Miracle-dz/UniFAR-FTUs huggingface dataset:
3D Modeling using Multi-View Images

ii ABSTRACT There is a growing interest in the creation of three-dimensional (3D) images and videos due to the growing demand for 3D visual media in commercial markets. A possible solution to produce 3D media files is to convert existing 2D images and videos to 3D. The 2D to 3D conversion methods that estimate the depth map from 2D scenes for 3D reconstruction present an efficient approach to save on the cost of the coding, transmission and storage of 3D visual media in practical applications. Various 2D to 3D conversion methods based on depth maps have been developed using existing image and video processing techniques. The depth maps can be estimated either from a single 2D view or from multiple 2D views. This thesis presents a MATLAB-based 2D to 3D conversion system from multiple views based on the computation of a sparse depth map. The 2D to 3D conversion system is able to deal with the multiple views obtained from uncalibrated hand-held cameras without knowledge of the prior camera parameters or scene geometry. The implemented system consists of techniques for image feature detection and registration, two-view geometry estimation, projective 3D scene reconstruction and metric upgrade to reconstruct the 3D structures by means of a metric transformation. The implemented 2D to 3D conversion system is tested using different multi-view image sets. The obtained experimental results of reconstructed sparse depth maps of feature points in 3D scenes provide relative depth information of the objects. Sample ground-truth depth data points are used to calculate a scale factor in order to estimate the true iii depth by scaling the obtained relative depth information using the estimated scale factor. It was found out that the obtained reconstructed depth map is consistent with the ground-truth depth data. iv ACKNOWLEDGEMENTS
An Adversarial Robust Behavior Sequence Anomaly Detection Approach Based on Critical Behavior Unit Learning

Sequential deep learning models (e.g., RNN and LSTM) can learn the sequence features of software behaviors, such as API or syscall sequences. However, recent studies have shown that these deep learning-based approaches are vulnerable to adversarial samples. Attackers can use adversarial samples to change the sequential characteristics of behavior sequences and mislead malware classifiers. In this paper, an adversarial robustness anomaly detection method based on the analysis of behavior units is proposed to overcome this problem. We extract related behaviors that usually perform a behavior intention as a behavior unit, which contains the representative semantic information of local behaviors and can be used to improve the robustness of behavior analysis. By learning the overall semantics of each behavior unit and the contextual relationships among behavior units based on a multilevel deep learning model, our approach can mitigate perturbation attacks that target local and large-scale behaviors. In addition, our approach can be applied to both low-level and high-level behavior logs (e.g., API and syscall logs). The experimental results show that our approach outperforms all the compared methods, which indicates that our approach has better performance against obfuscation attacks.
Nanomaterials Reinforced Polymer Filament for Fused Deposition Modeling: A State-of-the-Art Review

For the past years, fused deposition modeling (FDM) technology has received increased attention in the applications of industrial manufacturing fields, particularly for rapid prototyping, small batch production and highly customized products, owing to the merits of low-cost, user-friendliness and high design freedom. To further expand the application potential and promote the performance of the as-manufactured products, many efforts have been spent on the development of suitable materials for FDM applications. In recent years, the involvement of nanomaterials in the FDM-based polymer matrix, which has been demonstrated with great opportunities to enhance the performance and versatility of FDM printed objects, has attracted more and more research interest and the trend is expected to be more pronounced in the next few years. This paper attempts to provide a timely review regarding the current research advances in the use of nanomaterials to reinforce polymer filaments for the FDM technique. Polymer composite filaments based on nanomaterials such as carbon nanotubes, nanoclay, carbon fibers, graphene, metal nanoparticles and oxides are discussed in detail regarding their properties and applications. We also summarized the current research challenges and outlooked the future research trends in this field. This paper aims at providing a useful reference and guidance for skilled researchers and also beginners in related fields. Hopefully, more research advances can be stimulated in the coming years.
Effect of Arsenic Soil Contamination on Stress Response Metabolites, 5-Methylcytosine Level and CDC25 Expression in Spinach

Experimental spinach plants grown in soil with (5, 10 and 20 ppm) arsenic (As) contamination were sampled in 21 days after As(V) contamination. Levels of As in spinach samples (from 0.31 ± 0.06 µg g−1 to 302.69 ± 11.83 µg g−1) were higher in roots and lower in leaves, which indicates a low ability of spinach to translocate As into leaves. Species of arsenic, As(III) and As(V), were represented in favor of the As (III) specie in contaminated variants, suggesting enzymatic arsenate reduction. In relation to predominant As accumulation in roots, changes in malondialdehyde levels were observed mainly in roots, where they decreased significantly with growing As contamination (from 11.97 ± 0.54 µg g−1 in control to 2.35 ± 0.43 µg g−1 in 20 ppm As). Higher values in roots than in leaves were observed in the case of 5-methylcytosine (5-mC). Despite that, a change in 5-mC by As contamination was further deepened in leaves (from 0.20 to 14.10%). In roots of spinach, expression of the CDC25 gene increased by the highest As contamination compared to the control. In the case of total phenolic content, total flavonoid content, total phenolic acids content and total antioxidant capacity were higher levels in leaves in all values, unlike the roots.
Bayesian Models for Targeted Cyber Deception Strategies (Student Abstract)

We propose a model-driven decision support system (DSS) based on a Bayesian belief network (BBN) to support cyber deception based on a detailed model of attacker beliefs. We discuss this approach using a case study based on passively observed operating system (OS) fingerprinting data. In passive reconnaissance attackers can remain undetected while collecting information to identify systems and plan attacks. Our DSS is intended to support preventative measures to protect the network from successful reconnaissance, such as by modifying features using deception. We validate the prediction accuracy of the model in comparison with a sequential artificial neural network (ANN). We then introduce a deceptive algorithm to select a minimal set of features for OS obfuscation. We show the effectiveness of feature-modification strategies based on our methods using passively collected data to decide what features from a real operating system (OS) to modify to appear as a fake [different] OS.
Is Social Training Delivered with a Head-Mounted Display Suitable for Patients with Hereditary Ataxia?

Social cognition is fundamental in everyday life to understand “others’ behavior”, which is a key feature of social abilities. Previous studies demonstrated the efficacy of a rehabilitative intervention in semi-immersive virtual reality (VR) controlled by whole-body motion to improve the ability of patients with cerebellar disorders to predict others’ intentions (VR-SPIRIT). Patients with severe ataxia that have difficulties at multiple levels of social processing could benefit from this intervention in terms of improving their social prediction skills, but they may have difficulties in controlling VR with whole-body movements. Therefore, we implemented VR-SPIRIT on a wearable, affordable, and easy-to-use technology, such as the Oculus Quest, a head-mounted display. The aim of this work was to evaluate the usability and tolerability of this VR application. We recruited 10 patients (37.7 ± 14.8 years old, seven males) with different types of hereditary ataxia who performed a single VR-SPIRIT session using the Oculus Quest viewer. After the session, patients answered a series of questionnaires to investigate the overall usability of the system and its potential effects in terms of cyber sickness. The preliminary results demonstrated system usability and tolerability. Indeed, only three patients did not complete the session due to different problems (dizziness, nausea, and boredom). In future studies, more patients will be enrolled to assess the effectiveness of the application, paving the way for the implementation of social training that can also be delivered at home.
Ensemble of Fine-tuned Deep Learning Models for Monkeypox Detection: A Comparative Study

Monkeypox is a rare viral disease that is caused by the monkeypox virus. Monkeypox has clinical symptoms that are similar to those of other diseases such as measles and chickenpox, which makes early detection challenging. The early detection of monkeypox is essential to prevent its spread and reduce the risk of human-to-human transmission. Our study introduces a new method for detecting monkeypox at an early stage using modified transfer learning (TL) algorithms and an ensemble algorithm. The proposed approach can effectively distinguish it from other diseases that have similar symptoms. We used two different datasets, the “Monkeypox Skin Images Dataset (MSID)” and the "Monkeypox-dataset-2022(MD-2022)", which contain images from four classes, including monkeypox, measles, chickenpox, and normal images. We used stratified cross-validation to ensure that each fold of the cross-validation procedure contains a representative sample of each class, which is important when dealing with imbalanced datasets. To evaluate our proposed approach, we trained five pre-trained models, namely DenseNet121, ResNet152V2, ResNet50, InceptionV3, and EfficientNetV2B3, on each dataset separately. The achieved accuracy scores for the MD-2022 dataset were 89.4%, 84.2%, 89.4%, 84.2%, and 84.2%, respectively, while for the MSID dataset, the accuracy scores were 97.4%, 96.2%, 93.6%, 93.6%, and 95% for DenseNet121, ResNet50, InceptionV3, EfficientNetV2B3, and ResNet152V2, respectively. Subsequently, we constructed an ensemble model using a majority voting approach, which combined the predictions of the five models. Our findings indicate that the ensemble model outperformed certain individual models and demonstrated higher efficacy in monkeypox detection by achieving an accuracy score of 89.4% and 98.7% for the "Monkeypox-dataset-2022" and “Monkeypox Skin Images Dataset (MSID)” respectively.
HG-SL: Jointly Learning of Global and Local User Spreading Behavior for Fake News Early Detection

Recently, fake news forgery technology has become more and more sophisticated, and even the profiles of participants may be faked, which challenges the robustness and effectiveness of traditional detection methods involving text or user identity. Most propagation-only approaches mainly rely on neural networks to learn the diffusion pattern of individual news, which is insufficient to describe the differences in news spread ability, and also ignores the valuable global connections of news and users, limiting the performance of detection. Therefore, we propose a joint learning model named HG-SL, which is blind to news content and user identities, but capable of catching the differences between true and fake news in the early stages of propagation through global and local user spreading behavior. Specifically, we innovatively design a Hypergraph-based Global interaction learning module to capture the global preferences of users from their co-spreading relationships, and introduce node centrality encoding to complement user influence in hypergraph learning. Moreover, the designed Self-attention-based Local context learning module first introduce spread status to highlight the propagation ability of news and users, thus providing additional signals for verifying news authenticity. Experiments on real-world datasets indicate that our HG-SL, which solely relies on user behavior, outperforms SOTA baselines utilizing multidimensional features in both fake news detection and early detection task.
Trashcano: Developing a quantitative teaching tool to understand ballistics accelerated by explosive volcanic eruptions

Accurate predictions of volcanological phenomena, such as the trajectory of blocks accelerated by volcanic explosions, require quantitative skills training. Large outdoor experiments can be useful to convey concepts of volcanic processes to students in an exciting way. Beyond the fun aspects, these experiments provide an opportunity to engage with the physics of projectile flight and help promote mathematical learning within the Earth Sciences. We present a quantitative framework required to interpret ballistic trajectories and the outdoor experiment known commonly as “trashcano”, taking a step-by-step approach to the physics of this problem, and deriving a range of mathematical solutions involving different levels of complexity. Our solutions are consistent with the predictions from established computer programs for volcanic ballistic trajectory modelling, but we additionally provide a nested set of simplified solutions for a range of teaching scenarios as well as simulated datasets for use where the full experiment may not be possible.
Let's twist again

We exhibit a Hopf superalgebra structure of the algebra of field operators of quantum field theory (QFT) with the normal product. Based on this we construct the operator product and the time-ordered product as a twist deformation in the sense of Drinfeld. Our approach yields formulas for (perturbative) products and expectation values that allow for a significant enhancement in computational efficiency as compared to traditional methods. Employing Hopf algebra cohomology sheds new light on the structure of QFT and allows the extension to interacting (not necessarily perturbative) QFT. We give a reconstruction theorem for time-ordered products in the spirit of Streater and Wightman and recover the distinction between free and interacting theory from a property of the underlying cocycle. We also demonstrate how non-trivial vacua are described in our approach solving a problem in quantum chemistry.
SH waves in layered transversely isotropic media—An asymptotic expansion approach

abstract An asymptotic expansion of the displacement vector in terms of inverse powers of frequency is employed to investigate the dynamic (amplitude) properties of SH waves propagating in plane-layered transversely isotropic media. Both reflected and head waves are considered in terms of the asymptotic expansion, and their ranges of validity and accuracy are discussed. Although the solution of the most general case of wave propagation in an anisotropic medium has been presented in the literature (C˘ervený, 1972), it is instructive to consider this simple case in which many quantities inherent to wave propagation in anisotropic media can be more readily understood and can be solved analytically rather than reverting to numerical methods.
The Influence of E-Servqual on Customer E-Satisfaction, A Case of A State-Owned Bank

The goal of this study is to determine the influence of e-servqual on customer e-satisfaction. The study took a case in a state-owned bank that has been applied digital service. It focused on a unit located in Semarang and used purposive sampling for selecting the respondents who use the bank’s digital service of particularly BRIMO, QRIS, Brizzi, and SMS Banking. The data from 120 customers was gathered using questionnaire and was analyzed using multiple regression. E-servqual was measured using nine dimensions of efficiency, reliability, responsiveness, personal needs, fulfillment, security/trust, ease of use, site organization, and privacy. The results show that e-satisfaction is significantly influenced by the dimensions of e-servqual. However, of the nine dimensions, only site organization that has a negative effect on e-satisfaction.
pdRide: Privacy-Preserving Distributed Online Ride-Hailing Matching Scheme

Privacy-preserving online ride-hailing (ORH) service enables riders and drivers to conveniently establish optimized ride-hailing through mobile applications without disclosing their location information. In order to alleviate the load of central server and unnecessary increased response latency caused by centralized schemes, we investigate the privacy-preserving ORH matching service in distributed deployment environment. In this paper, we first design three secure outsourced calculation protocols based on Distributed Two-Trapdoor Public-Key Cryptosystem (DT-PKC), including ciphertext packing, blinding and decryption protocol across domains (CPBD), secure Euclidean square distance calculation protocol across domains (SESDC) and secure minimum distance selection protocol (SMDS). Then, we apply the protocols to construct a privacy-preserving distributed ORH matching scheme named pdRide. Geographically distributed road-side unit (RSU) and computation service provider (CSP) collaborate to securely select the matching driver for the requesting rider within a range. Specifically, SESDC can effective calculate the Euclidean square distances between multiple drivers and requesting rider over the encrypted location information with different keys. SMDS can select the driver with the minimum distance for the requesting rider on the encrypted distances. Finally, experiment results demonstrate its effectiveness in terms of communication overhead, computation overhead and transmission latency.
A Critical Race Theory Assessment of Law Student Needs

Background/Context: Law students of color have been struggling to recover from the heightened challenges they endured during the first two years of the pandemic. Struggles with food insecurity, financial anxiety, and emotional strain contribute to declining academic success for populations that were marginalized on law school campuses long before COVID. Legislative support is necessary to support students through this era so they can maximize their full potential. Purpose/Objective/Research Question/Focus of Study: The study seeks to understand law students’ challenges during COVID and consider ways that administrators, legislators, and others can ameliorate their struggles. The objective is to provide greater support to already marginalized students during a time of significant stress and pressure. The focus of the study is on serving the needs of students of color, particularly women of color (drawing from an intersectional raceXgender framework from critical race theory). Research Design: The article draws on data from the Law School Survey of Student Engagement (LSSSE). With participation from partner law schools, LSSSE collects annual longitudinal data from law students, now housing responses from almost 300,000 students over 19 years. This study of law student needs draws from more than 13,000 responses collected in spring 2021, highlighting findings from the Coping with COVID module, a short set of questions focused on anticipated student challenges during the pandemic. The article presents findings disaggregated by race as well as raceXgender to underscore ways that COVID deepened existing disparities and heightened challenges for already vulnerable populations. Conclusions/Recommendations: The article concludes with recommendations for both institutional and legislative solutions to the identified student struggles. Law schools must allocate greater resources to student needs that range from mental health counseling to academic support—and only after first identifying the unique challenges facing women of color and other students traditionally left at the margins. Legislators must recognize that law students, while privileged in many ways, nevertheless need ongoing support to meet their basic needs; they should consider expanding Supplemental Nutrition Assistance Program (SNAP) benefits to cover law students, providing more financial aid and loan forgiveness, and prioritizing rental assistance so that law students can focus on their academic success and reach their full potential as attorneys.
Splitting of beams caused by multiple connections along the beam span

: In the past splitting of beams caused by connection perpendicular to grain has drawn attention. Models have mainly being developed considering one mid span connection. Some semi-empirical models assume the splitting capacity to be proportional with the number of connections when sufficiently spaced. However, recent experiments show this is not the case. Experiments show a considerable drop in splitting capacity per connection. Until now only a fracture mechanical model is able to explain the result for beams loaded by two connections. Experiments with three connections along the span of a simply supported beam show an even greater drop in splitting capacity per connection. The test results are presented and evaluated and compared to model predictions.
Coffee Consumption and Risk of Hypertension in Adults: Systematic Review and Meta-Analysis

Objectives: The association between coffee intake and hypertension (HTN) risk is controversial. Therefore, this systematic review and meta-analysis aimed at summarizing the current evidence on the association of coffee with hypertension risk in observational studies. Methods: PubMed/Medline and Web of Science were searched for observational studies up to February 2023. Observational studies which assessed the risk of HTN in the highest category of coffee consumption in comparison with the lowest intake were included in the current meta-analysis (registration number: CRD42022371494). The pooled effect of coffee on HTN was evaluated using a random-effects model. Results: Twenty-five studies i.e., thirteen cross-sectional studies and twelve cohorts were identified to be eligible. Combining 13 extracted effect sizes from cohort studies showed that higher coffee consumption was associated with 7% reduction in the risk of HTN (95% CI: 0.88, 0.97; I2: 22.3%), whereas combining 16 effect sizes from cross-sectional studies illustrated a greater reduction in HTN risk (RR = 0.79, 95% CI: 0.72, 0.87; I2 = 63.2%). These results varied by studies characteristics, such as the region of study, participants’ sex, study quality, and sample size. Conclusions: An inverse association was found between coffee consumption and hypertension risk in both cross-sectional and cohort studies. However, this association was dependent on studies characteristics. Further studies considering such factors are required to confirm the results of this study.