Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploring Consumer Experience and Continuance Intentions Towards Food Delivery Applications in Indonesia

The Indonesian government's implementation of mobility restrictions due to the Covid-19 pandemic has led to an increase in the usage of food delivery applications (FDAs) as a means of fulfilling the citizens' food needs. This study examines consumer experiences and the continuance intentions of using FDAs, focusing on factors that contribute to their continued use based on the Technology Acceptance Model, such as ease of use, usefulness, and actual use. A qualitative research approach was employed through semi-structured interviews. The results show that ease of use, including the simplicity of installing the app and conducting transactions, as well as perceived benefits, such as the fulfillment of food needs and reducing exposure to Covid-19, play a crucial role in maintaining continuous FDA usage. The drivers' attitudes, communication skills, and credibility also influence consumer decisions to continue using FDAs. Promotions and discounts emerged as significant factors in increasing the frequency of FDA usage.
Evaluation of Decision Tree for predicting Patients' Length of Stay After Arthroplasty Surgical Procedures in the Rural Healthcare

Predicted inpatient length of stay (LOS) after total joint arthroplasty (TJA) is important information that sets the expectation for the post-surgical recovery period, which helps hospital management plan, and manage surgical resources as efficiently as possible. In this study, the accuracy of prediction models for predicting patients' LOS in a rural community hospital was conducted in the Northwest United States of America. Data were collected from 181 patients for hip, knee, and shoulder surgeries. A decision tree (DT) prediction model was compared with the National Surgical Quality Improvement Program (NSQIP) calculator for predicting LOS of TJA. The DT model (RMSE = 0.67) provided a more accurate LOS prediction than the NSQIP calculator (RMSE = 1.18). Furthermore, a greater level of interpretability of the decision-making process makes the DT model very applicable in “high stakes” environments like healthcare.
Coprime Visible Regions Assisted Angle Unfolding for Sparse ESPRIT

In many applications, such as automotive radar for autonomous vehicles, a sparse linear array (SLA) is more attractive than a uniform linear array (ULA). SLAs not only make hardware costs lower to design antenna arrays with a large aperture but also reduce the mutual coupling among antenna elements. When estimation of signal parameters via rational invariance techniques (ESPRIT) is applied on SLAs with shift among subarrays being larger than half wavelength, there would be ambiguities in the field of view (FoV) of the sensor array due to angle folding. In this paper, we present novel SLA geometry with non-uniform subarrays that do not necessarily have a centrally symmetric geometry, and corresponding coprime FoV aided approach to do the angle unfolding. The main goal of the sparse array design is to increase the array aperture size using fewer sensors while maintaining shift invariant geometry. By carefully designing the shifts among these sparse subarrays following a coprime relationship, the angles can be resolved uniquely by a consistent comparison of the angle estimations reported separately by different shifted subarrays.
Adaptive Semi-Blind Receiver for TDD Massive MIMO Systems

This paper presents to use an orthogonal property of code for identifying base station and users in a cellular network. In addition, the principle of semi-blinds is applied with LS algorithm to estimate fading channel and reduce the effect of pilot contamination. The mixing parameter combines the coefficients of weight vector obtained from the pilot signal and the data. The simulation results confirm that the proposed technique can improve performance achievable downlink rate higher than that of both the LS with code for identifying base station and conventional LS algorithms.
Detecting Style of Processing and Understanding on Puzzle Game: HELLTAKER Use Case

The current learning style analysis process uses questionnaires as a classification tool that cannot cause students to show instinctive behavior like playing video games. Therefore, we propose a method for analyzing learners' learning styles based on Felder & Silverman theory. This study presents a framework for developing an approach to assess learners' understanding and processing style through observing their behavior while playing the game HELLTAKER. This study analyzes the information gathered from learners using a statistical technique called “Correlation” to compare it with the information resulting from The index of learning style(ILS), which is questionable. We propose a framework for the test and hypothesis with a group of students in a classroom with no more than 30 members per room. For defined as the learning style analysis results, is divided into H1AA representing the Active Learning style based on the number of movements, H1AR representing the Reflective Learning style based on some reset decisions, H1BG represents a Global Learning style based on game progression. Furthermore, finally, H1BS represents a Sequential Learning style that relies on variations in the problem-solving order. The expected value of the statistical significance of the hypothesis is accepted at 0.15 or more than 85%.
Association of Camera and Radar Detections Using Neural Networks

Automotive radar and camera fusion relies on linear point transformations from one sensor's coordinate system to the other. However, these transformations cannot handle non-linear dynamics and are susceptible to sensor noise. Furthermore, they operate on a point-to-point basis, so it is impossible to capture all the characteristics of an object. This paper introduces a method that performs detection-to-detection association by projecting heterogeneous object features from the two sensors into a common high-dimensional space. We associate 2D bounding boxes and radar detections based on the Euclidean distance between their projections. Our method utilizes deep neural networks to transform feature vectors instead of single points. Therefore, we can leverage real-world data to learn non-linear dynamics and utilize several features to provide a better description for each object. We evaluate our association method against a traditional rule-based method, showing that it improves the accuracy of the association algorithm and it is more robust in complex scenarios with multiple objects.
Distributed Device-Specific Anomaly Detection using Deep Feed-Forward Neural Networks

The Internet of Things (IoT) requires sophisticated security measures because of heterogeneity and resource constraints. Current approaches in Anomaly Detection (AD) do not meet both challenges. Device-specific AD models can account for the heterogeneity of devices. However, existing approaches fail to run on constrained devices. This paper presents the approach of distributed device-specific AD models. Each model processes only data from one device. Through this simplification of the prediction task, lightweight AD models can be created. They provide the ability to counter the resource constraints of devices. With less requirements in processing power, IoT devices can perform AD on their own. The novel approach improves the optimization metrics detection performance, latency, and model complexity. The evaluation uses the publicly available UNSW-NB15 dataset. It shows that models can be simplified to run on IoT devices. Measurements with a device-specific model on a Raspberry PI show only a little increase in training and processing time compared to central processing on a desktop PC. While the accuracy maintains >98%, the F1-score increases from 0.64 up to 0.89 in the distributed approach.
Conversion of Ultrasonic Waves with Symmetric Notches in Isotropic Plate

Lamb waves are ultrasonic technic usedto control the quality of mechanical parts and to monitor their integrity statebefore their entire fracture. In this paper, we aim to study the ultrasonicpropagation theory and numerical simulation in Aluminum plate. Firstly, the twoanalytical equations of the symmetrical and anti-symmetrical lamb modes arecalculated numerically by MATLAB algorithm based on the SAFE (Semi AnalyticalFinite Element Method) to compute the dispersion curves: wave number and phasevelocity in function of the frequency - thickness (f.e). Secondly, simulations werecarried out using ABAQUS CAE based on Finite Element Method (FEM) toinvestigate the propagation and the interaction of lamb modes by defect typenotches. Thirdly, the propagating modes are identified using two-DimensionalFast Fourier Transform (2D-FFT).
Detection of Alzheimer's Risk Level using Inception V3 Transfer Learning Model

Alzheimer's disease is a degenerative brain disease that worsens with time. Alterations characterize it in the brain, leading to specific proteins' deposition. Alzheimer's disease produces a reduction in brain volume, which eventually leads to brain cell death. More than 70% are 75 years of age or older. Alzheimer's disease is estimated to impact 60–70 percent of the world's 55 million dementia patients. Researchers have uncovered many elements that may contribute to the development of Alzheimer's disease. Machine learning techniques have the potential to improve the diagnosis method for Alzheimer's disease dramatically. Deep learning algorithms have seen a lot of success in the field of medical image analysis in recent years. Yet, there has been little study into the use of deep learning algorithms for detecting and categorizing Alzheimer's disease. This paper presents a deep-learning model for identifying and classifying different types of Alzheimer's disease using brain MRI data. The data collection contains a total of 6400 photographs. There are 1279 photographs used for training and 5121 for testing purposes; in this case, data augmentation is also employed to diversify the dataset. A CNN-based transfer learning model named Inception V3 is used to create results in this work, and the outcomes are compared based on precision, recall, F1-Score, and accuracy. The model's total accuracy is 89%. The use of categorization in predicting Alzheimer's disease is precious to medical experts involved in the condition's treatment.
Emotional Virtual Reality Stroop Task

Anxiety classification in VirtualReality using biosensors is an ongoing challenge for the area. This paperpresents the development and validation of a Virtual Reality version of thewell-established cognitive task; the Emotional Stroop Colour-Word Task. Thisenvironment aims to be able to differentiate between varying states of anxietyof the user. The task is split into three “stages” to correspond with threedifferent states; no anxiety, low anxiety and anxiety. To validate whether theenvironment can accurately separate between these states, we have used arepeated measures design comparing each stage based on the mean (1) averageelectrodermal activity, (2) average self-reported measure of anxiety, and (3)average performance of the users in the stage. Repeated measures ANOVA resultsshow that there is a significant difference between stages in electrodermalactivity and self-reported anxiety, but not in response time. This signifiesthat it might be possible to record different anxiety states using theapplication. Further research must be conducted to test this hypothesis.
Multi-Agent Advantage Actor-Critic Learning For Message Content Selection in Cooperative Perception Networks

Recent advancements in autonomous vehicle perception haveexposed limitations of onboard sensors such as radar, lidar, and cameras, which road obstacles and adverse weather conditions can impede. Connected and Autonomous Vehicles (CAVs) are leveraging wireless communications to share perception information through a process called Cooperative Perception (CP), aiming to provide a more comprehensive understanding of their environment. However, this can result in excessive redundant and useless information in the network, as the same road objects may be detected and exchanged simultaneously by multiple CAVs. This not only consumes more network resources but also may overload the communication channel, reducing the delivery of perception information to CAVs and ultimately decreasing the overall CP awareness in the network. This paper introduces MCORM, a multi-agent learning method based on the advantage actor-critic algorithm to maximize object usefulness and reduce redundancy in the network. Our evaluations demonstrate that through this method, CAVs learn optimal CP message content selection policies that maximize usefulness. Further more, our proposal proves to be more effective in mitigating object redundancy and improving network reliability in comparison to existing approaches.
A Scalable Asynchronous Traffic Shaping Mechanism for TSN with Time Slot and Polling

The IEEE 802.1 Time Sensitive Networking (TSN) task group is devoted to improving deterministic delay during data communication. To schedule traffic in TSN, Asynchronous Traffic Shaping (ATS) has been introduced to guarantee bounded maximum delays without complicated time synchronization, and Urgency Based Scheduler (UBS) becomes the default implementation for ATS. However, due to the traffic fluctuation, UBS cannot achieve best delay bounds if some parameters are not configured appropriately in real time. What is more, UBS needs to consumes excessive butter resources, which prevents TSN from being deployed in real world. To solve these problems, we propose Time Sensitive Queuing (TSQ), a novel ATS mechanism. TSQ lowers the difficulty of TSN deployment by removing the parameter that need to be configured in real time. In addition, to reduce the butter consumption, TSQ applies time-slot-based packet allocation mechanism as the enqueue strategy, and time-slot-based packet polling mechanism as the dequeue strategy, respectively. Our Network Calculus analysis shows that TSQ can provide bounded delays and up to 40% butter resource reduction compared to UBS. The extensive experiments implemented in ns-3 show that TSQ consumes 33% less butter resource compared to UBS.
Cold Start for Cloud Anomaly Detection

Cloud providers need to constantly monitor their network and provide accurate timely alerts when the service level degrades. In order to do so, many providers use Anomaly Detection (AD) systems that detect deviation of the network parameters from the normal pattern. However, the accuracy of such systems strongly depends on acquiring enough data to learn the normal behavior. This problem, known as cold start, limits the ability to detect anomalies of newly created objects and thus significantly reduces the coverage of VM anomaly detection systems.In this paper we address this problem in the context of modeling VM traffic patterns in a big cloud provider setting. We first observe that the models of the deployed VMs are clustered into a relatively small number of clusters. Thus, a small number of appropriately selected models can provide an accurate modeling for a large fraction of the VMs. We then turn to the algorithmic problem and show how to efficiently find an appropriate model for a specific newly created VM. Our evaluation, based on a large set of VMs from a major cloud provider, indicates that using our algorithms, one can significantly improve anomaly detection coverage while maintaining a compatible accuracy level.
A Fast 2D Super-resolution Imaging Method via Bayesian Compressive Sensing for mmWave Automotive radar

Millimeter-wave (mmW) automotive radar imaging technology is widely applied to advanced driver assistance systems (ADAS). Existing super-resolution imaging methods can improve angular resolution for automotive radar with a limited aperture. However, these super-resolution methods have high computational complexity, meanwhile have poor imaging performance in single-snapshot. To address these problems. we propose a fast 2D super-resolution imaging method for real-time and high-quality automotive radar imaging. First, a novel Bayesian compressive sensing with the Kailath-Variant (BCS-KV) imaging method is proposed to achieve superior angular super-resolution in single-snapshot. And the K-V is used to reduce the complexity of matrix inversion. Then, in the range dimension, a Multi-Channel Accumulation (MCA) is utilized to detect the effective range unit to further reduce the 2D imaging computational complexity. Finally, both simulated and experimental results demonstrate that the proposed method has lower computational complexity and compelling imaging performance than other imaging methods.
Creativity: Mining of Innovative Thinking Using Educational Data

Innovation i.e., the process of new ideas that are useful or effective in different types of domains. Innovation should be taught in schools, homes, and other extra places. Innovative will be increased or improved with the help of Education Data Mining (EDM), which will improve the quality of Information. By employing BERT- type, objectives are to train representation from sequence process data, then fine-tunes these representations on subsequent prediction tasks, by WEKA and machine learning algorithms. In addition, we investigated students’ performance in exams using machine learning and symmetry-based learning algorithms. This article highly predicts female students pass more than boys students.
Enabling Auditable Trust in Autonomous Networks with Ethereum and IPFS

Operation and management of telecommunication networks are increasingly difficult with the demands and behaviors of users exceeding the capacity of network engineers to keep pace. This has led to increased automation of the network, enabled by various forms of intelligent software. One such proposal from the ITU-T Focus Group on Autonomous Networks (standardization group) is an architecture to achieve self-driven automation (i.e. autonomy) of network operation, whereby technology from different operators and third parties is self-assembled and deployed in production networks. This raises questions and challenges regarding transparency, auditability, and trust while maintaining interoperability.This work presents an initial study of a distributed and decentralized marketplace to bring transparent and auditable trust to the proposed architecture without sacrificing interoperable functionality. We demonstrated this by our proof of concept implementation of both the proposed architecture and marketplace based on the combination of Ethereum and IPFS.
Multi-Scale Dense Networks for Ship Classification Using Dual-Polarization SAR Images

As one of crucial remote sensing applications, ship classification using synthetic aperture radar (SAR) images has increasingly been studied in modern maritime surveillance. Nowadays, the prevailing classification paradigm for SAR ship targets is to utilize the deep network models, which presents superior performance over the traditional handcrafted feature driven methods. Of which the SAR ship classification method using densely connected convolutional neural networks (CNNs) is among the state-of-the-art. However, the general CNNs cannot fully explore the SAR ship feature representations, which limits its potentials for better classification performance. In this paper, we propose a novel multi-scale framework for the CNNs to further improve the ship classification performance with dual-polarization SAR images. Particularly, the convolutional feature maps from different spatial scales are fused to acquire multi-scale global representations of the dual-polarization SAR images, which are finally integrated by the group bilinear pooling operation in the classification layer and will further be processed by multiple classifiers for better network training. Extensive experiments have proved that the proposed method can improve the robustness and classification performance against the state-of-the-art algorithms on the OpenSARShip datasets.
Channel State Information for Human Activity Recognition with Low Sampling Rates

In this paper, it is shown that lower transmission/sampling rates can be used in human activity recognition using channel state information (F1-scores > 85%) and that extremely high sampling rates are unnecessary once the system has been deployed. This is done by analysing the effects of interpolating different sampling rates on Wi-Fi dynamic channel state information for human activity recognition. While current research focuses on training and testing with homogeneous and very high sampling rates (> 100 Hz), this paper outlines some issues with higher sampling rates and explores the impact of training and testing with heterogeneous sampling rates in order to advance more towards joint communication and sensing, where one cannot be certain of the received data rate over time while not knowing the exact training set due to weight sharing in Federated Learning. This paper shows the effect of training and testing with heterogeneous sampling rates (including interpolated datasets) on convolutional neural networks in WiFi sensing.
Neural Network Self Driving Car: A Platform for Learning and Research on a Reduced Scale

We present a self-driving car platform using vision-based neural networks for controls as a guide to those developing their own platforms. As the self-driving car industry continues to make improvements, it is crucial that the learning platforms also continue to develop to keep up with state of the art methods. In this paper, we discuss our platform: our car, our driving city, our simulator, our reinforcement learning framework for steering within the lane, and real time object detection. All of them perform on a smaller scale than real world using remote control (RC) cars with NVIDIA Jetson TX2s onboard, although other edge computing may be used in its place. We provide our code for our project at: https://github.com/byu-rvl/BYU_ECEN_Racer_Sample_Code
Low-Power and Cost-Effective Context Information Estimation for Wearables

Context awareness helps Artificial Intelligence (AI) better communicate with humans. It has a wide application range from entertainment and marketing to education and healthcare. In this paper we propose our method for a low-power, low-cost, and versatile context-aware system based on chest-worn Inertial Measurement Units (IMUs). The ability to recognize subjective feelings enables the proposed system to perform affective computing next to activity recognition, posture recognition, swallow detection, and voice activity detection. We set up a number of experiments and collected relevant data on which we are currently performing our methods.
A 6T-SRAM-Based Computing-In-Memory Architecture using 22nm FD-SOI Device

A computing in-memory architecture based on 22nm Fully Depleted Silicon On Insulator (FD-SOI) device is presented. By using FD-SOI devices, logic operations such as “and” “nor” or “ x “ Access Memory (SRAM) through the effect of body biasing in the $22\mathrm{~nm}$ FD-SOI technology. The proposed architecture contains six modules: the SRAM-based Computing InMemory module, the Data Buffer module, the Pulse Generation module, the pre-charge module, AdditionActivation-Binarization module and the System Controller module. Complex ADC and DAC circuits are not involved in this design. Thereby, by using the FD-SOI devices, the convolution or dot product operations can be realized, which are always used in artificial intelligence (AI) algorithms in a very efficient way. A Binary Multi-Layer Perception (BMLP) is mapped to examine the design. Simulations shows that our design achieves 94% accuracy in the MNIST digit recognition task. And the energy efficiency is 73.03 TOPS/W, which is far beyond traditional AI accelerators and provides an efficient path for massive computing in-memory operation.