Reviewerly/exHarmony huggingface dataset:
Trade-Off Analysis Between Parallelism and Accuracy of SLIC on Apache Spark

In this paper, we present a parallel algorithm for SLIC on Apache Spark, which we call PSLIC-on-Spark. To this purpose, we have extended the original SLIC algorithm to use the operations in Apache Spark, supporting its parallel processing on multiple executors in the Apache Spark cluster. Then, we analyze the trade-off relationship of PSLIC-on-Spark between its processing speed and accuracy due to partitioning of the original image data sets. Especially, we identify two limitations in PSLIC-on-Spark, which degrade the accuracy of the original SLIC. Through experiments, we verify the trade-off relationship. Specifically, we show that PSLIC-on-Spark using 8 CPU cores reduces the processing time of SLIC by 2. 24~2.93 times while it reduces the boundary recall (BR) of SLIC by 1. 54~6.32 % and increases under-segmentation error (UE) by 1. 79~6.2 %. In contrast, PSLIC-on-Spark using 2 CPU cores reduces the processing time of SLIC by 1.38~1.45 times while it reduces the BR of SLIC by 0. 28~1.5 %, and increases UE by 0. 25~1.77 %. We also verify the effectiveness of PSLIC-on-Spark to deal with a large-scale image by showing that the processing speed of PSLIC-on-Spark becomes much more efficient as the image size becomes large. Specifically, compared to the original SLIC, the proposed SLIC-on-Spark reduces its processing time by 2.23 times for the image of 480×320 pixels and by 5.59 times for the image of 2002×1335 pixels, respectively.
An Extensive Review on Machine Learning and Deep Learning Based Cervical Cancer Diagnosis and Classification Models

Cervical cancer is a commonly occurring deadliest disease among women, which needs earlier diagnosis to reduce the prevalence. Pap-smear is considered as a widely employed technique to screen and diagnose cervical cancer. Since classical manual screening techniques are inefficient in the identification of cervical cancer, several research works have been started to develop automated machine learning (ML) and deep learning (DL) tools for cervical cancer diagnosis. This paper surveys the recent works made on cervical cancer diagnosis and classification. The recently presently ML and DL models for cervical cancer diagnosis and classification has been reviewed in detail. Besides, segmentation techniques developed for cervical cancer diagnosis also surveyed. At the end of the survey, a brief comparative study has been carried out to identify the significance of the reviewed methods.
State-of-Art: Similarity Assessment for Content Based Image Retrieval System

CBIR (Content-Based Image Retrieval) is an approach in which similar images are retrieved for a particular query image depends on the image content similarity. The similarity measurement among the feature vectors and the feature mining process is the foremost image retrieval task. The performance of CBIR is depends on both ideal features mined from the image and the suitable choice of distance metrics (similarity measures). Various retrieval procedures have been established to improve the CBIR performances. This paper presents a summary of several similarity measures utilized in CBIR and the comparative study of these distance metrics on texture and color features. By HSV and color moments, color features are mined and texture features by Gabor wavelet filter (GWF). Similarity measures such as Manhattan distance (MHD), Cosine Angle distance (CAD), Euclidean distance (ED), Geodesic distance (GD), Minkowski distance (MKD), Jaccard distance (JD), Chebyshev distance (CD), Hamming distance (HD), Mahalanobis distance (MHBD), Earth mover distance (EMD), Kullback-Leibler divergence (KLD) and Bhattacharya distance (BD) were analyzed for feature similarity. The goal of this work is to analyze the performance of various similarity measures for CBIR. A systematic comparison of various similarity measures in the CBIR system for different texture and color feature vectors are done in this paper. The performance of two CBIR systems such as color and texture features (HSV, Moments, and GWF) and color and edge directive descriptor (CEDD) are computed. Using support vector regression with butterfly optimization algorithm (SVR-BOA) classifier, the performance of both the systems have been compared for different similarity features. The comparative results are tested on the WANG database and the performance of all these similarity measures in terms of precision, accuracy, and recall are evaluated for two CBIR systems.
Railway cyber safety: An intelligent threat perspective

Cybersecurity threats to railways are increasing, both due to improvements in the techniques of hackers and the increasing merger of cyber and physical spheres. Accepted approaches to safety can be extended to consider the risks from cyber, however the nature of railways as complex cyber-physical systems of systems may require a broader approach beyond functional safety. This paper explores some of the cybersecurity hazards using a war gaming approach. The authors find that, while standard engineering approaches are effective in building new rail control system components, a broader and more creative consideration of attacks has benefits. In particular they identify the ability to cause mass disruption by targeting the fail-safes designed to ensure safety or auxiliary systems that are not directly classified within the scope of the ICS.
Low‐space bit‐serial systolic array architecture for interleaved multiplication over GF(2 <sup> <i>m</i> </sup> )

This article offers a new bit-serial systolic array architecture to implement the interleaved multiplication algorithm in the binary-extended field. The exhibited multiplier structure is more proper for VLSI implementation as it has regular cell structures as well as local communication wires between the cells. The ASIC implementation results of the suggested bit-serial multiplier structure and the existing competitive bit-serial multiplier structures previously described in the literature indicate that the recommended design achieves a notable reduction in area and significant improvement of area-time complexities by at least 28.4% and 35.7%, respectively. Therefore, it is more proper for cryptographic applications forcing more restrictions on the space.
Making the Transition to Virtual Methods in the Literacy Classroom: Reframing Teacher Education Practices

In response to the COVID 19 pandemic universities and colleges abruptly closed and teacher educators had little time to move instruction from face-to-face classrooms to digital learning environments. This sudden shift created a myriad of obstacles as instructors worked to retain pedagogically sound and effective instruction digitally—while also preparing novice teachers to teach online themselves. Adding another layer of complexity was prospective teachers’ lack of knowledge and hesitation regarding technology tools, as well as how to meaningfully integrate the tools into their teaching. Facing these challenges, we as literacy teacher educators drew upon effective methods of teacher education, literacy practices and digital literacy to rethink the way we design lessons and assignments for our literacy methods courses. The framework we created for restructuring the integration of technology into courses can be duplicated across disciplines and guide instructors to reconceptualize their use of tech tools to re-envision face-to-face and digital instruction to expand learning outcomes.
Exploring Cross-lingual Singing Voice Synthesis Using Speech Data

State-of-the-art singing voice synthesis (SVS) models can generate natural singing voice of a target speaker, given his/her speaking/singing data in the same language. However, there may be challenging conditions where only speech data in a non-target language of the target speaker is available. In this paper, we present a cross-lingual SVS system that can synthesize an English speaker's singing voice in Mandarin from musical scores with only her speech data in English. The pro-posed cross-lingual SVS system contains four parts: a BLSTM based duration model, a pitch model, a cross-lingual acoustic model and a neural vocoder. The acoustic model employs encoder-decoder architecture conditioned on pitch, phoneme duration, speaker information and language information. An adversarially-trained speaker classifier is employed to discourage the text encodings from capturing speaker information. Objective evaluation and subjective listening tests demonstrate that the proposed cross-lingual SVS system can generate singing voice with decent naturalness and fair speaker similarity. We also find that adding singing data or multi-speaker monolingual speech data further improves generalization on pronunciation and pitch accuracy.
Development of multiple computer-based testing system using open source programing model

Abstract The traditional methods of examination process is often characterized by human errors, question leakage and recording of score. In this study, the active application for generating multiple exam modules based on different types of e-exams have been presented. The first user is the instructor who submits the required exam questions based on suitable scores and different evaluation scales. The final user is the student who can find many selected e-exam modules by using PC screen interface. An Immediate scoring system can follow the final score. The used tools in the present system involves a Hypertext Markup Language (HTML), Visual Basic 6 language and MySQL correlated with oracle and system implementation. Many types of e-exam are considered such as True &amp; False, answer in one word multiple choices, matching between words in two columns and dragging into the screen. The system has been tested successfully based on multiple e-exam modules. The result of this system observed an efficient examination process due to robust generating and managing tests and less human error in the condition of multi-student with multi-lecturer.
Quantum Techniques for Image Processing

Quantum computing is revolutionizing every field of knowledge. With advent of affordable quantum computing almost possible the importance and relevance of quantum computing for all the recent research trends cannot be overemphasized. We have already witnessed quantum analogues of most celebrated classical algorithms starting from Shor’s algorithm for integer factorization, Grover’s search algorithms for unstructured database. Quantum Image Processing is an emerging field borne by applying quantum computing techniques to the Image Processing problems. This paper attempts to summarize and review the state of development in the field of Quantum Image Processing. All major quantum computing platforms have been presented, At the end a discussion on the current trends and future possibilities is presented.
An Anonymous Communication Method for Wireless Sensor Networks Based on Bilinear Pairings

With the wide application of wireless sensor networks in various fields, the security issues in wireless sensor networks have become increasingly important. The security threats of privacy information such as identity, routing, and communication relationship of nodes in the network have become the main bottlenecks for large application and deployment. In this paper, an anonymous communication scheme with enhanced anonymity and less overhead is designed for the characteristics of wireless sensor networks by using bilinear problem, which aims to prevent attackers accessing to the privacy information by attacking the network. The scheme not only provides anonymous session key, anonymous authentication and anonymous data transmission, but also provides privacy protection for transmission data and effectively resists active attacks and passive attacks. The theoretical computation shows that our scheme reduces the time delay by 39% and the communication cost by 20% compared with the classical anonymous communication scheme in [6], [8] and [9].
MnasNet Based Lightweight CNN for Facial Expression Recognition

Since the recognition of facial expressions from images and videos has great commercial and academic potential, it has become a common topic of discussion in the fields of artificial intelligence and computer vision. Considering mobile phones, versatile models should be small and quick, and precise as well. Until recently, some works have proposed various facial expression recognition models, but although these works perform better, it can still be said that there are incredible opportunities for improvement. This paper proposes a facial expression recognition method based on Automated mobile neural architecture search (MNAS). On the FER2013 dataset, our MnasNet model achieved an accuracy of 70.82%.
Image Interpolation Using Non-adaptive Scaling Algorithms for Multimedia Applications—A Survey

Image interpolation is a technology of maintaining the quality of digital images by introducing new high-quality pixels. The interpolation schemes are used in several image processing applications like computer graphics, reconstructing medical images, producing high resolution in still images, and in television pictures as well as processing remote sensing images. This proposed survey deals with the study of different non-adaptive image interpolation techniques used for multimedia applications. This study analyzes the characteristics of different non-adaptive image interpolation algorithms in terms of peak signal to noise ratio (PSNR) and structural similarity index measure (SSIM). From the review on interpolation schemes, this work identifies that several low-complexity conventional non-adaptive image interpolation schemes outperform the recently developed non-adaptive image interpolation algorithms in terms of PSNR. Further, it suggests that for any decisive applications, the conventional non-adaptive interpolation schemes can also be used to maintain the quality of picture.
Why Those Biscuits Are Relevant and on the Sideboard

Abstract In this paper, we explain why the antecedent of a biscuit conditional is relevant to its consequent by extending Douvenʼs evidential support theory of conditionals making use of utilities. By this extension, we can also explain why a biscuit conditional gives rise to the inference that the consequence is (most likely) true. Finally, we account for the intuition that (indicative) biscuit sentences are false when the antecedent is false and allow for counterfactual biscuits.
Can Service Robots Help Best Practices for COVID?

We present the design of a mobile robot that delivers hand sanitizer on the Oregon State University campus. The goal is to encourage people to follow the best health practices under COVID-19. The current hardware involves a hands-free hand sanitizer dispenser mounted atop a TurtleBot base. A wizard teleoperates the robot to approach bystanders, communicating via its approach that it would like them to participate. Future work will evaluate what communication modes best serve this goal of distributing hand sanitizer in particular contexts, and consider distributing services to where there is the most human demand.
Boosting in Univariate Nonparametric Maximum Likelihood Estimation

Nonparametric maximum likelihood estimation is intended to infer the unknown density distribution while making as few assumptions as possible. To alleviate the over parameterization in nonparametric data fitting, smoothing assumptions are usually merged into the estimation. In this paper a novel boosting-based method is introduced to the nonparametric estimation in univariate cases. We deduce the boosting algorithm by the second-order approximation of nonparametric log-likelihood. Gaussian kernel and smooth spline are chosen as weak learners in boosting to satisfy the smoothing assumptions. Simulations and real data experiments demonstrate the efficacy of the proposed approach.
Comparative Study of Color and Texture Features for Image Retrieval on Natural Datasets

The size of image datasets is increasing with a rapid pace having many application areas. To search and retrieve an image with its best matches, one needs to apply different image retrieval algorithms. The popular algorithm for retrieving images based on low level visual features is known as content based visual image retrieval system. These algorithms are distinctly dependent on their capability to extract low level features and their similarity matching mechanism for getting similar images of high accuracy and precision.This paper compares some latest methods of feature extraction having texture and color features based on their retrieval performance. The similarity into images is calculated using several distance based dissimilarity metrics.A GUI based software application (written in MATLAB) is built for comparison and evaluation of retrieval performance.
A Graph Convolutional Network for Emotion Recognition in Context

In this paper, we propose a novel framework based on graph convolutional neural network for emotion recognition by utilizing the region-based semantic information. To extract informative sematic region, we first utilized a bottom-up attention module to extract salient images. After that, these images are used for establishing graph features. This graph convolutional neural network extracts emotion features from graph node features for emotion recognition. Additionally, to remove redundant features, this paper utilizes Gate Recurrent Unit that consists of gate and memory units. In the end, we conduct some emotion recognition experiments to prove the superior performance of our model on CEAR dataset compared to the state-of-the-art approaches.
Hypervisor memory acquisition for ARM

Cyber forensics use memory acquisition in advanced forensics and malware analysis. We propose a hypervisor based memory acquisition tool. Our implementation extends the volatility memory forensics framework by reducing the processor's consumption, solves the in-coherency problem in the memory snapshots and mitigates the pressure of the acquisition on the network and the disk. We provide benchmarks and evaluation.
Fall detecting clothes in realtime based seniors full body motion capture system using multiple inertial sensors

Abstract The physical ability of the elderly will decrease along with the aging process, thus increasing the potential for falls. An elderly person who has experienced a fall and is not detected for a long time will bring many possible consequences. Early detection of an elderly fall will help to minimize this possibility by reducing the time between the occurrence of an event and the arrival of medical assistance The purpose of this research is to build a Fall Detecting Clothes in Realtime Based Art Full Body Motion Capture System Using Multiple Inertial Sensors. This system builds by Waterfall methodology where every research step is done in sequence, start from analysis, design, coding, testing. Testing methodology is black box, which is a test every feature function. The Fall Detecting Clothes in Real Time Based Systems Full Body Motion Capture System Using Multiple Inertial Sensors has been tested using black testing box with the results of all feature functions working properly. The system has been tested with several conditions with the results of all controls and output data works properly as well.
Two parallel versions of VF3: Performance analysis on a wide database of graphs

An important combinatorial problem is subgraph isomorphism, which formalizes the task of searching for occurrences of a known substructure within a larger structure represented by a graph: applications are in the fields of chemistry, biology, medicine, databases, social network analysis. Subgraph isomorphism has been proven to be NP-complete in the general case, but several algorithms exist that use heuristics to achieve an affordable run time for common classes of graphs. The need of working with larger and larger graphs makes attractive the idea of parallelizing this task; however, a consensus has not yet been reached on what is the best strategy for doing so. In this paper, we present two versions of a new, parallel algorithm that is based on a re-design of the well known VF3 algorithm. We discuss the changes that were made to efficiently distribute the work among multiple processors. The algorithms have been evaluated with a comprehensive experimentation, using several publicly available graph datasets, to demonstrate their effectiveness in exploiting the parallelism.
An Intelligent Virtual Environment for Training with Dynamic Parameters

The paper proposes a new approach for training people in Virtual Reality environment. The key idea is the intellectualization of the Virtual Environment by changing the dynamic parameters, taking into account the performance metrics. The performance metrics include the time spent for the current task and the mistakes made. If the learner makes some errors within a long time interval, then the environment is rebuilt by using the dynamic parameters. The mechanism for the changing the dynamic parameters in the Virtual Reality, based on the fuzzy model with a set of rules and the membership functions, bind the performance metrics and the characteristic for those parameters. At the same time, the complex environment can include hundreds of dynamic parameters and for this case, an optimization of the fuzzy model is needed. For this reason, a genetic algorithm with a single fuzzy rule for changing the dynamic parameters is applied. This approach is used in the development of a training simulator for a forest cutting machine (harvester). The results obtained could be beneficial to modeling of training systems and for improving the performance during the training session.
Advanced Weighted Round Robin Procedure for Load Balancing in Cloud Computing Environment

Cloud computing is based on the notions of load balancing to carry tasks to idle virtual machines for efficient distribution of the assets. The scheduling of non-preemptive jobs is an irretrievable limit and therefore been allocated to the suitable virtual machines in the beginning placement the aforementioned. Almost, the onset tasks incorporate several reliant jobs and they may complete self-governing jobs in several virtual machines and in the same virtual machines several cores. Moreover, tasks occur in the course of server execution in fluctuating arbitrary interims in several load situations. The contributing varied resources are accomplished by distributing the jobs to suitable assets by dynamic and static scheduling to build the cloud more effective and thus it advances the customer fulfillment. The aim of this study advocates the load balancing and scheduling procedure by keeping in mind the competences of virtual machines, the job dimension of every demanded task or interdependency of several tasks.
Deep Neural Query Understanding System at Expedia Group

Understanding customer intent expressed through search queries is necessary to not only provide the best shopping experience to Expedia Group customers but also to maximize marketing returns. Natural language Understanding (NLU) has ubiquitous commercial application in search, conversational platforms and more. Search queries are notoriously terse, noisy and lack grammatical cues making NLU a challenging task. Multi-lingual market scalability - a highly desirable feature for global travel agent - further add complexity. In this work, we present our NLU System for such search queries in the travel domain using multi-lingual deep learning models that perform these broad tasks: intent classification, named entity recognition and linking. We propose an alternate framework that significantly improves recognition and resolution of ill-defined sparse entities. Our system also includes cross-lingual transfer learning components featuring active learning loop to scale these models to multiple languages with minimal but high quality annotation by localization experts. We explain the business problem these models address, idiosyncrasies of our data, architecture details and implementation trade-offs.