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Articles 1711 - 1740 of 6273
Full-Text Articles in Entire DC Network
Discoverpath: A Knowledge Refinement And Retrieval System For Interdisciplinarity On Biomedical Research, Yu-Neng Chuang, Guanchu Wang, Chia-Yuan Chang, Kwei-Herng Lai, Daochen Zha, Ruixiang Tang, Fan Yang, Alfredo Costilla Reyes, Kaixiong Zhou, Xiaoqian Jiang, Xia Hu
Discoverpath: A Knowledge Refinement And Retrieval System For Interdisciplinarity On Biomedical Research, Yu-Neng Chuang, Guanchu Wang, Chia-Yuan Chang, Kwei-Herng Lai, Daochen Zha, Ruixiang Tang, Fan Yang, Alfredo Costilla Reyes, Kaixiong Zhou, Xiaoqian Jiang, Xia Hu
Faculty, Staff and Student Publications
The exponential growth in scholarly publications necessitates advanced tools for efficient article retrieval, especially in interdisciplinary fields where diverse terminologies are used to describe similar research. Traditional keyword-based search engines often fall short in assisting users who may not be familiar with specific terminologies. To address this, we present a knowledge graph based paper search engine for biomedical research to enhance the user experience in discovering relevant queries and articles. The system, dubbed DiscoverPath, employs Named Entity Recognition (NER) and part-of-speech (POS) tagging to extract terminologies and relationships from article abstracts to create a KG. To reduce information overload, DiscoverPath …
Hallucination Detection: Robustly Discerning Reliable Answers In Large Language Models, Yuyuan Chen, Qiang Fu, Yichen Yuan, Zhihao Wen, Ge Fan, Dayiheng Liu, Dongmei Zhang, Zhixu Li, Yanghua Xiao
Hallucination Detection: Robustly Discerning Reliable Answers In Large Language Models, Yuyuan Chen, Qiang Fu, Yichen Yuan, Zhihao Wen, Ge Fan, Dayiheng Liu, Dongmei Zhang, Zhixu Li, Yanghua Xiao
Research Collection School Of Computing and Information Systems
Large language models (LLMs) have gained widespread adoption in various natural language processing tasks, including question answering and dialogue systems. However, a major drawback of LLMs is the issue of hallucination, where they generate unfaithful or inconsistent content that deviates from the input source, leading to severe consequences. In this paper, we propose a robust discriminator named RelD to effectively detect hallucination in LLMs' generated answers. RelD is trained on the constructed RelQA, a bilingual question-answering dialogue dataset along with answers generated by LLMs and a comprehensive set of metrics. Our experimental results demonstrate that the proposed RelD successfully detects …
Spoken Language Processing And Modeling For Aviation Communications, Aaron Van De Brook
Spoken Language Processing And Modeling For Aviation Communications, Aaron Van De Brook
Doctoral Dissertations and Master's Theses
With recent advances in machine learning and deep learning technologies and the creation of larger aviation-specific corpora, applying natural language processing technologies, especially those based on transformer neural networks, to aviation communications is becoming increasingly feasible. Previous work has focused on machine learning applications to natural language processing, such as N-grams and word lattices. This thesis experiments with a process for pretraining transformer-based language models on aviation English corpora and compare the effectiveness and performance of language models transfer learned from pretrained checkpoints and those trained from their base weight initializations (trained from scratch). The results suggest that transformer language …
Catching Elusive Depression Via Facial Micro-Expression Recognition, Xiaohui Chen, Tony Tie (T.) Luo
Catching Elusive Depression Via Facial Micro-Expression Recognition, Xiaohui Chen, Tony Tie (T.) Luo
Computer Science Faculty Research & Creative Works
Depression is a common mental health disorder that can cause consequential symptoms with continuously depressed mood that leads to emotional distress. One category of depression is Concealed Depression, where patients intentionally or unintentionally hide their genuine emotions through exterior optimism, thereby complicating and delaying diagnosis and treatment and leading to unexpected suicides. In this article, we propose to diagnose concealed depression by using facial micro-expressions (FMEs) to detect and recognize underlying true emotions. However, the extremely low intensity and subtle nature of FMEs make their recognition a tough task. We propose a facial landmark-based Region-of-Interest (ROI) approach to address the …
Analyzing Human - Nonhuman Primate Conflict Mitigation Techniques In Mto Wa Mbu, Northern Tanzania, Lily Adams
Analyzing Human - Nonhuman Primate Conflict Mitigation Techniques In Mto Wa Mbu, Northern Tanzania, Lily Adams
Independent Study Project (ISP) Collection
Human – non-human primate conflict is particularly crucial due to primates’ high capacity to live among human populations. The study to analyze Human - Nonhuman Primate Conflict Mitigation Techniques, was carried in November 2023 at Mto wa Mbu, Northern Tanzania. To analyze techniques currently being used to mitigate human – non-human primate conflict, this study collected data through semi-structured interviews. Over 87% (n=35) of respondents used multiple mitigation techniques simultaneously. 80% of respondents (n=32) reported using projectiles to ward off foraging primates, 75% of respondents (n=30) reported using loud noises (made either by the voice/body or by manipulating noisemakers), 67.5% …
Voucher Abuse Detection With Prompt-Based Fine-Tuning On Graph Neural Networks, Zhihao Wen, Yuan Fang, Yihan Liu, Yang Guo, Shuji Hao
Voucher Abuse Detection With Prompt-Based Fine-Tuning On Graph Neural Networks, Zhihao Wen, Yuan Fang, Yihan Liu, Yang Guo, Shuji Hao
Research Collection School Of Computing and Information Systems
Voucher abuse detection is an important anomaly detection problem in E-commerce. While many GNN-based solutions have emerged, the supervised paradigm depends on a large quantity of labeled data. A popular alternative is to adopt self-supervised pre-training using label-free data, and further fine-tune on a downstream task with limited labels. Nevertheless, the "pre-train, fine-tune" paradigm is often plagued by the objective gap between pre-training and downstream tasks. Hence, we propose VPGNN, a prompt-based fine-tuning framework on GNNs for voucher abuse detection. We design a novel graph prompting function to reformulate the downstream task into a similar template as the pretext task …
Balanced Blended Space: Proposing A Universal Theoretical Framework For Combinative Reality, David Smith, Frederick Bianchi
Balanced Blended Space: Proposing A Universal Theoretical Framework For Combinative Reality, David Smith, Frederick Bianchi
Publications and Research
In today's fragmented societies, a unified framework for communication and collaboration across different realities is crucial. We introduce Balanced Blended Space (BBS) as a framework for describing combinative reality, encompassing virtual, physical, and conceptual realms, all intrinsically connected. Interactions within these environments shape our perceptual space. This paper outlines key axiomatic assumptions, criteria for a universal framework, and fundamental terminology. We identify deep symmetries enabling the BBS framework, including Cognitive and Computational Symmetry, Physical and Virtual Symmetry, Mediation Pathway Symmetry, Space-Time Symmetry, and Sensory Symmetry. We propose tests to determine its viability, emphasizing virtual intelligence as a collaborative partner. We …
Enhancing Autism Education: Exploring Interactive Videos And Ai Integration For Effective Teaching, Fatima Ahmed Alraeesi
Enhancing Autism Education: Exploring Interactive Videos And Ai Integration For Effective Teaching, Fatima Ahmed Alraeesi
Theses
This research focuses on enhancing autism education by integrating interactive videos and AI solutions to improve teacher training. As the number of autistic students rises, it becomes crucial for special education teachers to employ effective teaching strategies tailored to individual needs. The most effective teaching methods for autistic students involve understanding the condition and incorporating customized instruction strategies, such as adapting assignments to suit the student's needs, assisting those with difficulty speaking, and employing visual aids for better organization. The proposed solution involves utilizing interactive video technology to train teachers, bridging the gap between research and practical implementation of educational …
A Novel Method Of Decision Making Based On Plithogenic Contradictions, Nivetha Martin, Florentin Smarandache, Sudha S
A Novel Method Of Decision Making Based On Plithogenic Contradictions, Nivetha Martin, Florentin Smarandache, Sudha S
Neutrosophic Systems with Applications
Plithogenic decision-making models are evolved integrating the Plithogenic modelling approach with various methods of multi-criteria decision-making (MCDM). The earlier Plithogenic based decision methods are primarily based on the degrees of appurtenance. This paper introduces a novel Plithogenic ranking genre of decision-making paradigm based on degrees of contradiction. The method of Decision Making on Plithogenic Contradictions (DMPC) developed in this research work is indigenous and unique as the modeling procedure doesn’t resemble any of the decision methods. This simple and logical approach proposed in this paper is applied in making optimal decisions on supplier selection. The proposed contradiction based Plithogenic model …
Flacgec: A Chinese Grammatical Error Correction Dataset With Fine-Grained Linguistic Annotation, Hanyue Du, Yike Zhao, Qingyuan Tian, Jiani Wang, Lei Wang, Yunshi Lan, Xuesong Lu
Flacgec: A Chinese Grammatical Error Correction Dataset With Fine-Grained Linguistic Annotation, Hanyue Du, Yike Zhao, Qingyuan Tian, Jiani Wang, Lei Wang, Yunshi Lan, Xuesong Lu
Research Collection School Of Computing and Information Systems
Chinese Grammatical Error Correction (CGEC) has been attracting growing attention from researchers recently. In spite of the fact that multiple CGEC datasets have been developed to support the research, these datasets lack the ability to provide a deep linguistic topology of grammar errors, which is critical for interpreting and diagnosing CGEC approaches. To address this limitation, we introduce FlaCGEC, which is a new CGEC dataset featured with fine-grained linguistic annotation. Specifically, we collect raw corpus from the linguistic schema defined by Chinese language experts, conduct edits on sentences via rules, and refine generated samples manually, which results in 10k sentences …
Evlog: Identifying Anomalous Logs Over Software Evolution, Yintong Huo, Cheryl Lee, Yuxin Su, Shiwen Shan, Jinyang Liu, Michael Lyu
Evlog: Identifying Anomalous Logs Over Software Evolution, Yintong Huo, Cheryl Lee, Yuxin Su, Shiwen Shan, Jinyang Liu, Michael Lyu
Research Collection School Of Computing and Information Systems
Software logs record system activities, aiding maintainers in identifying the underlying causes for failures and enabling prompt mitigation actions. However, maintainers need to inspect a large volume of daily logs to identify the anomalous logs that reveal failure details for further diagnosis. Thus, how to automatically distinguish these anomalous logs from normal logs becomes a critical problem. Existing approaches alleviate the burden on software maintainers, but they are built upon an improper yet critical assumption: logging statements in the software remain unchanged. While software keeps evolving, our empirical study finds that evolving software brings three challenges: log parsing errors, evolving …
Poor Water Quality Persists In Diverse Urban Communities, Rae Cade, Danlin Yu, Kyle Whyte, Pankaj Lal, Cortni Borgerson
Poor Water Quality Persists In Diverse Urban Communities, Rae Cade, Danlin Yu, Kyle Whyte, Pankaj Lal, Cortni Borgerson
Department of Earth and Environmental Studies Faculty Scholarship and Creative Works
While humans need water to survive, water can also efficiently transmit microbial diseases and heavy metals to consumers. Over 50,000 water violations occurred in 2020 and such burdens were not borne equally amongst consumers. Yet, the factors that drive such variation remain unclear. We examined the interactions between water quality and inherited equity gaps in one of the most diverse urban areas in the United States. We used publicly available drinking water datasets to determine which contaminant was most prevalent between 2010–2020 and who was significantly more likely to be affected by this contamination. We found that a lack of …
Academic Evaluation From The Perspective Of Multi-Source Eterogeneous Data:Connotation, Status And Prospect, Sanhong Deng, Jie Yang, Hao Wang, Jia Shen
Academic Evaluation From The Perspective Of Multi-Source Eterogeneous Data:Connotation, Status And Prospect, Sanhong Deng, Jie Yang, Hao Wang, Jia Shen
Journal of Scientific Information Research
[Purpose/significance]Academic evaluation and scientometric methods of synthesizing multisource heterogeneous data have gradually become hot spots in the field. At present， there is no systematic theoretical discussion on academic evaluation from the perspective of multisource heterogeneous data.[Method/process]In view of insufficient utilization of multi-source heterogeneous data， inappropriate research methods， and unclear research objectives， this paper combs the development of academic evaluation from the perspective of multi-source heterogeneous data， and clarifies the connotation and extension of the field， and analyzes the frontiers and hot spots in the field.[Result/conclusion]The results state clearly that from the perspective of multi-source heterogeneous data， academic evaluation manifests the …
Application And Use Of Artificial Intelligence (Ai) For Library Services Delivery In Academic Libraries In Kwara State, Nigeria, Abdullahi Olayinka Isiaka
Application And Use Of Artificial Intelligence (Ai) For Library Services Delivery In Academic Libraries In Kwara State, Nigeria, Abdullahi Olayinka Isiaka
Library Philosophy and Practice (e-journal)
The application and use Artificial Intelligence (AI) in library services delivery and operations has modernized traditional practices, enabling libraries to adapt to the evolving information needs of patrons in the digital era. The main purpose of this study is to investigate the application and use of Artificial Intelligence (AI) Technologies for Library Services Delivery in Academic Libraries in Kwara State, Nigeria. The study used a descriptive survey approach. The population was the 108 librarians in academic libraries in Kwara State, Nigeria. A total enumeration technique was employed, and a questionnaire was used to collect data from the library staff. The …
Joint Intent Detection And Slot Filling For Turkish Natural Language Understanding, Osman Büyük
Joint Intent Detection And Slot Filling For Turkish Natural Language Understanding, Osman Büyük
Turkish Journal of Electrical Engineering and Computer Sciences
Intent detection and slot filling are two crucial subtasks of a text-based goal-oriented dialogue system. In a goal-oriented dialogue system, users interact with the system to complete a goal (or to fulfill their intent) and provide the necessary information (slot values) to achieve that goal. Therefore, a user?s text input includes information about the user?s intent and contains required slot values. Recently, joint models that simultaneously detect the intent and extract the slots are proposed to benefit from the interaction between the two tasks. The proposed methods are usually tested using benchmark data sets in English such as ATIS and …
A Machine Learning Approach For Dyslexia Detection Using Turkish Audio Records, Tuğberk Taş, Muhammed Abdullah Bülbül, Abas Haşi̇moğlu, Yavuz Meral, Yasi̇n Çalişkan, Gunay Budagova, Mücahi̇d Kutlu
A Machine Learning Approach For Dyslexia Detection Using Turkish Audio Records, Tuğberk Taş, Muhammed Abdullah Bülbül, Abas Haşi̇moğlu, Yavuz Meral, Yasi̇n Çalişkan, Gunay Budagova, Mücahi̇d Kutlu
Turkish Journal of Electrical Engineering and Computer Sciences
Dyslexia is a learning disorder, characterized by impairment in the ability to read, spell, and decode letters. It is vital to detect dyslexia in earlier stages to reduce its effects. However, diagnosing dyslexia is a time-consuming and costly process. In this paper, we propose a machine-learning model that predicts whether a Turkish-speaking child has dyslexia using his/her audio records. Therefore, our model can be easily used by smart phones and work as a warning system such that children who are likely to be dyslexic according to our model can seek an examination by experts. In order to train and evaluate, …
A Gentle Introduction To Chatgpt, Steven W. Holloway
A Gentle Introduction To Chatgpt, Steven W. Holloway
Libraries
A guest lecture on the state of commercial generative transformer technology, mid-2023, to a general audience at Staunton Public Library.
Overview Of The Clef-2023 Checkthat! Lab Task 4 On Factuality Of Reporting Of News Media, Preslav Nakov, Firoj Alam, Giovanni Da San Martino, Maram Hasanain, Dilshod Azizov, Rabindra Nath Nandi, Panayotov Panayot
Overview Of The Clef-2023 Checkthat! Lab Task 4 On Factuality Of Reporting Of News Media, Preslav Nakov, Firoj Alam, Giovanni Da San Martino, Maram Hasanain, Dilshod Azizov, Rabindra Nath Nandi, Panayotov Panayot
Natural Language Processing Faculty Publications
We present an overview of the CLEF-2023 CheckThat! lab Task 4, which focused on predicting the factuality of reporting of entire news outlets. This is a different level of granularity compared to previous efforts, which focused on fact-checking, where the target is a claim, or fake news detection, where the target is an article. We briefly summarize the participating systems and discuss the dataset, the task, and the evaluation setup. The task attracted a large number of registrations, and eventually five teams made submissions. All participants improved over the baseline by a margin using both deep learning and traditional machine …
Asset-Based Approaches To Multilingual Students’ Computer Science Identity Development, Sharin Rawhiya Jacob, Mark Warschauer
Asset-Based Approaches To Multilingual Students’ Computer Science Identity Development, Sharin Rawhiya Jacob, Mark Warschauer
Journal of Computer Science Integration
While computer science identity development has been examined in several studies, there is much to learn about the development of multilingual students’ computer science (CS) identities. To develop strong CS identities, multilingual students must engage in culturally and linguistically sustaining curriculum, pedagogy, and interaction that draws from their rich and varied resources. This theoretical paper is grounded in a justice-centered, asset-based framework that views the traditions and practices in students’ cultures and communities as strong contributors to knowledge construction in STEM. We draw on multiple studies exploring multilingual student CS identity development to better understand how their personal, familial, community-based, …
U.S. Parents' Intentions To Use Anti-Bullying Apps: Insights From A Comprehensive Model, Brittany Wheeler, Katie Baumel, Deborah Hall, Yasin Silva
U.S. Parents' Intentions To Use Anti-Bullying Apps: Insights From A Comprehensive Model, Brittany Wheeler, Katie Baumel, Deborah Hall, Yasin Silva
Computer Science: Faculty Publications and Other Works
Cyberbullying has become a prominent risk for youth and an increasing concern for parents. To help parents reduce their child's cyberbullying risk, anti-bullying apps (ABAs)—mobile applications for identifying and preventing instances of cyberbullying—have been developed in recent years. Given that ABAs are an emerging technology, limited research has been conducted to understand the factors predicting parents' intentions to use them. Drawing on three interdisciplinary theoretical frameworks, a sample of parents in the U.S. recruited through Amazon Mechanical Turk completed an online survey to assess parents' knowledge of, attitudes about, and intentions to use ABAs. Participants also rated the importance of …
Near Peer Pedagogies For Culturally Sustaining Computer Science And Mathematics, Cliff Freeman, Eli Tucker-Raymond, Juan Gutierrez
Near Peer Pedagogies For Culturally Sustaining Computer Science And Mathematics, Cliff Freeman, Eli Tucker-Raymond, Juan Gutierrez
Journal of Computer Science Integration
In this study, College STEM Literacy Workers act as co-teachers in 9th and 10th grade classrooms alongside mathematics teachers as they both learn to integrate a 30-hour integrated computer science/mathematics curriculum. As part of the curriculum for educator learning, we adapted the Algebra Project’s “Model of Excellence” for culturally relevant-sustaining pedagogies. We used the framework to explore how teachers understand College STEM Literacy Workers’ contributions in the model as well as College STEM Literacy Workers’ own experiences in the classroom. We found that while teachers and College STEM Literacy Workers did not individually instantiate the model of excellence, they were …
Analysing Gamma Frequency Components In Eeg Signals: A Comprehensive Extraction Approach, Tanvir Hasib, Vijayakumar Vengadasalam
Analysing Gamma Frequency Components In Eeg Signals: A Comprehensive Extraction Approach, Tanvir Hasib, Vijayakumar Vengadasalam
Journal of Informatics and Web Engineering
Gamma band activity is a high-frequency (30-100 Hz) oscillation of the electroencephalogram (EEG) that has been linked to a variety of cognitive processes including attention, memory and learning. However, extracting gamma band activity from EEG data can be challenging due to the relatively low signal-to-noise ratio of gamma band signals and the presence of other frequency bands such as beta and alpha. In this paper, we present a method for extracting gamma band activity from EEG data. We evaluated our method on a dataset of EEG data recorded from dyslexic patients. We found that our method was able to successfully …
Utilizing Fuzzy Algorithm For Understanding Emotional Intelligence On Individual Feedback, Elham Abdulwahab Anaam, Su-Cheng Haw, Kok-Why Ng, Palanichamy Naveen, Rasha Thabit
Utilizing Fuzzy Algorithm For Understanding Emotional Intelligence On Individual Feedback, Elham Abdulwahab Anaam, Su-Cheng Haw, Kok-Why Ng, Palanichamy Naveen, Rasha Thabit
Journal of Informatics and Web Engineering
Although previous studies looked at how employees should seek assistance, the issue is the researchinvestigation into how behavioral intelligence affects employee satisfaction is limited. This study examines several significant usages and developments of fuzzy mental modelling. The primary objective of the current section is to provide an innovative technique for modelling an emotion-based acceleration of the compressor for individuals. Methodologies of experiential thinking postulate that our comprehension of facial emotional reactions depends significantly on facial behavior imitation and the reactions as opportunities. Considering the theoretical foundations of combined logical reasoning. In addition, the hypothesis of probability, it additionally is not …
Impact Of Green Building Certification On The Rent Of Commercial Properties: A Review, Thebuwena Arachchige Chandana Hemantha Jayakody, Anthony Vaz
Impact Of Green Building Certification On The Rent Of Commercial Properties: A Review, Thebuwena Arachchige Chandana Hemantha Jayakody, Anthony Vaz
Journal of Informatics and Web Engineering
The world is currently facing two major problems, namely, increasing energy costs and global warming. As a result, it is crucial to take proactive measures to effectively address and mitigate the detrimental impacts arising from elevated energy costs, the pressing issue of global warming, and various types of environmental degradation. As a reaction, international organizations are advocating for the development of eco-friendly, sustainable, or green buildings as a strategy to reduce the harmful effects of the construction sector on the environment. While green development may entail higher costs for developers, it is imperative to evaluate the return on investment from …
Engaging Learning Experience: Enhancing Productivity Software Lessons With Screencast Videos, Usha Vellappan, Lim Liyen, Lim Su Yin
Engaging Learning Experience: Enhancing Productivity Software Lessons With Screencast Videos, Usha Vellappan, Lim Liyen, Lim Su Yin
Journal of Informatics and Web Engineering
The use of screencast videos can improve the effectiveness of the teaching and learning process, whether it is face-to-face or online. Screencast videos are digital resources that capture the computer screen and create an audio-visual experience, and they can be shared online to aid the learning process. It eliminates the need for educators to repeat information multiple times and creates an uninterrupted personalised learning environment for the students. This method of learning gives students a more personalised sense, as if they were given one-on-one guidance from the educator, with students having access to the educator and receiving immediate feedback during …
Bare-Bones Based Salp Swarm Algorithm For Text Document Clustering, Mohammed Azmi Al-Betar, Ammar Kamal Abasi, Ghazi Al-Naymat, Kamran Arshad, Sharif Naser Makhadmeh
Bare-Bones Based Salp Swarm Algorithm For Text Document Clustering, Mohammed Azmi Al-Betar, Ammar Kamal Abasi, Ghazi Al-Naymat, Kamran Arshad, Sharif Naser Makhadmeh
Machine Learning Faculty Publications
Text Document Clustering (TDC) is a challenging optimization problem in unsupervised machine learning and text mining. The Salp Swarm Algorithm (SSA) has been found to be effective in solving complex optimization problems. However, the SSA’s exploitation phase requires improvement to solve the TDC problem effectively. In this paper, we propose a new approach, known as the Bare-Bones Salp Swarm Algorithm (BBSSA), which leverages Gaussian search equations, inverse hyperbolic cosine control strategies, and greedy selection techniques to create new individuals and guide the population towards solving the TDC problem. We evaluated the performance of the BBSSA on six benchmark datasets from …
An Analysis Of Obesity In School Children During The Pandemic Covid-19 Using Plithogenic Single Valued Fuzzy Sets, S. P. Priyadharshini, F. Nirmala Irudayam
An Analysis Of Obesity In School Children During The Pandemic Covid-19 Using Plithogenic Single Valued Fuzzy Sets, S. P. Priyadharshini, F. Nirmala Irudayam
Neutrosophic Systems with Applications
The objective of this research is to examine the perception that school children with obesity, when excluded from organized academic performance and constrained to their residences during the coronavirus epidemic 2019, will reveal negative consequences in health behaviors. To meet the objective, the concept of Plithogenic Single valued fuzzy sets (PSFS) and their aggregation operators were introduced. Based on the proposed theory, an analysis is presented with the case study to highlight its practicality and preciseness.
The Mean Sum Of Squared Linking Numbers Of Random Piecewise-Linear Embeddings Of $K_N$, Yasmin Aguillon, Xingyu Cheng, Spencer Eddins, Pedro Morales
The Mean Sum Of Squared Linking Numbers Of Random Piecewise-Linear Embeddings Of $K_N$, Yasmin Aguillon, Xingyu Cheng, Spencer Eddins, Pedro Morales
Rose-Hulman Undergraduate Mathematics Journal
DNA and other polymer chains in confined spaces behave like closed loops. Arsuaga et al. \cite{AB} introduced the uniform random polygon model in order to better understand such loops in confined spaces using probabilistic and knot theoretical techniques, giving some classification on the mean squared linking number of such loops. Flapan and Kozai \cite{flapan2016linking} extended these techniques to find the mean sum of squared linking numbers for random linear embeddings of complete graphs $K_n$ and found it to have order $\Theta(n(n!))$. We further these ideas by inspecting random piecewise-linear embeddings of complete graphs and give introductory-level summaries of the ideas …
Toward Energy Transformation: Intelligent Decision-Making Model Based On Uncertainty Neutrosophic Theory, Mona Mohamed, Nissreen El-Saber
Toward Energy Transformation: Intelligent Decision-Making Model Based On Uncertainty Neutrosophic Theory, Mona Mohamed, Nissreen El-Saber
Neutrosophic Systems with Applications
There has been an increasing tendency for the generation of energy from diverse renewable resources because of the application of contemporary pollution mitigation and justification regulations. Precisely a consequence, choosing the best renewable energy source might be considered a challenging issue given the complexity of the future conditions in any society. Environmental, economic, social, and technical aspects are merely some of the factors that are taken into consideration while evaluating renewable energy sources (RnESs). The suitable RES selection problem, which relies on ambiguous and imprecise data, is also influenced by a variety of circumstances. Hence, this study constructs multi-stages intelligent …
Frank At Checkthat! 2023: Detecting The Political Bias Of News Articles And News Media Notebook For The Checkthat! Lab At Clef 2023, Dilshod Azizov, Preslav Nakov, Shangsong Liang
Frank At Checkthat! 2023: Detecting The Political Bias Of News Articles And News Media Notebook For The Checkthat! Lab At Clef 2023, Dilshod Azizov, Preslav Nakov, Shangsong Liang
Machine Learning Faculty Publications
This paper addresses the challenge of detecting political bias in news articles and media outlets from CheckThat!lab Task 3 [1, 2] by proposing an automated method for classifying these as left, center, or right-leaning. As mass media consumption continues to grow, the capability to identify bias in news reporting is crucial due to the potential societal impact of unaddressed political bias. To tackle this issue, we present a comprehensive approach employing machine learning techniques to detect political leaning in news media and articles. Our model, CatBoost, is evaluated on a diverse dataset comprising over 55,000 news articles sourced from AllSides1 …