Miracle-dz/UniFAR-FTUs huggingface dataset:
MoFuzz: A Fuzzer Suite for Testing Model-Driven Software Engineering Tools

Fuzzing or fuzz testing is an established technique that aims to discover unexpected program behavior (e.g., bugs, security vulnerabilities, or crashes) by feeding automatically generated data into a program under test. However, the application of fuzzing to test Model-Driven Software Engineering (MDSE) tools is still limited because of the difficulty of existing fuzzers to provide structured, well-typed inputs, namely models that conform to typing and consistency constraints induced by a given meta-model and underlying modeling framework. By drawing from recent advances on both fuzz testing and automated model generation, we present three different approaches for fuzzing MDSE tools: A graph grammar-based fuzzer and two variants of a coverage-guided mutation-based fuzzer working with different sets of model mutation operators. Our evaluation on a set of real-world MDSE tools shows that our approaches can outperform both standard fuzzers and model generators w.r. t. their fuzzing capabilities. Moreover, we found that each of our approaches comes with its own strengths and weaknesses in terms of fault finding capabilities and the ability to cover different aspects of the system under test. Thus the approaches complement each other, forming a fuzzer suite for testing MDSE tools.
Sorted Phase Rotation Spatial Modulation in MISO Channel

In this paper, a sorted phase rotation assisted spatial modulation (SM) is developed to optimize the spatial constellation of the received signals in SM multiple input single output (MISO) system. The proposed scheme improves the conventional phase alignment based SM by sorting the Euclidean norm of the channel, and utilizing the sorting results to adjust the phase of each transmit antenna to further optimize the minimum Euclidean distance (MED) of the received constellation. By adopting a phase rotation criterion that is based on the sorted Euclidean distance at the transmitter, the propsed scheme, termed as SM-SPR is presented in this paper. An upper bound of the bit error rate (BER) for the proposed SM-SPR with MISO configuration is also derived in this paper. Simulation results demonstrate that the proposed SM-SPR scheme achieves significant performance gain than conventional SM system. Furthermore, it outperforms the conventional phase alignment assisted SM with improved MED of the constellation.
Investigation of dynamic behavior of the Bray–Liebhafsky reaction in the CSTR. Properties of the system examined by pulsed perturbations with I−

In order to investigate the properties of the dynamic states in the Bray–Liebhafsky (BL) reaction, pulsed perturbations with iodide are applied. In particular, the excitability thresholds of several stable stationary states corresponding to the same non-equilibrium stationary state branch, as well as the excitability thresholds and phase response behavior of one oscillatory state, are investigated and characterized quantitatively. In the oscillatory state, the phase response behavior corresponding to perturbations of different strengths is determined, and characterized by phase response curves (PRC and PTC). Finally, all values obtained experimentally are also calculated, using one variant of a model of the Bray–Liebhafsky reaction.
Prolongation of Longitudinal Relaxometry Characteristics in Healthy Aging: a Whole Brain MRI Study

Conventional MRI studies have reported several structural changes such as brain atrophy and ventricular enlargement in healthy aging. Quantitative MRI (qMRI) allows the measurement of tissue characteristics such as the longitudinal relaxation times (T1) which provides unique and complementary information to widely used measures of brain signal characteristics. In this study, the T1 values on entire brain were mapped with an ROI based method. T1 prolongation with aging was demonstrated on numerous cortical and subcortical areas such as caudate, thalamus and prefrontal cortex. This outcome was interpreted as increased demyelination in these structures.
Trust and Distrust: The Antecedents of Intention to Donate in Digital Donation Platform

As the most generous country, Indonesia embedded altruism as their lifestyle. However, the donation that is often made conventionally now has been shifted to online donation. Online donation platform such as Kitabisa.com has significant users growths in recent years. Along with the increasing number of online donations, many fraud cases occurred which harm users' trust in online donation platforms. Thus, this study aims to examine the institutional mechanism and information systems success factors that influence trust and distrust in the online donation platform and how it influences attitude and online donation intention. We collected 865 data using an online survey and then analyzed it using Partial Least Square - Structural Equation Modeling (PLS-SEM). The result of this study indicates that quality aspects such as system quality, information quality, and institutional mechanism aspects such as perceived platform rules and perceived monitoring influence trust and distrust in online donation platforms. Additionally, trust and distrust in online donation platforms influence attitude towards donation and online donation intention significantly.
Multi-Agent Deep Reinforcement Learning for Traffic optimization through Multiple Road Intersections using Live Camera Feed

Traffic signals provide one of the primary means to administer conflicting traffic flows. Existing signal control strategies, operating on hand-crafted rules, fail to efficiently, autonomously adapt to the changing traffic patterns. Each signal control system independently manages one intersection at a time and regulates navigation of vehicles through that intersection. Current systems cannot co-operate to optimize aggregate traffic flows through multiple road intersections. Consequently, they are susceptible to making myopic signal control decisions that might be effective locally, but not globally. Instead, we propose a system of multiple, coordinating traffic signal control systems. This paper presents the first application of multi-agent deep reinforcement learning (DRL) to achieve traffic optimization through multiple road intersections solely based on raw pixel input from CCTV cameras in real time. This set of traffic control agents is shown to significantly outperform independently operating (both DRL-trained and loop-induced) adaptive signal control systems, by increasing traffic throughput and reducing the average time a vehicle spends in an intersection. Additionally, this paper, introduces attention-based visualization to interpret and validate the proposed multi-agent signal control methodology.
Multi-Objective Optimisation of Photovoltaic Systems and Unified Power Quality Conditioners with Simultaneous Distribution Network Reconfiguration

Nowadays, renewable energy sources are being increasingly integrated to power systems. This research analyses various cases of distribution network reconfiguration (DNR) as well as the placement and sizing of photovoltaic (PV) systems and unified power quality conditioners (UPQC). Non- dominated Sorting Genetic Algorithm-II (NSGA-II), Strength Pareto Evolutionary Algorithm-2 (SPEA2) and Multi-objective Evolutionary Algorithm based on Decomposition (MOEA/D) were applied to minimise the active power loss, voltage deviation and total cost subject to constraints. The efficacy of the three algorithms was analysed according to their simulation time and performance metrics. The simulation results revealed that the integration of PV systems and UPQC with the simultaneous reconfiguration of the network produces acceptable compromises among the objective functions. NSGA-II outperformed the other algorithms in terms of simulation time at the expense of a minor deterioration in the quality of the solutions in some cases. With equal importance of each objective, smaller PV systems are required to achieve the optimal solution when UPQCs are present in the distribution system. The integration of UPQC with simultaneous DNR further reduces the size of PV systems required.
Identification of Electrically Stressed Regions in AlGaN/GaN-on-Si Schottky Barrier Diode Using EBIC Technique

The mapping of the current induced by a focused electron beam in a scanning electron microscope (SEM) has been used to localize electrically stressed regions in the AlGaN/GaN-on-Si Schottky barrier diode (SBD) structures cross-sectioned by the focused ion beam (FIB) technique. We have shown that homogeneously distributed electron beam induced current (EBIC) intensity detected below the Schottky contact at 0 V changes with increasing reverse voltage ${V}_{R}$ and peaks at the edges of a field-plate region. The build-up of local microavalanches at high electric voltages has been indicated by overexposed EBIC signal at areas following the edges of the field plate structure. Interpretation of EBIC measurements is supported by electro-physical modeling and simulations employing the 2-D finite element method in Synopsys TCAD Sentaurus. The simulations prove that the electric field intensity in the SBD locally reaches values sufficiently high to trigger multiplication of the excessive carriers generated by an electron beam, which helps one to visualize and localize critical regions in GaN-based power electronic devices by the EBIC method.
Harmonic Reflection Detection from 915-MHz SDR- Based RF Energy Harvesting Prototype System

In this paper the reflection detection of second harmonic from a 915-MHz radio-frequency (RF) energy harvesting prototype system is demonstrated. The prototype system contains an RF power emitter and a power receiver. The power receiver is composed of a rectenna and a commercial power management demoboard. The power emitter includes two commercial software-defined radio (SDR) transceivers to act as a RF power radiator and a harmonic detector for energy harvesting and harmonic reflection detection, respectively. From the experimental results, the transmission power of about +18 dBm are delivered to the rectenna to start up the power management demo board to hunting electrical energy at a distance of 50 cm. By directly reading the measured harmonic power strength with a SDR tool, a detectable harmonic power level difference can be observed. The difference over 10 dB can be obtained. Thus, it can be used as an index signal to identify the directional position of the power receiver.
Complementary Analysis of Thermal Transition Multiplicity of Hen Egg-White Lysozyme at Low pH Using X-ray Scattering and Scanning Calorimetry

In this present paper we present a new method of small-angle X-ray scattering (SAXS) data analysis (called the TMA method) to examine multiplicity of structural transition of denaturation processes of proteins. The TMA method gives us a molar fraction of spatial-conformational-state and an indication of deviation from the two-state structural transition hypothesis in a structural transition process. Using this method we have successfully compared a spatial-conformational-state transition observed by SAXS with a thermodynamicmicrostate transition observed by differential scanning calorimetry (DSC) in the thermal denaturation process of hen egg-white lysozyme (HEWL) at the pH range of 2.4-3.1. The TMA method is able to dissect spatialconformational-state transition multiplicity depending on structural hierarchy. We have found that the thermal structural transition of HEWL is well-characterized mostly in terms of two different spatial-conformationalstate transitions in the tertiary and intramolecular structures. Upon heating, the transition-midpoint temperature of the spatial-conformational-state transition of the tertiary structure mostly agrees with that of DSC, while the transition-midpoint temperature of the intramolecular structure greatly differs from that of DSC. The tertiary structural transition essentially obeys the two-state transition feature with a sharp critical temperature in comparison with the intramolecular structural transition. When the pH is lowered from 3.1 to 2.4, the former transition tends to shows a maximum deviation from the two-state transition feature around the transition temperature. On the other hand, the intramolecular structural transition proceeds continuously with a relatively large deviation in the whole temperature range measured at all pH values. The present results obtained by the TMA method essentially agree with our previous report using standard SAXS analyses in HEWL system at different pH range of 7.0-1.2, and would afford us further aspects to discuss experimental evidences with theoretical models of protein folding, such as nucleation-collapse models.
A MIMO UHF-RFID SAR 3D Locating System for Autonomous Inventory Robots

As ultrahigh frequency (UHF) radio-frequency identification (RFID) is a cost-efficient, reliable technology, its use for Internet of Things applications is very attractive. The goal of three-dimensional (3D) product maps for smart warehouses is very challenging, but can be achieved with UHF-RFID. Therefore, we equipped a mobile robot platform with a commercial RFID reader and added a switching matrix to integrate a multiple input—multiple output (MIMO) synthetic aperture radar based localization concept (SARFID) with the use of eight antennas. The mobile robot platform and the MIMO localization technique together demonstrate good tag localization results. We evaluate both single-dimensional and two-dimensional synthetic aperture radar (SAR) trajectories. Their shapes are the main contributor to the achievable accuracy. The robot position is acquired by the robot’s odometry data. Despite the substantial error in the robot position, we generate 3D product maps with tags labeled on objects and achieve a 3D root mean square error (RMSE) of 3.8 cm.
Unveiling Purchase Intention for .id Domain using E-WOM Adoption on Social Media

Domain. id is a unique name given to identify private individuals or institutions (both private and public) on Indonesia's Internet. The number of. id domains was still less than the target. This gap made Indonesian Internet Domain Name Manager (PANDI) improve adequate and effective strategies, especially digital marketing. PANDI should identify factors that influence people's intention according to social media usage to formulate strategies. Social media could establish qualified electronic Word of Mouth (e- WOM) involving brand image and brand awareness. This study aimed to determine those factors and analyze whether the . id domain's adoption can affect brand awareness, brand image, and purchase intention of the . id domain. Data collection was carried out by distributing online questionnaire surveys then processed using PLS-SEM with 167 respondents. They represented people who owned. id domain or other domain. Their perception exposed why they purchased. id and prioritize other domains. Out of 13, this research captured 12 accepted hypotheses. Generally, source credibility influenced information credibility, while information credibility influenced the adoption of e-WOM regarding. id domains on social media. Also, e-WOM affects one's brand awareness, brand image, and attitude towards the . id domain. A person's attitude towards the . id domain positively influenced the purchase intention of the . id domain.
SOCA: Domain Analysis for Highly Automated Driving Systems

Highly automated driving systems need to master a highly complex environment and are required to show meaningful behavior in any situation occurring in mixed traffic with humans. Deriving a sufficiently complete and consistent set of system-level requirements capturing all possible traffic situations is a significant problem that has not been solved in existing literature. In this paper, we propose a new method called SOCA addressing this problem by introducing a novel abstraction of traffic situations, called zone graph, and using this abstraction in a morphological behavior analysis. The morphological behavior analysis enables us to derive a set of system-level requirements with guarantees on completeness and consistency. We illustrate our method on a slice-of-reality example from the automated driving domain.
AdaDiffGrad: An Adaptive Batch Size Implementation Technique for DiffGrad Optimization Method.

Stochastic Gradient Descent is a major contributor to the success of the deep neural networks. The gradient provides basic knowledge about the function direction and its rate of change. However, SGD changes the step size equally for all parameters irrespective of their gradient behavior. Recently, several efforts have been made to improve the SGD method, such as AdaGrad, RMSprop, Adam, and diffGrad. The diffGrad is an appropriate and enhanced technique that uses fraction constant based on previous gradient information for gradient calculation. This fraction constant decreases the momentum resulting in slow convergence towards an optimal solution. This paper addresses the slow convergence problem of the diffGrad algorithm and proposed a new adaDiffGrad algorithm. In adaDiffGrad an adoptive batch size is implemented for the diffGrad to overcome the problem of slow convergence. The proposed model is experimented for image categorization and classification over CIFAR10, CIFAR100, and FakeImage dataset. The results are compared with the state of art models, such as Adam, AdaGrad, DiffGrad, RMSprop, and, SGD. The results show that adaDiffGrad outperforms other optimizers and improves the accuracy of the diffGrad.
A Latent Feelings-aware RNN Model for User Churn Prediction with only Behaviour data

User Churn Prediction is a cutting-edge research area in the web service industry, it is the key for managing the user in the virtual world and provide feedback information for improving the corresponding web service. At present, most of the relevant work is to design a questionnaire to collect data of users' characteristics and feelings and then develop a general model by finding relevance. However, that kind of methods requires quite a time and manpower, and most web services can only obtain logs of users' behaviours and have no access to users' feature data. Therefore, it is a big challenge to conduct user churn prediction with only behavior data and get users' latent feelings from their action data in order to improve the accuracy of churn prediction. In this paper, a novel Latent Feelings-aware RNN model, namely LaFee, has been proposed to solve the user churn prediction problem by using only behaviour data. The latent feelings, proven to be satisfaction and aspiration, can be estimated through the intermediate variable of the trained LaFee. We also designed experiments on a real dataset and the results show that our methods outperform the baselines.
From trips database to real-world fuel consumption. model and large-scale simulation framework

Due to the well-known environmental issues, European CO2 emission standards are more and more stringent. However a direct measurement of these CO2 emissions is actually unachievable. Our work aims to predict such a real-world fuel consumption by modeling its three influencing factors: the vehicle, the driving behavior and the use case. The vehicle factor is modeled through a quasi-static model, parametrized by vehicle parameters only (i.e. mass body, engine power, etc.). Driving behavior and use case are modeled thanks to a data analysis of a real-world vehicles and trips database by performing a classification on features derived from speed and acceleration trace recordings. Our approach was evaluated on trips of 162 vehicles and compared to real-fuel consumption gathered in an online self-report fuel tracking service database, by evaluating the gap between the official fuel consumption and the real one given by the database or our model, according to the identified driving behaviors and use cases. A maximal difference of around 10% is recovered for 85% of the vehicles and 20% difference for 96% of the vehicles.
Deep Learning Model for Financial Time Series Prediction

Stock market is considered complex, fickle, and dynamic. Undoubtedly, prediction of its price is one of the most challenging tasks in time series forecasting. Traditionally, there are several techniques to effectively predict the next t lag of time series data such as Logistic Regression and Random Forest. With the recent progression in sophisticated machine learning approaches such as deep learning, new algorithms are developed to analyze and forecast time series data. This paper employs Long-Short Term Memory (LSTM) deep learning approach to predict future prices for low, medium, and high risk stocks. To the best of our knowledge, we are proposing an innovating technique to evaluate deep learning and other prediction techniques w.r. t. the stocks’ risk factor. The proposed approach is compared with other traditional algorithms over different periods of training data. The results show that our LSTM approach outperforms other traditional approaches for all stock categories over different time periods. Experimental results illustrate that, for low and medium risk stocks, it is better to use LSTM with long time period of training data. However, for high risk stocks, short time period of training data provides more accurate predictions.
Adversarial Attack against LSTM-based DDoS Intrusion Detection System

Nowadays, machine learning is a popular method for DDoS detection. However, machine learning algorithms are very vulnerable under the attacks of adversarial samples. Up to now, multiple methods of generating adversarial samples have been proposed. However, they cannot be applied to LSTM-based DDoS detection directly because of the discrete property and the utility requirement of its input samples. In this paper, we propose two methods to generate DDoS adversarial samples, named Genetic Attack (GA) and Probability Weighted Packet Saliency Attack (PWPSA) respectively. Both methods modify original input sample by inserting or replacing partial packets. In GA, we evolve a set of modified samples with genetic algorithm and find the evasive variant from it. In PWPSA, we modify original sample iteratively and use the position saliency as well as the packet score to determine insertion or replacement order at each step. Experimental results on CICIDS2017 dataset show that both methods can bypass DDoS detectors with high success rate.