Miracle-dz/UniFAR-FTUs huggingface dataset:
What Can I Do There? Controlling AR Self-Avatars to Better Perceive Affordances of the Real World

This work explores a new usage of Augmented Reality (AR) to extend perception and interaction within physical areas ahead of ourselves. To do so, we propose to detach ourselves from our physical position by creating a controllable “digital copy”; of our body that can be used to navigate in local space from a third-person perspective. With such a viewpoint, we aim to improve our mental representation of distant space and understanding of action possibilities (called affordances), without requiring us to physically enter this space. Our approach relies on AR to virtually integrate the user’s body in remote areas in the form of an avatar. We discuss concrete application scenarios and propose several techniques to manipulate avatars in the third person as a part of a larger conceptual framework. Finally, through a user study employing one of the proposed techniques (puppeteering), we evaluate the validity of using third-person embodiment to extend our perception of the real world to areas outside of our proximal zone. We found that this approach succeeded in enhancing the user’s accuracy and confidence when estimating their action capabilities at distant locations.
Detecting Malware in Android Applications by Using Androguard Tool and XGBoost Algorithm

Android is the most popular operating system for smartphones and tablets. With its popularity, Android mal ware has also grown dramatically. Many conventional malware detection techniques are now not sufficient, due to sophisticated detection avoidance strategies. According to ongoing research, one harmful Android software is released every 10 seconds. To counter these significant mal ware campaigns, scalable detection approaches require that can provide quick and accurate identification of mal ware apps. To overcome the above issues, we proposed a method to detect malware in Android applications by extracting features like activities, services, requested permissions, and intent filters from the manifest file. Furthermore, the androguard tool is used to disassemble the code and extract all suspicious API calls by reading the dex code. These extracted features are serialized in feather data format for efficient retrieval. After that, the XGBoost algorithm is used to detect the malware. The result of the proposed method gives 97% accuracy.
The Third BATSE Gamma-Ray Burst Catalog

The Burst and Transient Source Experiment (BATSE) on the Compton Gamma Ray Observatory (CGRO) has triggered on 1122 cosmic gamma-ray bursts between 1991 April 19 and 1994 September 19. These events constitute the Third BATSE (3B) burst catalog. This catalog includes the events previously reported in the 2B catalog, which covered the time interval 1991 April 19 to 1993 March 9. We present tables of the burst occurrence times, locations, peak fluxes, fluences, and durations. In general, results from previous BATSE catalogs are confirmed here with greater statistical significance. The angular distribution is consistent with isotropy. The mean galactic dipole and quadrupole moments are within 0.6 a and 0.3 a, respectively, of the values expected for isotropy. The intensity distribution is not consistent with a homogeneous distribution of burst sources, with V/V(sub max) = 0.33 +/- 0.01. The duration distribution (T(sub 90)) exhibits bimodality, with peaks at approx. 0.5 and approx. 30 s. There is no compelling evidence for burst repetition, but only weak limits can be placed on the repetition rate.
Event-Based Rumor Detection using LSTM Models For Arabic Content on Twitter

Due to the flexibility of sharing and exchanging information, Microblogging networks such as Twitter have become more and more popular over the last few years. The nature of social media as well provides a rich and facilitated ground for rumormongers to spread false stories and information that may result in major mess and unpredictable reactions from involved individuals. Detecting rumors automatically at their early formative and diffusion phases is particularly crucial to minimize their negative and catastrophic effects. Many effective solutions to detect rumors on Twitter have been presented over the last few years. However, most of the researches were limited to certain languages, such as English and Chinese. Few studies focused on the Arabic Language; Most of those studies were concerned only to detect rumors at the tweet-level and neglecting event-level based detection. In this paper, we built two RNN-LSTM deep learning models to classify Arabic Twitter content as rumor or non-rumor more efficiently utilizing linguistic features. The constructed models are capable to detect rumor events on Twitter. An attention mechanism was embedded to optimize the performance of these models where they achieved accuracy above 93%.
Detection of Pathologies in X-Ray Chest Images using a Deep Convolutional Neural Network with Appropriate Data Augmentation Techniques

Current advances in trained Deep Learning models have allowed architectures, such as Convolutional Neural Networks to outperformed radiologists in developing complex tasks, including detecting pathologies in chest X-ray images. To train these detection models, labeled data sets for identifying pathologies and its bounding box are necessary. However, most available datasets in the scientific community contain the classification labels only, and those with the bounding boxes need to be cleaned and analyzed. In this work, we propose a cleaning methodology and data augmentation techniques that could help perform efficient training procedures, in particular for pathology detection models in chest X-ray images. We found that using these approaches, the models’ training Mean Average Precision (mAP) reached 0.93.
Suicidal Ideation Detection on Social Media: A Machine Learning Approach

Mental illness is a huge problem among the population, identifying the individual who is at risk of suicide is necessary and is the first and foremost step to averting suicide. The purpose of the study is to distinguish between suicidal and non-suicidal posts that have been gathered from social media. The pre-processed data was utilised to perform a variety of machine learning algorithms, including Support Vector Machine, Logistic Regression, and AdaBoost, as well as term frequency-inverse document frequency for embedding. The results indicate that Support Vector Machine has the highest precision (80.72%), while Logistic Regression has the best accuracy (80.75%) and recall (77.81%).
Study on visual odometry and optimization of SLAM system

With the development of computer technology and the increasing data processing capability and speed, the robot vision-based SLAM problem is gradually emerging. In this paper, based on the traditional vision odometry implementation scheme, the ORB feature extraction algorithm is used to extract the feature points of the image and calculate the BRIEF descriptors, and then a combination of PnP and improved ICP is used to solve the bit pose of the camera. In order to improve the accuracy and stability of the algorithm, the PROSAC algorithm is used to reject the mis-match in this paper. Finally, the algorithm is tested using the public dataset of TUM, which proves that the algorithm has faster computational speed and stability.
Convergent Born series for large refractive indices

It is shown that a generalized overrelaxation method, when applied to the domain integral equation that arises in scattering by penetrable objects, results in a convergent iterative solution. This modified Born series converges when the original Born series diverges for a wide range of indices of refraction and scatterer size. Moreover the modified series converges more rapidly than the original Born series in those cases for which the original series converges. Numerical examples demonstrating the effectiveness of this method are presented in one, two, and three dimensions.
High-sensitivity pesticide detection via silicon nanowires-supported acetylcholinesterase-based electrochemical sensors

We report the use of a silicon-based nanocomplex, i.e., gold nanoparticles-coated silicon nanowires, for the improvement of acetylcholinesterase (AChE)-based electrochemical sensors for pesticide detection. Owing to the high electrical conductivity of the nanocomplex and its compatibility with the enzyme, the sensor exhibited significantly enhanced performance. The AChE enzyme bound to the surface possessed Michaelis-Menton constant of 81 mu M, resembling that in its free form. The sensor showed rapid response toward substrate acetylcholine in the concentration range of 1.0 mu M-1.0 mM. This AChE nanosensor could detect as low as 8 ng/L dichlorvos, an organophosphate pesticide. (C) 2008 American Institute of Physics.
Disease Detection in Cactus (Beles) via the Use of Machine Learning: A Proposed Technique

Machine learning is a crucial technology that may assist individuals in various fields (agriculture, healthcare, household, transportation, etc.) and stages of life. Machine learning improves performance precision (prediction). It utilises numerous data formats (picture, video, audio, and text) for varied applications and purposes. Our effort has centred on the early detection of cactus diseases to prevent the quantitative and qualitative decline of crop yield. To do this, we have utilised photos of diseased and healthy cacti. Using the imadjust, guided filter, and K-means clustering approaches, the images were improved, noises were removed, and the images were segmented to generate a better model. After executing each technique and evaluating their performance, these picture preparation techniques were picked from a large pool of options. As part of the model creation process, feature extraction approaches (Color histogram, Bag of features, and GLCM) were used to extract colour, bag of features, and texture features, respectively. After testing the model with these features, a bag of features was determined to be the best for generating a better model, hence they were chosen as the model's features. Our proposed machine learning model will be developed utilising a bag of features and linear SVM. We will use VGG16 to clear and enhanced the image quality. Other machine learning techniques will tried to train and evaluate the model for disease detection, however linear SVM will demonstrate the highest performance (97.2%) as we analyze and predict it from the previous work (average).
Stereoscopic Video See-Through Head-Mounted Displays for Laser Safety: An Empirical Evaluation at Advanced Optics Laboratories

Nowadays, high-power and multi-spectral lasers are used in many scientific experiments and industrial processes. Those laser sources can rapidly cause permanent damage to human eyes. Research and development work with those laser sources requires typically wearing personal protective equipment (PPE), such as laser safety goggles as eye protectors. Currently, laser safety goggles are based on optical spectral filters, which block spectral bands where hazardous laser radiation is emitted. Such laser safety goggles can filter up to 99% of the visible spectrum, rendering researchers working in hazardous and complex laboratory environments visually impaired. Video see-through head-mounted displays (VST-HMD) could be used as eye protectors without reducing users’ visibility of the environment since they can be constructed such that all laser and ambient light is blocked from the human eye. To date, this application domain is still largely unexplored in the MR community. To our best knowledge, there has been no comprehensive work that investigates the human factors of such an eye protection method at an advanced optics laboratory. In this work, we present the results of an empirical study where we evaluate the usability, perceived safety, advantages, and limitations of using VST-HMDs as laser safety goggles. We use a stereoscopic VST-HMD developed through a human-centered design approach at one of the most advanced optics laboratories in the world. 18 participants including 14 laser experts evaluated the current prototype. Our user evaluation and field studies confirm that the complex and hazardous working conditions at high-energy laser laboratories could be significantly improved with MR technology.
Substrate Integrated Waveguide Antenna with Slow Wave Effect to Minimize Dimensions

On the basis of a previously developed slow wave substrate integrated waveguide, this study offers a new design, simulation, and measurement results of a circularly polarized square slot antenna backed by cavities (SW-SIW). 11.2 GHz is the intended operating frequency for the planned antenna. The size of the proposed antenna has been reduced to 13% when compared to the reference design and 49% when compared to the traditional SIW version of antenna thanks to the internal metallized via that creates the slow wave effect for the physical separation of the electric and magnetic field in the designed antenna structure. The designed antenna has been tested and simulated, and the results show that it performs better than expected in terms of gain, radiation loss, and return loss. According to the suggested design, the maximum return loss achieved by the proposed antenna is 37 dB at 11.2 GHz frequency, and the return loss is less than 10 dB in the frequency range of 11 to 11.3 GHz. With proposed antenna we got the gain of 5.9 dB as compare to reference antenna with 4.8 dBi of gain.
Microstrip Patch Antennas with Enhanced Linearly-or Circularly-Polarized Beamwidth: A Mini-Review

In this paper, the linearly- and circularly-polarized (LP or CP) beamwidths of low-profile microstrip patch antennas (MPAs) are summarized based on our previous works. Herein, the equivalent magnetic currents (EMCs) are used to analyze the radiation beams. Firstly, the half-power beamwidth (HPBW) of the LP MPA is studied, its HPBW could be significantly widened by using a single EMC. Secondly, the axial-ratio beamwidth (ARBW) of the CP MPA is extensively analyzed. It shows that its ARBW could be obviously extended by controlling the spacing between these EMCs. With these arrangements, both of these antennas are fabricated and measured finally. The measured results show that the HPBW of LP antenna could be obviously extended from 80° to around 135°. Meanwhile, the 3-dB ARBW of the CP antenna could be increased from 50° to 140° as desired.
Determinants of Continuance Intention to Use Mutual Fund Investment Apps: The Changing of User Behavior During the Pandemic Crisis

The COVID-19 pandemic and the implementation of large-scale social restrictions have caused economic and social disruptions globally, including in Indonesia. These difficult situations have shifted the financial attitudes, especially among the working-age group in Indonesia, to be more focused on saving and investing. One of the popular options among new investors is mutual fund investment because it requires a lower cost than other options, offers diversified holdings, and supports a variety of risk profiles. New investors primarily use an investment app, such as a mutual fund investment application, to manage their investment portfolios safely and seamlessly. In line with the rapid growth of mutual fund investors, the number of mutual fund investment apps in Indonesia has increased to more than 80 providers in 2022, resulting in fiercer competition between service providers. Therefore, retaining and increasing the apps’ investment usage is crucial to providers’ business success. This study investigates factors affecting the investor’s continuance intention to use mutual fund investment apps by adapting the expectation-confirmation model (ECM) with other key constructs, i.e., perceived security, trust, and self-efficacy. The collected data from 384 participants were analyzed using Structural Equation Modelling. The results indicate that perceived usefulness, trust, and user satisfaction positively affect the continuance use of mutual fund investment apps. Based on findings, this study proposes practical strategies to retain the application usage, such as implementing security measures, personalized investment courses and advisories based on the user’s profile risks and preferences, and recommendations on achieving the user’s investment goals based on machine learning solutions. Digital solutions, such as financial and investment apps, may contribute to expanding access to banking, financial services, and financial inclusion in the Indonesian population.
SARS-CoV-2 outbreak uplifted collectivistic expression in the Hubei Daily

Following the outbreak of SARS-CoV-2, collectivistic words have been used more frequently on Sina Weibo and the People’s Daily. However, the studies on Sina Weibo and the People’s Daily can only reflect the overall impact by SARS-CoV-2 in China. To examine the influence of SARS-CoV-2 on collectivism/individualism, we first investigated the Hubei Daily, an authoritative local media in Hubei, the first province to discover SARS-CoV-2, to see the impact of SARS-CoV-2 on collectivism/individualism. We analyzed data from the Hubei Daily, using the same collectivistic/individualistic words identified in prior studies and found that the outbreak of SARS-CoV-2 increased collectivism and decreased individualism significantly. Next, we analyzed the same data using the individualistic/collectivistic word bank created in a different cross-culture study based on Sina Weibo posts. The results showed that, during the SARS-CoV-2, collectivistic words were used more frequently; no significant changes were seen regarding individualist words. Lastly, we created a COVID-19 word bank and conducted a regression analysis to examine the relationship between collectivistic word frequency and COVID-19 word frequency in the after-breakout period of SARS-CoV-2 and found that the severity of SARS-CoV-2 predicted collectivist word frequency change in the Hubei Daily.
Research on Image Fusion Methodof SAR and Visible Image Based on CNN

Synthetic aperture radar (SAR) imaging guidance has all-weather detection capability, and has been applied to aerial vehicles in the field of air combat. The research on sar/ visible image fusion technology has important theoretical and practical significance for improving the ability of target detection and precision guidance in complex combat environment. The imaging mechanism of visible light image and SAR image is different, and there are great differences in characteristics between images. Using image fusion technology to organically combine different images, complement their advantages and disadvantages, and can better interpret their scene information. Aiming at the difference feature extraction of SAR optical images, the modal classification task and ground object classification task are proposed, and the fine-tuning VGG convolution neural network model is designed, which uses less training time and achieves better classification results. For SAR optical image mapping, space-frequency consistent generation countermeasure network model framework (SFGAN) is proposed, which makes the texture details of the generated image more realistic and the contour features of roads and rivers more clear. Training from the original data, ablation experiments were designed to verify the effectiveness of the loss function. Experimental results show that the SFGAN method can learn the matching relationship from the small-scale SAR optical image pair data, so as to realize the mapping from SAR image to optical image.
4D Recurrent Neural Network Based on Time-Space-Frequency Domain Fusion for EEG Emotion Recognition

In recent years, EEG emotion recognition has had broad prospects in various research fields. Most of the existing EEG identification studies use a single feature and do not simultaneously consider the effects of EEG information on time, space and frequency. However, neuroscience shows that the response degree of emotion depends on the EEG channels of different frequencies. In this paper, A recursive neural network approach is proposed to analyze EEG signals at different times and regions by integrating the time domain, frequency domain, and space domain. Firstly, the 2D channel map is extracted to obtain frequency domain features, and then the 2D channel map with different frequency domain features is fused to obtain 3D spatial frequency domain data. Finally, the 3D data in different time periods are connected to obtain the 4D feature structure used for the training depth model. 4D data feature makes up for the defect that a single feature cannot perceive channel information of different frequencies and produce a more significant effect than single feature emotion recognition. Experiments on the SEED data set show that the multi-feature fusion model performs better than other models in EEG emotion recognition.
Numerical simulations of a lobed fuel injector

Numerical modeling of the nonreactive mixing processes associated with a lobed fuel injector in a coflowing air stream is presented. The lobed fuel injector is a device which generates strong streamwise vorticity, producing locally high strain rates which can enhance the molecular mixing of reactants while delaying ignition in a controlled manner. Vortex element modeling is used to simulate flow field evolution and fuel element mixing characteristics for this lobed fuel injector. Quantitative predictions for vorticity generation and qualitative results for streamwise rollup compare well qualitatively with recent experimental investigations of this flow field [Smith et al., Phys. Fluids 9, 667 (1997)]. Parametric studies of the effects of lobe amplitude-to-wavelength ratio, lobe angle, and lobe shape for given flow conditions suggest that geometrical features may be optimized to enhance mixing and control reaction processes.
Can Operational Transparency Enhance the Perception of Waiting Time? A Case Study from the Healthcare Sector

Patient satisfaction has been a vital organizational metric in medical facilities since it promotes a responsive healthcare system, increases revenue, sustains a higher quality of service, and ensures its continuity. Waiting time for getting health care services is a major metric for patient satisfaction. We hypothesize that technology can be used to enhance people’s perception of waiting time. We examine the real-time communication of operational transparency in particular, i.e., the workflow and its current status. Examples of such information include the amount of effort put into delivering a service, the current activities of doctors, the position in the queue, and the priority of the patient case. 21 participants took part in our experiment. The results showed that patients in the group that received real-time operational transparency perceived their waiting time to be shorter than those in the group that did not. Also, participants who received the information perceived stress and frustration to be less. Our study is exploratory and meant to assess the potential of operational transparency in making waiting time better. Future research can assess that on a larger sample, and also assess the side-effects of transparency, e.g., information overload, the need for extra explainability, and stress on medical care personnel.