Reviewerly/exHarmony huggingface dataset:
Efficient hardware prototype of ECDSA modules for blockchain applications

This paper concentrates on the hardware implementation of efficient and re- configurable elliptic curve digital signature algorithm (ECDSA) that is suitable for verifying transactions in Blockchain related applications. Despite ECDSA architecture being computationally expensive, the usage of a dedicated stand-alone circuit enables speedy execution of arithmetic operations. The prototype put forth supports N-bit elliptic curve cryptography (ECC) group operations, signature generation and verification over a prime field for any elliptic curve. The research proposes new hardware framework for modular multiplication and modular multiplicative inverse which is adopted for group operations involved in ECDSA. Every hardware design offered are simulated using modelsim register transfer logic (RTL) simulator. Field programmable gate array (FPGA) implementation of var- ious modules within ECDSA circuit is compared with equivalent existing techniques that is both hardware and software based to highlight the superiority of the suggested work. The results showcased prove that the designs implemented are both area and speed efficient with faster execution and less resource utilization while maintaining the same level of security. The suggested ECDSA structure could replace the software equivalent of digital signatures in hardware blockchain to thwart software attacks and to provide better data protection.
Efficient Bootstrapping for Approximate Homomorphic Encryption with Non-Sparse Keys.

We present a bootstrapping procedure for the full-RNS variant of the approximate homomorphic-encryption scheme of Cheon et al., CKKS (Asiacrypt 17, SAC 18). Compared to the previously proposed procedures (Eurocrypt 18 & 19, CT-RSA 20), our bootstrapping procedure is more precise, more efficient (in terms of CPU cost and number of consumed levels), and is more reliable and 128-bit-secure. Unlike the previous approaches, it does not require the use of sparse secret-keys. Therefore, to the best of our knowledge, this is the first procedure that enables a highly efficient and precise bootstrapping with a low probability of failure for parameters that are 128-bit-secure under the most recent attacks on sparse R-LWE secrets.
Differential Privacy for Credit Risk Model

The use of machine learning algorithms to model user behavior and drive business decisions has become increasingly commonplace, specifically providing intelligent recommendations to automated decision making. This has led to an increase in the use of customers personal data to analyze customer behavior and predict their interests in a companys products. Increased use of this customer personal data can lead to better models but also to the potential of customer data being leaked, reverse engineered, and mishandled. In this paper, we assess differential privacy as a solution to address these privacy problems by building privacy protections into the data engineering and model training stages of predictive model development. Our interest is a pragmatic implementation in an operational environment, which necessitates a general purpose differentially private modeling framework, and we evaluate one such tool from LeapYear as applied to the Credit Risk modeling domain. Credit Risk Model is a major modeling methodology in banking and finance where user data is analyzed to determine the total Expected Loss to the bank. We examine the application of differential privacy on the credit risk model and evaluate the performance of a Differentially Private Model with a Non Differentially Private Model. Credit Risk Model is a major modeling methodology in banking and finance where users data is analyzed to determine the total Expected Loss to the bank. In this paper, we explore the application of differential privacy on the credit risk model and evaluate the performance of a Non Differentially Private Model with Differentially Private Model.
Improving the Accuracy of Writer Detection of Handwritten Text Using Image Hashing

In this paper, we describe a new approach to improve the accuracy of writer detection of a given text. There are various methods for writer identification, but most of them require image analysis of the text. Although, our method is also image-dependent, but the approach focuses on comparing the pressure versus time plots rather than comparison of the text image. The identification is regarded as finding similarity in the curve of the plot. We applied the techniques of image hashing and dynamic time warping to fulfill the identification task. We conducted experiments using 10 different English handwriting samples from 20 people and achieved promising results.
Implementation Of Message Security In Pictures Using Playfair Chiper Algorithm: Implementation Of Message Security In Pictures Using Playfair Chiper Algorithm

Cryptography implementations are now widely solved by cryptanalyst in the process of securing a messaging system, and therefore the need to add a method of steganography. Steganography is the art of hiding secret messages in any medium other than the sender and receiver that no one would know or realize there is a secret message in the media is sent. In this journal, conducted a study on the implementation of cryptography in the form of encryption and decryption of a message using the Playfair cipher algorithm is modified by letter hijaiyah. Then the implementation of steganography in a PNG image media, the testing process is done by sending the media image using email, whatsapp, facebook, and telegram. research aims to make the encryption process stronger and more secure,
Using The 5W1H Method In Writing Important Information With Google Forms In Elementary Schools

This research is based on the lack of understanding of students in writing important information in the text. This research was aimed to determine the ability of primary school students in writing important information by using the method of 5W1H through the google form. The method used in this research is descriptive qualitative. The instrument used to measure the understanding of the students in writing important information is a test given through google form. Collecting data was given to 39 students of class 6 primary school. The sample was conducted by purposive sampling method. The Object of the research is grouped into four categories, they are understanding the whole, understanding the partial, misunderstanding, and not understanding. From the results of this research, it can be concluded that students can mostly understand partially, one-tenth of them cannot understand, and a fifth of them misunderstood, and only one-sixth of them understand the use of 5W1H in writing important information. Therefore, it is necessary to improve the quality of teaching and learning about the method of writing important information in a text at the primary school level.
STL-SGD: Speeding Up Local SGD with Stagewise Communication Period

Distributed parallel stochastic gradient descent algorithms are workhorses for large scale machine learning tasks. Among them, local stochastic gradient descent (Local SGD) has attracted significant attention due to its low communication complexity. Previous studies prove that the communication complexity of Local SGD with a fixed or an adaptive communication period is in the order of $O (N^{\frac{3}{2}} T^{\frac{1}{2}})$ and $O (N^{\frac{3}{4}} T^{\frac{3}{4}})$ when the data distributions on clients are identical (IID) or otherwise (Non-IID), where $N$ is the number of clients and $T$ is the number of iterations. In this paper, to accelerate the convergence by reducing the communication complexity, we propose \textit{ST}agewise \textit{L}ocal \textit{SGD} (STL-SGD), which increases the communication period gradually along with decreasing learning rate. We prove that STL-SGD can keep the same convergence rate and linear speedup as mini-batch SGD. In addition, as the benefit of increasing the communication period, when the objective is strongly convex or satisfies the Polyak-\L ojasiewicz condition, the communication complexity of STL-SGD is $O (N \log{T})$ and $O (N^{\frac{1}{2}} T^{\frac{1}{2}})$ for the IID case and the Non-IID case respectively, achieving significant improvements over Local SGD. Experiments on both convex and non-convex problems demonstrate the superior performance of STL-SGD.
Multi Class Data Classification to Improve Accuracy in Sentiment Analysis using Machine Learning

Sentiment analysis means classifying a text into different emotional classes. These days most of the sentiment analysis techniques divide the text into either binary or ternary classification in this paper we are classifying the movie reviews into 5 classes. Multi class sentiment analysis is a technique which can be used to know the exact sentiment of a review not just polarity of a given textual statement from positive to negative. So that one can know the precise sentiment of a review . Multi class sentiment analysis has always been a challenging task as natural languages are difficult to represent mathematically. The number of features are also generally large which requires huge computational power so to reduce the number of features we will use parts-of-speech tagging using textblob to extract the important features. Sentiment analysis is done using machine learning, where it requires training data and testing data to train a model. Various kinds of models are trained and tested at last one model is selected based on its accuracy and confusion matrix. It is important to analyze the reviews in textual form because large amount of reviews is present all over the web. Analyzing textual reviews can help the firms that are trying to find out the response of their products in the market. In this paper sentiment analysis is demonstrated by analyzing the movie reviews, reviews are taken from IMDB website.
Learning Accurate and Interpretable Decision Rule Sets from Neural Networks

This paper proposes a new paradigm for learning a set of independent logical rules in disjunctive normal form as an interpretable model for classification. We consider the problem of learning an interpretable decision rule set as training a neural network in a specific, yet very simple two-layer architecture. Each neuron in the first layer directly maps to an interpretable if-then rule after training, and the output neuron in the second layer directly maps to a disjunction of the first-layer rules to form the decision rule set. Our representation of neurons in this first rules layer enables us to encode both the positive and the negative association of features in a decision rule. State-of-the-art neural net training approaches can be leveraged for learning highly accurate classification models. Moreover, we propose a sparsity-based regularization approach to balance between classification accuracy and the simplicity of the derived rules. Our experimental results show that our method can generate more accurate decision rule sets than other state-of-the-art rule-learning algorithms with better accuracy-simplicity trade-offs. Further, when compared with uninterpretable black-box machine learning approaches such as random forests and full-precision deep neural networks, our approach can easily find interpretable decision rule sets that have comparable predictive performance.
Pelatihan penulisan artikel ilmiah penelitian tindakan kelas untuk publikasi pada jurnal iIlmiah

One of the classical issues teachers face in East Nusa Tenggara is the lack of academic articles based on scientific research, including Classroom Action Research (CAR). It was happened because of the low interest, motivation, and ability of teachers to write the research in the form of an academic article and publish it in an academic journal. Therefore, it was essential to hold training on writing CAR articles for publication in academic journals. It aims to improve the ability of teachers to write CAR article and publish it in academic journals. This activity is running in the form of online training. The methods used in implementing activities include webinars through zoom meetings, presentations, discussion, training, and mentoring through WhatsApp Group. This activity hold in two stages, namely the preparation stage and the implementation stage. The result of the activity indicates the high enthusiasm of participants both during the webinar and training and mentoring. In addition, this activity can also improve the participants' understanding and abilities in making or writing good CAR article for publication in academic journals
MAGIC: Learning Macro-Actions for Online POMDP Planning.

The partially observable Markov decision process (POMDP) is a principled general framework for robot decision making under uncertainty, but POMDP planning suffers from high computational complexity, when long-term planning is required. While temporally-extended macro-actions help to cut down the effective planning horizon and significantly improve computational efficiency, how do we acquire good macro-actions? This paper proposes Macro-Action Generator-Critic (MAGIC), which performs offline learning of macro-actions optimized for online POMDP planning. Specifically, MAGIC learns a macro-action generator end-to-end, using an online planner's performance as the feedback. During online planning, the generator generates on the fly situation-aware macro-actions conditioned on the robot's belief and the environment context. We evaluated MAGIC on several long-horizon planning tasks both in simulation and on a real robot. The experimental results show that the learned macro-actions offer significant benefits in online planning performance, compared with primitive actions and handcrafted macro-actions.
$\text{#NFA}$ admits an FPRAS: Efficient Enumeration, Counting, and Uniform Generation for Logspace Classes

In this work, we study two simple yet general complexity classes, based on logspace Turing machines, which provide a unifying framework for efficient query evaluation in areas like information extraction and graph databases, among others. We investigate the complexity of three fundamental algorithmic problems for these classes: enumeration, counting and uniform generation of solutions, and show that they have several desirable properties in this respect. Both complexity classes are defined in terms of non-deterministic logspace transducers (NL transducers). For the first class, we consider the case of unambiguous NL transducers, and we prove constant delay enumeration, and both counting and uniform generation of solutions in polynomial time. For the second class, we consider unrestricted NL transducers, and we obtain polynomial delay enumeration, approximate counting in polynomial time, and polynomial-time randomized algorithms for uniform generation. More specifically, we show that each problem in this second class admits a fully polynomial-time randomized approximation scheme (FPRAS) and a polynomial-time Las Vegas algorithm for uniform generation. Interestingly, the key idea to prove these results is to show that the fundamental problem $\text{#NFA}$ admits an FPRAS, where $\text{#NFA}$ is the problem of counting the number of strings of length $n$ (given in unary) accepted by a non-deterministic finite automaton (NFA). While this problem is known to be $\text{#P}$-complete and, more precisely, $\text{SpanL}$-complete, it was open whether this problem admits an FPRAS. In this work, we solve this open problem, and obtain as a welcome corollary that every function in $\text{SpanL}$ admits an FPRAS.
LibGuides: LibAnswers Training Guide for UL Employees: Getting to LibChat & Connecting

This is a guide for how to use LibAnswers at the CMU University Libraries. The guide includes materials for learning about the system, and our policies for use.
SubDEx: Exploring Ratings in Subjective Databases

We demonstrate SubDEx, a dedicated framework for Subjective Data Exploration (SDE). SubDEx enables the joint exploration of items, people, and people's opinions on items, in a guided multi-step process where each step aggregates the most useful and diverse trends in the form of rating maps. Because of the large search space of possible rating maps, we leverage pruning strategies to enable interactive running times. We demonstrate the need for a dedicated SDE framework and the effectiveness and efficiency of our approach, by interacting with the ICDE'21 participants who will act as data analysts.
Understanding the identity function in SML by theory

Motivation Being one of the representative languages in the paradigm of functional programming, Standard ML (SML) offers many advanced features that are rarely seen in imperative programming. One s...
Sublinear classical and quantum algorithms for general matrix games

We investigate sublinear classical and quantum algorithms for matrix games, a fundamental problem in optimization and machine learning, with provable guarantees. Given a matrix $A\in\mathbb{R}^{n\times d}$, sublinear algorithms for the matrix game $\min_{x\in\mathcal{X}}\max_{y\in\mathcal{Y}} y^{\top} Ax$ were previously known only for two special cases: (1) $\mathcal{Y}$ being the $\ell_{1}$-norm unit ball, and (2) $\mathcal{X}$ being either the $\ell_{1}$- or the $\ell_{2}$-norm unit ball. We give a sublinear classical algorithm that can interpolate smoothly between these two cases: for any fixed $q\in (1,2]$, we solve the matrix game where $\mathcal{X}$ is a $\ell_{q}$-norm unit ball within additive error $\epsilon$ in time $\tilde{O}((n+d)/{\epsilon^{2}})$. We also provide a corresponding sublinear quantum algorithm that solves the same task in time $\tilde{O}((\sqrt{n}+\sqrt{d})\textrm{poly}(1/\epsilon))$ with a quadratic improvement in both $n$ and $d$. Both our classical and quantum algorithms are optimal in the dimension parameters $n$ and $d$ up to poly-logarithmic factors. Finally, we propose sublinear classical and quantum algorithms for the approximate Carath\'eodory problem and the $\ell_{q}$-margin support vector machines as applications.
Computing Branching Distances Using Quantitative Games

We lay out a general method for computing branching distances between labeled transition systems. We translate the quantitative games used for defining these distances to other, path-building games which are amenable to methods from the theory of quantitative games. We then show for all common types of branching distances how the resulting path-building games can be solved. In the end, we achieve a method which can be used to compute all branching distances in the linear-time--branching-time spectrum.
SuperSim: a test set for word similarity and relatedness in Swedish.

Language models are notoriously difficult to evaluate. We release SuperSim, a large-scale similarity and relatedness test set for Swedish built with expert human judgments. The test set is composed of 1,360 word-pairs independently judged for both relatedness and similarity by five annotators. We evaluate three different models (Word2Vec, fastText, and GloVe) trained on two separate Swedish datasets, namely the Swedish Gigaword corpus and a Swedish Wikipedia dump, to provide a baseline for future comparison. We release the fully annotated test set, code, baseline models, and data.
Thermal energy pattern related with the temperature increasing due to intraplate and induced seismicity, Southern Norway, 2017-2018

&amp;lt;p&amp;gt;Small seismological events recorded in southern Norway in the period of 2017-2018 were used to calculate the sudden co-seismic temperature increase using a simple stress-drop model. In order to estimate the net production of thermal energy, both, industry explosions and natural events were included.&amp;amp;#160; The range of resultant average temperature rise, with a maximum of ~ 143 &amp;amp;#176; C for a M&amp;lt;sub&amp;gt;w =&amp;lt;/sub&amp;gt;3.5, is proposed as an additional constituent that explain the weakness areas related to the high amount of intracrustal seismicity, mainly regarding the anisotropic thermal expansion of rocks and the flash heating thermal process produced by historical earthquakes with magnitudes over . The temperature values were subsequently used to estimate the thermal energy in 2D and 3D cumulative patterns in the area, as well as the total amount of energy that is available regarding seismic activity. The results were correlated with existing geological information, considering lineaments and heat flux data. Areas with high values of thermal energy seems to be spatially linked with both high heat flux zones and high density of lineaments, mainly to the south of the Tr&amp;amp;#248;ndelag Platform as well as to the south of the M&amp;amp;#248;re basin.&amp;lt;/p&amp;gt;
Design of Secure and Efficient Product Information Retrieval System Model in Cloud Computing

Cloud computing is a promising technique for organizing a vast number of IT resources in a cost-effective and flexible manner. Increasingly many companies are planning to shift their local data management systems to the cloud and uses cloud servers to store and mange product information. A concomitant challenge is determining how to secure the privacy of commercially confidential data while retaining the capacity to search the data. In this paper we propose a privacy-preserving data search scheme for secure and efficient outsourcing and retrieval of product information in cloud computing. We design the proposed scheme and its implementation and analysis results demonstrate the effectiveness of proposed system in terms of security and efficiency in retrieval of product information.
ASSOCIATION RULE DALAM MENENTUKAN CROSS-SELLING PRODUK MENGGUNAKAN ALGORITMA FP-GROWTH

Sales transaction data at Bigmart stored in a database will be able to become new knowledge if processed using the data mining process. In addition, inventory is also a problem that is being faced by Bigmart. Data mining is able to analyze data into information in the form of transaction patterns that are useful in increasing revenue, one of which is Cross-Selling products. Association rule is one of the data mining methods included in the Market Basket Analysis method. The algorithm used is the FP-Growth algorithm because it has the virtue of shorter time processing data. The pattern obtained is determined by the value of support (support) and the value of confidence (confidence). To find the association rules the FP-Growth algorithm is used. To get more accurate association rules, use the Weka 8.3 tool. There are 11 association rules obtained using the Weka 8.3 tool which is classified as a Stong Rule that meets the Minimum support value of 10% and Minimum confidence 80%. Keywords: Database, Cross-selling, Market Basket Analysis, Association Rule, FP-Growth
A Proxy Node Resource Load Balancing Strategy Based on the Swift System

The internet of Things (IoT) uses wireless sensor devices to build a huge network transmission center, the data is increasing, and the data types have diverse characteristics. Such data raise a certain challenge for data storage and resource scheduling in cloud platforms. Swift can solve the needs of massive data storage capacity, but it is impossible to effectively achieve load balancing and waste resources. Therefore, according to the resource load status of the proxy node, this paper proposes a node resource load balancing strategy based on Linux Virtual Server (LVS). Through the study of scheduling algorithms, a dynamic feedback load balancing algorithm based on a weighted minimum connection is proposed. Experimental results show that the dynamic feedback load balancing algorithm based on a weighted minimum connection can effectively solve the resource scheduling problem.
Chatbot using Lex

Chatbot have been popular in recent times and are gaining speed as a program for communication. Some chatbot respond like humans and mimic a conversation same to same like a normal human. This is known as Chatbot This paper addresses the Design of Chatbot system We will also understand existing system and scope of the system.
LibGuides: Market Research: Industry Research

Sources of information about the potential consumers, competitors and/or industry information.