Reviewerly/exHarmony huggingface dataset:
Sustainability in migrating workloads to public clouds

In recent times, there has been a considerable increase in Cloud-Based applications and infrastructure. This has led to quicker innovations, agile businesses, availability of new services over the internet, improved collaboration, and better security. With the growth of new technologies like blockchain, quantum computing, mobility-focused applications, and edge computing, there has been an increased interest in adopting cloud services. In this paper, we highlight the different sustainability metrics and benefits while migrating workloads from the on-prem data center to the public clouds. Also, the clouds are elastic, scalable, cost-efficient, robust, and overall a better alternative to host the client applications and services. We present how the major Cloud Service Providers (CSPs) are continuously working on improving their infrastructure for a more energy efficient cloud. But with so many factors like the cost of cloud services, the location of the data center to name a few, it becomes quite a tedious task for the clients to select a cloud service provider when moving from their on-premise data center(s). Hence, we also briefly propose our solution that we are currently working on. The final goal is to have a cross-platform advisory that based on a wide-range of client-based inputs and a rich repository of current energy efficient clouds and their sustainability metrics, aims to provide them a detailed recommendation about their preferred cloud service provider. In case the client does not provide any such preference, the advisory should also recommend an ideal cloud service provider for their particular workload. This suggested action will be able to fulfill the client's constraints as well as provide them an energy efficient cloud along with a sustainability score. This score is indicative of how much improvement in the energy consumed and carbon footprint can be achieved through this migration to the suggested cloud.
Nullspaces of entanglement breaking channels and applications

Abstract Quantum entanglement breaking channels are a fundamental class of quantum operations; originally investigated for quantum information theoretic reasons, their study has since grown to touch on many aspects of quantum information science. Here we investigate the nullspace structures of entanglement breaking channels and we derive a pair of related applications. We show that every operator space of trace zero matrices is the nullspace of an entanglement breaking channel. We derive a test for mixed unitarity of quantum channels based on complementary channel behaviour and entanglement breaking channel nullspaces. We identify conditions that guarantee the existence of private algebras for certain classes of entanglement breaking channels.
Efficient Privacy-Preserving Similarity Range Query based on Pre-Computed Distances in eHealthcare

The advance of smart eHealthcare and cloud computing techniques has propelled an increasing number of healthcare centers to outsource their healthcare data to the cloud. Meanwhile, in order to preserve the privacy of the sensitive information, healthcare centers tend to encrypt the data before outsourcing them to the cloud. Although the data encryption technique can preserve the privacy of the data, it inevitably hinders the query functionalities over the outsourced data. Among all practical query functionalities, the similarity range query is one of the most popular ones. However, to our best knowledge, many existing studies on the similarity range query over outsourced data still suffer from the efficiency issue in the query process. Therefore, in this paper, aiming at improving the query efficiency, we propose an efficient privacy-preserving similarity range query scheme based on the precomputed distance technique. In specific, we first introduce a pre-computed distance based similarity range query (PreDSQ) algorithm, which can improve the query efficiency by precomputing some distances. Then, we propose our privacy-preserving similarity query scheme by applying an asymmetric scalar-product-preserving encryption technique to preserve the privacy of the PreDSQ algorithm. Both security analysis and performance evaluation are conducted, and the results show that our proposed scheme is efficient and can well preserve the privacy of data records and query requests.
GemAR: Geometry Augmented Reality Application for Elementary School Students

Much research on AR developed to support the learning process. One of them is for learning mathematics in the field of geometry for elementary school students. However, some of the developed applications based on a marker. So, It is very dependent on the availability of print markers in operating them. This research develops markerless-based Geometry Augmented Reality (GemAR) to help a 6th-grade elementary school student learning about Geometry. GemAR application has eight 3D Geometry objects that can display. There were 60 students (28 students in the experimental group and 32 students in the control group) and two class teachers involved in this study. The experimental group used the GemAR application during the teaching and learning process, while the control group used a whiteboard and slides displayed via the Projector LCD. The result is the experimental group experienced an increase in learning outcomes based on the pre-test and post-test results in both groups. GemAR application testing results with an experimental group and two class teachers, 75% agreed that the GemAR application helped students understand the content taught, and 85.71% agreed that the GemAR application was proper to use in the learning process.
Visual Reconstruction of Ancient Coins Using Cycle-Consistent Generative Adversarial Networks

In this paper, our goal is to perform a virtual restoration of an ancient coin from its image. The present work is the first one to propose this problem, and it is motivated by two key promising applications. The first of these emerges from the recently recognised dependence of automatic image based coin type matching on the condition of the imaged coins; the algorithm introduced herein could be used as a pre-processing step, aimed at overcoming the aforementioned weakness. The second application concerns the utility both to professional and hobby numismatists of being able to visualise and study an ancient coin in a state closer to its original (minted) appearance. To address the conceptual problem at hand, we introduce a framework which comprises a deep learning based method using Generative Adversarial Networks, capable of learning the range of appearance variation of different semantic elements artistically depicted on coins, and a complementary algorithm used to collect, correctly label, and prepare for processing a large numbers of images (here 100,000) of ancient coins needed to facilitate the training of the aforementioned learning method. Empirical evaluation performed on a withheld subset of the data demonstrates extremely promising performance of the proposed methodology and shows that our algorithm correctly learns the spectra of appearance variation across different semantic elements, and despite the enormous variability present reconstructs the missing (damaged) detail while matching the surrounding semantic content and artistic style.
Atmospheric Turbulence-Controlled Cryptosystems

To overcome the limitations of QKD, post-quantum cryptography, and computational security-based cryptography protocols in this paper, an atmospheric turbulence-controlled cryptosystem is proposed. The proposed encryption scheme employs the traditional scheme to utilize the atmospheric turbulence as the common source of randomness only in the initialization stage to determine the common parameters to be used in the proposed encryption scheme. To overcome low secret-key rates of traditional scheme, dictated by the long coherence time Tc of turbulence channel, the proposed encryption scheme updates the parameters of gamma-gamma distribution, used to generate irradiance samples for cumulative distribution function-based determination of the key, every Tc seconds and as such the final key is shaped by the atmospheric turbulence channel. We also describe a scheme that randomly selects one of several available paths in which the simultaneously measured irradiance samples, after interleaving, are used to generate the raw key. The secret-key rates of the proposed schemes are orders of magnitude higher compared to corresponding traditional QKD and source type physical-layer security schemes and are comparable with the state-of-the-art optical communication data rates.
Mining serial positioning episode rules by natural exponent inertia weight based swallow swarm optimization algorithm with constraint based event sequences

The Frequent Episode Mining (FEM) is a challenging framework to identify frequent episodes from a sequence database. In a sequence, an ordered collection of events defines an episode, and frequent episodes are only considered by the earlier studies. Also, it doesn’t support for the serial based episode rule mining. In this work, the episode rules are mined with precise and serial based rule mining considering the temporal factor, so that, the occurrence time of the consequent is specified in contrast to the traditional episode rule mining. The proposed work has a larger number of candidates and specific time constraints to generate the fixed-gap episodes, and mining such episodes from whole sequence where the time span between any two events is a constant which is utilized to improve the proposed framework’s performance. In order to improve the efficiency, an Optimal Fixed-gap Episode Occurrence (OFEO) is performed using the Natural Exponent Inertia Weight based Swallow Swarm Optimization (NEIWSSO) algorithm. The temporal constraints significantly evaluate the effectiveness of episode mining, and a noticeable advantage of the present work is to generate optimal fixed-gap episodes for better prediction. The effective use of memory consumption and performance enhancement is achieved by developing new trie-based data structure for Mining Serial Positioning Episode Rules (MSPER) using a pruning method. The position of frequent events is updated in the precise-positioning episode rule trie instead of frequent events to reduce the memory space. The benchmark datasets Retail, Kosarak, and MSNBC is used to evaluate the proposed algorithm’s efficiency. Eventually, it is found that it outperforms the existing techniques with respect to memory consumption and execution time. On an average, the proposed algorithm achieves 28 times lesser execution time and consumes 45.5% less memory space for the highest minimum support value on the Retail dataset compared to existing methods.
Efficient Task Allocation for Cloud Using Bat Algorithm

Cloud computing is the shared pool of heterogeneous computing, storage, and network resources across the globe. The resources are allocated to cloud users using a service level agreement. The resources are accessed by the end-users based on pricing models. In such scenario placement and management of resources over cloud datacenters is a critical issue. Task allocation in cloud places an important role in the performance of cloud and manage utilization of resource and performance of tasks. There exist various static and dynamic algorithm to solve this issue. In this work a Bat Algorithm inspired task allocation algorithm for cloud infrastructure is proposed to improve the performance of cloud in term of execution time and start time as compared to existing algorithms.
Some Features of Sentiment Analysis for Russian Language Posts and Comments from Social Networks

Abstract Sentiment analysis of different language texts is one of the very popular machine learning tasks. The complexity of its solution depends both on the characteristics of a particular language, and on the length of the evaluated texts. In our work, we consider the task of creating a sentiment analysis software tool for Russian posts and comments from the most popular social networks without any domain restriction. The features of constructing both the algorithmic and the software parts of the problem are described, some quality and performance metrics of the suggested neural network system are presented.
The quantum Zeno and anti-Zeno effects with driving fields in the weak and strong coupling regimes

Abstract Repeated measurements in quantum mechanics can freeze (the quantum Zeno effect) or enhance (the quantum anti-Zeno effect) the time-evolution of a quantum system. In this paper, we present a general treatment of the quantum Zeno and anti-Zeno effects for arbitrary driven open quantum systems, assuming only that the system–environment coupling is weak. In particular, we obtain a general expression for the effective decay rate of a two-level system subjected to arbitrary driving fields as well as periodic measurements. We demonstrate that the driving fields change the decay rate, and hence the quantum Zeno and anti-Zeno behavior, both qualitatively and quantitatively. We also extend our results to systems consisting of more than one two-level system, as well as a two-level system strongly coupled to an environment of harmonic oscillators, to further illustrate the non-trivial effect of the driving fields on the quantum Zeno and anti-Zeno effects.
Deep Likelihood Network for Image Restoration With Multiple Degradation Levels

Convolutional neural networks have been proven effective in a variety of image restoration tasks. Most state-of-the-art solutions, however, are trained using images with a single particular degradation level, and their performance deteriorates drastically when applied to other degradation settings. In this paper, we propose deep likelihood network (DL-Net), aiming at generalizing off-the-shelf image restoration networks to succeed over a spectrum of degradation levels. We slightly modify an off-the-shelf network by appending a simple recursive module, which is derived from a fidelity term, for disentangling the computation for multiple degradation levels. Extensive experimental results on image inpainting, interpolation, and super-resolution show the effectiveness of our DL-Net.
An Ethereum-based Energy Trading Protocol (EETP)

Nowadays, the proportion of the global supply of green energy sources (e.g., solar and wind) is increasing dramatically. Integrating these sources into the current smart grid is very challenging, given that the smart grid has centralized topology which is costly and inefficient. In particular, the current procedures used in the wholesale energy market are slow, time-consuming and thus expensive for small-scale energy sources (e.g., small-scale solar energy farms). Indeed, these sources are excluded in practice from the energy market. Thus, to integrate the increasing number of small-scale energy sources, there is a need to transform the smart grid into a decentralized system. A promising technology to realize this decentralization is Blockchain. With its characteristics like immutability, security, and decentralization, we believe that Blockchain will be the core technology for the future decentralized smart grid. In this paper, we propose a peer-to-peer (P2P) energy trading system based on Ethereum. The objective is to allow any participant (e.g., big/small producers and consumers) to securely trade energy with minimum cost. The proposed approach moves most of the processing and storage off-chain, with the objective to minimize the cost of on-chain (i.e., smart contract) processing and storage while guaranteeing the security of the trading process. Stable coins are used to pay for the trade so as to overcome the problem of the instability regarding cryptocurrency exchange rates. Our proposal has been implemented, tested and deployed on the Ethereum official test network Ropsten. The results show that the proposed approach implements the P2P trading process with very small cost, making it an affordable solution for all participants, including small-scale sources.
Face Identification System Using Convolutional Neural Network for Low Resolution Image

This research aims to determine the performance of face identification on closed circuit television (CCTV) cameras. There are two data classifications used, namely training data and testing data. The training data use the CASIA-Webface dataset. Meanwhile, the testing data consists of two data, namely the source data and the target data. The source data are form of photos taken using a Digital Single Lens Reflex (DSLR) camera, while the target data use video data taken with CCTV. The source data consists of 10 IDs, each of them has 1 image for each size, so the total images used in the source data are 50 IDs. While the target data are 20 IDs, each of them has 20 face images with low resolution characteristics, less light and face capture not parallel to CCTV. This research uses Convolutional Neural Network (CNN) method with ResNet50 architecture, ArcFace as a loss function in the training process and Cosine Similarity for the face identification process. ResNet50 and ArcFace use an embedding size of 512 and in the training process, ArcFace's scale and margin parameters are 64 and 0.5. The results indicate differences in accuracy, True Positive Rate (TPR) and False Positive Rate (FPR) from the face identification process between the image sizes used and the respective IDs in the source data. The method used had the highest performance for face image identification scenarios of 128 × 128 pixels with accuracy and FPR reached 99.30% and 0.02%. From the TPR, the method used had high performance at size of 512 × 512 pixels namely 91.50%.
ABSTRACT FORMS OF QUANTIFICATION IN THE QUANTIFIED ARGUMENT CALCULUS

Abstract The Quantified argument calculus (Quarc) has received a lot of attention recently as an interesting system of quantified logic which eschews the use of variables and unrestricted quantification, but nonetheless achieves results similar to the Predicate calculus (PC) by employing quantifiers applied directly to predicates instead. Despite this noted similarity, the issue of the relationship between Quarc and PC has so far not been definitively resolved. We address this question in the present paper, and then expand upon that result. Utilizing recent developments in structural proof theory, we develop a G3-style sequent calculus for Quarc and briefly demonstrate its structural properties. We put these properties to use immediately to construct direct proofs of the meta-theoretical properties of the system. We then incorporate an abstract (and, as we shall see, logical) predicate into the system in a way that preserves all the structural properties. This allows us to identify a system of Quarc which is deductively equivalent to PC, and also yields a constructive method of demonstrating the Craig interpolation theorem (which speaks in favor of the aforementioned predicate being logical). We further generalize this extension to develop a bivalent system of Quarc with defining clauses that still maintains all the desirable properties of a good proof system.
Low-Rank Nonlocal Representation for Remote Sensing Scene Classification

The nonlocal mechanism (NM) has shown its effectiveness in many real-world applications. However, it is usually criticized for its costly computation complexity in time and space, which are both <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathscr {O}(H^{2}W^{2})$ </tex-math></inline-formula> , where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$H\times W$ </tex-math></inline-formula> is the spatial dimension of input feature maps in height and width. In this letter, we first show that the NM can be expressed as a low-rank representation by theory. Then we propose a down-to-earth low-complexity global context acquisition mechanism, termed as the low-rank nonlocal representation (LNR), whose complexity in time and space are both approximatively <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathscr {O}(HW)$ </tex-math></inline-formula> . LNR is a general module that can be deployed on an arbitrary convolutional neural network (CNN) hierarchy for any visual recognition tasks. To demonstrate its superiority, experiments are carried out on four standard remote sensing scene classification benchmarks. Experimental results show that our proposed LNR can significantly reduce the computation cost by boosting the performance gain. Implementing LNR on the classical ResNet, in particular, can reduce at most 3.93 M model parameters.
An Effective Technique of Protocol-Aware Network Traffic Classification for Performance Enhancement of SDN

Software Defined Network (SDN) decouples arrange control and sending capacities, empowering system control to turn out to be straightforwardly a programmable network and the fundamental framework that is disconnected from various applications programs and system services. Traffic classification in SDN is very crucial as the unique feature of SDN can be exploited for better traffic management, flow control, security, reliability and overall performance of the network. Classifying network traffic using Machine Learning (ML) algorithms helps in better decision making for monitoring security loopholes of a network. In this paper a protocol aware traffic classification framework which is able to classify the network traffic based on different flow features has been proposed. This framework not only helps in identifying different protocols but also transitively helps in identifying benign and anomalous traffic in a network which enhances the performance of a network by minimizing the interprotocol communication time. Various supervised machine learning algorithms have been used to predict the protocol from network traffic and the results have been compared to identify an effective technique for classification of network traffic.
Spatio-Temporal Statistics With R.

Spatio-Temporal Statistics With R is an excellent book both for those seeking a broad overview of the current spatio-temporal practices and for those wanting practical spatio-temporal examples or a...