Reviewerly/exHarmony huggingface dataset:
Open Access: an opportunity to redesign scholarly communication in history

While the adoption of open access to academic publications was slower and more difficult in the humanities and in particular in history than in the scientific and technical disciplines, it is today more and more widely accepted by the community of historians. The number of open access peer-reviewed journals is growing, thanks to the support of multidisciplinary platforms and tools, such as OpenEdition, Scielo and OJS. However, many journals still adopt an embargo period to protect the sales of their print edition. Research monographs in history are also more frequently available in open access. It is interesting to see that new academic presses, purely digital and adopting completely open access, such as UCL Press, Ubiquity Press and OBP were created in recent years to address the growing need of researchers in the humanities and social sciences. Finally, in the context of open science, which is larger than open access to publications, new forms of communication are experimented, that combine with open data (e.g. data journals) or with new ways of interacting with society (e.g academic blogs). These more recent explorations in open scholarly communications are promising perspectives for historians, in particular when they are involved in public history.
Evidence Identification Methods for Android and iOS Mobile Devices with Facebook Messenger

Facebook Messenger (FBM) is widely used by most mobile users. FBM is used for normal communication in addition to its involvement in criminal cases. Following a scientific mobile forensic analysis approach keeps the evidence admissible. This study follows the NIST mobile forensic process to retrieve data from FBM. The study provides several techniques for device identification, data acquisition, and analysis of FBM data from both Android and iOS devices. Several tools were used for acquisition including Libimobiledevice, iTunes, Belkasoft, AXIOM, and ADB. Additionally, several tools were used for data analysis including AXIOM Examine, Belkasoft, and DB viewer for SQLite. This study shows that the appropriate forensic tool for FBM analysis is AXIOM; based on the results of analyzing encrypted iTunes images for iOS, Belkasoft was better in performance for analyzing ADB images for Android, while FBM data extraction requires the device to be rooted.
Multiplatform Comparative Analysis of Intelligent Robots for Communication Efficiency in Smart Dialogs

The objective of the paper is to analyze the algorithms and methods used in dialogue chatbot systems in terms of usability and quality of their functioning. The research used two knowledge corpuses differing in subject matter, number of specific intents, availability of entities, and number of training and test data. The research was performed on three platforms - RASA, Dialogflow, and IBM Watson. The influence of the number of intents and the presence of entities on the quality of the dialogue system using predefined metrics was examined. Additionally, the experiments concerned the analysis of chatbot operation for different ratios of training to test data. Moreover, the influence of the platform and corpus selection on the quality of operation and acceptance level of intent was examined.
Consulting Application of Building Block Dynamic Repair Technology in the Restoration of Students’ Learning Power

Building block dynamic repair technology; Repair of learning power; Consulting application
Variable Step Size Firefly Algorithm for Automatic Data Clustering

Nowadays with the rapid development of data availability proper data analysis technique is required. Clustering is a popular unsupervised data analysis process which can be used to identify similar or dissimilar set of objects based on their characteristics. Data clustering algorithms can be applied to a wide range of areas. Several clustering methods have been considered to resolve data clustering issues. The very popular Firefly algorithm (FA) is a nature motivated optimization algorithm that depends on the natural light emission of fireflies. In this paper a modified firefly algorithm is used to resolve the data clustering issues. The suggested algorithm is compared with very popular particle swarm optimization algorithm (PSO) and existing standard firefly algorithm. The experimental results are calculated on six medical datasets from UCI and Kaggle machine learning repository. In addition to this, Davies-Bouldin (DB) cluster validity index is applied to analysis experimental results of discussed algorithms. With proper performance study we concluded that the modified firefly algorithm namely variable step size firefly algorithm (VSSFA) can perform better than PSO and FA.
Ensuring Security of Ecommerce Online Payment Systems

Nowadays E-commerce is important because most businesses are done online.Examples of e-commerce are Alibaba express, Amazon…etc.As businesses are done on e-commerce, many transactions are done so it is important to provide security.There are three main security issues relevant to doing business online: verifying the identity of the person through which doing business, ensuring that messages sends and received have not been tampered performed.Cryptography is about constructing and analyzing protocols that overcome the influence of adversaries and which are related to various aspects in information security such as data confidentiality, data integrity, authentication, and non-repudiation, Application of cryptography include ATM cards, computer passwords, and electronic commerce.The aim is to study the security of different online payment systems, the threats to which users are exposed and some solutions to strengthen the security.
Measurement and calculation of indoor climate of swallow’s nests house in Minahasa

Abstract Swallow birds (Collocalia fuciphaga) are found in many islands in Indonesia. In general, swallows like sites which are warm and humid for their house and making nests. Swallow’s nest is a product of high economic value. Many people are interested in building houses for swallow to gain profit from the nest. The aim of the study is to describe the thermophysical characteristics of building materials of the house, and its indoor air temperature. The methodology used is descriptive analysis. The study includes measurements of outdoor and indoor climate variables which are air temperature, surface temperature and humidity. Identification of thermophysical material include heat specific capacity, thermal conductivity and density of wall and roof material. The results of climatic measurements carried out on 7 and 8 May 2021 in Minahasa showed that the indoor temperature was in the range of 25 to 27 °C, and relative humidity of around 84 to 85 %. Geometric and thermophysical modelling were made for simulation calculations by using HEAT2 software. The calculation results show that the distribution of indoor air temperature is in range of 25.6 to 25.8 °C, where in average there is a difference of about 0.5 °C lower than the measurement results.
Verso una nuova definizione di maniera attraverso il parlato spontaneo italiano

"Towards a new definition of manner through Italian spontaneous dialogues. Manner has been traditionally considered as a semantic prime expressing the way in which an action is performed. In previous studies, it has been treated as a category fulfilling a basically descriptive function, encoded by an inventory of lexical or morpho-syntactic items (such as verb roots or adverbial phrases). In this paper, we propose a functional and corpus-driven definition of this notion. We present an annotation scheme of Manner constructions and we show how we applied it to 40 spontaneous Italian dialogues from the VOLIP corpus. The analysis of Manner constructions in spoken interactional data shows that, far from being a static descriptive notion, Manner can be regarded as an interactive, gradable category, co-constructed by speakers. Keywords: Manner, corpus-driven analysis, spoken dialogues, co-construction of meaning "
Regional Mercury Background Values in Soils and Saprolites in the Gold-Producing Greenstone Belt of Suriname, Guiana Shield

Background values of mercury have been established in 3 pilot profiles and 62 natural unpolluted soil and saprolite profiles on 16 different rock types in the greenstone belt of Suriname. The data were obtained to serve as a yardstick to distinguish natural from polluted materials in the area where artisanal small-scale gold mining is ubiquitous. The analytical data on Hg and other major and trace elements in the pilot projects show that Hg is residually concentrated in topsoils and indurated horizons by the laterization process up to levels of 200 ppb. This is up to 100 times the original concentration in the underlying hard rock. Unmined bauxites from the Nassau Mountains show commonly Hg values over 2000 ppb, the highest value is 11668 ppb. In the deeper pallid zone horizons of the profiles, Hg has often been leached almost completely together with iron by reducing groundwater action. In this way the range of Hg values within a single profile can be greater than between profiles. Maximum and average Hg values of 196 samples from the 16 different rock types fall apart into two groups. Felsic rocks with predominance of quartz and feldspar have maximum Hg values around 100 ppb and averages around 50 ppb, mafic ones with less silica and higher Fe, Mg and Ca have maximum values around 250-300 ppb and averages around 150 ppb. In general the natural soil and saprolite Hg values fall in the same order of magnitude as many mine tailings and stream sediments, and therefore cannot be used to separate polluted from unpolluted materials.
Time-bin Quantum Key Distribution exploiting the iPOGNAC polarization moulator and Qubit4Sync temporal synchronization

Here we present cross-encoded Quantum Key Distribution where state encoding is performed with a self-compensating and calibration-free polarization modulator, while transmission is performed in time-bin encoding resistant to perturbances from the fiber channel.
A Proposal for the Development of Practical Korean Textbook for Office Manager

The purpose of this study is to propose a direction for the development of business Korean textbooks for foreign learners who want to work as middle managers in office positions in Korean companies. First, previous studies on business Korean education were reviewed. And, 4types of business Korean textbooks were analyzed to see if the current educational content meets the needs of learners. As a result of analysis, though the business textbooks aim for the common purpose of foreign learners to find employment in Korean companies, there are differences in the content composition. The level of learners was not clear, and the scope and difficulty of topic selection were significantly different. The vocabulary and grammar of the text were not related to actual practical situations. The reading activity was the main activity rather than a speaking activity that consumers wanted. The conversations and materials in those books were closer to everyday communication than to introducing the work. We judged that there is a limit to understanding Korean companies and workplace culture for prospective foreign workers due to the lack of introduction to workplace culture. Therefore, based on the results of textbook analysis, this study proposes to divide the business Korean textbook system into two for general office and specialized fields and divide the learning stage according to the level of the learner. In detail, this study proposed ‘composition of content suitable for purpose, presentation of vocabulary expressions related to topics, strengthening of work-related speaking-listening, and introduction of various workplace cultures’ as directions for the development of practical Korean textbooks for middle office managers.
I termini di colore nelle aree bianco, nero e grigio nella storia dell’italiano

"Colour Terms in the WHITE, BLACK and GRAY Areas in the History of the Italian Language. The present article is part of several studies that the authors have dedicated to the history of Italian colour terms. The Introduction illustrates some of the theses of Berlin, Kay (1969), with particular reference to the distinction between basic and non-basic terms. § 2 is dedicated to the Latin colour terms in the WHITE, BLACK and GRAY areas and their reflexes in the Romance languages. In § 3 the resources used for the research are presented. In §§ 3.1. (From Old Italian to the 19th century) and 3.2. (From the end of the 19th century to today’s Italian) the historical evolution and contemporary use of basic terms and other non-transparent colour terms are analyzed, also focusing on their figurative meanings and on the fixed collocations in which they occur. § 3.3. deals briefly with the terms formed by means of various morphological and syntactic devices that are most frequently used in Italian for the enrichment of the colour lexicon. Keywords: semantics, word-formation, colour terms, Italian "
Sentiment analysis using convolution neural network

Face recognition and sentiment analysis used in many surveillance and security application such as biometric identifications, health care applications, and criminal detection. Face recognition framework provide the concept of recognizing the respective face image features to value in the digital forms via using various classification techniques, In this paper, we present the face recognition and sentiment analysis by using the convolution neural network for improving the accuracy of the sentiment analysis system, sentiment analysis covers various gestures such as the happy, sad, neutral, surprise, angry and fear, here our present model based on the convolution neural network and provide the accuracy for the sentiment analysis.
CONSTRUCTION OF AN OPTIMAL COLLECTIVE DECISION ON THE BASIS OF A CLUSTER ENSEMBLE

This article presents methods of image analysis based on supervised learning and an algorithm consisting of two stages of determining the optimal classifier using a cluster ensemble. At the first stage, the averaged co-association matrix is calculated using a cluster ensemble. In the clustering ensemble, we used a scheme of a single clustering algorithm that constructs base partitions with parameters taken at random. At the second stage, the optimal classifier is determined using the resulting kernel matrix as input data. Numerical experiments were carried out with real hyperspectral images. The experimental results showed that the proposed algorithm has classification accuracy comparable to some modern methods.
Phishing Website Detection and Classification

Websites are used nowadays for a lot of online transactions like E-commerce which involve making online payments. These websites may or may not ask user sensitive data such as username, password, credit/debit card, and details. Websites which ask users for these sensitive data for malicious or illegal activities are termed as phishing websites. Detection of phishing website is a crucial task today to prevent phishing attacks. For detection and prediction of phishing/fraudulent websites, we propose a system that works on classification techniques and algorithm and classifies the datasets as phishing/legitimate. It is detected on various characteristics like uniform resource locator (URL), domain name, domain entity, etc. When the user makes the online transaction after the payment is done through the gateways of the website, data mining algorithm will be used to determine it as phishing/non-phishing. This application will find any real-life applications primarily in e-commerce, so that the whole transaction process is secure. The performance of data mining algorithm is better than the other classification algorithms which are used traditionally. This application enables the user to perform online transactions fearlessly. Phishing/fake website URL can be added by the admin in the system database, after which it will be scanned using the data mining algorithm, once the scan is completed, it will add new keywords in the database. Phishing has become one of the major methods of cyber-attack where credentials of users are obtained using illegal ways. URL of the website will used mainly as the input data, and apart from that the system will also scan for already present blacklisted keywords, link in the website and classify it as phishing/safe. In order to ensure maximum accuracy, exhaustive dataset will be used to train the model.
DIGITAL TECHNOLOGIES AND THEIR IMPACT ON THE EDUCATIONAL POLICY OF THE STATE

Digital technologies are becoming an increasingly prominent feature of modern education. The educational value of digital technologies has been reinforced by the widespread use of digital educational resources during the pandemic. Thus, the hope that digital technologies can transform education in the direction of empowerment and influence the economic efficiency and competitiveness of the country, despite their uneven history to date, is actively promoted. The purpose of the study is to identify the difficulties of introducing digital technologies into the domestic education system and the directions of their improvement. The article also presents the results of the analysis of the work on the introduction and use of digital technologies in the national education systems of the leading countries of Europe and the Republic of Kazakhstan in particular. This study allowed us to conclude that the introduction of digitalization into the domestic education system creates new difficulties, such as misunderstanding of the processes taking place, insufficient funding, and others. However, it should be noted that countries can reach a new level of digitalization of the economy only with the targeted support of the state.
Toward Users' Perception of Sustainable Transportation:A Social Media Analytics Approach

Sustainability is one of the considerable challenges we widely face these days. Transformation into a more vibrant life provides a city for social, economic, and environmental interaction in which people can prosper, and hence sustainable transportation comes into play. Accordingly, public concerns about sustainable solutions in the urban transportation industry have increased, followed by discussions on public transportation, taxi apps, and ride-sharing. On the other hand, with the increasing emergence and development of social networks, customers can freely express their opinions without being bound by predefined issues. Moreover, social network analytics is an opportunity for organizations to understand the priorities of a large portion of their customers while spending less time and money compared to traditional research methods such as surveys. Twitter is now an accessible and valuable resource to access customers' feedback and what is on their minds. The present study has tried to identify the mentality of users of a transportation system by using data mining and text mining on Twitter so that the factors affecting the tendency of people to use any urban transportation mode have been investigated and prioritized. This article provides a model for improving the sustainability of urban transportation systems.
Video Deblurring with Deconvolution and Aggregation Networks

In contrast to single-image deblurring, video deblurring has the advantage that neighbor frames can be utilized to deblur a target frame. However, existing video deblurring algorithms often fail to properly employ the neighbor frames, resulting in sub-optimal performance. In this paper, we propose a deconvolution and aggregation network (DAN) for video deblurring that utilizes the information of neighbor frames well. In DAN, both deconvolution and aggregation strategies are achieved through three sub-networks: the preprocessing network (PPN) and the alignment-based deconvolution network (ABDN) for the deconvolution scheme; the frame aggregation network (FAN) for the aggregation scheme. In the deconvolution part, blurry inputs are first preprocessed by the PPN with non-local operations. Then, the output frames from the PPN are deblurred by the ABDN based on the frame alignment. In the FAN, these deblurred frames from the deconvolution part are combined into a latent frame according to reliability maps which infer pixel-wise sharpness. The proper combination of three sub-networks can achieve favorable performance on video deblurring by using the neighbor frames suitably. In experiments, the proposed DAN was demonstrated to be superior to existing state-of-the-art methods through both quantitative and qualitative evaluations on the public datasets.
Parity-time Symmetric Optical Neural Networks

An optical neural network architecture is proposed that utilizes parity-time symmetric couplers as its building blocks. Gain–loss contrasts across the array are adjusted as a means to train the network.
Bridging the Gap Between Formal Semantics and Neurolinguistics: The Case of the N400 and the LPP

We outline a formal framework that combines results from neurolinguistic research on two ERP components, the N400 and the LPP, with formal semantics. At the semantic level we combine de Groote’s continuation-based version of Montague semantics with van Eijck’s Incremental Dynamics enriched with frames. We analyze them in terms of complex properties that apply both to the semantic and the discourse level and which combine world knowledge with syntagmatic and paradigmatic relationships. DPs and common nouns are interpreted as sequences of update operations which are related to these properties. In turn, each ERP component is correlated to at least one update operation. Whereas the N400 is related to success of these operations and the way they reduce uncertainty about the situation described by a discourse, the LPP is correlated to the failure to execute these updates.
Bayesian Hypothesis Testing

This chapter gives a brief overview of Bayesian hypothesis testing. We first describe a standard Bayesian analysis of a single binomial response, going through the prior distribution choice and explaining how the posterior is calculated. We then discuss Bayesian hypothesis testing using the Bayes factor, a measure of how much the posterior odds of believing in one hypothesis changes from the prior odds. We show, using a binomial example, how the Bayes factor may be highly dependent on the prior distribution, even with extremely large sample sizes. We next discuss Bayes hypothesis testing using decision theory, reviewing the intrinsic discrepancy of Bernardo, as well as the loss functions proposed by Freedman. Freedman’s loss functions allow the posterior belief in the null hypothesis to equal the p-value. We next discuss well-calibrated null preferences priors, which applied to parameters from the natural exponential family (binomial, negative binomial, Poisson, normal), also give the posterior belief in the null hypothesis equal to valid one-sided p-values, and give credible intervals equal to valid confidence intervals.