Reviewerly/exHarmony huggingface dataset:
Two Approaches for Recognizing the Structure of Block Diagrams

Introduction. Working with graphic images is an essential element of almost any modern computer-aided design systems. The result of neural network, including deep image processing, can be recognition of the belonging classes of objects present on them. Objects of the real world require large expenditures for the development and implementation of highly specialized computer vision systems. In recent years, there has been an improvement of the quality characteristics obtained in the field of technical vision. This is made possible by artificial neural networks. The article deals with the recognition of a flat, black and white flowchart image. These are two-dimensional images or their selected parts, which are displayed in an arbitrary graphic format by system means on a computer monitor screen. Basic block diagram shapes: rectangle, rhombus, parallelogram, circle, ellipse (oval), etc. The purpose of the article is to solve the problem of graphic image recognition. The systems for the recognition of graphic images include modern systems of computer-aided design, management and document management. The article has formed a basic set of training and test images of block diagram nodes. Neural network models are proposed to improve the detection accuracy of block diagram nodes based on fully connected and convolutional neural networks. Results. The basic procedures of the block diagram image recognition algorithm were partially tested at the software level, and allow us to conclude the effectiveness of the proposed structural methods. A comparative analysis of neural network and syntactic structural approaches for solving this problem is carried out. Conclusions. Two methods of recognizing flat graphic figures and recognizing connections between figures in flowcharts are proposed: a method of recursive traversal of all branches of the tree of the current union of connections between figures and the figures themselves, and also a study was carried out for the created neural network in PyTorch to solve this problem using trained neural network methods. Keywords: image recognition, convolutional neural networks, syntactic analysis.
Reprint of: Using Trellis software to enhance high-quality large-scale network data collection in the field

Trellis is a mobile platform created by the Human Nature Lab at the Yale Institute for Network Science to collect high-quality, location-aware, off-line/online, multi-lingual, multi-relational social network and behavior data in hard-to-reach communities. Respondents use Trellis to identify their social contacts by name and photograph, a procedure especially useful in low-literacy populations or in contexts where names may be similar or confusing. We use social network data collected from 1,969 adult respondents in two villages in Kenya to demonstrate Trellis’ ability to provide unprecedented metadata to monitor and report on the data collection process including artifactual variability based on surveyors, time of day, or location.
Tiny Generative Image Compression for Bandwidth-Constrained Sensor Applications

Deep image compression algorithms based on Generative Adversarial Networks (GANs) are a promising direction to address the strict communication bandwidth limitations commonly encountered in IoT sensor networks (e.g. Low Power Wide Area Networks). However, current methods do not consider that the sensor nodes, which perform the image encoding, usually only offer very limited computation and memory capabilities, e.g. a resource-constrained tiny device such as a micro-controller. In this paper, we propose the first tiny generative image compression method specifically designed for image compression on micro-controllers. We base our encoder on the well-known MobileNetV2 network architecture, while keeping the decoder side fixed. To cope with the resulting asymmetric design of the compression pipeline, we investigate the impact of different training strategies (end-to-end, knowledge distillation) and integer quantization techniques (post-training, quantization-aware training) on the GAN-training stability. On the Cityscapes dataset, we achieve a compression performance that is very close to the state-of-the-art, while requiring 99% less SRAM size, 97% smaller flash storage and 87% less multiply-add operations. Our findings suggest that tiny generative image compression is particularly well suited for application-specific domains.
Graph elements of banknotes of the Republic of China detection and recognition based on deep learning algorithm

Aiming at the problem of banknotes of the Republic of China to be classified in the museum and the difficulty of establishing a banknote database, a small sample of Republic of China banknote graph elements detection and recognition model based on YOLO v4 algorithm was proposed. Created a specific dataset based on images provided by the museum, and enhance difficult data. Extended the application of the target detection algorithm based on deep learning to the field of banknote cultural relics. In our dataset, the mean Average Precision (mAP) of this modal reaches 91.83%, which is improved by 21.12%, 10.65%, 12.70% compared with SSD algorithm, YOLO v3 algorithm, and Faster R-CNN algorithm. The detection time of a single image at 0.37s. Experimental results show that the proposed modal can achieve efficient and accurate positioning detection of the elements of the Republic of China banknote graph. Lay the foundation for the cultural relics sorting, clustering, and the establishment of a banknote retrieval system.
Review of Deep Learning-Based Style Transfer Research

Currently, style transfer solves a lot of tedious work and greatly improves efficiency and cost. With the continuous development of style transfer, there is a growing demand for using intelligence to solve the work of game rendering, animation production, advertising design, film production, and so on. Thus, we introduce two main types of current style transfer methods: image style transfer methods based on image iteration and image style transfer method based on model iteration. The basic principles and methods of the two methods are explained in detail, and their advantages and disadvantages are illustrated. Finally, the current application environment and development direction of style transfer based on deep learning are summarized.
Image Forgery Detection by CNN and Pretrained VGG16 Model

Image forgery has been existing in various fields in some manner. Its severity can range from innocuous social media editing to forged medical records or legal documents. Especially at a time when most organizations have taken a virtual form, it is necessary to verify the quality and validity of records, bills, etc. In this research, various deep learning approaches have been used, such as CNN and pretrained CNN models. These are used to identify image distortions and to compare and interpret the outcomes. In this paper, CNN models and pretrained CNN models are designed to assess their efficacy in the recognition and detection of image datasets. The algorithms are applied to the dataset to calculate the outcome of the model. In proposed work, the obtained accuracy of CNN model and pretrained CNN model is 74.95% and 91.62%, respectively. These CNN models have been designed to observe the better performance of deep learning neural networks over the pre-existing active and passive approaches.
Mal-LSGAN: An Effective Adversarial Malware Example Generation Model

Various Machine Learning (ML) models have been developed for malware detection. But their widespread application is challenged by adversarial attacks using adversarial malware examples. Generative Adversarial Networks (GAN) is one of the effective approaches to help build possible unknown attacks and expose the vulnerability of targeted systems. The existing GAN-based ML models have the weaknesses of unstable training and low-quality adversarial examples. In this paper, we propose a novel Mal-LSGAN model to tackle these weaknesses. By using a Least Square (LS) loss function and new activation function combinations, Mal-LSGAN achieves a higher Attack Success Rate (ASR) and a lower True Positive Rate (TPR) in 6 ML detectors, compared with the existing MalGAN and Imp-MalGAN. In Multi-Layer Perceptron (MLP), Mal-LSGAN can even decrease TPR from 97.81&#x0025; of original examples to 2.92&#x0025; of adversarial examples. The experimental results also demonstrate that Mal-Lsgangets the preferable transferability of adversarial malware examples.
An Empirical Study on the Implementation of AI Assisted Language Teaching for Improving Learner's Learning Ability

Intelligent technology shows great potential in education in terms of integrating intelligent technology with meaningful and deep teaching. Thus, the value of technology has aroused many scholars&#x0027; thinking. As a basic component of students&#x0027; academic achievement evaluation, foreign language competence has become one of the core issues of common concern in English teaching. Because of the &#x201C;digital aboriginal&#x201D;, the English learning ability of contemporary primary school students has gradually become one of the main goals of the subject of language, which has become a core quality of students to be considered competent enough for future challenges. This study aims to determine whether the AIALL teaching model can help improve students&#x0027; learning abilities in English learning. The research method was to apply the AIALL teaching model to English teaching for experimental classes, while the control class was taught the traditional way. A questionnaire of English learning ability was used as a measure of students&#x0027; English learning abilities. Results were analyzed by single-factor stratified group experiments, and the conclusions showed that the AIALL teaching model can improve the overall level of primary school students&#x0027; English performance. Furthermore, the AIALL teaching model can improve the overall level of learning ability of primary school students.
A Partial Outsourcing Decryption of Attribute-Based Encryption for Internet of Things

Internet of things (IoT) is a fast-growing technology in the modern digital era that attracts people from industries as well as academic fields. IoT provides the platform to connect all the devices to share data over the Internet. This emerging technology suffers from many security issues due to the heterogeneity of the environment and resource-constrained IoT devices. Attribute-based encryption (ABE) is a modern encryption scheme that provides security along with an access control mechanism. This technique is applied for secure communication among IoT devices over unsecured communication channels. The bilinear pairing operation is generally used for encryption and established sharing between parties. In this paper, outsourcing-based decryption of ABE using a three-party Diffie–Hellman key exchange protocol is proposed for the IoT environment where devices have limited computational and battery power. The main aim of the proposed scheme is to lessen the complexity of computation for the decryption operation of ABE for the resource-constrained devices of IoT.
Classification

Classification, the task of assigning objects to a given set of categories, is used in almost every field. One important sub-branch of classification consists of methods that learn classification functions from example data. The following chapter will provide an overview of the most basic concepts and methods of this type of data-driven classification. We will first highlight the basic ideas behind classification, along with some examples related to tourism. Thereafter, we will introduce measures of classification performance, which are necessary to direct data-driven training of classification functions and/or to evaluate classification results. As an essential part of this chapter, we will provide self-contained, yet stripped-down, descriptions of the most crucial data-driven classification methods. As such, we will focus on nearest neighbor classifiers, logistic regression, Naïve Bayes, decision trees and ensemble variants thereof, support vector machines, and finally, artificial neural networks. All of the concepts and methods will then be applied to a specific use case in an accompanying Jupyter notebook, demonstrating the practical implementation of these concepts and methods through the use of Python and the machine learning framework scikit-learn.
Design and Research of Remote Sensing Process Language based on XML

In the era of rapid development of remote sensing technology, in order to improve the production efficiency of remote sensing data processing flow and further optimize the editing of remote sensing processes in the visual interface. In this paper we have analyzed the characteristics and attributes of remote sensing processes and algorithms. We made the production of processes and algorithms into scripts, and realizes to create remote sensing processes by inputting scripting language in the visual interface. A remote sensing language based on XML is designed to read and store key information. This language will use XML to drive the production of remote sensing process. With the language we design, a remote sensing process customization system has been implemented. It can improve the editing and production efficiency of remote sensing data processing flow.
Criteria of an Alternative Empathy Research Method to Uncover End-User's Supra-Functionality Needs in Home Sofa Furniture

Many products have reached a level of functional maturity, especially in products that are not technology-based like furniture. To draw the end-user’s eye, designers must provide them with added value in the realm of pleasure which called ‘Supra-functional needs’. This kind of end-user’s needs are related to their emotion directly that are difficult to express and understand.The most common approach in the Design Thinking process to understand end-users’ needs and gain insight into them is empathy. Several empathy methods mostly concentrate on the end-user’s functionality and usability needs not their Supra-functional needs. With the help of an alternative empathy technique that focuses on extracting the end-user’s emotional needs, designers can reach more real empathy.This study investigates to determine the criteria of an alternative empathy research method to uncover Home Sofa End-user’s Supra-Functionality Needs. The home sofa is a useful furniture as people have a prolonged and intimate sensory interaction with this product which makes it a good case to study end-user’s supra-functionality needs. The researcher applied twenty-seven face to face interviews to collect qualitative data from furniture designers, furniture producers and furniture sellers as the main stakeholders in the furniture industry. ATLAS.ti software was used for coding and analysing the data. There were three levels of analysis, open coding, selective coding, and theoretical coding. Research findings show that an alternative empathy research method that uses online services and visual approaches, can uncover more end-user’s Supra-functionality needs and deliver more pleasure products.
An expressive ciphertext-policy attribute-based encryption with outsourced decryption in blockchain

Ciphertext-Policy Attribute-Based Encryption (CP-ABE) allows data to be encrypted under an access policy, which is specified as a logical combination of attributes. However, CP-ABE schemes usually use expensive operations (i.e. modular exponentiations and bilinear pairings) to generate long ciphertexts and secret key, which makes them difficult to implement in practical applications, especially for resource constrained devices. Fortunately, with the development of cloud computing and blockchain technology, users can outsource most of the decryption costs to proxy, while the underlying plaintext is still confidential. In this scheme, we proposed an expressive ciphertext-policy attributebased encryption with outsourced decryption in blockchain. Suppose that ABE ciphertexts are stored in the InterPlanetary File System (IPFS), the user can provide the proxy with a single transformation key that allows he/she to translate any ABE ciphertext satisfied by that users attributes into a few ciphertexts which can support user to verify and the a summarized ElGamalstyle ciphertext. The scheme is selectively RCCA-secure in the random oracle model for large message spaces.
Active Learning for Language Identification with N-gram Technique

Language identification is the task of identifying the language a given document is written in. It is a fundamental and vital step for many language-processing tasks since models are trained using data from a specific language and utilizing correct language models for corresponding data is essential. However, it would be time-consuming and expensive to collect great amounts of labeled training instances for this task. To help alleviate this problem, this experiment implements an active learning method. This machine learning algorithm can achieve higher accuracy with less labeled data to help recognize language. The active learner repeatedly picks unlabeled data it wants to learn and makes queries to the oracle [1]. The dataset used for this experiment consists of 22 languages each with 1000 paragraphs. Three methods of uncertainty sampling of active learning are trained and tested on the dataset. To compare with the results of the active learner, the practice of random sampling is applied to the data as well. It turns out that active learning outperforms random sampling with higher accuracy in fewer epochs.
New Trends in Training Methodologies

AR was used in the secondary school study program Electronics mechanics. Students were trained in building various assemblies backed by AR. They were expected to learn how to input various components into the assembly after the components had been assorted from component catalog. We used qualitative research approach: analyses of class observation, interviews with teachers and students. As a result of our survey, the impact of AR on student personality at cognitive and motivational level was suggested for further discussion.
Warped Ensembles: A Novel Technique for Improving CTC Based End-to-End Speech Recognition

Combining outputs from predictive models trained for similar tasks generally perform better than using a single model. Models trained on different domains could for example help in improving the performance with the help of information from complementary domains. The weights in the ensemble could also be adjusted to best suit the desired target domain. However, in End-to-End (E2E) based speech recognition solutions, this is not a simple task owing to the lack of time alignments in the outputs. This work presents a novel ensembling technique for E2E ASR systems trained using Connectionist Temporal Classification (CTC) loss, which uses a multi-sequence alignment technique for synchronising output time-frames across models, and then combines the outputs using weights obtained using a calibration technique introduced in our earlier work. Several experiments are conducted on multi-accent English data and the Librispeech corpus. Word Error Rates (WER) are evaluated and compared against the performance of ROVER (a system combination technique), and the best model in the system. When compared to the best model, average relative improvements of 6.46% and 8.94% are observed in unadapted in-domain and out-of-domain experiments respectively. Comparison to ROVER shows average relative improvements of 19.29% and 9.21% in in-domain and out-of-domain experiments.
Vulnerable Road Users Detection and Tracking using YOLOv4 and Deep SORT

Over the years, The detection and tracking of Vulnerable Road Users (VRUs) have become one of the most critical features of self-driving car components. Because of its processing efficiency and better detection algorithms, tracking-by-detection appears to be the best paradigm. In this paper, a detection-based tracking approach is presented for Multiple VRU Tracking of video from an inside-vehicle camera in real-time. YOLOv4 scans every frame to detect VRUs first, then Simple Online and Realtime Tracking with a Deep Association Metric (Deep SORT) algorithm, which is customized for multiple VRU tracking, is applied. The results of our experiments on both the Joint Attention in Autonomous Driving (JAAD) and Multiple Object Tracking (MOT) datasets exhibit competitive performance.
Domain Adaptive Visual Tracking with Multi-scale Feature Fusion

Accuracy and speed have been two fundamental issues that are difficult to balance in object tracking. Trackers with high accuracy often have quite large network structures that require huge amounts of computing resources, therefore leading to a lower tracking speed. To address the problem, we propose a novel domain adaptive tracking algorithm to obtain a better balance between tracking speed and accuracy. A simple and effective domain adaptation component is employed to transfer features from the image classification domain to the object tracking domain. In addition, we construct an adaptive spatial pyramid pooling layer to substitute for the fully- connected layer connected to convolutional layers, which can significantly reduce computational complexity while achieving high tracking accuracy. Experiments on VOT2018, TrackingNet and OTB2015 shown the effectiveness of the proposed method. Compared with the state-of-the-art trackers, our tracker can obtain real-time tracking with a speed of 35 FPS.
Elements of satellite quantum network

Long-distance quantum key distribution (QKD) is a critical component of a quantum network. When network users are within a city, they often use existing telecommunication fiber-optic networks. This method is problematic for QKD on a large scale between cities and countries. Direct connecting of distant consumers with quantum single-photon lines is not possible, because optical fiber involves attenuation. This problem is addressed by constructing a large number of trusted intermediate nodes, but there are concerns with the reliability of each node. To increase the quantum network's dependability, a method of building a satellite quantum network is actively developing. The only one trusted satellite node is required for satellite QKD between any two points on the Earth's surface. Furthermore, a satellite quantum link can also be utilized to add another communication channel in the existing fiber-optic networks that are thousands of kilometers apart. We describe the creation of ground infrastructure elements for receiving single photons in various polarization quantum states from satellites. Ground-based receiving nodes are based on two telescopes with an aperture of 1.2 m and 0.6 m located in remote cities.
Ultrasound Thyroid Nodule Segmentation Based On Multi-branch and Color Space Volume

Ultrasonic segmentation of thyroid nodules is an indispensable part of computer aided system and diagnosis of thyroid diseases. Due to the fact that ultrasound images have the characteristics of asymmetric irradiation, uneven appearance, low signal-to-noise ratio, low contrast and blurred boundary, thyroid nodule segmentation is a challenging task. In order to improve the precision of thyroid nodule segmentation, this paper presents an Ultrasound thyroid nodule segmentation algorithm based on multi-branch and color space volume. In this method, an image pyramid is constructed to compensate for the spatial information loss by convolutional and pooling operations. We use pretrained ResNet block as the Feature Encoder Module, a multi-branch convolutional fusion (MBCF) block is proposed to increase the receptive field and capture high-level spatial and semantic information. To further improve the segmentation performance, the color space volume transformation is used to highlight the contrast between the target area and the background in the data preprocessing part. Experimental results on an Ultrasound thyroid nodule segmentation dataset show that the proposed method performs well over the state-of-the-art methods.