Miracle-dz/UniFAR-FTUs huggingface dataset:
ROS-sensitive Dyes in Lipid Nanoparticles for in vivo Imaging

Reactive oxygen species (ROS) actively participate in many metabolic processes in liver cells, including cell proliferation and apoptosis. Intracellular ROS not only contribute in signaling, but also oxidize lipids, proteins, and DNA. As a result, ROS are involved in the development of many diseases. Lifetime visualization of ROS using sensors that are sensitive to the presence of free radicals is an urgent but difficult task due to short lifetimes of radicals and varied concentrations in different cell compartments. We synthesized lipid nanoparticles (LNP) with average diameters $100 \pm 10$ nm and two different ROS sensitive dyes (L-012 and hydrocyanine 5) for effective detection of ROS in liver cells. We demonstrated that L-012 sodium salt leaks from the nanoparticles, while hydrocy5 labeled LNP are stable and shows an increase of fluorescence in more than 5 times after reaction with hydrogen peroxide. We also validated animal model with increased ROS production by scanning ion-conductance microscopy. Both lipopolysaccharide (LPS) and carbon tetrachloride (CCl4) treatment caused intensive ROS burst in 24 h after injection. Using fluorescent tomography, we analyzed LNP biodistribution in Balb/c mice and observed its preferable accumulation in liver. Thus, incorporation of ROS-sensitive dyes into LNP can improve the stability of the sensors, minimize degradation during blood circulation, and increase its accumulation in the target organ. Creating delivery systems for lifetime ROS imaging can be a useful tool for studying changes in ROS in liver diseases.
The Effects of Adaptive Control on Learning Directed Locomotion

This study is motivated by evolutionary robot systems where robot bodies and brains evolve simultaneously. In such systems robot ‘birth’ must be followed by ‘infant learning’ by a learning method that works for various morphologies evolution may produce. Here we address the task of directed locomotion in modular robots with controllers based on Central Pattern Generators. We present a bio-inspired adaptive feedback mechanism that uses a forward model and an inverse model that can be learned on-the-fly. We compare two versions (a simple and a sophisticated one) of this concept to a traditional (open-loop) controller using Bayesian optimization as a learning algorithm. The experimental results show that the sophisticated version outperforms the simple one and the traditional controller. It leads to a better performance and more robust controllers that better cope with noise.
Comparison and Application of Multiple 3D LIDAR Fusion Methods for Object Detection and Tracking

Environment perception is a critical part of autonomous driving which is required to get a reliable and accurate object information from environment. LIDAR sensors are thought to be a key enabler for autonomous cars through their significant advantages on wide field-of-view and high-resolution capabilities. Automotive companies’ interest in LIDAR sensors is also thought to increase with slashed sensor prices over the years. Our main aim in this research is to get more precise object detection and tracking (ODT) system in real time for autonomous vehicles. In this paper, we have developed, applied and tested two different (low and high) realtime sensor fusion methods on multiple 3D LIDAR sensors for environment perception. The first contribution of this work is proposing and implementing “high level track-to-track fusion” method on multiple 3D LIDAR sensors. To the best of our knowledge, this is the first automotive application of track-to-track fusion method on multiple 3D LIDARs. Another contribution is the analysis and comparison of track-to-track fusion method performance with the well-studied low-level real-time fusion method. These two real-time fusion strategies are implemented in the experimental test truck which is instrumented with two 3D LIDAR sensors and the performance of the fusion strategies are tested under three different driving scenarios. Additionally, the ground truth data is collected with the help of global navigation satellite system (GNSS) in high accuracy for performance evaluation. The test results are analyzed in terms of defined performance criteria and the benefits & weaknesses of the proposed approach are discussed in this work.
Mining Patients' Reviews in Online Health Communities for Adverse Drug Reaction Detection of Antiepileptic Drugs

In pharmacovigilance, the detection of adverse drug reactions is a task of utmost importance. This paper presents a data mining-based method to detect adverse drug reactions of anti-epileptic drugs from a dataset of patients' reviews collected from an online health community. The dataset is preprocessed and the unigram, bigram, and trigram are generated and then the adverse drug reactions of each anti-epileptic drug are extracted with the help of consumer health vocabulary and adverse drug reactions lexicon. Proportional reporting ratio is used to measure the association between each adverse drug reaction and antiepileptic drug. A list of ranked adverse drug reactions for each anti-epileptic drug is generated and validated against Drugs.com database. The results show the validity and utility of using patients' reviews in online health communities as a source for adverse drug reactions detection.
An Energy-Aware Host Resource Management Framework for Two-Tier Virtualized Cloud Data Centers

The high energy consumption of cloud data centers is one of the key issues restricting the future development of the cloud computing industry. For two-tier virtualized data centers in which containers are deployed on VMs, this paper conducts an in-depth study of this problem from the aspect of resource management. First, the working model of two-tier virtualized data center is defined. By mapping tasks to containers and using VMs to isolate different jobs, this model improves the resource utilization of hosts while ensuring the isolation and security of jobs. Then, an energy-aware host resource management framework is constructed, which includes two algorithms. The initial static placement is a two-tier scheduling algorithm, including the load balancing alternate placement and the two-sided matching methods to complete the placement of containers to VMs and VMs to hosts respectively. The runtime dynamic consolidation algorithm takes the initial placement scheme as input, and utilizes the dynamic consolidation method to use the least active hosts to meet the real-time resource requirements of containers. Finally, simulation experiments compared with related algorithms are conducted using real workload traces. The results show that the proposed two algorithms have better performance in host resource utilization, number of active hosts, number of container migrations and SLA metric. And the entire framework achieves an energy-saving effect of 13.8% at least.
Two-stage Unsupervised Approach for Combating Social Spammers

Spammers use Online Social Networks (OSNs) as a popular platform for spreading malicious content and links. The nature of OSNs allows the spammers to bypass the combating techniques by changing their behaviours. Classification based approaches are the most common technique for spam detection. “Data labelling” “spam drift” “imbalanced datasets” and “data fabrication” are the most common limitations of classification techniques that hinder the accuracy of spam detection. The paper presents a two-stage fully unsupervised approach using a user’s peer acceptance within OSN to distinguish spammers from genuine users. User’s common shared interest over multiple topics and the mentioning behaviour are used to derive the peer acceptance. The contribution of the paper is a pure unsupervised method to detect spammers based on users’ peer acceptance without labelled datasets. Our unsupervised approach is able to achieve 95.9% accuracy without the need for labelling.
Combined Deep Learning With Directed Acyclic Graph SVM for Local Adjustment of Age Estimation

In order to further improve the accuracy of age estimation, a locally adjusted age estimation algorithm based on deep learning and directed acyclic graph SVM is proposed. In the training phase, SE-ResNet-50 network pre-trained by the VGGFace2 dataset is first fine-tuned. Once the network converges, and the vector consisting of the parameters of the last fully connected layer is used as a representation and train multiple One-Versus-One SVMs. In the test phase, we first sent the face image to be estimated into SE-ResNet-50 to obtain a rough age estimation value, then set the specific neighborhood, and finally combined the trained SVM into a directed acyclic graph SVM and set specific neighborhood with the global estimate as the center for accurate age estimate. In order to show the universality of the proposed coarse-to-fine or/and global-to-local method, experiments were carried out on MORPH and AFAD images of different races, and the results verified the effectiveness of the algorithm.
Deep Learning Based Ancient Asian Character Recognition

Ancient documents record a lot of information such as politics, economy, culture and so on, which may help people know the history and culture. However, a lot of ancient documents have not be organized yet. One of the reasons is some kinds of character are not be used now and only few of specialists can understand them, especially in Asian. These ancient Asian characters include Oracle Bone Inscriptions, Kuzushi characters, etc. Furthermore, these ancient documents are found one after another, letting document organization becomes a hard work, and aging processing increases the difficult of organization. This paper aims to recognize some kinds ancient character for helping people organize the ancient Asian documents by deep learning method. For achieving better accuracy, we applied the most state-of-the-art deep learning model –self-attention, which is different with current convolutional neural network. The experimental results shows the self-attention also achieves good accuracy in Oracle Bone Inscriptions and Kuzushi characters recognition, which also prove the effectiveness self-attention in ancient character recognition.
Design Optimisation of an 8-pole Line-Start Permanent-Magnet Synchronous Motor

This paper presents an optimisation study of an 8-pole, 3-phase, 1kW line-start permanent magnet synchronous motor (LSPMSM) designed based on a benchmark induction motor (IM). The initial IM (IM0) is designed using a machine sizing technique and then the initial LSPMSM (L0) is designed based on some modifications in the rotor of the IM0. The finite element method (FEM) is employed to analyze the motor’s performance. The initial LSPMSM (L0) operates with higher efficiency and power factor compared with the IM0. L0 efficiency is categorized in the IE4 efficiency standard class while IM0 efficiency is in the IE3 class. Then, IM0 and L0 are individually optimised based on a FEM optimisation technique using genetic algorithm (GA) to improve their transient and steady-state performance through maximizing the starting torque and the efficiency as two optimisation objectives, respectively. The optimised LSPMSM (L1) has better performance in terms of the starting torque (transient representative) and the efficiency (steady state representative) in comparison to other L0. The results indicate that, it is not necessarily true that a LSPMSM designed based on an optimised IM has better performance than a LSPMSM design based on a non-optimised IM (in this case L0).
Cyber Secured Power Orders for Resilient HVDC Systems

We propose a technique to secure HVDC stations from executing power order commands that may cause overloading of associated AC lines in the interconnected electric grid. The method is designed to be hosted in a HVDC station computational platform, and not from a SCADA/EMS, and thus will check for potential system issues locally in the station, without models. This method is used to identify malicious commands resulting from cyber intrusions. It is used to detect insecure and/or inadvertent power order commands requested by systems external to the HVDC station's electronic security perimeter (ESP). It alarms and could stop external HVDC power orders that will trigger the operation of installed special protection systems or remedial action systems in interconnected electric grid. The method is based on 1) a continuous estimation of identified AC power line flow(s) resulting from the power order change command, 2) predicting the operation of identified remedial action schemes based on this continuous estimation and determination if the resulting power order change exceeds the protection trip threshold or security thresholds and 3) stopping the full execution of the HVDC power order change and alarming the HVDC control house and the central dispatch center. Numerical results from a HVDC cyber physical test bed indicates the accuracy of the method as well as its speed of execution.
The Study of Surface Film Formation on Noble-Metal Electrodes in Alkyl Carbonates/Li Salt Solutions, Using Simultaneous in Situ AFM, EQCM, FTIR, and EIS

In this study, surface film formation on nonactive-metal electrodes was analyzed using several in situ spectroelectrochemical techniques. These techniques included in situ Fourier transform infrared spectroscopy in both internal and external reflectance modes, impedance spectroscopy, electrochemical quartz crystal microbalance, and atomic force microscopy. The solutions studied included ethylene carbonate−dimethyl carbonate (EC−DMC) and EC−tetrahydrofurane (EC−THF) mixtures with Li salts, such as LiAsF6, LiPF6, LiClO4, and LiBr, part of which are essential for practical Li ion batteries. This work aimed to determine the onset of surface film formation, the impact of the solvent and salt used, and to compare the stability of the surface films formed in the various systems. The onset of surface film formation in these systems usually approximated 1.5 V (Li/Li+). EC reduction products, probably (CH2OCO2Li)2, are dominant constituents in the surface films. However, in LiPF6 solutions, Li salt reduction produc.
Impact of Imperfect Repairs and Diagnostic Coverage on the Reliability of Multi-Channel IEC-61850 Substation Communication Network

The reliability evaluation of IEC-61850 based Substation Communication Network (SCN) is often achieved by using combinatorial methods such as Reliability Block Diagram (RBD) because it is considered to be conceptually simple and easy to apply. These methods, however, assume that all system faults can be identified and fully repaired; which is not practically achievable. As a result, the methods overstate the performance of the system during its useful life. System diagnostic coverage is one of the critical factors that necessitate consideration during the evaluation of system reliability when SCN interface with IEC-61508 based Safety Related System (SRS) in power distribution centres. In this article, the impact and significance of quality of repairs comprising repair efficiency and diagnostic coverage on the reliability of the system are investigated using Markov and symbolic dynamics based on linear dynamical systems. The originality of the research is in the utilisation of transition probability matrix and its eigenvalues to analyse the effectiveness of repair efficiency and the dynamical behaviour of the system under varying levels of repair efficiency and system diagnostic coverage. The results indicate that the magnitudes of the eigenvalues can be used to optimally select the individual factors in order to attain the desired reliability and availability of IEC-61850 based SCN, this aspect is characterised the spectral gap on the complex plane. The results also indicate that the effectiveness of the repair efficiency factors is dependent on the level of system diagnostic coverage, wherein high impact is noticeable at low system diagnostic factors.
Island-based Modified Harmony Search Algorithm with Neighboring Heuristics Methods for Flow Shop Scheduling with Blocking

The flow shop scheduling with blocking involves assigning several jobs to machines with minimum time complexity. This problem can be considered as a combinatorial optimization problem that does not have an algorithmic solution. Recently, the modified harmony search algorithm with neighboring heuristics methods (MHSNH) is proposed to tackle this problem. In this paper, the flow shop scheduling with blocking problem is tackled using the island harmony search version of MHSNH. Recently, a new version of harmony search algorithm (iHS) is proposed for global optimization problems. In i HS, the population stored in the harmony memory is divided into a set of sub-populations called islands. After a predefined number of iterations, some of the migrant individuals determined by migration rate are exchanged between islands following a migration topology to control the population diversity. In order to evaluate the island modified harmony search algorithm with neighboring heuristics methods (iMHSNH), a de facto standard job scheduling dataset, Taillard’s benchmark, is used. The proposed algorithm is compared to a number of well-established methods in terms of the mean total flow time and the average relative percentage deviation. The proposed method outperforms other comparative algorithms. Finally, the proposed algorithm is compared against MHSNH in terms of locating multiple optimal solutions, which has not been studied before in the literature.
Smart Governance on Taxation: Tax Data Integration through Host-to-Host e-Invoices towards Collaborative Compliance in Indonesia

Information technology has an essential role in efforts to improve tax compliance in Indonesia. The collaborative compliance approach utilizes information technology expected to build mutual trust in a new era of relations between taxpayers and tax authorities in increasing the tax ratio in Indonesia. During numerous disputes and misuse of tax invoices, tax reform was conducted by utilizing technology to provide convenience in implementing taxpayer’s rights and obligations. Furthermore, using technology, it is expected to improve the quality of tax authority services. This research was conducted to analyze the Piloting Project of Tax Data Integration using Host-to-Host (H2H) e-Invoice to implement collaborative compliance as one of the government’s efforts to implement smart governance to handle tax evasion issues. This research was conducted using a post-positivist approach with qualitative methods with innovation in the tax compliance concept. The data collection was carried out through in-depth interviews conducted through studies at PT X. The results showed that the application of the tax data integration program at PT X went quite well, viewed based on the dimensions of law enforcement, provision of facilities, and trust. In this case, law enforcement activities related to taxpayer supervision carried out better and more efficiently since there has been an increased competence of tax authorities in carrying out their duties. Providing tax facilities by providing convenience to the taxpayers may influence taxpayer compliance in carrying out its tax obligations. Facilities provision may also affect the dimension of trust where trust between actors leads to a better direction. The implementation of this program creates mutual trust and mutual understanding between PT X and tax authorities.
Detecting Communities in Networks: a Decentralized Approach Based on Multiagent Reinforcement Learning

An important problem in network science is finding relevant community structures in complex networks. A community structure is a partition of the network nodes into clusters or modules, such that each cluster is densely connected. Current community detection algorithms have time complexity, centralization, and scalability issues. In this paper, to solve this problem, we implement a multi-agent reinforcement learning algorithm that optimizes a quality metric known as modularity. We model each node of the network as an autonomous agent that can choose other nodes to form a cluster with. They receive a reward and learn a policy that maps actions to their values. Experiments on known real-world networks show results similar to other modularity optimization methods while providing answers for decentralization, data privacy, and scalability.
Comparison of Covid-19 Cases in Indonesia and Other Countries for Prediction Models in Indonesia Using Optimization in SEIR Epidemic Models

Coronavirus Disease 2019 or COVID-19 is a new disease that can cause respiratory and inflammatory disorders. As a new model virus the general public has difficulty finding its match and then consider it trivial. The spread of the disease caused by COVID virus 19 is set to become a pandemic by the WHO as of March 12, 2020. Development of covid-19 pandemic data in Indonesia, has claimed 1089 lives on May 17, 2020 (source: http://covid19.bnpb.go.id/) and is a major threat to global public health especially Indonesia. The pandemic behavior in one area can be learned by comparing behavior in other regions. We propose SEIR epidemic models (S = Suspect, E = Expose, I = Infected, and R = Recovered) to predict the behavior of covid-19 transmission in Indonesia with parameters of distribution, cure rate, mortality rate, communication rate and movement. The appropriate parameters to predict the behavior of the Covid-19 virus spreading in Indonesia, firstly, the number of cases that occurred in Indonesia are compared with other countries that were first exposed to this pandemic. Several countries in Asia, Australia, Europe and America are chosen for comparison. Comparisons are performed by examining the maximum correlation values in each country. The pattern of the number of cases that occurred in Indonesia is very similar to the UK, Malaysia and Australia. The first prediction maximum number of new cases per daily is 1,343 people occurring on May 15, 2020. The end of the pandemic is predicted on August 8-10, 2020 (circumstance 1). The second prediction maximum number of new cases per daily is 1,034 people occurring on May 30, 2020. The end of the pandemic is predicted on September 9-10, 2020 (circumstance 2). The SEIR model for predicting the number of Covid-19 cases is sufficient when there is no further development of this pandemic.
Device Simulation of Poly (3-Hexylthiophene) HTL Based Single and Double Halide Perovskite Solar Cells

The materials used to fabricate solar cell, affect the efficiency of the solar cell significantly. In recent times lead halide perovskite has become one of the emerging material due to its excellent optical properties like high absorption coefficient and high tolerance to defects. Perovskite solar cells (PSCs) consist of active (absorber) layer sandwiched between ETL (electron transport layer) and HTL (hole transport layer). ETL and HTL play an essential role in PSC. In this work, two devices namely, single halide device (SHD) and double halide device (DHD) using poly (3-hexylthiophene) (P3HT) (HTL) and TiO2 (ETL) are simulated. In the case of SHD, a narrow bandgap halide (NBH) has been utilized as an absorber layer which is further sandwiched between P3HT and TiO2. While in the case of DHD, a thin layer of wide bandgap halide (WBH) has been deployed on the top of NBH sandwiched between ETL and HTL. After performing comparative analysis, it has been realized that SHD provides 15% efficiency with 0.88V open-circuit voltage (VOC), while DHD reports 21% conversion efficiency with 1.13V VOC. The findings can be carried forward for further research work like lead-free perovskite solar cell and tandem approach.