Reviewerly/exHarmony huggingface dataset:
Consensus Mechanism Using Sharding Approach in Consortium Blockchain

Blockchain is a hyped technology which is used in various fields nowadays. Different applications use blockchain for storing their data securely. For validating the data to be stored in blockchain network, generally consensus mechanism known as Proof of Work is used. Proof of work demands high computation power with huge amount of electricity due to which its alternatives like Proof of Stake, Proof of Luck, etc. comes into existence for different types of blockchain. The organisations that are involved in reaching consensus have given origin to different kinds of blockchain. In this paper, blockchain as a data structure is explained. Different types of blockchain are elaborated and existing consensus algorithms for consortium blockchain are discussed. Framework using sharding approach has been proposed for consortium blockchain. Experiments conducted prove that convergence time increases almost linearly with the number of iterations and number of nodes. Also, challenges related to the proposed framework are presented in this paper.
Generating an Extract Summary from a Document

The rapid growth of digital text emphasises the necessity to create and build efficient summary tools to find and extract information in short form. Text overviews are typically handled using extraction techniques in natural language processing, which include choosing a portion of the original text to capture the core concept of the subject. Unlike the abstractive technique, it is utilised less than the extractive method, but outcomes are considerably closer to the human summary. In our study, we concentrate on the automated extractive kind of summary. In our study, we utilised Word2Vec, a collection of lexical integration models from Word Embedding. Word2Vec turns the word into a vector, and these segments are the derivatives of the word in our instance a 100 segment vector. We also utilised the CNN and auto-encoder. We also used the CNN. We use our model on Amazon Food Reviews datasets and assess rebuilt paragraphs using standard metrics (such as ROUGE). The aim of this research was to automatically generate the accurate and best summary.
Implementing Client-Side Encryption for Enforcing Data Privacy on the Web Using Symmetric Cryptography: A Research Paper

The Internet provides remote access to resources such as storage and computation available worldwide. The virtual interaction of people on the Internet makes it difficult to recognize the identity of a person. In addition, the open-network structure of the Internet might allow third parties to read and change data in a communication over the network. Hence, every organization must maintain the integrity of data by preventing unauthorized modification. To secure confidential information on the Internet, one must put into practice necessary security measures. Cryptography is a technique that helps to reduce security risks to the confidentiality and integrity of the data on the Internet. Moreover, the encryption of confidential data can be done either at the client machine (Web client) or on the server (Web server). The side (client/server) at which the encryption has to take place is decided based on the confidentiality of data to be protected. The critical data that need highest security in an organization is considered as in Level 1. In Level 2, we have information that is important but not critical as in Level 1. Level 3 comprises of data that are not important in the daily operations of an organization. Among the three levels of confidentiality, data exist at Level 1 and Level 2 need encryption so that they can be kept confidential on the Web. As the data in Level 1 is both confidential and important, it must be encrypted at the client-side itself before their migration onto the Web. Whereas, data in Level 2 can be encrypted after their migration since the criticality of data in Level 2 is less compared to that of Level 1. In this paper, the authors have identified some of the symmetric cryptographic algorithms with the help of which client-side encryption of data can be achieved for enforcing data privacy on the Web. They have also proposed an extended symmetric algorithm using which searchable encryption is possible on the encrypted data stored in a remote server.
Recurrent Neural Network: A Flexible Tool of Computational Neuroscience Research

Recurrent neural networks (RNNs) are a set of computational models which are repeatedly used as a mechanism to acquire insights into neurological phenomena, by considering computational, electrophysiological and anatomical constraints. RNNs can either be trained by data and facts examples or can be designed to implement some dynamical concepts. Recently, trained RNNs have been progressively utilised both for explaining neurological phenomenon and for performing computational tasks. In this paper, we have reviewed how trained RNNs are combined with different methodologies and provided a framework for modelling in computational and theoretical neuroscience and help in generating hypothesis.
AMSS: A Novel Take on Web Page Ranking

There is a heavenly improvement in Web-based information sources and organizations. It is surveyed that the measure of pages has been expanded colossally. Most existing Web page ranking algorithms are not capable enough to have appealing Time and Space intricacies (JAMA 65(24), 2013). The proposed research work examines various impediments of the common ways to deal with page search and situating systems basically in the ebb and flow period of tremendous data. The fundamental target is to support a web customer by presenting the most modified site page situating as a response to a customer's interest question by pondering the taste and examining the historical backdrop of the customer while previously looking on the web and providing the customer with optimized web pages according to the customers search. In proposed Page Positioning Algorithm (PPA) the calculation is made by utilizing the plan and improvement of the Advance Meta Search System (AMSS) device to help the end client in effectively finding the optimized web page to all the more likely fulfill his/her requirements. The proposed PPA calculation arms the client with an unfathomable meta search instrument to energize the client by giving a site page situating solicitation to satisfy the tweaked needs quickly, especially when the hunt inquiry is mixed up or insufficient (JAMA pp 189–196, 2017). The flow of the research paper holds a comprehensive logical and exploratory appraisal of the proposed calculation and device by going through different examinations and evaluation measurements (JAMA 2018). Finally, the improved measurements showed the upgraded effectiveness when it stood out from other regular page positioning methodologies referenced in writing.
Evaluation of an Online Oral English Teaching Model Using Big Data

In order to improve the online oral English teaching model, enhance its efficiency, and widen its utilization in university teaching, this study developed a rigorous oral English teaching model, constructed a comprehensive oral English evaluation model, and used PBL to guide the direction of oral English curriculum under the influence of big data-driven background. The use of online oral English teaching mode was realized to ensure that students can internalize knowledge in class. A complete teaching method was used to ensure that learners can master more knowledge in English class, promote the operation of online English learning in the network environment and create a safe learning environment for students, improve students’ oral English ability, improve oral teaching, and improve the integrity of oral teaching. The deepening of big data and online review teaching will be the requirements of the future teaching environment. Therefore, it was decided that only by making full use of all the advantages of information technology, combined with the background of education and teaching, can we explore the new education and training methods needed in the new semester.
Always-Sustainable Software Security

In this paper, we depict a new approach applicable to any types of software security solutions with the aim of regulating the protection level according to measured execution conditions. We define the inherent security requirement for this regulation and for that sake, a scalable use of Intel SGX trusted execution environment. We expose the merits of the solution assembling sustainability and security and describe a first implementation with its results and elaborate future works.
Efficient Representation of Large-Alphabet Probability Distributions via Arcsinh-Compander

A number of engineering and scientific problems require representing and manipulating probability distributions over large alphabets, which we may think of as long vectors of reals summing to 1. In some cases it is required to represent such a vector with only b bits per entry. A natural choice is to partition the interval [0,1] into 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b</sup> uniform bins and quantize entries to each bin independently. We show that a minor modification of this procedure – applying an entrywise non-linear function (compander) f(x) prior to quantization – yields an extremely effective quantization method. For example, for b = 8(16) and 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">5</sup> -sized alphabets, the quality of representation improves from a loss (under KL divergence) of 0.5(0.1) bits/entry to 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−4</sup> (10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−9</sup> ) bits/entry. Compared to floating point representations, our compander method improves the loss from 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−1</sup> (10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−6</sup> ) to 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−4</sup> (10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−9</sup> ) bits/entry. These numbers hold for both real-world data (word frequencies in books and DNA k-mer counts) and for synthetic randomly generated distributions. Theoretically, we set up a minimax optimality criterion and show that the compander $f(x) \propto \operatorname{ArcSinh} (\sqrt {(1/2)(K\log K)x} )$ achieves near-optimal performance, attaining a KL-quantization loss of ≍ 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−2b</sup> log <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> K for a K-letter alphabet and b →∞. Interestingly, a similar minimax criterion for the quadratic loss on the hypercube shows optimality of the standard uniform quantizer. This suggests that the ArcSinh quantizer is as fundamental for KL-distortion as the uniform quantizer for quadratic distortion.
Protecting Semantic Information Using An Efficient Secret Key

We consider a semantic cipher system, in which we protect only the semantic information of the source. The optimal tradeoff is characterized among the coding rate, the secret key rate, the semantic information leakage rate, the source reconstruction distortion, and the semantic distortion. It is shown that an efficient key with a small size suffices to protect the semantic information.
Alpha-NML Universal Predictors

Inspired by Sibson’s alpha-mutual information, we introduce a new parametric class of universal predictors. This class interpolates two well-known predictors, the mixture estimator, that includes the Laplace and the Krichevsky-Trofimov predictors, and the Normalized Maximum Likelihood (NML) estimator. We point out some advantages of this class of predictors and study its performance in terms of known regret measures under logarithmic loss, in particular for the well-studied case of discrete memoryless sources.
Hybrid Multiplicative Non-perfect Secret Sharing

A secret sharing scheme is said to be d-multiplicative if the scheme allows the players to multiply shared d secrets by locally converting their shares into an additive sharing of the product. This paper considers the most general setting where d secrets are independently shared by different adversary structures (hybrid) and a partial information can be leaked (non-perfect) and presents a necessary and sufficient condition for d-multiplication to be possible. In particular, even for the extreme case that the i-th secret is known by all but the i-th player who has partial information on the secret (e.g., all but one bit), d-multiplication is impossible.
Maximum Mutual Information under Local Differential Privacy Constraint

Local differential privacy (LDP) is a state-of-the-art technique for privacy preservation. In this paper, we provide upper bounds for mutual information, a common information-theoretic metric, under pure LDP and approximate LDP constraints. Compared to existing results, our results have the advantage of holding for any discrete distribution and any privacy budget, and are tighter over some regions of the distribution and privacy budget.
Optimizing Federated Averaging over Fading Channels

Deep fading represents the typical error event when communicating over wireless channels. We show that deep fading is particularly detrimental for federated learning (FL) over wireless communications. In particular, the celebrated FEDAVG and several of its variants break down for FL tasks when deep fading exists in the communication phase. The main contribution of this paper is an optimal global model aggregation method at the parameter server, which allocates different weights to different clients based on not only their learning characteristics but also the instantaneous channel state information at the receiver (CSIR). This is accomplished by first deriving an upper bound on the parallel stochastic gradient descent (SGD) convergence over fading channels, and then solving an optimization problem for the server aggregation weights that minimizes this upper bound. The derived optimal aggregation solution is closed-form, and achieves the well-known O(1/t) convergence rate for strongly-convex loss functions under arbitrary fading and decaying learning rates. We validate our approach using several real-world FL tasks.
Maximum-Length Low-Density MDS Codes and Near Resolvable Designs

We study the relation between near resolvable designs and ${\mathbb{F}_q}$ -linear codes over $\mathbb{F}_q^b$. We use the incidence matrix of a near resolvable design to construct a parity-check matrix of an ${\mathbb{F}_q}$-linear code. We show an equivalence between the construction of a new class of near resolvable designs NRB(rb + 1, r), that we call r-complete, and the well-known ${\mathbb{F}_q}$-linear codes over $\mathbb{F}_q^b$ with length n and dimension n – r which have the following good properties: (i) they are maximum distance separable (MDS), (ii) they are low-density, and (iii) they reach the maximum length of any MDS lowest density code.
Symmetries in Linear Programming for Information Inequalities

Secret sharing is a central topic in information-theoretical cryptography. In a secret sharing scheme a random secret is distributed among participants so that only the qualified subsets of parties can recover the secret. In this paper we improve previously known [13] lower bounds on the size of the largest share for several specific access structures. Our approach is based on non-Shannon-type information inequalities. We employ the linear programming (LP) technique that permits to apply new information inequalities indirectly. To reduce computational complexity of the involved linear programs we extensively use symmetry considerations.
Weak Superimposed Codes of Improved Asymptotic Rate and Their Randomized Construction

Weak superimposed codes are combinatorial structures related closely to generalized cover-free families, superimposed codes, and disjunct matrices in that they are only required to satisfy similar but less stringent conditions. This class of codes may also be seen as a stricter variant of what are known as locally thin families in combinatorics. Originally, weak superimposed codes were introduced in the context of multimedia content protection against illegal distribution of copies under the assumption that a coalition of malicious users may employ the averaging attack with adversarial noise. As in many other kinds of codes in information theory, it is of interest and importance in the study of weak superimposed codes to find the highest achievable rate in the asymptotic regime and give an efficient construction that produces an infinite sequence of codes that achieve it. Here, we prove a tighter lower bound than the sharpest known one on the rate of optimal weak superimposed codes and give a polynomial-time randomized construction algorithm for codes that asymptotically attain our improved bound with high probability. Our probabilistic approach is versatile and applicable to many other related codes and arrays.
Optimal and Almost Optimal Cyclic (r,δ)-LRCs With Large Code Lengths

There has been a lot of works about constructing optimal LRCs via cyclic codes because of their elegant algebraic structure and efficient encoding procedure. Constructing optimal cyclic LRCs with large code lengths for relatively large minimum distances has been an attractive problem. Recently, Fang et al. firstly constructed two classes of q-ary cyclic Singleton-optimal 2-LRCs with length n > q + 1 and minimum distance d = 6 in [23]. In this paper, we generalize the constructions to the (r,δ)-LRCs. Specifically, we obtain two classes of optimal cyclic (2,δ)-LRCs with length $n = \frac{{(\delta + 1)(q + 1)}}{{{2^t}}}$ and minimum distance 2δ + 2, two classes of almost optimal cyclic (2,δ)-LRCs with length $n = \frac{{(\delta + 1)(q + 1)}}{{{2^t}}}$ and minimum distance 2δ +1, where t is a non-negative integer.
Algebraic Chase Decoding of Elliptic Codes Through Computing the Gröbner Basis

This paper proposes two interpolation-based algebraic Chase decoding for elliptic codes. It is introduced from the perspective of computing the Gröbner basis of the interpolation module, for which two Chase interpolation approaches are utilized. They are Kötter’s interpolation and the basis reduction (BR) interpolation. By identifying η unreliable symbols, 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">η</sup> decoding test-vectors are formulated, and the corresponding interpolation modules can be defined. The re-encoding further helps transform the test-vectors, facilitating the two interpolation techniques. In particular, Kötter’s interpolation is performed for the common elements of the test-vectors, producing an intermediate outcome that is shared by the decoding of all test-vectors. The desired Gröbner bases w.r.t. all test-vectors can be obtained in a binary tree growing fashion, leading to a low complexity but its decoding latency cannot be contained. In contrast, the BR interpolation first performs the common computation in basis construction which is shared by all interpolation modules, and then conducts the module basis construction and reduction for all test-vectors in parallel. It results in a significantly lower decoding latency. Finally, simulation results are also presented to demonstrate the effectiveness of the proposed Chase decoding.
On Multiple and Hierarchical Universality

Universal coding, prediction and learning usually consider the case where the data generating mechanism is unknown or non-existent, and the goal of the universal scheme is to compete with the best hypothesis from a given hypothesis class, either on the average or in a worst-case scenario. Multiple universality considers the case where the hypothesis class is also unknown: there are several hypothesis classes with possibly different complexities. In hierarchical universality, the simpler classes are nested within more complex classes. The main challenge is to correctly define the universality problem. We propose several possible definitions and derive their min-max optimal solutions. Interestingly, the proposed solutions can be used to obtain Elias codes for universal representation of the integers. We also utilize this approach for variable-memory Markov models, presenting a new interpretation for the known bound over the regret of the celebrated context-tree weighting algorithm and proposing a 3-part code that (slightly) out-performs it.
Successive Approximation for Coded Matrix Multiplication

Coded computing was recently introduced to mitigate the effect of stragglers on distributed computing systems. This paper combines ideas of approximate and coded computing to further accelerate computation. We propose approximated coded distributed computing (ACDC) that realizes a tradeoff between accuracy and speed, allowing the distributed computing system to produce approximations that increase in accuracy over time. If a sufficient number of compute nodes finish their tasks, ACDC exactly recovers the desired computation. We theoretically provide design guidelines for ACDC, and numerically show its benefits over previous methods.
Gaussian Data Privacy Under Linear Function Recoverability

A user’s data is represented by a Gaussian random variable. Given a linear function of the data, a querier is required to recover, with at least a prescribed accuracy level, the function value based on a query response provided by the user. The user devises the query response, subject to the recoverability requirement, so as to maximize privacy of the data from the querier. Recoverability and privacy are both measured by ℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> -distance criteria. An exact characterization is provided of maximum user data privacy under the recoverability condition. An explicit achievability scheme for the user is given and its privacy compared with a converse upper bound.
Learning quantum circuits of T -depth one

In this paper, we study the problem of learning an unknown quantum circuit of a certain structure. If the unknown target is an n-qubit Clifford circuit, we devise an algorithm to reconstruct its circuit representation by using O(n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) queries to it. It is unknown for decades how to handle circuits beyond the Clifford group for which the stabilizer formalism cannot be applied. Herein, we study quantum circuits of T -depth one on the computational basis. We show that their output states can be represented by a certain stabilizer pseudomixture. By analyzing the algebraic structure of the stabilizer pseudomixture, we can generate a hypothesis circuit that is equivalent to the unknown target T -depth one quantum circuit U on computational basis states, using Pauli and Bell measurements. If the number of T gates in U is of the order O(log n), our algorithm requires O(n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) queries to U to produce its equivalent circuit representation on the computational basis in time O(n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ). Using further additional O(4 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3n</sup> ) classical computations, we can derive an exact description of U for arbitrary input states. Our results greatly extend the previously known facts that stabilizer states can be efficiently identified based on the stabilizer formalism.The full manuscript can be found at [1].