Miracle-dz/UniFAR-FTUs huggingface dataset:
Social Media Marketing and Fashion Purchase Intention in Indonesia's Transitional Economy

The research set out to look at how social media marketing in the fashion business in Indonesia, a developing nation, affects consumers' propensity to make a purchase. This study surveyed 118 people in Indonesia using an online survey instrument. The proposed theoretical model was tested using Smart PLS and structural equation modeling (SEM). The bootstrapping method was used to test the hypotheses and the mediating roles of brand awareness and customer satisfaction. Despite the direct influence of social media marketing on purchase intention, brand awareness and customer satisfaction do not mediate the relationship between the two. This study contributes to the literature by shedding light on the impact of social media marketing on brand awareness, customer satisfaction, and purchase intention in transition economies, such as Indonesia. Findings from this study have practical applications because they can help marketers in a variety of contexts design social media marketing strategies that are more likely to be successful in reaching their intended audiences despite economic uncertainty.
Automatic Number Plate Recognition and QR Code Double Authentication System for a Carpark

Parking is one of the most important elements in a transportation system in highly populated urban areas. Unattended vehicles have the chance of being stolen if there is no way to authenticate the driver of the vehicle. Given this, Automatic Number Plate Recognition (ANPR) systems are one of the ways to monitor and track vehicles based on the information of the number plate. This information, along with the information about the driver can be used to verify and authenticate if the driver of the vehicle is the actual owner. Quick Response (QR) codes can encode information by transforming alphanumeric data into a unique pattern of data points. Therefore, this study aims to design a double authentication system by integrating ANPR and QR code authentication sub-systems to create a double authentication system that improves the security and safety of vehicles at carparks by creating a double authentication system. The study shows results that are working.
Object Detection in Images and Videos Using OpenCV: A Comparative Study of Deep Learning and Traditional Computer Vision Techniques

Using OpenCV, this research compares the performance of deep learning with standard computer vision approaches for detecting object in photos and videos. Recognizing and localizing objects inside an image or video is a fundamental task in computer vision. Convolutional neural networks, for example, have recently demonstrated greater accuracy in object detection trials. Traditional computer vision approaches, such as the Viola-Jones algorithm, remain popular due to their ease of use and performance. In this work, we use OpenCV, a famous computer vision toolkit, to compare the efficacy of both of these methods. On diverse data sets, we evaluate the precision, speed, and complexity of each strategy and provide insights into the strengths and drawbacks of each method. This paper offers a complete overview of current object identification strategies and can assist researchers and practitioners in selecting the best effective approach for their individual application.
Multa: Enabling Cost-efficient Multi-task Network Traffic Analysis

Machine learning based network traffic analysis has been widely used to combat malicious behaviors in various network scenarios. However, most of the applications are specialized for a single task and cannot cover a wide spectrum of security threats. Building a machine learning pipeline from scratch for each traffic analysis task is painstaking and time-consuming. And it is not practical to deploy several traffic analysis tasks together due to the high runtime overhead. In this paper, we propose Multa to enhance the deployability of traffic analysis applications. Multa is a framework that leverages Multi-Task Learning (MTL) - an emerging paradigm in machine learning - to enable the efficient parallel deployment of multiple traffic analysis tasks. Specifically, we devise a sequence-based traffic feature representation that is informative and general enough to serve a diverse range of tasks. Then we use the LSTM-based Multi-gate Mixture-of-Experts (LMMoE) model to learn task characteristics and propose a size tuning algorithm to optimize its performance. Considering different data curating methods, we also present two different training paradigms to construct Multa. Our experiments show that Multa can reduce the space occupancy of the model by up to 50% and accelerate the inference process by more than 100% in multi-task scenarios, which indicates better deployability in real-world networks.
Advances in Nanoarchitectonics: A Review of “Static” and “Dynamic” Particle Assembly Methods

Particle assembly is a promising technique to create functional materials and devices from nanoscale building blocks. However, the control of particle arrangement and orientation is challenging and requires careful design of the assembly methods and conditions. In this study, the static and dynamic methods of particle assembly are reviewed, focusing on their applications in biomaterial sciences. Static methods rely on the equilibrium interactions between particles and substrates, such as electrostatic, magnetic, or capillary forces. Dynamic methods can be associated with the application of external stimuli, such as electric fields, magnetic fields, light, or sound, to manipulate the particles in a non-equilibrium state. This study discusses the advantages and limitations of such methods as well as nanoarchitectonic principles that guide the formation of desired structures and functions. It also highlights some examples of biomaterials and devices that have been fabricated by particle assembly, such as biosensors, drug delivery systems, tissue engineering scaffolds, and artificial organs. It concludes by outlining the future challenges and opportunities of particle assembly for biomaterial sciences. This review stands as a crucial guide for scholars and professionals in the field, fostering further investigation and innovation. It also highlights the necessity for continuous research to refine these methodologies and devise more efficient techniques for nanomaterial synthesis. The potential ramifications on healthcare and technology are substantial, with implications for drug delivery systems, diagnostic tools, disease treatments, energy storage, environmental science, and electronics.
Integrated Sensing and Communication: NOMA vs Cooperative NOMA

This paper examines the integrated sensing and communication technology (ISAC) in the downlink scenario where a base station exploits cooperative non-orthogonal multiple access (CNOMA) to jointly offer communication functions to users and sensing functions to targets. CNOMA allows the user with good channel conditions to assist another user with a weak channel using the decode and forward strategy in full duplex mode while forming a beam-pattern that is capable of sensing the targets. The main objective in this work is to maximize the sum rate of the users by jointly optimizing the communication beamformers and the power allocation of the near user subject to the quality of service requirements for sensing and communication functions. The formulated problem is non-convex and hard to solve using traditional solvers. For that reason, a penalty-based approach is adopted to provide an efficient solution. Numerical results demonstrated the advantage of C-NOMA in ISAC, showing gains reaching up to 38% compared to the traditional NOMA, and 65% compared to the spatial division multiple access (SDMA).
Analyzing Textual Data for Mental Health Assessment: Natural Language Processing for Depression and Anxiety

In this study, depression and anxiety markers found in text data are carefully targeted in order to use Natural Language Processing (NLP) techniques to the evaluation of mental health. The work gives a thorough assessment of NLP models that include sentiment analysis, emotion identification, as well as linguistic pattern detection along with shows significant improvements in F1 scores, recall, and accuracy. The varied dataset, gathered from social media, online discussion boards, and healthcare records, is rigorously preprocessed as well as qualitatively evaluated, enhancing the validity of the method. The findings might advance early identification and intervention by bridging the technical and mental health spheres. Cross-cultural datasets, multi-modal data integration, moral concerns, and cooperation with mental health specialists are important future prospects.
DDPG-based Sum Rate Optimization for Opportunistic Backscatter NOMA Networks

In today's world of burgeoning IoT and 6G communications, supporting low-powered devices is crucial to fully capitalizing on the Next Generation Internet of Things (NG-IoT) revolution. The proliferation of these devices will unlock unprecedented opportunities for energy efficiency, sustainability, and ubiquitous connectivity. This paper investigates the sum rate optimization of a quality-of-service (QoS)-aware EH-enabled passive IoT device in a cognitive radio inspired non-orthogonal multiple access (CR-NOMA)-assisted backscatter communication network. Our goal is to optimize the sum rate of a secondary passive IoT device while guaranteeing the QoS requirements of the scheduled primary device. The deep deterministic policy gradient (DDPG) algorithm is employed to dynamically adjust the reflection coefficient of the backscatter node, yielding optimal performance. Our results demonstrate significant improvements in the sum rate, highlighting the importance of incorporating advanced machine learning (ML) techniques into IoT and wireless communication domains to address critical challenges and enhance the overall performance of NG-IoT networks.
Efficient and Privacy-Preserving Eclipse Query Over Encrypted Data

As the mobile Internet grows rapidly, location-based services (LBSs) are widely applied in the tourism and transportation fields. To fully mine the data collected, location service providers (LSPs) intend to offer various query services to users, which include eclipse query. The eclipse query can generalize nearest neighbor queries and skyline queries and allow users to set more rough and customizable preference ranges. In addition, with the boom of cloud computing, more and more LSPs hope to leverage the cloud to offer better query services. Given that the data could potentially contain confidential information, the data are required to be encrypted prior to outsourcing them. Therefore, eclipse queries need to be executed on the ciphertext. Although several schemes for eclipse queries have been proposed in existing works, they have little focus on privacy issues. To address this issue, we propose an efficient and privacy-preserving scheme for eclipse queries (EPEQ) in this paper. First, we develop a MinValue tree to construct an index for the dataset. Then, by utilizing the MinValue tree and a symmetric homomorphic encryption technique, we design a secure minimum value comparison protocol to obtain a skyline data and a secure undominated data acquisition protocol to obtain the data not skyline dominated by the skyline data. After that, we present our scheme. We analyze the security of the EPEQ scheme and perform experimental evaluations, demonstrating the security and efficiency of our EPEQ scheme.
On Optimizing RIS-aided SWIPT-IoTs with Power Splitting-based Non-Linear Energy Harvesting

Future generation of Wireless Communications encompasses massive connectivity of energy-starved and heavy-data driven billions of Internet-of-Things (IoT) devices. In this vein, Reconfigurable Intelligent Surface (RIS) holds great promise while providing improved performance and efficiency in terms of energy, cost and spectrum. Simultaneous Wireless Information and Power Transmission (SWIPT) in conjunction with RIS makes a great partnership to suffice the IoT demands. This paper examines a SWIPT-IoT system that utilizes power-splitting (PS) and non-linear energy harvesting (EH) model to achieve more data rates in constraint environment. The IoT node receives both energy and information from the base station via RIS. We present a combined problem that aims to optimize the individual objectives of rate, EH, and transmit power, while taking into account various sets of quality-of-service (QoS)-based constraints. We introduce a set of iterative optimization algorithm that utilize a divide-and-conquer approach to effectively solve the aforementioned problems. Based on our computational results, we confer that in order to reap the benefits of PS-based SWIPT-IoTs, it is imperative to increase the size of RIS and position them in optimal proximity to both the base station and the user.
Evapotranspiration estimation using remote sensing data

Evapotranspiration (ET) is a critical hydrological link between the earth surface and the atmosphere. It is therefore important point of issues involving many aspects of climate, climate change, and ecosystem response. It is well known that ET is the process responsible for the transfer of the moisture from soil and vegetated surface to the atmosphere. Changes in ET are likely to have large impacts on terrestrial vegetation. Since the distribution and abundance of plant communities are controlled to a large extent by the quantity and seasonality of moisture. If the changes in water balance are significant, major shifts in vegetative patterns and condition are a likely result of climate change. Equally changes in ET are likely to impact atmospheric composition of green house gases, and climate, as the hydrological cycle increases in intensity with warming. Therefore, in this paper, it is attempted to estimate the ET over vegetative and bare field using National Oceanic and Atmospheric Administration (NOAA)/ Advanced Very High Resolution Radiometer (AVHRR) data at coarse spatial resolution of 1.1 km as a function of Normalized Difference Vegetation Index (NDVI) and with semi-empirical approach. For this purpose, a model has been proposed to estimate the ET over vegetative and bare field. The dependence of ET on NDVI-Surface temperature has been checked by multiple regression analysis and quite good percentage of dependence of ET on NDVI-Surface temperature has been observed. This type of estimation will be helpful for climate modeler, climatologists, ecosystem modeler and regional planer.
Use of Chilled Mirror /Dew-Point Method (WP4C) to Develop Soil Water Characteristic Curve for Coastal Silty Sand with a Comparison of the Existing Analytical Models

Soil Water Characteristic Curve (SWCC) is one of the methods to study the behaviour of unsaturated soil. This study presents the SWCC for Sri Lankan coastal silty sand developed using chilled mirror dew-point technique WP4C and compares the performance of a few prediction models. SWCC was also developed experimentally using pressure plate and suction-moisture sensors, to verify the results obtained using WP4C. The fitted SWCC curve was developed using Van Genuchten's (VG) model. The prediction models of Arya and Paris (1981), Aubertin et al. (2003), and Wang et al. (2017) were used to compare the performance using their own suggested values and the more suitable values for this study as the model parameters which were estimated using the least square method. This study shows that chilled mirror dew-point technique which is incorporated in WP4C instrument can be used as a fast and reliable experimental method to develop the SWCC for sandy soils compared to other experimental methods. The Wang et al. (2017) model is better suited for estimating the SWCC through analytical methods for this soil type, using either the recommended values in this study or proposed values by the authors for the modal parameters.
Design and Modeling of Inductive Wireless Power Transfer for Electric Vehicles

Due to the increasing issue of global warming and climate change, electric vehicle(EV) penetration in the automobile market has increased exponentially. Infrastructure and charging technology must also be created to enable the adoption of EVs and meet their charging needs. This paper proposes a simulation model for Wireless Power Transfer(WPT) using the Inductive WPT method for the transmission of power while using the induction of voltage between the transmitter and receiver. The proposed system introduces a low-cost and flexible method that provides new benefits over conventional methods of power transmission.
A Novel Sustainable Bandwidth Allocation Strategy for Multiple Service Migration in 5G/6G Edge Computing

Multi-containerized applications are becoming more common. Modern mobile Internet-of-Things applications, e.g., augmented reality and online gaming, have increased the need for ultra-low and real-time application responses. Edge computing is a solution for decreasing response times by relocating services closer to users. Migrating multiple services in this environment is unavoidable, given user mobility and load balancing. We utilize the MiGrror migration method, to propose a novel strategy for increasing bandwidth for short critical periods instead of maintaining increased bandwidth throughout the entire migration process. Our novel strategy drastically reduces downtime and migration time while enhancing bandwidth utilization by freeing up additional bandwidth during migration, which increases bandwidth availability for other services. The findings indicate that, even without using additional bandwidth, adjusting the timing of a bandwidth increase/decrease for services improves performance. The results are comparable to increasing the bandwidth for the entire migration duration with equivalent bandwidth consumption. Additionally, this study demonstrates that the proposed strategy is more sustainable than when using the original bandwidth, which results in less data transfer.
M-BANKING AND THE MICROCREDIT AUDIENCE: a study based on the UTAUT 2

Purpose: Small entrepreneurs have difficulty accessing credit, but they can be served with microcredit lines through digital channels, such as mobile banking (MB). The present study aimed to measure the impact of antecedent factors (performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation, price value and habit) on the intention and behavior of MB use by the urban microcredit public
The Natchez Diaspora: A History of Indigenous Displacement and Survival in the Atlantic World

Author(s): Smyth, Edward Glenn | Advisor(s): Westerkamp, Marilyn | Abstract: In 1731, the French colonists of Louisiana and their Native American allies defeated the Natchez after a bloody conflict and drove the Natchez survivors from their ancestral homelands. The dissertation responds to the question: what happened to the Natchez survivors after 1731? Instead of thinking about the year 1731 as an end to Natchez history, I argue that the war with the French marked the beginning of a new phase of Natchez history that is best characterized as a diaspora. Indeed, the Natchez established many new communities after 1731: some Natchez eventually settled in colonial South Carolina while others established Natchez communities among the Chickasaws, Cherokees and Creeks. This is the first project to explore Natchez history after the 1730s and it enlarges the temporal scope of Natchez history and its significance to larger colonial processes in the Atlantic World. Through the use of French and English sources, as well as Natchez oral history that I have collected by working with contemporary Natchez communities in Oklahoma and South Carolina, this project examines Natchez communities in an extended Indigenous diasporic network. This network enabled Natchez people to survive multiple colonial displacements and to establish a network of contacts with several different European and Native American populations during the eighteenth century and beyond. In tracking the diaspora, the dissertation departs from scholarship that focuses on the adaptation of Native American polities, culture, and society in response to European colonialism. Rather than only looking at how Natchez adapted to European colonialism, which clearly had a major impact on Natchez history, this project also examines how Natchez people responded to living with other Native American groups. The diasporic Natchez communities reveal that the Natchez adapted to changes in multiple directions, not just in response to European colonists, but also in response to their interactions with Chickasaws, Creeks, and Cherokees. By examining how each Natchez community integrated themselves into new areas and with new peoples, the dissertation argues that the limited choices that were available to the Natchez were as much conditioned by Native American societies as European colonization.
Optimization of Torque-Control Model for Quasi-Direct-Drive Knee Exoskeleton Robots Based on Regression Forecasting

The choice of torque curve in lower-limb enhanced exoskeleton robots is a key problem in the control of lower-limb exoskeleton robots. As a human–machine coupled system, mapping from sensor data to joint torque is complex and non-linear, making it difficult to accurately model using mathematical tools. In this research study, the knee torque data of an exoskeleton robot climbing up stairs were obtained using an optical motion-capture system and three-dimensional force-measuring tables, and the inertial measurement unit (IMU) data of the lower limbs of the exoskeleton robot were simultaneously collected. Nonlinear approximations can be learned using machine learning methods. In this research study, a multivariate network model combining CNN and LSTM was used for nonlinear regression forecasting, and a knee joint torque-control model was obtained. Due to delays in mechanical transmission, communication, and the bottom controller, the actual torque curve will lag behind the theoretical curve. In order to compensate for these delays, different time shifts of the torque curve were carried out in the model-training stage to produce different control models. The above model was applied to a lightweight knee exoskeleton robot. The performance of the exoskeleton robot was evaluated using surface electromyography (sEMG) experiments, and the effects of different time-shifting parameters on the performance were compared. During testing, the sEMG activity of the rectus femoris (RF) decreased by 20.87%, while the sEMG activity of the vastus medialis (VM) increased by 17.45%. The experimental results verify the effectiveness of this control model in assisting knee joints in climbing up stairs.