Reviewerly/exHarmony huggingface dataset:
Human Instance Segmentation

Currently, the more popular instance segmentation frameworks are based on powerful object detection methods, such as Faster R-CNN, Mask R-CNN, etc. Although they use different methods and different structures, these methods are roughly similar: first generating multiple regions proposal in the image, and then using the non-maximum suppression algorithm (NMS) to delete the regions proposal that do not meet the requirements in the generated regions proposal. However, when there are two highly overlapping objects in the image, the NMS will treat the bounding box of one of them as a duplicate candidate area and delete it. This means that these algorithms cannot distinguish between two highly overlapping objects. However, in instance segmentation, people are a special category, and we can use human bones to segment people. Also in the example is divided, an example of the classification confidence score is used as a mask quality divided frame most instances. However, it is quantified as IoU mask quality is usually not a good correlation between the scores and classification instance masks its basic facts. Through this problem, we propose a new module scoring module (Evaluation), using the scoring module to combine instance features with corresponding masks, which can improve the quality of instance segmentation.
Evaluación de un clasificador de textos digitales basado en el contenido semántico a través de ontologías

Nowadays, the generation of information through digital text documents has increased exponentially, so there is a need to store documents in mass storage devices such as high capacity hard discs, storage servers, the cloud and others. However, the storage that is carried out lacks a thematic organization, therefore, a search for information becomes complex. Given this problem, this publication describes the development of a system that has the purpose of classifying a digital text document based on the thematic content. This system implements ontologies to achieve a better classification by taking advantage of its characteristics. The system is divided into five tasks: the first is the implementation of a word count to create a frequency vector; The second task performs a refinement on the frequency vector to eliminate the sentence connectors and prepositions; the third task orders the vector from the highest to the lowest frequency; the fourth task takes the most significant set of frequencies vector, in which the ontology of a domain is applied and the relation that the words have to determine the thematic of the document is sought; and the fifth task is to organize the documents in a folder structure based on the identified domains. The system was developed with the incremental development methodology. To validate the operation of the system, a set of tests was carried out in a controlled scenario in order to verify the correct classification of the documents.
Current Issues of Science, Education and Development Integration

nThe existing process of integration in the socio-economic, cultural and educational spheres of the world, as well as in the field of science, leads them to globalization in all directions. In recent years, the expansion and deepening of cooperation in education, science and industry in our country has led to a broader study of the concepts of innovation and integration in our lives and a more scientific approach.
A Radar and Monocular Camera-based Fusion Approach for Pedestrian Detection

Since single sensor has its own shortcomings in pedestrian detection, fusion detection of using radar and camera sensor is currently an effective solution for unmanned driving. This paper proposes a new strategy based on the fusion of radar and camera. First, Kalman filter is used to filter out the invalid data and measurement noise in the radar measurement process, and the radar candidate rectangle is generated based on the target distance and the calibration of the radar/camera sensor. For the camera, extract the foreground information in the video frame and use the information about human area and aspect ratio to screen out suitable pedestrian motion rectangle. Finally, the features are extracted from the candidate rectangle fused by radar and camera and the optimized XGBoost classifier is apply to implement pedestrian recognition. The experimental results show that the detection time of pedestrian after fusion is reduced, and the average precision is increased by 19.41%.
Quantitative evaluation model of desensitization algorithm

Today, with the explosive growth of data volume, data has become an increasingly important strategic resource for various enterprises, but Big Data not only facilitates human society, but also brings huge privacy leakage problem. Because the data in business production often contains a large amount of user sensitive privacy information, in order to avoid the privacy leakage of these data in the test system and other environments, enterprises begin to pay more attention to desensitization technology. Because desensitization is mainly used in the process of software development in the business field, and is often used to generate internal test data, most of the results are classified or protected by patents. In addition, the evaluation of desensitization algorithm mostly stays at the level of general and qualitative evaluation. At present, there is no intuitive, accurate and universal quantitative evaluation standard. In this paper, aiming at the pain point of desensitization evaluation model, a data oriented quantitative desensitization evaluation model based on association rule mining method is designed. The model does not need to understand the desensitization algorithm design process, but only faces the data set before and after desensitization, which reduces the risk of desensitization algorithm leakage. In addition, the model also covers the common fields of various industries, and accurately and intuitively describes the availability of desensitization data in the subsequent test environment in the form of quantitative numbers, which provides an innovative idea for the unification of desensitization algorithm evaluation standards.
Automatical Enhancement and Denoising of Extremely Low-light Images

Deep convolutional neural networks (DCNN) based methodologies have achieved remarkable performance on various low-level vision tasks recently. Restoring images captured at night is one of the trickiest low-level vision tasks due to its high-level noise and low-level intensity. We propose a DCNN-based methodology, Illumination and Noise Separation Network (INSNet), which performs both denoising and enhancement on these extremely low-light images. INSNet fully utilizes global-ware features and local-ware features using the modified network structure and image sampling scheme. Compared to well-designed complex neural networks, our proposed methodology only needs to add a bypass network to the existing network. However, it can boost the quality of recovered images dramatically but only increase the computational cost by less than 0.1%. Even without any manual settings, INSNet can stably restore the extremely low-light images to desired high-quality images.
HFGCNET: High-Frequency Graph Reasoning for Finer Semantic Image Segmentation

Semantic segmentation is a fundamental task in computer vision and image processing. Although existing methods based on the fully convolutional network (FCN) have greatly improved the accuracy, it still does not show satisfactory results on tiny objects and boundary regions. One of the problems is that the current FCN-based methods ignore details such as the image’s contours and edges because of over downsampling operations in the CNN encoder backbone. In signal processing, excessive down-sampling will incur spectrum aliasing, thus losing high-frequency details. This work presents a high-frequency graph convolution operation to solve the above problems. Traditional image processing generally uses the high-pass filter to extract image contours. We accordingly suppose that the high-frequency information is vital for the extractions of boundary clues and details. We implement our strategy and conduct experiments on the Cityscapes dataset, which demonstrate the effectiveness of our high-frequency graph convolution block on semantic segmentation. The proposed method achieves comparable performance and dramatically improves the performance of small objects like the rider, traffic signs, etc.
A Comparative Study on Recognition of Degraded Urdu and Devanagari Printed Documents

So far, distinguishing proof of many significant scripts has been accomplished for fine printed documents. Without a doubt, many degraded scripts exist that has gotten no evident consideration particularly in Urdu and Devanagari. This paper examines the sorts of degradation and strategies used to evacuate those degradation in printed reports. Further feature extraction and classification techniques are talked about to finish up which strategy can give better outcomes for Urdu and Devanagri archives.
Adaptive Importance Sampling Via Auto-Regressive Generative Models and Gaussian Processes

The quality of importance distribution is vital to adaptive importance sampling, especially in high dimensional sampling spaces where the target distributions are sparse and hard to approximate. This requires that the proposal distributions are expressive and easily adaptable. Because of the need for weight calculation, point evaluation of the proposal distributions is also needed. The Gaussian process has been proven to be a highly expressive non-parametric model for conditional density estimation whose training process is also straightforward. In this paper, we introduce a class of adaptive importance sampling methods where the proposal distribution is constructed in a way that Gaussian processes are combined autoregressively. By numerical experiments of sampling from a high dimensional target distribution, we demonstrate that the method is accurate and efficient compared to existing methods.
Unmanned vehicles scheduling system with data fusion technology

Unmanned transportation mode can save a lot of human resources and economic costs. It has a wide range of application scenarios and good development prospects. In this paper, we design an unmanned vehicles scheduling system that combines data fusion technology in order to realize unmanned transportation mode. We also optimize the vehicle routing algorithm to improve the efficiency of unmanned transportation. The system will synthesize speed, economy, safety to generate the optimal transportation scheme. We evaluate the real-time performance of the system, and the results meet the requirements of data fusion transmitted by multiple information sources.
Weakly Supervised Geodesic Segmentation of Egyptian Mummy CT Scans

In this paper, we tackle the task of automatically analyzing 3D volumetric scans obtained from computed tomography (CT) devices. In particular, we address a particular task for which data is very limited: the segmentation of ancient Egyptian mummies CT scans. We aim at digitally unwrapping the mummy and identify different segments such as body, bandages and jewelry. The problem is complex because of the lack of annotated data for the different semantic regions to segment, thus discouraging the use of strongly supervised approaches. We, therefore, propose a weakly supervised and efficient interactive segmentation method to solve this challenging problem. After segmenting the wrapped mummy from its exterior region using histogram analysis and template matching, we first design a voxel distance measure to find an approximate solution for the body and bandage segments. Here, we use geodesic distances since voxel features as well as spatial relationship among voxels is incorporated in this measure. Next, we refine the solution using a GrabCut based segmentation together with a tracking method on the slices of the scan that assigns labels to different regions in the volume, using limited supervision in the form of scribbles drawn by the user. The efficiency of the proposed method is demonstrated using visualizations and validated through quantitative measures and qualitative unwrapping of the mummy.
Multiple secrets sharing scheme based on Eigenvalue

Currently, relevant secret sharing schemes are mainly extended based on Shamir's threshold secret sharing scheme and Blakley's vector scheme. In this paper, we extend the threshold secret sharing scheme based on the eigenvalue proposed by Zhang Yanshuo et al, use the diagonalized matrix with different invertible matrices, distribute the square matrices similar to the diagonal matrix to different participants as their sub-secret shares. In this scheme, multiple secrets can be shared in each secret sharing process. The analysis results show that the scheme is correct.
Graph Spectral Feature Learning for Mixed Data of Categorical and Numerical Type

Feature learning in the presence of a mixed type of variables, numerical and categorical types, is important for related modeling problems. In this work, we propose a novel strategy to explicitly model the probabilistic dependence structure among the mixed type of variables by an undirected graph. The dependence structure among different pairs of variables are encoded by a suitable mapping function to estimate the edges of the graph. Spectral decomposition of the graph Laplacian provides the desired feature transformation. We numerically validate the implications of the feature learning strategy on various datasets in terms of data clustering.
Localization Technology in Health Sector: A Study of PCTS in Rajasthan

In any society the most susceptible and at risk sections are woman and children and when it comes to health services perspective the problem becomes severe especially in the case of pregnant woman and infants health. The advancement in technology doesnâ€™t fulfil its purpose till it caters to the need of this section in terms of improvement in their lives and better access to services. The pregnancy and child immunization is key of any health system and it is necessary to ensure the time specific and quality services in this context. For this purpose only, Government of Rajasthan developed a tracking system known as pregnancy child tracking and health services management system (PCTS). PCTS was built, designed and developed by the National Informatics Centre as an Integrated system for Health information management system (HIMS). This is a name based tracking system through which a pregnant woman and child will be tracked for ANCs and immunization. The thought behind this is to improve service delivery to the rural areas in the state of Rajasthan, India. This study was conducted to analyse the different tools and techniques used in tracking the mother and child from child tracking and health services management system (PCTS). Main objective of the study was to study and analyse the issues and challenges in the application of PCTS in Rajasthan. The study shows that PCTS has been successful to an extent in providing basic health care services to pregnant woman and child but still there are many issues which need to be addressed to make it more prompt and more comprehensive in approach.
Representing Data Protection Aspects in Process Models by Coloring

Business processes typically operate on personal data, e.g., customer data. This requires compliance with data protection regulations, e.g. the European Union General Data Protection Regulation (GDPR). The modeling of business processes is a widespread methodology to visualize and optimize business processes. This should include also data protection concerns. However, standard modeling languages like the Business Process Modeling and Notation (BPMN) do not offer an adequate notation for expressing data protection aspects. In this paper, we propose a methodology for visualizing privacy concerns in BPMN models. We suggest using colors for marking critical activities and data in the process models. This provides an easy documentation of data protection problems and supports the optimization of processes by eliminating the privacy violation issues.
Research on Distributed Power Planning Based on Supply Equipment Capacity Limitation

Due to the rapid economic development in recent years, the original transformers and other power supply equipment is difficult to meet the demand of the rapid growth of electricity consumption. In order to reduce the cost of power supply, adding photovoltaic/wind power/energy storage system is an effective means to meet the demand of users to achieve regional balance without the overall reform of the system. In this paper, with reference to Ganzhou region under different growth rate of electricity a internal power supply area, considering the three-phase imbalance and photovoltaic (pv) power supply under different weather factors efficiency under different conditions, building composed of piconets and time division at micro network life period earnings are applied to solve the highest net present value as the goal on the premise of micro power grid planning for the next step, meet the increasing social demand of power supply area, improve power supply reliability, provide strong theoretical support. Finally, based on the power supply load of a certain region in Ganzhou, the method is calculated and proved to be effective.
Blockchain-based IoT Consensus Mechanism System

Abstract With the continuous development of the Internet of Things (IoT), the application of blockchain technology to the Internet of Things has become a trend of the times. As we all know, the core of the blockchain lies in the consensus mechanism. In order to apply the blockchain to the Internet of Things, and for subsequent research needs, this research proposes a lightweight, high-throughput consensus mechanism system. Using scalable technology, any number of nodes and gateways can be set up. The experimental results show that the system throughput increases synchronously with the increase of the test load; in addition, when the test load is 800tps, the system throughput reaches the maximum value, which is close to 600tps. When the test load exceeds 800tps, the actual system throughput begins to drop, and about 90% of transactions have a delay time of 5000ms. This blockchain consensus system can be used for the simulation of lightweight IoT systems.
An LDA-Based Approach Towards Word Sense Disambiguation in Malayalam

Word Sense Disambiguation refers to the task of correctly identifying the sense of an ambiguous word in a given context. This is considered as an AI-complete problem in the field of natural language processing. In this paper, an LDA-based WSD model is proposed which, to the best of our knowledge, is the first work that makes use of LDA for disambiguation in Malayalam. We had selected three ambiguous words and manually collected sentences including those from different novels, short stories, and web documents. The corpus consists of more than 200 instances of each ambiguous word. The model performed well compared to the state-of-the-art WSD systems in Malayalam.
Targets of “Constant-holistic Shadow” Space Based on the Features of Different Requirements of Activities in Hot Summer Regions

Abstract When participating in the outdoor activities during the sunny and hot days in the regions with a hot summer, people are looking forward to staying in the “constant-holistic shadow” space which is always in shadow during the period of activities. It is necessary to set the targets of “constant-holistic shadow” space differently based on the different temporal-spatial feature requirements of varied types of activities. This paper has qualitatively compared the coupling degree of the three main factors of crowd outdoor activities in summer, analyzed the varied feature requirements of different activity types for the “constant-holistic shadow” space, and discussed the targets and setting methods of “constant-holistic shadow” space of the three typical activity requirements, so as to provide a basis for setting rational targets of the “constant-holistic shadow” space.
A Multi-head Self-relation Network for Scene Text Recognition

The text embedded in scene images can be seen everywhere in our lives. However, recognizing text from natural scene images is still a challenge because of its diverse shapes and distorted patterns. Recently, advanced recognition networks generally treat scene text recognition as a sequence prediction task. Although achieving excellent performance, these recognition networks consider the feature map cells as independent individuals and update cells state without utilizing the information of their related cells. And the local receptive field of traditional convolutional neural network (CNN) makes a single cell that cannot cover the whole text region in an image. Due to these issues, the existing recognition networks cannot extract the global context information in a visual scene. To deal with the above problems, we propose a Multi-head Self-relation Network(MSRN) for scene text recognition in this paper. The MSRN consists of several multihead self-relation layers, which are designed for extracting the global context information of a visual scene. Then the information of the related cells can be fused by multi-head self-relation layer. Furthermore, experiments over several public datasets demonstrate that our proposed recognition network achieves superior performance on several benchmark datasets including IC03, IC13, IC15, SVT-Perspective.
Support Vector Machine - Based Classification of Wireless Transceivers

The wireless device authentication based on the impairments of radio frequency front-end is a promising method how to increase the physical layer security of future wireless networks. This paper is demonstrating the use of one of the most-used machine learning methods - Support Vector Machines in such an application. Besides sketching the multi-class authentication on an example of data from a set of software defined radios, we also evaluate how is the used classifier sensitive to changes of working temperature during the learning and testing phases.
FPGA Hardware Image Encryption Co-simulation Utilizing Hybrid LFSR and Chaos Maps

This paper introduces a new version of the chaotic map-based image encryption system that uses Xilinx System Generator (XilinxSG) systems. Two (PRBGs) are proposed and named XOR LFSR Fixed Point Chaotic Maps-PRBG (XORLFPCM-PRBG) plus Combined XOR and XNOR for LFPCM-PRBG (XXLFPCM-PRBG) in which LFSR is combined with FPCM to encrypt an image. The Randomized Test Benchmarks at the (NIST) are applied for texting the randomization suggested PRBGs. Also, the security analysis includes the correlation-coefficient, the information-entropy, and the attacks differential (NPCR and UACI) are being used to test the intelligibility of the suggested encryption system. FPGA co-simulation hardware was also provided via FPGA SP605 XC6SLX45T to check the image encryption method of reality. The outcome showed that both XORLFPCM-PRBG and XXLFPCM-PRBG proposed systems are suitable for ciphering images founded on stream cipher an besides surpass about traditional algorithms.