Reviewerly/exHarmony huggingface dataset:
Color Image Encryption using Synchronous CML-DNA and Weighted Bi-objective Genetic Algorithm

The proposed work in this paper exhibits a synchronous permutation-diffusion color image encryption technique that exploits Coupled Map Lattice (CML) and Deoxyribonucleic acid (DNA). Weighted Bi-objective Genetic Algorithm (BOGA) based on both entropy and Correlation Coefficient (CC) is also applied for the optimization of the same. The proposed algorithm presents an efficacious scheme which is based on three phases: Permutation, Diffusion and Optimization. The first phase involves permuting the original image pixels using three chaotic sequences produced by CML with three different initial parameters. The second phase comprises of diffusion of the permuted pixels using DNA XOR operation. The last phase optimizes the entropy as well as CC, generating efficient results of encryption. The results confirm that the using BOGA along with synchronous CML-DNA meaningfully improves the encryption performance.
Secured Sharing of Data in Cloud via Dual Authentication, Dynamic Unidirectional PRE, and CPABE

Cloud computing is an emergent computing paradigm; however, data security is a significant issue in recent time while outsourcing the data to the cloud preventing users to upload their data on cloud. The data forwarded to cloud can be protected using some cryptographic techniques based on identity, attributes, and prediction. But these algorithms lack their performance when a revoked user collude with cloud; therefore, it becomes essential to develop a secure data sharing framework with some enhanced cryptographic techniques. The proposed methodology presented a secure privacy preserving data sharing with encryption technique called dynamic unidirectional proxy re-encryption (PRE) with cipher text policy attribute-based encryption. The technique ensures the privacy, integrity, and security of the data while retrieving through the cloud. The framework is implemented in the cloud sim with java language. Experimental results proved that proposed frame work attains reasonable results compared to traditional methods.
Subjective Evaluation of 360-degree Sensory Experiences

Traditionally, most multimedia content has been developed to stimulate two of the human senses, i.e., sight and hearing. Due to recent technological advancements, however, innovative services have been developed that provide more realistic, immersive, and engaging experiences to the audience. Omnidirectional (i.e., 360-degree) video, for instance, is becoming increasingly popular. It allows the viewer to navigate the full 360-degree view of a scene from a specific point. In particular, when consumed through head-mounted displays, 360-degree videos provide increased immersion and sense of presence. The use of multi-sensory effects —e.g., wind, vibration, and scent— has also been explored by recent work, which allows an improved experience by stimulating other users' senses through sensory effects that go beyond the audiovisual content. Understanding how these additional multi-sensory effects affect the users' perceived quality of experience (QoE) in 360-degree, however, is still an open research problem at large. As a step to better understand the QoE of immersive sensory experiences, this paper presents a testbed and discusses a user-focused study on a scenario in which the user is immersed in the 360-degree video content and is stimulated through additional sensory effects. Quantitative results indicated that the sensorial effects can considerably increase the sense of presence of 360-degree videos. Qualitative results provided us with a better view of the limitations of current technologies and interesting insights such as the users' sense of surprise.
Comparison of Three CPU-Core Families for IoT Applications in Terms of Security and Performance of AES-GCM

This article describes the implementation of the AES-GCM for IoT-oriented low-end 8/16/32-bit general-purpose processors. Although various aspects of implementations of the AES-GCM for high-end processors and hardware were examined in detail, the low-end processors to a lesser extent. This article estimates the speed and memory demand for various approaches to ensuring resistance to attacks, such as timing analysis and simple power analysis by ensuring the constant algorithm execution time. A particular attention is paid to the low-level multiplication implementation in GF (2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">128</sup> ) for each architecture as a key galois/counter mode operation, because low-end processors do not have ready-made instructions for carry-less multiplication. For each AVR/MSP430/ARM Cortex-M3 processor core, a constant time implementation of carry-less multiplication is proposed, the performance of which approaches the Not Constant Time algorithm.
Transforming Wikipedia Into Augmented Data for Query-Focused Summarization

The limited size of existing query-focused summarization datasets renders training data-driven summarization models challenging. Meanwhile, the manual construction of a query-focused summarization corpus is costly and time-consuming. In this paper, we use Wikipedia to automatically collect a large query-focused summarization dataset (named W <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">iki</small> R <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ef</small> ) of more than 280,000 examples, which can serve as a means of data augmentation. We also develop a BERT-based query-focused summarization model (Q-BERT) to extract sentences from the documents as summaries. To better adapt a huge model containing millions of parameters to tiny benchmarks, we identify and fine-tune only a sparse subnetwork, which corresponds to a small fraction of the whole model parameters. Experimental results on three DUC benchmarks show that the model pre-trained on W <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">iki</small> R <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ef</small> has already achieved reasonable performance. After fine-tuning on the specific benchmark datasets, the model with data augmentation outperforms strong comparison systems. Moreover, both our proposed Q-BERT model and subnetwork fine-tuning further improve the model performance.
Optimal container resource allocation in cloud architecture: A new hybrid model

A huge variety of fields and industries depend upon cloud computing based microservice due to its high-performance capability. Also, the merit of container usage is enormous; it enable larger portability, easier and faster deployment and restricted overheads. However, the rapid evolution causes issues in terms of container automation and management, Till now, a number of research works has concentrated on solving the open issues in container automation and management. In fact, container resource allocation is the major key hole for cloud providers since it directly influences the resource consumption and system performance. In this manner, this paper introduces a new optimized container resource allocation model by proposing a new optimization concept. To make the possibility of optimal container resource allocation, a new hybridized algorithm is implanted; namely, Whale Random update assisted Lion Algorithm (WR-LA), which is the hybrid form of Lion Algorithm (LA) and Whale Optimization Algorithm (WOA) is introduced. Moreover, the solution of optimized resource allocation is made by considering objectives like Threshold Distance, Balanced Cluster Use, System Failure, and Total Network Distance, respectively. Finally, the performance of the proposed model is compared over other conventional models and proves its superiority.
Producing Trust Among Illicit Actors: A Techno-Social Approach to an Online Illicit Market

Illicit market exchanges in cybercriminal markets are plagued by problems of verifiability and enforceability: trust is one way to ensure reliable exchange. It is fragile and hard to establish. One way to do that is to use the administrative structure of the digital market to control transactions. This is common among a specific type of market – darknet cryptomarkets. These are sites for the sale of illicit goods and services, hosted anonymously using the Tor darknet. However, reliance by users on the technology and the market administrators exposes users to excessive risk. We examine a case of a market that rejects several key technological features now common in cryptomarkets but that is nonetheless reliable and robust. We apply a techno-social approach that looks at the way participants use and combine technologies with trust relationships. The study was designed to capture the interactional context of the illicit market. We aimed to examine both person-to-person interaction and the technical infrastructure the market relied on. We find that the social space of the market maintains itself through a shared common security orientation, community participation in key decisions about products sold, performing trust signalling, and relying on lateral trust between members. There are implications for how resilience in cryptomarkets is understood.
An Improved Feature Extraction Method for Texture Classification with Increased Noise Robustness

This paper presents an improved feature extraction method based on the use of state-of-the art filtering techniques and Local Binary Patterns-derived feature descriptors with applications in texture classification. The method is adaptive, being capable to determine the type of noise present in the input image and to apply the appropriate operator for the filtering step of the feature extraction technique. The improved approaches, labelled BM3DELBP (Block Matching and 3D filtering Extended Local Binary Pattern) and SARBM3DELBP (Synthetic Aperture Radar Block Matching and 3D filtering Extended Local Binary Pattern) bring significant improvements both in terms of robustness to Gaussian and speckle noise and in terms of classification accuracy, being invariant to different image transformations. We tested our approach both on synthetic textures from two standard Outex databases and on real polarimetric Synthetic Aperture Radar (SAR) images of pine forests. On all considered databases, the proposed approach proved to be above state-of-the-art LBP variants in terms of classification accuracy, even in the presence of high Gaussian and speckle noise levels.
Deep Convolutional Neural Networks for plane identification on Satellite imagery by exploiting transfer learning with a different optimizer

Object identification is on an available problem. Automating plane identification on Satellite imagery can be applied for activity and traffic patterns to monitoring airports, and including defense intelligence issues. This paper implements Deep Convolutional Neural Networks(CNN) to classify a plane in the planesnet dataset. Pre-trained model and transfer learning are deployed to overcome a limitation of computation resources by adding new top layer consists of a fully-connected layer and softmax layer to identify the new classes and re-train it. Besides, the experimental designs for testing an implementation of a pretrained model with some kinds of the optimizer to comparing a result. There are four types of optimizer. The first two are well-known optimizer namely Stochastic Gradient Descent optimizer and Adam Optimizer, while others are PowerSign and AddSign optimizer. PowerSign and AddSign optimizer are methods to minimize cost, which discover by using Recurrent neural network(RNN) and Reinforcement Learning. A result demonstrates that a plane identification on Satellite imagery can be achieved by implementing the pre-trained model and obtains an exceptional result with Adam optimizer.
Content-Aware Video Analysis to Guide Visually Impaired Walking on the Street

Although many researchers have developed systems or tools to assist blind and visually impaired people, they continue to face many obstacles in daily life—especially in outdoor environments. When people with visual impairments walk outdoors, they must be informed of objects in their surroundings. However, it is challenging to develop a system that can handle related tasks. In recent years, deep learning has enabled the development of many architectures with more accurate results than machine learning. One popular model for instance segmentation is Mask-RCNN, which can do segmentation and rapidly recognize objects. We use Mask-RCNN to develop a context-aware video that can help blind and visually impaired people recognize objects in their surroundings. Moreover, we provide the distance between the subject and object, and the object’s relative speed and direction using Mask-RCNN outputs. The results of our content-aware video include the name of the object, class object score, the distance between the person and the object, speed of the object, and object direction.
Applications of Deep Learning in Healthcare and Biomedicine

The increasing advancements and improvements in medicine and healthcare in the past few decades have ushered us into a data-driven era where a huge amount of data is collected and stored. With this change, there is a need for analytical and technological upgradation of existing systems and processes. Data collected is in the form of Electronic Health Data taken from individuals or patients which can be in the form of readings, texts, speeches or images. A means to Artificial Intelligence—'Machine Learning' is the study of models that computer systems use to self-learn instructions based on the weight of parameters without being provided explicit instructions. Parallelly with biomedical advancements in the past decade, it has been observed that there has been an increasing refinement of algorithms and tools of machine learning. Deep Learning is one of the more promising of these algorithms. It is an Artificial Neural Network that designs models computationally that are composed of many processing layers, in order to learn data representations with numerous levels of abstraction. Research suggests that deep learning might have benefits over previous algorithms of machine learning and its' suggestive better predictive performance is, hence garnering significant attention. With their multiple levels of representation and results that surpass human accuracy, deep learning has particularly found widespread applications in health informatics and biomedicine. These are in the field of molecular diagnostics comprising pharmacogenomics and identification of pathogenic variants, in experimental data interpretation comprising DNA sequencing and gene splicing, in protein structure classification and prediction, in biomedical imaging, drug discovery, medical informatics and more. The aim of this chapter is to discuss these applications and to elaborate on how they are being instrumental in improving healthcare and medicine in the modern context. Algorithms of deep learning show an improved potential in learning patterns and extracting attributes from a complex dataset. We would first introduce deep learning and developments in artificial neural network and then go on to discuss its applications in healthcare and finally talk about its' relevance in biomedical informatics and computational biology research in the public health domain. In the end future scope of deep learning algorithms would be discussed from a modern healthcare perspective.
Harmonic Networks with Limited Training Samples

Convolutional neural networks (CNNs) are very popular nowadays for image processing. CNNs allow one to learn optimal filters in a (mostly) supervised machine learning context. However this typically requires abundant labelled training data to estimate the filter parameters. Alternative strategies have been deployed for reducing the number of parameters and / or filters to be learned and thus decrease overfitting. In the context of reverting to preset filters, we propose here a computationally efficient harmonic block that uses Discrete Cosine Transform (DCT) filters in CNNs. In this work we examine the performance of harmonic networks in limited training data scenario. We validate experimentally that its performance compares well against scattering networks that use wavelets as preset filters.
IoT in Education: Its Impacts and Its Future in Saudi Universities and Educational Environments

Currently, there has been a growing concern in the Internet of Things especially in education that allows physical objects (things), sensors and actuators to communicate via the Internet, which has changed universities and educational institutions. By using Internet of Things, universities can create a sophisticated environment for its learners and staff. So, this paper proposes a literature review to illustrate the impact of Internet of Things on the educational environment. It also aims to examine user acceptance toward this technology within Saudi universities and educational institutions by using the Technology Acceptance Model which encourage them to use this technology. Overall, the results reveal that Internet of Things has been a positive impact on the educational process and university campuses. Also, there is a strong agreement among the study individuals on the future of the Internet of Things in Saudi Arabia regarding the perceived usefulness and the ease of use of this technology.
Engagement of Pregnant Women and Mothers over WhatsApp

WhatsApp, the world's most popular messaging application, offers significant opportunities for people to engage with each other, share their knowledge and experiences. In this paper, we investigate the nature of engagement in three WhatsApp groups, meant for promoting maternal and child health, consisting of 364 pregnant women, 249 new mothers, and three medical practitioners. We conducted thematic analysis of data obtained from these groups. Based on our analysis - 1732 messages, 121 multimedia artifacts and discussion with the medical practitioners - we found the participants' expectation from medical practitioners, their problem of repeatedly asking similar questions and multimedia practices of participants and practitioners in the groups.
Linear Codes From Perfect Nonlinear Functions Over Finite Fields

In this paper, a class of p-ary 3-weight linear codes and a class of binary 2-weight linear codes are proposed respectively by virtue of the properties of the perfect nonlinear functions over F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p(m)</sub> and (m, s)-bent functions from F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2(m)</sub> to F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2(s)</sub> , where p is an odd prime and m, s are positive integers. The weight distributions are completely determined by the sign of the Walsh transform of weakly regular bent functions and the size of the preimage of the employed (m, s)-bent functions at the zero point, respectively. As a special case, a class of optimal linear codes meeting Griesmer bound is obtained from our construction.
Learning to Generate Personalized Product Descriptions

Personalization plays a key role in electronic commerce, adjusting the products presented to users through search and recommendations according to their personality and tastes. Current personalization efforts focus on the adaptation of product selections, while the description of a given product remains the same regardless of the user who views it. In this work, we propose an approach to personalize product descriptions according to the personality of an individual user. To the best of our knowledge, we are the first to address the problem of generating personalized product descriptions. We first learn to predict a user's personality based on past activity on an e-commerce website. Then, given a user personality, we propose an extractive summarization-based algorithm that selects the sentences to be used as part of a product description in accordance with the given personality. Our evaluation shows that user personality can be effectively learned from past e-commerce activity, while personalized descriptions can lead to a higher interest in the product and increased purchase likelihood.
A Deep Learning Approach to Radio Signal Denoising

This paper proposes a Deep Learning approach to radio signal de-noising.Being data-driven, it allows de-noising signals (corresponding to distinct protocols) without requiring explicit use of expert knowledge: in this way it grants higher flexibility.The core component of the Artificial Neural Network architecture used in this work is a Convolutional De-noising AutoEncoder.We report about the performance of the system in spectrogram-based denoising of the preamble of protocols from the IEEE 802.11 family, studied using simulation data.This approach can be used within a machine learning pipeline: the denoised data can be fed to a protocol classifier.A further perspective advantage of using the AutoEncoders in such a pipeline is that they can be co-trained with the downstream classifier (the protocol detector), to optimize its accuracy.
Design of Quantum Computing Circuits

Quantum computing is an emerging method of computation that promises to accomplish computational tasks and algorithms that are too difficult to perform on existing computing paradigms. Some of the promising applications of quantum computing includes number theory, encryption, search, and scientific computation. Circuits must be designed to apply quantum machines to these applications. This work introduces quantum circuit design. We provide a brief introduction to quantum computing, and then, we illustrate gates used in quantum circuit design. We conclude by introducing quantum circuits for arithmetic operations.
Block change learning for knowledge distillation

Deep neural networks perform well but require high-performance hardware for their use in real-world environments. Knowledge distillation is a simple method for improving the performance of a small network by using the knowledge of a large complex network. Small and large networks are referred to as student and teacher models, respectively. Previous knowledge distillation approaches perform well in a relatively small teacher network (20–30 layers) but poorly in large teacher networks (50 layers). Here, we propose an approach called block change learning that performs local and global knowledge distillation by changing blocks comprised of layers. The method focuses on the knowledge transfer without losing information in a large teacher model, as the approach considers intra-relationships between layers using local knowledge distillation and inter-relationships between corresponding blocks. The results are demonstrated this approach as superior to state-of-the-art methods using feature extraction datasets (Market1501 and DukeMTMC-relD) and object classification datasets (CIFAR-100 and Caltech256). Furthermore, we showed that the performance of the proposed approach was superior to that of a fine-tuning approach using pretrained models.