Reviewerly/exHarmony huggingface dataset:
A Well Organized Model in Cloud Computing Platform for Data Accessing

Cloud Computing is a trending technology. The main benefit is user will pay only for the resources which have been utilized in the cloud services. Data which are stored in cloud can be accessed by the people from anywhere in the world using internet connection. Because of difficulties in data access and lack of security, in the current database system people are moving to Cloud Service Provider (CSP). Network backup and recovery method are used in CSP so there is no data loss in case of hardware failure. In this paper, we planned an efficient model in cloud computing for data accessing which will reduce the search time of providing the public key of the data owner. Not only data storage and security, data access also plays an important role to consume less time. So, in this proposed system we are going to increase the time efficiency for the data accessing.
Small-variance asymptotics for non-parametric online robot learning

Small-variance asymptotics is emerging as a useful technique for inference in large-scale Bayesian non-parametric mixture models. This paper analyzes the online learning of robot manipulation tasks...
Neighborhood-history quantum walk

History dependent discrete time quantum walks (QWs) are often studied for their lattice traversal properties. A particular model in the literature uses the state of a memory qubit at each site to record visits and to control the dynamics of the walk. We generalize this model to the neighborhood-history quantum walk (NHQW), in which the walk dynamics and the state of the memory qubits in a neighborhood of the particle's position are interdependent. To demonstrate it, we construct an NHQW on a one-dimensional lattice, with a simple neighborhood. Several dynamically interesting history dependent QWs can be realized as single-particle sectors of quantum lattice gas automata. In contrast, the NHQW constructed in this paper is realized as a single-particle sector of the more general quantum cellular automaton. The complexity of the NHQW dynamics presents a promising avenue toward richer walk strategies and a potentially useful model of QWs for the Noisy Intermediate-Scale Quantum era of quantum computing. It also modifies QWs to conceivably allow for modeling fundamental physics incorporating quantum field interactions with particles.
Enhancing Students? Learning In Electronic Engineering Technology Courses By Using Mobile Tablet Pc Technology

In this article, the authors address a modern method of instruction using the new instructional technology in the classroom.This project is
Critical Analysis on the Contribution Made by Qualitative Research to English Language Teaching

The primary objective of this paper is to assert the contribution of qualitative research in ELT. This paper has presented qualitative theories and features advantages and limitations, the criteria of good qualitative research, and analysis of two articles based on Tracy‟s model. The analysis result showed that both articles have some limitations such as the uncovering of potential resources and perspectives on how effective extensive reading programs should be implemented in tertiary education in Indonesia. However, most of the criteria that Tracy established were found in these articles, such as relevance of the area of study, timeliness and significance; sufficient, abundant, appropriate and complex use of theoretical constructs, as well as data collection and analysis processes.
Methods of removal wide-stripe noise in short-wave infrared hyperspectral remote sensing image

Purpose This paper aims to study the removal of wide-stripe noise in hyperspectral remote sensing images. There is a great deal of stripe noises in short-wave infrared hyperspectral remote sensing image, especially wide-stripe noise, which brings great challenge to the interpretation and application of hyperspectral images. Design/methodology/approach To remove the noise and to reduce the impact based on in-depth study of the mechanism of the stripe noise generation and its distribution characteristics, this paper proposed two statistical local processing and moment matching algorithms for the elimination of wide-stripe noise, namely, the gradient mean moment matching (GMMM) algorithm and the gradient interpolation moment matching (GIMM) algorithm. Findings The experiments were carried out with the practical short-wave infrared hyperspectral image data and good experiment results were obtained. Experiments show that both can reduce the impact of wide-stripe noise, and the filtering effect and the application range of the GIMM algorithm is better than that of the GMMM algorithm. Originality/value Using new methods to deal with the hyperspectral remote sensing images, it can effectively improve the quality of hyperspectral images and improve their utilization efficiency and value.
Homomorphic Rank Sort Using Surrogate Polynomials

In this paper we propose a rank based algorithm for sorting encrypted data using monomials. Greedy Sort is a sorting technique that achieves to minimize the depth of the homomorphic evaluations. It is a costly algorithm due to excessive ciphertext multiplications and its implementation is cumbersome. Another method Direct Sort has a slightly deeper circuit than Greedy Sort, nevertheless it is simpler to implement and scales better with the size of the input array. Our proposed method minimizes both the circuit depth and the number of ciphertext multiplications. In addition to its performance, its simple design makes it more favorable compared to the alternative methods which are hard to parallelize, e.g. not suitable for fast GPU implementations. Furthermore, we improve the performance of homomorphic sorting algorithm by adapting the SIMD operations alongside message slot rotation techniques. This method allow us to pack N integers into a single ciphertext and compute N comparisons at once, thus reducing $$\mathcal {O}(N^2)$$ comparisons to $$\mathcal {O}(N)$$ .
Participative Biogeography-Based Optimization

Biogeography-Based Optimization (BBO) has recently gained interest of researchers due to its simplicity in implementation, efficiency and existence of very few parameters. The BBO algorithm is a new type of optimization technique based on biogeography concept. This population-based algorithm uses the idea of the migration strategy of animals or other species for solving optimization problems. the original BBO sometimes has not resulted in desirable outcomes. Migration, mutation and elitism are three Principal operators in BBO. The migration operator plays an important role in sharing information among candidate habitats. This paper proposes a novel migration operator in Original BBO. The proposed BBO is named as PBBO and new migration operator is examined over 12 test problems. Also, results are compared with original BBO and others Meta-heuristic algorithms. Results show that PBBO outperforms over basic BBO and other considered variants of BBO.
Adaptive Fitness Predictors in Coevolutionary Cartesian Genetic Programming

In genetic programming (GP), computer programs are often coevolved with training data subsets that are known as fitness predictors. In order to maximize performance of GP, it is important to find the most suitable parameters of coevolution, particularly the fitness predictor size. This is a very time-consuming process as the predictor size depends on a given application, and many experiments have to be performed to find its suitable size. A new method is proposed which enables us to automatically adapt the predictor and its size for a given problem and thus to reduce not only the time of evolution, but also the time needed to tune the evolutionary algorithm. The method was implemented in the context of Cartesian genetic programming and evaluated using five symbolic regression problems and three image filter design problems. In comparison with three different CGP implementations, the time required by CGP search was reduced while the quality of results remained unaffected.
Tree-Based Modeling Techniques

Tree-based learning techniques are considered to be one of the best and most used supervised learning methods. Tree-based methods empower predictive models with high accuracy, stability, and ease of interpretation. Unlike linear models, they map non-linear relationships pretty well. These methods are adaptable at solving any kind of problem at hand (classification or regression). Methods like decision trees, random forest, gradient boosting are being widely used in all kinds of machine learning and data science problems. Hence, for every data analyst, it is important to learn these algorithms and use them for modeling. This chapter guide the learner to learn tree-based modeling techniques from scratch.
Comments on “Traffic Sign Recognition Using Kernel Extreme Learning Machines With Deep Perceptual Features”

This paper comments on the recently published article entitled “Traffic sign recognition using kernel extreme learning machines with deep perceptual features”. This paper presents a disagreement on the claim of the above-mentioned article related to the reduction in computational complexity. This paper also suggests not to use a single notation for two different terms.
Pyndl: Naive discriminative learning in python

Moves code to read and write events into pyndl.io module. This changes the existing API! Deprecation warnings are added accordingly.improves documentation
Pseudorandomness for read-k DNF formulas

The design of pseudorandom generators and deterministic approximate counting algorithms for DNF formulas are important challenges in unconditional derandomization. Numerous works on these problems have focused on the subclass of small-read DNF formulas, which are formulas in which each variable occurs a bounded number of times.Our first main result is a pseudorandom generator which e-fools M-term read-k DNFs using seed length poly(k, log(1/e)) · log M + O(log n). This seed length is exponentially shorter, as a function of both k and 1/e, than the best previous PRG for read-k DNFs. We also give a deterministic algorithm that approximates the number of satisfying assignments of an M-term read-k DNF to any desired (1 + e)-multiplicative accuracy in time poly(n) · min{(M/e)poly(k,log(k/e)), (M/e)O(log((k log M)/e))}.For any constant k this is a PTAS, and our runtime remains almost-polynomial (MO(log log M)) for k as large as any polylog(M). Prior to our work, the fastest deterministic algorithm ran in time MO (log M) even for k = 2, and no PTAS was known for any non-trivial subclass of DNFs. The common essential ingredients in these pseudorandomness results are new analytic inequalities for read-k DNFs. These inequalities may be of independent interest and utility; as an example application, we use them to obtain a significant improvement on the previous state of the art for agnostically learning read-k DNFs.
Constructive logical characterizations of bisimilarity for reactive probabilistic systems

Larsen and Skou characterized bisimilarity over reactive probabilistic systems with a logic including negation, conjunction, and a diamond modality decorated with a probabilistic lower bound. Later on, working on continuous state spaces, Desharnais, Edalat, and Panangaden proved that negation is not necessary to characterize the same equivalence. In this paper, we redemonstrate the former result with a simpler proof and the latter result directly on discrete state spaces without resorting to measure-theoretic arguments. Moreover, we show that conjunction can be replaced by disjunction in both logics, still characterizing the same bisimilarity. To these ends, we introduce reactive probabilistic trees , a fully abstract model for reactive probabilistic systems that allows us to uniformly prove expressiveness of the four probabilistic modal logics by means of a compactness argument. Our proofs are constructive, as they induce for each considered logic an algorithm that builds a distinguishing formula in case of two inequivalent reactive probabilistic systems.
Democratic Blockchain Design

Democracy requires rules that are designed to prevent the abuse of power and money. Blockchain technology is sometimes heralded as a solution to mitigate the problems associated with voting procedures, such as counting errors, fraud,or the improper use of money to influence the outcome of the collective choice procedure. In this article, I argue that the democratic potential of the blockchain hinges on the specific design of the rules governing the validation of blocks.In support of this argument, I shed light on the governance problems raised by standard protocols such as proof-of-work, proof-of-stake, and on-chain voting.
Extended Bat Algorithm (EBA) as an Improved Searching Optimization Algorithm

This paper presents a new searching technique by using a new variant of Bat Algorithm (BA) known as Extended Bat Algorithm (EBA). EBA introduces the spiral searching method instead of randomly searching used in original BA. Spiral searching method taken from Spiral Dynamic Algorithm (SDA) is performed to improve the accuracy and efficiency of the original algorithm such as stabilizing the convergence when reaching ideal value. EBA conserves the robustness of BA and SDA and increases the performance of the proposed algorithm. The proposed algorithm is tested by using numerical experiments with three different objective functions. The results show that EBA outperforms original Bat Algorithm (BA) and Particle Swarm Optimization (PSO) in almost test functions and successfully optimizes the numerical problems.
Cloud Computing : An Overview

Cloud computing is a technology that allows user to use computation, storage and data access services. Cloud computing is about moving services, computation and data for cost and business advantage. It is a location-transparent, centralized facility. These services are provided on the basis of cast. By making data available in the cloud, it can be more easily accessed often at much lower cost, increasing its value by enabling opportunities for enhanced collaboration, integration, and analysis on a shared common platform. This paper explores the definition, nature and potential of cloud computing and research questions related to cloud computing.
The state of web vulnerabilities

Computer Fraud & SecurityVol. 2019, No. 1 RegularsThe state of web vulnerabilitiesTom ParsonsTom ParsonsTenableSearch for more papers by this authorTom ParsonsPublished Online:5 Nov 2021https://doi.org/10.1016/S1361-3723(19)30011-9AboutSectionsView articleView Full TextPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareShare onFacebookTwitterLinked InEmail View article FiguresReferencesRelatedDetails 1 January 2019Volume 2019Issue 1ISSN (print): 1361-3723ISSN (online): 1873-7056 Metrics History Published online 5 November 2021 Published in print 1 January 2019 Information© MA Business Ltd.PDF download
Visualizing Multi-document Semantics via Open Domain Information Extraction

Faced with the overwhelming amounts of data in the 24/7 stream of new articles appearing online, it is often helpful to consider only the key entities and concepts and their relationships. This is challenging, as relevant connections may be spread across a number of disparate articles and sources. In this paper, we present a system that extracts salient entities, concepts, and their relationships from a set of related documents, discovers connections within and across them, and presents the resulting information in a graph-based visualization. We rely on a series of natural language processing methods, including open-domain information extraction, a special filtering method to maintain only meaningful relationships, and a heuristic to form graphs with a high coverage rate of topic entities and concepts. Our graph visualization then allows users to explore these connections. In our experiments, we rely on a large collection of news crawled from the Web and show how connections within this data can be explored. Code related to this paper is available at: https://shengyp.github.io/vmse .
An Enhancement Approach for Securing Neighbor Discovery in IPv6 Networks

IPv6 is willing to be the most used protocol in the future Internet even its deployment takes more time due to some constraints. Indeed, IPv6 allows addressing all objects on the Internet with public addresses. One of the new associated IPv6 protocols is Neighbor Discovery Protocol (NDP). Duplicate address detection (DAD) is one of the functions of NDP to make sure a generated IPv6 address is unique. However, since the NDP is not secure by default, the DAD is vulnerable to attacks. The attacker can prevent a new node from using a new address by failing the DAD procedure. The purpose of our technique is to secure the DAD process in an IPv6 network using a new field in NS message called Hash_Target_64. Our algorithm called DAD-Hide-Target is going to secure the DAD process by using a hash function SHA-256 and hiding the target address. Overall, the experimental results show a significant effect in term of Address Configuration Success Probability.
Visual-Semantic Alignment Across Domains Using a Semi-Supervised Approach

Visual-semantic embeddings have been extensively used as a powerful model for cross-modal retrieval of images and sentences. In this setting, data coming from different modalities can be projected in a common embedding space, in which distances can be used to infer the similarity between pairs of images and sentences. While this approach has shown impressive performances on fully supervised settings, its application to semi-supervised scenarios has been rarely investigated. In this paper we propose a domain adaptation model for cross-modal retrieval, in which the knowledge learned from a supervised dataset can be transferred on a target dataset in which the pairing between images and sentences is not known, or not useful for training due to the limited size of the set. Experiments are performed on two target unsupervised scenarios, respectively related to the fashion and cultural heritage domain. Results show that our model is able to effectively transfer the knowledge learned on ordinary visual-semantic datasets, achieving promising results. As an additional contribution, we collect and release the dataset used for the cultural heritage domain.
A Survey On Image Spam Detection Techniques

Today very important means of communication is the e-mail that allows people all over the world to communicate, share data, and perform business.Yet there is nothing worse than an inbox full of spam; i.e., information crafted to be delivered to a large number of recipients against their wishes.In this paper, we present a numerous anti-spam methods and solutions that have been proposed and deployed, but they are not effective because most mail servers rely on blacklists and rules engine leaving a big part on the user to identify the spam, while others rely on filters that might carry high false positive rate.
Provenance Analysis for Instagram Photos

As a feasible device fingerprint, sensor pattern noise (SPN) has been proven to be effective in the provenance analysis of digital images. However, with the rise of social media, millions of images are being uploaded to and shared through social media sites every day. An image downloaded from social networks may have gone through a series of unknown image manipulations. Consequently, the trustworthiness of SPN has been challenged in the provenance analysis of the images downloaded from social media platforms. In this paper, we intend to investigate the effects of the pre-defined Instagram images filters on the SPN-based image provenance analysis. We identify two groups of filters that affect the SPN in quite different ways, with Group I consisting of the filters that severely attenuate the SPN and Group II consisting of the filters that well preserve the SPN in the images. We further propose a CNN-based classifier to perform filter-oriented image categorization, aiming to exclude the images manipulated by the filters in Group I and thus improve the reliability of the SPN-based provenance analysis. The results on about 20, 000 images and 18 filters are very promising, with an accuracy higher than $$96\%$$ in differentiating the filters in Group I and Group II.
Ignorance is strength: May human mind’s unique capabilities stem from its limitations?

Radical views on heuristics and biases, and more generally, non-optimal patterns of human judgement, construe them either as an unwelcome mental handicap or a great evolution-based advantage. A more moderate position recognizes both sides, depending on the context and the situation at hand. This paper suggests that at least in some cases, apparently unsound human judgment may be viewed as a hallmark of true intelligence, enabling its major insights while also making it so hard to analyze and fully comprehend.
Singular Patterns in Optical Flows as Dynamic Texture Descriptors

This work introduces a novel approach to dynamic texture description. The proposed method is based on statistics from a vector field feature extractor that decomposes and describes features of distinctive local vector patterns as composites of singular patterns from a dictionary. The extractor is applied to a time-varying vector field, namely a dynamic texture’s optical flow frames. An interest point pooling method statistically highlights the recurring texture patterns, generating a histogram signature that is descriptive of the temporal changes in the texture. The proposed descriptor is used as feature vector on classification experiments in a widespread dataset. The classification results demonstrate our method improves on the state of the art for dynamic textures with non-trivial motion, while employing a smaller feature vector.
The coreset variational Bayes (CVB) algorithm for mixture analysis

The pressing need for improved methods for analysing and coping with big data has opened up a new area of research for statisticians. Image analysis is an area where there is typically a very large number of data points to be processed per image, and often multiple images are captured over time. These issues make it challenging to design methodology that is reliable and yet still efficient enough to be of practical use. One promising emerging approach for this problem is to reduce the amount of data that actually has to be processed by extracting what we call coresets from the full dataset; analysis is then based on the coreset rather than the whole dataset. Coresets are representative subsamples of data that are carefully selected via an adaptive sampling approach. We propose a new approach called coreset variational Bayes (CVB) for mixture modelling; this is an algorithm which can perform a variational Bayes analysis of a dataset based on just an extracted coreset of the data. We apply our algorithm to weed image analysis.
Post-Quantum Security of Communication and Messaging Protocols: Achievements, Challenges and New Perspectives

Widely used communication and messaging protocols, e.g., TLS 1.3 and Signal, are insecure against an adversary with a quantum computer. We expect that post-quantum security will become mandatory for a new generation of cryptographic protocols in the nearest 10-15 years. The analysis of existing post-quantum secure cryptographic tools for key exchange, encryption and message authentication based on hard problems in codes, lattices, ring learning with errors and elliptic curve isogenies revealed their advantages and drawbacks. In particular, we conclude, that the main threat to communication and messaging protocols is only pre-quantum security of currently used Diffie-Hellman key exchange protocols. We summarize efficiency and safety results for each class of post-quantum cryptographic schemes and present an experimental implementation of isogeny-based Diffie-Hellman-like key exchange protocol as an illustration of their applicability.