Miracle-dz/UniFAR-FTUs huggingface dataset:
The Effects of COVID-19 and Workplace Mobility to Stock Price and Exchange Rate in Indonesia: An Econometric Approach

The COVID-19 pandemic has reached its 20th month in Indonesia and still damaged various sectors, particularly economy. The policies imposed by the government impacted mainly the stock price. exchange rate, and people mobility in Indonesia. However, there are limited studies that incorporate these variables in Indonesia context. Thus, this study investigates the relationship between the COVID-19 pandemic, stock price, exchange rate, and workplace mobility simultaneously. This study employs Vector Autoregressive (VAR) as the analysis considering its advantages in finding the causal relationship between variables and periodic interpretation using Impulse Response Function (IRF). The VAR results show that from the Granger Causality Test, it turns out that the shocks from COVID-19 positivity rate and mobility in workplaces caused the changes in stock price and exchange rate. On the other hand, the IRF results exhibit the depreciating responses of stock price and exchange rate due to the shocks of COVID-19 positivity rate and mobility are enormous in the short term. In the longer term, the stock price response needs a longer time to return to the initial condition than the exchange rate. Therefore, further policy evaluation and formulation become essential to maintain the stock price and exchange rate, mainly due to the effect of COVID-19 and workplace mobility.
Autonomous Mobile Robot as a Cyber-Physical System Featuring Networked Deep Learning and Control

In this article, we proposed an autonomous mobile robot (AMR) capable of conducting multiple deep learning inferences by a computing network as a Cyber-Physical System (CPS). In view of industry 4.0, a five-layered CPS architecture including Component, Intelligence, Cyber, Configuration, and Deployment was devised. Mechatronic control of the two-wheeled self-balancing AMR presents the first challenge to embedded computing, whereas multiple deep learning modules for visual navigation and localization further escalate the stake. To meet the challenge of real-time and affordable computing, we constructed an embedded cluster based on Raspberry Pi4s and Intel NCS2 for simultaneous intelligent inference and data exchange. We tested the performances of three networking methods, Network File System (NFS), Socket Server with Multiple Clients (SSMC), and ROS TO PIC (ROS-T), and reported the comparison. We also conducted a series of field tests on autonomous corridor navigation, localization, object delivery, and object pick-and-place to verify the performance of the self-balancing AMR. The results showed high success rates with sufficient spatial precision, stability, and robust loop closure. The web-based console realizes the functions of the digital twin and provides connectivity to higher-level management systems.
Using Real-Time Kinematics Algorithm in Mission Critical Communication for Accurate Positioning and Time Correction over 5G and Beyond Networks

At 5G and beyond networks, accurate localization services and nanosecond time synchronization are crucial to enabling mission-critical wireless communications technologies and techniques such as autonomous vehicles and distributed multiple-input and multiple-output (MIMO) antenna systems. This paper investigates how to improve wireless time synchronization by studying time correction based on the Real-Time Kinematics (RTK) positioning algorithm. Using the multiple Global Navigation Satellite System (GNSS) receiver references and the proposed binary GNSS satellite formation to reduce the effect of the ionosphere and troposphere delays and recede the measurement phase-range and pseudorange errors. As a result, it improves user equipment’s (UE) localization and measures the time difference between the Base Station (BS) and the UE local clocks. The results show that the positioning accuracy has been increased, and a millimetre accuracy has been achieved while attaining the sub-nanosecond time error (TE) between the UE’s and BS local clocks.
A Multi-Objective Genetic Optimization Algorithm for Charger Selection in Static Charger Deployment Scheme for WRSN

Existing research on wireless rechargeable sensor networks has focused on scheduling mobile chargers to recharge fixed or mobile nodes, while a few studies have begun to investigate the problem of charging nodes with uncertain mobility by static chargers. The main challenge in charging nodes with non-deterministic mobility is to determine the locations where the static chargers are deployed. Thus, once mobile nodes move within the effective charging range of the charger, they can be charged. The current studies have deconstructed this problem in two steps: first, generate a candidate set of chargers' locations, and then select some of them as the final deployment solution. The latter is called the charger selection problem, which is a typical multi-objective optimization problem with two goals: (1) prolonging the network lifetime, and (2) reducing the cost of network deployment. In our work, we propose a genetic-algorithm-based multi-objective optimization scheme (GAMOS) to address the charger selection problem. First, construct a D-dimensional vector, where $D$ is the cardinality of the candidate set. Each dimension of the vector represents a candidate charger and takes the value of 0 or 1, while 1 means that the candidate location is selected as the deployment solution and 0 means not. We initialize several vectors randomly in the D-dimensional solution space, and then iteratively optimize them. Finally, we use a dual indicator selection method for final solution selection. We pioneered the use of multi-objective optimization algorithms to solve the energy supply problem in WRSN, what's more, the results of simulation experiments based on the NCSU dataset[1] show that the average network survival rate of GAMOS is 90.60% and the average number of chargers is 7.20, outperforming other existing algorithms overall.
High-Speed Long-Haul Multi-Channel Nonlinear Optical Communication Systems Influenced by Equalization Enhanced Phase Noise

In this work, the performance of high-speed long- haul nonlinear Nyquist-spaced multi-channel coherent optical fiber communication systems utilizing electronic dispersion compensation and digital nonlinearity compensation is explored taking into consideration the enhanced equalization phase noise. The analytical model has also been developed to estimate the system performance under different transmission scenarios.
Combining Structural with Metabolic Networkbased on PET/MR Imaging to Classify AD Continuum

Predicting Alzheimer's disease (AD) and early diagnosis is clinically important. In this study, we proposed a dual-model connectome analysis with support vector machine model to investigate the classification performance using ab-noraml brain connectivity associated with cognitive decline. $^{18}\mathbf{F}$-Fluorodeoxyglucose (FDG) positron emission tomography (PET) and diffusion tensor imaging (DTI) data obtained from the AD Neuroimaging Initiative database, were collected from 90 healthy controls and 78 patients with preclinical AD. We constructed metabolic and structural brain connectivity from each individual's FDG PET and DTI images using the Kullback-Leibler divergence similarity estimation and white matter fiber methods. Connectome analysis was used to extract a total of 8010 metabolic and structural features. The classification results show that the combined metabolic and structural connectivity model had superior classification performance in the test dataset (AUC, $\mathbf{0.83 \pm 0.03)}$. These results suggested that a combination of metabolic and structural connectivity may be powerful tools for personalized precision medicine patients with potential to discover vaulable imaging biomarker.
Psychology Inspired Model for Hierarchical Image Aesthetic Attribute Prediction

Deep neural network has proved its effectiveness in image aesthetic quality assessment (IAQA), but still lacks reasonable interpretability. Aesthetic attributes provide rich intermediate-level information for understanding the underlying principles of image aesthetics, but has not been fully investigated. Psychological studies have shown that aesthetic experience involves hierarchical stages, i.e., human process image aesthetics following a staged information processing mechanism. Motivated by this, this paper presents a Hierarchical Image Aesthetic Attribute (HIAA) prediction model, aiming to imitate the staged mechanism of human aesthetic experience. Image aesthetic attributes are first divided into several hierarchical groups. Then, hierarchical features are extracted from the cascaded layers of the deep neural network to predict the aesthetic attributes in a group-wise manner. The overall image aesthetic score is also predicted by aggregating the hierarchical features. Experimental results demonstrate that the proposed HIAA model outperforms the state-of-the-arts in terms of both aesthetic attribute prediction and aesthetic score regression.
QSAR Model for Prediction PTP1B Inhibitor as Anti-diabetes Mellitus using Simulated Annealing-Support Vector Machine

Diabetes mellitus or diabetes is a kind of disease characterized by a raised in blood sugar. This disease can deal with long-term damage, such as dysfunction and failure of various organs. In Indonesia, diabetes is one of the major causes of death, with more than 10 million people living with diabetes. To date, no drug can cure diabetes. So far, people with diabetes must take responsibility for their daily routine. Drug discovery is needed to find the cure for diabetes. protein tyrosine phosphatase 1B (PTP1B) is one inhibitor that proved as a promising target for anti-diabetes Mellitus. Drug discovery takes a lot of time and effort, and thus, in silico methods, such as quantitative structure-activity relationship (QSAR), can be used to accelerate this process. We aim to build a QSAR model of PTP1B inhibitor as anti-diabetes Mellitus using the simulated annealing (SA)-Support Vector Machine (SVM) method. The data were retrieved from the ChEMBL database by selecting the SMILES from each compound. By calculating the SMILES using PaDEL, we got 1443 descriptors for each compound, and by using SA, we decreased the number of descriptors. The best result shows that SA selected 600 descriptors out of 1443 descriptors for each compound. The RBF kernel on SVM has the best value with accuracy, F1 score, and AUC of 94.508%, 95.048%, and 0.943, respectively.
REVIEW ARTICLE: Transverse dynamics in cavity nonlinear optics (2000 2003)

We review publications on transverse dynamics in cavity nonlinear optics during the years 2000–2003. Topics covered are transverse pattern dynamics, localized structures, scaling of turn-on transients, properties of nonlinear PDEs, VCSELs, quadratic media, light valves with optical feedback, quantum properties of light, and quantum images.
Transfer Learning to adapt 5G AI-based Fingerprint Localization across Environments

Fingerprint-based indoor positioning has attracted a lot of interest due to its potential to meet a positional accuracy that enables many location-based 5G indoor services. However, the accuracy of fingerprinting decreases with changes in the environment which prevents positioning in new scenarios. On the other hand, naively acquiring up-to-date training data from the changed environment to retrain the model is often time-consuming. It is unclear whether after a change in the environment, a fingerprint model can be (data-)efficiently updated.This paper examines the generalizability (with respect to accuracy, robustness, and effort in recording data) of state-of-the-art fingerprint models based on a convolutional neural network (CNN) in realistic setups with changes in the environment. We propose a transfer learning (TL) method that exploits realistic synthetic Channel State Information (CSI) obtained with the Quasi Deterministic Radio channel Generator (QuaDRiGa), used to pre-train the CNN-based fingerprint model so that it can be adapted to any real (NLoS) propagation scenario with a low number of real training samples. Our experiments show that the positioning accuracy using fine-tuning improves by 37% in changed and by 19% in new environments.
Energy Trading in Prosumer based Smart Grid Integrated with Distributed Energy Resources

Energy trading in a smart microgrid based Peer to Peer (P2P) microgrid requires a novel framework. Where in a microgrid each prosumer equipped with Distributed Energy Resources (DER) is encouraged to trade energy. Energy is traded with peers in the microgrid or sold to power grid to generate revenue whenever there is a surplus of energy. As there is an economic benefit, more prosumers are attracted to install DER. The peers in the microgrid are incentivized to buy locally produced energy which is available at a cheaper cost compared to buying from power grid. This results in reducing energy consumption from power grid which in turn results in reducing power losses in transmission lines and results in reduction of $CO_{2}$ emissions. Three market paradigms are considered in this paper, i.e. Bill sharing, Supply to Demand Ratio method and Mid-market rate method. Each of the pricing models are elaborated with local energy trading price and prosumers energy costs. In this case, these methods are studied concerning a smart microgrid equipped with DER, Solar Photovoltaic (SPV) system in this case, and the effectiveness of the pricing mechanisms and their respective benefits are established.
Automated Data Format Identification and Processing for Security Analysis

With the growing use of data intensive systems, more endpoints are available for threat actors to leverage vulnerabilities in order to gain access to remote systems and/or sensitive data. These endpoints can potentially accept data from a numerous amount of sources, opening a variety of attack vectors for threat actors to use. The use of files in varying formats and levels of quality present a challenge when attempting to effectively perform analysis of large structured data sets for indicators-of-compromise (IOC’s) in a timely manner. This paper proposes a solution to this issue through the use of a modular system which incorporates a client-server architecture to monitor specified files for IOC’s. The strategy incorporates a centralized application programming interface (API) that accepts files from a variety of general formats, normalization to a standard format, preprocessing, and automated analysis with the use of pre-trained machine learning models (ML), where each model is trained on a specific attack pattern.
A Review of Architecture and Control Strategies of Hybrid AC/DC Microgrid

This paper reviews architecture of hybrid AC/DC microgrid and several controlling strategies for hybrid AC/DC microgrid. Interconnected group of networks of loads, energy storage system and distributed energy sources defines a microgrid. To avoid multiple conversion that occurs in individual AC as well as DC grid in the microgrid system, hybrid microgrid is a solution for such issues. Balancing of power between both the microgrids is done with an Interlinking converter. It will balance power by transfer of power through one microgrid to another. This paper summarizes various controlling methods from the several aspects and existing issues in every method presented here.
Self-Attention based Semi-Supervised Learning for Time-varying Wi-Fi CSI-based Adjoining Room Presence Detection

Device-free indoor human presence detection problem has been studied in recent years based on supervised learning and wireless signals such as channel state information (CSI). Thanks to the abundant spatial information of CSI, we can perform indoor presence detection more accurately. Nevertheless, the CSI is susceptible to humidity, temperature, and even machine restarts, resulting in unexpected changes and prediction failures of the trained model. The most intuitive approach to resolve this time-varying phenomenon is to retrain the model by recollecting and labeling CSI data. However, it is time- and labor-consuming to label the dataset every time we retrain the model. Our proposed self-attention based semi-supervised learning for adjoining room presence detection (SAS-PD) system provides an alternative solution to retrain the detection model without any effort to label data and consequently overcome the time-varying issue. The proposed teacher/student training strategy can effectively classify four classes based on a single permanent labeled CSI dataset and recollected unlabeled CSI datasets. In addition, the positional encoder is adopted to enhance time domain correlations of CSI data. Experimental results show that our proposed SAS-PD system can provide enhanced presence detection accuracy under time-varying environments, and it can almost reach the upper bound of the training algorithm with supervised learning.
Cooperative Path Planning Using Responsibility-Sensitive Safety (RSS)-based Potential Field with Sigmoid Curve

Potential field (PF)-based path planning is reported to be highly efficient for autonomous vehicles because it performs risk-aware computation and has a simple structure. However, the inherent limitations of the PF make it vulnerable in some specific traffic scenarios, such as local minima and oscillations in close obstacles. Therefore, a hybrid path planning with the sigmoid curve has recently been presented to generate better trajectories than those generated by the PF for collision avoidance. However, it is time-consuming and less applicable in complex dynamic environments, especially in traffic emergencies. To address these limitations, we propose a cooperative hybrid path planning (CHPP) approach that involves collaboration with adjacent vehicles for emergency collision avoidance via V2V communication. Moreover, the responsibility-sensitive safety (RSS) model is introduced to enhance the PF and sigmoid curve for safe-critical and time-saving requirements. The effectiveness of the proposed CHPP method compared with the state-of-the-art methods is studied through simulation of both static and dynamic traffic emergency scenarios. The simulation results prove that the CHPP approach performs better in terms of computation time (0.02 s faster) and driving safety (avoiding collision) than other methods, which are more supportive for emergency cooperative driving.
Resource Management Based on OCF for Device Self-Registration and Status Detection in IoT Networks

Recently, there are heterogeneous devices that connect to the Internet to provide ubiquitous and intelligent services based on sensors and actuators in the network of the Internet of Things (IoT). The resources of IoT represent the physical entities on the Internet to expose functions through services. Resource management is necessary to enable a massive amount of IoT-connected devices to be discoverable and accessible in the network of IoT. In this paper, we propose an IoT resource management to provide schemes of device self-registration and status detection for devices based on the Open Connectivity Foundation (OCF) standard. This device self-registration scheme is based on an agent that is proposed for registering devices itself which deployed in the OCF network. The devices host the OCF resources to provide IoT services such as sensing and controlling through the sensors and actuators. For a group of devices, an agent-based self-registration is proposed to register the resources. Through the proposed self-registration, the information of IoT devices is published using profile and saved in the management platform that enables the clients to discover the resources and access the services. For accessing the IoT resources in the OCF network, an interworking proxy is proposed to support the communications between web clients and devices over Hypertext Transfer Protocol (HTTP) and Constrained Application Protocol (CoAP) based on OCF. Furthermore, through the interoperability of the resources using the registered information, a real-time monitoring scheme is proposed based on periodic request and response for the status detection of deployed devices.
Simulation Investigation of Propagation Channel inside and outside of the High-Speed Trains

To enhance the intelligentization level of the high speed train (HST), wireless train communication network (WTCN) is a promising technology to improve the networking flexibility and situational awareness capabilities of HST. Different from traditional wireless network, not only the coverage quality inside of the HST should be guaranteed, but also the mutual interference between the adjacent HSTs should be avoided. In this paper, we investigate the indoor and outdoor radio propagation simulation model of HST. The three-dimensional (3D) structure model of HST are build, and then the ray-tracing based simulation method is adopted to investigate the effects of train body on radio propagation inside and outside of HST. The proposed propagation simulation model are optimized based on the measurements in the HST. Based on the simulation results, the radio propagation characteristics inside and outside of the HST are analyzed. The proposed simulation model can be used for WTCN deployment and optimization.
Tokenizing Features for Fast Video Object Segmentation

This paper investigates how to take full advantage of the tem-poral and spatial information in videos with minimal compu-tational cost in the semi-supervised video object segmentation (VOS) task. Current state-of-the-art methods have achieved remarkable performance by matching features of the current frame with those of past frames to propagate the past segmen-tation masks to the current. However, the inference speeds of such matching-based methods are limited due to the tremen-dous amount of computation on pixel-to-pixel matching. To address this problem, we propose a fast matching mechanism for VOS that extracts essential object information as a handful of token vectors for matching. By extracting succinct but suf-ficient information from the pixel-wise features, we develop a fast VOS model which achieves competitive segmentation performance (81.6% $J$ & $F$ on DAVIS-2017), maintaining a high inference speed (FPS = 42.1).
Dynamic Fault Detection Based on Variational Bayesian Mixture Probabilistic Principal Component Analysis

It is essential to achieve real-time fault detection of the industrial process to reduce the occurrence of accidents during the industrial process. However, there are some problems in the actual monitoring process, such as the instability of sensor measurement, losing packet while transferring data, which causes problems like missing data and outliers while sampling from actual monitoring data. In addition, the monitoring data are interrelated in time series. Thus, a fault detection method based on DMPPCA model is proposed in this study. DMPPCA can not only consider the above problems at the same time, but also determine the dimension of latent variables automatically. Finally, the effect of proposed DMPPCA model was validated by a three-phase flow facility.
Optimal Tracking Control of Vehicle Cooperative Platoon Based on Reinforcement Learning

This paper proposed a data-driven reinforcement learning control method to achieve optimal control of trajectory tracking for the centralized connected vehicle platoon model. It minimized distance and velocity error and improved fuel effi-ciency, where autonomous vehicles transmit and receive each other's vehicle status data via wireless vehicle-to-vehicle (V2V) communication device. Adaptive dynamic programming techniques are used to obtain optimal tracking control strategy in the presence of unknown system dynamics. The effectiveness of the proposed method is verified by an online learning control simulation of connected vehicles.