Miracle-dz/UniFAR-FTUs huggingface dataset:
A Multi-Modal Approach to Measuring the Effect of XAI on Air Traffic Controller Trust During Off-Nominal Runway Exits

Lack of transparency has been demonstrated to be a stumbling block in building Air Traffic Controller (ATCO) trust towards intelligent decision aids. To address this issue, a runway exit prediction decision aid, with explainability, was developed with the trait of providing explanations involving the top three contributing features to its predictions. To evaluate the influence of the intelligent decision aid's explanations on ATCO Trust, the decision aid was used in a human-in-the-loop study during off-nominal runway exits, utilizing 12 participants and a total of 67 trials. A multi modal approach was adopted with three types of data (questionnaire, behavioural, physiological) being collected in this study to ensure a more comprehensive understanding of the effects of explainability on ATCO trust. The results indicated that higher levels of perceived transparency led to an increase in trust levels, with an accompanied increase in cognitive load and complacency, even with low prediction accuracy by the intelligent decision aid. As such, these effects must be accounted for in designing XAI decision aids, which are defined as decision aids that rationalize their recommendations, for off-nominal events, when attempting to enhance trust levels by increasing transparency.
Squashing Bugs And Improving Design: Using Data Farming To Support Verification And Validation Of Military Agent-Based Simulations

Verification and validation of complex agent-based human behavior simulation models is a challenging endeavor, particularly since a dearth of real-world data makes it impossible to use most traditional validation methods. Data farming techniques have stepped up to the challenge, proving to be a valuable tool for verification and validation of complex models. In this paper we demonstrate how data farming and analysis aids in the verification and validation of complex models by presenting specific examples pertaining to WRENCH, an agent-based simulation model that represents complex interactions between security forces and civilians during civil security stability operations. We first provide an overview of data farming and its relevance for verification and validation of military agent-based simulation models, then give an overview of WRENCH, and finally demonstrate with examples how we have used data farming to aid in the verification and validation of WRENCH.
Analysis of RC delay using Multi level interconnects

Very Large-Scale Integration (VLSI) is a specialized field within the design of integrated circuits (ICs) in electrical engineering for delay optimization. Among the myriad challenges encountered in VLSI design, one of the most critical is the delay incurred by interconnections, which can profoundly impact the overall performance of the circuit. As technology continues to advance, interconnect delay becomes an increasingly significant bottleneck in VLSI design. However, interconnect delay pertains to the time it takes for the output of one device to reach the input of another, and vice versa. Our analysis sheds light on the complexities of interconnect delay in contemporary VLSI design and proposes potential avenues for future research. To gain a deeper understanding of circuit delay and mitigate its adverse effects, we conducted 120 simulations using the LT Spice Simulator. These simulations encompassed both intermediate and global layers of interconnects, offering valuable insights into the dynamics of circuit delay. This paper aims to comprehensively analyze various aspects of interconnect delay in both lumped and distributed circuits, with the objective of enhancing overall circuit performance. Traditionally, circuit delay is primarily attributed to individual device. Our findings are intended to inform strategies for minimizing delay and optimizing the performance of VLSI circuits.
Automatic Layout Design and Implementation for Three Phase Voltage Source Converters

The converter level automatic layout is challenging because multiple types of components need to adhere to different placement rules. This paper proposes an implementation method for computers to automatically layout three three-phase voltage source converters. The proposed method utilizes a hierarchical tree to establish layout rules and prioritize components. The layout optimization process involves employing simulated annealing (SA) based heuristic optimization for the phase leg and applying specialized algorithms for different component groups. The layout of the phase leg is optimized for a minimum switching loop inductance. The layout of other components is solved as a floor plan problem or by directly following the design rules defined in the hierarchical tree. The whole converter layout can be visualized, and the switching node capacitance can be extracted. These parasitics enable more accurate switching performance evaluation for the design tool applications. Moreover, the proposed layout design can be easily extended to other types of converters.
Composition Optimization of Moving Objects Using Recursive Gaussian Process for Photography Drone

In this study, we developed a process of heuristically searching and selecting the optimal viewpoint for capturing a good composition of a group of moving subjects with an autonomous indoor drone camera. The subjects on the drone's camera screen are represented using a Gaussian mixture model. The Kullback–Leibler divergence between the Gaussian mixture model and a user-defined reference composition is evaluated and defined as the composition evaluation value. The drone searches for a viewpoint in a 3D space to optimize this value using particle swarm optimization. To facilitate the search, a recursive Gaussian process is employed to update the prediction of the observation result. Through the proposed method, a sufficient optimal viewpoint can be obtained, even for moving subjects.
A Control Scheme for Grid Connected Solar Powered EV Charging Station With Hybrid Energy Storage System

Renewable energy sources, along with energy storage systems, are presently experiencing an enhanced trajectory of hybridization to relieve the pressure on the utility grid. An effective power-sharing method is required due to the hybridization of energy storage. The Proportional integral-based power-sharing technique is suggested in this paper as a practical way to make use of hybrid energy storage in grid connected solar-powered Electric Vehicle (EV) charging stations. In this study, solar energy generation serves as the main source of the hybrid energy storage system, which consists of a battery and supercapacitor (SC). The supercapacitor and the battery work in collaboration to support transient and average power respectively. By examining a battery’s state of charge (SOC), the average power distribution between the power supply and battery is determined, and a practical and effective energy management strategy is suggested. Additionally, when there is an unexpected difference between the amount of power produced and the amount needed by the load, using a supercapacitor reduces the current stress on the battery system.
Frequency Regulation in Low-Inertia Microgrid

In recent years, the substantial integration of Renewable Energy Systems (RESs), such as solar PhotoVoltaic (PV) and wind energy, into the power grid has raised concerns about the declining inertia of the electrical network. This poses a significant threat to the stability and reliability of the grid, necessitating innovative solutions to reinforce and incorporate inertia effectively. While Energy Storage Systems (ESS) offer a potential solution, their economic feasibility is hampered by limited lifespans, high costs, and maintenance requirements. One emerging strategy to address this issue is the de-loading of PV systems, which enables the generation of an active power reserve to ensure reliable frequency stabilization. However, the conventional approach of maintaining a fixed reserve in PV systems for frequency support is not cost-effective. This paper presents a novel de-loading strategy that leverages a fuzzy logic controller, taking into account irradiance and PV penetration levels as input variables. The proposed strategy is inherently adaptive and dynamically adjusts the system’s reserve, considering its changing dynamics. This is facilitated through a modified Maximum Power Point Tracking (MPPT) algorithm for the boost converter, resulting in reduced expenses compared to the traditional fixed reserve maintenance approach. The findings reiterate the significant performance improvements achievable with the proposed controller, emphasizing its potential to enhance the stability and reliability of the power grid in the face of increasing RES integration.
Visual-Based Deep Reinforcement Learning for Robot Grasping with Pushing

Robot grasping is a hot topic in the field of intelligent robot. The synergistic effect of robot grasping and pushing is of great help to manipulation tasks of robots. Someone proposed a model-free grasping method for robot based on deep reinforcement learning combined with a pushing and grasping network, which can determine action choices in pushing and grasping based on visual scene states and then obtain rewards. The network was trained through trial and error. Based on this method, we improve the model by adding feature fusion module and attention module to the deep Q-network, and train and test it in a simulated environment. The experiments show that our model has significantly improved grasping success rate and efficiency. Moreover, in complex unknown environments, the grasping performance of our model is also better than that of the original model.
The Yolov5 model combined with EfficientFormerV2 for lightweight applications in vehicle detection

In the practical application of vehicle detection, the biggest problem to be solved is the lightweight problem of the application, and the lightweight application can better reduce the application cost and application scenarios. Therefore, more and more scientific research projects pay more attention to improving the efficiency and accuracy of target detection, so that the model can complete lightweight application. In this paper, Transformer architecture based on YOLOv5 model combined with EfficientFormerV2 is proposed to improve the accuracy and efficiency of YOLOv5 in vehicle detection tasks, and more lightweight applications are generated based on this. In applications, the combination of EfficientFormerV2 and YOLOv5 reduces the size and computational effort of the model, making it suitable for smaller devices and low-power applications. This combination can also improve the robustness and generalization ability of the model, making it more suitable for practical application scenarios.
"I'm so going to nuke Helsinki" - Newsgames in the Nordic media landscape

This presentation looks at newsgames in the context of Nordic countries. By focusing on selected cases from the region, the presentation gives insight into the design choices behind newsgames, including their promises and pitfalls. With the transition to a digital age continuing to challenge news production and distribution, the interplay between games and journalism continues to offer interesting avenues of research.
Research on the Quality of Automotive Electronic Software Based on Code Checking

The quality of automotive electronic software is critical to vehicle performance and safety, and it is of great significance to study relevant effective code analysis methods and testing tools to ensure software quality. A multi-static analyzer-fusion code analysis technique is proposed in this paper to detect problems such as defects and vulnerabilities in code, and generate readable reports to help developers identify and solve such challenges. In order to verify the effectiveness of this method, this article developed a code check based automotive electronic software testing tool for analyzing potential quality issues, tested multiple actual automotive electronic software projects, and compared it with four mainstream code testing tools. Results indicate that the self-developed tool can identify 90% of potential problems, reduce testing time by an average of 20%, and have high consistency with manual review results.
Weakly-Supervised Point-Cloud Classification in Transmission Engineering

Laser point cloud technology is now widely used in power transmission engineering measurement operations. However, the raw point cloud data suffers from poor point cloud classification accuracy and lack of a large number of annotated samples. To address the problems mentioned above, the paper proposes a large-scale point cloud classification model based on weakly supervised learning. It is capable of capturing local information from point clouds at different spatial scales, and it enables the network to learn local features of different volume point clouds. Actually, it can accurately classify the point clouds of transmission engineering into ground, tower, line, insulator and jumper with 0.1% labeled training data, and realize the accurate semantics of each point cloud The results of the paper can significantly improve the efficiency of engineering operations.
A Single Camera Vision-Based Pose Tracking and Control System

This paper presents a closed-loop single-camera pose tracking and control system by adding the yaw calculation to existing systems. This improvement makes a capable system that only requires the use of a single imager and base station computer for the location and pose tracking and the control of a UAV (unmanned aerial vehicle). An additional LED marker was added to allow for the computation of the heading. This system employs complete directional control of the target UAV with a single camera, while stabilization and velocity are handled onboard the target drone by its internal processing and input from the inertial measurement unit (IMU) sensor. This system was verified with a real-time flight test and comparisons to motion-capture system measurements.
Energy and beam delivery upgrade of the TOP-IMPLART proton linear accelerator

This contribution describes the energy upgrade to 71 MeV of the TOP-IMPLART linear accelerator of ENEA Frascati Research Centre, its final layout with a foreseen maximum available proton energy of 110 MeV and an active scanning delivery system. While the accelerator is specifically designed for radiotherapy, it has already been applied as a radiation source for different applications, including characterization of electronic components radiation tolerance. Once fully commissioned, the TOP-IMPLART accelerator will constitute a significant addition to ENEA portfolio of already available radiation facilities (gamma and X rays, neutrons, electrons).
Common-Ground Double Gain Five Level Inverter for Distributed Energy Resources

A common-ground transformer-less five-level (5L) inverter topology is presented in this research with a voltage-boosting ability of four times compared to the typical neutral point clamped multi-level inverter topologies for single and three-phase distributed energy resources. The proposed topology depends on flying capacitor operation and requires ten active power semiconductor switches and two flying capacitor units per leg. A simple level-shifted sinusoidal pulse width modulation strategy is employed to self-balance the flying capacitors, while also mitigating the output current ripples and lowering the filter rating. The merits of the proposed five-level inverter topology are: (i) no leakage current in the industrial system due to the common ground operation; (ii) the input DC link voltage requirement is half of the grid voltage peak, whereas the neutral point is clamped; and (iii) the capability of reactive power compensation. The principles of operation, modulation techniques, and steady-state analyses for the proposed inverter topology are discussed. The proposed inverter topology is validated through both the MATLAB/Simulink and PSIM platforms. To analyze the effectiveness of the proposed inverter architecture, loss distribution, current and voltage stress across the switches, and efficiency of the inverter topology are provided.
Estimation of Concrete Compressive Strength: A Neural Ordinary Differential Equation Approach

The 28-day concrete compressive strength(CCS) is one of the most significant indicators required by the construction industry. The existing methods require making concrete samples and conducting compressive strength tests through physical experiments in the sample age of 28 days. Waiting time of the experiments increases the cost in time and financial, which can not meet the actual demand. The advent of computational intelligence makes it possible to estimate the CCS. Many scholars use regression models to estimate the CCS. However, the formation of concrete strength is a complex dynamic system. The use of regression models can place limitations on estimates, limiting the potential for effects. It is inappropriate to simplify a complex dynamic system as a regression problem. Differential equations are widely used to describe dynamical systems. Neural ordinary differential equations draw on the idea of differential equations. But unlike ordinary differential equations in mathematics, the derivative functions of ordinary differential equations are modeled by neural networks. We proposed a novel approach to model the dynamical system of concrete. Furthermore, it provides a novel approach to estimating the 28-day CCS by modeling concrete dynamic systems based on Neural ODE. The experimental results show our approach achieves acceptable accuracy.
Analysis of Consumer Coffee Brand Preferences Using Brain-Computer Interface and Deep Learning

Neuromarketing is an emerging technology that helps to understand consumers' preferences and their decision-making process in an excellent manner. Lately, the coffee industry has experienced growing competitiveness, and meeting consumers' evolving tastes has been imperative for all coffee brands to thrive. This research study proposes an innovative methodology to discern consumer preferences. This approach integrates Brain-Computer Interface (BCI) technology with the execution of deep- learning neural networks. A g. Nautilus headset is used to record the Electroencephalography (EEG) signals of 12 subjects while they taste coffee from 3 different brands. EEG signals are pre-processed to train the Convolutional Neural Network model using the MNE-Python library. The model suggested can predict and categorize customer preferences with an accuracy of 85.90%.
Development of Highly Efﬁcient Hydroisodeparafﬁnization Catalysts Based on Micro-Mesoporous SAPO-31: New Approaches in the Synthesis of SAPO-31 and the Preparation of Bifunctional Catalysts

: A method is proposed for the synthesis of micro-mesoporous SAPO-31 with a high external surface area and large mesopore volume using tetraethoxysilane as a source of silicon. The synthesis of SAPO-31 is based on the principles underlying the Stöber process approach, which allows control of the pore diameter and volume and, hence, the external surface area of the product material. The use of the proposed methods for depositing the hydro-dehydrogenating component on the synthesized micro-mesoporous SAPO-31 made it possible to obtain highly efﬁcient bifunctional hydroisodeparafﬁnization catalysts with the platinum or palladium content not higher than 0.2 wt. %. As a result, the process temperature can be decreased by 50–60 ◦ C in comparison with the known analogs. Therewith, for example, in the test reaction of n-decane hydroisodeparafﬁnization at a temperature of 300 ◦ C and feed rate of 1.5 h − 1 , the conversion above 90%, and the 97–98% yield of liquid products, the 95% selectivity to isomeric products were achieved.
Networks of Brain-Heart Interactions During Elicited Emotional States

Understanding brain-heart interactions (BHI) is essential to understanding the emotional and physiological complexities associated with emotional granularity. This study examined the network physiology in terms of BHI as well as the associations of distinct brain regions and frequency bands in various affective states. BHI was evaluated utilizing the controlled time-delay stability (cTDS) method. The statistical analysis revealed significant differences between brain-to-heart and heart-to-brain networks (P-values < 0.0001), with heart-to-brain networks having stronger connections across emotional states, indicating the important role the heart plays in influencing brain activity associated with arousal and valence. Higher frequency bands exhibited stronger network connections, indicating their applicability to the BHI in this context. Notably, the networks representing various emotional states displayed symmetrical distributions of average link strength across brain regions. The research indicates that cTDS is a useful instrument for investigating BHI coupling and network interactions, emphasizing the importance of considering the directionality of the brain-heart connection under distinct emotional conditions, including differences in levels of arousal and valence.
Are Prompt Engineering and TODO Comments Friends or Foes? An Evaluation on GitHub Copilot

Code intelligence tools such as GitHub Copilot have begun to bridge the gap between natural language and programming language. A frequent software development task is the management of technical debts, which are suboptimal solutions or unaddressed issues which hinder future software development. Developers have been found to “self-admit” technical debts (SATD) in software artifacts such as source code comments. Thus, is it possible that the information present in these comments can enhance code generative prompts to repay the described SATD? Or, does the inclusion of such comments instead cause code generative tools to reproduce the harmful symptoms of described technical debt? Does the modification of SATD impact this reaction? Despite the heavy maintenance costs caused by technical debt and the recent improvements of code intelligence tools, no prior works have sought to incorporate SATD towards prompt engineering. Inspired by this, this paper contributes and analyzes a dataset consisting of 36,381 TODO comments in the latest available revisions of their respective 102,424 repositories, from which we sample and manually generate 1,140 code bodies using GitHub Copilot. Our experiments show that GitHub Copilot can generate code with the symptoms of SATD, both prompted and unprompted. Moreover, we demonstrate the tool’s ability to automatically repay SATD under different circumstances and qualitatively investigate the characteristics of successful and unsuccessful comments. Finally, we discuss gaps in which GitHub Copilot’s suc-cessors and future researchers can improve upon code intelligence tasks to facilitate AI-assisted software maintenance