Reviewerly/exHarmony huggingface dataset:
Instance and Foreground Aware Single-Stage Outdoor 3D Object Detector from Point Clouds

In the context of 3D object detection solely based on point cloud data, it's crucial to extract a compact subset of representative point clouds from complex scenes. This approach offers several benefits, like reduced computational consumption and enhanced practical applicability. While the current leading algorithm for pure point cloud-based object detection has demonstrated the capability to effectively retain most foreground points, it still encounters a significant challenge: an imbalance between foreground and background points within the final point cloud. This issue results in the emergence of false positives. To address this problems, we propose a balanced detector, named IFA-SSD, which includes the foreground/background balance strategy (FBBS). In the detection algorithm based on pure point cloud type, IFA-SSD achieves the state-of-the-art in multi-class detection on KITTI and ONCE datasets.
Color Image Scrambling Technique Based on Chinese Remainder Theorem and Arnold Transformation

Image scrambling technology plays a vital role in the fields of digital watermarking and information hiding. Although traditional image scrambling technology can preprocess images to enhance the security of algorithms, more robust security measures are needed due to the rapid development of artificial intelligence techniques. In order to further improve the security of color image encryption, a color image scrambling technique based on the Chinese remainder theorem and the Arnold transformation is proposed, which combines traditional cryptography with image scrambling techniques. The method initially applies the Arnold transformation to color images, and then encrypts the pixel values of the three channels in the transformed pixel matrix using the Chinese remainder theorem to obtain the final scrambled image. The performance of the algorithm was tested using MATLAB, and the Structural Similarity Index (SSIM) between the original image and the attacked recovered image exceeded 0.9. The correlation between adjacent pixels of the scrambled image approached 0, and the running time was only 46.7s. The algorithm provides a significantly improved and extensive key space compared to using the Chinese remainder theorem or Arnold transform alone. The experimental results show that the algorithm has good resistance to attacks, good scrambling effect and high operational efficiency. The algorithm thoroughly considers both security and operational efficiency aspects, making it suitable for watermark image preprocessing in the field of digital watermarking research, so as to further realize the copyright protection of digital products.
A Real-Time Pedestrian Tracking Method Based on Enhanced Tracktor

In the domain of multi-object video tracking, the issue of target identity loss, which results from detection biases caused by interactive occlusions and other influences, is addressed in this paper. We propose AUTracktor, a pedestrian multi-object tracking algorithm based on an enhanced Tracktor. Initially, a dynamic updating module is designed for bounding box regression, leveraging a Siamese network for further refinement and localization of the proposed bounding boxes. Subsequently, a temporal data optimization unit is employed to update a more suitable template for the current frame, establishing a connection between frames. Feature fusion is conducted through pixel correlation, thereby enhancing the edge and scale information of the target. Experimental evaluations on the public MOT16 dataset demonstrate that our proposed approach exhibits superior tracking accuracy compared to the state-of-the-art methods, with commendable robustness and a stable frame rate of 24 FPS.
Real-time road scene segmentation based on knowledge distillation

Aiming at the problem that it is difficult to balance the accuracy and computational efficiency of the road scene semantic segmentation algorithm, a real-time road scene segmentation algorithm based on knowledge distillation is proposed. The algorithm introduces three key functional modules; (1) using an asymmetric convolutional pyramid module on the top of the encoder; (2) incorporating a coordinate attention module in the network; (3) Using high accuracy non-real time models to distill knowledge on lightweight model. The method in this paper achieves a balance of accuracy and real-time performance on the CamVid dataset. the inference speed is 63.7 FPS and the GFLOPs are 109.2 while the mIoU is 79.3% when computed on an NVIDIA GTX 1080Ti.
Research and analysis of big data based on decision model

Big data a key role in airport vehicle scheduling and other issues. Therefore, research on taxi-related issues is of great significance to airport passengers' efficiency and driver income. This paper studies the issues related to airport taxi driver decision-making and storage yard management. First of all, we obtained the current number of passengers waiting through the model of the number of passengers in the airport, and the number of taxis in the queue was used to obtain the number of taxis in the queue. The total number of passengers, using the data obtained from the two models for the first comparison, then a decision can be made, and then a second comparison can be made through the driver's waiting time and the time to return to the city, and then a decision can be made again Finally, the third comparison is made through the benefits of A and B programs, and a decision can be made at this time. In summary, the driver's choice decision model is established, which has certain reference value.
Integral Calculation based on Transformer and Prior Knowledge

In response to the problem that the Transformer model in integral calculation usually only models character vocabulary and fails to fully utilize the mathematical meaning of character combinations and character distance perception, a new integral solving model that integrates prior knowledge into the Transformer is proposed in this paper. Firstly, numerical segmentation is performed on input sequence characters to further perceive the meaning of numbers; Secondly, one-dimensional convolution is introduced into the embedding layer to encode the sequence, enhancing digital perception while obtaining the mathematical meaning of operator, variable and constant combinations. Finally, by introducing a distance-based Gaussian prior attention mechanism, the perception of adjacent characters is improved. The experimental results on the integrated dataset show that the proposed model has higher solving accuracy and better performance than mathematical solvers such as Mathematica and Matlab.
Handwritten Chinese Character Recognition Based on Residual Neural Network

The application of handwritten Chinese character recognition technology can enable computers to recognize and read text efficiently and accurately, thereby can improve people's work efficiency. Using Residual Neural Network algorithm, based on Matlab his paper proposes a handwritten Chinese character recognition method. The system can recognize handwritten Chinese character information on the GUI interface, and has been verified to have a high recognition rate. The handwritten Chinese character recognition system can obtain Chinese character images through two methods: data set and tablet handwriting. This Chinese character recognition method is widely used in sorting express delivery automatically, reviewing report, inputting text automatically, and grading test papers.
Improved White-box SM4 Design and Implementation

SM4, as a block cipher standard in China, has received extensive attention since the date of its publication. Its implementation correctness and security have rapidly become a research hotspot. With the popularity of cryptographic algorithms in network security, attack methods such as key whitening attack, software static analysis, and reverse engineering in the software implementation environment are also constantly developing. In order to resist these attack methods, a white-box implementation of the key in the cryptographic algorithm is required. Common white-box implementation schemes add a large number of linear or nonlinear transformations, which greatly reduces the implementation efficiency while enhancing the security. In this paper, we optimized the white-box design method of Xiao-Lai SM4 algorithm. According to its structural characteristics, we reduced one affine transformation and increased nonlinear encoding without disclosing the relevant information of intermediate data. Also, our work distinguished the implementation of the first, intermediate and last rounds. We operated the simulation implementation, and the implementation efficiency is significantly improved. Then the white-box diversity and white-box ambiguity in the optimized scheme are calculated, which proves the security of the optimized algorithm. After the correctness and implementation efficiency tests, the encryption speed is improved by 18.6% and the decryption speed is improved by 20.8% compared with the original scheme.
Aircraft Remote Sensing Image Target Detection Based on Improved Mask-RCNN Algorithm

In this paper, an improved Mask-RCNN algorithm is proposed for target detection of airplanes in remote sensing images. The method introduces multi-scale feature fusion and attention mechanism to improve the accuracy and robustness of target detection. By conducting experiments on DOTA dataset and comparing with other similar algorithms, the experimental results show that the algorithm achieves an average detection accuracy of 91.22% with a small number of additional parameters and an FPS of 22.56. This indicates that the improved Mask-RCNN algorithm has excellent performance and generalization ability for aircraft detection in remote sensing images.
A Lightweight Object Tracking Method Based on Transformer

Transformer is widely used in the field of object tracking by virtue of its powerful global modeling capability, but the existing Transformer-based object tracking method is slow due to the large number of parameters, which limits the use of the algorithm on mobile smart devices, in order to reduce the number of parameters to further improve the tracking speed, a lightweight object tracking based on Transformer is proposed. Transformer-based lightweight object tracking method is proposed. First, in the feature extraction part, the first three layers of Swin-Transformer network are used to extract deep features from the input initial template, dynamic template, and search area, respectively. Second, in order to make full use of the initial template information, the extracted features are feature-enhanced with the help of the cross-attention module, and the features after the enhancement of the initial template and the dynamic template are sparsified by the sparsification module. Then, the fused features are fed into the simplified encoder and decoder modules for feature fusion. Finally, the output features go through a bounding box prediction header to predict the tracked object and a quality scoring header to determine whether to update the dynamic template. The algorithm is experimented on the LaSOT dataset, and the tracking success rate reaches 70.8% and the tracking speed reaches 78.7 FPS. The tracking speed is improved by significantly reducing the number of parameters while guaranteeing the tracking accuracy, which proves the effectiveness of the lightweight tracking method.
Evaluation of the Blended Learning Program at SD Negeri 1 Baturan Klaten

The Covid-19 pandemic has hampered learning while at the same time making teachers and students learn to carry out online learning. There are obstacles in the implementation of online learning such as the difficulty for teachers to engage active students and decreased student achievement. To overcome this, there is a need for learning innovation by implementing blended learning. The blended learning program is a learning model that combines face-to-face learning with online learning. The purpose of this study was to evaluate the blended learning program at SD N 1 Baturan and to find out the supporting and inhibiting factors in implementing blended learning. The method used in this research is a case study with a qualitative approach. Data collection techniques are carried out by observation, interviews, and documentation. The results of the evaluation show that the blended learning program at SD N 1 Baturan has been implemented well even though it has several obstacles and there are several supporting and inhibiting factors for the blended learning program. Supporting factors include the school environment, government, and teaching staff, while inhibiting factors include facilities and infrastructure, students' technological literacy, and face-to-face time limitations.
On improving the professional training of future biology teachers

Relevance. Higher education is designed to transfer to the student the knowledge, skills, and abilities in a particular field, promote the formation of humanistic values, broaden the student’s horizons, develop his/her ability to make creative solutions, and practice self-study. Professional training of future biology teachers shifts the focus to upbringing, developmental functions of education, the formation of a future teacher's personality, his/her holistic spiritual and moral development, high psychological stability, and readiness for the profession. Purpose. The study aimed to develop and substantiate the professional training of future teachers when studying pedagogical, methodological, and natural disciplines in pedagogical universities to promote the development of cognitive and professional interest of graduates, the formation of deep and persisting knowledge, and activate their professional self-determination. Methodology. We have identified the main directions for the formation of professional qualities of students in the upbringing and training process and during production practice (visits to the best schools; conducting lectures, practical and laboratory classes with the elements of profiling; the use of professional literature, elective courses, etc.). Results. Professional training of future biology teachers allows the subject of activity to reach the level that is legally required and sufficient for pedagogical activity. Conclusions. A 21st-century teacher should possess the following qualities: reflectivity in thinking and practical activity; tolerance; affection, love, tenderness; interest in technology and digital information; global awareness.
Identification of Zones of Hydrothermally Altered Rocks Using WorldView-2 Data at the Talman Site (Talmanskaya Area, South-Eastern Transbaikal, Russia)

The work is aimed at the practical application of satellite imagery data for the selection of promising areas for geological exploration in conditions of medium-low mountain relief and a sharply continental climate. The results of processing and analysis of WorldView-2 data within the Talmanskaya area are presented in order to identify zones of hydrothermal-metasomatic changes in rocks that are promising for the identification of gold-polymetallic mineralization. The choice of the study area is due to sufficient geological knowledge and the absence of technogenic formations that affect the result of processing satellite imagery materials. To increase the spectral information content of the WorldView-2 data, the spectral channel ratio method was used, as a result of which a pseudo-color RGB composite was created that displays the spectral characteristics of objects on the Earth’s day surface, in particular, minerals of the oxide/hydroxide group containing transitional iron ions (Fe3+ and Fe3+/Fe2+). Comparison of the results of processing satellite data along with geological information made it possible to identify spectral anomalies as indicators of the presence of near-ore changes, which are an important search criterion for hydrothermal deposits.
Application of the Landsat-8 Data Set and the Digital Elevation Model SRTM to Prediction Gold-Polymetallic Mineralization to the Central Part of the Malouralskaya Zone, the Polar Urals

For the first time for the central part of the Malouralskaya zone of the Polar Urals, a new approach to processing data from remote sensing of the Earth was applied. The data were obtained using the Landsat-8 spacecraft. It consists in integration hydrothermal alteration propagation patterns and lineament density maps. They are based on the results of statistical processing of remote sensing data and digital elevation model SRTM (The Shuttle Radar Tpography Mission). The work was carried out in order to identify morphological features and patterns, features of the deep structure and identify promising areas of localization of gold-polymetallic mineralization in the study area. As a result of the study, it was found that areas promising for the gold-polymetallic type of mineralization in the central part of the Malouralskaya zone are localized within trans-regional fault zones, crossing favorable horizons and structures, and controlling ore mineralization, morphostructures, complicated by radial discontinuous faults of the 1st order NE and NW direction with a length of up to 30 km, as well as zones of increased indices II and III, less often hydroxide-(Al–OH, Mg–OH) and carbonate-containing minerals.
Territory Suitability Assessment for Conducting Detailed Geological and Mineralogical Mapping Based on Statistical Methods of Remote Sensing Data Processing Landsat-8: A Case Study in the Southeastern Transbaikalia, Russia

The work considers the suitability of using multispectral satellite remote sensing data Landsat-8 for conducting regional geological and mineralogical mapping of the territory of south-eastern Transbaikalia (Russia) in conditions of medium- low-mountain relief and continental climate. The territory was chosen as the object of study due to its diverse metallogenic specialization (Au, U, Mo, Pb-Zn, Sn, W, Ta, Nb, Li, fluorite). Diversity in composition and age of ore-bearing massifs of intrusive, volcanogenic and sedimentary rocks are also of interest. Statistical processing algorithms to increase spectral information content of satellite data Landsat-8 were used; they include: principal component analysis (PCA); minimum noise fraction (MNF) and independent component analysis (ICA). Eigenvector matrices analysed on the basis of statistical processing results and two-dimensional correlation graphs were built to compare thematic layers with geological material classes: oxide/hydroxide group minerals containing transition iron ions (Fe3+ and Fe3+/Fe2+); a group of clay minerals containing A1–OH and Fe, Mg–OH; minerals containing Fe2+ and vegetation cover. Pseudo-coloured RGB composites representing the distribution and multiplication of geological materials classes was generated and interpreted. Integration of informative thematic layers with using fuzzy logic model was carried out to construct a prospectivity map. Received map was compared with geological information, and positive conclusions about territory suitability for further remote mapping research of hydrothermally altered zones and hypergenesis products in order to localize areas promising for identifying hydrothermal-metasomatic mineralization were made.
Application of the Model of a Spatially-Hierarchical Quadtree with Truncated Branches to Improve the Accuracy of Image Classification

The article considers the problem of increasing the accuracy of the classification of satellite image samples under the assumption that their samples are independent. The increase in accuracy was achieved by additional processing of the image as a spatially hierarchical quadtree, which is a type of random Markov field. A modification of this model was proposed – a spatially hierarchical quadtree with truncated branches. For the original and modified models, we compared the classification results of a real radar image characterized by a large amount of noise. The classification accuracy was estimated as the proportion of correctly classified pixels within the selected homogeneous areas. It has been established that within the framework of the modified model, homogeneous areas of images are more correctly classified by transferring the properties of accumulated images of the same region to them. The modified model makes it possible to obtain a classification result of higher accuracy than the original one when processing noisy images, while having less resource intensity.
Satellite-Based Mapping of the Negative Impact of Gold Mining Enterprises on the Natural Environment of the Cryolithozone (On the Example of the Magadan Region)

Gold mining at ore and alluvial deposits causes a substantial negative impact on the natural environment, in particular land degradation and contamination of watercourses with suspended solids. In this study, we consider a methodology for identifying and mapping the negative impact of gold mining enterprises on the natural environment based on a long-term series of free-available Landsat and Sentinel-2 satellite images. The study was carried out on the example of Tenkinsky, Susumansky and Yagodninsky districts of the Magadan region, where the largest gold deposits are located. Identification features of active mining areas, as well as abandoned ones (on which vegetation began to recover), have been found on satellite images. Based on expert interpretation of the images and NDVI analysis, it was found that about 2% of the study area was affected by gold mining. The processes of vegetation recovery were identified only on 10% of the degraded lands. In the Tenkinsky district, the area of disturbed lands for the period 2001–2021 increased by more than 7 times, which is associated with a substantial increase in gold mining. Using the C2RCC processor (module of the SNAP software package), the content of suspended solids in the water of the most impacted rivers Berelekh, Ayan-Yuryakh and Kolyma, was estimated in comparison with natural values (typical for non-contaminated water). We found that the main source of suspended matter in the rivers is the alluvial gold deposits located in the floodplain of the Berelekh river. At the same time, the seasonal variability of water contamination is determined by hydrological situation. In particular, water turbidity decreases during low water periods and increases during high-flow periods.
Forecasting of Gold-Silver Mineralization within the Pepenveem Ore Zone of the Chukchi Peninsula Based on Space Surveys of Resource, Canopus and ASTER

When deciphering the materials of space sensing of the eastern part of the Chukchi Peninsula on a scale of 1 : 50 000, structural elements of tectonic-volcanogenic genesis were revealed, represented by linear, arc, annular, zonal-concentric structural elements of various ranks. Ring structures with a diameter of up to 2 km and their dissecting disjunctiva mainly of the north-eastern and submeridional strike control the localization of gold mineralization of the Pepenveem ore-placer zone. On the basis of computer processing of the ASTER infrared satellite image in the Pepenveem zone, the areas of development of hydrothermally altered rocks (berezites, secondary quartzites, sericite-hydrosluidic-quartz metasomatites) genetically associated with mineralization were identified. The use of high-resolution satellite imagery in conjunction with geophysical data made it possible, on a scale of 1 : 10 000, to identify gold-prospective areas associated with volcanic dome structures, which are characterized by an increased density of lineaments and are associated with the formation of subvolcanic rhyolites.