Reviewerly/exHarmony huggingface dataset:
MatriVasha: A Multipurpose Comprehensive Database for Bangla Handwritten Compound Characters

Bengali or Bangla language is native to Bengal. Bengali speaks their mother tongue, which they feel comfortable. In 1952, UNESCO announced that 21st February is the international mother language day. People remember this day with great respect. We achieved our mother languages for our martyrs. And, there are belongs to a different type of characters. Bangla's handwritten compound character is one of them. For helping Bengali people in different purposes, Bangla's handwritten compound character recognition performs a vital role. Such as Bangla character recognition, they are extracting data from handwritten paper and digitalization of any kind of documents, etc. In Bangla language, there are have ten numerical digits, 50 basic characters, greater than 200 compound characters, and ten modifiers. But there is no strong dataset for Bangla character recognition. Most of the researchers are working for only digit or basic characters or a few numbers of compound characters. But works with compound characters are complicated because of their different shapes and styles. For this kind of reason, MatriVasha has proposed a dataset designed to recognize 120 compound characters in Bangla that consist of 306,240‬ images written by 2552 distinct writers from Bangladesh. Our proposed dataset is the most comprehensive dataset for handwritten compound characters in Bangla; so far, MatriVasha datasets can be found on this link https://shahariarrabby.github.io/matrivasha.
An Intelligent Model to Predict the Development Time and Budget of Software Projects

Today, software projects are a major part of information and business technology. Estimating the cost and delivery time of a project is one of the most important aspects of its development process. The results of this estimate are directly related to the failure or success of the whole project. Project anomalies, high diversity, intangibility, and poor standards are obstacles that have hampered various current forecasting models. In this regard, the aim of this paper is to provide an intelligent hybrid model that is able to weigh the project features properly. The main idea of the proposed model is based on mathematical methods, soft computing and using previous estimation methods. After the complete introduction of the new model, its efficiency is evaluated using Desharnais and ISBSG databases. The results indicate higher accuracy and greater flexibility of the proposed model. Improvement rate shows in the various metrics such as MdMRE, Pred(0.25) and MMRE separately, but the average improvement rate is 31%.
Multi-matrix verifiable computation

The problem of securely outsourcing computation to cloud servers has attracted a large amount of attention in recent years. The verifiable computation of Gennaro, Gentry, Parno (Crypto'10) allows a client to verify the server's computation of a function with substantially less time than performing the outsourced computation from scratch. In a multi-function model (Parno, Raykova, Vaikuntanathan; TCC'12) of verifiable computation, the process of encoding function and the process of preparing input are decoupled such that any client can freely submit a computation request on its input, without having to generate an encoding of the function in advance. In this paper, we propose a multi-matrix verifiable computation scheme that allows the secure outsourcing of the matrix functions over a finite field. Our scheme is outsourceable. When it is used to outsource m linear functions, the scheme is roughly m times faster and has less communication cost than the previously best known scheme by Fiore and Gennaro (CCS'12), both in the client-side computation and in the server-side computation. We also show the cost saving with detailed implementations.
Social Robots as Teaching Assistance System in Higher Education: Conceptual Framework for the Development of Use Cases

This paper provides an overview of the current state of research on social robots in higher education and the existing frameworks to categorize and develop social robot applications. Based on the existing work, we present our own framework to develop use cases for social robots in the education sector. Our framework is based on a heuristic and symbiotic design approach that serves as a guideline for developing use cases and views human-robot interaction as two complementary and mutually reinforcing roles. We illustrate our framework by means of a use case that we have conducted in 2019 during the initial lecture of the large-scale course ‘Introduction to academic writing’.
Improving Online Clustering of Chinese Technology Web News With Bag-of-Near-Synonyms

In the Internet era, online clustering of technology web news can help discover scientific breakthroughs and grasp technology trends. To do that automatically, the news documents to be clustered must be represented appropriately with numerical vectors. However, traditional representations such as Term Frequency-Inverse Document Frequency (TF-IDF) cannot distinguish near-synonyms and may cause “dimension disaster.” To overcome these problems, this article proposes the Bag-of-Near-Synonyms (BoNS) model based on the idea to construct near-synonym sets using word embeddings and agglomerative clustering, and then to represent a document with a Set Frequency-Inverse Document Frequency (SF-IDF) vector in which each dimension corresponds to a near-synonym set rather than a single word. To speed up computation, we further propose the hashed version of SF-IDF and name it hSF-IDF, which employs a hash function to map each near-synonym set to a unique number as the key and hence reduces the computation of SF to linear time. In addition, we apply hSF-IDF to online clustering of Chinese technology web news and propose an improved batch-based method. Extensive experiments have been conducted on a real-world dataset. The results show that our model outperforms some strong baselines including TF-IDF, average pooling of word or character embeddings, Latent Dirichlet Allocation (LDA), and bag-of-concepts in terms of both accuracy and efficiency.
FlorUniTo@TRAC-2: Retrofitting Word Embeddings on an Abusive Lexicon for Aggressive Language Detection.

This paper describes our participation to the TRAC-2 Shared Tasks on Aggression Identification. Our team, FlorUniTo, investigated the applicability of using an abusive lexicon to enhance word embeddings towards improving detection of aggressive language. The embeddings used in our paper are word-aligned pre-trained vectors for English, Hindi, and Bengali, to reflect the languages in the shared task data sets. The embeddings are retrofitted to a multilingual abusive lexicon, HurtLex. We experimented with an LSTM model using the original as well as the transformed embeddings and different language and setting variations. Overall, our systems placed toward the middle of the official rankings based on weighted F1 score. However, the results on the development and test sets show promising improvements across languages, especially on the misogynistic aggression sub-task.
Implementation Data Mining With K-Means Algorithm For Clustering Distribution Rabies Case Area In Palembang City

Abstract This research aims to classify the vulnerability of Rabies cases by using the CRISP-DM methodology through the process of business understanding, data understanding, data preparation, modeling, evaluation and deployment. The data that has the same characteristics are grouped in a single cluster then the data with different characteristics are grouped in another cluster. The algorithm used in clustering technique is K-Means algorithm. In this case, the cluster is divided into three clusters. The first cluster is a result of an expenditure that has an area with very vulnerable cases of rabies, the second cluster is an expenditure result that has an area with the vulnerable level of rabies cases, the third cluster is the result of expenditure has areas with no vulnerable cases of rabies.
Open-Source High Quality Speech Datasets for Basque, Catalan and Galician

This paper introduces new open speech datasets for three of the languages of Spain: Basque, Catalan and Galician. Catalan is furthermore the official language of the Principality of Andorra. The datasets consist of high-quality multi-speaker recordings of the three languages along with the associated transcriptions. The resulting corpora include over 33 hours of crowd-sourced recordings of 132 male and female native speakers. The recording scripts also include material for elicitation of global and local place names, personal and business names. The datasets are released under a permissive license and are available for free download for commercial, academic and personal use. The high-quality annotated speech datasets described in this paper can be used to, among other things, build text-to-speech systems, serve as adaptation data in automatic speech recognition and provide useful phonetic and phonological insights in corpus linguistics.
Halide implementation of weighted median filter

With the recent extension of camera applications, image filtering is essential in image processing. Weighted median filtering is one of the image denoising method. The weighted median filter can be more useful for removing noise and blurring correction; however, its computational cost is high. Halide is a domain-specific language for image processing. By using Halide, we can easily optimize the code of image processing. In this study, we present weighted median filter with Halide code. Experimental results show that we can easily write the weighted median filter code.
How (UN) Happiness Impacts on Software Engineers in Agile Teams?

Information technology (IT) organizations are increasing the use of agile practices, which are based on a people-centred culture alongside the software development process.Thus, it is vital to understand the social and human factors of the individuals working in agile environments, such as happiness and unhappiness and how these factors impact this kind of environment.Therefore, five case-studies were developed inside agile projects, in a company that values innovation, aiming to identify how (un)happiness impacts software engineers in agile environments.According to the answers gathered from 67 participants through a survey, interviews and using a cross-analysis, happiness factors identified by agile teams were effective communication, motivated members, collaboration among members, proactive members, and present leaders.
Satisfiability and Model Checking for the Logic of Sub-Intervals under the Homogeneity Assumption

The expressive power of interval temporal logics (ITLs) makes them one of the most natural choices in a number of application domains, ranging from the specification and verification of complex reactive systems to automated planning. However, for a long time, because of their high computational complexity, they were considered not suitable for practical purposes. The recent discovery of several computationally well-behaved ITLs has finally changed the scenario. In this paper, we investigate the finite satisfiability and model checking problems for the ITL D, that has a single modality for the sub-interval relation, under the homogeneity assumption (that constrains a proposition letter to hold over an interval if and only if it holds over all its points). We first prove that the satisfiability problem for D, over finite linear orders, is PSPACE-complete, and then we show that the same holds for its model checking problem, over finite Kripke structures. In such a way, we enrich the set of tractable interval temporal logics with a new meaningful representative.
From a Use Case Categorization Scheme Towards a Maturity Model for Engineering Distributed Ledgers

This contribution focuses on the maturity of the engineering of business applications for a trusted collaboration in business networks. Distributed ledgers emerge as technology enabler for establishing trust across business partners while blockchain is often used as a synonym. Hence, mature knowledge for application engineering and quality assured methods for selecting technology platforms for distributed collaboration are essential. When choosing a Distributed Ledger Technology (DLT) it is difficult to compare the different technologies in order to identify the one technology best suitable for a specific use case. Platforms’ maturity for distributed ledgers cannot be assessed sufficiently. Detailed knowledge about the technological details of platforms and functional characteristics are sometimes sparse. To start with, we propose a characterization approach for distributed ledgers based on various classification schemas. This characterization is founded in an evaluation of use cases and prototypical implementations as well as a record of projects conducted. The approach allows one to sort out unsuitable technologies at an early stage. Since the automation of business cooperation is one of the core benefits of DLT, Smart Contracts for the automation of business processes and Distributed Autonomous Organizations (DAO) for the specification of collaboration networks furnish a key benefit for business re-engineering with DLT. Levels of maturity for collaboration specification are defined to distinguish different computational and organizational powers in contract enforcements.
The A to Zoom of Digital Book Events: How One Press Managed the Overnight Transition to Virtual Events Due to Coronavirus

When COVID-19 caused a full season of book events to be cancelled in spring 2020, the MIT Press decided immediately to take its author events online instead. This article describes the process of making a swift transition from festivals, book tours, launches, and conferences to a fully digital events landscape.
A two-stage Bayesian semiparametric model for novelty detection with robust prior information

Novelty detection methods aim at partitioning the test units into already observed and previously unseen patterns. However, two significant issues arise: there may be considerable interest in identifying specific structures within the novelty, and contamination in the known classes could completely blur the actual separation between manifest and new groups. Motivated by these problems, we propose a two-stage Bayesian semiparametric novelty detector, building upon prior information robustly extracted from a set of complete learning units. We devise a general-purpose multivariate methodology that we also extend to handle functional data objects. We provide insights on the model behavior by investigating the theoretical properties of the associated semiparametric prior. From the computational point of view, we propose a suitable $\boldsymbol{\xi}$-sequence to construct an independent slice-efficient sampler that takes into account the difference between manifest and novelty components. We showcase our model performance through an extensive simulation study and applications on both multivariate and functional datasets, in which diverse and distinctive unknown patterns are discovered.
Generate and Test for Formulated Product Variants With Information Extraction and an In-Silico Model

The chemical industry is expanding its focus from process-centered products to product-centered products. Of these, consumer chemical products and other similar formulated products are especially ubiquitous. State of the art in the formulated product design relies heavily on experts and their expertise, leading to extended time to market and increased costs. The authors show that it is possible to construct a graph database of various details of products from textual sources, both offline and online. Similar to the “generate and test” approach, they propose that it is possible to generate feasible design variants of a given type of formulated product using the database so constructed. If they restrict the set of products that are applied to the skin, they propose to test the generated design variants using an in-silico model. Even though this chapter is an account of the work in progress, the authors believe the gains they can obtain from a readily accessible database and its integration with an in-silico model are substantial.
An Incentives-Based Mechanism for Corporate Cyber Governance Enforcement and Regulation

A growing literature in finance examines the impact of cybercrime on equity markets and publicly traded corporations, with an emerging strand of this literature investigating the contagion channel from cybersecurity breaches against the publicly traded companies, to broader market volatility. The dominant responses by the corporations to these threats can be described as ‘test internally for internal vulnerabilities’, and the ‘insure and forget’ approach, both of which imply a lack of significant preventative actions by companies under the risk of an external cybersecurity attack. The evidence of growing adverse impact and risk of hacking events on firms’ market valuations is highlighted by (i) the rising cumulative abnormal returns impact of such events, (ii) the rising systemic contagion effects of hacks, and (iii) the lack of robust regulatory mechanisms for systematic prevention, mitigation, and enforcement of data security breaches. This supports our proposal that when acting under regulatory authority’s supervision from within a ring-fenced incentives system, ‘white knight’ hackers may provide the appropriate mechanism for discovery and deterrence of weak corporate cybersecurity practices and systems. This mechanism can help alleviate the systemic weaknesses in the existent mechanisms for cybersecurity oversight and enforcement in financial markets.
RZcoin: Ethereum-Based Decentralized Payment with Optional Privacy Service

As the blockchain 2.0 platform, Ethereum’s turing complete programming language and smart contract components make it play an important role in the commercialization of blockchain. With the further development of blockchain applications, the privacy and security issues of Ethereum have gradually emerged. To solve this problem, we proposed a blockchain privacy protection model called RZcash in the previous work. It implements the dynamically updateable and verifiable hiding of the asset information in Ethereum, namely the account balance and transaction amount. However, RZcash does not pay attention to the key redundancy problem that may be caused by the creation of secret accounts. In addition, the large size of proofs gives it high communication costs. In response to these problems, we further improve RZcash. For the key redundancy problem, we construct a new signature scheme based on the ciphertext equivalent test commitment. Moreover, we use the Schnorr signature and bulletproof to improve the corresponding proof scheme in RZcash, thereby reducing the size of proof. Based on these improvements, we propose a decentralized payment system, called RZcoin, based on Ethereum. Finally, we implement the algorithm model of RZcoin and evaluate its security and performance. The results show that RZcoin has higher security and Lower communication cost than RZcash.
Two-Round Oblivious Linear Evaluation from Learning with Errors

Oblivious Linear Evaluation (OLE) is the arithmetic analogue of the well-know oblivious transfer primitive. It allows a sender, holding an affine function $$f(x)=a+bx$$ over a finite field or ring, to let a receiver learn f(w) for a w of the receiver’s choice. In terms of security, the sender remains oblivious of the receiver’s input w, whereas the receiver learns nothing beyond f(w) about f. In recent years, OLE has emerged as an essential building block to construct efficient, reusable and maliciously-secure two-party computation. In this work, we present efficient two-round protocols for OLE over large fields based on the Learning with Errors (LWE) assumption, providing a full arithmetic generalization of the oblivious transfer protocol of Peikert, Vaikuntanathan and Waters (CRYPTO 2008). At the technical core of our work is a novel extraction technique which allows to determine if a non-trivial multiple of some vector is close to a q-ary lattice.
Validating Mathematical Structures

Sharing of notations and theories across an inheritance hierarchy of mathematical structures, e.g., groups and rings, is important for productivity when formalizing mathematics in proof assistants. The packed classes methodology is a generic design pattern to define and combine mathematical structures in a dependent type theory with records. When combined with mechanisms for implicit coercions and unification hints, packed classes enable automated structure inference and subtyping in hierarchies, e.g., that a ring can be used in place of a group. However, large hierarchies based on packed classes are challenging to implement and maintain. We identify two hierarchy invariants that ensure modularity of reasoning and predictability of inference with packed classes, and propose algorithms to check these invariants. We implement our algorithms as tools for the Coq proof assistant, and show that they significantly improve the development process of Mathematical Components, a library for formalized mathematics.
Artificial Neural Network Overview

In this chapter artificial neural networks are thoroughly studied through the deconstruction of the model. Its several parts are described, and a comparison of the different used methods for each of these components is made, such as the functioning of each neuron, the learning process that makes use of optimization tools, the hyperparameters that define the model’s architecture, and the influence of the selected features.
Reviews Analysis of Apple Store Applications Using Supervised Machine Learning

Application distribution platforms such as Apple App Store and Google Play enable users to search, buy and install software applications with a few clicks. Additionally, application developers are working in different directions, which leads to the production of a variety of applications. This chapter provides techniques for classifying the reviews based on their metadata (star rating), keyword frequencies and sentiment analysis. It compares the accuracy of multiple Supervised machine learning (ML) classification algorithms. The chapter utilizes feature extraction (FE) algorithms combined with Supervised Machine Learning algorithm and natural language processing to add strength in the classification of, and then prediction for, text reviews. It describes the project implementation and design, including data collection and methodology. The chapter focuses on the results, comparing the accuracy and the performance of both FE algorithms combined with the variety of ML classification algorithms.
Nonlocal dispersion cancellation for three or more photons

The entanglement of quantum systems can produce a variety of nonclassical effects that have practical applications in quantum information science. One example of this is nonlocal dispersion cancellation, in which the effects of dispersion on one photon can be canceled out by the dispersion experienced by a second photon at a distant location. In this paper, we extend the analysis of nonlocal dispersion cancellation to three or more photons. We find that energy-time entanglement of three or more photons can lead to a complete or partial cancellation of dispersion depending on the experimental conditions. These results may be useful in implementing quantum key distribution in networks with three or more nodes.