Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving Search-based Test Case Generation with Local Search using Adaptive Simulated Annealing and Dynamic Symbolic Execution

DynaMOSA is an effective search-based test case generation algorithm. However, it uses an alternating variable method for local search. This method follows a greedy strategy that considers each input variable of an optimization function independently and attempts to optimize it. Some problems with this kind of search are that it can easily become stuck in the local optimal solution and its search capability becomes inadequate in the late stage of the search. Such constraints may lead to a dramatic drop in search performance. To solve these problems, this study proposed a local search algorithm based on adaptive simulated annealing and symbolic path constraints to generate test cases with high coverage for multiple testing criteria within a limited time budget. On the one hand, the simulated annealing algorithm was selected to explore the neighborhood of candidate solutions during the search. On the other hand, various simulated annealing operators were designed for the search of each statement to enhance the applicability of the algorithm in various programs. Additionally, symbolic execution was introduced as a supplement to the simulated annealing algorithm for local search to generate test cases for inputs with complex structures. Furthermore, the proposed algorithm was implemented in EvoSuite framework. From an SF110 open-source benchmarking dataset, 49 projects or 110 classes were selected according to the complexity and number of objectives of each class under test to conduct the experiments. The proposed algorithm outperformed the original algorithm in generating high coverage test cases on most projects in terms of line, mutation, and multicriteria coverage as well as search efficiency.
Active Power Optimization of a Turning Process by Cutting Conditions Selection: A Q-Learning Approach

In the context of Industry 4.0, the optimization of manufacturing processes is a challenge. Although in recent years many of the efforts have been in this direction, there is still improvement opportunities in these processes. The optimisation of the power consumed by the processes can be improved by means of the parameters of control. To date, this challenge has been addressed by Multi-Objective optimization techniques, however, Reinforcement Learning based approaches are raising with promising results in many industrial fields.In this paper, we propose a Reinforcement Learning (RL) based approach to optimize the active power consumption of a machining process by the cutting conditions selection. Through the application of Q-Learning algorithm, the agent self-learns the optimal solution through interacting with the environment. The approach was validated in three different scenarios demonstrating the feasibility of RL application to determine the cutting conditions values in order to optimize the active power consumption.
Method of Algorithmic Correction of Dynamic Properties of Special-Purpose Electric Drive

The technique of digital algorithmic correction of dynamic properties of the electric drive is offered. The application of the technique allowed achieving quasi-invariant properties of the electric drive in relation to the angular velocity of its rotor. The method allows synthesizing three digital algorithms: proportional-differential regulator; regulator with correction drive modal dynamic properties; algorithmic meter of angular speed of electric drive rotor. The joint use of these three algorithms allows to reduce the mean-square error of tracking the optimal law of motion by nearly 4 times and to reduce the influence of shock (impulse) disturbances by 3 times. Thus, it becomes reasonable and possible for practical implementation to use only an algorithmic method of improving the dynamic properties of electric drive, which does not require the use of additional equipment.
Co-optimizing Dataflow Graphs and Actors with MLIR

Dataflow programming is considered a good solution for the implementation of parallel signal processing applications. However, the strict separation between kernel and coordination codes limits the variety of possible optimizations and the compatibility with state-of-the-art compiler frameworks. We present a prototype static dataflow compiler, built with the LLVM MLIR framework, that overcomes these limitations and enables a previously impossible combination of optimization strategies that leverages information from the dataflow topology. Initial results show 30% wall time improvement and 53% memory usage improvement on a video processing workload.
Simulation Mathematical Modeling of Radar Signals with a Low Probability of Their Unauthorized Reception

a technique and software for modeling radar signals with a low probability of their unauthorized reception at the input of the monitoring tool receiver are presented. The technique is illustrated by the formation of a signal model based on the Costas sequence. The methodology and software are designed to develop methods and algorithms: detection, measurement of parameters, recognition of radio emissions of radio electronic equipment, recognition of radio electronic equipment
Towards Identification of Appliances in Conventional Homes using ML and Descriptive Statistics

Providing ancillary services for future smart grids is challenging because of the rapidly growing electricity demand, while having uncertainties in renewable power generation, limited availability of conventional spinning reserves, and expensive storage systems. Thus, Home Energy Management Systems (HEMSs) have been gaining increased attention nowadays. To capitalize on the potential of HEMS, which supports customer participation and two-way power communication so as to maintain the generation-load balance, two interconnected challenges, i.e., load monitoring and identification of appliances consumption, need to be addressed. In this contribution, a comprehensive nonintrusive load monitoring (NILM) algorithm for appliance identification is proposed, which only requires a single sensing point from conventional homes, i.e., the aggregated power signal. Machine learning algorithms and both time-domain and frequency-domain based feature extraction are utilized in the development of the proposed solution. Simulation experiments are performed using the Reference Energy Disaggregation Dataset (REDD), a real household power consumption dataset. Simulation results demonstrate the effectiveness of the proposed NILM strategy with F1-score values of 97.659%, higher than those reported in the state-of-the-art.
Local Matrix Stack Graph Convolutional Networks for Classification

In order to make full use of the spectral and spatial information of hyperspectral images, a spatial spectral joint composition method based on nuclear spectral Angle method is designed in this paper. Graph Convolutional Networks(GCN) does not use regular convolution kernels for convolution, so it can adaptively capture geometric changes of different object regions in hyperspectral images. However, for the construction of adjacency matrix and the determination of graph structure, the traditional graph convolution network method needs very high computational cost. To solve the above problems, this paper developed a Graph Convolutional Networks based on local pixel discrimination (LS-GCN), which can predict the whole image according to part of the sampled pixels and their neighborhoods, reducing the time complexity of the composition process by an order of magnitude and improving the image recognition rate to a certain extent.
Renewable powered Battery Swapping Stations for sustainable urban mobility

Due to sustainability concerns raised by the transportation sector, still relying mostly on oil as main energy source, urban mobility is quickly shifting towards the adoption of electric vehicles (EVs), The EV charging process should heavily rely on Renewable Energy Sources (RES) and be smartly scheduled to promote sustainability and pollution reduction. In this context, renewable powered Battery Swapping Stations (BSS) represent a promising solution to enable sustainable and feasible e-mobility. Focusing on a BSS powered by photovoltaic panels, we investigate the issue of properly dimensioning its capacity (in terms of number of sockets) and the renewable energy supply to satisfy the battery swapping demand, trading off cost, Quality of Service and feasibility constraints. In addition, we analyse the potential benefits of smart scheduling strategies for battery recharging. Our results show that considerable cost saving of up to almost 40% can be achieved with a local RE supply to power the BSS. Furthermore, a proper tuning of the scheduling strategy configuration parameters is required to better trade off cost and Quality of Service, based on the desired performance targets.
Advanced Intelligent Platform for Small Autonomous Vehicles

Intelligent small autonomous vehicle architectures are currently a priority area for the use of modern technologies and systems in both civilian and military fields. The aim of this paper is to design a complex advanced methodology and platform for the development of a prototype of an autonomous vehicle system (small UGV) using nested sensing, control and monitoring elements of systems for reconnaissance, servicing and selected combat activities. This paper presents a novel complex methodology for design of an advanced complex platform for small autonomous vehicle based on robust and intelligent SW and HW sensors, communication, visualization and control based on embedded computing. Algorithms for self-tuning control and navigation in uncertain complex processes are proposed. Several types of intelligent control and strategy are analyzed and compared. Simulation and practical experiments results are provided to illustrate high performance in the practical platform design of small autonomous vehicles. The obtained practical results are reviewed and potential shortcomings of the analyzed methods are discussed.
Regular Self-Pulsations in External Cavity Mid-IR Quantum Cascade Lasers

We show theoretically and experimentally that a quantum cascade laser (QCL) in an external Fabry-Perot cavity (EC) can produce regular self-pulsations (SP) of the output intensity at frequencies ~75 GHz. We recognize that the propagation delay in EC provides QCL with a "memory" mechanism to preserve the regularity and coherence of the pulse train on the intervals significantly exceeding the sub-picosecond gain coherence and gain recovery times. These results may point to novel practical approaches for producing regular time-domain SPs and pulse trains in the Mid-IR QCLs. Otherwise, due to a multimode Risken-Nummedal-Graham-Haken (RNGH) instability, free-running FP QCLs exhibit SPs at Rabi flopping frequencies in the THz range, which in addition, reveal a quasi-periodic chaotic behavior if the cavity round trip time is a few picoseconds or more.
Use of Machine Learning for COVID-19 Disease Detection

The main goal of this work is to analyze the use of machine learning to predict Covid-19 disease. An important task is to train models to predict Covid-19 from lung X-rays. When training models, it is important to choose the right parameters to improve success. An essential part of the work is the selection of suitable data and subsequent work with this data. The output of the work are several trained models and their evaluation.
ZSCORE06: Stata module to calculate anthropometric z-scores using the 2006 WHO child growth standards

zscore06 calculates anthropometric z-scores using the 2006 WHO child growth standards. Lenght/heigh-for-age, weight-for-height, BMI-for-age and weight-for-age Z-scores are calculated for children 0 to 5 years of age. If the age, height or weight values are outside the range of reference values, a value of 99 is given for the corresponding z-score(s). Note: in contrast with the WHO's Igrowup program, none of the z-scores are calculated if child age is missing.
Overview of Bidirectional Unfolding Converters for Battery Energy Storage Systems

In order to support wider use of distributed renewable sources and mitigate the negative effects of their intermittent behavior, nowadays they are often installed together with energy storages, located on-site or nearby. Battery energy storage systems (BESS) allow not only to match consumption and generation profiles more closely, but also they provide various ancillary grid-supporting services. The enabling technology for these emerging opportunities is related to power electronic interfaces between the DC battery and the AC grid. Generally, a two-stage power conversion approach is used, where the battery-side DC-DC stage is responsible for controlling the battery current and grid-side DC-AC stage controls the current taken or injected to the grid. These two stages are connected through a bulky DC-link capacitor with stabilized voltage. Such an approach has however reached its limits in terms of efficiency, power density and cost. Therefore, a lot of research is currently focused on alternative topologies, enabling higher performances than the well-developed two stages approach. One of the promising configurations of such alternative topology includes a DC-DC converter, that forms sine voltage half-waves and an unfolding circuit, which provides the commutation of these half-waves to the grid. This paper presents a review of unfolding converters in the context of BESS. Configurations of unfolding circuits with DC-DC choppers, dual active bridge converters and multilevel converters, as well as the partial power converters are analyzed.
Design and Realization of Bent Y-Shaped Ceramic Dual-Mode Resonators and Filters

In this paper, the design and realization of second-and fourth order bent Y-shaped ceramic dual-mode resonators and filters is discussed. The resonators are based on uniform cylindrical ceramic TM mode rods, which are connected to a metal core. This approach allows the cost-efficient realization of complex shaped dual-mode resonators. The filters are designed to use a pair of degenerated TM modes, which can be divided into a mode with electric as well as a mode with magnetic symmetry plane. Each dual-mode resonator contributes two resonances as well as one transmission zero (TZ) to the filter response. By varying the angles between the ceramic rods, it is shown that the filter parameters can be adjusted. Due to the TM mode operation, a proper contact between the ceramic rods and the metallic housing is required. Two different solutions are proposed in this paper: On the one hand, the cavity is made from a copper sheet. This approach enables the possibility that the ceramic resonators are soldered with the surrounding housing. On the other hand, threads are soldered to the ceramics, with which the ceramic core can be screwed to the housing. Second- and fourth order filters are manufactured as proof of concept.
Data-Driven Metrics Applied to Traffic Crashes to Improve Observability in Smart Cities

Data is a crucial factor for monitoring and understanding events related to Smart Cities. Data can be discovered and integrated from different sources and has the potential to be interpreted in multiple ways. Traffic crashes, for example, are common events that occur in cities. A significant amount of historical data related to traffic crashes is publicly available for analysis and can be used by a wide range of stakeholders. Measuring the impact of Smart Cities solutions usually relies on data collection, analysis, and metrics before and after such solutions are implemented. This paper presents an observable data-driven bottom-up methodology to create the Critical Composite Index (CCI), a Key Performance Indicator developed to measure traffic crash severity as a singular value. The CCI can be used by both domain experts and non-domain experts to be informed about traffic crashes on the roadways. This paper the development of the CCI using historical, government agency reported, and publicly accessible traffic crash data. The CCI can be modified or extended to align with specific reporting traffic crash criteria by modifying the weights of traffic crash features. The observable data-driven bottom-up methodology development enables the transformation of raw data into a metric that can contribute to the observability of Smart Cities.
OW-LOAM: Observation-Weighted LiDAR Odometry and Mapping

Simultaneous Localization and Mapping (SLAM) is essential for robots, especially in unfamiliar indoor environments where other localization methods such as GNSS, UWB are unavailable. LOAM, as a state-of-the-art LiDAR SLAM method, works by extracting corner and surf points from raw point clouds and matching them with accumulated maps. However, the bisquare weight it uses for each observation is derived from the observation residual, which cannot reflect the actual observation quality and is of little help in improving the system accuracy. In this paper, we propose a novel method termed OW-LOAM, which takes the difference in the observation qualities into account by replacing the bisquare weight in LOAM with the inverse of the estimated variance of the observation noise based on Bayesian estimation theory. We conduct a series of experiments in various indoor environments of different scales, and the results show that the proposed OW-LOAM outperforms the original LOAM in both accuracy and robustness.
A DEEP SEARCH FOR CO J = 2−1 EMISSION FROM A Lyα BLOB AT z ∼ 6.595

We have used the Green Bank Telescope to carry out a deep search for redshifted CO J = 2−1 line emission from an extended (>17 kpc) Lyα blob (LAB), “Himiko,” at z ∼ 6.595. Our non-detection of CO J = 2−1 emission places the strong 3σ upper limit of L′CO < 1.8 × 1010 × (ΔV/250)1/2 K km s−1 pc2 on the CO line luminosity. This is comparable to the best current limits on the CO line luminosity in LABs at z ∼ 3 and lower-luminosity Lyα emitters at z ≳ 6.5. High-z LABs appear to have lower CO line luminosities than the host galaxies of luminous quasars and submillimeter galaxies at similar redshifts, despite their high stellar mass. Although the CO-to-H2 conversion factor is uncertain for galaxies in the early universe, we assume XCO = 0.8 M☉ (K km s−1 pc2)−1 to obtain the limit M(H2) <1.4 × 1010 M☉ on Himiko's molecular gas mass; this is a factor of ≳ 2.5 lower than the stellar mass in the z ∼ 6.595 LAB.
TOF Estimation of Single Antenna for Commercial Wi-Fi

With the explosive growth of mobile devices, the need for precise positioning in indoor environments is becoming more prominent. Yet these state-of-the-art solutions require either multiple devices or multiple antennas that not available on all devices. The positioning of devices with simpler structures should be investigated. In this paper, we propose a method to treat channel state information (CSI) data as a random process, construct an autocorrelation matrix and apply the MUSIC algorithm to achieve the purpose of single antenna estimation of time of flight (TOF). We study the distribution of symbol time offset (STO) errors by means of statistical tests and provide a theoretical basis for real experiments. Finally, we use Intel5300 commercial wireless network card to conduct experiments in typical indoor environments of NLOS and LOS, and the median errors of 3.3m and 1.2m are achieved respectively.
Updating the Linux TAPRIO Scheduler in Deterministic Time

In flexible production systems with distributed control systems which communicate with each other via a real time network, changes in the requirements for real time communication during operation must not lead to a temporary failure of the deterministic communication. Rather, the changes of the network configuration must become active at exactly predictable times in order to guarantee the functionality of the deterministic communication all the time. This paper shows a realization of how the configuration of a real time network schedule under Linux can be adjusted at predetermined times without interrupting the communication. For this purpose, the real time scheduler TAPRIO is integrated into the library libnl, and the performance of this extension is evaluated in a test case. It is shown that the modification of the network configuration is reliably possible in several successive communication cycles.
Considering polarization mismatch in modeling the RFID phase offset variability for tag localization

In most of the existing localization approaches based on RFID phase measurements, the model of the phase of the received signal accounts for the distance but not for the orientation between the reader’s and tag’s antennas. This lack is not very relevant if both antennas are circular polarized and/or if they are not reciprocally rotating. However, in other conditions, like in robotic localization with antennas having different polarization, the dependence on the relative orientation becomes important. In this paper we analytically characterize this phenomenon and provide some experimental data to support the model. Then, to give an idea of the importance of modeling this phenomenon, we compare a localization algorithm based on an Extended Kalman filter, where this factor is included in the model, with an Extended Kalman filter where, on the contrary, this phenomenon is ignored. In this second case, the algorithm tries to cope with the offset variability by including the offset among the variables estimated but the performance of the approach where the phenomenon is considered remains significantly higher, as illustrated through numerical results.
Heritage Properties Price Prediction Using Random Forest Classifier

Recently, machine learning has been gaining popularity for its use and because it holds a great promise for real estate valuation and prediction. However, the application of machine learning in predicting the prices of heritage property has limited adoption. Therefore, the present study aims to test the best machine learning in predicting the price of heritage properties. This paper provides a report from the data collection method, preliminary data analysis with statistical method, and the experimental implementation of the machine learning algorithms from training and validating to the testing stage. The results show that the Random Forest model produced better performance on heritage property, which was tested with another five hold-out data. The testing results show that the machine learning approach with Random Forest was able to predict the prices of heritage property within the acceptable valuation ranges.
Quantum state preparation of an atomic ensemble via cavity-assisted homodyne measurement

The quantum spin states of an atomic ensemble are of special interest to both fundamental studies and precision measurement applications. Here, we propose a scheme to prepare collective quantum states of an atomic ensemble placed in an optical cavity via homodyne measurement of a probing light field. The effective interactions of atoms mediated by photons are enhanced by the optical cavity, and the output probe light could also be entangled with the collective spin states. By selectively measuring the quadrature of the output light, we can prepare various quantum states, including superposition states of Dicke states and Dicke squeezed states. It is also demonstrated that the fidelity of the prepared quantum state can be enhanced by repetitive homodyne detection and using longer probe laser pulses. With the experimental parameters, we discuss some imperfections, including background noise, fluctuation of the measurement outcomes, and free-space scattering.We believe that our scheme should be feasible for experimental realization with current technologies, which may be used in the future study of quantum mechanics and quantum metrology.