Miracle-dz/UniFAR-FTUs huggingface dataset:
A Framework for Robustness Evaluation in AI-Based Malware Detectors

This paper presents a framework for the robustness evaluation of AI-based malware detectors. The framework is composed of three elements: Training phase, Evaluation phase, and Attack experimental. Researchers upload their own dataset, feature extraction scrips, or trained model in the framework for evaluation. The evaluation metrics measure the accuracy, F-1, false positive, and false negative rate of the detectors, and evaluation results are used to confirm effectiveness and e ciency. Our proposed framework aids in resolving the issue that researchers don’t need to possess an entire experimental setting, especially considering that some research sources are di cult to collect and integrate.
HyCU: Hybrid Consistent Update for Software Defined Network

Software Defined Network (SDN) enables network operators to achieve the customization of network services, which tends to be more dynamic and fine-grained. However, the distributed nature of rule updating in SDN brings consistency problems, i.e., packets travel according to different versions of rules. It leads to the issues of blackholes, loops, congestion, and deadlock in the data plane, which may further affect the service quality of the application plane. With the emergence of new computing paradigms such as edge computing and fog computing, the heterogeneity of network devices and links, as well as the diversity of network application requirements have become increasingly prominent. Traditional update methods ignore these key factors when modeling, so they cannot cope with the increasingly complex network environment, resulting in delays or packet loss rates that do not meet service requirements. This paper proposes HyCU, which takes device performance as a constraint and optimizes updates based on flow service requirements. We conduct experiments under different scenarios and constraints over two real-world topologies with real-time running flows, demonstrating the effectiveness of HyCU.
Remote sensing estimation and spatiotemporal variation analysis of vegetation carbon sink in the Yangtze River Economic Belt

The Yangtze River Economic Belt is the most economically active region in China, with huge development potential. It is a globally influential inland economic belt and a leading demonstration belt for the construction of ecological civilization. Therefore, ecological construction in this region has long received attention on the regional and national levels. However, at present, there are few studies on the temporal and spatial distribution of NEP in vegetation in China, and studies using this area as a research area are even rarer. In order to accurately to estimate the carbon sink of vegetation in the Yangtze River Economic Belt, we mainly adopt methods such as image preprocessing, land to use type classification, image analysis, model simulation tests, and comparative analysis. Specifically, ENVI is used to preprocess remote sensing images such as radiometric calibration and FLASSH atmospheric correction, and then 17 land types are merged and reclassified based on the IGBP classification system in ArcGIS. At the same time, the improved CASA model is used to accurately estimate the carbon sink of vegetation in the Yangtze River Economic Zone, estimate the net primary productivity of vegetation in the Yangtze River Economic Zone, and calculate the net ecosystem productivity that represents the carbon sink of vegetation. Then, the response and spatiotemporal change characteristics of NEP in the Yangtze River Economic Zone to climate change are studied through regression analysis, trend analysis, and other methods, and the spatiotemporal dynamic change characteristics of vegetation carbon sink in the Yangtze River Economic Zone are analyzed, Based on the prediction model, the trend of future changes is analyzed to provide a scientific method for monitoring the ecological environment of the Yangtze River Economic Belt and provide a basis for formulating ecological environmental protection policies in the region. At the same time, it also provides a reference from future scholars to study the net ecosystem productivity of vegetation in provinces and cities, and has important significance of enriching and improving research results.
Egocentric Pedestrian Trajectory Prediction

Pedestrian trajectory prediction is one of the important research topics for autonomous vehicles. Many factors such as Pedestrians’ actions and waypoints in the scene can affect the pedestrians’ routes. Currently, most researches focus on predicting trajectories in Bird’s Eye View (BEV), which limits the application in monocular cameras. In this paper, we propose to combine BEV trajectory prediction with deterministic tracking method and create a new egocentric algorithm for predicting pedestrian trajectories. Experiment results showed that our method can accurately predict pedestrian trajectories in egocentric scenes.
Evaluating Intertwined Effects in Fake News Spreading by using MCDM Approaches

What news is real and fake has formed a hot topic in recent years. The current discussions concerning fake news indicate that academics and practices still lack consensus on measuring the influencing factors. For dealing with this issue, the purpose of this study is to focus on investigating and assessing influence factors to reduce the gaps in spreading of fake news by interdependence and feedback problems among dimensions and criteria to find the most significant factors. We used DEMATEL-based Analytic Network Process (DANP) to solve these problems. Then we conducted several experts to investigate the root-and-cause of spreading of fake news. These results can provide government understanding of how to prevent strategies and fill the gaps between academic and practice.
Closed-loop simulation testing of a probabilistic DR framework for Day Ahead Market participation applied to Battery Energy Storage Systems

In this manuscript, we test the operational performance decrease of a probabilistic framework for Demand Response (DR). We use Day Ahead Market (DAM) price scenarios generated by a Combined Quantile Regression Deep Neural Network (CQR-DNN) and a Non-parametric Bayesian Network (NPBN) to maximise profit of a Battery Energy Storage System (BESS) participating on the DAM for energy arbitrage. We apply the generated forecast time series to a stochastic Model Predictive Control (MPC), and compare the performance using a point and perfect forecast. For the probabilistic forecasts, we test two control strategies; 1) minimising the Conditional Value at Risk (CVaR) for making costs, and 2) minimising the expected value of the cost. We apply the MPC in a closed-loop simulation setting and perform a sensitivity analysis of the profit by changing the ratio between battery capacity and the max power, the cluster reduction method, and the number of scenarios used by the MPC. We show that the proposed framework works, but the approach does not increase profit compared to a deterministic point forecast. This can possibly be explained by the deterministic forecast capturing the shape of the price curve with less noise than a probabilistic forecast without enough scenarios. We show that the value of a good forecast becomes smaller as the charging time of the battery becomes larger, due to the battery being unable to exploit small price differences optimally.
1-bit Conformal Programmable Metasurface for RCS Reduction

Based on conformal array synthesis theory, a 10×10 conformal programmable metasurface for RCS reduction is designed in this paper. The designed 1-bit unit cell achieves a phase difference of 180°±20° in the range of 4.8~5.7GHz by switching the ON and OFF states of the PIN diode with the reflection amplitude less than -1.7dB. Based on this unit, the conformal array is modeled and the coding sequence is optimized by binary particle swarm optimization (BPSO). Both simulation and calculation results show that the coded conformal metasurface can achieve diffuse reflection and RCS reduction. The reconfigurable characteristics of the metasurface also extend the possibility of time domain control, which has a great application prospect in the field of new stealth technology.
Intention to Use Shared Electric Scooters in Outlying Islands

Air pollution has always been an important issue of concern, especially emissions from transportation. Because the main primary sources of pollutant emissions are PM2.5, NOx and CO, most of them come from "vehicles". According to statistics, the total number of registered motor vehicles in Taiwan is as high as 22.844 million, which means that each citizen owns one motorcycle on average [1]. Therefore, encouraging the use of electric vehicles, or even sharing them, may help to improve the pollution and congestion caused by urban traffic.
Axial Ratio Enhancement for Circularly Polarized Array Antenna Using a Dual-Phase Technique

In this paper, we present a dual-phase technique for improving the axial ratio (AR) performance of circularly polarized (CP) array antenna. Both Berry phase and propagation phase are employed to control the co-polarization and cross-polarization radiation patterns independently. As a proof of concept, a CP array antenna consisting of 8 × 8 dual-phase elements is proposed. The array well validates our method for AR enhancement, which exhibits a 1 dB AR bandwidth covering from 8.5 to 12.5 GHz. Besides, the simulated results show a 3 dB gain bandwidth of 35.9% from 8.7 to 12.5 GHz.
Emotion Prediction in Music Based on Artificial Intelligence Techniques

Music is often described as the "language of emotion," and emotion prediction is important as it can impact future behavior. This paper proposes an audio-based emotion prediction model using a One-Dimensional Convolutional Neural Network (1D-CNN) approach, with Mel-Frequency Cepstral Coefficients (MFCCs) extracted as audio features. Preliminary results show an overall accuracy of 93%, but the imbalanced dataset used may cause bias in the accuracy of each emotion. Further research is needed to investigate the classification of audio features and 1D-CNN layers.
Design of a Switchable Reflectionless Filter with Bandpass and Bandstop Modes

This paper introduces a switchable filter with two operating modes, reflectionless bandpass (RBPF) and reflectionless bandstop (RBSF). By turning PIN diodes loaded between microstrip lines ON or OFF states, a switchable filtering structure equipped with a switchable absorptive circuit can be achieved. The measured 3-dB/10-dB fractional bandwidth (FBW) for RBPF/RBSF mode is 36.7% and 13%, and 10-dB reflectionless bandwidth (RBW) is 61.9% and 100%, respectively.
Passive Damping Stabilization for Constant-Power Loaded Boost Converter

This paper presents a methodology to stabilize an open-loop boost power converter feeding a constant power load. The stabilization is ensured by means of three different passive damping filters, an RC parallel branch placed at the output port, an RL series branch placed at the input, and an RL parallel branch placed also in the input port. The presented methodology allows the design of the system under a pre-defined dynamic behavior and the established size, weight, and cost limitations. Analytical models are obtained for each topology and a theoretical study is carried in order to determine the optimal resistance that ensures the damping of the peak of the output impedance of the converter with each stabilizer. The loci of the poles of the system in the complex plane is studied in terms of the stabilizer parameter values to estimate the dynamics of the complete system. Finally, numerical examples and PSIM simulations are discussed for the RC damping topology to validate the presented concepts.
Vehicles of Everything combined Yolov7 with Traffic enforcement camera on the roadside system

This study proposes an intelligent roadside system based on a V2X vehicle network and intelligent traffic signals to address the important issues of traffic accidents and congestion in traffic management. With the V2X vehicle network, vehicles can obtain real-time information from traffic signals and use better methods to control their speed and direction, thus reducing the occurrence of accidents and congestion. In addition, the system uses CNN technology to improve detection performance under required conditions, and YOLOv7 technology can accurately identify vehicles. With these technologies, the intelligent roadside system can more sensitively detect vehicle behavior on the road, better coordinate traffic flow, improve road traffic efficiency, reduce traffic congestion, and also reduce the occurrence of traffic accidents. Overall, this intelligent roadside system based on a V2X vehicle network and intelligent traffic signals is expected to play an important role in urban traffic management, effectively reducing the occurrence of traffic accidents and congestion, improving road traffic efficiency, and providing a safer and more convenient driving experience for the public.
RS-SAGAN: Boosting Remote Sensing Sample Diversity via Seasonal Adaptive GAN

While remote sensing training sample volume is increasing, the seasonal characteristics are still lacking despite their crucial role in intelligent interpretation. We address this issue via the remote sensing seasonal adaptive generative adversarial network (RS-SAGAN). RS-SAGAN is composed of a seasonal domain adaptive learning module and a season transfer module. First, the seasonal domain adaptive learning module unifies the data distributions of the source and target domains into the same feature space. Second, the season transfer module converts the original samples to different seasons through a generative adversarial network (GAN). Using Postdam, and Vaihingen datasets as semantic samples, we achieve the competitive Fréchet Inception Distance (FID) score by approximately 5% over the existing approaches. The report on the SEN1-2 dataset with seasonal information demonstrates that RS-SAGAN can eliminate the domain gap and obtain high-quality seasonal samples, enhancing the generalization and robustness of remote sensing interpretation.
Micro-manipulation Method of Dual Directions Magnetic Driving Synthetic Micro-Wedge Structure

With the development of micro-electronics and ultra-precision machining technology, the precision picking and releasing of micro-scale devices are urgently needed in the manufacturing process of micro-electronic products. However, the existing robot micromanipulation techniques have the problems of low release success rate and difficult to guarantee the stability of the picking process. This paper explores the micro-wedges deformation under the action of external magnetic field and proposes two controllable interface adhesion regulation methods: the single positive direction magnetic field controls deformation and recovery of wedges as the first regulation method, which can ensure the stability in the picking process, and the second regulation method is anisotropic deformation of wedges under the dual directions magnetic field, which can greatly improve the success rate of release due to contact only at the single wedge in release stage. The normal adhesion force test and the micron-scale silicon wafer transport experiment also proves the feasibility of the two regulation methods, among which normal adhesion force can be decreased 78-fold by changing contact area via the dual directions magnetic field.
Region-of-Interest Detection Based on Graph Convolutional Network and H.266/VVC Encoded Video

This paper proposes a novel region-of-interest (ROI) detection method that utilizes a graph convolutional network (GCN) and information from H.266/Versatile Video Coding (VVC) encoded video. The GCN-based model takes various features, including the geometric features, coding mode, motion information, and quantization parameter of the coding unit (CU), as input and produces an output that indicates whether the CU belongs to the ROI. By combining the GCN-based model with the encoded video, the proposed approach significantly reduces the computation time, with an average time saving of 60.88% compared to that of YOLOv7. This paper provides a fast ROI detection method and facilitates post-processing tasks such as the quality enhancement of ROIs for H.266/VVC video.
Morphometric variation in Indian samples of the Common Myna, Acridotheres tristis (Aves: Sturnidae)

Morphometric variation in 9 characters of 347 Common Mynas from 10 localities in India was analysed statistically. Both sexes have differentiated similarly among localities in all characters. Character variability within localities is not significantly correlated with that among localities. The patterns of geographic variation are not clinally ordered; contiguous localities often are not most similar morphometrically. Size variation, as represented multivariately by principal factor I, is not related linearly to corresponding environmental variation among localities. However, relative appendage proportions, as defined by principal factor II, regresses significantly on an altitude factor for both males and females. The comparatively small proportion of the total morphometric variation among localities accounted for by environmental variation suggests that other factors are operative in determining body size. It is speculated that geographic variation in interspecific competition and food particle size may also act as selective pressures in the evolution of optimal body size at each locality.
Satellite remote sensing for assessment of irrigation system performance: a case study in India

Describe el uso de la deteccion alejada basada en los satelites y del sistema de informacion geografica para obtener informacion sobre el funcionamiento agricola primario del sistema de la productividad y de irigacion en un sistema de riego grande de arroz en la India. Discute el potencial y la rentabilidad de las tecnicas de deteccion alejada basada en los satelites para hacer inventarios y supervisar productividad agricola
Predictable Internet Clients and In-Switch Deep Packet Inspection

Deep packet inspection (DPI) is important for network security and is currently provided by complex black-box firewalls. This raises the question: Can network administrators build their own DPI-capable filter using a standard programmable switch? The common answer is that standard switches support P4, which allows users to specify how to parse packet headers, but not packet payload fields (e.g. URL) thus DPI tasks, like URL filtering, require dedicated middleboxes. In this paper, we challenge this common answer. First, we demonstrate that clients send packets with a predictable structure, so a P4 switch can perform some DPI (enough for URL filtering). Second, we demonstrate a URL-filtering firewall completely in the data plane, with no external help from the SDN controller, firewalls, etc. and no custom logic. Our proof-of-concept, P4Wall, handles multiple protocols (HTTP, HTTPS, DNS) with high performance - orders of magnitude faster than a standard Linux (netfilter) firewall.
Experiences and Lessons Learned from Real-World Projects in Software Engineering Subject

Teamwork in software development life cycle (SDLC) and Software Engineering (SE) is a cooperative process that all Computing Science (CS) undergraduates need to undergo. It is a critical skill for the industry and is usually trained through group projects in Higher Education. Due to the nature of software development, most software projects involve collaborative efforts of a group of developers. Although teamwork has been studied in many prior works, it is still considered as a dynamic element in SDLC. As the level of complexity, type of deliverables and range of stakeholders in software projects can vary widely, prior experience cannot be applied directly to new projects. The current implementation of SE education in the Professional Software Development (PSD) and Team Project (TP) subjects contains elements to promote teamwork. Students are required to work in groups on real-world problems. This paper examines the current teamwork simulating real-world software projects through an evaluation with the existing and previous cohorts of students, who have experienced the PSD and TP subjects. Several improvements are then proposed by this study. Based on the results, the majority of the respondents agree that our proposed methods such as self-selection of groups, pair programming, and prototyping model will bring about improved teamwork in their group projects.
DDHANet: Dual-Domain Hybrid Attention-Guided Network For CT Scatter Correction

Scattered x-ray photons generated by the interactions between objects and x-rays will produce artifacts in X-ray computed tomography (CT) images and detrimentally affect the diagnosis. The success of deep learning has inspired us to reduce scatter artifacts in CT images. In this paper, we propose a novel dual-domain network combined with hybrid attention modules. We train the network to reduce artifacts in the projection and image domains. Hybrid attention modules help the network focus on important information and suppress less relevant information. In addition, we describe a joint loss function to preserve the image details. Compared with other state-of-the-art methods, our proposed method shows that it can improve peak signal-to-noise and structural similarity.
Three-phase EV Charging System Based on Matrix Converters with Improved Current Commutation

Matrix converters (MC) enable direct AC-to-AC power conversion without large DC-link electrolytic capacitors and have been applied to various wireless power transfer (WPT) systems. However, traditional MC-based WPT systems suffer from commutation problems, requiring additional switching strategies to ensure safe operation, which increases the control complexity. To solve this problem, this paper proposes a novel three-phase wireless electric vehicle (EV) charging system based on MC. First, the three-phase AC voltage generated by the MC is directly used to drive the three-phase LCC-compensated WPT circuit, which eliminates the bulky short-life DC-bus capacitor and provides a compact rotational magnetic field. Furthermore, a dedicated modulation method is proposed to realize the frequency coordination of the MC stage and the WPT stage. Accordingly, the system can operate safely without additional commutation strategy. Finally, finite element simulations and circuit simulations are used to verify the analysis and prove the effectiveness of the proposed system.
Instance Segmentation of Dislocations in TEM Images

Quantitative Transmission Electron Microscopy (TEM) during in-situ straining experiment is able to reveal the motion of dislocations - linear defects in the crystal lattice of metals. In the domain of materials science, the knowledge about the location and movement of dislocations is important for creating novel materials with superior properties. A longstanding problem, however, is to identify the position and extract the shape of dislocations, which would ultimately help to create a digital twin of such materials. In this work, we quantitatively compare state-of-the-art instance segmentation methods, including Mask R-CNN and YOLOv8. The dislocation masks as the results of the instance segmentation are converted to mathematical lines, enabling quantitative analysis of dislocation length and geometry - important information for the domain scientist, which we then propose to include as a novel length-aware quality metric for estimating the network performance. Our segmentation pipeline shows a high accuracy suitable for all domain-specific, further post-processing. Additionally, our physics-based metric turns out to perform much more consistently than typically used pixel-wise metrics.