Miracle-dz/UniFAR-FTUs huggingface dataset:
Declarative procedural generation of architecture with semantic architectural profiles

Procedural content generation (PCG) for architecture is widely used in a variety of digital media, most notably in games. However, such methods are often limited in their expressive range, and require considerable technical knowledge to create non-trivial architectural structures. We present a novel tile-based PCG approach for generating architecture, that proposes the use of architectural profiles, a declarative characterization of architectural typology, within a generic tile solving framework. An architectural profile consists of a set of tiles, representing atomic architectural building blocks, and a set of declarative constraints and rules, specifying which conditions a tile configuration has to satisfy to be valid. These conditions are translated into logic constraints, and used by a tile solver to control tile placement in a bottom-up manner. Eventually, each valid model output by the solver is a representative instance of its architectural profile. We describe an implementation of this approach with Answer Set Programming, using an off-the-shelf constraint solver for model generation. We performed an expressive range analysis, and concluded that our declarative method is quite controllable and can be steered over a broad range of architectural structures, regarding density and repetitiveness. Due to this expressive range and control, our tile-based method is very suitable for the customized development of urban environments for games.
IOT Based – Automated Indoor Air Quality and LPG Leak Detection Control System using Support Vector Machine

The rapid increase of population every year causes a great distress in the environment. Air pollution is becoming more imminent due to emissions from transportation, urbanization, and energy consumption. Hence, harmful elements contaminate the outdoor air, which then affects the health of individuals. These contaminants are the CH4, CO, CO2, and particulate matter, which also affect the indoor air quality. The indoor air quality was said to be twice to five times more dangerous than the outdoor air due to harmful substances entering and being trapped. LPG leakage is also considered as an indoor health risk. The study therefore aims to create a device to not only to monitor the different parameters of indoor air quality (IAQ), but also to notify the user about the potential risks in the specific location. The control system utilizes wireless transmission of data and a mobile application to notify the user. The sensor system also utilized a supervised machine learning using Support Vector Machines which helped the system to try and predict new data as accurate as possible. This study was able to accurately monitor the IAQ of a given location notify the user. Careful reading of sensors revealed percent differences ranging from 0.8% to 4% compared to an industrial grade air quality detector. The researchers also found a variety of opportunities for improvement within the design of the prototype.
38.5 Gb/sRoF Based Optical Receiver for 5G Mobile Remote Radio Head Applications

A 38.5 Gb/s optical receiver has been fabricated in a 130 nm SiGe-BiCMOS technology for being employed in a Radio over Fiber transceiver for 5G remote radio head application. As a transimpedance topology, a common base amplifier is used to provide a low resistance input impedance to relax connecting a photodiode with relatively high capacitance. The transimpedance amplifier (TIA) is followed by the capacitive degeneration limiting amplifier to create a degree of freedom and relax the trade-off between the gain-bandwidth product and the linearity of the receiver. Analytical methods have been investigated to provide a low-noise and high gain design. Calibrated S-parameter measurement has been done which represents a 27-GHz 3-dB bandwidth with a differential gain of 49 dB$\Omega$ with a total power consumption of 160 mW for the entire receiver. The optical receiver has also been tested in the time domain by using a 35-GHz photodetector with a responsivity of 0.1 A/W and a capacitance of 90 fF. Eye diagram measurements have revealed that the optical receiver can provide a BER of $6.37\times 10^{-6},1.3\times 10^{-4}$, and $1.7\times 10^{-3}$ for a data rate of15, 25, and 35 Gb/s at an input current of 32$\mu$A respectively. The fabricated receiver occupies an area of 930 $\mu m\times 930\mu m$ including bond pads.
Corner Detection with 2-D RPLIDAR to Detect Furniture on TCIFFRC Tracks

Trinity College International Firefighting Robot Contest (TCIFFRC) is a firefighting robot competition organized by Trinity College, United States. In the TCIFFRC track, there are four types of room with different characteristics. When the game starts, the robot can be located in any room with randomly placed tube-shaped obstacles (simulating furniture). The robot must be able to detect those obstacles in order to know its position, so that the robot can go out of the room avoiding the obstacles quickly. The focus of this research is to improve the ability of the robot by using corner detection to detect the furniture correctly so that the robot can know the position of the furniture. The robot in this research is a quadruped robot that has been equipped with a 2-dimensional (2-D) laser range scanner (RPLIDAR A1M8). The experiment results showed that the robot was able to detect the furniture up to 5 meters away with an error of 12.5% and 6.12% for the measured angle and the measured distance, respectively. The success rate of the robot achieved 94.7% in being able to escape from the given room with the furniture.
MSD-Net: Multi-Scale Discriminative Network for COVID-19 Lung Infection Segmentation on CT

Since the first patient reported in December 2019, 2019 novel coronavirus disease (COVID-19) has become global pandemic with more than 10 million total confirmed cases and 500 thousand related deaths. Using deep learning methods to quickly identify COVID-19 and accurately segment the infected area can help control the outbreak and assist in treatment. Computed tomography (CT) as a fast and easy clinical method, it is suitable for assisting in diagnosis and treatment of COVID-19. According to clinical manifestations, COVID-19 lung infection areas can be divided into three categories: ground-glass opacities, interstitial infiltrates and consolidation. We proposed a multi-scale discriminative network (MSD-Net) for multi-class segmentation of COVID-19 lung infection on CT. In the MSD-Net, we proposed pyramid convolution block (PCB), channel attention block (CAB) and residual refinement block (RRB). The PCB can increase the receptive field by using different numbers and different sizes of kernels, which strengthened the ability to segment the infected areas of different sizes. The CAB was used to fusion the input of the two stages and focus features on the area to be segmented. The role of RRB was to refine the feature maps. Experimental results showed that the dice similarity coefficient (DSC) of the three infection categories were 0.7422,0.7384,0.8769 respectively. For sensitivity and specificity, the results of three infection categories were (0.8593, 0.9742), (0.8268,0.9869) and (0.8645,0.9889) respectively. The experimental results demonstrated that the network proposed in this paper can effectively segment the COVID-19 infection on CT images. It can be adopted for assisting in diagnosis and treatment of COVID-19.
Performance Analysis of DVB-T2 System Based on MIMO Using Low Density Parity Check (LDPC) Code Technique and Maximum Likelihood (ML) Detection

The MIMO (Multiple Input Multiple Output) system works very well on multipath components. Some multipath components are exploited to increase bandwidth diversity and efficiency. In addition, this system also provides a significant increase in data throughput and communication coverage without expand the frequency and transmit power. Even so, this system is still vulnerable to InterSymbol Interference. Low Density Parity Check (LDPC) technique which is has the ability to correct errors near the Shannon limit (theoretical maximum error correction limit) and Maximum likelihood (ML) detection which is able to provide performance minimum bit errors are believed to be able to handle interference arising when passing through a channel.This research made a MIMO 2X2 simulation system on DVB-T2 technology with LDPC coding technique and Maximum Likelihood detector. The results obtained from running this simulation system indicate that the system which is has smaller rate is the better system. The ideal system without error correction code shows the Maximum Likelihood performance forming a waterfall curve and no more errors at 20 dB. While the system with LDPC and Maximum Likelihood is able to reaches BER (bit error rate) 10-2 at SNR (Signal to Noise Ratio) around 1 dB from the 1/2 rate system, and the 3/4 rate system reaches BER 10-2 at around 4 dB, and the 5/6 rate system reaches BER 10-2 at SNR around 6 dB.
Detection and Mitigation of Cyber Attacks on Voltage Stability Monitoring of Smart Grids

This paper investigates the possible cyber-physical attacks on voltage stability monitoring of a power transmission system. By considering a smart adversary that can launch a vector attack based on power flow equations, conventional voltage stability monitoring systems based on voltage stability indexes will fail to detect the instability issue. This can mislead the Power System Operator (PSO) to take inappropriate corrective action. In order to prevent and to combat such a malicious attack, a new indicator for efficient intrusion detection and a novel mitigation algorithm is proposed. This proposed detection algorithm employs the multi-port equivalent circuit of the power system to calculate the Thevenin Equivalent (TE) parameters. These TE parameters are then used to calculate an indicator, which reveals the attack on Phasor Measurement Unit (PMU) data. The proposed mitigation scheme then helps PSO to detect which PMU is under-attack. Furthermore, the amount of injected attack vectors is computed or estimated depending on the number of compromised PMUs. The effectiveness of the proposed techniques is demonstrated via illustrative case studies.
Molecular docking and molecular dynamics simulation of Bacillus thuringiensis dehalogenase against haloacids, haloacetates and chlorpyrifos

Abstract The high dependency and surplus use of agrochemical products have liberated enormous quantities of toxic halogenated pollutants into the environment and threaten the well-being of humankind. Herein, this study performed molecular docking, molecular dynamic (MD) simulations, molecular mechanics-Poisson Boltzmann Surface Area (MM-PBSA) calculations on the DehH2 from Bacillus thuringiensis, to identify the order of which the enzyme degrades different substrates, haloacids, haloacetate and chlorpyrifos. The study discovered that the DehH2 favored the degradation of haloacids and haloacetates (−3.3 − 4.6 kcal/mol) and formed three hydrogen bonds with Asp125, Arg201 and Lys202. Despite the inconclusive molecular docking result, chlorpyrifos was consistently shown to be the least favored substrate of the DehH2 in MD simulations and MM-PBSA calculations. Results of MD simulations revealed the DehH2-haloacid- (RMSD 0.15 − 0.25 nm) and DehH2-haloacetates (RMSF 0.05 − 0.25 nm) were more stable, with the DehH2-L-2CP complex being the most stable while the least was the DehH2-chlorpyrifos (RMSD 0.295 nm; RMSF 0.05 − 0.59 nm). The Molecular Mechanics Poisson-Boltzmann Surface Area calculations showed the DehH2-L-2CP complex (−24.27 kcal/mol) having the lowest binding energy followed by DehH2-MCA (−22.78 kcal/mol), DehH2-D-2CP (−21.82 kcal/mol), DehH2-3CP (−21.11 kcal/mol), DehH2-2,2-DCP (−18.34 kcal/mol), DehH2-2,3-DCP (−8.34 kcal/mol), DehH2-TCA (−7.62 kcal/mol), while chlorpyrifos was unable to spontaneously bind to DehH2 (+127.16 kcal/mol). In a nutshell, the findings of this study offer valuable insights into the rational tailoring of the DehH2 for expanding its substrate specificity and catalytic activity in the near future. Communicated by Ramaswamy H. Sarma
Shallow-Water Bathymetry Estimation Using Single Band Algorithm and Green Band Algorithm

The provision of more comprehensive bathymetric data, for not just apply to the modern marine application but also the development of national data infrastructure. This demand has created a series of development in bathymetric data acquisition throughout the year from the lead line, ship-borne sounding, to air-borne Lidar. However, it has pros and cons in each bathymetric data acquisition related to cost, time, manpower and accessibility. The integration idea drives from the successful of Satellite-Derived Bathymetry technique in solving the problem, moreover, this the way to achieve the data with a uniform resolution in acceptable time and cost. Therefore, this study conducted to generate bathymetric mapping at '‘Pantai Tok Jembal'’ using Single Band Algorithm (SBA) for two visible bands (Blue and Green) and Green Band Algorithm (GBA). Two comparative analysis conducted in this study first is between SBA-Blue and SBA-Green to evaluate the ability of each band to estimate bathymetry at '‘Pantai Tok Jembal'’ shallow-coastal water area and while second is between SBA-Green and GBA method to evaluate the ability of each statistical approach in computing absolute water depth. Further, the result evaluated using SBES ground truth data. The root mean square error (RMSE) and R-squared (R2) computed between Single Beam Echo Sounding (SBES) data and Satellite-Derived Bathymetry (SBA and GBA), and the result shows that SBA-Green stand out in both analysis; moreover, this study successfully produces 49.69 km2 area (Extended Area) bathymetric mapping at '‘Pantai Tok Jembal'’, Terengganu, Malaysia. However, to support marine application and national data infrastructure, it requires more comprehensive bathymetric data which is giving high in accuracy and reliability, further research needs to undertake to fulfil this support
A Review of Agency Architectures in Interactive Drama Systems

This paper provides a review of the interactive drama field, attempting to create a taxonomy for classifying system affordances for emergence and authorial control, referred to in this paper as "agency architecture". The classification of interactive drama systems according to a spectrum of agency architectures, helps to identify open questions in the field, providing a better understanding of which architectures would benefit from greater attention from the research community. In this paper, several key interactive drama systems from the field’s literature are categorised by agency architecture. This is followed by a summary and analysis of their architectural classifications, alongside justifications for their assigned categories. It is then concluded with the identification of a number of research gaps, revealed by the compiled classifications, that highlight potential future research questions.
Centralised Versus Decentralised Traffic Optimisation of Urban Road Networks: A Simulation Study

One of the pivotal challenges presented to urban road traffic controllers is the effective utilisation of transport infrastructure, as a result of growing urbanisation, the finite network capacity, and of the increasing number of road vehicles. In this context, the arrival of connected autonomous vehicles (CAVs) represents a unique opportunity for a fundamental change in urban traffic optimisation, and urban traffic control should take an active role in integrating CAVs into the mobility ecosystem in order to maximise benefits. Traditional approaches, commonly exploited by SATNAVs, are based on a decentralised logic, where each vehicle decides the route to follow in isolation, possibly by considering the current network conditions. The arrival of connected vehicles would allow the exploitation of centralised traffic optimisation, where a central urban traffic controller can suggest routes to vehicles by taking into account the current network conditions, and predicted future evolution. This paper introduces a centralised approach for traffic optimisation of urban road networks, and presents an extensive evaluation of the capabilities of centralised and decentralised approaches. Evaluation is based on a validated and calibrated SUMO simulation model of the town centre of Milton Keynes, United Kingdom.
MobRec — Mobile Platform for Decentralized Recommender Systems

Recommender systems recommend new movies, music, restaurants, etc. Typically, service providers organize such systems in a centralized way, holding all the data. Biases in the recommender systems are not transparent to the user and lock-in effects might make it inconvenient for the user to switch providers. In this paper, we present the concept, design, and implementation of MobRec, a mobile platform that decentralizes the data collection, data storage, and recommendation process. MobRec’s architecture does not need any backend and solely consists of the users’ smartphones, which already contain the users’ preferences and ratings. Being in proximity in public places or public transportation, data is exchanged in a device-to-device manner, building local databases that can recommend new items. One of biggest challenges of such a system is the implementation of unobtrusive device-to-device data exchange on off-the-shelf Android devices and iPhones. MobRec facilitates such data exchange, building on Google Nearby Messages with Bluetooth Low Energy. We achieve the successful exchange of data within 3 to 4 minutes, making it suitable for the described scenario. We demonstrate the feasibility of decentralized recommender systems and provide blueprints for the development of seamless multi-platform device-to-device communication.
Security and Reliability Analysis of a Two-Way Half-Duplex Wireless Relaying Network Using Partial Relay Selection and Hybrid TPSR Energy Harvesting at Relay Nodes

In recent years, physical layer security has been considered as an effective method to enhance the information security beside the cryptographic techniques that are used in upper layers. In this paper, we provide the security analysis for a two-way relay network, where the two sources can only communicate through the intermediate relay nodes. In particular, we consider the scenario that there is an eavesdropper in the vicinity of one source node. Both reliability and security aspects are taken into consideration in our work. To enhance the reliability of communication, the intermediate relays are supplied with the energy harvested from the sources’ radio frequency (RF) signals using hybrid time-switching and power splitting (TPSR) protocol. Also, we apply the relay selection technique to select the best relay for the information exchange between two sources. Regarding security, the secrecy of information is improved with the help of friendly jammers nearby the eavesdropper. We provide the in-dept reliability and security analysis in terms of the closed-form expressions of the outage probability (OP) at the source nodes, the intercept probability (IP) at the eavesdropper, the secrecy outage probability (SOP), and the average secrecy capacity (ASC) of the system. Finally, the Monte Carlo simulations are also conducted to verify the correctness of our analysis and the effectiveness of the proposed scheme. Numerical results confirms that with the appropriate and feasible choices of involved parameters, both outage OP and IP can be kept at small values to guarantee the reliable and secure communication of the system.
Cloning and Expression of Small Hepatitis B Surface Antigen (sHBsAg) In Hansenula polymorpha

Recombinant small hepatitis B surface antigen (sHBsAg) is used as a vaccine component to prevent hepatitis B virus infection. As an attempt to produce local recombinant sHBsAg, a PCR-amplified DNA fragment encoding Indonesia sHBsAg which belongs to B genotype and adw2 subtype was cloned into Hansenula polymorpha expression vector pHIPX4 by using recombination method. The resulted pHIPX4-sHBsAg was integrated into the alcohol oxidase (AOX) locus of H. polymorpha NCYC495 genome and the sHBsAg expression was regulated under the control of H. polymorpha AOX promoter. H. polymorpha NCYC495 carrying the sHBsAg coding sequence was grown in mineral medium and methanol 0.5% (v/v) was added to induce the expression of recombinant sHBsAg. The expression of sHBsAg was detected by HBsAg diagnostic kit test, ELISA, and Western blot analysis.
Fuzzing Binaries for Memory Safety Errors with QASan

Fuzz testing techniques are becoming pervasive for their ever-improving ability to generate crashing trial cases for programs. Memory safety violations however can lead to silent corruptions and errors, and a fuzzer may recognize them only in the presence of sanitization machinery. For closed-source software combining sanitization with fuzzing incurs practical obstacles that we try to tackle with an architecture-independent proposal called QASan for detecting heap memory violations. In our tests QASan is competitive with standalone sanitizers and adds a moderate 1.61x average slowdown to the AFL++ fuzzer while enabling it to reveal more heap-related bugs.
A profile of a retrospective cohort of 22 patients with COVID-19 and active/treated tuberculosis

We read with interest the two articles by Tadolini et al. [1] and Stochino et al. [2], which described recent cohorts of either current or former tuberculosis (TB) patients with coronavirus disease 2019 (COVID-19) and studied their clinical course. India has the majority of global burden of TB, along with highest rising number of daily COVID-19 cases in the world [3, 4]. The information about COVID-19 and active/former TB co-infection reported so far is sparse, but it can be assumed that person with TB, when co-infected with COVID-19, may be at more risk of poor outcomes [1, 5]. The present study describes the first-ever cohort of current or treated TB patients co-infected with COVID-19 from a high TB burden country, recruited by a tertiary care hospital in India. Both tuberculosis and COVID-19 being communicable and prevalent diseases in India, the co-existence can lead to worse outcomes, as seen in this study, where there was high mortality among active as well as treated TB patients with COVID-19 co-infection https://bit.ly/3jHcGbQ
Design of a Sliding Mode Observer-Based Fault Tolerant Controller for Automobile Active Suspensions With Parameter Uncertainties and Sensor Faults

This paper presents a sliding mode observer-based fault tolerant controller for a class of active suspension with the parametric uncertainties and sensor faults. First, T-S fuzzy approach is employed to represent the inertial parameters uncertainties and sensor faults, and then an augmented vehicle dynamics model is established. To estimate both system state variables and sensor fault signal simultaneously, a sliding-mode observer is investigated, and the fault tolerant control law is further derived in terms of the estimation of state and state error within Lyapunov theory framework, moreover, the sufficient conditions for the existence of this controller is deduced and solved via a set of linear matrix inequalities. Finally, a complete comparative simulation case is provided to demonstrate the effectiveness and feasibility of the designed control method.