Computer Sciences Commons^™

Simulating The Machine Translation Of Low-Resource Languages By Designing A Translator Between English And An Artificially Constructed Language, Michaela Snyder

Mahurin Honors College Capstone Experience/Thesis Projects

Natural language processing (NLP), or the use of computers to analyze natural language, is a field that relies heavily on syntax. It would seem intuitive that computers would thrive in this area due to their strict syntax requirements, but the syntax of natural languages leaves them unable to properly parse and generate sentences that seem normal to the average speaker. A subfield of NLP, machine translation, works mainly to computerize translation between different languages. Unfortunately, such translation is not without its weaknesses; language documentation is not created equal, and many low-resource languages—languages with relatively few kinds of documentation, most often …

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 …

Detection Of False Data Injection In Smart Water Metering Infrastructure, Ayanfeoluwa Oluyomi, Shameek Bhattacharjee, Sajal K. Das

Computer Science Faculty Research & Creative Works

Smart water metering (SWM) infrastructure collects real-Time water usage data that is useful for automated billing, leak detection, and forecasting of peak periods. Cyber/physical attacks can lead to data falsification on water usage data. This paper proposes a learning approach that converts smart water meter data into a Pythagorean mean-based invariant that is highly stable under normal conditions but deviates under attacks. We show how adversaries can launch deductive or camouflage attacks in the SWM infrastructure to gain benefits and impact the water distribution utility. Then, we apply a two-Tier approach of stateless and stateful detection, reducing false alarms without …

Disagreement Matters: Exploring Internal Diversification For Redundant Attention In Generic Facial Action Analysis, Xiaotian Li, Zheng Zhang, Xiang Zhang, Taoyue Wang, Zhihua Li, Huiyuan Yang, Umur Ciftci, Qiang Ji, Jeffrey Cohn, Lijun Yin

Computer Science Faculty Research & Creative Works

This paper demonstrates the effectiveness of a diversification mechanism for building a more robust multi-attention system in generic facial action analysis. While previous multi-attention (e.g., visual attention and self-attention) research on facial expression recognition (FER) and Action Unit (AU) detection have been thoroughly studied to focus on "external attention diversification", where attention branches localize different facial areas, we delve into the realm of "internal attention diversification" and explore the impact of diverse attention patterns within the same Region of Interest (RoI). Our experiments reveal that variability in attention patterns significantly impacts model performance, indicating that unconstrained multi-attention plagued by redundancy …

Introduction: Imagined And Real Ai, Michael Paulus

SPU Works

The increasing role and power of artificial intelligence in our lives and world require us to imagine and shape a desirable future with this technology. Since visions of AI often draw from Christian apocalyptic narratives, current discussions about technological hopes and fears present an opportunity for a deeper engagement with Christian eschatological resources. This book argues that the apocalyptic imagination can transform how we think about and use AI, helping us discover ways artificial agency may help us create a better world.

Ethics Of Emerging Communication And Collaboration Technologies For Children, Juan Pablo Hourcade, Elizabeth Bonsignore, Tamara Clegg, Flannery Currin, Jerry A. Fails, Georgie Qiao Jin, Summer R. Schmuecker, Lana Yarosh

Computer Science Faculty Publications and Presentations

This SIG will provide child-computer interaction researchers and practitioners, as well as other interested CSCW attendees, an opportunity to discuss topics related to the ethics of emerging communication and collaboration technologies for children. The child-computer interaction community has conducted many discussions on ethical issues, including a recent SIG at CHI 2023. However, the angle of communication and collaboration has not been a focus, even though emerging technologies could affect these aspects in significant ways. Hence, there is a need to consider emerging technologies, such as extended reality, and how they may impact the way children communicate and collaborate in face-to-face, …

Development Of Open-Source Software 'Pystage', Victor T. Norman, Brandon Husted, Zhonglin 'Loya' Niu

Summer Research

PyStage is an open-source project that seeks to help students bridge the gap between block-based programming in Scratch and text-based programming in Python.

A Reference Framework For Variability Management Of Software Product Lines, Saiqa Aleem, Luiz Fernando Capretz, Faheem Ahmed

Electrical and Computer Engineering Publications

Variability management (VM) in software product line engineering (SPLE) is introduced as an abstraction that enables the reuse and customization of assets. VM is a complex task involving the identification, representation, and instantiation of variability for specific products, as well as the evolution of variability itself. This work presents a comparison and contrast between existing VM approaches using “qualitative meta-synthesis” to determine the underlying perspectives, metaphors, and concepts of existing methods. A common frame of reference for the VM was proposed as the result of this analysis. Putting metaphors in the context of the dimensions in which variability occurs and …

Scheduling Electric Vehicle Charging For Grid Load Balancing, Zhixin Han, Katarina Grolinger, Miriam Capretz, Syed Mir

Electrical and Computer Engineering Publications

In recent years, electric vehicles (EVs) have been widely adopted because of their environmental benefits. However, the increasing volume of EVs poses capacity issues for grid operators as simultaneously charging many EVs may result in grid instabilities. Scheduling EV charging for grid load balancing has a potential to prevent load peaks caused by simultaneous EV charging and contribute to balance of supply and demand. This paper proposes a user-preference-based scheduling approach to minimize costs for the user while balancing grid loads. The EV owners benefit by charging when the electricity cost is lower, but still within the user-defined preferred charging …

Towards Full Authorship With Ai: An Interactive User Interface For Supporting Revision, Kenneth C. Arnold, Edom Maru, Jiho Kim, Noelle Haviland, Ray Flanagan, Saron Melesse, Souad Yakubu, Zeai Sun

Summer Research

Recent breakthroughs in large language models (LLMs) have led to the development of tools like ChatGPT and Bing Chat, which can produce texts comparable to those written by competent writers. The affordances of these tools encourage users to offload large parts of their thinking and writing onto the LLM. But in doing so, users surrender autonomy and authorship over their writing. Could LLMs instead encourage writers to reflect on texts that they wrote themselves? Could these reflections lead them to make meaningful revisions to their texts? Our goal is to provide writers with a tool that promotes reflection and encourages …

Environmentally-Aware And Energy-Efficient Multi-Drone Coordination And Networking For Disaster Response, Chengyi Qu, Francesco Betti Sorbelli, Rounak Singh, Prasad Calyam, Sajal K. Das

Computer Science Faculty Research & Creative Works

In a Disaster Response Management (DRM) Scenario, Communication and Coordination Are Limited, and Absence of Related Infrastructure Hinders Situational Awareness. Unmanned Aerial Vehicles (UAVs) or Drones Provide New Capabilities for DRM to Address These Barriers. However, There is a Dearth of Works that Address Multiple Heterogeneous Drones Collaboratively Working Together to Form a Flying Ad-Hoc Network (FANET) with Air-To-Air and Air-To-Ground Links that Are Impacted By: (I) Environmental Obstacles, (Ii) Wind, and (Iii) Limited Battery Capacities. in This Paper, We Present a Novel Environmentally-Aware and Energy-Efficient Multi-Drone Coordination and Networking Scheme that Features a Reinforcement Learning (RL) based Location Prediction …

Sigmoid Activation-Based Long Short-Term Memory For Time Series Data Classification, Sajal Das

Computer Science Faculty Research & Creative Works

With the enhanced usage of Artificial Intelligence (AI) driven applications, the researchers often face challenges in improving the accuracy of the data classification models, while trading off the complexity. In this paper, we address the classification of time series data using the Long Short-Term Memory (LSTM) network while focusing on the activation functions. While the existing activation functions such as sigmoid and tanh are used as LSTM internal activations, the customizability of these activations stays limited. This motivates us to propose a new family of activation functions, called log-sigmoid, inside the LSTM cell for time series data classification, and analyze …

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

Computer Science Faculty Research & Creative Works

No abstract provided.

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.

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 …

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, …

Towards 6g: Key Technological Directions, Chamitha De Alwis, Pardeep Kumar, Quoc-Viet Pham, Kapal Dev, Ashuman Kalla, Madhusanka Lianage, Woo-Joo Hwang

Department of Mathematics Publications

Sixth-generation mobile networks (6G) are expected to reach extreme communication capabilities to realize emerging applications demanded by the future society. This paper focuses on six technological directions towards 6G, namely, intent-based networking, THz communication, artificial intelligence, distributed ledger technology/blockchain, smart devices and gadget-free communication, and quantum communication. These technologies will enable 6G to be more capable of catering to the demands of future network services and applications. Each of these technologies is discussed highlighting recent developments, applicability in 6G, and deployment challenges. It is envisaged that this work will facilitate 6G related research and developments, especially along the six technological …

A Monitoring Infrastructure To Improve Flipped Learning In Technological Courses, Francisco Ortin, Jose Quiroga, Miguel Garcia

Department of Mathematics Publications

Flipped learning changes the traditional instructional approach of lectures. In flipped classrooms, the students work at home, while in-class sessions are used for other more interactive and active learning approaches guided by the lecturer. While flipped learning has shown many benefits, it is not easy to include remote students in face-to-face in-class sessions, and there is still reluctance from shy students to participate. In technological courses, classroom sessions in flipped learning require the installation of many software packages together with those tools commonly used by the students. Our objective is to improve these limitations in flipped learning by using an …

Co-Existence With Ieee 802.11 Networks In The Ism Band Without Channel Estimation, Muhammad Naveed Aman, Muhammad Ishfaq, Biplab Sikdar

School of Computing: Faculty Publications

Any new deployment of networks in the industrial, scientific, and medical (ISM) band, even though it is license-free, has to co-exist with IEEE 802.11 networks. IoT devices are typically deployed in the ISM band, creating a spectrum bottleneck for competing networks. This paper investigates the issue of co-existence of wireless networks with WiFi networks. In our scenario, we consider WiFi as the “primary” or higher priority network co-existing with multiple “secondary” networks that may be used for low priority devices, with both networks operating in the ISM band. Towards this end, we first develop an analytical model for a metric …

On Approximating Total Variation Distance, Arnab Bhattacharyya, Sutanu Gayen, Kuldeep S. Meel, Dimitrios Myrisiotis, A. Pavan, N. V. Vinodchandran

School of Computing: Faculty Publications

Total variation distance (TV distance) is a fundamental notion of distance between probability distributions. In this work, we introduce and study the problem of computing the TV distance of two product distributions over the domain {0, 1}ⁿ. In particular, we establish the following results.

1. The problem of exactly computing the TV distance of two product distributions is #P-complete. This is in stark contrast with other distance measures such as KL, Chisquare, and Hellinger which tensorize over the marginals leading to efficient algorithms.
2. There is a fully polynomial-time deterministic approximation scheme (FPTAS) for computing the TV distance of two …

A Machine Learning Approach To Deepfake Detection, Delaney Conrad

All Undergraduate Theses and Capstone Projects

The ability to manipulate videos has been around for decades but a process that once would take time, money, and professionals, can now be created by anyone due to the rapid advancement of deepfake technology. Deepfakes use deep learning artificial intelligence to make fake digital content, typically in the form of swapping a person’s face in a video or image. This technology could easily threaten and manipulate individuals, corporations, and political organizations, so it is essential to find methods for detecting deepfakes. As the technology for creating deepfakes continues to improve, these manipulated videos are becoming increasingly undetectable. It is …

Why Fractional Fuzzy, Mehran Mazandarani, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

In many practical situation, control experts can only formulate their experience by using imprecise ("fuzzy") words from natural language. To incorporate this knowledge in automatic controllers, Lotfi Zadeh came up with a methodology that translate the informal expert statements into a precise control strategy. This methodology -- and its following modifications -- is known as fuzzy control. Fuzzy control often leads to a reasonable control -- and we can get an even better control results by tuning the resulting control strategy on the actual system. There are many parameters that can be changes during tuning, so tuning usually is rather …

A Multistage Framework For Detection Of Very Small Objects, Duleep Rathgamage Don, Ramazan Aygun, Mahmut Karakaya

Published and Grey Literature from PhD Candidates

Small object detection is one of the most challenging problems in computer vision. Algorithms based on state-of-the-art object detection methods such as R-CNN, SSD, FPN, and YOLO fail to detect objects of very small sizes. In this study, we propose a novel method to detect very small objects, smaller than 8×8 pixels, that appear in a complex background. The proposed method is a multistage framework consisting of an unsupervised algorithm and three separately trained supervised algorithms. The unsupervised algorithm extracts ROIs from a high-resolution image. Then the ROIs are upsampled using SRGAN, and the enhanced ROIs are detected by our …

A Web-Based Synchronized Architecture For Collaborative Dynamic Diagnosis And Therapy Planning, Qi Zhang

Faculty Publications - Information Technology

Clinical treatment delivery often involves multiple medical professionals, where collaborative teamwork is crucial to ensure the quality of diagnosis and therapeutic decision making. With the development of Internet of Medical Things (IoMT) and its applications in mobile health (mHealth), healthcare services can be delivered to remote users. However, a challenging situation arises when the data are volumetric and dynamic, it will be difficult to achieve real-time performance in information streaming and data dynamic rendering and synchronization over Internet, as is the case with cardiac procedures, where both the anatomy and dynamic function characteristics of the organ must be considered. To …

Framework For Trustworthy Ai In The Health Sector, Mykhailo Danilevskyi

Academic Posters Collection

The European Commission defines that Trustworthy AI should be lawful, ethical and robust. The ethical component and its technical methods are the main focus of the research. According to this, the initial research goal is to create a methodology for evaluating datasets for ML modeling using ethical principles in the healthcare domain. Ethical risk assessment will help to ensure compliance with principles such as privacy, fairness, safety and transparency which are especially important for the Health Care sector. At the same time, risks must be evaluated with respect to AI model performance and possible scenarios of risk mitigation. Ethical risk …

A New Credit Scoring Model To Reduce Potential Predatory Lending: A Design Science Approach, Anna Zakowska

CGU Theses & Dissertations

This research examines the potential impact of implementing a novel credit scoring model that integrates attributes beyond the traditional FICO model. It aims to address issues related to predatory lending and the financial exclusion affecting individuals often categorized as 'credit invisible,' 'credit unscorable,' 'unbanked,' and 'underbanked.' These individuals typically face difficulties in establishing or repairing a credit history, which poses a challenge for financial institutions in accurately evaluating their creditworthiness. This gap in the credit assessment process often opens doors to unfair lending practices. To tackle this problem, a systematically designed, built, tested, and evaluated innovative credit scoring model was …

An Equivalence Checking Framework For Agile Hardware Design, Yanzhao Wang, Fei Xie, Zhenkun Yang, Pascuale Cocchini, Jin Yang

Computer Science Faculty Publications and Presentations

Agile hardware design enables designers to produce new design iterations efficiently. Equivalence checking is critical in ensuring that a new design iteration conforms to its specification. In this paper, we introduce an equivalence checking framework for hardware designs represented in HalideIR. HalideIR is a popular intermediate representation in software domains such as deep learning and image processing, and it is increasingly utilized in agile hardware design.We have developed a fully automatic equivalence checking workflow seamlessly integrated with HalideIR and several optimizations that leverage the incremental nature of agile hardware design to scale equivalence checking. Evaluations of two deep learning accelerator …

Rdkg: A Reinforcement Learning Framework For Disease Diagnosis On Knowledge Graph, Shipeng Guo, Kunpeng Liu, Pengfei Wang, Weiwei Dai, Yi Du, Yuanchun Zhou, Wenjuan Cui

Computer Science Faculty Publications and Presentations

Automatic disease diagnosis from symptoms has attracted much attention in medical practices. It can assist doctors and medical practitioners in narrowing down disease candidates, reducing testing costs, improving diagnosis efficiency, and more importantly, saving human lives. Existing research has made significant progress in diagnosing disease but was limited by the gap between interpretability and accuracy. To fill this gap, in this paper, we propose a method called Reinforced Disease Diagnosis on Knowlege Graph (RDKG). Specifically, we first construct a knowledge graph containing all information from electronic medical records. To capture informative embeddings, we propose an enhanced knowledge graph embedding method …

The Role Of Preprocessing For Word Representation Learning In Affective Tasks, Nastaran Babanejad, Heidar Davoudi, Ameeta Agrawal, Manos Papagelis

Computer Science Faculty Publications and Presentations

Affective tasks, including sentiment analysis, emotion classification, and sarcasm detection have drawn a lot of attention in recent years due to a broad range of useful applications in various domains. The main goal of affect detection tasks is to recognize states such as mood, sentiment, and emotions from textual data (e.g., news articles or product reviews). Despite the importance of utilizing preprocessing steps in different stages (i.e., word representation learning and building a classification model) of affect detection tasks, this topic has not been studied well. To that end, we explore whether applying various preprocessing methods (stemming, lemmatization, stopword removal, …

An Improved Lower Bound For Sparse Reconstruction From Subsampled Walsh Matrices, Jaroslaw Blasiok, Patrick Lopatto, Kyle Luh, Jake Marcinek, Shravas Rao

Computer Science Faculty Publications and Presentations

We give a short argument that yields a new lower bound on the number of uniformly and independently subsampled rows from a bounded, orthonormal matrix necessary to form a matrix with the restricted isometry property. We show that a matrix formed by uniformly and independently subsampling rows of an N ×N Walsh matrix contains a K-sparse vector in the kernel, unless the number of subsampled rows is Ω(KlogKlog(N/K)) — our lower bound applies whenever min(K,N/K) > logC N. Containing a sparse vector in the kernel precludes not only the restricted isometry property, but more generally the application of those matrices for …