Miracle-dz/UniFAR-FTUs huggingface dataset:
Applying Learning Techniques to Oracle Synthesis

Software reliability is a primary concern in the construction of software, and thus a fundamental component in the definition of software quality. Analyzing software reliability requires a specification of the intended behavior of the software under analysis. Unfortunately, software many times lacks such specifications. This issue seriously diminishes the analyzability of software with respect to its reliability. Thus, finding novel techniques to capture the intended software behavior in the form of specifications would allow us to exploit them for automated reliability analysis. Our research focuses on the application of learning techniques to automatically distinguish correct from incorrect software behavior. The aim here is to decrease the developer's effort in specifying oracles, and instead generating them from actual software behaviors.
Dynamic State Estimation of Power System Considering Asynchronous Measurement

In order to solve the problems that PMU measurement and SCADA are asynchronous and the measurement model is different, it is difficult to use them together. In this paper, a robust unscented Kalman filter based on quadratic constrained quadratic estimation is proposed to realize the fusion estimation of hybrid measurements. The algorithm realizes the following three functions: Firstly, the time scale of SCADA measurement is given and synchronized by sequential comparison and interpolation synchronization method; secondly, the strong tracking algorithm is introduced to solve the problem that the filtering effect of UKF is reduced when the process noise is abnormal; finally, the proposed algorithm is simulated in IEEE 30 test system. The test results show that the algorithm can realize the synchronization of asynchronous measurement, filter out the error caused by asynchronous problem, and effectively correct the influence of abnormal process noise on the filtering result, so as to realize the real-time tracking of the system state.
Pathways and Organisms Involved in Ammonia Oxidation and Nitrous Oxide Emission

Ammonia oxidation is a central step in the global nitrogen cycle that involves several different-conditions and metabolic bioprocesses, including aerobic versus anaerobic ammonia oxidation, and autotrophic versus heterotrophic ammonia oxidation. With the development and application of metagenomics and other modern molecular approaches, some new organisms (particularly ammonia-oxidizing archaea) and novel pathways related to ammonia oxidation have been identified. Consequently, the understanding of nitrogen cycling processes and the microorganisms that mediate them have been greatly improved. Here the authors summarize the biochemistry, microbiology, and ecophysiology of these organisms (including autotrophic ammonia-oxidizing bacteria, heterotrophic nitrifying bacteria, anaerobic ammonia-oxidizing bacteria, and ammonia-oxidizing archaea) and discuss the current knowledge and important concepts associated with their corresponding pathways. Factors influencing their distribution, abundance, community structure and potential ammonia oxidation rates in natural and engineered ecosystems are also addressed. Furthermore, the mechanism of nitrous oxide emission during these processes and the specific control strategies are explained or proposed. The significant roles of these organisms in novel biological wastewater treatment processes are also evaluated. Finally, several urgent issues and significant perspectives related to these novel pathways and players have been addressed to evoke the involvement of researchers in broadening future studies.
Detection of Sore-risk Regions on the Foot Sole with Digital Image Processing and Passive Thermography in Diabetic Patients

According to the International Diabetes Federation, approximately four million people die from diabetes each year, and ten million more suffer disabling or potentially terminal complications, such as diabetic foot disease. Therefore, the proposal is the early detection by detecting non-homogeneous temperature regions with radiometric data and image processing by IR thermography. The samples were taken from 12 patients diagnosed with diabetes mellitus without a history of diabetic foot or visible anatomical alterations, by a cooled IR sensor programmed exclusively to measure human skin temperature. The radiometric data was processed to eliminate interferences and segment the lower limb since the acquisition was carried out in a non-controlled environment; where lies the relevance of this paper. The results were presented as binary images with a defined segmented region against a contrasting background, reviling areas corresponded to higher temperatures regard the rest of the sole fo the foot as a consequence of non-homogeneous temperature pattern. A total of 6 cases showed a normal homogeneous temperature, 2 cases had fault origins and 4 cases presented spotted regions that are prospective to prone lesions. This procedure set a precedent for the analysis of early detection of the diabetic foot with radiometric data processing instead of using IR images as input; which could be used to retrieve the coordinates and carry out an exhaustive numerical analysis or pattern recognition (i.e. thermal fingerprint) to support the anticipated diagnosis based on support systems such as medical thermography as perspective.
GMSK Modulated DSSS Signal Separation Based on Principal Component Analysis

Blind source separation (BSS) has been introduced in direct sequence spread spectrum (DSSS) system to suppress multiple access interference (MAI) without prior information. BSS or independent component analysis (ICA) based anti-interference receiver can separate the legal information sequence from the interference to improve the communication performance. However, the previous BSS or ICA based receiver is proposed for antipode CDMA signal where the information sequence is binary. Because of the continuous phase characteristics of GMSK, it is difficult for the BSS or ICA based receiver to achieve good performance in GMSK-DSSS systems. According to this, a principal component analysis (PCA) based receiver is proposed in this paper. Firstly, the waveform data of the signal is transformed into multi-channel signals. Secondly, for each channel signal, the covariance matrix is estimated. Then the covariance matrix is decomposed to obtain the eigenvalues and the eigenvectors. The un-mixing matrix is calculated with eigenvalues and eigenvectors. Finally, the information sequence of legal user is separated from the interference user with the un-mixing matrix. To verify the performance, simulation experiment is carried out. The Results indicate the proposed method outperforms the ICA or BSS based method.
A Train Arrival Delay Prediction model using XGBoost and Bayesian optimization*

This paper proposes a data-driven method that combines eXtreme Gradient Boosting (XGBoost) and Bayesian optimization algorithm to predict train arrival delays. First, 11 characteristics that may affect train arrival delay at the next station are identified as independent variables. Second, an XGBoost prediction model that capture the relation between train arrival delays and various characteristics of a railway system is established Third, the Bayesian optimization algorithm is applied to the hyper-parameter optimization of XGBoost model to improve the prediction accuracy. Finally, a case study is illustrated to show the prediction accuracy of the proposed method. The results demonstrate that the proposed method has a higher prediction precision and outperforms other benchmark methods(i.e). , Random Forest, Deep Extreme Learning machine and Gradient Boosting Regression Tress).
Mapping Gaming Elements with Gamification Categories: Immersion, Achievement, and Social in a MOOC Setting

The growing participation of educational institutions in the movement of massive open online courses (MOOCs) generates a great multitude of opportunities and challenges. Firstly, the high dropout rate and the lack of learners’ motivation are significant issues in MOOCs, which cast doubt on the quality of teaching and learning. Secondly, MOOC users’ diversity creates problems and barriers to MOOC providers in designing successful and effective courses that will suit all types of users. It is still unclear to think about how to reach various types of MOOC participants. In this regard, gamification is proposed as a complement to existing learning approaches to provide learners with a powerful and motivational learning experience. For decreasing the high dropout rates in MOOCs and to engage the learners and improve their motivation by adopting gamification in education, this study aims to classify gamification into three categories: achievement, social, and immersion (ASI). Furthermore, 27 gaming elements are identified that have been used in a MOOC context and are mapped to the aforementioned categories in order to help the MOOC developers and designers better understand how the various gamification categories can influence the users.
Risk-based Trust Evaluation Model for VANETs

Vehicular ad hoc networks (VANETs) have drawn a lot of attention in recent years due to their potential in improving traffic safety applications. Evaluating trust between peers in such networks is an essential component that determines whether a received report from a neighboring vehicle should be accepted or refused. For this purpose, many VANET trust management models have been proposed, differing in their architecture, trust establishment process, and flexibility. However, risk estimation has not been taken into consideration in all of these models. In this paper, we propose a risk-based trust evaluation model that overcomes the information oversampling issue in VANETs. The proposed model provides a decision-making process for vehicles receiving conflicting reports regarding an event’s occurrence according to the risk estimation for each required action of both reports. The risk is estimated according to the likelihood of taking an incorrect action and its associated impact. Finally, a decision is made corresponding to the action with the lowest risk. We show that a risk-based decision-making scheme may take different actions than a purely trust-based method. Simulation results show that the risk-based trust model outperforms a purely trust-based model.
Optimization of implant-type wireless rechargeable battery

Lithium-ion batteries (LIBs) are served as a power source for various biomedical devices. Compared to the conventional LIBs, which are used in portable electric devices and electric vehicles, the implantable LIBs are required high safety and stable performance. In detail, the implantable LIBs should have high energy density due to limited space in the human body. Therefore, there has been a need for implantable LIBs having high energy density and long lifespan. In this study, the implantable LIB is designed by optimization of electrode composition in consideration of the energy density and cha rging/discharging performance. The designed implantable LIB has 30 mm x 9 mm x 3 mm (Length x width x thickness), sho wed high performance with 52 mAh of capacitance, and 166.4 mWh of power.
Novel Beam-Forming Technology for WPT System to Flying Drone

We are developing the microwave power transfer (MPT) system for the flying drone and designing an optimum beam form to maximize the MPT efficiency, consisting of a beam efficiency from a transmitting antenna to a receiving antenna and an RF-DC conversion efficiency at a rectenna, rectifying antenna. Some beam-forming technologies exist to increase the beam efficiency but we do not mind the beam pattern at the receiving antenna surface. The RF-DC conversion efficiency of the rectenna has an input microwave power dependence and has an optimum microwave input power for maximum efficiency. If there is an amplitude taper at the receiving antenna surface in the maximum beam efficiency case, it is suitable for a signal transfer but not for the MPT because the RF-DC conversion efficiency of the rectenna is not maximum. We, therefore, propose a flat-top beam pattern to increase efficiency, which is beam efficiency × RF-DC conversion efficiency. We apply the Woodward Lawson method to make the flat-top beam.
FlashRegex: Deducing Anti-ReDoS Regexes from Examples

Regular expressions (regexes) are widely used in different fields of computer science such as programming languages, string processing and databases. However, existing tools for synthesizing or repairing regexes were not designed to be resilient to Regex Denial of Service (ReDoS) attacks. Specifically, if a regex has super-linear (SL) worst-case complexity, an attacker could provide carefully-crafted inputs to launch ReDoS attacks. Therefore, in this paper, we propose a programming-by-example framework, FlashRegex, for generating anti-ReDoS regexes by either synthesizing or repairing from given examples. It is the first framework that integrates regex synthesis and repair with the awareness of ReDoS-vulnerabilities. We present novel algorithms to deduce anti-ReDoS regexes by reducing the ambiguity of these regexes and by using Boolean Satisfiability (SAT) or Neighborhood Search (NS) techniques. We evaluate FlashRegex with five related state-of-the-art tools. The evaluation results show that our work can effectively and efficiently generate anti-ReDoS regexes from given examples, and also reveal that existing synthesis and repair tools have neglected ReDoS-vulnerabilities of regexes. Specifically, the existing synthesis and repair tools generated up to 394 ReDoS-vulnerable regex within few seconds to more than onehour, while FlashRegex generated no SL regex within around five seconds. Furthermore, the evaluation results on ReDoS-vulnerable regex repair also show that FlashRegex has better capability than existing repair tools and even human experts, achieving 4 more ReDoS-invulnerable regex after repair without trimming and resorting, highlighting the usefulness of FlashRegex in terms of the generality, automation and user-friendliness.
Stock Price Prediction using Bi-Directional LSTM based Sequence to Sequence Modeling and Multitask Learning

The stock market is a dynamic and volatile platform which provides an environment for traders to invest and trade in shares. The price of a stock is dependent on numerous static and dynamic features. Predicting the future price of a particular company’s stock can be extremely beneficial for traders. Seq2Seq modelling helps map an input sequence to an output sequence. In this paper, we propose a system to predict the future Open, High, Close, Low (OHCL) value of a stock using a Bi-Directional LSTM based Sequence to Sequence Modelling. Each OHCL price is an independent sequence and multitask learning helps map the interrelations between them. A multitask system is also proposed which uses sub tasks and shared tasks to model the prices. Stock prices of Tata Consumer Products Limited from the National Stock Exchange (NSE) of India is used. To evaluate the efficiency of the proposed systems, they are compared against various machine learning algorithms. The proposed Seq2Seq and multitask systems comfortably outperform the existing algorithms with RMSE values of 3.98 and 7.87 respectively.
Building Multi-domain Service Function Chains Based on Multiple NFV Orchestrators

Service Function Chains (SFCs) are compositions of Virtual Network Functions (VNFs) designed to provide complex network services. In this work, we propose a strategy to build an SFC across multiple domains and multiple clouds using multiple NFV platforms, which we call a Multi-SFC. To the best of our knowledge, this is the first solution to allow an SFC to be built across multiple different orchestrators - although there are other solutions for multiple domains and clouds. The basic building block of the proposed strategy is the SFC segment, in which all VNFs are connected within a single cloud/domain/platform. A pair of different segments is interconnected through a VNF tunnel that consists of a pair of VNFs, each interfacing one of the connected segments. A tunnel can be implemented with different technologies such as a VPN or VXLAN. The main advantage of the Multi-SFC strategy is that it is a holistic approach that allows operators to deploy SFCs on multiple clouds/domains/platforms without having to deal with a myriad of minute details required to configure and interconnect the different underlying technologies. A prototype was implemented as a proof of concept and experimental results are presented.
Maneuver Planning and Learning: a Lane Selection Approach for Highly Automated Vehicles in Highway Scenarios.

Highway scenarios are highly dynamic environments where several vehicles interact following their own goal, leading to different combinations of scenes that also change over time. Human drivers adapt their driving behavior integrating current information with their former experiences. In a similar way, an autonomous system performing any driving activity should be able to integrate information learned from former interactions. Reinforcement Learning has shown promising results, but it should only be applied to autonomous vehicles if the system is also able to fulfill safety and integrity requirements on a deterministic and reproducible way. This paper presents a planning system that is able to learn over time, always complying to the safety requirements. Our planner integrates several layers interacting with each other, combining the advantages of Reinforcement Learning based systems and reactive systems. We present a planner that ensures driving safety on short horizons and integrates previous experiences to optimize the expected reward. We evaluate our method in simulation comparing different learning techniques. Our results show that the planning system is able to adaptively integrate this experience outperforming rule-based strategies.
Evaluation of Ambient Light Displays for Requests to Intervene and Minimal Risk Maneuvers in Highly Automated Urban Driving

Driver take-overs at system limits and the corresponding requests to intervene are not obligatory in highly automated driving. Therefore, minimal risk maneuvers may occur. In order to clearly communicate the automation status and the driver’s task at such system limits, ambient light displays seem to have a high potential. Therefore, two ambient light displays were investigated in a driving simulation experiment, mounted either at the bottom of the windshield or on the steering wheel. Forty participants experienced two request to intervene scenarios, and two minimal risk maneuver scenarios during a highly automated drive. In general, both ambient light displays seem to ensure correct driver’s reactions and safety, and lead to positive subjective ratings. Results revealed no significant differences between the two ambient light display positions regarding the take-over behavior, gaze behavior and subjective rating, except in terms of the perceived brightness.
Real time ECG R-peak detection by extremum sampling

In this paper, we propose and validate a novel self-adaptive real-time event-driven ECG R-peak detection algorithm based on extremum sampling. The criteria for algorithm development were low complexity and energy efficiency facilitating wearable and mobile applications. The proposed algorithm consists of three major steps: event-driven identification of the QRS window, detection of ECG signal extremum, and awaiting the end of the current QRS complex. The threshold adaptation mechanism reduces the algorithm sensitivity to varying ECG amplitude range. The algorithm performance has been examined on MIT-BIH Arrhythmia Database recordings achieving the following results: sensitivity and positive prediction rate equal respectively to 99.55% and 99.88% which effected in total false detection rate equal to 0.58%. These results significantly outweigh the others reported in the area of low power R-peak detection algorithms.
Assessment of cell cycle and viability of magnetic levitation assembled cellular structures

Label-free magnetic levitation is one of the most recent Earth-based in vitro t echniques that simulate the microgravity. This technique offers a great opportunity to biofabricate scaffold-free 3-dimensional (3D) structures and to study the effects of microgravity on these structures. In this study, self-assembled 3D living structures were fabricated in a paramagnetic medium by magnetic levitation technique and effects of the technique on cellular health was assessed. This magnetic force-assisted assembly system applied here offers broad applications in several fields, such as space biotechnology and bottom-up tissue engineering.
The Betweenness Identities and Their Applications

Betweenness is a crucial measure to evaluate the significance of the nodes and link in the networks, which is widely used in centrality ordering, failure cascading modeling and path planning. However, we have very little knowledge about the betweenness, which prevents the deep analysis. In this paper, we discovered two betweenness identities, which represent that the node and link betweenness is closely relative to the average length of the shortest paths in the network. Betweenness distribution is a globally parameter and uniquely determines the small-world property of the network.
A Framework of 5G Mobile-health Services for Ambulances

Before the communication technology was mature, the ambulance was only used as a transport tool for emergency patients to ensure that the patient's vital signs were stable before arriving at the hospital. With the advent of the 5G era, the use of 5G's high-speed, low-latency, and large connection characteristics can transform the ambulance into a mobile information platform, which will greatly optimize the entire process of pre-hospital care in the ambulance. This paper presents the overall architecture of a framework of 5G mobile-health services for ambulances. This will enable the doctors in-hospital to remotely assist and guide the ambulance crew to provide initial medical care to in-ambulance patients.
UI Obfuscation and Its Effects on Automated UI Analysis for Android Apps

The UI driven nature of Android apps has motivated the development of automated UI analysis for various purposes, such as app analysis, malicious app detection, and app testing. Although existing automated UI analysis methods have demonstrated their capability in dissecting apps' UI, little is known about their effectiveness in the face of app protection techniques, which have been adopted by more and more apps. In this paper, we take a first step to systematically investigate UI obfuscation for Android apps and its effects on automated UI analysis. In particular, we point out the weaknesses in existing automated UI analysis methods and design 9 UI obfuscation approaches. We implement these approaches in a new tool named UIObfuscator after tackling several technical challenges. Moreover, we feed 3 kinds of tools that rely on automated UI analysis with the apps protected by UIObfuscator, and find that their performances severely drop. This work reveals limitations of automated UI analysis and sheds light on app protection techniques.