Reviewerly/exHarmony huggingface dataset:
Video Text Type Classification

In the previous chapter, words and characters are segmented from the text lines in video and natural scene images. In the case of video, one can see two types of texts, namely caption, which is edited text, and scene, which is part of image. To achieve better recognition performance, it is necessary to classify caption and scene text.
Text Classification Based on BertRCNNATT Hybrid Model

The Recurrent Convolutional Neural Network (RCNN) has achieved excellent performance in text classification. However the RCNN ignores the context of the information and the use of the max-pooling will causes the loss of feature information. Therefore, We proposed a text classification method based on the BertRCNNATT hybrid model to solve these problems. First, the BERT pre-training is used to generate semantic vectors rich in context information. Then, we use the RCNN and the max-pooling attention that considers the weight distribution problem for further classification. Our model has an accuracy rate of 94.656% on the SST-2 data set, which outperforms the state-of-the-art classification model.
Technolegosprudence and its challenges: Investigating its relevance in the 21st Century

The evolution of technology, while necessary and inevitable, has often been met with scepticism, and often rightfully so. However, from the advent of technology in personal computers (PCs) to the latest developments in the internet, technology has advanced by leaps and bounds. One wonders whether the legal framework to regulate the same has been able to keep pace. The authors note the undoubted benefits of technology in breeding innovation and the developments in technology that have brought humanity closer than ever before. Additionally, the authors explain that the march of technology is inevitable. The best way to deal with the advancements is to adapt the law accordingly so that no one suffers from technological ineptitude through what the authors call technolegosprudence. In this regard, the authors explain how the data management systems need to improve and why the gap of technological ineptitude between the developing and the developed nations in the world requires to be addressed urgently. Moreover, the authors show why techno-legal development needs to be uniform globally and why robust data protection, data security and data privacy regimes across the world is the need of the hour. The authors, through this paper, thus trace the evolutionary route of the jurisprudence related to technology, arguing why better laws and regulatory policies can help to reduce the adverse effects of the advancement of technology while also recommending measures as to how the governments and other relevant stakeholders can take better steps to regulate technology in a way that enables technology to be utilised data breach the betterment of humanity.
The Effective Approach to Content-Based Image Retrieval Based on ANN Classifier

Due to the existence of vast quantities of digital data, a program must be established which can retrieve similar data from the application. In this proposed system content-based retrieval method to get similar data based on the query image data is added. The proposed method is split into three stages, preprocessing, extraction of the element and classification. The preprocessed images are considered for the characteristics of extraction, form, color, and texture dependent features. CCM and BPF are removed and they train the machine. ANN is used as a classification to classify the system. Unit efficiency is measured using the efficiency parameters and tests indicate the improved result.
Marathi and Konkani Speech Recognition using Cross-Correlation Analysis

Systems that extract information from speech are common in communications and automation, where an individual's voice is analysed and recognised by the system to follow the intended course of action. Extant research on speech recognition deals with the application of complex performance techniques of Artificial Intelligence (AI) and Machine Learning (ML) to study large non-isolated voice patterns. This paper uses simplistic performance parameters, namely, cross correlation for the identification of individual voice samples consisting of isolated Marathi and Konkani words in MATLAB. The study examines a dataset of 90 samples comprising 10 words from both languages for 10 individuals. The study presents a comparative analysis of. wav and. ogg formats of the audio samples with efficiencies of 93.5% and 88.5% for Marathi and Konkani respectively.
Irregular Gabor frames of Cauchy kernels

Clustering algorithms partition a dataset into groups of similar points. The clustering problem is very general, and different partitions of the same dataset could be considered correct and useful. To fully understand such data, it must be considered at a variety of scales, ranging from coarse to fine. We introduce the Multiscale Environment for Learning by Diffusion (MELD) data model, which is a family of clusterings parameterized by nonlinear diffusion on the dataset. We show that the MELD data model precisely captures latent multiscale structure in data and facilitates its analysis. To efficiently learn the multiscale structure observed in many real datasets, we introduce the Multiscale Learning by Unsupervised Nonlinear Diffusion (M-LUND) clustering algorithm, which is derived from a diffusion process at a range of temporal scales. We provide theoretical guarantees for the algorithm's performance and establish its computational efficiency. Finally, we show that the M-LUND clustering algorithm detects the latent structure in a range of synthetic and real datasets.
Late Breaking Results: Heterogeneous Circuit Layout Centerline Extraction for Mask Verification

With the continued feature-size shrinking in modern circuit designs, the layout performance estimation and parasitic import calculation based on the extracted centerline result play an important role in mask verification. Most previous works on layout centerline extraction focus on identifying the connectivity among the devices in a mask layout, with few ones collecting accurate centerline information for mask verification while considering design constraints. In this paper, we first formulate the centerline extraction problem as a Voronoi diagram to collect centerline points. Then, we present a graph-based invalid centerline removal algorithm to generate an initial centerline result. Finally, a complexity-driven centerline optimization method is proposed to further optimize the centerline while considering design constraints. Compared with the commercial 3D-RC parasitic parameter extraction tool RCExplorer and the 1st place in the 2019 EDA Elite Challenge Contest, experimental results show that our algorithm achieves the highest average precision ratio of 99.8% on centerline extraction while satisfying all design constraints in the shortest runtime.
Intelligent Grasping Method of Multi-joint Manipulator Based on Deep Learning

Mechanical arm is widely used in various fields of industrial production, and grasping operation is the most common application of mechanical arm in assembly line operation. When grasping an object, the robot arm should first acquire the position of the target object and adjust its posture according to the position of the target object to complete the grasping task. At present, the construction of hand-eye system based on depth camera and traditional mechanical arm, which enables the mechanical arm to grasp intelligently and autonomously, is the mainstream development trend of mechanical arm. The visual recognition based on deep learning in the visual grasping system of multi-joint manipulator can realize rapid multi-target recognition by training the deep convolution neural network without changing the recognition algorithm. In this paper, a depth neural network is built based on the idea of regression, and a reliable grasping position of a non-specific object is obtained from a given depth image, so that the multi-joint manipulator can further grasp. As an important part of machine learning, deep learning can establish an interactive relationship between multi-joint manipulator and complex dynamic environment.
A Proposed Entropy based Recommender Framework for Disaster Management

Disaster management system employs an Early Warning System (EWS) to generate warnings. The limitations of existing early warning systems are that the quality of warning is missing, and usefulness of suggested actions has not been measured. A recommender system when integrated with an EWS has the potential to overcome it. This paper proposes an entropy-based recommender system which works offline on the warning web log obtained from an EWS. The warning web log essentially stores past warnings along with a set of pre-indicators and post-indicators of the warnings. By using novel pre-disaster similarity and post-disaster similarity between warning pairs, a warning knowledge base is generated. It consists of set of trustworthy warnings for each warning in the web log. The trustworthy warnings for the current warning issued by EWS collectively form the Nearest Neighbor Warning Set (NN-WSet) for the target user. The quality of the selection of NN-WSet governs the generation of priority of actions to be taken by the target user. Considering the fact that user has limited response time, this arrangement is capable of minimising pre-disaster risk. Here authors are focussing on floods only, however, the proposed framework is generic and can be applied to management of all kinds of disasters.
Multi-unit Collaborative Monitoring and Optimal Control for Hydraulic Turbine Governing Systems

The hydraulic turbine governing system is a core part of a hydropower plant. Its’ performance has a direct impact on the safety of the power plant and power quality sending to the grid. Since the hydraulic turbine governing system is a complex nonlinear system, traditional single-unit-based prognostics and health management systems often face the problems such as fewer learning data, low accuracy, and unstable monitoring performance. In this work, we propose a multi-unit collaborative monitoring and optimal control platform and an intelligent fault diagnosis framework to solve the problems. The platform adopts a multi-layer and multi-unit governing system coordinative monitoring structure which enables the platform to obtain more monitoring states and running data. The intelligent fault diagnosis framework integrates statistical analysis, expert knowledge, and data-driven approaches to fulfill fault warning and fault diagnosis. Moreover, the platform is deployed in Fengman intelligent hydropower plant (China) and improves the performance of hydropower plant health management.
Twitter Spam Review Detection Using Hybrid Machine Learning Techniques

This paper focuses on detection and classification of spam reviews in a data set of reviews scraped from Twitter. Detection of spam reviews is a major step in screening and blocking irrelevant information posing as a review and can further be used to blacklist users. It is not possible to conclusively classify anything as spam even by human beings. Expecting a machine to do so requires extensive training and exploration of different kinds of models that may offer a multi-dimensional look at the problem that is superior to the discerning ability of human beings. Models should be devised in such a way that they look beyond simple sentiment analysis. In this work, Random Forests and Support Vector machines are designed to work together as a hybrid model to classify reviews. With hybrid methods that utilize ensemble learning, it will be possible to exploit the advantages of multiple models and combine the results to classify a review as spam.
Instance-dependent Label-noise Learning under a Structural Causal Model

Label noise will degenerate the performance of deep learning algorithms because deep neural networks easily overfit label errors. Let X and Y denote the instance and clean label, respectively. When Y is a cause of X, according to which many datasets have been constructed, e.g., SVHN and CIFAR, the distributions of P(X) and P(Y|X) are entangled. This means that the unsupervised instances are helpful to learn the classifier and thus reduce the side effect of label noise. However, it remains elusive on how to exploit the causal information to handle the label noise problem. In this paper, by leveraging a structural causal model, we propose a novel generative approach for instance-dependent label-noise learning. In particular, we show that properly modeling the instances will contribute to the identifiability of the label noise transition matrix and thus lead to a better classifier. Empirically, our method outperforms all state-of-the-art methods on both synthetic and real-world label-noise datasets.
Sensor Virtualization for Efficient Sharing of Mobile and Wearable Sensors

Users are surrounded by sensors that are available through various devices beyond their smartphones. However, these sensors are not fully utilized by current end-user applications. A key reason sensor use is so limited is that application developers must exactly identify how the sensor data can be used by smartphone apps. To mitigate this problem, we present SenseWear, a sensor-sharing platform that extends the functionality of a smartphone to use remote sensors with limited additional developer effort. Sensor sharing has several uses, including augmenting the hardware in smartphones, creating new gestural interactions with smartphone applications, and improving application's Quality of Experience via higher-quality sensors from other devices, such as wearables. We developed and present six use cases that use remote sensors in various smartphone applications. Each extension requires adding fewer than 20 lines of code on average. Furthermore, using remote sensors did not introduce a perceptible increase in latency, and creates more convenient interaction options for smartphone apps.
Manual Preliminary Coarse Alignment of 3D Point Clouds in Virtual Reality

The alignment of 3D point clouds consists of coarse alignment and precise alignment. The preliminary coarse alignment must be implemented for point clouds with a significant initial pose difference before time-consuming precise alignment. However, this procedure is normally finished on 2D interfaces manually, which leads to a partial perception of the 3D point clouds. The biased understanding may affect the operation efficiency and alignment accuracy. In this paper, we developed a VR-based prototype for manual preliminary coarse alignment of point clouds. A user study was conducted to compare the efficiency, accuracy, and usability in a controlled alignment task with both the 2D interface and the developed system. The task was graded into three levels based on the complexity of matched points clouds (simple, complex, and incomplete). The result indicated that the prototype system was effective and useful for supporting the preliminary coarse alignment task. It displayed outstanding performance for the coarse alignment of complex and incomplete point clouds.
Face Image Super Resolution using a Generative Adversarial Network

Traditional image super resolution centered around purely mathematical models are capable of creating gradient based textures, but fail to render the specific lineaments that would be expected in a realistically upscaled image. This is especially problematic in scenarios involving images of subjects whose recognition is reliant on the presence of specific characteristics, for example, faces. In this paper, we describe a deep learning model that is capable of generating an 8x upscaled photo from a low resolution image of a face. The underlying model is based on the SRGAN architecture that deviates from the conventional GAN approach of the adversarial back and forth between generator and discriminator by incorporating an added content loss whose value is dependent on the detection of the natural features in the generated image by a pre-trained VGG model. The model is trained on the Celeb Faces Attributes dataset with over 1,00,000 data points and can produce upscaled images that are realistic with a coherent presence of the natural attributes of a face.
Fast Action Recognition Based on Local and Nonlocal Temporal Feature

In this paper, we propose a mixed time-asymmetric (MTA) CNN which uses time-asymmetric convolution to extract non-local temporal feature and uses normal convolution to extract local temporal features. With the fusion of local and non-local temporal feature, our MTA CNN can achieve better action recognition accuracy while keeping the network lightweight and fast. Specially, temporal feature fusion method is designed to replace the common global average pooling in our MTA CNN so as to obtain higher-dimensional feature vector and retain more information. Extensive experimental results demonstrate that our methods can achieve comparable results on Kinetics-400 and UCF101 among leading methods with less parameters and more faster recognition speed.
RAYONISM IN EARLY MODERN ART – A FLEETING RAY OF LIGHT

Current research paper explores the fleeting Russian avant-garde artistic movement of the first half of the 20th century known as Rayonism (Rayism or Luchizm). Comparative formalist and conceptual analyses with its contemporary movements and predecessors (Cubo-Futurism, Orphism, Cubism, Futurism) are chosen as the main methods of research as permitting some clarity based on the objectively observable elements while remaining aware of ideological connotations. The article opens up with the introduction of the movement and then proceeds with comparative analyses infused with relevant elements of historical and ideological context highlighting evident similarities and distinct unique features of Rayonism as art and idea. The article is concluded with discussion of possible reasons for the (inevitable) decline of the movement (trauma of the main artists, lack of provocative edge) as well as its conceptual weaknesses (lack of genuine innovation). Its role in the greater context of the artistic world evolution is attributed to the reconceptualization of the role of artist as scientist and providing a shift from objective representation towards pure abstraction.
CP-GNN: A Software for Community Detection in Heterogeneous Information Networks

Recently, the topic of community detection (CD) in heterogeneous information networks (HINs), which contain multiple types of nodes and edges, has received much attention. However, existing CD methods could not well exploit the high-order relationship among the nodes to detect communities. To alleviate this issue, we propose to use the concept of context path to model the high-order relationship among nodes, and develop a novel context path-based graph neural network (GNN) software, called CP-GNN. It can not only learn the embeddings of nodes for detecting communities accurately, but also well capture the high-order relations between nodes unsupervisedly, which enables great convenience to the study of communities and real-world applications.
Video Text Enhancement for Recognition

The previous chapter classifies two types of text in video. When text or image is affected by blur, recognition performance degrades.
Artefact: A UML-based framework for model-driven development of interactive surface prototypes

While interactive surface prototypes may be highly application-specific, existing prototypes hint at common, recurring design considerations. Given the rapid accumulation of near-identical prototypes, there is a need to promote design reuse. In this context, existing research prototypes motivate abstracting generic structures, architectural views, and descriptions to inform future designs. This paper proposes Artefact: a UML-based framework for model-driven development of interactive surface prototypes. We define flexible base models using existing research prototypes: initial hardware and middleware abstractions to support developers in the early design stages. For validation, we use the proposed framework to capture existing research prototypes. We then conduct an interview study to learn expert perceptions towards the captured model representations. Our initial findings highlight three significant benefits: (1) an accessible graphical syntax with unambiguous model representation, (2) a system for capturing arbitrary technical specifications, and (3) flexible model representation with consistent notation. While we can not draw any absolute conclusions, initial results suggest benefits in the model-driven approach.
The Utilization of Tracker Video Analysis App to Measure Centripetal Force for Physics Teaching

The purpose of this study was to investigate the use of the Tracker video analysis application to measure the centripetal force on the circular motion in the traditional Tari Piring dance. A qualitative descriptive study was conducted. This study discovered that: 1) the Tracker application measured physical quantities related to the concept of centripetal force by analyzing the position of an object in each frame of video to a specified unit size; 2) video analysis with the Tracker application depicted physics concepts through real-world phenomena; and 3) video measurement results with the Tracker application were more precise and less uncertainty. The researchers concluded that when compared to analog measurements, the Tracker application offered several advantages. However, this application has not been widely used for educational purposes in schools. The researchers advocated for using this Tracker application in physics education, particularly when it comes to motion phenomena.
More on Fuzzy Graph Connectivity

This chapter discusses some new connectivity concepts in fuzzy graphs, namely cyclic connectivity and connectivity status. They have potential for large number of applications and a few are mentioned in the chapter. This chapter also gives a theoretical study of these concepts.
Towards a Unified Information-Theoretic Framework for Generalization

In this work, we investigate the expressiveness of the conditional mutual information (CMI) framework of Steinke and Zakynthinou (2020) and the prospect of using it to provide a unified framework for proving generalization bounds in the realizable setting. We first demonstrate that one can use this framework to express non-trivial (but sub-optimal) bounds for any learning algorithm that outputs hypotheses from a class of bounded VC dimension. We prove that the CMI framework yields the optimal bound on the expected risk of Support Vector Machines (SVMs) for learning halfspaces. This result is an application of our general result showing that stable compression schemes Bousquet al. (2020) of size $k$ have uniformly bounded CMI of order $O(k)$. We further show that an inherent limitation of proper learning of VC classes contradicts the existence of a proper learner with constant CMI, and it implies a negative resolution to an open problem of Steinke and Zakynthinou (2020). We further study the CMI of empirical risk minimizers (ERMs) of class $H$ and show that it is possible to output all consistent classifiers (version space) with bounded CMI if and only if $H$ has a bounded star number (Hanneke and Yang (2015)). Moreover, we prove a general reduction showing that leave-one-out analysis is expressible via the CMI framework. As a corollary we investigate the CMI of the one-inclusion-graph algorithm proposed by Haussler et al. (1994). More generally, we show that the CMI framework is universal in the sense that for every consistent algorithm and data distribution, the expected risk vanishes as the number of samples diverges if and only if its evaluated CMI has sublinear growth with the number of samples.