Reviewerly/exHarmony huggingface dataset:
Human Activity Recognition in Untrimmed Video using Deep Learning for Sports Domain

Rapid changing in technology automated human activity recognition most research topic nowadays. As motion camera very problematic for monitoring the human activity in different area like in workplace, office, industry, hospitals and so on in various application. Tracking and take care of the health of the live camera and evaluate the operation done correctly or not. The computer vision area to find the activity done by a human with the help of object detection, feature extraction, cluttered background, occlusion and applying deep learning approaches to reach the solution. In this paper considering the sports activity because the sports activity is the combination of the different sub-activity present. To identify the main activity based on the sub-activity, most of the sports game observed that having the starting activity will be the same and but next sub activity of a sports game will different of differentiated the sports game like high-jump, long-jump, cricket bowling, all this sport game starting with the same sub-activity i.e. running, but second activity differentiate the game, in high-jump is ’high jump after running’, in Long-jump is the ’long jump after running’, and in cricket, bowling is ’bowling after the running’. Video is one of the strongest media for knowledge and transcends the field of sport. How instructors or trainers at any level may use video analysis to focus on any variances in human activity in sport specially in competition and training recording where as well as how to enhance efficiency. Video is a grat source of knowledge, and using it correctly will significantly improve any mentor willing to do the right thing. So, all second sub-activity differentiate the sports game. This is the main objective performance based to find sub-activity and recognized the correct and improve main sport activity using sub-activity using deep learning methodology CNN and LSTM algorithm will discuss in the paper.
Constructing Immune Cover for Secure Steganography Based on an Artificial Immune System Approach

Artificial Immune Systems (AIS) are a class of computationally intelligent systems inspired by the principles and processes of the Biological Immune System (BIS), and has been applied to many fields successfully by exploit its characteristics of self-learning and self-organizing. In this paper, we open a new field for applications of AIS, namely, immune cover construction for steganography application. We proposed an AIS-based steganography framework to improve the security of steganography by constructing immune cover image in spatial domain. Texture complexity is a major factor in resisting steganalysis in images, so the proposed framework is designed by immune processing to adaptively accentuate the texture region as well as maintain the original characteristics of images, and then obtain more suitable and secure immune cover image for steganography. This approach allows to construct more setganalysis-secure data embedding using standard steganography algorithms. Compared with state-of-the-art methods, the proposed method has an improved ability to resist steganalysis.
Research on Pressure Comfort of Yoga Suit and Optimization Scheme of Pattern Based on CLO 3D Software

Abstract Based on the improvement of yoga clothing pressure comfort, this paper used Fuyi CAD garment system to make 2d clothing pattern, then, the 3D virtual fitting experiment of the main part of this paper was carried out. In the experiment, the static and dynamic pressure data of the virtual model were collected by using ClO 3D software, and the experimental data were analyzed by using the comparative experimental method. After analysis, the optimization was carried out in the garment piece, and then the same method was adopted to conduct data acquisition again, and it turned out to be that the overall virtual pressure reduces 247.93Kpa. Finally, the optimization effect of each garment piece was evaluated. The results showed that the sample can be optimized and the pressure comfort can be improved through the virtual pressure test method of CLO 3D platform.
Face and age recognition using three-dimensional discrete wavelet transform and rotational local binary pattern with radial basis function support vector machine method

Interest in facial recognition hypotheses and algorithms has grown steadily over the last few decades. Video monitoring, criminal identification, building access control, and unmanned and autonomous vehicles are only a few examples of concrete applications that are becoming increasingly attractive to industry. Various techniques are being developed, including local, holistic, and hybrid approaches, which use only a few face image characteristics or the entire facial features to provide a face image description. Many methods have good results, if there are sufficiently representative training samples per person, in the face recognition system. Facial part finding and extraction show the utmost vital role in face and age recognition. In this research work a new algorithm is proposed for Face and Age Recognition (FAR) by using Discrete Wavelet Transform (DWT), Radial Basis Function Support Vector Machine (RBF-SVM) classifier, and Rotational Local Binary Pattern (RLBP). RLBP is utilized for the selection and extraction of features from the face image. In this algorithm, extract the face component like Nose, Mouth, Left and Right eye. In the preprocessing stage median filter is used to remove noises from the face image. By using this, there is an improvement in the feature extraction procedure. In pattern recognition, a basic errand is finding a picture from the picture parts. For the implementation of results FG-NET ((Face and Gesture Recognition Network) and AT&amp;T datasets are used. The detection rate of face recognition has reached up to 92–98% and the detection rate for age recognition is 87%. The proposed algorithm is compared with SVM shows better over previous algorithms and also estimate the value of accuracy.
Unsupervised Extractive Summarization Based on Context Information

Unsupervised extractive summarization do not need parallel corpus pairs for training, so they are widely used in many scenarios. Many unsupervised extractive summarization methods usually lose the context information of sentences. Based on the context information, this paper proposes a model of unsupervised extractive summarization. At the same time, we tested many ways to express the context information. We also studied the relationship between sentences in the abstract. We have proved that the context information and the relationship between the sentences are very helpful to the task and we developed an unsupervised summarization system without any training.
Removal of Floating Particles from Underwater Images Using Image Transformation Networks

In this paper, we propose three methods for removing floating particles from underwater images. The first two methods are based on Generative Adversarial Networks (GANs). The first method uses CycleGAN which can be trained with an unpaired dataset, and the second method uses pix2pixHD that is trained with a paired dataset created by adding artificial particles to underwater images. The third method consists of two-step process – particle detection and image inpainting. For particle detection, an image segmentation neural network U-Net is trained by using underwater images added with artificial particles. Using the output of U-Net, the particle regions are repaired by an image inpainting network Partial Convolutions. The experimental results showed that the methods using GANs were able to remove floating particles, but the resolution became lower than that of the original images. On the other hand, the results of the method using U-Net and Partial Convolutions showed that it is capable of accurate detection and removal of floating particles without loss of resolution.
Extending Behavior Trees with Market-Based Task Allocation in Dynamic Environments

Behavior tree (BT) is a novel control architecture in the robotic field. Being modular and reactive, BTs show great advantages in autonomous systems when applied to robot control. However, there are unsolved problems on the use of BTs in multi-robot scenarios include but are not limited to task allocation and robot coordination. In this work, we propose a market-based behavior tree method for task perception, allocation, and execution. This method introduces an auction mechanism to adjust commitments between different robots as the change of environment and extends BTs into multi-robot systems. We validate the proposed method quantitatively and qualitatively by simulation with Gazebo.
Reference-Free Despeckling of Synthetic-Aperture Radar Images Using a Deep Convolutional Network

This work proposes a deep learning based method to de-speckle SAR images that does not require noise-free reference data. Instead, our method exploits the redundancy between images of the same area at different times to train a residual convolutional neural network in a regression framework to predict speckle-free images. Moreover, thanks to end-to-end training of the network, our approach does not require explicit parameter tuning. Experiments show the relevance of our approach on Sentinel 1 images acquired over volcanic areas. The method is shown to compete well with well-known approaches such as the Lee filter and the more recent SAR-BM3D filter.
Hadamard Extensions and the Identification of Mixtures of Product Distributions

The Hadamard Extension <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathbb H({\mathrm {m}})$ </tex-math></inline-formula> of an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n \times k$ </tex-math></inline-formula> matrix m is the collection of all Hadamard products of subsets of its rows. This construction is essential for source identification (parameter estimation) of a mixture of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> product distributions over <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> binary random variables. A necessary requirement for such identification is that <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathbb H({\mathrm {m}})$ </tex-math></inline-formula> have full column rank; conversely, identification is possible if apart from each row there exist two disjoint sets of rows of m, each of whose extension has full column rank. It is necessary therefore to understand when <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathbb H({\mathrm {m}})$ </tex-math></inline-formula> has full column rank; we provide two results in this direction. The first is that if <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathbb H({\mathrm {m}})$ </tex-math></inline-formula> has full column rank then there exists a set of at most <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k-1$ </tex-math></inline-formula> rows of m, whose extension already has full column rank. The second is a Hall-type condition on the values in the rows of m, that suffices to ensure full column rank of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathbb H({\mathrm {m}})$ </tex-math></inline-formula> .
A General Solution and Practice for Automatically Constructing Domain Knowledge Graph

A knowledge graph is a semantic network that reveals the relationships between entities. Domain knowledge graphs can assist practitioners in complex analysis applications and decision support, and build industry barriers. A good construction system can help companies construct domain knowledge graphs efficiently and quickly. However, due to the differences in data models and business requirements in different fields, there is no general method for constructing a domain knowledge graph. Moreover, the current construction methods require a lot of manpower investment, which is relatively inefficient. To solve the inefficiency and nonuniversal problems in the process of domain knowledge graph construction, in this paper, we study the automated construction mechanism of Chinese domain knowledge graphs and propose a general automatic construction solution. Experimental results show that our proposed solution is more effective in constructing a domain knowledge graph.
SketCHI 4.0: Hands-On Special Interest Group on Remote Sketching in HCI

Sketching is a physical activity: moving a stylus to create marks on paper or screen, from mind to visual output. But sketching can also translate to the virtual space. When we sketch collaboratively, we look for cues, exchange ideas, and annotate work via mark-making or comment. The digital medium has evolved to explore the potentials of sketching online, and this Special Interest Group aims to bring together researchers and practitioners interested in Sketching in HCI to explore the new virtual landscape of sketching, popularised by the constraints of the current world situation. We invite you to join our virtual group, discuss and share sketches, query the existing state-of-the-art, and help pave the way for the development of this medium in the virtual space with your imagery and ideation.
NLA-Bit: A Basic Structure for Storing Big Data with Complexity O(1)

This paper introduces a novel approach for storing Resource Description Framework (RDF) data based on the possibilities of Natural Language Addressing (NLA) and on a special NLA basic structure for storing Big Data, called “NLA-bit”, which is aimed to support middle-size or large distributed RDF triple or quadruple stores with time complexity O(1). The main idea of NLA is to use letter codes as coordinates (addresses) for data storing. This avoids indexing and provides high-speed direct access to the data with time complexity O(1). NLA-bit is a structured set of all RDF instances with the same “Subject”. An example based on a document system, where every document is stored as NLA-bit, which contains all data connected to it by metadata links, is discussed. The NLA-bits open up a wide field for research and practical implementations in the field of large databases with dynamic semi-structured data (Big Data). Important advantages of the approach are as follow: (1) The reduction of the amount of occupied memory due to the complete absence of additional indexes, absolute addresses, pointers, and additional files; (2) reduction of processing time due to the complete lack of demand—the data are stored/extracted to/from a direct address.
A Simple and Effective Baseline for Out-of-Distribution Detection using Abstention

Refraining from confidently predicting when faced with categories of inputs different from those seen during training is an important requirement for the safe deployment of deep learning systems. While simple to state, this has been a particularly challenging problem in deep learning, where models often end up making overconfident predictions in such situations. In this work we present a simple, but highly effective approach to deal with out-of-distribution detection that uses the principle of abstention: when encountering a sample from an unseen class, the desired behavior is to abstain from predicting. Our approach uses a network with an extra abstention class and is trained on a dataset that is augmented with an uncurated set that consists of a large number of out-of-distribution (OoD) samples that are assigned the label of the abstention class; the model is then trained to learn an effective discriminator between in and out-of-distribution samples. We compare this relatively simple approach against a wide variety of more complex methods that have been proposed both for out-of-distribution detection as well as uncertainty modeling in deep learning, and empirically demonstrate its effectiveness on a wide variety of of benchmarks and deep architectures for image recognition and text classification, often outperforming existing approaches by significant margins. Given the simplicity and effectiveness of this method, we propose that this approach be used as a new additional baseline for future work in this domain.
Proposing 2-tier Architecture for Permission-ed and Permission-less Blockchain Consensus Algorithms Based on Voting System

With the rapid use of internet technology, security and performance are always at risk. The data on the internet can be compromised and the performance can be bottlenecked. The solution to this problem is the blockchain technology. Reason for choosing this field is because blockchain is a new technology and now it is one of the most demanding field in the IT sector. Blockchain is based on peer-to-peer topology containing connected nodes. Each node has cryptographic hash of the previous node (no malicious or unauthorized node can enter the linked nodes), timestamp and relevant data. Blockchain provides resistance against the unwanted interference and modification of the data and each node has separate processing power thus enhancing security and performance of the host network. Permission-ed and Permission-less blockchain have their own properties and limitations and they can be used in different scenarios. In permission-less blockchain the nodes can be public, anyone can add nodes to increase the performance (Bitcoin), while permission-ed blockchain is used within enterprise or between enterprises where the nodes are private and new nodes need authorization to be added in link nodes. The aim of this paper is to study the permission-ed and permission less blockchain, their similarities and differences, some of the consensus algorithms they use. Outcome of the paper will be understanding of some of the consensus algorithms and proposing a two-tier architecture which will contain voting based algorithm for enhanced performance.
Variational Autoencoder for Data Analytics in Internet of Things Based on Transfer Entropy

Variational autoencoders (VAEs) are generative models which combine deep learning and Bayesian machine learning. The VAEs are trained via minimizing the loss function, and the most popular loss function in VAEs is the evidence lower bound function in which the Kullback–Leibler divergence has been used. Motivated by the idea that information flow from VAEs input to output should be reflected in the loss function, we propose to incorporate transfer entropy (TE) to loss function to quantify the information flow. Subsequently, we apply our TE-based VAEs to data analytics in Internet of Things, including data compression and generative modeling. Simulation results show that our TE-based VAEs works much better than the Kullback–Leibler divergence-based VAE in terms of reconstruction error and label error. We further analyze the latent space to clarify why our TE-based VAE performs better. We compare our TE-based VAE against compressive sensing and demonstrate that our TE-based VAE could have 30 times stronger compression power than compressive sensing.
Automatic Creation of a Domain Specific Thesaurus Using Siamese Networks

Recent trends have increasingly indicated a shift in search technologies across all applications from syntactic and lexical matching approaches to semantic methods, aiming to understand the intent and contextual meaning of search queries, in order to yield more relevant and accurate results. Such methods often rely on semantic ontologies to map query words to concepts and aid in expansion. However, most applications require a domain specific language definition in order to overcome issues of ambiguity and misinterpretation of meaning. General purpose ontologies are often lacking in this purpose and fail to yield appropriate results in specific applications. In this paper, we propose a novel method of building a domain specific thesaurus for aiding semantic search through automatically creating a refined general thesaurus, followed by training a Siamese Network in two phases to classify candidate synonyms as relevant or non-relevant to the particular domain. We focus on the application of tag-based gallery image retrieval and extract and utilise information from Google's Conceptual Captions dataset in order to improve our model's performance. In order to investigate and justify our training method and architecture, we conduct an ablation study and compare results with our model. We further analytically and empirically demonstrate the advantage of representing terms in a domain-specific environment through semantic vectors fine-tuned on corpora related to the domain. Although our experiments are focused on building a word ontology specific to image retrieval, our method is generic and can be generalised to any field requiring a domain specific semantic language.
WITHDRAWN: High-fidelity reversible data hiding scheme using hybrid generalized singular value decomposition and Rivest Shamir Adleman algorithm

Steganography is the method of embedding data in a harmless medium of cover. It offers higher safety than cryptography although cryptography hides the message's contents but not the message's presence. Therefore, no one will be conscious of the presence of confidential information apart from the approved sender and receiver. In this paper, a novel High-Fidelity Reversible data hiding Scheme using a hybrid Generalized Singular Value Decomposition and Rivest Shamir-Adleman (GSVD-RSA) algorithm is proposed to provide the lossless and secured transformation. The experimental outcomes indicate that the suggested algorithm has not only a high rate of embedding then large visual quality and can accomplish complete retrieval of the recovered original image. This algorithm has some benefits compared to other algorithms. Reversible data hiding can fully restore the cover image without distortion after the hiding information is obtained. The proposed technique accomplished a PSNR of 1.65% when compared with Switching Tree Coding based Vector Quantization method.
Role of Public Libraries in the Evolution of Digital Bharat into Atmanirbhar Bharat

Access to information is the hallmark of democracy. The government of India is trying its best to provide free and timely information to all citizens. For bringing effectiveness and efficiency in the governance, an electronic mode of governing has been adopted. This paper endeavors to relate the digital transformation of Bharat and various information needs of citizens. The article also highlights how public libraries can help in achieving the goals of digital Bharat and end with the discussion that the government should frame the proper policy for the provision of e-government services in public libraries.