Reviewerly/exHarmony huggingface dataset:
Personality Trait Prediction Using Handwriting Recognition with KNN

Handwriting analysis is the study of the relationship between handwriting and personality. It is a particular way to represent one’s thought process with the help of handwriting. That is why it is also called brain writing. Handwriting is also a unique feature just like fingerprint of a particular individual it cannot be same of two different people. It also helps us to recognize emotional status of person. Handwriting recognition is mainly done in an orthodox way in which a professional handwriting expert take handwriting samples of person and examine them for various personality traits which take a lot of time and energy of a person. Hence in this paper we focused on the method to create automated system that would help to predict personality traits of a specific person by their handwriting sample using machine learning algorithm KNN. As in today’s modern world it becoming more important for predicting personality either by face, voice, handwriting and many other techniques for different traits. To make this method we are considering diverse feature like letter size, word spacing, slant of words, pen pressure, baseline etc in a document for prediction of personality of writer
Standardized Description of the Feature Extraction Process to Transform Raw Data Into Meaningful Information for Enhancing Data Reuse: Consensus Study

Despite the many opportunities data reuse offers, its implementation presents many difficulties, and raw data cannot be reused directly. Information is not always directly available in the source database and needs to be computed afterwards with raw data for defining an algorithm.The main purpose of this article is to present a standardized description of the steps and transformations required during the feature extraction process when conducting retrospective observational studies. A secondary objective is to identify how the features could be stored in the schema of a data warehouse.This study involved the following 3 main steps: (1) the collection of relevant study cases related to feature extraction and based on the automatic and secondary use of data; (2) the standardized description of raw data, steps, and transformations, which were common to the study cases; and (3) the identification of an appropriate table to store the features in the Observation Medical Outcomes Partnership (OMOP) common data model (CDM).We interviewed 10 researchers from 3 French university hospitals and a national institution, who were involved in 8 retrospective and observational studies. Based on these studies, 2 states (track and feature) and 2 transformations (track definition and track aggregation) emerged. "Track" is a time-dependent signal or period of interest, defined by a statistical unit, a value, and 2 milestones (a start event and an end event). "Feature" is time-independent high-level information with dimensionality identical to the statistical unit of the study, defined by a label and a value. The time dimension has become implicit in the value or name of the variable. We propose the 2 tables "TRACK" and "FEATURE" to store variables obtained in feature extraction and extend the OMOP CDM.We propose a standardized description of the feature extraction process. The process combined the 2 steps of track definition and track aggregation. By dividing the feature extraction into these 2 steps, difficulty was managed during track definition. The standardization of tracks requires great expertise with regard to the data, but allows the application of an infinite number of complex transformations. On the contrary, track aggregation is a very simple operation with a finite number of possibilities. A complete description of these steps could enhance the reproducibility of retrospective studies.
Context-based Adblocker using Siamese Neural Network

This paper proposes a new content-based ad-blocker to minimize the amount of human effort required to effectively combat pushed advertisements. Current ad-blocker models are expensive to maintain and not always effective in identifying confusable images that may play different roles across diverse websites. We investigated the possibility of solving these problems with the introduction of a deep learning, content-based ad-blocker model. More specifically, the proposed ad-blocker identifies advertisement images by combining the contained information of a given image and the content of the website it originated from. The proposed solution was prototyped and applied to a diverse selection of popular websites, achieving a detection accuracy of 98%.
The Framework and Implementation of a Highly Immersive Learning Environment for China Science and Technology Museum

The China Science and Technology Museum exerts significant influence over knowledge dissemination, but its traditional learning and dissemination methods cannot fulfill the diversified and digital demands of visitors. With development of computer graphics, it has increased the viability of applying immersive learning supported by Virtual Reality (VR) to educational exhibits. According to the Gibson’s theory of perceptual learning, this study proposes a technical framework for China Science and Technology Museum to construct an immersive learning environment. Force feedback technology was used in both physics exhibit and mathematical exhibit to enhance the haptic perception and to create more scene dynamism and immersion. Through user experience assessment, it showed that this virtual environment enabled students to obtain a good sense of presence and engagement, and participants reported a satisfying experience in the virtual immersive learning system.
Anonymity-driven Measures for Privacy

In today&#x2019;s world, digital data are enormous due to technologies that advance data collection, storage, and analyses. As more data are shared or publicly available, privacy is of great concern. Having privacy means having control over your data. The first step towards privacy protection is to understand various aspects of privacy and have the ability to quantify them. Much work in structured data, however, has focused on approaches to transforming the original data into a more anonymous form (via generalization and suppression) while preserving the data integrity. Such anonymization techniques count data instances of each set of distinct attribute values of interest to signify the required anonymity to protect an individual&#x2019;s identity or confidential data. While this serves the purpose, our research takes an alternative approach to provide quick privacy measures by way of anonymity especially when dealing with large-scale data. This paper presents a study of anonymity measures based on their relevant properties that impact privacy. Specifically, we identify three properties: uniformity, variety, and diversity, and formulate their measures. The paper provides illustrated examples to evaluate their validity and discusses the use of multi-aspects of anonymity and privacy measures.
Evaluation Study on Privacy Policies of Express Companies Based on Cloud Model

In the era of the Internet of things (IoT), smart logistics is quietly rising, but user privacy security has become an important factor hindering its development. Because privacy policy plays a positive role in protecting user privacy and improving corporate reputation, it has become an important part of smart logistics and the focus of express companies. In this paper, through the construction of the privacy policy evaluation index system of express companies, aiming at qualitative indicators that are difficult to evaluate, we introduce the cloud model evaluation method that can combine the qualitative and quantitative together, and comprehensively evaluate the privacy policy of five express companies in China from four indicators: general situation, user informed consent, information security control and personal rights protection. The results show that: Overall, the privacy policies of the five express companies have not reached the "good" level, and there is a certain gap between the privacy policies of different express companies. From the comparison of indicators, the five express companies generally score relatively good; However, the overall score of information security control index is relatively poor, and the other two indexes are quite different. Cloud model evaluation method has strong applicability for the evaluation of express company privacy policy, which provides a reference for improving the privacy policy formulation and improving the privacy protection level of China&#x2019;s express delivery industry in the era of IoT.
Computational Refinements for Post-Quantum Elliptic Curve Security

Computer security in a post-quantum world is a topic of great significance. The security of a vast number of public key encryption and key distribution techniques is dependent upon various number theoretic frameworks such as factoring, discrete logarithms and elliptic curves. Yet, variations on Shor&#x2019;s algorithm have provided a theoretical basis for rendering such systems vulnerable to quantum attacks. In this work, we review quantum solutions for typical number theoretic problems. After leading up to elliptic curve systems, we highlight the relevance of computing modular inverses. Finally, refinements to quantum versions of the extended Euclidean algorithm are presented.
The Effects of Virtual Classroom Instruction: The Pre-service Teacher Preparation Program at the University of Ljubljana

Over the past few decades, a strong presence of the reflective model of teacher-training has been observed. It presupposes intense teacher-trainees’ involvement in the development of teaching practices and engagement in teacher self-development through the process of self-reflection. Subject to internalization, this process facilitates transmutation of personal learning experience into effective teaching practices. With the current pandemicPandemic still well underway, these systems of self-reflection and internalization have significantly shifted as the process of transmutation had to be replaced or at least supplemented by development and implementation of new teaching practices, inadvertently demanding of students and teachers alike to adapt to the new reality. The shifts in teaching practices have been marked by a number of modifications in the teacher-training experience, which are reflected in the recent research, conducted within our teacher-training program at the end of the online Teaching Practicum (TP). The present chapter discusses the research findings based on the teacher-trainees’ responses in two separate surveys designed for use at the end of the academic year 2020–21, which was heavily marked by the lockdown mode. These findings are further supplemented by the analysis of the relevant content from the teacher-trainees’ reflective essays, which are submitted as part of their TP portfolioPortfolio requirement. Supported by the research results, the present chapter highlights the respondents’ concerns over as well as benefits of their online teaching experience and draws relevant conclusions pertaining to future development of (online) teaching practices.
Msfe-Panet: Improved Yolov4-Based Small Object Detection Method in Complex Scenes

Purpose With the rapid development of computer vision and artificial intelligence technology, visual object detection has made unprecedented progress, and small object detection in complex scenes has attracted more and more increasing attention.Because of the limited feature information of small objects, the resolution is low. Therefore, in the actual complex scene, the effect of detecting small objects is not ideal. Methods To solve the problems of ambiguity, overlap and occlusion in small object detection in complex scenes. In this paper, a multiscale fusion feature enhanced path aggregation network MSFEPANet is proposed. By adding an attention mechanism and feature fusion, the fusion of strong positioning information of the deep feature map and strong semantic information of the shallow feature map is enhanced, which helps the network find interesting areas in complex scenes and improves its sensitivity to small objects. The rejection loss function and network prediction scale are designed
An Empirical Study of Intelligent Feedback Mechanism in Promoting College English Translation Learning

It is common for students to have low learning engagement, low learning motivation, and difficulty in improving translation performance due to insufficient feedback information, single form of feedback, and untimely feedback, etc. How to improve students’ translation ability and learning engagement becomes the key to improve translation learning effect. In the translation teaching based on the intelligent joint feedback mechanism, the translation learning activities including the conceptualization stage, the writing stage, the feedback stage and the revision stage are designed by incorporating the theory of process writing method. The results of the empirical analysis showed that the translation learning based on the intelligent joint feedback mechanism significantly improved students’ translation performance and enhanced students’ learning engagement; compared with the translation learning based on the online automatic feedback mechanism, there was a stronger correlation between students’ reading tendency and translation performance in the translation learning based on the intelligent joint feedback mechanism. It can be seen that translation learning based on intelligent joint feedback mechanism can help promote students’ learning engagement and the transfer of reading accumulation to translation ability, and realize the improvement of translation learning effect.
Optimized Video Compression with Residual Split Attention and Swin-Block Artifact Contraction

The existing learning-based algorithms continue to be plagued by erroneous motion compression and ineffective motion compensation architectures. To overcome these challenges, we present an end-to-end video compression method through a set of primary operations. A deep residual attention split (DRAS) block is introduced for motion compression networks to pay attention to certain image regions to create more effective features for the decoder while boosting the rate-distortion optimization (RDO) efficiency. A channel residual block (CRB) is proposed in motion compensation to yield a more accurate predicted frame, potentially improving the residual frame. Due to mitigating the compression errors, an artifact contraction module (ACM) by residual swin convolution UNet block is included in this model to improve the reconstruction quality. These modules combine with a loss function by assessing the trade-off and enhancing the decoded video quality. Experimental results show the competitive performance of the proposed method on four benchmark datasets.
Fixing ReDigi: NFT Tethered Sound Recordings

One of the problems with digital sound recordings has been how to permit the lawful resale of music fixed in freely copyable, nonrivalrous digital files. Historical Congressional resistance and the recent 2018 decision in Capitol Records, LLC v. ReDigi Inc., seem to foreclose any such possibility of digital resale, or the extension of copyright’s venerable first sale doctrine to digital files. In Capitol Records, the Second Circuit noted the impossibility of lawful secondary resale without an act of reproduction facilitating further distribution. By focusing on Section 106(1), the Second Circuit avoided having to resolve the more difficult first sale implications of DRM technologies. This Article argues that NFTs (non-fungible tokens) are the first DRM technology to permit lawful digital resale, with many musicians, like Calvin Cordozar Broadus Jr., known professionally as Snoop Dogg, paying serious attention. Just days before his February 2022 Super Bowl Performance, Mr. Broadus sold his latest album, Bacc on Death Row, as an exclusive, limited-time release of NFT stashboxes for $5,000.00 each. Sales of his NFT totaled $44.3 million in just five days, with the industry predicting that the NFT marketplace for sound recordings will become a multibillion dollar one as early as 2025. More importantly, NFTs, like BODR, are the first tethering DRM technology that adequately addresses the public policy concerns raised in the legislative history and subsequent Registrar of Copyrights 2001 report on the Digital Millennium Copyright Act rejecting extension of Section 109’s first sale doctrine to digital files.However, the promise of ownership of NFT tethered sound recordings may be an empty one, when viewed through the lens of the Copyright Act. Despite industry marketing, copyrights are a statutory monopoly, and do not create property interests, making the question of ownership an unsettled one. Further muddying the waters are industry agreements containing downstream royalty provisions. Congress has two immediate paths in response to consumer adoption of NFT tethered sound recordings: (1) do nothing, and allow Section 115 to control the imposition of downstream royalties on NFTs; or (2) amend Section 119 to recognize a digital first sale doctrine for NFTs.
ICT Training of Pre-service Bilingual Teachers: Present, Past, and Future Needs

Twenty-first century skills were part of the ATC21S (University of Melbourne 2012) international research project entitled AssessmentAssessmentand Teaching of twenty-first Century Skills (Dudeney et al. 2014) which further specify as creativity and innovation, collaboration and teamwork, critical thinking, problem-solving, autonomy, flexibility, and lifelong learning. BilingualBilingualpre-service teachersPre-service teacher are entitled to develop technologyTechnology-related skills for both their own education and that of their future students (Savage and Barnett 2015). This research analyzed the perceived ICTInformation and Communications Technology (ICT)competenceCompetence and needs of 34 pre-service bilingualBilingual teachers from the University of Córdoba (SpainSpain) based on their opinions on their attitudes, skill, tools, and pedagogy on what they think they will need in the future, what they need to fulfil their Undergraduate training as teachers, and what they have needed so far in their school placements. Quantitative and qualitative data were scrutinized under a mixed-methods research design. Findings showed that pre-service students viewed their technologyTechnology training positively in the four categories analyzed (i.e., Will, Skill, Tools, and Pedagogy). The conclusions indicate that twenty-first century teachers are entitled to be ‘digital citizens’, who have a digital identity which, for more than sure, will instil in future generations.
Can Code Switching Indicate Personal Branding?: A Case Study of Higher Education Students in Indonesia

&lt;p&gt;&lt;em&gt;The goal of this research is to have a better understanding of how &lt;/em&gt;&lt;em&gt;students&lt;/em&gt;&lt;em&gt; can grow into their own personal brands&lt;/em&gt;&lt;em&gt; by switching their languages&lt;/em&gt;&lt;em&gt;. The study takes a qualitative approach, and the data w&lt;/em&gt;&lt;em&gt;ere&lt;/em&gt;&lt;em&gt; gathered through interviews via &lt;/em&gt;&lt;em&gt;G&lt;/em&gt;&lt;em&gt;oogle &lt;/em&gt;&lt;em&gt;F&lt;/em&gt;&lt;em&gt;orms. The interviewees were all current Brawijaya University students &lt;/em&gt;&lt;em&gt;from departments of &lt;/em&gt;&lt;em&gt;English Education and English Literature&lt;/em&gt;&lt;em&gt;.&lt;/em&gt;&lt;em&gt; There are fifty students &lt;/em&gt;&lt;em&gt;telling&lt;/em&gt;&lt;em&gt; their experiences while comitting code switching and how they &lt;/em&gt;&lt;em&gt;obtained&lt;/em&gt;&lt;em&gt; the stereotype. &lt;/em&gt;&lt;em&gt;According to the findings, the primary goal of developing a personal brand is to gain confidence, where a personal brand will help them stand out among their competitors. It is also discovered that code switching and personal branding &lt;/em&gt;&lt;em&gt;are &lt;/em&gt;&lt;em&gt;not yet being practiced to the extent that it could be due to the limited research in this area previously. However, we have discovered that people are interested in this phenomenon because it is more a matter of individuals' lack of knowledge about personal branding that is limiting them.&lt;/em&gt;&lt;/p&gt;
Benin

The Beninese government embarked, five years ago, on an ambitious reform agenda (“Revealing Benin”) to revive the economy and improve the wellbeing of the people. Economic activity accelerated prior to the COVID-19 pandemic, with sound macroeconomic management and enhanced budget transparency culminating in access to international capital markets in 2019 and issuance of the first-ever SDG bond by an African sovereign last year. However, today, Benin faces significant headwinds from a deteriorating security situation at its northern borders, pandemic-induced scars, and higher cost of living amidst the war in Ukraine, which could impact hard-won macroeconomic gains and cause hardship. The authorities have requested a Fund-supported program to meet pressing financing needs, preserve macroeconomic stability, and anchor the country’s National Development Plan centered on achieving the Sustainable Development Goals (SDGs).
Isolated Bangla Handwritten Character Classification using Transfer Learning

Bangla language consists of fifty distinct characters and many compound characters to be named. Several notable studies have been performed to recognize the Bangla characters both as handwritten and optical characters. Our approach is to use transfer learning to classify the basic distinct as well as compound Bangla Handwritten characters avoiding the vanishing gradient problem. Deep Neural Network techniques such as 3D Convolutional Neural Network (3DCNN), Residual Neural Network (ResNet), and MobileNet have been applied to generate an end-to-end classification of all the possible standard formation of the handwritten characters in Bangla language. Bangla Lekha Isolated dataset is used to apply this classification model, which has a total of 1,66,105 Bangla Character images sample data categorized in 84 distinct classes. This classification model achieved 99.82% accuracy on training data and 99.46% accuracy on test data. Comparison has been made among the various state-of-the-art benchmarks of Bangla Handwritten Characters classification, which shows that this proposed model got better accuracy in classifying the data.
Evaluation of Deep Learning models on UV ink : a Fake Money detection scheme with RPN

As soon as coins or money were invented, people were trying to make counterfeits. Counterfeit money is fake money that is produced without the permission of the state or government, usually to imitate the currency and deceive the intended recipient. In Bangladesh, this is a significant problem that is becoming an increased phenomenon as the days pass by. Today’s modern banknotes have several security features that make it easier to identify fake notes. One of the security features is the use of UV ink. Banknotes deliberately put random flecks of color scattered all over the surface of the money - which acts as an extra layer of protection against counterfeiters. We proposed an automatic authentication model for identifying counterfeit money based on these random flecks of color which are visible under UV light. To obtain a benchmark result, existing object detection pre-trained models were used, followed by MobileNet, Inception, ResNet50, ResNet101, and Inception-ResNet architectures. After that, using the Region Proposal Network (RPN) method with Convolutional Neural Network (CNN) based classification the optimal model was proposed. The proposed model had a 96.3% accuracy. It is critical to reduce the circulation of counterfeit money in a country’s economy to stop inflation. This study will aid in the detection of counterfeit money and, hopefully, reduce its spread.