SACM - United States of America
Permanent URI for this collectionhttps://drepo.sdl.edu.sa/handle/20.500.14154/9668
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Item Restricted Automating the Resolutions for Software Merge Conflicts(Virginia Polytechnic Institute and State University, 2024-11-22) Aldndni, Waad; Meng, Na; Servant, FranciscoDuring collaborative software development, developers engage in parallel work on separate branches, which are eventually merged at regular intervals. However, conflicts can arise when edits from different branches overlap in the text. Resolving such conflicts involves three strategies: keeping the local version only (KL), keeping the remote version only (KR), or manually editing them (ME). Nonetheless, manually resolving merge conflicts can be a laborious and error-prone process. Thus, researchers proposed tools to aid in conflict resolution by combining edits from both branches as many as possible, although these tools often fail to consider the preferences of the developers involved adequately. Recent studies show that developers predominantly resolve textual conflicts via KL or KR. This suggests that existing tools do not fully consider the resolution preferences of developers but only focus on the technical feasibility of merging branch edits. Our research focuses on predicting developers’ resolutions automatically for software merge conflicts and suggesting resolution edits to developers. We designed and implemented three tools to automatically predict resolution strategies for merge conflicts and to automatically apply some of the strategies by producing merged versions. The tool evaluation shows promising results. Our research will help developers resolve conflicts effectively and efficiently; it will also shed light on future research for software merge and automatic conflict resolution.21 0Item Restricted Behavior and Design of Composite Rebars Interfaced with Concrete(university of colorado Denver, 2024) Alatify, Ali; Kim, JimmyAbstract This dissertation studies different aspects of the interfacial behavior of composite reinforcement embedded in concrete. GFRP rebars are known for its none-corrosiveness, light weight, and high strength compared to conventional steel rebars, and became predominantly employed in different structural applications such as bridge construction. Thus, the serviceability and interfacial behavior of GFRP bars in different structural applications is investigated in four phases in this research. Chapter three presents an experimental study on the residual bond of glass fiber reinforced polymer (GFRP) rebars embedded in ultra-high performance concrete (UHPC) subjected to elevated temperatures, including a comparison with ordinary concrete. Based on the range of thermal loading from 25oC (77oF) to 300oC (572oF), material and push-out tests are conducted to examine the temperature-dependent properties of the constituents and the behavior of the interface. Also performed are chemical and radiometric analyses. The average specific heat and thermal conductivity of UHPC are 12.1% and 6.1% higher than those of the ordinary concrete, respectively. The temperature-induced reduction of density in these mixtures ranges between 5.4% and 6.2% at 300oC (572oF). Thermal damage to GFRP, in the context of microcracking, is observed after exposure to 150°C (302°F). Fourier transform infrared spectroscopy reveals prominent wavenumbers at 668 cm-1 (263 in.-1) and 2,360 cm-1 (929 in.-1), related to the bond between the fibers and resin in the rebars, while spectroradiometry characterizes the thermal degradation of GFRP through diminished reflectivity in conjunction with the peak wavelength positions of 584 nm (2,299×10-8 in.) and 1,871 nm (7,366×10-8 in.). The linearly ascending bond-slip response of the interface alters after reaching the maximum shear stresses, leading to gradual and abrupt declines for the ordinary concrete and UHPC, respectively. The failure mode of the ordinary concrete interface is temperature-sensitive; however, spalling in the bonded region is consistently noticed in the UHPC interface. The fracture energy of the interface with UHPC exceeds that of the interface with the ordinary concrete beyond 150oC (302oF). Design recommendations are provided for estimating reductions in the residual bond of the GFRP system exposed to elevated temperatures. The interface shear between ordinary concrete and ultra-high-performance concrete (UHPC) connected with glass fiber reinforced polymer (GFRP) rebars is presented in chapter four. Following ancillary tests on the fracture of the rebars under in-plane shear loading, concrete-rebar assemblies are loaded to examine capacities and failure modes that are dependent upon the size, spacing, and number of the rebars. While the transition of load-resisting axes in the glass fibers and their quantity dominates the shear behavior of the bare rebars, the size and spacing of the reinforcement control the capacities of the interface by altering load-transfer mechanisms from the rebar to the concrete. The degree of stress distribution affects the load-displacement response of the interface, which is characterized in terms of quasi-steady, kinetic, and failure regions. The primary failure modes of the interface comprise rebar rupture and concrete splitting. The formation of cracks between ordinary concrete and UHPC results from interfacial deformations, leading to spalling damage when applied loads exceed service levels. An analytical model is formulated alongside an optimization technique. The capacities of the interface in relation to the rebar rupture and concrete splitting failure modes are predicted. Furthermore, a machine learning algorithm is utilized to define a failure envelope and propose practice guidelines through parametric investigations. The serviceability of concrete beams with continuous and spliced glass fiber reinforced polymer (GFRP) rebars is investigated and detailed in chapter five. An experimental program is undertaken using 18 beams incorporating various reinforcing schemes to examine the effects of rebar distribution and spacing on flexural and cracking responses. The cracking load of the beams with the continuous rebars (Category C) is 24.2% higher than that of the beams with the spliced rebars (Category S) experiencing stress concentrations. The distributed configuration of the rebars enhances interactions between the concrete and reinforcement, thereby increasing bond transfer in the beams. Contrary to the linear load-displacement behavior of the C-category beams after cracking, parabolic trends are observed in the S-category beams owing to the slip of the spliced rebars, which degrades composite action at the rebar-concrete interface and reduces the flexural rigidity of the beams. The crack opening of the C-category beams under service loading is within the tolerable limits of published guidelines, whereas the opening of the S-category beams exceeds the limits. Through statistical characterization, the significance of the rebar distribution in crack opening and depth is demonstrated at a 5% significance level (95% confidence interval). Design recommendations include a slip multiplier of 0.63 for calculating the stress of spliced GFRP rebars and a bond coefficient of 0.88 for determining the flexural capacity of beams with this type of reinforcement. The implications of variable bond for the behavior of concrete beams with glass fiber reinforced polymer (GFRP) bars alongside shear-span-dependent load-bearing mechanisms is evaluated in chapter six. Experimental programs are undertaken to examine element- and structural-level responses incorporating fully and partially bonded rebars, which are intended to represent sequential bond damage. Conforming to published literature, three shear-span-to-depth (av/d) ratios are considered: arch action (av/d < 2.0), beam action (3.5 ≤ av/d), and a transition from arch to beam actions (2.0 ≤ av/d < 3.5). When sufficient bond is provided for the element-level testing (over 75% of 5db, where db is the rebar diameter), the interfacial failure of GFRP is brittle against a concrete substrate. An increase in the shear-span-to-depth ratio, aligning with a change from arch action to beam action, decreases the load-carrying capacity of the beams and the slippage of the partially bonded rebars dominates their flexural stiffness. Compared with the case of beams under beam action, the mutual dependency of the bond length and shear span is apparent for those under arch action. As far as failure characteristics are concerned, the absence of bond in the arch-action beam prompts crack localization; by contrast, partially bonded ones demonstrate diagonal tension cracking adjacent to the compression strut that transmits applied load to the nearby support. The developmental process of rebar stress is dependent upon the shear-span-to-depth ratios and, in terms of utilizing the strength of GFRP, beam action is favorable relative to arch action. Analytical modeling suggests design recommendations, including degradation factors for the calculation of rebar stresses with bond damage when subjected to arch and beam actions.31 0Item Embargo ENHANCING LOCATION INFORMATION PRIVACY AND SECURITY IN IoBT USING DECEPTION-BASED TECHNIQUES(Florida Atlantic Uniiversity, 2024-09) Alkanjr, Basmh; Imadeldin, MahgoubIoBT stands for the Internet of Battlefield Things. This concept extends the principles of the Internet of Things (IoT) for military and defense use. IoBT integrates smart devices, sensors, and technology on the battlefield to improve situational awareness, communication, and decision-making in military operations. Sensitive military data typically includes information crucial to national security, such as the location of soldiers and equipment. Unauthorized access to location data may compromise operational confidentiality and impede the element of surprise in military operations. Therefore, ensuring the security of location data is crucial for the success and efficiency of military operations. We propose two systems to address this issue. First, we propose a novel deception-based scheme to enhance the location-information security of IoBT nodes. The proposed scheme uses a novel encryption method, dummy IDs, and dummy packets technology. We develop a mathematical model to evaluate our scheme in terms of safety time (ST), probability of failure (PF), and the probability of identifying the real packet in each location information update (PIRP). Then, we develop NetLogo simulations to validate the mathematical model. The proposed scheme increases ST, reduces PF and PIRP. We develop a scheme to protect the node's identity using dummy ID, silence period, and sensitive area’s location privacy enhancement concepts. We generate a pseudonym location for each node in the IoBT environment to protect the node's real location information. We propose a new metric called the average probability of linkability per dummy ID (DID) change to assess the attacker's effectiveness in linking the source node with its new DID following the silent period. We develop Matlab simulations to evaluate our scheme in terms of average anonymity and average probability of linkability per DID change. The results showed further privacy enhancement by applying the sensitive area concept. Tampering with location information, such as falsification attacks, can lead to inaccurate battlefield assessments and personnel safety risks. Thus, we design ANFIS and ensemble methods for detecting position falsification attacks in IoBT. Using the VeReMi dataset, our method achieved high detection accuracy while reducing false negative rate and computation complexity. Cross-validation further supports the reliability of our model.32 0Item Restricted Detecting Makeup Activities using Internet-of-Things(University of Maryland Baltimore County, 2019-07) Alqurmti, Fatimah; Roy, NirmalyaThis thesis focuses on identifying human activities for rendering make-up activities using sensors’ data and a supervised machine learning approaches. We considered five make-up activities in our work, such as, applying cream, lipsticks, blusher, eyeshadow, and mascara. We collected the data from ten participants using two smart-watch built-in sensors, accelerometer and gyroscope. We preprocessed the data and trained with different predictive machine learning models and we evaluated make-up activity prediction built on using Naïve Bayes, Simple Logistic, k-nearest neighbors’, and the random forest algorithms. We investigated the models' performance on three different datasets that differ by the environment they were collected in. The first dataset was collected from the participants using a controlled environment. In this staged setting, we provided the participants specific instructions on how to perform the five make-up activities. The second dataset was collected from the participants in an uncontrolled environment. We did not inform the participants with any prior instructions on how to perform the five activities and therefore, naturally they performed the make-up activities in their own way. Third, we synthetically generated a dataset by combining the existing datasets from the participants who were under both controlled and uncontrolled environments. Our results showed a 92.7 % accuracy for the controlled environment case given by the Gradient Boosting classifier and an 89.20 % accuracy for the uncontrolled environment case shown by the Random Forest classifier. Finally, Random Forest classifier registered the highest accuracy 92%, for the hybrid case where both the datasets from controlled and the uncontrolled environments were combined. We believe that this early work on recognizing and discovering a multitude of make-up activities has potential application in assessing and training the performance of various stakeholders in the future work of fashion industry.7 0Item Restricted Detecting Makeup Activities using Internet-of-Things(University of Maryland Baltimore County, 2019-07-30) Alqurmti, Fatimah; Roy, NirmalyaThe make-up market is one of the most furnished fashion markets in product retailing and training demands. Each of the makeup activities involves very delicate movements of hands and requires good amount of training and practice for perfection. The available choices in the make-up training industry depends on practical workshops by professionalinstructors, and still evaluating the perfection of makeup activities lacks certainty. In this work, we introduced a novel application for human activity recognition using sensors’ data and a supervised machine learning approaches for rendering make-up activities. We considered five make-up activities in our work, such as, applying cream, lipsticks, blusher, eyeshadow, and mascara and collected data from ten participants. We built supervised make-up activity recognition using different predictive machine learning algorithms i.e. Naïve Bayes, Simple Logistic, k-nearest neighbors’, and the random forest algorithms. We investigated the models' performance for detecting five make-up activities with or without instructions. Our results show that shallow machine learning algorithms achieve up to 92% accuracy in detecting make-up activities.20 0Item Restricted MISINFORMATION DETECTION IN THE SOCIAL MEDIA ERA(Howard University, 2024-04-22) Alzahrani, Amani; Rawat, Danda B.As social media becomes the main way of getting information, the spread of misinformation is a serious and widespread problem. Misinformation can take many forms, such as text, video, and audio, and it can travel quickly through different platforms, affecting the quality and trustworthiness of the information that users access around the world. Misinformation can have negative effects on how people think, act, and interact, and it can even endanger social peace. This study aims to tackle the complex problem of misinformation by presenting a comprehensive approach that addresses various forms of deceptive content on social media with a focus on Twitter ( currently X). Twitter stands out as a dynamic and influential microblogging service that enables users to share real-time updates, news, and opinions in concise 280-character messages known as tweets. We introduce a hybrid deep learning model that incorporates Feature-based models at both tweet and user levels, complemented by pre-trained text embedding models such as Global Vectors (GloVe) and Universal Sentence Encoders (USE). Through careful evaluation on a real-world dataset, our approach proves effective in detecting textual misinformation. Recognizing the vital need to verify the reliability of information on social media, we propose a method to assess user credibility. Our solution involves evaluating the credibility of users based on their profiles to enhance the rumors detection model. This study proposes a novel mechanism for assessing a user’s credibility. Additionally, we extended our study capabilities to address the challenges posed by deceptive video content spread on social media using DeepFake technology. As the rapid advancement of deepfake technology threatens the integrity of audio and video content, we present a novel approach combining Optical Flow (OF) algorithms with a Convolutional Neural Network (CNN) to enhance deepfake video detection. This comprehensive strategy addresses the diverse challenges posed by misinformation, credibility assessment, and deepfake detection in the dynamic landscape of social media.37 0Item Restricted Simulating Dynamical Systems from Data(Massachusetts Institute Of Technology, 2024-02-21) Alomar, Abdullah; Shah, DevavratThe ever-increasing availability of data from dynamical systems offers an opportunity for automated data-driven decision-making in various domains. However, a significant barrier to realizing this potential is the issues inherent to these datasets: high-dimensionality, noise, sparsity, and confounding. In this thesis, we propose methods to exploit the richness in the structure of such datasets to overcome the above-mentioned problems while undertaking various inference tasks. Central to these methods is a key factorization characterizing the function governing the dynamics. Specifically, we harness trajectories from different, yet related, dynamical systems. We posit that the function governing the dynamics of each individual system can be factorized into a linear combination of latent separable functions of the state and action. Crucially, these latent functions are shared across the different dynamical systems. This principled factorization structure provides guidance on how to devise theoretically sound methods that perform well empirically across a variety of tasks. These tasks include time series imputation and forecasting, change point detection, reinforcement learning, and trace-driven simulation in networked systems. Exploiting the principled factorization structure has paved the way for the contributions we make in different tasks. First, we propose and analyze algorithms for mean and variance estimation and forecasting of time series with varying noise models, data missingness patterns, and assumptions on the factorization structure. These algorithms employ variants of the classical multivariate singular spectrum analysis (mSSA) algorithm and establish a link between time series analysis and Matrix/Tensor Completion. Second, we develop and analyze an algorithm for change point detection inspired by the factorization structure and based on the cumulative sum (CUSUM) statistic. This work extends the analysis of CUSUM statistics traditionally done for the setting of independent observations. Finally, we explore the potential gains of considering the factorization structure in simulating Markov Decision Processes (MDPs). We then build upon this approach to accommodate MDPs with time varying parameters with the specific application of trace-driven simulation in networked systems.36 0Item Restricted Predicting Pedestrian Crossing Intention(Saudi Digital Library, 2024) Alofi, Afnan; Trivedi, MohanAutonomous vehicles face significant challenges in understanding pedestrian behavior, particularly in urban environments. The system must recognize pedestrians’ intentions and anticipate their actions to achieve intelligent driving. This paper focuses on predicting pedestrian crossings, aiming to enable oncoming vehicles to react in a timely manner. We investigate the effectiveness of various input modalities for pedestrian crossing prediction, including human poses, bounding boxes and ego vehicle speed features. We propose a novel lightweight architec- ture based on LSTM and attention to accurately identifying crossing pedestrians. Our methods evaluated on two widely used public datasets for pedestrian behavior, PIE and JAAD datasets, and our algorithm achieved a state-of-the-art performance in both datasets34 0Item Restricted Applications of Continuous Flow Reactors Towards Screening Catalytically Active Nanoparticle Syntheses(USC Digital Library, 2023) Madani, Majed; Malmstadt, NoahThe dissertation presented herein is structured into chapters that delve into various research domains within milli- and microfluidic systems. Part of this dissertation includes collaborative authorship. Chapter 1 introduces the fundamentals of fluid mechanics. In this chapter, some highlights of the important physical phenomena that are dominant in milli- and microscale flow systems are presented, focusing on flow dynamics, diffusion, and computational fluid dynamics simulations. It emphasizes the importance of fluid behavior in microscale systems and introduces a case study on microfluidics applications in biomolecular systems in which a portion of a manuscript I participated in as a third author is presented. Chapter 2 covers applications of continuous flow synthesis of colloidal nanoparticles using milli-and microfluidics systems, highlighting the advantages of miniaturized systems in reaction-based nanoparticle syntheses. Chapter 3 is adapted from a published manuscript in which I am a joint primary author. Chapter 3 describes the use of continuous flow methods for screening the reaction parameters of catalytically active molybdenum carbide nanoparticle synthesis with an emphasis on throughput optimization using a Design of Experiment approach. Chapter 4 introduces machine learning-assisted spectrophotometry, showcasing the integration of machine learning algorithms for the kinetic analysis of ionic liquid-based platinum nanoparticle synthesis. Chapter 5 introduces in-situ characterization for continuous flow reactors with a particular objective of studying the nucleation and growth kinetics of nanoparticle synthesis using X-ray scattering. This chapter provides a critical evaluation of flow reactor designs for in situ X-ray scattering analysis, focusing on the synthesis of ionic liquid-based Pt nanoparticles.30 0Item Restricted Machine learning applications for the optimization of renewable energy systems(Saudi Digital Library, 2023-10-10) Maghfuri, Abdullah M.; Wright, Mark MbaThis thesis aims to establish the sectors and applications of renewable energy. The applications of sustainable energy have grown every year in different uses. Three separate studies stand out as a source of creativity. Together, they point the way to a better and more accountable future. Each study explores an important aspect of energy optimization and environmental stewardship and offers remarkable insights and responses. This thesis begins with climate simulation, concentrating on projected solar irradiation and wind patterns in the following decades. These estimations emphasize preemptive energy management. This study compares Saudi Arabia's metropolitan solar power systems to wind and fossil fuel infrastructure. Current and future climates are analyzed. The company's recent solar energy strategy shows its ability to produce sustainable energy and reduce climate change. Wastewater is the topic of the second part of this thesis. Wastewater treatment is a priority due to rising water needs and environmental concerns. Here, renewable energy sources and electrochemical technologies are combined to improve wastewater treatment efficiency and save operational costs. The study uses machine learning predictive models and extraction of high-value by-products from the treatment process blends technology and ecology, enabling sustainable water management. The third study project, which explores the complicated field of predictive modeling, is essential to prioritize the accurate projection of energy output given the rising importance of renewable energy. This research paper thoroughly examines the effectiveness of statistical and machine-learning approaches in predicting renewable energy generation. The findings demonstrate the prevalence of machine learning techniques, which bring a sense of innovation and efficacy to the realm of renewable energy fields.57 0