University of Nebraska - Lincoln
Governors State University
Biola University
SUNY Fredonia
University of Pennsylvania
Bryant University
California State University, San Bernardino
Simulated Annealing With Reinforcement Learning For The Set Team Orienteering Problem With Time Windows,
2024
Singapore Management University
Simulated Annealing With Reinforcement Learning For The Set Team Orienteering Problem With Time Windows, Vincent F. Yu, Nabila Y. Salsabila, Shih-W Lin, Aldy Gunawan
Research Collection School Of Computing and Information Systems
This research investigates the Set Team Orienteering Problem with Time Windows (STOPTW), a new variant of the well-known Team Orienteering Problem with Time Windows and Set Orienteering Problem. In the STOPTW, customers are grouped into clusters. Each cluster is associated with a profit attainable when a customer in the cluster is visited within the customer's time window. A Mixed Integer Linear Programming model is formulated for STOPTW to maximizing total profit while adhering to time window constraints. Since STOPTW is an NP-hard problem, a Simulated Annealing with Reinforcement Learning (SARL) algorithm is developed. The proposed SARL incorporates the core concepts …
Transiam: Aggregating Multi-Modal Visual Features With Locality For Medical Image Segmentation,
2024
Central South University
Transiam: Aggregating Multi-Modal Visual Features With Locality For Medical Image Segmentation, Xuejian Li, Shiqiang Ma, Junhai Xu, Jijun Tang, Shengfeng He, Fei Guo
Research Collection School Of Computing and Information Systems
Automatic segmentation of medical images plays an important role in the diagnosis of diseases. On single-modal data, convolutional neural networks have demonstrated satisfactory performance. However, multi-modal data encompasses a greater amount of information rather than single-modal data. Multi-modal data can be effectively used to improve the segmentation accuracy of regions of interest by analyzing both spatial and temporal information. In this study, we propose a dual-path segmentation model for multi-modal medical images, named TranSiam. Taking into account that there is a significant diversity between the different modalities, TranSiam employs two parallel CNNs to extract the features which are specific to …
Recommendations With Minimum Exposure Guarantees: A Post-Processing Framework,
2024
Singapore Management University
Recommendations With Minimum Exposure Guarantees: A Post-Processing Framework, Ramon Lopes, Rodrigo Alves, Antoine Ledent, Rodrygo L. T. Santos, Marius Kloft
Research Collection School Of Computing and Information Systems
Relevance-based ranking is a popular ingredient in recommenders, but it frequently struggles to meet fairness criteria because social and cultural norms may favor some item groups over others. For instance, some items might receive lower ratings due to some sort of bias (e.g. gender bias). A fair ranking should balance the exposure of items from advantaged and disadvantaged groups. To this end, we propose a novel post-processing framework to produce fair, exposure-aware recommendations. Our approach is based on an integer linear programming model maximizing the expected utility while satisfying a minimum exposure constraint. The model has fewer variables than previous …
Dilf: Differentiable Rendering-Based Multi-View Image-Language Fusion For Zero-Shot 3d Shape Understanding,
2024
Chinese Academy of Sciences
Dilf: Differentiable Rendering-Based Multi-View Image-Language Fusion For Zero-Shot 3d Shape Understanding, Xin Ning, Zaiyang Yu, Lusi Li, Weijun Li, Prayag Tiwari
Computer Science Faculty Publications
Zero-shot 3D shape understanding aims to recognize “unseen” 3D categories that are not present in training data. Recently, Contrastive Language–Image Pre-training (CLIP) has shown promising open-world performance in zero-shot 3D shape understanding tasks by information fusion among language and 3D modality. It first renders 3D objects into multiple 2D image views and then learns to understand the semantic relationships between the textual descriptions and images, enabling the model to generalize to new and unseen categories. However, existing studies in zero-shot 3D shape understanding rely on predefined rendering parameters, resulting in repetitive, redundant, and low-quality views. This limitation hinders the model’s …
Facilitating Mathematics And Computer Science Connections: A Cross-Curricular Approach,
2024
Utah State University
Facilitating Mathematics And Computer Science Connections: A Cross-Curricular Approach, Kimberly E. Beck, Jessica F. Shumway, Umar Shehzad, Jody Clarke-Midura, Mimi Recker
Publications
In the United States, school curricula are often created and taught with distinct boundaries between disciplines. This division between curricular areas may serve as a hindrance to students' long-term learning and their ability to generalize. In contrast, cross-curricular pedagogy provides a way for students to think beyond the classroom walls and make important connections across disciplines. The purpose of this paper is a theoretical reflection on our use of Expansive Framing in our design of lessons across learning environments within the school. We provide a narrative account of our early work in using this theoretical framework to co-plan and enact …
Big Code Search: A Bibliography,
2024
Singapore Management University
Big Code Search: A Bibliography, Kisub Kim, Sankalp Ghatpande, Dongsun Kim, Xin Zhou, Kui Liu, Tegawende F. Bissyande, Jacques Klein, Traon Yves Le
Research Collection School Of Computing and Information Systems
Code search is an essential task in software development. Developers often search the internet and other code databases for necessary source code snippets to ease the development efforts. Code search techniques also help learn programming as novice programmers or students can quickly retrieve (hopefully good) examples already used in actual software projects. Given the recurrence of the code search activity in software development, there is an increasing interest in the research community. To improve the code search experience, the research community suggests many code search tools and techniques. These tools and techniques leverage several different ideas and claim a better …
Μakka: Mutation Testing For Actor Concurrency In Akka Using Real-World Bugs,
2023
Oakland University
Μakka: Mutation Testing For Actor Concurrency In Akka Using Real-World Bugs, Mohsen Moradi Moghadam, Mehdi Bagherzadeh, Raffi Takvor Khatchadourian Ph,D,, Hamid Bagheri
Publications and Research
Actor concurrency is becoming increasingly important in the real-world and mission-critical software. This requires these applications to be free from actor bugs, that occur in the real world, and have tests that are effective in finding these bugs. Mutation testing is a well-established technique that transforms an application to induce its likely bugs and evaluate the effectiveness of its tests in finding these bugs. Mutation testing is available for a broad spectrum of applications and their bugs, ranging from web to mobile to machine learning, and is used at scale in companies like Google and Facebook. However, there still is …
Perception Of Bias In Chatgpt: Analysis Of Social Media Data,
2023
Slippery Rock University of Pennsylvania
Perception Of Bias In Chatgpt: Analysis Of Social Media Data, Abdullah Wahbeh, Mohammad A. Al-Ramahi, Omar El-Gayar, Ahmed El Noshokaty, Tareq Nasralah
Computer Information Systems Faculty Publications
In this study, we aim to analyze the public perception of Twitter users with respect to the use of ChatGPT and the potential bias in its responses. Sentiment and emotion analysis were also analyzed. Analysis of 5,962 English tweets showed that Twitter users were concerned about six main types of biases, namely: political, ideological, data & algorithmic, gender, racial, cultural, and confirmation biases. Sentiment analysis showed that most of the users reflected a neutral sentiment, followed by negative and positive sentiment. Emotion analysis mainly reflected anger, disgust, and sadness with respect to bias concerns with ChatGPT use.
On Hierarchical Clustering-Based Approach For Rddbs Design,
2023
Zayed University
On Hierarchical Clustering-Based Approach For Rddbs Design, Hassan I. Abdalla, Ali A. Amer, Sri Devi Ravana
All Works
Distributed database system (DDBS) design is still an open challenge even after decades of research, especially in a dynamic network setting. Hence, to meet the demands of high-speed data gathering and for the management and preservation of huge systems, it is important to construct a distributed database for real-time data storage. Incidentally, some fragmentation schemes, such as horizontal, vertical, and hybrid, are widely used for DDBS design. At the same time, data allocation could not be done without first physically fragmenting the data because the fragmentation process is the foundation of the DDBS design. Extensive research have been conducted to …
Algorithm Selection Using Edge Ml And Case-Based Reasoning,
2023
University of Peshawar
Algorithm Selection Using Edge Ml And Case-Based Reasoning, Rahman Ali, Muhammad Sadiq Hassan Zada, Asad Masood Khatak, Jamil Hussain
All Works
In practical data mining, a wide range of classification algorithms is employed for prediction tasks. However, selecting the best algorithm poses a challenging task for machine learning practitioners and experts, primarily due to the inherent variability in the characteristics of classification problems, referred to as datasets, and the unpredictable performance of these algorithms. Dataset characteristics are quantified in terms of meta-features, while classifier performance is evaluated using various performance metrics. The assessment of classifiers through empirical methods across multiple classification datasets, while considering multiple performance metrics, presents a computationally expensive and time-consuming obstacle in the pursuit of selecting the optimal …
Remote Side-Channel Disassembly On Field-Programmable Gate Arrays,
2023
University of South Alabama
Remote Side-Channel Disassembly On Field-Programmable Gate Arrays, Brandon R. Baggett
Theses and Dissertations
Over the last two decades, side-channel vulnerabilities have shown to be a major threat to embedded devices. Most side-channel research has developed our understanding of the vulnerabilities to cryptographic devices due to their implementation and how we can protect them. However, side-channel leakage can yield useful information about many other processes that run on the device. One promising area that has received little attention is the side-channel leakage due to the execution of assembly instructions. There has been some work in this area that has demonstrated the idea’s potential, but so far, this research has assumed the adversary has physical …
Enhanced Content-Based Fake News Detection Methods With Context-Labeled News Sources,
2023
East Tennessee State University
Enhanced Content-Based Fake News Detection Methods With Context-Labeled News Sources, Duncan Arnfield
Electronic Theses and Dissertations
This work examined the relative effectiveness of multilayer perceptron, random forest, and multinomial naïve Bayes classifiers, trained using bag of words and term frequency-inverse dense frequency transformations of documents in the Fake News Corpus and Fake and Real News Dataset. The goal of this work was to help meet the formidable challenges posed by proliferation of fake news to society, including the erosion of public trust, disruption of social harmony, and endangerment of lives. This training included the use of context-categorized fake news in an effort to enhance the tools’ effectiveness. It was found that term frequency-inverse dense frequency provided …
Software Jimenae Allows Efficient Dynamic Simulations Of Boolean Networks, Centrality And System State Analysis,
2023
Biozentrum der Universität Würzburg
Software Jimenae Allows Efficient Dynamic Simulations Of Boolean Networks, Centrality And System State Analysis, Martin Kaltdorf, Tim Breitenbach, Stefan Karl, Maximilian Fuchs, David Komla Kessie, Eric Psota, Martina Prelog, Edita Sarukhanyan, Regina Ebert, Franz Jakob, Gudrun Dandekar, Muhammad Naseem, Chunguang Liang, Thomas Dandekar
All Works
The signal modelling framework JimenaE simulates dynamically Boolean networks. In contrast to SQUAD, there is systematic and not just heuristic calculation of all system states. These specific features are not present in CellNetAnalyzer and BoolNet. JimenaE is an expert extension of Jimena, with new optimized code, network conversion into different formats, rapid convergence both for system state calculation as well as for all three network centralities. It allows higher accuracy in determining network states and allows to dissect networks and identification of network control type and amount for each protein with high accuracy. Biological examples demonstrate this: (i) High plasticity …
Durability Of Wireless Charging Systems Embedded Into Concrete Pavements For Electric Vehicles,
2023
Utah State University
Durability Of Wireless Charging Systems Embedded Into Concrete Pavements For Electric Vehicles, Pravin Poudel
All Graduate Theses and Dissertations, Fall 2023 to Present
Point clouds are widely used in various applications such as 3D modeling, geospatial analysis, robotics, and more. One of the key advantages of 3D point cloud data is that, unlike other data formats like texture, it is independent of viewing angle, surface type, and parameterization. Since each point in the point cloud is independent of the other, it makes it the most suitable source of data for tasks like object recognition, scene segmentation, and reconstruction. Point clouds are complex and verbose due to the numerous attributes they contain, many of which may not be always necessary for rendering, making retrieving …
Vertical Free-Swinging Photovoltaic Racking Energy Modeling: A Novel Approach To Agrivoltaics,
2023
Western University
Vertical Free-Swinging Photovoltaic Racking Energy Modeling: A Novel Approach To Agrivoltaics, Koami Soulemane Hayibo, Joshua M. Pearce
Electrical and Computer Engineering Publications
To enable lower-cost building materials, a free-swinging bifacial vertical solar photovoltaic (PV) rack has been proposed, which complies with Canadian building codes and is the lowest capital-cost agrivoltaics rack. The wind force applied to the free-swinging PV, however, causes it to have varying tilt angles depending on the wind speed and direction. No energy performance model accurately describes such a system. To provide a simulation model for the free-swinging PV, where wind speed and direction govern the array tilt angle, this study builds upon the open-source System Advisor Model (SAM) using Python. After the SAM python model is validated, a …
The Value Of Official Website Information In The Credit Risk Evaluation Of Smes,
2023
Hefei University of Technology
The Value Of Official Website Information In The Credit Risk Evaluation Of Smes, Cuiqing Jiang, Chang Yin, Qian Tang, Zhao Wang
Research Collection School Of Computing and Information Systems
The official websites of small and medium-sized enterprises (SMEs) not only reflect the willingness of an enterprise to disclose information voluntarily, but also can provide information related to the enterprises’ historical operations and performance. This research investigates the value of official website information in the credit risk evaluation of SMEs. To study the effect of different kinds of website information on credit risk evaluation, we propose a framework to mine effective features from two kinds of information disclosed on the official website of a SME—design-based information and content-based information—in predicting its credit risk. We select the SMEs in the software …
Estimating Propensity For Causality-Based Recommendation Without Exposure Data,
2023
Singapore Management University
Estimating Propensity For Causality-Based Recommendation Without Exposure Data, Zhongzhou Liu, Yuan Fang, Min Wu
Research Collection School Of Computing and Information Systems
Causality-based recommendation systems focus on the causal effects of user-item interactions resulting from item exposure (i.e., which items are recommended or exposed to the user), as opposed to conventional correlation-based recommendation. They are gaining popularity due to their multi-sided benefits to users, sellers and platforms alike. However, existing causality-based recommendation methods require additional input in the form of exposure data and/or propensity scores (i.e., the probability of exposure) for training. Such data, crucial for modeling causality in recommendation, are often not available in real-world situations due to technical or privacy constraints. In this paper, we bridge the gap by proposing …
Collaborative Task Completion For Simulated Hexapod Robots Using Reinforcement Learning,
2023
Utah State University
Collaborative Task Completion For Simulated Hexapod Robots Using Reinforcement Learning, Tayler Don Baker
All Graduate Theses and Dissertations, Fall 2023 to Present
There is growing interest in developing autonomous systems capable of exhibiting collaborative behaviors. Using methods such as reinforcement learning is another way to train multiple robots for collaborative task completion. This study was able to successfully in simulation train multiple hexapod robots to push a target to a designated goal collaboratively. This required each robot to learn how find the target and push that target to a goal. This work suggests that using reinforcement learning for collaborative task completion for hexapod robots may simplify the complexity of the software and improve the decisions that they make.
General Population Projection Model With Census Population Data,
2023
California State University, San Bernardino
General Population Projection Model With Census Population Data, Takenori Tsuruga
Electronic Theses, Projects, and Dissertations
The US Census Bureau offers a wide range of data, and within this array, the American Community Survey 5-Year Estimate (ACS5) serves as a valuable resource for understanding the US population. This project embarks on an exploration of Machine Learning and the Software Development process with the goal of generating effective population projections from ACS5 data. The project aims to provide methods to make predictions for every city and town in the US, encompassing their total population and population divided into 5-year age groups. It's worth noting that while the generation of these projections is grounded in the generalized statistical …
Disease Of Lung Infection Detection Using Cnn Model -Bayesian Optimization,
2023
California State University, San Bernardino
Disease Of Lung Infection Detection Using Cnn Model -Bayesian Optimization, Poojitha Gutha
Electronic Theses, Projects, and Dissertations
Auscultation plays a role, in diagnosing and identifying diseases during examinations. However, it requires training and expertise, for application. This study aims to tackle this challenge by introducing a model that categorizes respiratory sounds into eight groups: URTI, Healthy, Asthma, COPD, LRTI, Bronchiectasis, Pneumonia, and Bronchiolitis. To achieve this categorization the study utilizes a Convolutional Neural Network (CNN) model that has been optimized using techniques. The dataset used in the study consists of 920 audio samples obtained from 126 patients with durations ranging from 10 to 90 seconds. Impressively, the model demonstrates a noteworthy 83% validation accuracy and an impressive …
