Reviewerly/exHarmony huggingface dataset:
Optimizing Video Compression &amp; Decompression Using Deep Learning Networks

Over recent years, the necessity for video streaming has grown significantly. People have incorporated the habit of watching videos and sharing them over electronic devices very often into their daily lives. This activity has made the process of storing a video and transferring it much more challenging for service providers, and in turn, increased the need for rugged and robust algorithms that can compress videos. One of the latest video compression standards, High-Efficiency Video Coding (HEVC), also known as H.265, supports resolutions up to 8192Å∼4320Å, including 8K UHD. While maintaining a high-quality image, HEVC reduces the storage reduction by 50% as it encodes video at the lowest possible bit rate. In this paper, Recurrent Neural Networks (RNN) and Convolutional Neural Networks (CNN) aid in compressing the video frames, which are already known to give a better compression than JPEG. Further, the video frames are enhanced using Image Processing Filters (Guided, Bilateral and Gaussian filters). The standard Moving Picture Experts Group (MPEG) algorithm is applied to these video frames in the final stage. A significant data set of diverse videos are given as input to the training data to minimize error and distortion on a single video frame.
Cloud Computing Enhanced Hybrid Encryption Algorithm for Real Time Security Analysis

We can say that Cloud computing is a term that is deployed to illustrate multiple concepts of computing that include multiple computers linked through a real-time large network of communication such as the internet. Cloud computing is a developing paradigm that has in recent times attracted a lot of researchers because of its capability to decrease the costs related to computing.  Due to the growth of cloud-computing procedures, the rapid increase in services of the cloud became outstanding. In today’s unsecured world data security is an autonomously challenging problem. Security has become all the way so important these days. When considering the cloud the models of security are defined as confidential, authenticate, accessible, data integrity  and recovery.
Multi-modal Variational Faster R-CNN for Improved Visual Object Detection in Manufacturing

Visual object detection is a critical task for a variety of industrial applications, such as robot navigation, quality control and product assembling. Modern industrial environments require AI-based object detection methods that can achieve high accuracy, robustness and generalization. To this end, we propose a novel object detection approach that can process and fuse information from RGB-D images for the accurate detection of industrial objects. The proposed approach utilizes a novel Variational Faster R-CNN algorithm that aims to improve the robustness and generalization ability of the original Faster R-CNN algorithm by employing a VAE encoder-decoder network and a very powerful attention layer. Experimental results on two object detection datasets, namely the well-known RGB-D Washington dataset and the QCONPASS dataset of industrial objects that is first presented in this paper, verify the significant performance improvement achieved when the proposed approach is employed.
Time-series Analysis of Newspaper Articles for Automatic Event Detection using LDA

Newspapers are useful for understanding important past events. However, reading all articles is difficult and time-consuming. Automatic detection of characteristic events from past time series text data helps an interested user to find important past events. Automatic topic classification of newspaper articles and visualizing the change of topics over a certain period are used for past event detection. In this study, automatic topic classification is performed using Latent Dirichlet Allocation (LDA). The daily changes in the topics are measured by using different distance metrics to detect the changes which indicate important events. The idea has been implemented with a simple and small data set to check its validity.
The Uncontrolled Web: Measuring Security Governance on the Web

While websites are becoming more and more complex daily, the difficulty of managing them is also increasing. It is important to conduct regular maintenance against these complex websites to strengthen their security and improve their cyber resilience. However, misconfigurations and vulnerabilities are still being discovered on some pages of websites and cyberattacks against them are never-ending. In this paper, we take the novel approach of applying the concept of security governance to websites; and, as part of this, measuring the consistency of software settings and versions used on these websites. More precisely, we analyze multiple web pages with the same domain name and identify differences in the security settings of HTTP headers and versions of software among them. After analyzing over 8,000 websites of popular global organizations, our measurement results show that over half of the tested websites exhibit differences. For example, we found websites running on a web server whose version changes depending on access and using a JavaScript library with different versions across over half of the tested pages. We identify the cause of such governance failures and propose improvement plans.
Indonesian Customs and Excise Training Center During COVID-19 Pandemic: Innovative System of Educational Process Management

The COVID-19 pandemic has affected all aspects of Indonesia that changes various aspects of life. The Customs and Excise and Training Center (CETC) as one of the training organizer under the Financial Education and Training Agency (FETA), Ministry of Finance, also felt the impact. Since March 2020, all classical training activities will be stopped. Every Division in CETC synergized to carry out the strategy for implementing education and training during the pandemic. This research delivered the analysis of the training policy at FETA, the analysis of the strategies and creative decisions which is carried out by CETC in organizing the training program during the pandemic and the analysis of the challenges that CETC faced in organizing training during the pandemic and the ways of solving it. Qualitative Research Methods are used in the research. Minister of Finance Decrees and the other implementing of the decrees were analyzed as a regulatory framework for soft skills development decisions in a pandemic. The result of the study indicates that the training policy at the Customs and Excise Education and Training Center was innovative in organizing the educational process and the creative use of educational technologies, especially, in those training programs where personal contact between lectures and students is needed. Since 2018 FETA has made policies on human resource development and soft skills development by forming a strategy called the Ministry of Finance Corpu (Kemenkeu Corpu). The training policy at the CECT refers to the policy determined by FETA.
Deep homography convolutional neural network for fast and accurate image registration

In this paper, a deep convolution neural network for image registration using homography transformation is proposed to improve the speed and accuracy of image registration. The four-corner homography parameterization is carried out by randomly clipping and perturbing the image block, and then the mapping from one image to another is completed to form the homography image registration dataset. In this network architecture, the homography matrix is obtained by returning the mean square error to the corner variables of the local region. In the preprocessing stage, the image is equalized by the histogram and the feature is magnified. The trained homography matrix is used for the affine transformation of the registered image to verify the effectiveness of the model. We test the dataset of homography image registration and experiment on various noises and various image enhancement effects. We also compare several traditional algorithms. The results prove that the accuracy of the proposed model is state-of-art. The processing speed of a single image is only 0.28 seconds, which has strong noise adaptability and the best performance.
Visualization of trajectory-based queries in images database

In image databases, queries are usually carried out by comparing the similarity of features extracted from the images, such as texture, shape and color in order to find the images most similar to the defined query center. However, we propose in this work the visual analysis of trajectory-based queries, where instead of defining a single image as the query center, a set of images that represent different temporal instances (“query trajectory”) is defined, retrieving the trajectories belonging to the delimited search area surrounding this query trajectory. This work proposes techniques for visualization of complex data trajectories, considering similarity. The attribution of visual context in the visualization of these trajectories may help in the perception of knowledge in the hidden structures of the data. We developed techniques to summarize related trajectories of classified data and rendering options to improve the visual context of the query in a virtual reality visualization environment.
Towards Visualizing and Detecting Audio Adversarial Examples for Automatic Speech Recognition

Automatic speech recognition (ASR) systems are now ubiquitous in many commonly used applications, as various commercial products rely on ASR techniques, which are increasingly based on machine learning, to transcribe voice commands into text for further processing. However, audio adversarial examples (AEs) have emerged as a serious security threat, as they have been shown to be able to fool ASR models into producing incorrect results. Although there are proposed methods to defend against audio AEs, the intrinsic properties of audio AEs compared with benign audio have not been well studied. In this paper, we show that the machine learning decision boundary patterns around audio AEs and benign audio are fundamentally different. In addition, using dimensionality reduction techniques, we show that these different patterns can be distinguished visually in 2D space. Based on dimensionality reduction results, this paper also demonstrates that it is feasible to detect previously unknown audio AEs using anomaly detection methods.
Addressing Target Shift in Zero-shot Learning using Grouped Adversarial Learning

Zero-shot learning (ZSL) algorithms typically work by exploiting attribute correlations to make predictions for unseen classes. However, these correlations do not remain intact at test time in most practical settings, and the resulting change in these correlations leads to adverse effects on zero-shot learning performance. In this paper, we present a new paradigm for ZSL that: (i) utilizes the class-attribute mapping of unseen classes to estimate the change in target distribution (target shift), and (ii) propose a novel technique called grouped Adversarial Learning (gAL) to reduce negative effects of this shift. Our approach is widely applicable for several existing ZSL algorithms, including those with implicit attribute predictions. We apply the proposed technique (gAL) on three popular ZSL algorithms: ALE, SJE, and DEVISE, and show performance improvements on 4 popular ZSL datasets: AwA2, aPY, CUB, and SUN.
Web-Based Learning for Adolescents

This chapter assesses the innovations and challenges of web-based learning for adolescents. The technology acceptance model (TAM) was used to establish the determinants for the challenges and innovations of web-based learning for adolescents. Challenges coined include virtual engagement, adaptability, motivation, technical issues, time management, learning styles, destructions, communication skills, and the digital divide. Web-based learning is an emerging and innovative approach to teaching and learning that meets the evolving needs of adolescent learning. Adolescents should embrace the transformation from traditional brick-and-mortar classroom practices to a web-based environment. Stakeholders must continuously overcome the challenges of web-based learning. Future research can concentrate on developing well-defined web-based course content that supports the holistic learning needs of adolescents and the development of a web-based learning model for adolescents.
Hybrid Quantum-Classical Algorithms for Loan-Collection Optimization with Loan-Loss Provisions

Banks are required to set aside funds in their income statement, known as a loan-loss provision (LLP), to account for potential loan defaults and expenses. By treating the LLP as a global constraint, we propose a hybrid quantum-classical algorithm to solve a specific case of quadratic constrained binary optimization (QCBO) models for loan-collection optimization. The objective is to find a set of optimal loan-collection actions that maximizes the expected net profit presented to the bank as well as the financial welfare in the financial network of loanees, while keeping the LLP at its minimum. Our algorithm consists of three parts: a classical divide-and-conquer algorithm to enable a large-scale optimization, a quantum alternating operator ansatz (QAOA) algorithm to maximize the objective function, and a classical sampling algorithm to handle the LLP. We apply the algorithm to a real-world data set with 600 loanees and five possible collection actions. The QAOA is performed using up to 35 qubits on a classical computer. We show that incorporating the QAOA can enhance the expected net profit by approximately $70\mathrm{%}$ in comparison to scenarios where the QAOA is absent from the hybrid algorithm. Although this improvement does not constitute definitive evidence of quantum advantage, our work illustrates the use of near-term quantum devices to tackle real-world optimization problems.
Obstacle Avoidance Control for Multisteering Mode of Multiaxle Wheeled Robot Based on Trajectory Prediction Strategy

A multiaxle wheeled robot is difficult to be controlled due to its long body and a large number of axles, especially for obstacle avoidance and steering in narrow space. To solve this problem, a multisteering mode control strategy based on front and rear virtual wheels is proposed, and the driving trajectory prediction of the multiaxle wheeled robot is analyzed. On this basis, an obstacle avoidance control strategy based on trajectory prediction is proposed. By calculating the relationship between the lidar points of the obstacle and the trajectory coverage area, the iterative calculation of the obstacle avoidance scheme for the proposed steering is carried out, and the feasible obstacle avoidance scheme is obtained. The mechanical structure, hardware, and software control system of a five-axle wheeled robot are designed. Finally, to verify the effectiveness of the obstacle avoidance strategy, a Z-shaped obstacle avoidance experiment was carried out. The results confirm the effectiveness of the proposed control strategy.
Realization of multiaxial load compensation mechanism capable of adjusting compensation force in any position

This study aims at realization of multiaxial load compensation mechanism capable of adjusting compensation force in any position. By using a load compensating mechanism on an articulated manipulator, it is pointed out that the gravity of the manipulator and the object can be fully compensated regardless of those position. This mechanism reduces the energy consumption while operating and makes it possible to easily move the weightless manipulator. The purpose of this study is to clarify a design theory of a novel load compensating manipulator with the following three performance requirements. (Performance A) Since the load of the object is not uniquely determined, the compensating force can be adjusted without external energy inflow. (Performance B) Since the position of the object to be grasped is not uniquely determined, the compensation force can be adjusted in multiple positions. (Performance C) Since the picking work requires multiple degrees of freedom, it is possible to adjust the compensation force with multiple axes. For this purpose, a constant-torque spring and a transmission are used to enable adjustment of the compensation force in any position (Performance A and B), and differential gears are added to them due to enable easy multi-axis operation (Performance C). In order to evaluate the feasibility of the proposed design theory, the testing machine equipped with a constant-torque spring, a transmission for bicycles, and a differential gear for three-wheeled motorcycles are constructed to realize the theory. As a result, the error rate of the compensation force is less than 9%, and the standard deviation is less than 0.8 Nm when the compensation force is adjusted in 10 different positions. These results indicate that the proposed theory and the mechanism embody a multiaxial load compensation function that can adjust the compensation force in any position.
I-Generalized and Kullback - Leibler Divergences for Content-Based Image Retrieval

Technological advancement has increased the amount of information is generated every day. The social and economic relevance of image recovery systems has created the need for improvement. Computing similarity between two images is a fundamental step in a CBIR system to retrieve images of interest. A proper similarity measure choice is the essence for efficient and effective image retrieval. Therefore, recent studies have used Bregman divergences in researches because of its flexibility in similarity analysis. Thus, this work aims to propose an efficient and robust method using the divergences of Bregman I-Generalized and Kullback Leibler to be used as measures of similarity in CBIR systems. The model bag of the visual word approach based on image subregions was used to characterize the images. Experiments used public databases: Holiday and Caltech101. A result shows our proposal achieved satisfactory gains when compared to Euclidean and Cosine distances in different classifiers. Moreover, our approach shows promising and competitive results when compared to methods presented in the literature. Therefore, using divergences with appropriate treatment can be used as similarity functions in parametric classifiers to minimize semantic gap em CBIR.
Morphological Map based Single Image Defogging

foggy and hazy weather conditions causes several problems in different practical scenarios such as detection and tracking of object, automatic vehicle assistance, surveillance etc. Images captured during these poor weather conditions are deteriorated in terms of image sharpness and color contrast. In this paper, morphological map based defogging techniques has been proposed. Proposed technique is compared with state-of-the-art methods using different parameters. Comparison result shows that proposed technique gives better result in terms of image quality.
Assessment of Pronunciation in Language Learning Applications

This paper proposes a smartphone application meant to be used in the process of learning a new language. Our application introduces to its users a series of exercises oriented towards word reproduction, aiming to enhance one’s vocabulary alongside improving the pronunciation, being capable to indicate the flaws in the user’s utterance. The targeted users are Romanian language speakers willing to learn and practice English, with profiles for both children (beginners) and adults (advanced). The core of the application is the pronunciation module. It will be presented with two methods of analysing the accuracy of the pronunciation and the benefits and disadvantages brought by each of them. The users of this application will take advantage of two important factors involved in the process of studying a foreign language: applying it and receiving continuous feedback.
NFT-K: Non-Fungible Tangent Kernels

Deep neural networks have become essential for numerous applications due to their strong empirical performance such as vision, RL, and classification. Unfortunately, these networks are quite difficult to interpret, and this limits their applicability in settings where interpretability is important for safety, such as medical imaging. One type of deep neural network is neural tangent kernel that is similar to a kernel machine that provides some aspect of interpretability. To further contribute interpretability with respect to classification and the layers, we develop a new network as a combination of multiple neural tangent kernels, one to model each layer of the deep neural network individually as opposed to past work which attempts to represent the entire network via a single neural tangent kernel. We demonstrate the interpretability of this model on two datasets, showing that the multiple kernels model elucidates the interplay between the layers and predictions.
Web-Based Sales Information System Using the FAST Method and PIECES Frameworks

This study aims to determine the effect of the effectiveness of information systems and web-based information system development. This study used a quantitative method and data collection used are primary data. The primary data source was through interviews and questionnaires. The data processing technique will be performed using the computational calculation of the SPSS 23 program. The result of this research shows us that the effectiveness of information systems has a significant positive effect on the quality of information. The development of information systems is tailored for the company's need to be able to create an effective system that can improve the quality of information generated both in terms of accuracy, timeliness, relevance, completeness, and security. The information system is effective if the system can run well and easy to use in managing data.
Bibliographic References to Publications from Online Resources. Normalization Requirements

The article discusses the rules for compiling bibliographic references to cited or mentioned electronic documents placed in information and telecommunications networks of the following categories: all types of electronic documents placed in information and telecommunications networks, regardless of the primary source of their publication (including republications, integrated and multimedia electronic documents, software and databases), groups of homogeneous and heterogeneous electronic documents, components of an electronic document (the fragment of a text, part of a work or a publication, a block of information in an integrated or multimedia document). The authors specify the bibliographic record for making a reference to electronic documents that have analogies in the system of traditional scholarly communications (books, articles, conference materials, etc.). Alternative requirements for making a bibliographic record when referring to documents that are not included in other bibliographic standards are proposed: research data; an integrated electronic document and its component part (messages and responses to them in social networks, chats and forums, on news feeds and on information resources; a fragment of a document, including a multimedia one; an information resource (website, portal); computer programs.