Miracle-dz/UniFAR-FTUs huggingface dataset:
Character Attentiveness Analysis Based on Deep Learning

In recent years, with the advancement of science and technology, people have begun to study computer vision security systems. Aiming at the pain points that affected safety by a single detection in the past, a feature centralized analysis system was designed. Through the combination of computer vision technology and deep learning, the fatigue detection and attention behavior detection are combined, the driver is photographed by the on-board camera, and the driving status of the driver is detected from multiple angles and multiple levels, so as to provide comprehensive guarantee for people to travel. This paper is divided into four modules, namely the collection and labeling of image data, the training of the Yolo model, the design of the character set analysis algorithm based on deep learning, and the display of the character set analysis system. Through the use and display of the system, the driver's fatigue state and the driver's distracted behavior during driving can be well identified, and corresponding alarms will be issued. The system has high precision and relatively stable operation. It can detect the driver's attention in real time, protect the driver's driving safety, and reduce the occurrence of traffic accidents, which has important practical significance.
Detection of Brain Tumor from the MRI Images using Deep Hybrid Boosted based on Ensemble Techniques

A brain tumor puts one’s life in danger and interferes with the body’s regular operations. Early identification and treatment planning are essential for a correct diagnosis of a brain tumor. It is crucial to get an early diagnosis of brain tumors in order to increase patient survival and treatment results. Manually assessing the magnetic resonance imaging (MRI) images is challenging. Therefore, more accurate digital approaches for tumor diagnostics are required. Early detection of brain tumors can mean the difference between life and death. Due to their improved performance, deep learning-based brain tumor detection methods have recently attracted attention. However, enormous quantities of classified images are needed to train the deep models in order for these deep learning-based systems to work as predicted. Such data are typically tedious, time-consuming, and prone to human error, which makes it difficult to use such deep learning techniques. Hence, the paper aims to incorporate different ensemble models to show it better than the deep learning algorithms.
Software Vulnerability Detection Based on Anomaly-Attention

Software vulnerability is a decisive factor affecting the security of information systems. There are problems in vulnerability detection such as traditional static methods cannot cope with the detection scenarios of software without source code, and dynamic methods consume many resources are inefficient. Aiming at the potential vulnerabilities that may occur in the operation of executable programs without source code, this paper proposes a Transformer software vulnerability detection method based on Anomaly-Attention, which introduces the Anomaly-Attention mechanism in the traditional Transformer model to calculate the association differences between function call nodes to neighboring points and function call nodes to the whole sequence respectively, and then distinguish normal function call points from abnormal function call points through the association differences. The CallSeq tool is used to collect the sequence of function calls in program operation as timing data, and the fuzzy test results are used as labels to train the model in this paper. Experiments show that the method in this paper can detect vulnerabilities in unsourced executable programs with 87.73% accuracy, which is about 3% higher than the vulnerability detection accuracy of methods based on CNN, LSTM and other network models.
Bocher contractions of conformally superintegrable Laplace equations: Detailed computations

These supplementary notes in the ArXiv are a companion to our paper "Bocher contractions of conformally superintegrable Laplace equations" [arXiv:1512.09315]. They contain background material and the details of the extensive computations that couldn't be put in the paper, due to space limitations.
The New Generation Brain-Inspired Sparse Learning: A Comprehensive Survey

In recent years, the enormous demand for computing resources resulting from massive data and complex network models has become the limitation of deep learning. In the large-scale problems with massive samples and ultrahigh feature dimensions, sparsity has gradually drawn much attention from academia and the industrial field. In this article, the new generation of brain-inspired sparse learning is reviewed comprehensively. First, sparse cognition learning is introduced from the visual biology mechanism to modeling for the natural image. Second, the sparse representation algorithms are summarized to sort out the research progress of sparse learning. Third, the relevant research on sparse feature selection learning is reviewed. Then, the sparse deep networks and applications are summed up. Last but not least, ten public issues and challenges of sparse learning are discussed. By investigating the development process of sparse learning, this article summarizes the advantages, disadvantages, limitations, and future research directions of the algorithm, which can help readers conduct further study.
Analysis of the Thermal Behavior of Li-Ion Pouch Battery Cell – Part II: Circuit-based Modeling for Fast and Accurate Thermo-Electrochemical Simulation

This paper belongs to a two-part study dealing with thermal effects in Li-ion pouch battery cells. Here, a SPICE-compatible battery model suited for circuit-based simulations is presented. The model accounts for the thermo-electrochemical mechanisms taking place in batteries. Since it provides fast, yet accurate, results, the approach is aimed at supporting designers of electric/electronic systems based on storage supplies. Although the proposed solution can be applied to a generic product, a Li-ion pouch battery cell in nickel-manganese-cobalt was considered as a case study. The model accuracy is verified by comparing PSPICE results with those obtained through the 3-D finite-element method simulations described in Part I.
A Filter-Based Learning Approach for Intrusion Detection using The NSL-KDD Network Dataset

Wireless sensor network (WSN) attacks seek to disrupt or eliminate the network’s ability to execute its expected duties. Penetration testing is a wireless sensor network defence that detects unknown threats. Because of the significant advancement in computer based products and widespread Internet utilization, it is critical to guarantee hosting and information systems. A hacking technique has been proposed to breach computer security with infiltration. An intrusion detection system (IDS) identifies network breaches with machine learning (ML) methods. Classic ML methods based feature extraction frequently resulted in low efficiency and misreading incursions. To address these challenges, this research provides a novel architecture for IDS that may be facilitated by the filter-based learning methodL The anticipated model performance is comparable to ML techniques. The appropriate solution was built and evaluated using network security laboratories – discovering dataset information (NSL-ICDD). The experimental data reveal that the suggested model filter-based learning detects assaults with more accuracy (i.e., 99 percent) than LDA, CART, and other current approaches.
Real Time Facial Emotion Recognition Methods using Different Machine Learning Techniques

Artificial intelligence has faced a rapid growth and it has made a huge change in this world. In real time the traditional algorithm has go phut to reach the human demands. Due to this great success has been gained by machine and deep learning in many different implementations such as parameter classification system, output recommendation system, different pattern recognition, etc. The thoughts, behavior and feeling of a human can be determined by his/her emotions. By utilizing the characteristics of deep learning/machine learning and different methods such as input analysis, face unlocking, etc. A facial emotions recognition system can be built and can be implemented with good accuracy. The project mainly focuses on comparison of different available deep and machine learning algorithm and find different metrices like matrix, accuracy, estimated time, validation time, etc.
A versatile method to design an equidistant MIMO array

A design method to synthesize a uniformly weighted and equidistant multiple input multiple output (MIMO) array is proposed and explained. The method can be used to design 1D or 2D arrays without software tools or programming effort. The method is based on sequentially copying and displacing antenna groups with the help of a number scheme. A nomenclature is proposed to classify the degrees of freedom in the design procedure
Distributed Application Architecture and LinkNet Topology Processor for Distribution Networks Using the Common Information Model

Ongoing decarbonization efforts and increasing penetrations of distributed energy resources (DERs) may soon disrupt current operational paradigms, requiring a shift from centralized control and optimization to hierarchical and distributed applications. To support this transition, a distributed application architecture for communication and control of distribution network assets is introduced. The architecture defines a structure of communication and coordination layers based on the Laminar Coordination Framework. The challenge of defining distributed control areas is resolved through definition of switch-delimited topological areas based on the physical topology of the feeder instead of communications network layers. Implementation of the architecture in standards-based platforms is enabled by a robust graph-based topology processor leveraging the class structure of the Common Information Model (CIM), which differs significantly from traditional bus-branch representations by explicitly modeling the nodes and terminals of every type of power system equipment. The topology processor creates a set of LinkNet linked list structures for indexing the nodes and terminals of all CIM class object instances and mapping of the feeder topology in real-time. The data structures and algorithm are computationally lightweight, with performance tests presented for several IEEE distribution test systems.
Building Extraction from High Resolution Remote Sensing Images Based on Improved Mask R-CNN

In order to improve the accuracy of building extraction, an improved Mask R-CNN instance segmentation model based on deep learning is proposed in this paper. The convolutional block attention module (CBAM) is introduced into the feature extraction network to improve the significance of the features of the building area in the feature map. Augmented FPN (AugFPN) is used to replace FPN network, so as to reduce the lack of information in the process of feature fusion and further tap the potential of multi-scale features, so as to improve the accuracy of building extraction. Using this model, the improved algorithm is compared with the original algorithm in the marked dataset. The detection AP50 of the improved algorithm for buildings is 80.1%, and the segmentation AP50 is 75.9%, which are better than the original algorithm. The experimental results show that this algorithm improves the learning ability of the model on building features, and this method can effectively improve the accuracy of building extraction from high resolution remote sensing images.
Semantic Segmentation Of Natural And Man-Made Fruits Using A Spatial-Spectral Two-Branches-Cnn For Sparse Data

We propose a CNN for semantic segmentation with classes which may be non-separable in the spatial domain, but distinguishable by additionally exploiting the spectral domain. In this spatial-spectral two-branches-CNN (SS2B-net), firstly, a spatial-branch-CNN exploiting the spatial domain and a spectral-branch-CNN exploiting the spectral domain are independently trained. Then, their parameters are fixed and their outputs serve as inputs for a new tiny CNN trained for classification into the given classes. We reduce the number of hyperspectral bands from 186 to 96 by converting the VR-bands to RGB. Working on just RGB data in the spatial-branch-CNN, well-tried CNNs for RGB data can be applied. Finally, we use a pretrained VGG16-net to avoid overfitting caused by sparse data, together with a don’t care class at the borders of classes to avoid overfitting caused by mixed pixels. In the spectral-branch-CNN, a small 1D CNN is designed and applied. The segmentation results show improvements against the 3D hyper-U-net at class borders, at objects in their completeness, in mixed pixels caused by reflections and in wrongly classified objects. We support that by increasing the macro average recall from 0.90 to 0.97.
Classical logic and intuitionistic logic: equivalent formulations in natural deduction, Gödel-Kolmogorov-Glivenko translation

This report first shows the equivalence bewteen several formulations of classical logic in intuitionistic logic (tertium non datur, reductio ad absurdum, Pierce's law). Then it establishes the correctness of the G\"odel-Kolmogorov translation, whose restriction to the propositional case is due to Glivenko. This translation maps a formula F of first order logic to a formula F¬¬ in such a way that F is provable in classical logic if and only if F¬¬ is provable in intuitionistic logic. All formal proofs are presented in natural deduction. These questions are well-known proof theoretical facts, but in textbooks, they are often ignored or left to the reader. Because of the combinatorial difficulty of some of the needed formal proofs, we hope that this report may be useful, in particular to students and colleagues from other areas.
Square form factorization, II

We propose a new subexponential time integer factoring algorithm called SQUFOF2, based on ideas of D. Shanks and R. de Vogelaere. It begins by using a sieve like that in the multiple polynomial Quadratic Sieve to construct a square value of a binary quadratic form. It uses this value to produce a square form. Then it factors the integer N as the original SQUFOF does by taking an inverse square root and following a nonprincipal cycle to a symmetry point. This marriage with the Quadratic Sieve transforms SQUFOF from a O ( N 1 / 4 ) algorithm into one with subexponential time. On the way we prove new facts about infrastructure distance, which is used in the time complexity analysis.
Incorporating Attention Mechanism And Graph Regularization Into Cnns For Hyperspectral Image Classification

The rich spatial and spectral features contained in a hyper-spectral image (HSI) play an important role in the classification of land-cover classes. In this paper, we incorporate attention mechanism and graph regularization into CNN-based HSI classification model to improve the classification performance. The proposed spatial-spectral CNN with attention mechanism and graph regularization (ATGR-SS-CNN) has three advantages: 1) 3D-2D cascade CNN. The 3D-CNN facilitates the joint spatial-spectral features representation from 3D data cube and the 2D-CNN further learns more abstract spatial representation; 2) Convolutional Block Attention Module (CBAM). CBAM gives greater attentions to discriminative features; 3) graph regularization. Graph regularization aims to maintain the similarity between samples belonging to the same land-cover class. Experiments on three datasets show that the proposed method can effectively improve the classification accuracy of HSI, which is better than the compared models.
A Real-Time Detection Method of Non-Metallic Threats from Restored Image using Blended Image Network

Due to its excellent garment penetration and safety, passive millimeter waves have been used in surveillance assessment. Nevertheless, limited resolution as well as inherent noise is problems with passive millimeter wave imaging (PMWs). The approaches that have been des cribed seldom improve the appearance of pictures for PMW and solely conduct detection system based on selecting relevant PMW that results in a high percentage of false alarms. Furthermore, the changes in grayscale make it challenging to distinguish low-reflective non-metallic dangers. An approach to finding hidden hazards in the human body is suggested in this research. To rebuild high-quality images using multi-source PMWs, this research study proposes a GAN framework. A unique image recognition pipeline is designed by incorporating feature extraction, image normalization, and a thorough analyzer models. The registering system is suggested for analyzing the peronal data as well as the reduction of false alarms, while the categorization network uses multi-scale characteristics to combine the local and global data in both PMWs as well as visible images to gain accurate shape and performance indicators. Allows the prediction for metallic and non-metallic objects may be done using the grayscale and contour characteristics, accordingly. The recognition findings of each individual frame are then combined using a synthetic method. The efficiency of each module and that of the suggested approach is assessed by using the experimental measurements. With 92.35% accuracy and 90.3% recall, experimental findings show that the proposed technique functions better than the other existing techniques. It also has a high detection rate.
Back muscle response of seated individuals to single and superimposed sinusoidal, vertical vibration

Data exist on the erector spinae muscle activity during steady state and random vibration. The purpose of this study was to determine how the muscles responded to vibration between those limits. This was achieved by measuring the surface electromyographic (EMG) muscle response to single and superimposed vibration signals. Subjects were tested at the following single and combined frequencies: 2, 3,…, 10, 2 + 4, 3 + 6, 4 + 8, 5 + 10, 2 + 4 + 6, 3 + 6 + 9, (All) 2 + 3 + … + 10. The EMG activity for the combined signals was evaluated at each single frequency and the ensemble averaged peak‐to‐peak predicted torques computed [Seroussi et al., J. Biomechanics 22(3), 219–229 (1989)]. For the superimposed signals, the torque was significantly greatest at the following frequency evaluation windows (signals used): 4 Hz (2 + 4, 2 + 4 + 6, All); 6 Hz (3 + 6, 3 + 6 + 9); 8 Hz (4 + 8); and 5 Hz = 10 Hz (5 + 10). The natural frequency of the seated subject has been reported in the 4.5‐ to 6‐Hz range. The maximum EMG resp.
Efficiency of Decision Tree Algorithm For Lung Cancer CT-Scan Images Comparing With SVM Algorithm

Machine Learning [ML] based classification algorithms play a vital role in the process of lung cancer classification based on CT images. A total of 265 CT scan images of lung cancer patients are collected and classified into samples of training dataset (n = 185 [70%]) and test dataset (n = 80 [30%]). Decision Tree and SVM algorithms are used in the process of classification, and for implementation Weka tool is used. The proposed decision tree and SVM based Lung cancer CT scan image classification has achieved an accuracy of about 99% and 97% respectively. The sample independent T-Test result of (p >0.005) and the G-power estimate of 0.8 satisfy the decision tree and SVM algorithms statistically.
Diffusion effects on a superconductive model

A superconductive model characterized by a third order parabolic operator L" is analysed. When the viscous terms, represented by higher - order deriva- tives, tend to zero, a hyperbolic operator L0 appears. Furthermore, if P" is the Dirichlet initial boundary - value problem for L", when L" turns into L0; P" turns into a problem P0 with the same initial - boundary conditions as P". The solution of the nonlinear problem related to the remainder term r is achieved, as long as the higher-order derivatives of the solution of P0 are bounded. More- over, some classes of explicit solutions related to P0 are determined, proving the existence of at least one motion whose derivatives are bounded. The estimate shows that the diffusion effects are bounded even when time tends to infinity.
A Real-Time Fluorescence Feedback System for Infrared Laser Sealing of Blood Vessels

This study explores UV light induced fluorescence from blood vessels for indicating successful infrared laser sealing of vascular tissues. A light emitting diode (LED) with center wavelength of 340 nm and 0.1 mW power was used with a Y-shaped fiber bundle of seven 200-μm-core fibers. The central excitation fiber was connected to the LED, while the detection ring of six fibers was connected to a spectrometer. The fiber bundle was aligned with porcine renal arteries compressed between optical windows. Fluorescence was acquired before and after vessel sealing, with a 1470 nm laser for 5 s at 30 W (sealing, n = 10) or 5 W (control, n = 10). Signal increase in the 470–520 nm spectrum was correlated with vessel burst pressures (BP). Integrated fluorescence increased 71 ± 25% at 30 W vs. 19 ± 14% at 5 W (p < 0.05), corresponding to a successful BP of 639 ± 189 mmHg vs. failed seal BP of 39 ± 41 mmHg (p < 0.05). Real-time measurements showed a gradual increase in fluorescence with the signal reaching a plateau at 3–4 s, indicating that shorter seal times are possible. The increase in fluorescence signal during laser vessel sealing may provide a non-destructive, real-time, optical method for indicating hemostatic seals.
A Tensor Non-Convex Low Rank And Sparse Constrained Band Selection Scheme For Clustering Of Hyperspectral Paper Data

Conventional band selection methods, which vectorize the spectral bands of Hyperspectral Imaging (HSI) data, may fail to consider its intrinsic two dimensional structure. Hence, an effective tensor based band selection scheme, using submodule clustering is proposed, where this framework is capable of preserving the spatial structure of individual spectral bands. In the proposed optimization problem, a representation tensor is derived from the self-expressive representation of spectral bands in the HSI paper data. A similarity matrix is constructed from the representation tensor and then, appropriate number of non-redundant bands are determined by Silhouette clustering evaluation criterion. To capture better low rankness and self-expressiveness property, $l_{\frac{1}{2}}$ -induced Tensor Nuclear Norm (TNN) and $l_{\frac{1}{2}}$ norm regularization are incorporated in the formulated optimization model. Further, the reduced band subset is used in the clustering of hyperspectral paper data. Experimental results prove the efficacy of newly formed band subset on the clustering performance.