

The objective of this work is to develop error-bounded lossy compression methods to preserve topological features in 2D and 3D vector fields. Specifically, we explore the preservation of critical points in piecewise linear and bilinear vector fields. We define the preservation of critical points as, without any false positive, false negative, or false type in the decompressed data, (1) keeping each critical point in its original cell and (2) retaining the type of each critical point (e.g., saddle and attracting node). The key to our method is to adapt a vertex-wise error bound for each grid point and to compress …

Delivery With Uavs: A Simulated Dataset Via Ats, Giulio Rigoni, Cristina M. Pinotti, Bhumika, Debasis Das, Sajal K. Das

Computer Science Faculty Research & Creative Works

We consider a delivery food service operated by Unmanned Aerial Vehicles (UAVs). Due to the absence of a dataset on UAVs deliveries in the literature, and since it is not possible to perform real tests, we create a dataset using an open-Air Traffic Simulator (ATS). Precisely, we converted a set of food deliveries operated by wheeled vehicles, proposed in the literature [1], into a set of simulated UAVs deliveries. For each delivery, we ran a UAV flight from the source to the destination. The results showed that, as expected, the UAV's course is shorter than the vehicle trajectory on the …

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.

Tension-Stiffening Effect Consideration For Modeling Deflection Of Cracked Reinforced Uhpc Beams, Le Teng, Rongling Zhang, Kamal Khayat

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

Tension-stiffening effects can significantly influence the flexural performance of cracked reinforced concrete specimens. Such effect is amplified for fiber-reinforced concrete, given the fact that fibers can bridge the cracks. The objective of this study was to develop a model to predict the deflection of cracked reinforced ultra-high performance concrete (R-UHPC) beam elements. The modeling approach characterized the average bending moment of inertia by combining the existing model used for conventional reinforced concrete and the analytical model of stress distribution of UHPC along the cross-section. The finite element analysis (FEA) was employed to evaluate the flexural deflection based on the average …

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 …

Dccam-Mrnet: Mixed Residual Connection Network With Dilated Convolution And Coordinate Attention Mechanism For Tomato Disease Identification, Yujian Liu, Yaowen Hu, Weiwei Cai, Guoxiong Zhou, Jialei Zhan, Liujun Li

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

Tomato is an important and fragile crop. During the course of its development, it is frequently contaminated with bacteria or viruses. Tomato leaf diseases may be detected quickly and accurately, resulting in increased productivity and quality. Because of the intricate development environment of tomatoes and their inconspicuous disease spot features and small spot area, present machine vision approaches fail to reliably recognize tomato leaves. As a result, this research proposes a novel paradigm for detecting tomato leaf disease. The INLM (integration nonlocal means) filtering algorithm, for example, decreases the interference of surrounding noise on the features. Then, utilizing ResNeXt50 as …

The Nursing Care Of People With Class Iii Obesity In An Acute Care Setting: A Scoping Review, Bev Ewens, Vivien Kemp, Mandy (Amanda) Towell-Barnard, Lisa Whitehead

Research outputs 2022 to 2026

Background

Patients with Class III obesity pose unique challenges to health care staff and organisations. Care requirements of this population are unique and require specialised equipment and knowledge to meet these needs, maintain the quality of care, as well as the safety of patients and staff.

Aim

To synthesise the evidence on the nursing care of Class III obese patients in acute care settings.

Methods

A scoping review informed by JBI. CINAHL Plus, Medline, Scopus, Proquest Central, Web of Science and Embase were searched for primary research articles about the nursing management of people classified as Class III obese in …

The Safety And Efficacy Of Using Moxibustion And Or Acupuncture For Cancer-Related Insomnia: A Systematic Review And Meta-Analysis Of Randomised Controlled Trials, Carol Chunfeng Wang, Ellen Yichun Han, Mark Jenkins, Xuepei Hong, Shuqin Pang, Lisa Whitehead, Deborah L. Kirk, Anne Williams

Research outputs 2022 to 2026

Introduction:

This study aimed to synthesise the best available evidence on the safety and efficacy of using moxibustion and/or acupuncture to manage cancer-related insomnia (CRI).

Methods:

The PRISMA framework guided the review. Nine databases were searched from its inception to July 2020, published in English or Chinese. Randomised clinical trials (RCTs) of moxibustion and or acupuncture for the treatment of CRI were selected for inclusion. Methodological quality was assessed using the method suggested by the Cochrane collaboration. The Cochrane Review Manager was used to conduct a meta-analysis.

Results:

Fourteen RCTs met the eligibility criteria. Twelve RCTs used the Pittsburgh Sleep …

Matrilateral Bias Of Grandparental Investment In Grandchildren Persists Despite The Grandchildren’S Adverse Early Life Experiences, Samuli Helle, Antti O. Tanskanen, David A. Coall, Mirkka Danielsbacka

Research outputs 2022 to 2026

Evolutionary theory predicts a downward flow of investment from older to younger generations, representing individual efforts to maximize inclusive fitness. Maternal grandparents and maternal grandmothers (MGMs) in particular consistently show the highest levels of investment (e.g. time, care and resources) in their grandchildren. Grandparental investment overall may depend on social and environmental conditions that affect the development of children and modify the benefits and costs of investment. Currently, the responses of grandparents to adverse early life experiences (AELEs) in their grandchildren are assessed from a perspective of increased investment to meet increased need. Here, we formulate an alternative prediction that …

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 …

Educators’ Perspectives About Teaching And Supporting Students With Learning Difficulties In Reading, Tanya Serry, Pam Snow, Lorraine Hammond, Emina Mclean, Jane Mccormack

Research outputs 2022 to 2026

We explored the perspectives of school-based educators located in Victoria, Australia, regarding their support of students who have reading difficulties. An anonymous survey was completed by 523 participants, including educators, educational leaders and Student Support Services staff. Results revealed multiple areas of concern related to their capacity to work on reading intervention with these students. Although participants reported that students with reading difficulties were present in most classes, confidence to work effectively with these students was mixed. They described feeling poorly prepared by preservice programs and indicated that insufficient time and mentorship prevented them from serving these students optimally. As …