Reviewerly/exHarmony huggingface dataset:
EVSRNet: Efficient Video Super-Resolution with Neural Architecture Search

With the development of convolutional neural networks (CNN), the super-resolution results of CNN-based method have far surpassed traditional method. In particular, the CNN-based single image super-resolution method has achieved excellent results. Video sequences contain more abundant information compare with image, but there are few video super-resolution methods that can be applied to mobile devices due to the requirement of heavy computation, which limits the application of video super-resolution. In this work, we propose the Efficient Video Super-Resolution Network (EVSRNet) with neural architecture search for real-time video super-resolution. Extensive experiments show that our method achieves a good balance between quality and efficiency. Finally, we achieve a competitive result of 7.36 where the PSNR is 27.85 dB and the inference time is 11.3 ms/f on the target snapdragon 865 SoC, resulting in a 2nd place in the Mobile AI (MAI) 2021 real-time video super-resolution challenge. It is noteworthy that, our method is the fastest and significantly outperforms other competitors by large margins.
A Survey of Ledger Technology-Based Databases

The spread of crypto-currencies globally has led to blockchain technology receiving greater attention in recent times. This paper focuses more broadly on the uses of ledger databases as a traditional database manager. Ledger databases will be examined within the parameters of two categories. The first of these are Centralized Ledger Databases (CLD)-based Centralised Ledger Technology (CLT), of which LedgerDB will be discussed. The second of these are Permissioned Blockchain Technology-based Decentralised Ledger Technology (DLT) where Hyperledger Fabric, FalconDB, BlockchainDB, ChainifyDB, BigchainDB, and Blockchain Relational Database will be examined. The strengths and weaknesses of the reviewed technologies will be discussed, alongside a comparison of the mentioned technologies.
Aspect-Based Sentiment Analysis in Hindi Language by Ensembling Pre-Trained mBERT Models

Sentiment Analysis is becoming an essential task for academics, as well as for commercial companies. However, most current approaches only identify the overall polarity of a sentence, instead of the polarity of each aspect mentioned in the sentence. Aspect-Based Sentiment Analysis (ABSA) identifies the aspects within the given sentence, and the sentiment that was expressed for each aspect. Recently, the use of pre-trained models such as BERT has achieved state-of-the-art results in the field of natural language processing. In this paper, we propose two ensemble models based on multilingual-BERT, namely, mBERT-E-MV and mBERT-E-AS. Using different methods, we construct an auxiliary sentence from this aspect and convert the ABSA problem to a sentence-pair classification task. We then fine-tune different pre-trained BERT models and ensemble them for a final prediction based on the proposed model; we achieve new, state-of-the-art results for datasets belonging to different domains in the Hindi language.
Topic-Based Document-Level Sentiment Analysis Using Contextual Cues

Document-level Sentiment Analysis is a complex task that implies the analysis of large textual content that can incorporate multiple contradictory polarities at the phrase and word levels. Most of the current approaches either represent textual data using pre-trained word embeddings without considering the local context that can be extracted from the dataset, or they detect the overall topic polarity without considering both the local and global context. In this paper, we propose a novel document-topic embedding model, DocTopic2Vec, for document-level polarity detection in large texts by employing general and specific contextual cues obtained through the use of document embeddings (Doc2Vec) and Topic Modeling. In our approach, (1) we use a large dataset with game reviews to create different word embeddings by applying Word2Vec, FastText, and GloVe, (2) we create Doc2Vecs enriched with the local context given by the word embeddings for each review, (3) we construct topic embeddings Topic2Vec using three Topic Modeling algorithms, i.e., LDA, NMF, and LSI, to enhance the global context of the Sentiment Analysis task, (4) for each document and its dominant topic, we build the new DocTopic2Vec by concatenating the Doc2Vec with the Topic2Vec created with the same word embedding. We also design six new Convolutional-based (Bidirectional) Recurrent Deep Neural Network Architectures that show promising results for this task. The proposed DocTopic2Vecs are used to benchmark multiple Machine and Deep Learning models, i.e., a Logistic Regression model, used as a baseline, and 18 Deep Neural Networks Architectures. The experimental results show that the new embedding and the new Deep Neural Network Architectures achieve better results than the baseline, i.e., Logistic Regression and Doc2Vec.
Data with Turiyam Set for Fourth Dimension Quantum Information Processing

Recently, neutrosophic set is considered as one of the prominent tool to deal with human cognition in three-way fuzzy space. This set has given a way to characterize the human cognition in conscious, unconscious, or indeterminate state. The problem arises when a person realizes all of three states independently as silent mode. It is observed at voting time of Indian democratic system where some people vote in a favor of a party, do not vote in favor of a party, being absent and choose none of the above. The last consciousness is turiyam state which is independent from all. It also observed at time of feedback or rating given by an expert towards which used to based on internal communication rather than true, false and uncertain activity. The internal communication which prvodies the opinion towards rating about any organization or employee is called as Turiya or Fourth dimension cognition. This paper tries to introduce this fourth dimension of human cognition as a new set called as Turiyam set with its graphical visualization via an illustrative example.
Comparative study on hash functions for lightweight blockchain in Internet of Things (IoT)

Over the last few years, there has been a great interest in the Internet of Things (IoT). This is mainly because the IoT interacts directly with people's everyday lives in critical applications, such as in smart homes and healthcare applications. IoT devices typically have a resource-constrained architecture, rendering them vulnerable to cyberattacks. Accordingly, smart devices and stored data need to be secured through lightweight and energy-efficient security solutions, which have been identified as the main challenge facing adoption of IoT systems. To address this problem, a disruptive technology (blockchain) has been foreseen by the industry and research community as being able to deliver secure, fast, reliable, and transparent solutions for IoT systems. Hence, this work investigates the adoption of lightweight blockchain technology, mainly as a method of securing IoT systems. Because hashing plays a major role in creating a robust blockchain structure, we select a number of different hash techniques to be executed on a Raspberry Pi device. In summation, this work provides a numerical study to evaluate the performance of well-known hash functions that can be used for lightweight blockchain-based IoT.
A Deep Reinforcement Learning Approach to Resource Management in Hybrid Clouds Harnessing Renewable Energy and Task Scheduling

The use of cloud computing for delivering application services over the Internet has gained rapid traction. Since the beginning of the COVID-19 global pandemic, the work from home scheme and increased business presence online have created more demand for computing resources. Many enterprises and organizations are expanding their private data centres and utilizing hybrid or multi-cloud environments for their IT infrastructure. Because of the ever-increasing demand for computing resources, energy consumption and carbon emission have become a pressing issue. Renewable energy sources have been recognized as clean and sustainable alternatives to fossil-fuel based brown energy. However, due to the intermittent nature of availability of renewable energy sources, it brings many challenges to automatically and efficiently schedule tasks under renewable energy constraints and deadlines. Task scheduling with traditional heuristic algorithms are not able to adapt quickly with changing energy availability and stochastic task arrival. In this regard, this work aims at building a novel scheduling policy with deep reinforcement learning, which automatically applies scheduling techniques like workload shifting and cloud -bursting in a geographically distributed hybrid multi-cloud environment consists of multiple private and public clouds. Our primary goals are maximizing renewable energy utilization and avoiding deadline constraint violations. We also introduce user configurable hyper-parameters to enable multi-objective scheduling on cloud cost, makespan and utilization. Our experiment results show that the proposed scheduling approach can achieve the aforementioned objectives dynamically to varying renewable energy availability.
An Efficient Adaptive Salp Swarm Algorithm Using Type II Fuzzy Entropy for Multilevel Thresholding Image Segmentation

Salp swarm algorithm (SSA) is an innovative contribution to smart swarm algorithms and has shown its utility in a wide range of research domains. While it is an efficient algorithm, it is noted that SSA suffers from several issues, including weak exploitation, convergence, and unstable exploitation and exploration. To overcome these, an improved SSA called as adaptive salp swarm algorithm (ASSA) was proposed. Thresholding is among the most effective image segmentation methods in which the objective function is described in relation of threshold values and their position in the histogram. Only if one threshold is assumed, a segmented image of two groups is obtained. But on other side, several groups in the output image are generated with multilevel thresholds. The methods proposed by authors previously were traditional measures to identify objective functions. However, the basic challenge with thresholding methods is defining the threshold numbers that the individual must choose. In this paper, ASSA, along with type II fuzzy entropy, is proposed. The technique presented is examined in context with multilevel image thresholding, specifically with ASSA. For this reason, the proposed method is tested using various images simultaneously with histograms. For evaluating the performance efficiency of the proposed method, the results are compared, and robustness is tested with the efficiency of the proposed method to multilevel segmentation of image; numerous images are utilized arbitrarily from datasets.
A Searchable Encryption Scheme with Biometric Authentication and Authorization for Cloud Environments

Cloud computing offers the possibility of providing suitable access within a network for a set of resources. Many users use different services for outsourcing their data within the cloud, saving and mitigating the local storage and other resources involved. One of the biggest concerns is represented by storing sensitive data on remote servers, which can be found to be extremely challenging within different situations related to privacy. Searchable Encryption (SE) represents a particular case of Fully Homomorphic Encryption (FHE) and at the same time represents a method composed from a set of algorithms meant to offer protection for users’ sensitive data, while it preserves the searching functionality on the server-side. There are two main types of SE: Searchable Symmetric Encryption (SSE), where the ciphertexts and trapdoors for searching are performed using private key holders, and Public Key Searchable Encryption (PKSE), in which a specific number of users have the public key based on which are capable of outputting ciphertexts and giving the possibility of producing the trapdoors by using the private key from the holder. In this article, we propose a searchable encryption system that uses biometric authentication. Additionally, biometric data are used in the trapdoor generation process, such that an unauthorized user cannot submit search queries. The proposed system contains three components: classic user authentication (based on username, password, and a message with a code using short message service (SMS), biometric authentication, and the searchable encryption scheme. The first two components can be seen as two-factor authentication (2FA), and the second component represents the initialization step of the searchable encryption scheme. In the end, we show and demonstrate that the proposed scheme can be implemented with success for medium to complex network infrastructures. We have granted special attention to the trapdoor function, which generates a value that can be used to perform the search process and search function that is based on the trapdoor pair for searching within the index structure. We provide the correctness and security proof of the operations, which gives us the guarantee that the cloud servers return the correct documents. Additionally, we discuss measuring the performance of the authentication scheme in terms of performance indicators, introducing two indicators for measuring purposes—namely, cloud average number of non-legitim the user actions for cloud purposes (CANNL) and cloud average number of legitim user actionsCANLU.
Multiscale Multiinteraction Network for Remote Sensing Image Captioning

Much of the recent work in remote sensing image captioning is influenced by natural image captioning. These methods tend to fix the defects of the model architecture to improve the previous work, but pay little attention to the differences between remote sensing images and natural images. By considering these differences, we propose a multiscale multiinteraction remote sensing image captioning model. As in Fig. 1(a), the targets in remote sensing images have a wide range of scales; while the natural images are generally taken close-up, resulting in a similar scale for the foreground targets. Due to the difference in shooting methods, the model pretrained on close-up natural images cannot capture multiscale remote sensing targets well. To alleviate this problem, we propose a two-stage multiscale structure for feature representation, where we first finetune the CNN backbone on remote sensing images for domain adaption, then we collect features from different stages as the multiscale feature representation. Moreover, due to the shooting distance, the height information of the target in the remote sensing image is greatly weakened, thus some objects like low plants and grasses become difficult to identify, as in Fig. 1(b). Thus, we further propose a multiinteraction feature representation module, where information flow of the same and different layers could effectively interact. By calculating the similarity score among features, we fuse features with high similarity, and increase the distance between features of different categories, thereby enhancing the distinguishability. Results on RSICD, Sydney-Captions, and UCM-Captions show a clear improvement over the compared methods.
PromDA: Prompt-based Data Augmentation for Low-Resource NLU Tasks

This paper focuses on the Data Augmentation for low-resource Natural Language Understanding (NLU) tasks.We propose Promptbased Data Augmentation model (PromDA) which only trains small-scale Soft Prompt (i.e., a set of trainable vectors) in the frozen Pre-trained Language Models (PLMs).This avoids human effort in collecting unlabeled indomain data and maintains the quality of generated synthetic data.In addition, PromDA generates synthetic data via two different views and filters out the low-quality data using NLU models.Experiments on four benchmarks show that synthetic data produced by PromDA successfully boost up the performance of NLU models which consistently outperform several competitive baseline models, including a state-of-the-art semi-supervised model using unlabeled in-domain data.The synthetic data from PromDA are also complementary with unlabeled in-domain data.The NLU models can be further improved when they are combined for training.
Graph Regularized Sparse Non-Negative Matrix Factorization for Clustering

The graph regularized nonnegative matrix factorization (GNMF) algorithms have received a lot of attention in the field of machine learning and data mining, as well as the square loss method is commonly used to measure the quality of reconstructed data. However, noise is introduced when data reconstruction is performed; and the square loss method is sensitive to noise, which leads to degradation in the performance of data analysis tasks. To solve this problem, a novel graph regularized sparse NMF (GSNMF) is proposed in this article. To obtain a cleaner data matrix to approximate the high-dimensional matrix, the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$l_{1}$ </tex-math></inline-formula> -norm to the low-dimensional matrix is added to achieve the adjustment of data eigenvalues in the matrix and sparsity constraint. In addition, the corresponding inference and alternating iterative update algorithm to solve the optimization problem are given. Then, an extension of GSNMF, namely, graph regularized sparse nonnegative matrix trifactorization (GSNMTF), is proposed, and the detailed inference procedure is also shown. Finally, the experimental results on eight different datasets demonstrate that the proposed model has a good performance.
Everything You Should Know About Intel SGX Performance on Virtualized Systems

Intel SGX has attracted much attention from academia and is already powering commercial applications. Cloud providers have also started implementing SGX in their cloud offerings. Research efforts on Intel SGX so far have mainly concentrated on its security and programmability. However, no work has studied in detail the performance degradation caused by SGX in virtualized systems. Such settings are particularly important, considering that virtualization is the de facto building block of cloud infrastructure, yet often comes with a performance impact. This paper presents for the first time a detailed performance analysis of Intel SGX in a virtualized system in comparison with a bare-metal system. Based on our findings, we identify several optimization strategies that would improve the performance of Intel SGX on such systems.
Eagle Techniques in Cloud Computational Formulation

The eagle expresses of cloud computing plays a pivotal role in the development of technology. In this computing world everyone is active so the end users and providers use various applications which are working as a broker for providing and managing the services. The aim of the paper is to solve the problems in such a way that brokers will provide an optimized solution for cloud service providers and the end users. The key role of allocating the efficient resources by making the algorithm which works for the time and cost optimization keeping in consideration of its quality of services and characteristics. These both are affecting the performance of these techniques is a major drawback due to low accuracy and large computational complexity of the algorithms. As per the scenario, the approach of the research is based on the rough set theory (RST). It’s a strategy to found the information revelation and handle the issues like number of parameters (Virtualization, resource sharing, cloud standardization etc). The rough set theory is the new method in cloud service selection so that the best services to provide for cloud users and efficient service improvement for cloud providers.
Regulating Blockchain

This introductory chapter provides an overview of the main legal and policy implications of blockchain technology. It proceeds in four steps. First, the chapter traces the technical and legal evolution of blockchain applications since the early days of Bitcoin, highlighting in particular the political ambitions and tensions that have marked many of these projects from the start. Second, it shows how blockchain applications have created new calculative spaces of financial markets that seek to challenge existing forms of money. Third, it discusses the core points of friction with incumbent legal systems, with a particular focus on the regulability of decentralized systems in general and data protection concerns in particular. Fourth, the chapter provides an outline to the contributions to the volume, which span a wide array of topics at the intersection of blockchain, law, and politics.
Digital Forensics using Blockchain

On considering the integrity of electronic evidence, in particular,we can see that such evidence needs to be protected from a number of undesirable outcomes namely, alteration or destruction. We need to guard against these events and others when trying to maintain system integrity and preserve the purity of evidence so that it could be acceptable in the court. Chain of Custody is nothing but the consecutive documentation of records. The Chain of Custody has all the necessary steps that a crime investigator must follow to make sure whether the information is honest. The Chain of Custody is significant because it cannot be proven that evidence was not altered during the time between collection and its usage in court. Then the collected evidence is not credible. Blockchain technology,a decentralized network currently used by Bitcoins and other Cryptocurrency networks, helps provide a secure database with the help of hashing the data and storing it in blocks.We propose to implement blockchain technology for the process of Chain of Custody, which helps in tracking the people whoaccess the data and assist in assuring the credibility of the data provided during the time of submission in court.
Imprecise Probabilities

This chapter explores the topic of imprecise probabilities (IP) as it relates to model validation. IP is a family of formal methods that aim to provide a better representationRepresentation of severe uncertainty than is possible with standard probabilistic methods. Among the methods discussed here are using sets of probabilities to represent uncertainty, and using functions that do not satisfy the additvity property. We discuss the basics of IP, some examples of IP in computer simulation contexts, possible interpretations of the IP framework and some conceptual problems for the approach. We conclude with a discussion of IP in the context of model validation.
Algorithmic properties of first-order modal logics of finite Kripke frames in restricted languages

Abstract We study the effect of restricting the number of individual variables, as well as the number and arity of predicate letters, in languages of first-order predicate modal logics of finite Kripke frames on the logics’ algorithmic properties. A finite frame is a frame with a finite set of possible worlds. The languages we consider have no constants, function symbols or the equality symbol. We show that most predicate modal logics of natural classes of finite Kripke frames are not recursively enumerable—more precisely, $\varPi ^0_1$-hard—in languages with three individual variables and a single monadic predicate letter. This applies to the logics of finite frames of the predicate extensions of the sublogics of propositional modal logics $\textbf{GL}$, $\textbf{Grz}$ and $\textbf{KTB}$—among them, $\textbf{K}$, $\textbf{T}$, $\textbf{D}$, $\textbf{KB}$, $\textbf{K4}$ and $\textbf{S4}$.