Reviewerly/exHarmony huggingface dataset:
Forensic authenticity examination of PDF documents

With the rapid development and widespread popularization of information technology, Portable Document Format (PDF) documents have gradually become a type of digital files that are easily accessible and closely related to the public. In digital forensics, digital files in the form of PDF documents are often encountered, and their authenticity needs to be determined. Therefore, the research on its key forensic techniques has important theoretical research significance and practical application value. However, through searching the literature, we can find that there is still a lack of systematic research on forensic authentication of PDF documents. Based on the above situation, this paper first studies the file structure and digital composition of PDF documents; The digital data characteristics and the contents for examination of PDF files produced by the mainstream scenarios are studied, including PDF generated by scanners, converting directly from images, and transforming from DOCX documents, etc. Finally, a case study is carried out to explain the key technologies and examination contents for authenticity examinations of PDF documents in detail. Our study will provide theoretical basis and practical guidance for forensic investigations of authenticity examinations of PDF documents.
State-of-the-art video recognition of human action under natural scenes

Human action video recognition focuses on discussing the theories and new algorithms to make computers understand the meanings of variety videos, such as those from TV, sports or family videos, and recently it plays an important role in advanced human-machine interface, security monitor, video retrieval and so on.The step-by-step process of traditional computational algorithm for human action recognition from videos are described in this paper, and some important deep learning models are also addressed by which some advantages and disadvantages of each approach in different tasks are analysed.Finally, some current trends and prospects of video recognition models are concluded.
User experience evaluation methods and models for software products

Software can be improved through user experience metrics, so it is very important to choose the right measurement method. Through the desktop research and other research methods, the user experience research methods and model results are summarized. With software A and B as the research object, according to the product form and the characteristics of the business, the user experience evaluation model commonly used in the industry is used to measure the two software products by means of usability test and questionnaire data from the various attributes and indicators of the evaluation model, so as to the rationality and feasibility of the evaluation model and provide Suggestions for improvement as the research object.
Design of Intelligent Question Answering System for Hospital Online Triage based on Knowledge Graph

At present, people pay more and more attention to medical and health issues, and more and more Internet users are eager to obtain medical related knowledge from the Internet. However, despite the large amount of medical data spread on the Internet, it is difficult for ordinary Internet users to effectively obtain and use these data through search engines. To solve this problem, this paper designs and implements a medical question answering system based on knowledge map, so that users can quickly obtain the relevant knowledge in the medical field they want by asking questions, and realize the effective use of medical data on the Internet. At present, there are few question answering systems in the medical and health field, so the medical question answering system designed in this paper has a broad application prospect. The medical knowledge map constructed in the system can also be used in more application scenarios such as medical chat robots in the future, further promoting the process of intelligence in the medical field.
A convolutional neural network based on optimized structure and its lightweighting

In this paper, we design a convolutional neural network based on the ideas of depthwise separable convolution and inverted residual module. The scaling factor of BN layer is used as a measure for channel pruning of the network model to compress it. By analyzing the layer-by-layer pruning process of conventional convolution, the layer-by-layer pruning method with depthwise separable convolution and inverted residual structure is proposed to prune the channels of the network model, and finally, the channel pruning strategy of classification simplification network is developed. Tests on the selected dataset showed that the classification accuracy of the pruned and fine-tuned network model is 97.7% when the pruning rate is 0.7.
Design of intelligent Japanese teaching system based on web

The rapid economic growth has promoted the development of intelligent technology, which has been widely used in various fields, including education, and many intelligent teaching systems with good performance and sound functions have been developed. Through the application of this system, not only the teaching effect has been improved, but also the students have more knowledge, and the workload of teachers has been reduced, so that teachers have more time and energy to prepare for teaching. Therefore, this paper takes Japanese teaching as the research object and web technology as the core, and designs an intelligent Japanese teaching system to provide support for further improving Japanese teaching effect.
Human Action Recognition of Triangle Mesh Sequence Representation

Considering the existing research works of human motion recognition are based on skeleton and video representations, a human motion classification method for triangle mesh sequence representation is proposed. Firstly, selecting the first frame of the triangle mesh sequence as the template model, the difference between each subsequent frame of the sequence and the template model is calculated by using the shape difference operator, which is expressed as the shape difference information tensor; Then, the shape difference information tensor is input into the deep network composed of two-dimensional convolution network and long short-term memory network to extract sequential action features for human action classification. The experimental results show that the classification accuracy of this method on human motion dataset AMASS reaches 100.00%.
Bi-Directional Human Pose Completion Based on RNN and Attention Mechanism

To tackle the occlusion problems between pedestrians in surveillance videos, a human pose sequence completion method is proposed. The method can generate the missing poses in the middle when poses before and after them are visible. The method includes the following steps: (1) A sequence-to-sequence model based on attention mechanism is used to generate the target pose sequence in the middle by taking the visible poses at both ends as input. (2) The same sequence-to-sequence model based on attention mechanism is used to generate the target pose sequence again, but in a reverse direction. (3) The two-direction prediction results are mixed together to obtain the final target pose sequence. The proposed method handles well the poses recovery problem due to occlusions when the poses before and after the target ones are known. The proposed method is tested on Human3.6M, CASIA datasets, etc. The <italic>L</italic><sub>2 </sub>norm distance between the generated poses and their ground truth is used as the evaluation metric. Compared with previous approach, the average poses error is reduced from 81.6 to 42.5, and the performance is increased by 47.9%.
Improved Anomaly Detection by Using the Attention-Based Isolation Forest

A new modification of the isolation forest called the attention-based isolation forest (ABIForest) is proposed for solving the anomaly detection problem. It incorporates an attention mechanism in the form of Nadaraya–Watson regression into the isolation forest to improve the solution of the anomaly detection problem. The main idea underlying the modification is the assignment of attention weights to each path of trees with learnable parameters depending on the instances and trees themselves. Huber’s contamination model is proposed to be used to define the attention weights and their parameters. As a result, the attention weights are linearly dependent on learnable attention parameters that are trained by solving a standard linear or quadratic optimization problem. ABIForest can be viewed as the first modification of the isolation forest to incorporate an attention mechanism in a simple way without applying gradient-based algorithms. Numerical experiments with synthetic and real datasets illustrate that the results of ABIForest outperform those of other methods. The code of the proposed algorithms has been made available.
The Disappearing Author: New Challenges to the Law of Copyright Brought by the Development of Artificial Intelligence

Artificial intelligence (AI) products have brought a huge impact on the law of copyright. When answering the question whether AI products can be granted copyright, the first point to consider is the legal subjectivity and authorship of AI products. Concurrently, the scientific and technological nature of AI led to great complexity to the classification of the products, giving rise to the uncertainty in the definition of the copyright of AI products. Although many countries have carried out legislation and planning in various aspects, there are still a great number of risks in this field. The difficulty of AI product specification not only comes from its being a relatively new area, but also from its challenge to the traditional concepts and laws of the past society. As the future development trend, AI should be improved in legal aspects on the basis of the continuous rapid development of technology and policy. In addition to legislation, efforts should also be made on neighboring rights and policies. The determination of AI copyright issue is significant and urgent to be solved, which is an area worthy of attention.
Calibration of a Stereoscopic Vision System in the Presence of Errors in Pitch Angle

This paper proposes a novel method for the calibration of a stereo camera system used to reconstruct 3D scenes. An error in the pitch angle of the cameras causes the reconstructed scene to exhibit some distortion with respect to the real scene. To do the calibration procedure, whose purpose is to eliminate or at least minimize said distortion, machine learning techniques have been used, and more specifically, regression algorithms. These algorithms are trained with a large number of vectors of input features with their respective outputs, since, in view of the application of the procedure proposed, it is important that the training set be sufficiently representative of the variety that can occur in a real scene, which includes the different orientations that the pitch angle can take, the error in said angle and the effect that all this has on the reconstruction process. The most efficient regression algorithms for estimating the error in the pitch angle are derived from decision trees and certain neural network configurations. Once estimated, the error can be corrected, thus making the reconstructed scene appear more like the real one. Although the authors base their method on U-V disparity and employ this same technique to completely reconstruct the 3D scene, one of the most interesting features of the method proposed is that it can be applied regardless of the technique used to carry out said reconstruction.
Training a Logic Dendritic Neuron Model with a Gradient-Based Optimizer for Classification

The logic dendritic neuron model (LDNM), which is inspired by natural neurons, has emerged as a novel machine learning model in recent years. However, recent studies have also shown that the classification performance of LDNM is restricted by the backpropagation (BP) algorithm. In this study, we attempt to use a heuristic algorithm called the gradient-based optimizer (GBO) to train LDNM. First, we describe the architecture of LDNM. Then, we propose specific neuronal structure pruning mechanisms for simplifying LDNM after training. Later, we show how to apply GBO to train LDNM. Finally, seven datasets are used to determine experimentally whether GBO is a suitable training method for LDNM. To evaluate the performance of the GBO algorithm, the GBO algorithm is compared with the BP algorithm and four other heuristic algorithms. In addition, LDNM trained by the GBO algorithm is also compared with five classifiers. The experimental results show that LDNM trained by the GBO algorithm has good classification performance in terms of several metrics. The results of this study indicate that employing a suitable training method is a good practice for improving the performance of LDNM.
Use of Mobile Phones in Classrooms and Digitalisation of Educational Centres in Barcelona

In the wake of the COVID-19 pandemic, multiple educational contexts experienced a sudden and accelerated digital transformation. However, this is not a new phenomenon. For years, public and private initiatives have been designed and tested in Spain. In this regard, the role and use of cell phones in the classroom has been a key and, at the same time, controversial aspect. In Barcelona (Catalonia), for example, recent educational policies have promoted the pedagogical use of cell phones. Within this framework, this article analyses whether these initiatives to promote the use of mobile phones are effectively transferred and implemented in the classroom. Using qualitative research, based on co-design, case studies and content analysis, we examined the reality of three educational centres in Barcelona. In these three contexts, field observations, interviews with management teams and ICT coordinators, and discussion groups with teachers were conducted. The information generated was grouped into five main categories of analysis. As a result, it was observed that the mobile phone has been losing prominence in the classroom. Schools tend to prohibit the use of cell phones and prefer computers to give priority to the control of technological tools in order to use the Internet safely. Mobile phones, in this sense, are only used at certain times when there is a pedagogical objective, although there is still a need for more pedagogical and digital training for teachers.
What are the requirements of interactive learning media? : Needs analysis to develop “MeetLearnHope” on parabolic motion

This study aimed to analyze the needs of teachers in the field of physics learning to develop multiple representation learning material application with hybrid combined pictures (MeetLearnHope) on parabolic motion concept. Recent research shows many studies about the importance of multiple representations in learning physics and also the benefit of using interactive multimedia. Thus, this would be meaningful and necessary to study what stuff are requested when we want to develop an interactive multimedia in learning parabolic motion concept. The participants including in this research were 25 physics teachers (4 male and 21 female). The descriptive-qualitative method was used so we got the result as the majority teachers of physics require an interactive multimedia that contains some essentials features, e.g., simple main concept explanation (92%), nature phenomenon about the concept (100 %), sample questions, and solutions (96%) but 68 % respondent said the interactive media should be simple e.g. doesn’t need any exaggerate music and user interface interactions. Overall, we can conclude that developing this interactive multimedia to improving learning motivation when learning about parabolic motion is needed.
Multi-Objective Optimal Scheduling of Reservoirs Integrating the Internet of Things and Deep Learning Models

With the continuous upgrading and expansion of the concept and technology of the Internet of Things (IoT), the production methods of many traditional industries have undergone earth-shaking changes. IoT and Differential Evolution Algorithm (DEA) are integrated into the deep learning model for multi-objective optimal scheduling of reservoirs. Firstly, based on IoT, a multi-objective optimal dispatching system and a wireless sensor network module for the reservoir are designed, which are mainly used to collect and process data. Secondly, a constraint system that comprehensively considers the reservoir water balance and other aspects are proposed. A certain reservoir group is taken as the research object, combined with the collected information of the reservoir groups in previous years. The ecological dispatch is studied to ensure the ecological flow of 60m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> /s, and the preparation and calculation of the monthly and ten-day water dispatch plans for normal and low water levels are realized. The dispatching results confirm that the ecological dispatching scheme of this reservoir group has high operability and reliability in practice.
Secure Sharding Scheme of Blockchain Based on Reputation

Nowadays, the implemetation scheme of blockchain system generally has the defect of insufficient saclability, which can not meet the needs of large-scale applications, and imped its development. Sharding technology is regarded as the most likely solution to the current blockchain bottleneck, it divides the nodes of the blockchain network into each shard, which can maximize the transaction processing capacity of the system. In a sharding scheme, the blockchain is split into several blocks, networks with fewer nodes are created, so the reliability of the nodes in the network becomes critical. In this work, a blockchain sharding scheme based on Verifiable Random Functions(VRF) and node reputation is proposed. The nodes are randomly assigned to each shard by VRF, and the nodes are encouraged to work actively through the reputation score of the in-shard nodes. At the same time, the aggregation of malicious nodes is cotrolled to reduce the probability of fragmentation deterioration and influence of the whole blockchain.
Deep Learning Model Development for Detecting 22 kV Line-Post Insulator Faults

In electrical power distribution systems, the most common insulators are used. Power outages can be caused by insulator damage in a variety of ways. The purpose of this study is to develop a deep learning model for detecting 22 kV line-post insulator faults. To investigate image sizing, the quantity of datasets, viewing points, and the number of appropriately trained individuals, the training datasets were created on IDE Roboflow. Moreover, the model was created by YOLOv5 on Google Colab. The images of insulators were divided into two groups including normal and faulty insulator. These insulator images were collected from the different point of view including on the floor and on the distribution line. The image size employed was 416x416 pixels, which corresponded to 148 real images and 251 augmentation images. The experiment defined different batch sizes with Epoch counts ranging from 50 to 300. The results demonstrated that the developed model could detect cracks in insulators mounted on the distribution lines in various spots.
A Moving Window Based Approach to Multi-scan Multi-Target Tracking

Multi-target state estimation refers to estimating the number of targets and their trajectories in a surveillance area using measurements contaminated with noise and clutter. In the Bayesian paradigm, the most common approach to multi-target estimation is by recursively propagating the multi-target filtering density, updating it with current measurements set at each timestep. In comparison, multi-target smoothing uses all measurements up to current timestep and recursively propagates the entire history of multi-target state using the multi-target posterior density. The recent Generalized Labeled Multi-Bernoulli (GLMB) smoother is an analytic recursion that propagate the labeled multi-object posterior by recursively updating labels to measurement association maps from the beginning to current timestep. In this paper, we propose a moving window based solution for multi-target tracking using the GLMB smoother, so that only those association maps in a window (consisting of latest maps) get updated, resulting in an efficient approximate solution suitable for practical implementations.
Deep Learning-Based 3D Face Recognition Using Masked Facial Point Cloud

Face recognition is widely used across the world as a reliable authentication system. Mainly, it can be divided into two types based on input data, 2D and 3D. One works with images and the other with various data formats like depth maps, point clouds, etc. to name a few. Image-based Face recognition is fairly accurate but it has its limitations like dependence on light, failure with pose and occlusion variation, etc. 3D data like point clouds are generated by LiDARs, these are multiple points (x, y, z) representing an object/face. The main advantage of data acquired by a LiDAR is it doesn't depend on ambient light and captures fairly accurate depth data. Unfortunately, irregular data organization of 3D point clouds requires unique algorithms. Here, we present the use of PointNet++ for 3D face recognition. This model achieves an accuracy of 98.9% on the Casi a3D dataset and 98 % on the Bosphorus. Also, we show the results of this model on masked face recognition (using partial face data). We present an efficient hardware design and implementation of this algorithm for inference using High-Level Synthesis on the Xilinx Zynq ZC-706 board.
Object level frustum culling based frame rate acceleration method

Frame rate is an important index to evaluate the performance of simulation systems. The higher it is, the more intuitive and realistic the simulation application will be. The terrain database rendering process in the simulation field takes a lot of time and hardware resources. Therefore, optimizing it leads to a significant speedup of the simulation system. The frustum culling method is very helpful in avoiding computations of things that are not visible. In this method, instead of sending all information to the graphics processing unit, visible and invisible elements will be sorted, and only visible elements will be rendered. This paper presents an effective frustum culling technique at object level. In the proposed method, the rendered region was frustum culled by objects, so the number of objects to be rendered in each frame was significantly reduced. The purpose of the study is to improve the rendering performance of terrain data in the Unity 3D framework. Experimental results have shown that the given method is very competitive.