Miracle-dz/UniFAR-FTUs huggingface dataset:
Passive Infrared Image Detail Enhancement with Points and Lines for Visual Odometry

In order to use infrared thermal imaging for simultaneous localization and mapping of mobile robot vision navigation, a point-line feature extraction and matching algorithm after infrared image enhancement is proposed for visual odometry. We first used a guided filter to smooth the input image and separate it into the base layer and the detail layer. Then constraining the gradient of the detail will be used gain mask to enhance it. Finally, the two parts of the image combined with weighted coefficients will be exported into the second guided filter. The output image will use LSD to extract line feature, then using ORB algorithm will get point feature extraction and matching from line feature image. The results show the it can effectively improve the defect of infrared image blurred with similar background temperature and difficult to extract image features for mobile robot visual SLAM.
GOLD and Discourse: Domain- and Community-Specific Extensions

Corpora annotated for discourse–related phenomena have become an important source for the empirical study of whole texts and a source of training data for the automated parsing of whole texts. By discourse–related phenomena, we refer to various textual relations (e.g., anaphoric and rhetorical relations) that hold among textual units (e.g., whole texts, adjacency pairs, and discourse segments). One of the main problems associated with corpus linguistics—and for the markup of linguistic data in general—has been the lack of interoperability between disparately marked up resources. While it is theoretically possible to standardize the content and structure of markup, the richness of the data itself suggests that no one standard scheme would suffice for all data, all of the time. In fact one of the major contributions of the E–MELD project1 has been to show that annotation elements need not be standardized as such. Rather, by following certain parameters of “best–practice” (Bird & Simons 2003) the stage can be set for wide–spread interoperability among disparate corpora. For example, one of the key strategies of the E–MELD project has been to suggest that all markup elements used in annotating linguistic data (including discourse–related corpora) should be mapped to a semantic resource that defines the meaning of each element. Such a semantic resource for descriptive discourse categories does not exist at present. To rectify this situation, we propose a discourse–specific extension to the General Ontology for Linguistic Description (GOLD), as introduced by Farrar & Langendoen (2003) and explicated in Farrar (forthcoming).
Scale-ratio Asymptotics in Nonparametric Minimax Estimation

This paper studies a Bayesian approach to non-asymptotic minimax adaptation in nonparametric estimation. Estimating an input function on the basis of output functions in a Gaussian white-noise model is discussed. The input function is assumed to be in a Sobolev ellipsoid with an unknown smoothness and an unknown radius. Our purpose in this paper is to present a Bayesian approach attaining minimaxity up to a universal constant without any knowledge regarding the smoothness and the radius. Our Bayesian approach provides not only a rate-exact minimax adaptive estimator in large sample asymptotics but also a risk bound for the Bayes estimator quantifying the effects of both the smoothness and the ratio of the squared radius to the noise variance, where the smoothness and the ratio are the key parameters to describe the minimax risk in this model. Application to non-parametric regression models is also discussed.
СЕРІЯ « Техніка »

Brain-Computer Interfaces (BCIs) have emerged as a groundbreaking technology that enables direct communication between the human brain and computing systems. This article aims to address the formulation of the problem in BCIs and its connection to important scientific and practical tasks. By analyzing the latest research in the field, the initial steps taken by researchers to tackle the challenges associated with BCIs have been identified. The study delves into signal acquisition and processing, highlighting the complexity and variability of brain signals, and the need for robust techniques to capture and interpret neural activity accurately. Additionally, the challenges related to feature extraction and representation, as well as decoding and interpretation of brain signals, necessitating advanced algorithms to handle the non-stationarity and variability of brain activity have been explored. The main findings of our research highlight promising advancements in BCIs, particularly in signal decoding. Our results demonstrate improved accuracy and efficiency in decoding user intention and actions, enabling more effective communication and control between humans and machines. The implications of these findings in relation to the initial problem
Contests with Sequential Entry and Incomplete Information

This paper provides a general study of a contest modeled as a multi-player incomplete-information all-pay auction with sequential entry. The contest consists of multiple periods. Players arrive and exert eﬀorts sequentially to compete for a prize. They observe the eﬀorts made by their earlier opponents, but not those of their contemporaneous or future rivals. We establish the existence and uniqueness of a symmetric perfect Bayesian equilibrium (PBE) and fully characterize the equilibrium. Based on the equilibrium result, we show that a later mover always secures a larger ex ante expected payoﬀ. Further, we endogenize the timing of moves and show that all players choose to move in the last period in the unique equilibrium that survives iterated elimination of strictly dominated strategies (IESDS)
Punch-Needled Passive UHF RFID Tag Dipole Antennas – Design, Fabrication, and Initial Wireless Evaluation

This study presents design and fabrication as well as evaluates the initial performance of punch-needled passive UHF (Ultra high frequency) RFID (Radiofrequency identification) tag antennas. There are two different kinds of punch-needling techniques used: 1) by using conductive thread on the backside of the tag antenna and 2) by using conductive thread on both sides of the tag antenna. Both types of tag antennas were made with 100 % wool yarn onto Aida cloth and for conductive parts. The wireless performance of the tags was evaluated at the European frequencies (865.6-867.6 MHz), where they achieved a maximum read range of over 1.2 meters. According to these preliminary results, it is potential to implement this construction method, which is especially interesting for home crafters, who are interested in making textile-integrated electronics of on their own. In further research, we inspect how to increase the read range of these punch-needled tags and explore other innovative home crafting manners to create passive UHF RFID tags from different types of materials.
3D Reconstruction From Traditional Methods to Deep Learning

Vision is one of the important pathways for human perception of external information, with over 80% of perception being acquired through vision. How to enable computers to possess efficient and flexible visual systems similar to humans has always been a hot topic in the field of computer science. One of the main goals of computer vision research is to reconstruct the geometric structure of 3D objects visible on the visible surfaces from 2D photos. Recently, this technology has become mature enough and its applications range from autonomous driving, virtual reality, cultural heritage preservation and restoration, among others, with significant research value. In this paper, we summarize the key technical issues in 3D reconstruction from existing technologies, first by summarizing traditional methods of 3D reconstruction, then analyzing commonly used deep learning methods for 3D reconstruction and their application scenarios in different fields. Finally, we conclude and provide an outlook on future development directions.
Detection of Smart Grids Instability with Convolutional Neural Networks and Global Explainability

A smart grid is an advanced concept of power systems devoted to harmonize electricity and communication in system networks. It is able to provide real-time information to producers, operators and consumers. There is an urgent demand to efficiently route supplied energy to consumer domains such as, for instance, households, organisations, industries and also smart cities. In this context, a smart grid with a stable system is required to supply the dynamic energy demand. In this paper, we propose a method aimed to detect, in real-time, whether a smart grid is in an instable state. We exploit deep learning, by considering a neural network developed by authors and global explainability, with the aim to determine the features useful in the model prediction. We evaluate a dataset composed by 60000 smart grid observations we obtain interesting results, by demonstrating the effectiveness of the proposed method.
A Deep Fusion Model for Human $vs$. Machine-Generated Essay Classification

Essay classification is a crucially important task, considering the increasing attention towards AI tools such as ChatGPT, and the raised concerns with regard to academic integrity. Existing text classification approaches, including neural network-based methods and feature-based methods, are often unable to effectively analyze diverse types of information in a combined way. Moreover, they are mostly tailored for English data, and focus on addressing specific topics, such as fake news, hate speech, and misinformation. On the other hand, machine learning research conducted on essay data has been limited to automated grading and off-topic detection tasks. In this paper, we propose a deep neural network-based model that combines text modeling and linguistic features via data fusion, with the aim to provide robust classification capabilities. Our experiments with datasets containing short essays in English and Spanish written by L2 learners, show that it is possible to accurately perform essay classification, and that our model is competitive, even when compared to popular feature-based and neural network-based approaches.
Low Power and High Speed Charge Pump based Dual Stage Dynamic Comparator using 45nm CMOS Technology

A comparator is a key component in variety of applications such as data converters, mixed signal systems, signal processing, digital I/O circuits, biomedical devices, memory sensing circuits, and so on. These applications frequently necessitate low-power, high-speed and low-offset comparators. In particular for signal processing applications, enhancing the performance of data converters depends on the accuracy of comparators. The most effective way to enhance the sampling rate of data converters is to improve comparator speed. A pre-amplifier with a charge pump is used in the proposed dynamic comparator, followed by a decision circuit and an output stage. The simulated results demonstrate that comparator has achieved a low power consumption of 13. 13μW with a delay of 85.54ps making it more suitable for data converters. The comparator operates with clock frequency of 1GHz and a supply voltage of 1V, and it is simulated in 45nm CMOS technology using the Cadence virtuoso tool.
Using Obfuscators to Test Compilers: A Metamorphic Experience

Android compilers play a crucial role in Android app development. The correctness of the apps relies on the compilers because the source code of the app is translated into the target language by the compilers. The use of obfuscators is becoming the standard in app development to prevent reverse engineering or code tampering. Despite their importance, both compilers and obfuscators lack an oracle, which is the mechanism to determine the correctness of the execution, and hence they can be called untestable software. Metamorphic Testing (MT) is a state-of-the-art testing method that can test untestable software. MT tests software based on Metamorphic Relations (MRs). Recent studies have shown that program transformation, an MT-based compiler-testing strategy, is highly effective in revealing bugs in compilers. However, this strategy requires sophisticated tools that could take significant time to develop. Therefore, program transformation using obfuscators is proposed. Based on research into testing obfuscators using MT, it is suggested that an MT-based compiler-testing strategy could be achieved by using obfuscators. In addition, this method has the potential to detect bugs in both compilers and obfuscators. This paper reports on our experience using MT techniques to test compilers and obfuscators. We present three related MRs, two of which uncover evidence of faults.
A Shrinkage Dual Sign Subband Adaptive Filter with Individual Weight Factor

A dual sign subband adaptive filter with individual weight factor (IWF-DuSSAF) is proposed, which can increase the convergence rate with a little additional computational burden compared with the sign subband adaptive filter with individual weight factor (IWF-SSAF). The dual sign method is used for deriving the proposed IWF-DuSSAF algorithm. Selecting the proper parameters of proposed IWF-DuSSAF algorithm will lead to small steady-state errors and fast convergence. The proposed sign subband adaptive filter with individual weight factor has the tradeoff between steady-state misalignment and convergence rate. To overcome this drawback, we present the shrinkage variable step-size IWF-DuSSAF (SHIWF-DuSSAF) algorithm. To make the proposed SHIWF-DuSSAF algorithm robust against impulsive noise, a novel robust shrinkage method is adopted to estimate the noise free error signal, which uses two threshold parameters. We gain the threshold by deciding the confidence degree. The occurrence of impulsive noise is detected by novel method. Finally, simulations are carried out to demonstrate the advantages of the proposed IWF-DuSSAF and SHIWF-DuSSAF algorithms.
Application of Improved Physics-Informed Deep Learning Based on Activation Function for Solving Nonlinear Soliton Equation

Partial differential equations (PDEs) are indispensable tools for conducting scientific research in a variety of disciplines and play a vital role in fields such as meteorology, oceanography, and biology. These powerful mathematical tools enable scientists and researchers to describe and analyze complex phenomena. However, it is often difficult to obtain exact analytical solutions of PDEs, and the numerical methods for solving PDEs are often computationally intensive and time-consuming. In recent years, with the continuous research on deep learning, physics-informed neural networks (PINNs) have been successfully applied to find numerical solutions of PDEs and have shown great potential. Meanwhile, solitary waves have also attracted great interest from researchers in nonlinear science. In this paper, we use the improved PINNs to numerically simulate the solitary wave solutions of PDEs. The improved PINNs not only incorporate constraints on the control equations to ensure the interpretability of the prediction results, which is important for physical field simulations. In addition, an adaptive activation function is introduced. In this paper, the Sawada-Kotera equation and the Caudrey-Dodd-Gibbon-Sawada-Kotera (CDGSK) equation are selected for study and the accuracy of the predicted solitary wave solution is evaluated by analyzing the error of the simulation results. The experimental results show that the improved PINNs significantly outperform the original PINNs with shorter training time and more accurate prediction results.
Hardware Time-stamping in Network Time Protocol and NTP to 1PPS Generation

Network time protocol (NTP) works in client-server mode to synchronize clocks of computer systems in "packet switched networks". NTP is popular across internet, wherein multitude of computers acting as "NTP clients" get time from corresponding "NTP servers", without need to get connected with "GNSS time receivers". By default, NTP is configured to operate in software(s/w) time stamping, which means time stamping of NTP packet happens in "kernel or networking stack". With s/w time stamping mode, NTP can provide an accuracy up to microsecond order. Whereas another timing protocol namely, PTP (precision time protocol) uses hardware time stamping to achieve nanosecond accuracy. In this paper, a novel approach of providing "hardware time stamping" to NTP is introduced to achieve nano-second accuracy. Also, "NTP master-slave test bed" to verify the accuracy achieved is also mentioned.
The Bandit’s States: Modeling State Selection for Stateful Network Fuzzing as Multi-armed Bandit Problem

Network interfaces of Industrial Control Systems are a common entry point for attackers, and thus need to be thoroughly tested for vulnerabilities. One way to perform such tests is with network fuzzers, which randomly mutate network packets to induce unexpected behavior and vulnerabilities. Highly stateful network protocols pose a particular challenge to fuzzers, since a fuzzer needs to be aware of the states in order to find deep vulnerabilities. Even if a fuzzer is aware of the states of a stateful network protocol, there are still several challenges to overcome. The challenge we focus on is deciding which state to test next. To make this decision, the fuzzer needs to strike a balance between exploiting known states and exploring states not yet tested. We propose to model this exploration versus exploitation dilemma using a Multi-armed Bandit. In this work, we present two modeling approaches and preliminary experiments. We choose to model the state selection problem with (I) a stochastic Multi-armed Bandit, and (II) an adversarial Multi-armed Bandit. The latter takes into account that coverage can only be discovered once, and that the underlying reward probability therefore decreases over time. Although the adversarial Multi-armed Bandit models the state selection problem more accurately, our experiments show that both approaches lead to statistically indistinguishable fuzzer performance. Furthermore, we show that the baseline fuzzer AFLNet leads to significantly better results in terms of coverage. Building on these unintuitive preliminary results, we aim to investigate the behavior of the agents in more detail, to include additional modeling approaches, and to use additional Systems under Test for the evaluation.
Detection of Pneumonia and Tuberculosis using Deep Learning approach

Pneumonia and tuberculosis are two major problems which can be diagnosed through X-ray images. However, accurate and efficient detection of these diseases is a challenge for radiologists, as it requires careful examination of multiple images. A deep learning approach can help to automate this process and improve the accuracy of disease detection. Specifically, aim to investigate the effectiveness of transfer learning and the ResNet50 model for this task. A publicly available dataset of chest X-rays from Kaggle is used. The dataset includes images from patients with and without pneumonia and tuberculosis. Transfer learning with the ResNet50 model a multilayer CNN model along with a residual block is used, for effective image classification. Model is trained using fine tuning techniques. For quantitative treatment, accuracy is used as the main metric for evaluating the model's performance, and also used precision, recall, and F1 score to assess the model's ability to detect cases of pneumonia and tuberculosis. The accuracy of the model is 96.59% for detecting cases of pneumonia tuberculosis. Additionally, the precision, recall, and F1 score for both diseases were above 0.90. These results demonstrate the effectiveness of transfer learning and the ResNet50 model for disease detection in chest X-rays.
Koopformer: Robust Multivariate Long-Term Prediction via Mixed Koopman Neural Operator and Spatial-Temporal Transformer

Multivariate long-term prediction is ubiquitous in many domains, such as meteorology, hydrology, and finance. But, over time, the joint distribution shift of non-stationary time series data will increase, making long-term prediction more challenging. Previous studies were more inclined to design novel architectures, add more inductive bias to capture the overall trend and reduce the non-stationarity of the series to reduce the prediction error. These methods, however, disregard the multivariate correlation between multiple time series. Also, most prediction models use the mean squared error (MSE) as a loss function to help optimize the model. This reduces prediction error but is incapable of capturing abrupt local changes. This paper proposes a model based on the transformer architecture, called the Koopformer, by mixing the Koopman neural operator and spatial-temporal attention for the first time to tackle the above challenges. Specifically, we design a Koopman neural operator to solve the distribution shift problem caused by non-stationarity. The operator uses the learned Koopman space and global shared features to model time series in a less non-linear way and initially obtain robust predictions, resulting in a model with more substantial inductive bias. Second, we suggest a new spatial-temporal attention mechanism that can model time and multivariate dependencies in multiple time series and take into account the effects of local temporal and multivariate correlations on prediction results. We also suggest a new loss function that focuses on the difference between the prediction and the ground truth and the similarity of shape to get more accurate and realistic results. Evaluations on five baseline data sets show that Koopformer performs better than traditional and state-of-the-art methods, predicting more precise and realistic results. We will open-source the code after the paper is accepted.
Lens antenna design tool based on generalized superformula: preliminary results

In this work, a user-friendly electromagnetic tool for analysis and synthesis of a novel class of 3–D dielectric lens antennas is presented. The lens antenna shape is modeled using a generalized superformula allowing additional degree of freedom. Thanks to the free parameters characterizing this formula, the lens geometry can be adjusted in a simple and analytical way. The electromagnetic tool is based on the geometrical optics and tube tracing approximations as well as the physical optic approach. In this way, the multiple wave reflections within the lens region as well as the far field outside the lens can be calculated. Moreover, an efficient synthesis procedure based on swarm intelligence has been developed and integrated in the numerical code. Thanks to this design tool it is possible to identify the lens parameters yielding the optimal antenna characteristic and a desired radiation patterns with low computational burden.