Open Access. Powered by Scholars. Published by Universities.®

Computer Sciences Commons™

Open Access. Powered by Scholars. Published by Universities.®

Discipline
Institution
Keyword
Publication Year
Publication
Publication Type
File Type

Articles 10831 - 10860 of 63016

Full-Text Articles in Computer Sciences

On The Use Of Wireless Technologies For Wildlife Monitoring: Wireless Sensor Network Routing Protocols, Lilian Mutalemwa Jun 2023

On The Use Of Wireless Technologies For Wildlife Monitoring: Wireless Sensor Network Routing Protocols, Lilian Mutalemwa

Tanzania Journal of Engineering and Technology (TJET)

Traditional methods for wildlife monitoring are labor-intensive and time-consuming. Therefore, advanced technologies and remote monitoring methods are becoming increasingly popular. This paper presents a study on wireless technologies for wildlife monitoring. In the study, a review of the literature was done to identify the most commonly used wireless technologies. Various technologies were explored including unmanned aerial vehicles (UAVs), Internet of Things (IoT), wireless sensor networks (WSNs), artificial intelligence (AI), global positioning system (GPS), and very high frequency (VHF) radio. Then, a more detailed study was done on WSN technology. Investigations were done to observe the performance of routing protocols in …

Go to article

Webtracker: Real Webbrowsing Behaviors, Daisy Reyes, Eno Dynowski, Taryn Chovan, John Mikos, Eric Chan-Tin, Mohammed Abuhamad, Shelia Kennison Jun 2023

Webtracker: Real Webbrowsing Behaviors, Daisy Reyes, Eno Dynowski, Taryn Chovan, John Mikos, Eric Chan-Tin, Mohammed Abuhamad, Shelia Kennison

Computer Science: Faculty Publications and Other Works

With increased privacy concerns, anonymity tools such as VPNs and Tor have become popular. However, the packet metadata such as the packet size and number of packets can still be observed by an adversary. This is commonly known as fingerprinting and website fingerprinting attacks have received a lot of attention recently as a known victim’s website visits can be accurately predicted, deanonymizing that victim’s web usage. Most of the previous work have been performed in laboratory settings and have made two assumptions: 1) a victim visits one website at a time, and 2) the whole website visit with all the …

Go to article

Deep Learning Model Compression Techniques: Advances, Opportunities, And Perspective, Hubert Msuya Jun 2023

Deep Learning Model Compression Techniques: Advances, Opportunities, And Perspective, Hubert Msuya

Tanzania Journal of Engineering and Technology (TJET)

Recently, deep learning (DL) models have excelled in a wide range of fields. All of these successes are built on intricate DL models. The hundreds of millions or even billions of parameters and high-performance computing graphical processing units or tensor processing units are largely responsible for their achievement. DL model integration into real-time devices with tight latency limitations, limited memory, and power-constrained requirements is the key driving force behind investigation of DL model compression techniques. Also, there is an increase in data availability that encourages multimodal fusion in DL models to boost the models' predictive accuracy. In order to create …

Go to article

Path Of Trust: An Off-Chain Performance Enhancement Algorithm For Public Blockchains, Anthony Kigombola Jun 2023

Path Of Trust: An Off-Chain Performance Enhancement Algorithm For Public Blockchains, Anthony Kigombola

Tanzania Journal of Engineering and Technology (TJET)

Blockchain technology is one among the latest innovations in computing industry. Blockchains have gathered widespread interest in the industry due to their potential as secure data storage. Despite the potential benefits of blockchains, there are several limitations which hinders mass deployment, one of these limitations being low throughput. Low throughput has limited blockchain adoption in large scale applications such as banking or mobile payments. This study addresses this limitation through development of a performance enhancement algorithm called Path of Trust (PoT). PoT uses off-chain stratergy in which transactions meeting certain criteria bypass the main chain and sent direct to the …

Go to article

Neutrosophic Decision Making Model For Investment Portfolios Selection And Optimizing Based On Wide Variety Of Investment Opportunities And Many Criteria In Market, Ayman H. Abdel-Aziem, Hoda K. Mohamed, Ahmed Abdelhafeez Jun 2023

Neutrosophic Decision Making Model For Investment Portfolios Selection And Optimizing Based On Wide Variety Of Investment Opportunities And Many Criteria In Market, Ayman H. Abdel-Aziem, Hoda K. Mohamed, Ahmed Abdelhafeez

Neutrosophic Systems with Applications

Investment portfolio selection is a difficult subject due to the presence of competing factors. Choosing a portfolio for one's investments is a major choice that may have far-reaching effects on one's financial well-being. Risk tolerance, time horizon, investing objectives, asset allocation, and investment selection are only a few of the factors that will be studied in this article. The Stable Preference Ordering Towards Ideal Solution (SPOTIS) technique is the basis for our proposed integrated multi-criteria decision-making (MCDM) model. This paper used the single-valued neutrosophic set as a framework to deal with uncertain data. The purpose of the suggested SPOTIS–Neutrosophic model …

Go to article

Assessment Of Spatial Variability Of Groundwater Levels In Moroto District, Uganda, Augustina Clara Alexander Dr Jun 2023

Assessment Of Spatial Variability Of Groundwater Levels In Moroto District, Uganda, Augustina Clara Alexander Dr

Tanzania Journal of Engineering and Technology (TJET)

Globally, the variation of groundwater levels is increasingly overwhelming due to over exploitation resulting from population growth. The dynamic nature of socio-economic activities in Moroto District such as agriculture, settlements and increased trends in irrigation technology has been well-known worldwide as common parameters triggering groundwater variability. Nevertheless, determination of groundwater levels for groundwater management and development purpose is a challenge due to spatial variability in levels across Moroto District. In this study, geostatistical technique in Geographical Information System (GIS) was used to analyze spatial variability of water levels in the study area. The analysis utilized ordinary Kriging method to predict …

Go to article

Lagrange Multipliers And Neutrosophic Nonlinear Programming Problems Constrained By Equality Constraints, Maissam Jdid, Florentin Smarandache Jun 2023

Lagrange Multipliers And Neutrosophic Nonlinear Programming Problems Constrained By Equality Constraints, Maissam Jdid, Florentin Smarandache

Neutrosophic Systems with Applications

Operations research science is defined as the science that is concerned with applying scientific methods to complex problems in managing and directing large systems of people, including resources and tools in various fields, private and governmental work, peace and war, politics, administration, economics, planning and implementation in various domains. It uses scientific methods that take the language of mathematics as a basis for it and uses computer, without which it would not have been possible to achieve numerical solutions to the raised problems, those that need correct solutions, when the solutions abound and the options are multiple, so we need …

Go to article

Algorithm For Generation Of S-Box Using Trigonometric Transformation In Genetic Algorithm Parameters, Javokhir Rustamovich Abdurazzokov Jun 2023

Algorithm For Generation Of S-Box Using Trigonometric Transformation In Genetic Algorithm Parameters, Javokhir Rustamovich Abdurazzokov

Chemical Technology, Control and Management

This article presents a fairly reliable algorithm for generating an S-box using a trigonometric function in the parameters of the genetic algorithm. S-boxes are essential components of modern cryptography and are used for permutation operations in various block cipher algorithms. The efficiency of the algorithm was demonstrated by various experimental experiments, which showed that the created S-box is sufficiently resistant to linear and differential cryptanalysis attacks. During the experimental analysis, the proposed S-box showed good results in various criteria, where the average level of nonlinearity was 105, the criterion of strict avalanche efficiency was 0.4941, the probability of linear convergence …

Go to article

Lagrange Multipliers And Neutrosophic Nonlinear Programming Problems Constrained By Equality Constraints, Maissam Jdid, Florentin Smarandache Jun 2023

Lagrange Multipliers And Neutrosophic Nonlinear Programming Problems Constrained By Equality Constraints, Maissam Jdid, Florentin Smarandache

Neutrosophic Systems with Applications

Operations research science is defined as the science that is concerned with applying scientific methods to complex problems in managing and directing large systems of people, including resources and tools in various fields, private and governmental work, peace and war, politics, administration, economics, planning and implementation in various domains. It uses scientific methods that take the language of mathematics as a basis for it and uses computer, without which it would not have been possible to achieve numerical solutions to the raised problems, those that need correct solutions, when the solutions abound and the options are multiple, so we need …

Go to article

Economic Value Of User Interface Design, Anna Kruse Jun 2023

Economic Value Of User Interface Design, Anna Kruse

Honors Program: Senior Projects (Public)

The economic value of well-designed user interfaces (UI) and user experiences (UX) is challenging to quantify, a topic that the current literature does not sufficiently explore. Those responsible for deciding whether to invest resources in UI/UX typically base these decisions on monetary considerations. The link between effective UI/UX design and profit may not be immediately clear to most, yet it is universally acknowledged that satisfied customers lead to successful business. To underscore the importance of investing in UI/UX, it is crucial to define the relationship between effective design and economic success in a way that can be understood by designers, …

Go to article

Properties Of Redefined Neutrosophic Composite Relation, Sudeep Dey, Gautam Chandra Ray Jun 2023

Properties Of Redefined Neutrosophic Composite Relation, Sudeep Dey, Gautam Chandra Ray

Neutrosophic Systems with Applications

The notion of single-valued neutrosophic composite relation was redefined by S.Dey and G.C.Ray in the year 2022. In this article, we investigate some basic properties of the redefined neutrosophic composite relation.

Go to article

Harnessing Artificial Intelligence For Early And Evolution Of Alzheimer’S Disease Detections And Enhancing Senior Mental Health Through Innovative Art-Singing Therapies: A Multidisciplinary Approach, Jocelyne Kiss, Geoffreyjen Edwards, Rachel Bouserhal, Elaine Champagne, Thierry Belleguic, Valéry Psyché, Charles Batcho, Carol Hudon, Sylsvie Ratté, Ingrid Verdruyckt, Marie-Hélène Parizeau, Aaron Liu-Rosenbaum, James Hudson, Marie-Louise Bourbeau, Marie Lemieux, Annik Charbonneau Jun 2023

Harnessing Artificial Intelligence For Early And Evolution Of Alzheimer’S Disease Detections And Enhancing Senior Mental Health Through Innovative Art-Singing Therapies: A Multidisciplinary Approach, Jocelyne Kiss, Geoffreyjen Edwards, Rachel Bouserhal, Elaine Champagne, Thierry Belleguic, Valéry Psyché, Charles Batcho, Carol Hudon, Sylsvie Ratté, Ingrid Verdruyckt, Marie-Hélène Parizeau, Aaron Liu-Rosenbaum, James Hudson, Marie-Louise Bourbeau, Marie Lemieux, Annik Charbonneau

Faculty Scholarship

The well-documented therapeutic potential of group singing for patients living with Alzheimer’s disease (PLAD) has been hindered by COVID-19 restrictions, exacerbating loneliness and cognitive decline among seniors in residential and long-term care centers (CHSLDs). Addressing this challenge, the multidisciplinary study aims to develop a patient-oriented virtual reality (XR) interaction system facilitating group singing for mental health support during confinement and enhancing the understanding of the links between Alzheimer’s disease, social interaction, and singing. The researchers also propose to establish an early AD detection system using voice, facial, and non-invasive biometric measurements and validate the efficacy of selected intervention practices. The …

Go to article

System-Characterized Artificial Intelligence Approaches For Cardiac Cellular Systems And Molecular Signature Analysis, Ziqian Wu Jun 2023

System-Characterized Artificial Intelligence Approaches For Cardiac Cellular Systems And Molecular Signature Analysis, Ziqian Wu

Dartmouth College Ph.D Dissertations

The dissertation presents a significant advancement in the field of cardiac cellular systems and molecular signature systems by employing machine learning and generative artificial intelligence techniques. These methodologies are systematically characterized and applied to address critical challenges in these domains. A novel computational model is developed, which combines machine learning tools and multi-physics models. The main objective of this model is to accurately predict complex cellular dynamics, taking into account the intricate interactions within the cardiac cellular system. Furthermore, a comprehensive framework based on generative adversarial networks (GANs) is proposed. This framework is designed to generate synthetic data that faithfully …

Go to article

Optical Response Of 3d Model Topological Nodal-Line Semimetal, Sita Kandel, Godfrey Gumbs, Oleg L. Berman Jun 2023

Optical Response Of 3d Model Topological Nodal-Line Semimetal, Sita Kandel, Godfrey Gumbs, Oleg L. Berman

Publications and Research

Wepresent a semi-analytical expression for both longitudinal and transverse optical conductivities of a model TNLSM employing the Kubo formula with emphasis on the optical spectral weight redistribution, deduced from appropriate Green’s func tions. In this semimetal, the conduction and valence bands cross each other along a one- dimensional curve protected by certain symmetry group in the 3D Brillouin zone. Although the crossing cannot be removed by any perturbations, it can be adjusted by continuous tuning of the Hamiltonian with a parameter α. When α>0, the two bands cross each other near the Γ point in the (kx,ky) plane of …

Go to article

2023 Chairs’ Welcome, Daniel Moreira, Aparna Bharati, Cecilia Pasquini, Yassine Yousfi Jun 2023

2023 Chairs’ Welcome, Daniel Moreira, Aparna Bharati, Cecilia Pasquini, Yassine Yousfi

Computer Science: Faculty Publications and Other Works

Welcome to the 11th edition of the ACM Workshop on Information Hiding and Multimedia Security (IH&MMSec ‘23). This year’s workshop continues the tradition of representing one of the prime events in information hiding and multimedia security, attracting researchers and practitioners worldwide. Carrying on with the efforts of the previous edition to overcome the Pandemics and reunite the IH&MMSec community to present and discuss their work, this year’s meeting is held fully in person at the Water Tower Campus of Loyola University Chicago, located right at the heart of the Windy City. Bathed by the fresh waters of Lake Michigan, Chicago …

Go to article

Properties Of Redefined Neutrosophic Composite Relation, Sudeep Dey, Gautam Chandra Ray Jun 2023

Properties Of Redefined Neutrosophic Composite Relation, Sudeep Dey, Gautam Chandra Ray

Neutrosophic Systems with Applications

The notion of single-valued neutrosophic composite relation was redefined by S.Dey and G.C.Ray in the year 2022. In this article, we investigate some basic properties of the redefined neutrosophic composite relation.

Go to article

Can You Answer This? - Exploring Zero-Shot Qa Generalization Capabilities In Large Language Models, Saptarshi Sengupta, Shreya Ghosh, Preslav Nakov, Prasenjit Mitra Jun 2023

Can You Answer This? - Exploring Zero-Shot Qa Generalization Capabilities In Large Language Models, Saptarshi Sengupta, Shreya Ghosh, Preslav Nakov, Prasenjit Mitra

Natural Language Processing Faculty Publications

The buzz around Transformer-based Language Models (TLMs) such as BERT, RoBERTa, etc. is well-founded owing to their impressive results on an array of tasks. However, when applied to areas needing specialized knowledge (closed-domain), such as medical, finance, etc. their performance takes drastic hits, sometimes more than their older recurrent/convolutional counterparts. In this paper, we explore zero-shot capabilities of large language models for extractive Question Answering. Our objective is to examine the performance change in the face of domain drift, i.e., when the target domain data is vastly different in semantic and statistical properties from the source domain, in an attempt …

Go to article

Adversarial Alignment For Source Free Object Detection, Qiaosong Chu, Shuyan Li, Guangyi Chen, Kai Li, Xiu Li Jun 2023

Adversarial Alignment For Source Free Object Detection, Qiaosong Chu, Shuyan Li, Guangyi Chen, Kai Li, Xiu Li

Machine Learning Faculty Publications

Source-free object detection (SFOD) aims to transfer a detector pre-trained on a label-rich source domain to an unlabeled target domain without seeing source data. While most existing SFOD methods generate pseudo labels via a source-pretrained model to guide training, these pseudo labels usually contain high noises due to heavy domain discrepancy. In order to obtain better pseudo supervisions, we divide the target domain into source-similar and source-dissimilar parts and align them in the feature space by adversarial learning. Specifically, we design a detection variance-based criterion to divide the target domain. This criterion is motivated by a finding that larger detection …

Go to article

Corruption-Tolerant Algorithms For Generalized Linear Models, Bhaskar Mukhoty, Debojyoti Dey, Purushottam Kar Jun 2023

Corruption-Tolerant Algorithms For Generalized Linear Models, Bhaskar Mukhoty, Debojyoti Dey, Purushottam Kar

Machine Learning Faculty Publications

This paper presents SVAM (Sequential Variance-Altered MLE), a unified framework for learning generalized linear models under adversarial label corruption in training data. SVAM extends to tasks such as least squares regression, logistic regression, and gamma regression, whereas many existing works on learning with label corruptions focus only on least squares regression. SVAM is based on a novel variance reduction technique that may be of independent interest and works by iteratively solving weighted MLEs over variance-altered versions of the GLM objective. SVAM offers provable model recovery guarantees superior to the state-of-the-art for robust regression even when a constant fraction of training …

Go to article

Flying By Ml Or Cnn Inversion Of Affine Transforms, Lloyd Van Warren Jun 2023

Flying By Ml Or Cnn Inversion Of Affine Transforms, Lloyd Van Warren

Theses and Dissertations

This dissertation describes how to automate the reading of dials, gauges, and instruments using machine learning (ML) methods. This process can be described as analog to digital conversion through an air gap without any direct electronic connection. The goal is to convert the values of existing instruments into digital values for control and monitoring without requiring any intrusion, changes, or upgrades to the instruments being observed. Images of instrument faces can be distorted by various kinds of noise, but this can be overcome using a deep learning convolutional neural network (CNN) approach similar to handwriting recognition. One advantage of this …

Go to article

Graphprompt: Graph-Based Prompt Templates For Biomedical Synonym Prediction, Hanwen Xu, Jiayou Zhang, Zhirui Wang, Shizhuo Zhang, Megh Bhalerao, Yucong Liu, Dawei Zhu, Sheng Wang Jun 2023

Graphprompt: Graph-Based Prompt Templates For Biomedical Synonym Prediction, Hanwen Xu, Jiayou Zhang, Zhirui Wang, Shizhuo Zhang, Megh Bhalerao, Yucong Liu, Dawei Zhu, Sheng Wang

Computer Vision Faculty Publications

In the expansion of biomedical dataset, the same category may be labeled with different terms, thus being tedious and onerous to curate these terms. Therefore, automatically mapping synonymous terms onto the ontologies is desirable, which we name as biomedical synonym prediction task. Unlike biomedical concept normalization (BCN), no clues from context can be used to enhance synonym prediction, making it essential to extract graph features from ontology. We introduce an expert-curated dataset OBO-syn encompassing 70 different types of concepts and 2 million curated concept-term pairs for evaluating synonym prediction methods. We find BCN methods perform weakly on this task for …

Go to article

Stability-Based Generalization Analysis For Mixtures Of Pointwise And Pairwise Learning, Jiahuan Wang, Jun Chen, Hong Chen, Bin Gu, Weifu Li, Xin Tang Jun 2023

Stability-Based Generalization Analysis For Mixtures Of Pointwise And Pairwise Learning, Jiahuan Wang, Jun Chen, Hong Chen, Bin Gu, Weifu Li, Xin Tang

Machine Learning Faculty Publications

Recently, some mixture algorithms of pointwise and pairwise learning (PPL) have been formulated by employing the hybrid error metric of “pointwise loss + pairwise loss” and have shown empirical effectiveness on feature selection, ranking and recommendation tasks. However, to the best of our knowledge, the learning theory foundation of PPL has not been touched in the existing works. In this paper, we try to fill this theoretical gap by investigating the generalization properties of PPL. After extending the definitions of algorithmic stability to the PPL setting, we establish the high-probability generalization bounds for uniformly stable PPL algorithms. Moreover, explicit convergence …

Go to article

Class-Independent Regularization For Learning With Noisy Labels, Rumeng Yi, Dayan Guan, Yaping Huang, Shijian Lu Jun 2023

Class-Independent Regularization For Learning With Noisy Labels, Rumeng Yi, Dayan Guan, Yaping Huang, Shijian Lu

Computer Vision Faculty Publications

Training deep neural networks (DNNs) with noisy labels often leads to poorly generalized models as DNNs tend to memorize the noisy labels in training. Various strategies have been developed for improving sample selection precision and mitigating the noisy label memorization issue. However, most existing works adopt a class-dependent softmax classifier that is vulnerable to noisy labels by entangling the classification of multi-class features. This paper presents a class-independent regularization (CIR) method that can effectively alleviate the negative impact of noisy labels in DNN training. CIR regularizes the class-dependent softmax classifier by introducing multi-binary classifiers each of which takes care of …

Go to article

Deep Learning Enhancement And Privacy-Preserving Deep Learning: A Data-Centric Approach, Hung S. Nguyen Jun 2023

Deep Learning Enhancement And Privacy-Preserving Deep Learning: A Data-Centric Approach, Hung S. Nguyen

USF Tampa Graduate Theses and Dissertations

Deep Learning and its applications have become attractive to a lot of research recentlybecause of its capability to capture important information from large amounts of data. While most of the work focuses on finding the best model parameters, improving machine learning performance from data perspective still needs more attention. In this work, we propose techniques to enhance the robustness of deep learning classification by tackling data issue. Specifically, our data processing proposals aim to alleviate the impacts of class-imbalanced data and non- IID data in deep learning classification and federated learning scenarios. In addition, data pre-processing strategies such that dimensionality …

Go to article

Possible Attacks On Match-In-Database Fingerprint Authentication, Jadyn Sondrol Jun 2023

Possible Attacks On Match-In-Database Fingerprint Authentication, Jadyn Sondrol

Scholarly Horizons: University of Minnesota, Morris Undergraduate Journal

Biometrics are used to help keep users’ data private. There are many different biometric systems, all dealing with a unique attribute of a user, such as fingerprint, face, retina, iris and voice recognition. Fingerprint biometric systems, specifically match-in-database, have universally become the most implemented biometric system. To make these systems more secure, threat models are used to identify potential attacks and ways to mitigate them. This paper introduces a threat model for match-in-database fingerprint authentication systems. It also describes some of the most frequent attacks these systems come across and some possible mitigation efforts that can be adapted to keep …

Go to article

Probing As A Technique To Understand Abstract Spaces, Ashlen A. Plasek Jun 2023

Probing As A Technique To Understand Abstract Spaces, Ashlen A. Plasek

Scholarly Horizons: University of Minnesota, Morris Undergraduate Journal

Machine learning models, while very powerful, have their operation obfuscated behind millions of parameters. This obfuscation can make deriving a human meaningful process from a machine learning model very difficult. However, while the intermediate states of a machine learning model are similarly obfuscated, using probing, we can start to explore looking at possible structure in those intermediate states. Large language models are a prime example of this obfuscation, and probing can begin to allow novel experimentation to be performed.

Go to article

Deep-Learning Realtime Upsampling Techniques In Video Games, Biruk Mengistu Jun 2023

Deep-Learning Realtime Upsampling Techniques In Video Games, Biruk Mengistu

Scholarly Horizons: University of Minnesota, Morris Undergraduate Journal

This paper addresses the challenge of keeping up with the ever-increasing graphical complexity of video games and introduces a deep-learning approach to mitigating it. As games get more and more demanding in terms of their graphics, it becomes increasingly difficult to maintain high-quality images while also ensuring good performance. This is where deep learning super sampling (DLSS) comes in. The paper explains how DLSS works, including the use of convolutional autoencoder neural networks and various other techniques and technologies. It also covers how the network is trained and optimized, as well as how it incorporates temporal antialiasing and frame generation …

Go to article

Lidar Segmentation-Based Adversarial Attacks On Autonomous Vehicles, Blake Johnson Jun 2023

Lidar Segmentation-Based Adversarial Attacks On Autonomous Vehicles, Blake Johnson

Scholarly Horizons: University of Minnesota, Morris Undergraduate Journal

Autonomous vehicles utilizing LiDAR-based 3D perception systems are susceptible to adversarial attacks. This paper focuses on a specific attack scenario that relies on the creation of adversarial point clusters with the intention of fooling the segmentation model utilized by LiDAR into misclassifying point cloud data. This can be translated into the real world with the placement of objects (such as road signs or cardboard) at these adversarial point cluster locations. These locations are generated through an optimization algorithm performed on said adversarial point clusters that are introduced by the attacker.

Go to article

Identify The Most Productive Crop To Encourage Sustainable Farming Methods In Smart Farming Using Neutrosophic Environment, Ahmed Abdelhafeez, Hadeer Mahmoud, Alber S. Aziz Jun 2023

Identify The Most Productive Crop To Encourage Sustainable Farming Methods In Smart Farming Using Neutrosophic Environment, Ahmed Abdelhafeez, Hadeer Mahmoud, Alber S. Aziz

Neutrosophic Systems with Applications

Only with careful management can the data provided by crops be used to make smart, profitable choices. Data has become the central ingredient in contemporary agriculture, and the recent advancements in handling it are contributing greatly to the meteoric rise of smart farming. Gains in efficiency and longevity may be realized to a significant degree by using the objective data collected by sensors. These data-driven farms can maximize output while minimizing waste and environmental impact thanks to the information they collect and analyze. Various criteria and factors in smart farming can aid in productivity. Sensors, drones, Global Positioning System (GPS) …

Go to article

System Predictor: Grounding Size Estimator For Logic Programs Under Answer Set Semantics, Daniel Bresnahan, Nicholas Hippen, Yuliya Lierler Jun 2023

System Predictor: Grounding Size Estimator For Logic Programs Under Answer Set Semantics, Daniel Bresnahan, Nicholas Hippen, Yuliya Lierler

Computer Science Faculty Publications

Answer set programming is a declarative logic programming paradigm geared towards solving difficult combinatorial search problems. While different logic programs can encode the same problem, their performance may vary significantly. It is not always easy to identify which version of the program performs the best. We present the system PREDICTOR (and its algorithmic backend) for estimating the grounding size of programs, a metric that can influence a performance of a system processing a program. We evaluate the impact of PREDICTOR when used as a guide for rewritings produced by the answer set programming rewriting tools PROJECTOR and LPOPT. The results …

Go to article

First Prev Next Last