Reviewerly/exHarmony huggingface dataset:
Bar code positioning technology under complex background based on morphology and gradient

This paper presents a new location detection technology to find the bar code under complex background. Firstly, the contour of the grayscale image is extracted by the optimized Sobel operator and differential algorithm, and then we use the morphological algorithm to make extracted contours more complete and clear. Secondly, we select the area where the bar code may exist, with the area of contours as a standard. Then the paper uses the Hough transform to detect and filter the contour edges that meet the requirements. Then aiming at the area where the edge of the selected contour was located, we calculate the gradient in the relevant direction and statistic results. Furthermore, based on statistics, we filtered out the area where the bar code is located. Experiments show that the bar code location detection algorithm proposed in the paper is not only superior to the traditional gradient detection and morphological detection methods, but also has the advantage such as good robustness, small amount of processed data, fast processing speed, and high accuracy, which is fit for the image larger than 4 million pixels.
Activity Identification using Supervised Machine Learning on Wearable Activity Tracker Data

Wearable activity trackers are electronic devices which are used to monitor fitness, sleep and other physical activities. The popularity of these devices is increasing as their capability to identify and track different type of activities has grown while at the same time they continue to become more affordable. This paper evaluates several machine learning techniques to improve our ability to accurately predict various physical activities like lying, sitting, walking and running using data tracked by such activity trackers. The dataset includes 3656 of Apple Watch and 2608 minutes of Fitbit data. The results show that an XGBoost model achieved the maximum AUC of 0.98 for the Apple Watch dataset, 0.99 on the Fitbit dataset, and 0.98 on the combined dataset.
A Hybrid Approach for Semantic Image Annotation

In this study, a framework is proposed that generates natural language descriptions of images within a controlled environment. Previous work on neural networks mostly focused on choosing the right labels and/or increasing the number of related labels to depict an image. However, creating a textual description of an image is a completely different phenomenon, structurally, syntactically, and semantically. The proposed semantic image annotation framework presents a novel combination of deep learning models and aligned annotation results derived from the instances of the ontology classes to generate sentential descriptions of images. Our hybrid approach benefits from the unique combination of deep learning and semantic web technologies. We detect objects from unlabeled sports images using a deep learning model based on a residual network and a feature pyramid network, with the focal loss technique to obtain predictions with high probability. The proposed framework not only produces probabilistically labeled images, but also the contextual results obtained from a knowledge base exploiting the relationship between the objects. The framework’s object detection and prediction performances are tested with two datasets where the first one includes individual instances of images containing everyday scenes of common objects and the second custom dataset contains sports images collected from the web. Moreover, a sample image set is created to obtain annotation result data by applying all framework layers. Experimental results show that the framework is effective in this controlled environment and can be used with other applications via web services within the supported sports domain.
A Blockchain-based Framework Proposal for Online Entertainment Ecosystem

People are spending much time online to read articles and watch videos nowadays. Yet for the creators of these entertainment contents, they are lacking of support to work together by sharing their creation as well as have limited options to receive economic rewards. This paper proposes a blockchain-based online entertainment sharing and rewarding system framework to allow creators work together by sharing each other’s creation and sharing economic rewards. This paper exploits the advantage of blockchain technology to provide a smart contract that bonds the participants and stores transaction records on a secured, alteration resistance, and immutable shared ledger. In addition, this papers also propose to use cryptocurrency to support borderless payment for the online entertainment ecosystem. The proposed framework contains three main parts: register the ownership of a creation; register the shared intellectual property rights when the creation contains another people’s creation; receives and share payments from online audiences. The system provides a solution to empower online entertainment creator in working together and receive economic rewards directly form audience.
Automatic text summarization of konkani texts using pre-trained word embeddings and deep learning

Automatic text summarization has gained immense popularity in research. Previously, several methods have been explored for obtaining effective text summarization outcomes. However, most of the work pertains to the most popular languages spoken in the world. Through this paper, we explore the area of extractive automatic text summarization using deep learning approach and apply it to Konkani language, which is a low-resource language as there are limited resources, such as data, tools, speakers and/or experts in Konkani. In the proposed technique, Facebook’s fastText pre-trained word embeddings are used to get a vector representation for sentences. Thereafter, Deep Multi-Layer Perceptron technique is employed, as a supervised binary classification task for auto-generating summaries using the feature vectors. Using pre-trained fastText word embeddings eliminated the requirement of a large training set and reduced training time. The system generated summaries were evaluated against the ‘gold-standard’ human-generated summaries with ROUGE toolkit. The results thus obtained showed that performance of the proposed system matched closely to the performance of the human annotators in generating summaries.
The Streaming Approach to Training Restricted Boltzmann Machines

One of the greatest challenges facing researchers of machine learning algorithms nowadays is the desire to minimize the training time of these algorithms. One of the most promising and unexplored structures of the neural network is the Restricted Boltzmann Machine. In this paper, we propose to use the BBTADD algorithm for RBM training. The performance of the algorithm has been illustrated on one of the most popular data sets.
Fan Beam Transform and LU Decomposition based Blind Watermarking Technique for Color Images

The copyright protection of digital multimedia data has been achieved tremendous growth in this internet era through the digital watermarking technique. This paper proposes a blind image watermarking technique using fan-beam transform (FBT) and lower–upper (LU) decomposition. Primarily, separate the color host image into the RGB color model and then the $$B$$ component is processed with FBT to attain projection data. The $$B$$ component is subdivided into $$4 \times 4$$ non-overlapping blocks. The pseudo-random sequence algorithm is employed to choose the embedding blocks for accomplishing better security and robustness. Each selected block is processed using LU decomposition. The first row and fourth column element of the upper triangular matrix attained by LU decomposition is selected to embed the color watermark image because this element enriches the robustness and visual quality of the watermark. Furthermore, the Arnold scrambling is utilized for preprocessing the watermark to ensure its security. This scheme accomplishes good results for image quality and robustness by obtaining the peak signal-to-noise ratio (PSNR) value which ranges from 48.6880 to 50.4124 and high normalized cross-correlation (NCC).
3D Textures Analysis Based on Features Extraction

The Decimal Descriptor Patterns (DDP) operator is a recent method of analyzing textures. Based on structural analysis of pattern distribution, the DDP presents an excellent description of the spatial structure of image texture. In this chapter, two successful applications of this operator are highlighted. The first one consists in Multiple Sclerosis (MS) Lesions detection from 3D Magnetic Resonance (MR) brain images tissue. The second application is 3D face analysis. Different experimentations conducted on a two well-know three-dimensional databases. For comparison, two best known operators of texture measures that are considered as references in image analysis are chosen: the Grey Level Co-occurrence Matrix (GLCM) and the Local Binary Patterns (LBP). Several tests of texture classification are performed in the same conditions using the classifier multiclass Support Vector Machines (SVM). The performance of the three operators is tested in front of illumination, inhomogeneity, contrast, and noise level variations with a various and a large number of textures. Experimental results show clearly the robustness of the DDP operator.
PixRadio

Anaglyph Stereo Matching is a particular, harder, case of the widely studied Stereo Matching algorithms. When applied to Anaglyph Reversion problem helps to improve stereo content storage and transmission, stereoscopic visualization independence and access to legacy anaglyph content available on the Web. However, the reversion process is not trivial since anaglyphs have high radiometric differences between the two views of a stereo pair of images, preventing standard stereo matching algorithms to be directly used. Different from related work, this paper presents a new pixel descriptor specially designed to deal with radiometric differences, allowing for the improvement of the final reconstructed stereo pair image quality. Experimental results show the proposed method presents, in average, better image quality when compared with related techniques.
Who needs a laptop? Adopting a mobile laptop cart to address space and equity issues in information literacy instruction

This article details how librarians at California State University, Chico implemented a laptop cart as a solution to the increasing needs for information literacy instruction. We address how the laptop cart is managed and booked, how librarians are using it to increase and allow for point-of-need instruction, and readers will gain understanding of how to replicate this at their home institutions.
Clues on the Sampling of Mixed Igneous Bodies

Magma mixing involves many variables that are non-linearly coupled and this produces magmatic bodies showing a strong geochemical heterogeneity. An interesting question arises concerning the potential loss of information due to the sampling of these complex systems for petrological studies. In particular, commonly only small portions of the magma body are available for sampling. It is therefore important to address whether the sampling can be regarded representative of the whole magmatic system. Although this is a key point in evaluating the reliability of petrological hypotheses, it is rarely addressed or even mentioned. In this chapter we attempt to address theoretically the problem of sampling of mixed igneous bodies.
Application of Active Learning in Decoding

Improved technology has resulted in numerous computing devices. The more people spend time interacting with these computing devices, the more we become interested in coming up with new interaction methods that could help to facilitate application of active learning in decoding with the computing devices. Application of active learning in decoding technology will help achieve the goal of this journal as it helps to overcome eye limitations in speed and efficiency. Application of Active Learning Decoding for Python3 programming language was mainly created because it was flexible, easy to extend, and developed some other small models. The machine learning algorithm in python is often lying beneath the scikit-learn python library. The user can easily modify and build another new model to predict the model learning algorithm. The model can also design a good and quality novel model algorithm that can be used more easily.
A High Performance Pipelined Parallel Generative Adversarial Network (PipeGAN)

Generative Adversarial Networks (GANs) are gaining popularity with applications in unsupervised, supervised as well as reinforcement learning for generating images, videos, and other artefacts. However, the inherently sequential nature of GANs is an Achilles heel to widespread adoption. In this paper, we propose and experimentally evaluate a novel sophisticated pipelined parallel version of GANs by dividing the training process into different balanced pipeline stages. Our experimental evaluation of the proposed technique shows significant performance gain up to 30% and 23% with an average speed-up close to 23% and 15% as compared to the serial implementation in the context of NumPy and Pytorch respectively when used to accurately classify real and fake images from standard MNIST and Fashion MNIST datasets.
Image Recognition Based on Super-Resolution Wasserstein Generative Adversarial Nets with Gradient Penalty

Generative adversarial networks (GAN) are currently a hotly debated research topic in the field of machine vision; however, they possess various shortcomings that cannot be overlooked, such as unstable generated samples, collapsed modes, and slow convergence. The present paper combines the advantages of the super-resolution GAN model (SRGAN) and Wasserstein GAN with gradient penalty (WGAN-GP) and proposes a hybrid model, known as super-resolution Wasserstein generative adversarial network with gradient penalty (SRWGAN-GP). The generator of SRWGAN-GP utilizes the residuals block and Wasserstein distance to increase the resolution of the generated samples and solve the shortcomings of the unstable GAN training. Besides, the discriminator of SRWGAN-GP consists of a local discriminator and a global discriminator to enhance image recognition capacity. Finally, the discriminator is extracted for image recognition. The results of comparative experiments indicate that this method can effectively enhance the accuracy of image recognition.
Current State and Issues of Education for Older Adults in South Korea

In South Korea in 2018, the ratio of older adults (age 65+ years) was 13.8%, and the average life expectancy was 79 years for males and 85.2 years for females. South Korea is the fastest ageing country among the OECD countries. Beginning in the 1970s, senior citizen associations, welfare groups, religious communities, and private institutions began to provide learning opportunities for older adults. At the beginning of the 2000s, the South Korean Government established comprehensive packages of welfare policies for older adults, which have significantly increased the number of educational institutions and programmes for older adults. Policy for the older adults has been focused primarily on welfare aspects such as economic support and medical services. However, the new elderly generation of baby boomers are not satisfied with simple welfare programmes for them. They are interested in social participation and education in their old age in order to be able to utilise their experiences and knowledge. Lifelong education for older adults is important to improve the quality of life through improving their learning ability and active social participation. Thus, national and local educational administrations should change their school education-oriented policy and promote the education for older adults. In addition, South Korea must support voluntary institutions of education for older adults and facilitate civil actor participation in the governance of education for the elderly. It is also necessary to cultivate elderly education activists, develop elderly education programmes, and vitalise intergenerational education. Furthermore, for the development of South Korean educational gerontology, it is necessary to vitalise the academic research and discussion through the operation of the elderly education majors in the graduate courses.
Dense Blocks-Incorporated Capsule Networks for Image Classification on Complex Datasets

Capsule network (CapsNet) has received a special concern recently owing to its great capability of guaranteeing the model robustness against the affine transformation and adversarial images. However, most CapsNets perform poor while costing huge at image classification on complex datasets, due probably to the weak feature extraction, the gradient problems and many intermediate variables employed in dynamic routing. To address this issue, a novel CapsNet called dense CapsNet (DenseCap) is proposed. First, dense blocks with short-cuts are stacked before capsule layers to adequately extract abundant essential features from image input while avoiding vanishing gradient. Then, CapsNet ReLU (CapsReLU) function is put forward to squash the capsule such that the sensitivity to the parameter initiation and vanishing gradient are handled. Furthermore, the influence of various dimensions of capsules on the reconstructed images is carefully studied. Finally, empirical results on five benchmark datasets show that the proposed DenseCaps outperform state-of-the-art capsule models.
ThermoSteg—Covert Channel for Microbolometer Thermographic Cameras

The article presents a new concept-steganography in thermography. Steganography is a technique of hiding information in a non-obvious way and belongs to sciences related to information security. The proposed method, called ThermoSteg, uses a modification of one of the parameters of the thermal imaging camera-integration time-to embed the signal containing hidden information. Integration time changing makes the microbolometer array heat up while reading the sensors. The covert information can be extracted from the stream of thermograms recorded by another thermal camera that observes the first one. The covert channel created with the ThermoSteg method allows the transmission of covert data using a thermal sensor as a wireless data transmitter. This article describes a physical phenomenon that is exploited by the ThermoSteg method and two proposed methods of covert data extraction, and presents the results of experiments.
Discovering Associative Patterns in Healthcare Data

Health care has several knowledge discovery techniques. Among them are association rules, which provide quick access to standards. However, classic algorithms can generate many patterns or fail to identify rare cases relevant to healthcare professionals. This study identified asymmetric associative patterns in health-related data using the Health Association Rules (HAR) algorithm. We use a combined strategy of six metrics to filter, select, and eliminate contradiction steps to find patterns and identify possible rare cases. The proposed solution uses adjustment mechanisms to increase the quality of standards with knowledge of the health professional. The HAR assists health researchers and decision support systems. A survey of 597 studies identified the primary needs and problems of associative patterns in the health context. The HAR identifies characteristics with the highest cause and effect relationship. The experiments were carried out on 13 datasets, where we identified the most pertinent patterns for the datasets without losing relevant knowledge.
Application of Artificial Intelligence Technology in Electronic Information Engineering

The word "artificial intelligence" has appeared more and more frequently in our life in the past two years. More and more intelligent products have entered our ordinary people's homes. The new technology represented by artificial intelligence technology has gradually become the key force driving China's economic development. The integration of artificial intelligence and electronic information technology has become an inevitable trend, Electronic information engineering must be transformed, relying on artificial intelligence technology to walk in the forefront of upgrading and development. Based on the literature research method, After consulting a lot of literature about AI technology and electronic information engineering, and concludes that under the background of artificial AI technology, the scale of electronic information manufacturing industry is steadily expanding. It shows a steady growth trend. According to the big data platform, The trade volume of China's electronic information industry is as high as 9.3 trillion, an increase of 11% Compared with the previous income scale, with the support of AI technology, the development of electronic information engineering will be even more powerful.
Study on Algorithm and Parameters of Seismic Design Code for Medium and Long Span Bridges

China is an earthquake prone country, ensuring the safety of bridges under earthquake is of great significance to the protection of people’s lives and property. With the rapid development of medium and long-span bridge construction, the research of bridge seismic analysis method has become a research hotspot in academic circles at home and abroad. It is generally believed that the spatial variation effect of seismic excitation should be considered in the seismic analysis of medium and long-span bridges. But the response spectrum method, which is widely used in current design codes, can not deal with this kind of problem. The time history analysis method is difficult to be popularized in engineering application because of its huge amount of calculation. The random vibration method has great advantages in theory in dealing with this kind of problem, but it has not been applied in practical engineering because of the complexity of its traditional calculation method. The virtual excitation method breaks through the bottleneck of calculation efficiency and makes it possible to introduce the random vibration method into the new code for seismic design of highway bridges.
Recent research trends on Model Compression and Knowledge Transfer in CNNs

Convolutional neural network (CNN) is the main tool for deep learning and computer vision, and it has many applications in face recognition, sign language recognition and speech recognition. As deep learning becomes more and more mature, the application of convolutional neural networks will become more and more widespread. As we know, the deeper a neural network is, the higher its memory and computational power overhead. Many neural networks used in medicine, autonomous driving, and language recognition have large model complexity, which makes it difficult to apply these CNNs to people's daily life. Therefore, the development of simple, lightweight and small neural networks has become the focus of researchers nowadays. In this paper, we summarize the development of convolutional neural networks in recent years, as well as various methods for compressing models and migrating data from large models to small ones. In general, the main convolutional neural network compression approaches are: pruning, knowledge distillation, aggregating neurons of different scales, proposing new structures, etc. We start from the structure of neural networks, introduce the major structural changes experienced from the development of convolutional neural networks, and then analyze various pruning, compression and knowledge distillation methods. For specific methods, we run different models and compare the improvements of the new methods with respect to the old ones. We also debugged models on adversarial generative pruning, teacher-student networks, and other compressed CNNs during this period, and drew some constructive conclusions. Finally, we summarize the trends in CNN development in recent years and the challenges we may face in the future.