Miracle-dz/UniFAR-FTUs huggingface dataset:
Investigation of Coverage and Signal Quality of LoRaWAN Network in Urban Area

Internet of Things (IoT) is a promising technology that supports the smart city and Industries 4.0. But, in the applications, some cases such as IoT for disasters, require a wide-coverage network with high energy efficiency. LoRaWAN is a breakthrough solution to address issues related to the IoT network that supports long-range coverage, low energy consumption, and cost-effectiveness. Urban areas such as DKI Jakarta have their challenges in the deployment of the LoRaWAN network. In the LoRaWAN deployment process, careful link budget calculations and radio frequency planning are needed to create a reliable network. A network drive test is also needed to obtain specific information related to signal coverage and quality. Based on the drive-test conducted, there were 8 blank spots with coverage area reached 79.392% and the average median of Signal to Noise Ratio (SNR) was 5 dB. The average path loss exponent in the DKI Jakarta was 3.253.
Eve, You Shall Not Get Access! A Cyber-Physical Blockchain Architecture for Electronic Toll Collection Security

Cooperative intelligent transportation system (C-ITS) applications are generally susceptible to position spoofing-dependent attacks such as Sybil and DDoS attacks due to a lack of established solutions. This paper presents a novel cyber-physical blockchain cryptographic architecture to help prevent position spoofing attackers from becoming validated nodes in C-ITS applications. The solution also guarantees security requirements including the non-trivial non-repudiation in light of these and other attacks. With a use case of electronic toll collection (ETC), our architecture implements techniques based on Received Signal Strength Indication (RSSI) measurements in conjunction with blockchain authentication methods such as Proof-of-Location and smart contracts to determine the legitimacy of a node. We demonstrate our solution in experiments using ITS-G5 Cohda Wireless technology (a Road Side Unit and two On-Board Units programmed with the ITS Vanetza stack) with functionalities specified by the European Telecommunications Standardization Institute (ETSI). From our experimental results from several driving-based data gathering tests, we discovered that our solution is able to cope with noise and relative velocity challenges because it incorporates both OBUs and RSUs in the Proof of Location computation steps. In light of this, the proposed architecture may also be applicable to govern V2X in general.
Blockchain-based Scheme for Authentication and Capability-based Access Control in IoT Environment

Authentication and access control techniques are fundamental security elements to restrict access to critical resources in IoT environment. In the current state-of-the-art approaches in the literature, the architectures do not address the security features of authentication and access control together. Besides, they don’t completely fulfill the key Internet-of-Things (IoT) features such as usability, scalability, interoperability and security. In this paper, we introduce a novel blockchain-based architecture for authentication and capability-based access control for IoT environment. A capability is a token which contains the access rights authorized to the device holding it. The architecture uses blockchain technology to carry out all the operations in the scheme. It does not embed blockchain technology into the resource-constrained IoT devices for the purpose of authentication and access control of the devices. However, the IoT devices and blockchain are connected by means of interfaces through which the essential communications are established. The authenticity of such interfaces are verified before any communication is made. Consequently, the architecture satisfies usability, scalability, interoperability and security features. We carried out security evaluation for the scheme. It exhibits strong resistance to threats like spoofing, tampering, repudiation, information disclosure, and Denial-of-Service (DoS). We also developed a proof of concept implementation where cost and storage overhead of blockchain transactions are studied.
Using Computer-Generated Examples to Support Idea Generation

We present ComposIt, a creativity support tool that assists designers in finding inspiration by providing many computer-generated examples. These examples may be beyond humans’ imagination and thus are helpful to inspire users to create new ideas. To automatically produce examples, our system adopts the deconstruction and construction strategies that are commonly used in the creative industries. Given a 3D model, the model deconstruction module analyzes the model and divides it into several elements. Then the model construction module synthesizes examples by composing the elements from different models. To evaluate the effectiveness of our system, we conducted a user study, in which artists were invited to design using our system. The results conclude that our system is able to inspire artists in their design process. The artists also shared their usage experience of our system in various aspects.
GO/BiVO4 NANOCOMPOSITES FOR Escherichia coli K12 PHOTOCATALYTIC INACTIVATION

Graphene oxide, a carbon-based nanomaterial has gained a wide attraction in the antibacterial field due to its large surface area, surface charge density, and various physicochemical properties. In this work, water disinfection using Graphene oxide/Bismuth vanadate (GO/BiVO4) composite was studied against Escherichia coli K12 (E. coli K12), where the composite was developed through a facile and simple blending method at room temperature. Bismuth vanadate (BiVO4) nanoparticles synthesized through the sol-gel method having a particle size of 21.3 nm was used to decorate over graphene oxide (GO) sheets. The composites whereof five different combinations namely, 0.5 %, 1 %, 1.5 %, 2 % and 2.5 % of GO/BiVO4 (wt.% of GO). The as-synthesized composites were characterized by X-ray diffraction (XRD) for the crystal structure, Raman spectroscopy for the structural analysis, and UV-Vis diffuse reflectance (DR) to determine the bandgap of GO/BiVO4 semiconducting composites. The antibacterial ability of all the synthesized five different composites was tested under a visible-light-driven home-made photoreactor. The results revealed the efficiency of 1.5% GO/BiVO4 nanocomposite in 0.1 g/L was giving 90% disinfection in 30 mins under visible light irradiation.
Xception Architecture Transfer Learning for Garbage Classification

Solid waste management issue is main problem especially in developing countries, including Indonesia. Several efforts are made to solve waste management problem. Indonesia government has launched movement to sort different type of garbage on September 2019. Automatic garbage sortation is able to help this program. In order to be able to perform this task, the computer needs to differentiate each type of garbage. This process can be done by using machine learning method to differentiate garbage type. In this research, Transfer learning is used to perform classification task on TrashNet dataset. The models used in this research are ImageNet pretrained VGG16, ResNet-50 and Xception.The experiment result shows that Xception model is able to achieve highest accuracy of 88%, average precision of 84%, and average recall of 84%
Ensiling of Manured Crops—Does Repeated Spreading of Slurry Increase the Hygienic Risk?

The influence of cattle slurry applied to a silage crop was studied in three successive years. Each year, 40 t slurry ha-1 were applied in two equal applications. Unfertilised and mineral-fertilised field plots were used as controls. Before spreading, the slurry was contaminated with spores of Clostridium tyrobutyricum (103–104 g-1 FM). The fields were harvested two times a year, the yields were measured and the chemical composition and bacterial content of the crop were determined. The fresh material was chopped, ensiled without additives, and stored for 100 days before opening. The bacterial content of the soil surface and available N, P and K in soil were measured. Slurry did not seem to have any significance in a short-term perspective. Yield increase as an effect of slurry application was slight and inconsistent in comparison with the yield from unfertilised plots. The number of Clostridium or Bacillus spores in the soil surface was not significantly affected by the slurry application, nor was the number of detrimental organisms in harvested crop at ensiling. Nonetheless, in three of the four silages in which clostridial spores were estimated, numbers were higher in silage made from slurry-treated crops. © 1997 SCI.
Indonesia's News Topic Discussion about Covid-19 Outbreak using Latent Dirichlet Allocation

News related to Covid-19 or Corona dominates the news in several media. From various perspectives, a number of news have been produced regarding the corona outbreak. The purpose of this research is to look at various news topics related to corona, both in national online media and in local online media. This study uses the Latent Dirichlet Allocation (LDA) algorithm to find out news topics related to corona. Preprocessing was carried out on existing articles such as removing punctuation marks, numbers, and removing stopwords. This process is preceded by lowering the text to get unique words. The number of articles collected was 12.883 news titles from the national online media RMOL and the Aceh Tribunnews regional online media in the period January to May 2020. The news items came from various news categories such as politics, law, health, economy, sports and others. Based on this research, by using the LDA algorithm, evaluate using coherence value and visualize the topics, will have the best perspective of how many topics can be created. RMOL news articles have 12 topics discussion with the coherence value is 0.538795. Aceh Tribunnews has 8 topics with the coherence value of 0.522946.
DeepFlow: Towards Network-Wide Ingress Traffic Prediction Using Machine Learning At Large Scale

Describing incoming web traffic – as seen from large eyeball networks, i.e. ingress traffic – and estimating it into the future, are necessary operations for network service providers who need to efficiently organize the essential tasks of more dynamic network planning and capacity management. For that a network-wide view on ingress traffic processes and their predictions is necessary. We propose DeepFlow, a system that processes complete ingress traffic flow data on a carrier scale and produces forecasts for all traffic flows using Machine Learning techniques. The viability of DeepFlow is shown by comparing different prediction methods on recent and real-world data that covers three years from 2016 to 2019. We use neural and non-neural methods that produce accurate results in predicting the three largest ingress traffic flows. Furthermore, we investigate the case where the traffic time series data has high volatility. We also use a VAR model to generate directed acyclic graphs to get insights into the relationships between the different ASes. DeepFlow is currently deployed in a lab environment of a large European service provider. The initial evaluation results demonstrate the feasibility to realize system-wide, continuous, near real-time and configurable traffic flow prediction at large scale.
Further Investigation of The Feasibility of Using EBG Structure-Based Microstrip Antenna for Gain Enhancement

This paper discusses the feasibility of metamaterial-based rectangular microstrip antenna for gain and aperture efficiency enhancement. To improve the proposed antenna gain, a 3×3 array based on Electromagnetic Band Gap (EBG) metamaterials is implemented as a single planar array covering the radiating patch antenna. The proposed antenna is mounted on a FR4 substrate backed with a fully copper ground plane, and the radiating patch antenna is excited via SMA connector. The antenna is designed to operate at 5.8 GHz to match the WLAN and WiMAX applications. The microstrip antenna is loaded with Semi-H shaped unit cells of EBG structures. The antenna performance before and after adding the EBG structure is investigated in terms of reflection coefficient ($11), radiation pattern, bandwidth, aperture efficiency, and boresight gain. The antenna based on EBG structure is provided boresight gain enhancement from 1.8 dBi to 11.4 dBi for the frequency of interest. Since the introduction of the EBG structure with the conventional square patch antenna the proposed antenna has been achieved another achievement by enhancing the aperture efficiency from 3.2% to 29.6% and increase the bandwidth by 85 MHz and improving the Half Power Beam Width (HPBW) from 112° to 34° and 136° to 66° along the elevation (θ) and azimuth directions (φ), respectively
Computing All Equilibria in Ordinal Graphical Games

Graphical games allow to concisely represent games where the utility received by each player depends on strategies played by a (hopefully small) subset of the players only. Recently, Graphical Ordinal Games have been proposed as a framework for game theory where utility degrees are ordinal. The concept of probabilistic mixed-Nash equilibrium is irrelevant in this framework since ordinal utility degrees cannot be averaged. Instead, possibilistic mixed equilibria have been proposed as a principled solution concept in such games. A generic possibilistic mixed equilibrium computation algorithm has been proposed, which applies to ordinal games, be they in standard normal form or graphical form. However, this algorithm only computes a single least-specific equilibrium. When analyzing ordinal games, one may be interested in finding every least-specific equilibria or in counting them. In this paper, we propose two original algorithms for computing all least-specific possibilistic mixed equilibria in Ordinal Graphical games. We first focus on tree-structured Ordinal Graphical Games and propose the Possibilistic Tree-Nash algorithm (-Tree-Nash), a possibilistic counterpart of the Tree-Nash algorithm proposed by Kearns et al. for (cardinal) graphical games. Then, we propose the Search All Equilibria algorithm (SAE) which computes all least-specific mixed equilibria of an arbitrary Ordinal Graphical Game. We provide algorithmic complexity results as well as an experimental evaluation of both algorithms.
Relative intensity noise measurement of femtosecond laser beams in SRS microscope

“Noise” of lasers is a short term for random fluctuations of various output parameters, which can play a fundamental role in many applications. In non-linear optical microscopy, the laser beam properties can influence the non-linear contrast efficiency and the image quality. Therefore, laser noise effects should be reduced to acceptable levels.In Stimulated Raman Scattering (SRS) microscopy, two synchronized trains of femtosecond pulses with independent tunability over a wide spectral range are required. The high performance of the SRS imaging requires a reduction of the relative intensity noise (RIN) and timing jitter between these two synchronized pulses trains.In this paper, relative intensity noise (RIN) of the two femtosecond laser sources, in our case a Ti:Sapphire (Ti:Sa) oscillator and a femtosecond synchronized optical parametric oscillator (OPO) are measured.
A Preliminary Study on Cell Motility Analysis from Phase-Contrast Microscopy Image Series

Analyses of morphology, polarity, and motility of cells is important for cell biology research such as metastatic and invasive capacity of cells, wound healing, and embryonic development. Automation of such analyses using image series of phase-contrast optical microscopy, which allows label-free imaging of live cells in their living environment, is a need. With this purpose, in this study image series of a cell motility experiment is manually annotated, and an automation algorithm realizing motion and shape analyses of cells using the annotated data is developed. In addition, due to the low number of annotated data at hand, a U-Net based solution is devised for automated segmentation of the cells and its performance is evaluated.
kW-class Wireless Power Transmission Based on Microwave Beam

In the paper, a wireless power transmission system of receiving about 1.0 kW power using 2.45 GHz microwave beam is reported. The complete TX system is composed of a high power magnetron, a parabolic antenna of 36.5 dBi gain, and a power divider for feeding dual polarization. As a rectenna, a dual polarized square patch antenna is designed for better efficiency in wider receiving angle. The Rx system consists of two rectennas arrays of 36×40 and 36×16 which are connected back and forth. The number of total rectennas is 2016. The complete system including Tx and Rx was tested. The transmitting power measured by a power meter is 2.3 kW and the maximum Rx power is 1.02 kW at 50 m distance between Tx and Rx. The efficiency of 44.3% was measured.
Deep Learning for Improving Image Quality with Uneven Illumination Images

There are many mobile scanning apps available on the market today that you can download onto your smartphone. HP Smart, Microsoft Office Lens and Adobe Scanner are the most popular document scanning apps. These offer the convenience of converting printed text into digital text available for edits, copy and storage in any digital device. Uneven lighting is one of the leading disturbance sources that can affect the scanning result. Therefore, this paper aims to realize the scanning function. It improves the image quality by removing the noise using deep neural network. Compared with the existing Adobe Scan, our method performs better in presence of uneven lighting.
Design and Realization of Peristaltic Pump and Syringe Pump in Hemodialysis System

Hemodialysis is the most frequently used therapy for people with chronic kidney failure. However, there are several obstacles in hemodialysis therapy, first the formation of blood clots when the blood is pumped out of the body, then the speed of pumping blood is not stable causing the patient to experience in shock. This research focuses on design peristaltic pump and syringe pump using a motor stepper, by calibrating the amount step motor stepper to keep blood flow out stable at 200 ml/min and control the heparin injection so that blood clots do not form. The designed peristaltic pump can produce flow rate close to 200 ml/min and have levels with a precision of 98.4899% and an accuracy rate of 98.6155%. Syringe pump which is designed to inject heparin can work well. The resulting the precision level is 99.8797% and accuracy rate is 99.3861%.
Vehicle Localization Using a Traffic Sign Map

High-definition (HD) map data is considered a pre-requisite for automated driving since it delivers a rich source of information insensitive to line-of-sight limitations and weather conditions. In order to exploit HD map data, a vehicle must be able to localize within the map. Vehicle localization solutions based on only GNSS data, odometry and IMU data are insufficient both in terms of accuracy and reliability. Cameras are used in production vehicles and therefore offer a realistic source of additional sensor information that could be exploited for improving localization accuracy and robustness.This work proposes a vehicle localization solution that exploits a traffic sign map and images from a single camera for improving localization accuracy. A particle filter is used for estimating the vehicle’s position. Likelihoods are computed in the image domain to avoid the need for estimating 3D positions of 2D detections in camera images. During an experimental evaluation, the localization error is shown to decrease to approximately 0.7 m in both lateral and longitudinal direction.
PerfCI: A Toolchain for Automated Performance Testing during Continuous Integration of Python Projects

Software performance testing is an essential quality assurance mechanism that can identify optimization opportunities. Automating this process requires strong tool support, especially in the case of Continuous Integration (CI) where tests need to run completely automatically and it is desirable to provide developers with actionable feedback. A lack of existing tools means that performance testing is normally left out of the scope of CI. In this paper, we propose a toolchain - PerfCI - to pave the way for developers to easily set up and carry out automated performance testing under CI. Our toolchain is based on allowing users to (1) specify performance testing tasks, (2) analyze unit tests on a variety of python projects ranging from scripts to full-blown flask-based web services, by extending a performance analysis framework (VyPR) and (3) evaluate performance data to get feedback on the code. We demonstrate the feasibility of our toolchain by using it on a web service running at the Compact Muon Solenoid (CMS) experiment at the world's largest particle physics laboratory - CERN. Package. Source code, example and documentation of PerfCI are available: https://gitlab.cern. ch/omjaved/PerfCI. Tool demonstration can be viewed on YouTube: https://youtu.be/RDmXMKAlv7g. We also provide the data set used in the analysis: https://gitlab.cern. ch/omjaved/PerfCI-dataset.
Evolution of Deterministic Hill-climbers

Local search algorithms consist in evolving a solution guided by a fitness function, which is usually directly derived from the objective function of the problem. Solving difficulties appear when the fitness landscape, naturally induced by the problem instance, is not perfectly exploitable, has a certain level of ruggedness and therefore has many local optima. We propose here to shift the problem from searching for a solution, to searching for a fitness function, which maximizes the efficiency of a deterministic and basic hill-climber. Considering that a LS algorithm is defined by a starting point, a neighborhood structure, a fitness function and a move strategy, we propose here to fix all components but the fitness function, so that each fitness function induces an algorithm generating a unique search trajectory. Through a simple evolution strategy applied to NK fitness functions, we propose to search for a basic hill-climbing algorithm specifically dedicated to attain the best possible solution of the original problem.
Deep Learning for Dengue Fever Event Detection Using Online News

Dengue fever currently has been a hyperendemic infectious disease in Indonesia. Early detection ability of the dengue fever events are essential for a timely and effective response to prevent outbreaks. This paper presents dengue fever event detection using online news. A previous study conducted an event detection task from sentences using word frequency burst to detect an ongoing event. However, news do not only report about the event (i.e., the event of dengue fever case) but also information regarding the disease. This paper focuses on detecting an event of dengue fever from online news. An assessment of different deep learning models is reported in this paper. Using k-fold cross validation, convolutional neural network (CNN) achieved the best performance (in average, test accuracy: 80.019%, precision: 78.561%, recall: 77.747%, and f1-score: 77.234%).