Reviewerly/exHarmony huggingface dataset:
Quantum Computation-Based Secured Key Distribution Protocols

The cryptographic system ensures authentication between the communicating parties through keys, and it cannot be sent over insecure communication channel due to eavesdropping adversary. Distribution approaches are required to support either private or public key cryptosystem that allows key sharing secretly. We represent the existing classical key distribution protocols and relevant quantum methods that overcome the challenges in classical protocols. The keys must be shared at the end of distribution process, and continuum to this, we have quantum key distribution that uses quantum principles which guarantee that key cannot be intercepted. The objective of this paper is to explore quantum key distribution protocols like BB84 and Eckert’s protocol in detail and then to analyze them with classical approaches in comparative parameters and lastly concludes the quantum protocol to be highly secure, advantageous and superior to classical existing protocols.
A generalizable saliency map-based interpretation of model outcome

One of the significant challenges of deep neural networks is that the complex nature of the network prevents human comprehension of the outcome of the network. Consequently, the applicability of complex machine learning models is limited in the safety-critical domains, which incurs risk to life and property. To fully exploit the capabilities of complex neural networks, we propose a non-intrusive interpretability technique that uses the input and output of the model to generate a saliency map. The method works by empirically optimizing a randomly initialized input mask by localizing and weighing individual pixels according to their sensitivity towards the target class. Our experiments show that the proposed model interpretability approach performs better than the existing saliency map-based approaches methods at localizing the relevant input pixels. Furthermore, to obtain a global perspective on the target-specific explanation, we propose a saliency map reconstruction approach to generate acceptable variations of the salient inputs from the space of input data distribution for which the model outcome remains unaltered. Experiments show that our interpretability method can reconstruct the salient part of the input with a classification accuracy of 89%.
Design of a personal service robot to improve therapeutic adherence in people with motor disabilities

Around 15% of people suffer from some type of disability in the world. One of them is motor disability, which prevents people with it from walking easily and going up or down stairs. Recent studies reveal that the number one cause of disability is an illness, which is why it is common for people to follow a treatment and require medication. If there is no high therapeutic adherence, the benefits offered by the medications will not be as expected. This work proposes the design of a personal service robot whose purpose is to make it easier for people with motor disabilities to take their medications and monitor their treatment, thus hoping to increase their therapeutic adherence. The proposed robot moves following lines and stops at points where the person is normally within their house and at the time the medicine should be taken. In addition, it has a camera that allows monitoring of the person through an application that can also be used to configure the intervals and dosage of the medication. The tests showed promising results in terms of time measurement and robot movement, however, problems with locating the person were encountered.
Online Sequential Complex-Valued ELM for Noncircular Signals: Augmented Structures and Learning Algorithms

Online sequential extreme learning machine (OS-ELM) has become a popular online learning strategy for single-hidden layer feedforward neural networks, and complex-valued signals are ubiquitous in real applications. In order to cater for complex-valued signals, especially for noncircular signals, in this paper we extend OS-ELM to complex domain, and propose two augmented online sequential complex ELM models by incorporating the conjugates of the network input and the hidden layer respectively. In this way, the proposed models are equipped with the capability to capture the complete second-order statistics of noncircular signals, which results in the enhanced generalization ability. The corresponding regularized models are derived to avoid the possible overfitting problem. By exploiting the algebra structure resulted from the augmented architecture, several approaches to reducing the computational complexity are also proposed. Simulation results validate the efficiency of the proposed algorithms.
The Improvement on Self-Adaption Select Cluster Centers Based on Fast Search and Find of Density Peaks Clustering

In order to solve the problem of manual selection of cluster centers in density peaks clustering algorithm, an automatic selection algorithm of cluster centers was proposed in this paper, which can calculate the change rate and difference for each data. Firstly, the local density p and the high density nearest distance δ of each data point were multiplied and sorted to calculate the difference value A between two adjacent data points, where A is a group of finite sequences from big to small, and the ratio of each item in the sequence to its next term is θ. Through the threshold range of θ and A, the cluster centers can be selected adaptively, and the number of clusters can be determined automatically. Experiment results have shown that the algorithm is suitable for non-convex data with good clustering effect.
Research Hotspots Mining and Trend Analysis of Blockchain

Abstract In this paper, the bibliography included in Web of Science is taken as the data sample. This paper analyzes articles, reviews and proceedings papers published from 2013 to 2020 in the field of blockchain to reveal the major countries and institutions. We use co-word method to identify research hotspots in the field of blockchain. The results show that the research on blockchain is an emerging research area. China and The United States are always in the leading position. Beijing University of Posts &amp; Telecommunications publish most papers in the field of blockchain. Smart contract, Internet of Thing, bitcoin, security and privacy are the hottest research topics in the field of blockchain.
Detection and Tracking of Lane Crossing Vehicles in Traffic Video for Abnormality Analysis

In this paper, we present a novel approach for detection and tracking of lane crossing/illegal lane crossing vehicles in traffic video of urban highways. For that intention, an initial pace is performed that estimates the road region of the geometrical structure. After finding the road region, every vehicle is tracked in order to detect lane crossing vehicles according to the distance between lane lines and vehicle centre, it is followed by tracking of lane crossing vehicles based on model-based strategy. The proposed system has been evaluated using recall and precision metric, which are received using experiments carried on selected video sequences of GRAM-RTM dataset and publically available video sequences. The experimental results present that our method reaches the highest accuracy for detection of vehicles and tracking of lane crossing vehicles.
Summary on Thangka Image Segmentation

Image segmentation is a basic task in the field of computer vision and image processing. At present, researchers use existing image segmentation technology to analyze and detect Thangka images, extract interesting objects, or obtain potential damaged areas to repair damaged areas in the next step. This article mainly introduces the traditional image segmentation algorithm, the current popular Deep learning algorithm, and the application of these algorithms in Thangka image processing. Although the structure of the Thangka image is relatively simple, it is rich in color information and complex in texture. It is particularly important to find a suitable method for segmenting the Thangka image. At present, the application in the special field of image processing still needs further in-depth research, which is the future development trend.
An Empirical Study to investigate the Effectiveness of Different Variants of SMOTE for Improving Web Service Anti-Patterns Prediction

In today’s world, IT professionals must ensure that all enterprise applications are running smoothly and are communicating with each other. Service-Oriented Architecture(SOA) provides the organization with a framework that makes the management of information technology systems affordable and manageable. Service-Based Systems(SBS) need to adapt themselves over time to fit in the new client prerequisites. These outcomes in the weakening of the software systems quality and plan and may cause the emergence of poor solutions called Anti-patterns. An anti-pattern is a repeated application of code or design that leads to a bad outcome. The research uncovered that the presence of anti-patterns thwarts the software systems advancement and maintenance. The early prediction of these anti-pattern using extracted features from source code helps to reduce the software system’s maintenance and enhance the quality of the software. This present work’s ideology is to investigate the viability of different data sampling technique variants empirically and the machine learning technique, Naive Bayes, in the anti-patterns prediction in web services.
Kent-PSO optimized ELM fault diagnosis model in analog circuits

Abstract Fault information in analog circuits is complex and diverse, so as to improve the accuracy of fault diagnosis, a Kent mapping and Particle Swarm Optimization (PSO) combined optimization Extreme Learning Machine (ELM) model is proposed. Firstly, the original data set of the circuit is normalized to obtain the fault data set. Secondly, Kent mapping is used to initialize the position of particles in the particle swarm, which makes the initial particle swarm more evenly distributed in the search space and enhances the global optimization ability. Third, aiming at the problem of the input weight and hidden layer bias generated randomly by the ELM are easy to lead to poor generalization ability, the Kent-PSO algorithm is used to optimize the input weight and hidden layer bias of ELM to obtain better and more stable ELM network parameters and improve the fault diagnosis ability. The diagnosis example of Sallen-Key bandpass filter shows that the proposed method has better fault diagnosis performance than PSO-ELM model.
Eksplorasi Hasil Belajar Matematika melalui Penerapan Sistem Pembelajaran Daring (SIPEDAR) di Masa Pandemi Covid-19

This study aims to describe student learning outcomes through the application of the online learning system (SIPEDAR) during the COVID-19 pandemic and to find out what features are used in learning through SIPEDAR such as assignment (assignment), attendance (presence), bigbluebottonBN, choice, databases, external tools, feedback, forums, glossaries, quizzes, chats, quizzes, lessons, scrom packages, surveys, workshops, books, files, folders, IMS content packages, labels, pages, and URLs. This type of research is qualitative research. The subjects in this study were third semester students who took Mathematics at Elementary School Class High in the elementary school teacher education study program at Universitas Sarjanawiyata Tamansiswa. The instruments used were in the form of multiple choice questions, true false statements, and descriptions. The results of the study were the average quiz score and the average assignment score in class 3A, namely 83.27 and 72.90. The average quiz score and the average assignment score in the 3F class were 83.56 and 70.67. Student learning outcomes in both classes are included in the good category so that learning using SIPEDAR is carried out well and can be modified with other e-learning platforms.
Discussion on The Data Extraction Strategy Of FAQ System Based On Chat Records

Abstract Q&amp;A data based on chat records have one characteristic: customers ask, service answers. There is a large amount of knowledge between the questions and answers of customer service. By optimizing the Q&amp;A extraction algorithm to extract knowledge, a very excellent Q&amp;A library can be constructed, thus the accuracy of FAQ system is greatly improved. By analyzing the existing data, this paper cnosiders the extraction strategy of question answering from machine learning and non-machine learning respectively. Then We compares their performance from three aspects of precision, recall and Fl-score according to their different characteristics. In order to ensure the best classification performance, grid search and K-fold crossover are also used to test the optimized classifier performance. After selecting the optimal data extraction strategy, We developed a FAQ system use this strategy, the system results show that the performance is reliable.
Image Compression Approach using Segmentation and Total Variation Regularization

In this paper we present a hybrid model for image compression based on segmentation and total variation regularization. The main motivation behind our approach is to offer decode image with immediate access to objects/features of interest. We are targeting high quality decoded image in order to be useful on smart devices, for analysis purpose, as well as for multimedia content-based description standards. The image is approximated as a set of uniform regions: The technique will assign well-defined members to homogenous regions in order to achieve image segmentation. The Adaptive fuzzy c-means (AFcM) is a guide to cluster image data. A second stage coding is applied using entropy coding to remove the whole image entropy redundancy. In the decompression phase, the reverse process is applied in which the decoded image suffers from missing details due to the coarse segmentation. For this reason, we suggest the application of total variation (TV) regularization, such as the Rudin-Osher-Fatemi (ROF) model, to enhance the quality of the coded image. Our experimental results had shown that ROF may increase the PSNR and hence offer better quality for a set of benchmark grayscale images.
Digit Recognition Applied to Reconstructed Audio Signals Using Deep Learning

Compressed sensing allows signal reconstruction from a few measurements. This work proposes a complete pipeline for digit recognition applied to audio reconstructed signals. The reconstruction procedure exploits the assumption that the original signal lies in the range of a generator. A pretrained generator of a Generative Adversarial Network generates audio digits. A new method for reconstruction is proposed, using only the most active segment of the signal, i.e., the segment with the highest energy. The underlying assumption is that such segment offers a more compact representation, preserving the meaningful content of signal. Cases when the reconstruction produces noise, instead of digit, are treated as outliers. In order to detect and reject them, three unsupervised indicators are used, namely, the total energy of reconstructed signal, the predictions of an one-class Support Vector Machine, and the confidence of a pretrained classifier used for recognition. This classifier is based on neural networks architectures and is pretrained on original audio recordings, employing three input representations, i.e., raw audio, spectrogram, and gammatonegram. Experiments are conducted, analyzing both the quality of reconstruction and the performance of classifiers in digit recognition, demonstrating that the proposed method yields higher performance in both the quality of reconstruction and digit recognition accuracy.
Entropy-Based Multiview Data Clustering Analysis in the Era of Industry 4.0

In the era of Industry 4.0, single-view clustering algorithm is difficult to play a role in the face of complex data, i.e., multiview data. In recent years, an extension of the traditional single-view clustering is multiview clustering technology, which is becoming more and more popular. Although the multiview clustering algorithm has better effectiveness than the single-view clustering algorithm, almost all the current multiview clustering algorithms usually have two weaknesses as follows. (1) The current multiview collaborative clustering strategy lacks theoretical support. (2) The weight of each view is averaged. To solve the above-mentioned problems, we used the Havrda-Charvat entropy and fuzzy index to construct a new collaborative multiview fuzzy c-means clustering algorithm using fuzzy weighting called Co-MVFCM. The corresponding results show that the Co-MVFCM has the best clustering performance among all the comparison clustering algorithms.
Attribution Based Approach for Adversarial Example Generation

Neural networks with deep architectures have been used to construct state-of-the-art classifiers that can match human level accuracy in areas such as image classification. However, many of these classifiers can be fooled by examples slightly modified from their original forms. In this work, we propose a novel approach for generating adversarial examples that makes use of only attribution information of the features and perturbs only features that are highly influential to the output of the classifier. We call this approach Attribution Based Adversarial Generation (ABAG). To demonstrate the effectiveness of this approach, three somewhat arbitrary algorithms are proposed and examined. In the first algorithm all non-zero attributions are utilized and associated features perturbed; in the second algorithm only the top-n most positive and top-n most negative attributions are used and corresponding features perturbed; and in the third algorithm the level of perturbation is increased in an iterative manner until an adversarial example is discovered. All of the three algorithms are implemented and experiments are performed on the well-known MNIST dataset. Experiment results show that adversarial examples can be generated very efficiently, and thus prove the validity and efficacy of ABAG - utilizing attributions for the generation of adversarial examples. Furthermore, as shown by examples, ABAG can be adapted to provides a systematic searching approach to generate adversarial examples by perturbing a minimum amount of features.
Document Image Edge Detection Based on a Local Hysteresis Thresholding and Automatic Setting Using PSO

The problem of image segmentation is a persistent problem that fits within the framework of computer vision. We can see year after year new trends bringing in the research’s latest advances in the hope of reaching a goal, the one of having the optimal and ideal segmentation of images. In this article, we implement an optimization mechanism using PSO algorithm, it provides the key elements for a good analysis of the impact of parameterization on the quality of segmentation. We propose an improvement of Pratt’s metric which aims at providing symmetry, the use of a distance map favourable to computation times, a locally adaptive hysteresis thresholding approach which seems promising, and another one based on Hossain’s works. Our experiments have been performed on a new image bank that we have built by merging document image datasets presented during the DIBCO competitions, setting up a multitude of different challenges, varying from images of printed texts to degraded manuscripts.
NDNet

Virtual try-on networks is a fascinating application of generative models in fashion, providing users the ease of trying on the apparel of their choice in the comfort of their homes. Several challenges have to be addressed by these networks to provide an experience that emulates a physical changing room, which includes feature preservation, natural deformation of apparel, realistic generation of occluding body parts, etc. In this work, we present Natural Deformation Network (NDNet), which aims for a more natural deformation of apparel with respect to the pose of the person while conserving the important details of the apparel as well as the person. We propose a novel Siamese Attention UNet generator to fulfill this objective. By leveraging the benefits of the Siamese architecture and attention gates in the generator, our work shows a commendable improvement in both quantitative as well as qualitative performance compared to the widely-acclaimed CP-VTON, as well as significant qualitative improvements over other works. The Siamese Attention UNet generator can be employed in future works as well for the task of naturally deforming the apparel.
Deep Block Attention and Global-local Aggregation Network for Video-based Person re-identification

Video-based person re-identification (Re-ID) refers to matching the same person across different non-overlapping cameras. It is a research-worthy and challenging problem owing to its wide-spread use for real applications, including public safety and criminal investigation. Due to the dynamic nature of the video, the problems of background clutters and spatial misalignment are more serious than image-level person Re-ID. In this paper, we propose a Deep Block Attention and Global-local Aggregation Network (DBA-GAN) to employ attention mechanism to cope with background clutters and extract global-local features to alleviate spatial misalignment. Specifically, the network has two parts, where the first part introduces a convolution block attention module, which generates attention maps along channel and spatial dimensions to focus on important pedestrian features and suppress unnecessary background feature. The second part designs a feature aggregation layer to combine global features and local features in the entire sequence to obtain richer and more discriminative feature representations. We evaluate our proposed architecture on three datasets, iLIDS-VID, PRID2011 and MARS. Extensive comparative experimental results show that our proposed method achieves state-of-the-art results.
Learning to Rank Intents in Voice Assistants

Voice Assistants aim to fulfill user requests by choosing the best intent from multiple options generated by its Automated Speech Recognition and Natural Language Understanding sub-systems. However, voice assistants do not always produce the expected results. This can happen because voice assistants choose from ambiguous intents—user-specific or domain-specific contextual information reduces the ambiguity of the user request. Additionally the user information-state can be leveraged to understand how relevant/executable a specific intent is for a user request. In this work, we propose a novel Energy-based model for the intent ranking task, where we learn an affinity metric and model the trade-off between extracted meaning from speech utterances and relevance/executability aspects of the intent. Furthermore we present a Multisource Denoising Autoencoder based pretraining that is capable of learning fused representations of data from multiple sources. We empirically show our approach outperforms existing state of the art methods by reducing the error-rate by 3.8%, which in turn reduces ambiguity and eliminates undesired dead-ends leading to better user experience. Finally, we evaluate the robustness of our algorithm on the intent ranking task and show our algorithm improves the robustness by 33.3%.