Digital Commons Network^™

Unifying Threats Against Information Integrity In Participatory Crowd Sensing, Shameek Bhattacharjee, Sajal K. Das

Computer Science Faculty Research & Creative Works

This article proposes a unified threat landscape for participatory crowd sensing (P-CS) systems. Specifically, it focuses on attacks from organized malicious actors that may use the knowledge of P-CS platform's operations and exploit algorithmic weaknesses in AI-based methods of event trust, user reputation, decision-making, or recommendation models deployed to preserve information integrity in P-CS. We emphasize on intent driven malicious behaviors by advanced adversaries and how attacks are crafted to achieve those attack impacts. Three directions of the threat model are introduced, such as attack goals, types, and strategies. We expand on how various strategies are linked with different attack …

Preserving Privacy In Image Database Through Bit-Planes Obfuscation, Vishesh K. Tanwar, Ashish Gupta, Sanjay Kumar Madria, Sajal K. Das

Computer Science Faculty Research & Creative Works

The recent surge in computer vision applications has caused visual privacy concerns to people who are either users or exposed to an underlying surveillance system. To preserve their privacy, image obfuscation lays out a strong road through which the usability of images can also be maintained without revealing any visual private information. However, prior solutions are susceptible to reconstruction attacks or produce non-trainable images even by leveraging the obfuscation ways. This paper proposes a novel bit-planes-based image obfuscation scheme, called Bimof, to protect the visual privacy of the user in the images that are input into a recognition-based system. By …

Welcome From General Chairs, Sajal K. Das, Wen Zhan Song

Computer Science Faculty Research & Creative Works

No abstract provided.

Urban Air Mobility: Vision, Challenges And Opportunities, Debjyoti Sengupta, Sajal K. Das

Computer Science Faculty Research & Creative Works

Urban Air Mobility (UAM) involving piloted or autonomous aerial vehicles, is envisioned as emerging disruptive technology for next-generation transportation addressing mobility challenges in congested cities. This paradigm may include aircrafts ranging from small unmanned aerial vehicles (UAVs) or drones, to aircrafts with passenger carrying capacity, such as personal air vehicles (PAVs). This paper highlights the UAM vision and brings out the underlying fundamental research challenges and opportunities from computing, networking, and service perspectives for sustainable design and implementation of this promising technology providing an innovative infrastructure for urban mobility. Important research questions include, but are not limited to, real-Time autonomous …

Dispatching Point Selection For A Drone-Based Delivery System Operating In A Mixed Euclidean–Manhattan Grid, Francesco Betti Sorbelli, Federico Corò, Sajal K. Das, Cristina M. Pinotti, Anil Shende

Computer Science Faculty Research & Creative Works

In this paper, we present a drone-based delivery system that assumes to deal with a mixed-area, i.e., two areas, one rural and one urban, placed side-by-side. In the mixed-areas, called EM-grids, the distances are measured with two different metrics, and the shortest path between two destinations concatenates the Euclidean and Manhattan metrics. Due to payload constraints, the drone serves a single customer at a time returning back to the dispatching point (DP) after each delivery to load a new parcel for the next customer. In this paper, we present the 1 -Median Euclidean–Manhattan grid Problem (MEMP) for EM-grids, whose goal …

Reward Maximization For Disaster Zone Monitoring With Heterogeneous Uavs, Wenzheng Xu, Chengxi Wang, Hongbin Xie, Weifa Liang, Haipeng Dai, Zichuan Xu, Ziming Wang, Bing Guo, Sajal K. Das

Computer Science Faculty Research & Creative Works

In this paper, we study the deployment of $K$ heterogeneous UAVs to monitor Points of Interest (PoIs) in a disaster zone, where a PoI may represent a school building or an office building, in which people are trapped. A UAV can take images/videos of PoIs and send its collected information back to a nearby rescue station for decision-making. Unlike most existing studies that focused on only homogeneous UAVs, we here study the scheduling of $K$ heterogeneous UAVs, where different UAVs have different energy capacities and functionalities that lead to different monitoring qualities (monitoring rewards) of each PoI. For example, one …

Message From The Ieee Mdm 2023 Test-Of-Time Committee, Christian S. Jensen, Sanjay Kumar Madria, Timos Sellis

Computer Science Faculty Research & Creative Works

No abstract provided.

Lasa: Location-Aware Scheduling Algorithm In Industrial Iot Networks With Mobile Nodes, Marco Pettorali, Francesca Righetti, Carlo Vallati, Sajal K. Das, Giuseppe Anastasi

Computer Science Faculty Research & Creative Works

The Synchronized Single-hop Multiple Gateway (SHMG) is a framework recently proposed to support mobility into 6TiSCH, the standard network architecture defined for Industrial Internet of Things (IIoT) deployments. SHMG supports industrial applications with stringent requirements by adopting the Shared-Downstream Dedicated-Upstream (SD-DU) scheduling policy, which allocates to Mobile Nodes (MNs) a set of dedicated transmission opportunities for uplink data. Such allocation is performed on all the Border Routers (BRs) of the network without considering the location of MNs. Transmission opportunities are reserved also in BRs far from the current location of the MN, resulting in a waste of resources that limits …

Robust Federated Learning Against Backdoor Attackers, Priyesh Ranjan, Ashish Gupta, Federico Corò, Sajal K. Das

Computer Science Faculty Research & Creative Works

Federated Learning is a Privacy-Preserving Alter-Native for Distributed Learning with No Involvement of Data Transfer. as the Server Does Not Have Any Control on Clients' Actions, Some Adversaries May Participate in Learning to Introduce Corruption into the Underlying Model. Backdoor Attacker is One Such Adversary Who Injects a Trigger Pattern into the Data to Manipulate the Model Outcomes on a Specific Sub-Task. This Work Aims to Identify Backdoor Attackers and to Mitigate their Effects by Isolating their Weight Updates. Leveraging the Correlation between Clients' Gradients, We Propose Two Graph Theoretic Algorithms to Separate Out Attackers from the Benign Clients. under …

One-Shot Federated Learning For Leo Constellations That Reduces Convergence Time From Days To 90 Minutes, Mohamed Elmahallawy, Tie (Tony) Tie Luo

Computer Science Faculty Research & Creative Works

A Low Earth orbit (LEO) satellite constellation consists of a large number of small satellites traveling in space with high mobility and collecting vast amounts of mobility data such as cloud movement for weather forecast, large herds of animals migrating across geo-regions, spreading of forest fires, and aircraft tracking. Machine learning can be utilized to analyze these mobility data to address global challenges, and Federated Learning (FL) is a promising approach because it eliminates the need for transmitting raw data and hence is both bandwidth and privacy friendly. However, FL requires many communication rounds between clients (satellites) and the parameter …

Cyber-Agricultural Systems For Crop Breeding And Sustainable Production, Soumik Sarkar, Baskar Ganapathysubramanian, Arti Singh, Fateme Fotouhi, Soumyashree Kar, Koushik Nagasubramanian, Girish Chowdhary, Sajal K. Das, George Kantor, Adarsh Krishnamurthy, Nirav Merchant, Asheesh K. Singh

Computer Science Faculty Research & Creative Works

The Cyber-Agricultural System (CAS) Represents an overarching Framework of Agriculture that Leverages Recent Advances in Ubiquitous Sensing, Artificial Intelligence, Smart Actuators, and Scalable Cyberinfrastructure (CI) in Both Breeding and Production Agriculture. We Discuss the Recent Progress and Perspective of the Three Fundamental Components of CAS – Sensing, Modeling, and Actuation – and the Emerging Concept of Agricultural Digital Twins (DTs). We Also Discuss How Scalable CI is Becoming a Key Enabler of Smart Agriculture. in This Review We Shed Light on the Significance of CAS in Revolutionizing Crop Breeding and Production by Enhancing Efficiency, Productivity, Sustainability, and Resilience to Changing …

Q-Learning For Sum-Throughput Optimization In Wireless Visible-Light Uav Networks, Yuwei Long, Nan Cen

Computer Science Faculty Research & Creative Works

Unmanned Aerial Vehicles (UAVs) Have Been Adopted as Aerial Base Stations (ABSs) to Provide Wireless Connectivity to Ground Users in Events of Increased Network Demand, and Points-Of-Failure Infrastructure (Such as in Disasters). However, with the Existing Crowded Radio Frequency (RF) Spectrum, UAV ABSs Cannot Provide High-Data-Rate Communication Required in 5G and beyond. to Address This Challenge, Visible Light Communication (VLC) is Proposed to Be Equipped on UAVs to Take Advantage of the Flexible and On-Demand Deployment Feature of the UAV, and the High-Data-Rate Communication of the VLC. However, VLC Has Strong Alignment Requirements between Transceivers, Therefore, How to Determine the …

Lightesd: Fully-Automated And Lightweight Anomaly Detection Framework For Edge Computing, Ronit Das, Tie (Tony) T. Luo

Computer Science Faculty Research & Creative Works

Anomaly Detection is Widely Used in a Broad Range of Domains from Cybersecurity to Manufacturing, Finance, and So On. Deep Learning based Anomaly Detection Has Recently Drawn Much Attention Because of its Superior Capability of Recognizing Complex Data Patterns and Identifying Outliers Accurately. However, Deep Learning Models Are Typically Iteratively Optimized in a Central Server with Input Data Gathered from Edge Devices, and Such Data Transfer between Edge Devices and the Central Server Impose Substantial overhead on the Network and Incur Additional Latency and Energy Consumption. to overcome This Problem, We Propose a Fully Automated, Lightweight, Statistical Learning based Anomaly …

Smartlens: Robust Detection Of Rogue Device Via Frequency Domain Features In Lora-Enabled Iiot, Subir Halder, Amrita Ghosal, Thomas Newe, Sajal K. Das

Computer Science Faculty Research & Creative Works

A challenging problem in Long Range (LoRa) communications enabled Industrial Internet of Things (IIoT) is the detection of rogue devices, which attempt to impersonate real devices by spoofing their authentic identifications in order to steal information and gain access to the system. Although machine learning (ML) offers a promising approach to detecting rogue devices, existing ML models rely on domain knowledge yet exhibit low detection accuracy and vulnerability against adversarial attacks. This paper proposes SmartLens, a novel real-time frequency domain feature based rogue device detection system, using a lightweight statistical ML algorithm and Mahalanobis distance to achieve high accuracy and …

A Distributed Algorithm For Identifying Strongly Connected Components On Incremental Graphs, S. Srinivasan, A. Khanda, S. Srinivasan, A. Pandey, S. (Sajal) K. Das, S. Bhowmick, B. Norris

Computer Science Faculty Research & Creative Works

Incremental graphs that change over time capture the changing relationships of different entities. Given that many real-world networks are extremely large, it is often necessary to partition the network over many distributed systems and solve a complex graph problem over the partitioned network. This paper presents a distributed algorithm for identifying strongly connected components (SCC) on incremental graphs. We propose a two-phase asynchronous algorithm that involves storing the intermediate results between each iteration of dynamic updates in a novel meta-graph storage format for efficient recomputation of the SCC for successive iterations. To the best of our knowledge, this is the …

Strategic Information Design In Selfish Routing With Quantum Response Travelers, Sainath Sanga, Venkata Sriram Siddhardh Nadendla, Mukund Telukunta, Sajal K. Das

Computer Science Faculty Research & Creative Works

Selfish routing begets inefficiency in multi-agent transportation systems, leading to significant economic losses in our society. Although several powerful techniques (e. g., marginal cost pricing) have been proposed to mitigate price-of-anarchy (a measure of inefficiency), social welfare maximization still remains a huge challenge in selfish routing, especially when travelers deviate from maximizing their own expected utilities. This paper proposes a novel informational intervention to improve the efficiency of selfish routing, especially in the presence of quantal response travelers. Specifically, modeling the interaction between the system and travelers as a Stackelberg game, and develop a novel approximate algorithm, called LoRI (which …

Optimizing Federated Learning In Leo Satellite Constellations Via Intra-Plane Model Propagation And Sink Satellite Scheduling, Mohamed Elmahallawy, Tie (Tony) T. Luo

Computer Science Faculty Research & Creative Works

The advances in satellite technology developments have recently seen a large number of small satellites being launched into space on Low Earth orbit (LEO) to collect massive data such as Earth observational imagery. The traditional way which downloads such data to a ground station (GS) to train a machine learning (ML) model is not desirable due to the bandwidth limitation and intermittent connectivity between LEO satellites and the GS. Satellite edge computing (SEC), on the other hand, allows each satellite to train an ML model onboard and uploads only the model to the GS which appears to be a promising …

Rate-Monotonic Scheduler For Lora-Based Smart Space Monitoring System, Preti Kumari, Hari Prabhat Gupta, Sajal K. Das, Rahul Bansal

Computer Science Faculty Research & Creative Works

Smart spaces system equipped with sensors to collect data that can be used to generate insights about its environmental conditions. Those collected data is then transmitted to the applications to enhance the comfort, quality of life, and security of the space. Long Range (LoRa) technology provides long distance coverage and consumes low energy which makes it suitable for smart space application. There are six virtual channels to transmit data in LoRa, however network faces the interference problem when nodes transmitted data at the same time. The interference problem makes LoRa less suitable for time-critical applications. To mitigate the interference problem, …

Identifying Design-Build Decision-Making Factors And Providing Future Research Guidelines: Social Network And Association Rule Analysis, Ramy Khalef, Islam H. El-Adaway

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

There is a dire need to rebuild existing infrastructure with strategic and efficient methods. Design-build (DB) becomes a potential solution that provides fast-tracked delivery as a more time and cost-efficient project delivery method. Past research studied factors influencing DB but without providing a holistic analytic approach. This paper fills this knowledge gap. First, a systematic literature review is performed using the preferred reporting items for systematic reviews and meta-analyses techniques, and a set of factors affecting DB projects are then identified and clustered, using k-means clustering, based on the whole literature discussions. Second, a graph theory approach, social network analysis …

Supply Chain Insights From Social Media Users’ Responses To Panic Buying During Covid-19: The Herd Mentality, Violetta Wilk, Saiyidi Mat Roni, Ferry Jie

Research outputs 2022 to 2026

Purpose:

This study applied the herd mentality theory to explore local and global social media users’ responses to panic buying across the USA, UK and Australia during the COVID-19 crisis to understand the implications on operations and supply chains.

Design/methodology/approach:

A total of 208,806 social media user-generated content (UGC) pieces were collected from Twitter in three countries – the USA, UK and Australia. The analysis of this big qualitative data was performed using machine learning–based software – Leximancer.

Findings:

Positive and negative sentiment towards panic buying during the COVID-19 crisis was observed in the UGC. No significant differences in social …

Determinants Of Intention To Use E-Wallet: Personal Innovativeness And Propensity To Trust As Moderators, Madugoda Gunaratnege Senali, Mohammad Iranmanesh, Fatin Nadzirah Ismail, Noor Fareen Abdul Rahim, Mana Khoshkam, Maryam Mirzaei

Research outputs 2022 to 2026

This study aims to investigate the determinants of intention to use e-wallets. Drawing on the technology acceptance model (TAM), the conceptual framework was developed. The study extends the TAM in the context of e-wallets, by testing the influences of product-related factors namely perceived compatibility, perceived risk, and perceived emotions and investigating the moderating impacts of personal innovativeness and propensity to trust. To conduct an empirical study, the data were collected from Malaysian individuals with no experience with e-wallets using an online survey. Data from 374 participants were obtained and analyzed using the partial least squares technique. The results showed that …

Spatial Heterogeneity In Sediment And Carbon Accretion Rates Within A Seagrass Meadow Correlated With The Hydrodynamic Intensity, Jiarui Lei, Rachel Schaefer, Phil Colarusso, Alyssa Novak, Juliet C. Simpson, Pere Masqué, Heidi Nepf

Research outputs 2022 to 2026

The majority of the carbon stored in seagrass sediments originates outside the meadow, such that the carbon storage capacity within a meadow is strongly dependent on hydrodynamic conditions that favor deposition and retention of fine organic matter within the meadow. By extension, if hydrodynamic conditions vary across a meadow, they may give rise to spatial gradients in carbon. This study considered whether the spatial gradients in sediment and carbon accretion rates correlated with the spatial variation in hydrodynamic intensity within a single meadow. Field measurements were conducted in three depth zones across a Zostera marina L. (eelgrass) meadow in Nahant …

Judging Better Together: Understanding The Psychology Of Group Decision-Making On Panel Courts And Tribunals, Brian M. Barry Dr

Articles

While the psychological phenomena that affect group decisionmaking have been thoroughly investigated for decades, how these phenomena apply to decision-making by judges on panel courts is under-examined. This article examines the main psychological phenomena of group decision-making, both positive and negative, and considers their implications for panel courts and other groups of professional legal decision-makers such as adjudicators serving on tribunals. This article argues that experimental studies on judges and adjudicators testing the effects of these phenomena would improve understanding of legal decision-making by these groups and could help to devise ways to improve their decision-making processes to reach higher …

The Distribution Of Justices' Votes And Countering National Disunity, Nicholas L. Georgakopoulos

FIU Law Review

The estimation of the distribution that matches the voting of the justices of the Supreme Court shows that voting is correlated and reveals three phenomena: an outlier distribution produced by one composition of the Court, the surprising frequency of unanimous decisions, and the intensity with which the Court avoids 4–4 decisions. The intensity with which the Court avoids 4–4 splits and the strength of the drive to produce unanimous decisions seem sensitive to national disunity. At times of greater disunity, 1965 to 1975 and 2001 to 2020, the Court avoids 4–4 splits more intensely and has a greater fraction of …

Early Detection Of Sars-Cov-2 Variants Through Dynamic Co-Mutation Network Surveillance, Paul W. Bible, Qiang Huang, Huining Qiu, Yong Huang, Fangfang Zheng, Jing Gu, Jian Sun, Yuantao Hao, Yu Liu

Computer Science Faculty publications

Background: Precise public health and clinical interventions for the COVID-19 pandemic has spurred a global rush on SARS-CoV-2 variant tracking, but current approaches to variant tracking are challenged by the flood of viral genome sequences leading to a loss of timeliness, accuracy, and reliability. Here, we devised a new co-mutation network framework, aiming to tackle these difficulties in variant surveillance.

Methods: To avoid simultaneous input and modeling of the whole large-scale data, we dynamically investigate the nucleotide covarying pattern of weekly sequences. The community detection algorithm is applied to a co-occurring genomic alteration network constructed from mutation corpora of weekly …

School Library Advocacy: Enhancing Opportunities For All Learners, Elizabeth A. Burns

STEMPS Faculty Publications

There is a lack of consistency in how school librarians understand and engage in advocacy for their programs and the profession (Burns, 2015; Lance & Kachel, 2018). It is important that school librarians demonstrate the positive impact they contribute to improving instruction for student learners and advancing access so that all learners have equal opportunities. The school library and school librarian should be available to each student (Kachel, 2021). The School Library Manifesto (2021) includes language to support a strong school library with a qualified school librarian. Advocacy goals can be highlighted throughout this critical set of school library guidelines. …

How It Course Improves Digital Competencies: An Experimental Study In Science Education, Mustafa Tevfik Hebebci, Helen Crompton

STEMPS Faculty Publications

The rising significance of technology in society has caused a surge in the requirement of individuals possessing digital competencies. In the current era, digital skills are progressively vital for both personal and occupational growth. Governments have acknowledged the value of digital competencies and have incorporated them as part of fundamental proficiencies in numerous countries. Pre-service teachers have a paramount role in molding forthcoming generations, highlighting their necessity to possess robust digital competencies. This research centers on how an "Information Technologies in Science Education" course influences the digital competency progression in pre-service teachers. The study deployed a pre-test post-test experimental design …

Improving Reliability In Assessing Integrative Learning Using Rubrics: Does Group Norming Help?, Lanah Stafford, Erin Cousins, Linda Bol, Megan Mize

STEMPS Faculty Publications

Integrative learning is an important outcome for graduates of higher education. Therefore, it should be well-defined and assessed reliably. The American Association of Colleges & Universities has developed a rubric to define and assess integrative learning, but it has low reliability. This pilot study examines whether this rubric's reliability can be improved by training users on how to use the rubric in a group setting rather than individually. Twelve faculty were trained to score undergraduate ePortfolios using the Integrative and Applied Learning VALUE Rubric. Half of the faculty were trained in an individual setting and half in a group setting …

Dementia And Cognitive Impairment In French-Speaking Sub-Saharan Africa: A Comprehensive Review On Moving Out Of The Shadows Of Neglect, Cyrille D. Nkouonlack, Wepnyu Y. Njamnshi, Samuel A. Angwafor, Joseph N. Siewe Fodjo, Michel K. Mengnjo, Leonard Ngarka, Maggy Mbede, Leonard N. Nfor, Carlson Abomate, Gladys E. Maestre

School of Medicine Publications

Dementia is a global public health problem with increasing prevalence and incidence worldwide. The African continent is expected to bear the biggest brunt of the burden of dementia by 2050 because of the rapid demographic changes, including rapid population growth, an increase in life expectancy, and ageing. However, French-speaking Sub-Saharan African (FS-SSA) countries are underrepresented in research on dementia in Africa. While the reasons are diverse and complex, linguistic and cultural barriers to research, disproportionately affect these countries and may be significant factors. Any efforts, therefore, to redress the burden of dementia in Africa must consider the specific demographic, cultural, …

Maximizing Technology Acceptance Model In Accessing The Attitude Of Rural Farmers Using Ict Tools In Farming To Enhance Productivity, Francis Yao Anyan Dr., Michael Oppong Mr., Stephanie O. Ansah Mrs., Millicent Yengkangyi Ms., Akosua Boatemaa Adarkwa Mrs

Library Philosophy and Practice (e-journal)

The study was conducted using a multi-stage sampling technique involving simple random sampling, a probability sampling method, purposive sampling, and snowball sampling, which are non-probability methods. Two regions, namely, the Greater Accra and Eastern were purposively selected for the study. In the Greater Accra Region, Ada East District was selected while in the Eastern Region Asuogyaman District. In Greater Accra, the study was conducted in six farming villages namely: Big Ada, Dogo, Kasseh, Addokope, Korlekope, and Bedeku. In Eastern regions, the study was conducted in Asogyaman, where Tortibo, Sappor, Yenease, Adina Donor, and Ankyease. A snowball strategy was relied on …