Reviewerly/exHarmony huggingface dataset:
Awareness of Digital Imaging/Telepathology Among Dental Post Graduate Students: A Survey

Background: The collection, storage and sharing of pathological data among oral pathologists to this day is done mostly by physical transfer of slides and reports. Telepathology is a form of communication between medical professionals that includes the transmission of pathology images or testing data. Aim: The aim of the study is to assess the knowledge, attitude and perception of digital pathology/telepathology among dental post graduate students. Materials and Methods: A cross-sectional study consisting of a self constructed survey was distributed through an online portal to dental post graduate students. The survey focused on the type of respondent, knowledge of microscopic digital photography, current usage, strengths and weaknesses and perceived future direction of digital imaging in the pathology laboratory. Results: A total of 102 responses were collected. The overall response rate was 100%. 56.9% of dental post graduate students are aware of the term “Digital Pathology” or “Telepathology”. 11.8% of dental post graduate students use digital pathology in their routine practice. 20.6 % of dental post graduate students send specimens once a year. 75.5% of dental post graduate students believe that “Digital Pathology/Telepathology” can be effective for research purposes. 62.7% of dental post graduate students believe that digital pathology imaging provides faster diagnosis. 75.5% of dental post graduate students believe that digital pathology imaging has high equipment cost. Comparison between the age of the respondents and their awareness of the term Digital Imaging/Telepathology and its scope in India stated that dental post graduate students of age group between 21-30 years were aware of the term and believed it has scope in India, but the results were insignificant (p&gt;0.05). Conclusion: Improved knowledge and acceptance of digital pathology and telepathology throughout the dentistry community could facilitate the communication and cooperation necessary to realize the type of initiatives capable of advancing the importance of pathologists in the changing healthcare environment.
A free toolkit to foster open access agreements

In November 2021, with the support of the Association of Learned and Professional Society Publishers (ALPSP) and cOAlition S, four ‘task and finish’ working groups were established. The authors facilitated and supported these groups. Each group was responsible for producing tools that will enable library consortia and small independent publishers to negotiate transformative agreements, which is to say, agreements that will enable the publisher to fully transition to open access. The first task and finish group developed shared principles for transformative agreements. The second developed a data template to enable smaller independent publishers to reach agreements with library consortia and libraries, while the third developed example licence agreements. These groups recognized that the implementation of a transformative agreement crosses a complex ecosystem of technology, processes, policies, automated functions and manual functions that relate to contract management, article submission and peer review, content hosting and dissemination as well as financial management. For this reason, a fourth group produced a workflow framework that describes the process in all its phases. The members of these four groups were volunteers from stakeholder communities including libraries, library consortia, smaller independent publishers and intermediaries. This article explains why these tools are needed and the process behind their creation. The authors have combined these tools into a freely available toolkit, available under a CC BY licence.
RESEARCH ON CRYPTOGRAPHY AND NETWORK SECURITY

The goal of the paper is to present a general understanding of network security and cryptography.The study and application of methods to protect communication from malicious conduct is known as cryptography.On the other hand, using both software and hardware technologies, network security refers to a collection of regulations and configurations intended to safeguard the accessibility, integrity, and confidentiality of computer networks and data.Data that is kept in our systems and transmitted over wireless networks are protected using network security and cryptography.Through the use of codes (encryption) and other methods to conceal information, cryptography is a network security measure that is used to safeguard company information and communication from cyber attacks.
Heckling and free speech

Heckling receives little attention in the vast literature on freedom of speech.But, in a recent contribution, Jeremy Waldron explores a positive case for the practice.Waldron asks us to see heckling as a spectrum of activities, some of which should be both protected and encouraged.In his view, a primary speaker's right to speak is not a right to a perfectly tailored (or choreographed) presentation before a subdued audience.A great deal of heckling should be treated as legitimate counter speech, covered by audience members' own expression rights.Waldron also sees heckling as making an important contribution to broader free speech values such as pursuit of truth and political accountability.I argue that the case for treating heckling as wrongful interference with the free speech rights of primary speakers and their willing listeners is much stronger than Waldron makes out.More boisterous forms of heckling should usually be restricted to presentations by political officials or state sponsored events.In other cases, heckling should be restricted to visual forms and displays that do not directly interfere with a primary speaker's voice.
Mortar

Vanilla Deep Neural Networks (DNN) after training are represented with native floating-point 32 (fp32) weights. We observe that the bit-level sparsity of these weights is very abundant in the mantissa and can be directly exploited to speed up model inference. In this paper, we propose Mortar, an off-line/on-line collaborated approach for fp32 DNN acceleration, which includes two parts: first, an off-line bit sparsification algorithm to construct the target formulation by "mantissa morphing", which maintains higher model accuracy while increasing bit-level sparsity; second, the associating hardware accelerator architecture to speed up the on-line fp32 inference through manipulating the enlarged bit sparsity. We highlight the following results by evaluating various deep learning tasks, including image classification, object detection, video understanding, video & image super-resolution, etc.: We (1) increase bit-level sparsity up to 1.28~2.51x with only a negligible -0.09~0.23% accuracy loss, (2) maintain on average 3.55% higher model accuracy while increasing more bit-level sparsity than the baseline, (3)and our hardware accelerator outperforms up to 4.8x over the baseline, with an area of 0.031 mm2 and power of 68.58 mW.
Device Dependent Information Hiding for Images

A new method for hiding information in digital images is proposed. Our method differs from existing techniques in that the information is hidden in a mixture of colors carefully tuned on a specific device according to the device's signal-to-luminance (gamma) characteristics. Because these reproduction characteristics differ in general from device to device and even from model to model, the hidden information appears when the cover image is viewed on a different device, and hence the hiding property is device-dependent. To realize this, we modulated a cover image using two identically-looking checkerboard patterns and switched them locally depending on the hidden information. Reproducing these two patterns equally on a different device is difficult. A possible application of our method would be secure printing where an image is allowed to be viewed only on a screen but a warning message appears when it is printed.
Indoor Mobile Robot Socially Concomitant Navigation System

With the progress of science and technology and the development of economy, more and more robots have entered the family and become good helpers and partners in people’s daily life. As a good partner of human beings, the robot is endowed with humanized emotion and decision-making core, and has harmonious and natural social interaction ability. Among them, the socially concomitant navigation function is a very important social function. For indoor mobile robot to navigate socially with its human companions, a Single-Object Tracking deepsort(SOT deepsort)algorithm is proposed, and a fast, stable and simple socially concomitant navigation system is proposed. In order to make the robot more socialized, questionnaires are designed to investigate the comfortable distance of the robot in the process of following its companion. Endow the robot appropriate behavior when traveling with human companions, so that the robot can play the role of accompanying the pedestrian and complete the task of following the companion. Finally, the proposed system is tested on Fabo mobile robot. The experimental results show that the following accuracy of this method is high and meets the real-time requirements.
Exponential H∞ Filtering for Complex-Valued Uncertain Discrete-Time Neural Networks with Time-Varying Delays

The purpose of this paper is to design a compatible filter for a class of classical discrete-time neural networks having uncertain complex-valued weighting parameters and time-varying delayed responses subject to the H∞ performancemeasure. For this notion, the complex-valued filter scheme is designed for the considered class of discrete-time neural networks with regard to the available output measurements. At first, some novel complex-valued weighted summation inequalities are put forth to establish a more precise linearized lower bound for the quadratic summing terms resulting from the finite difference of the constructed Lyapunov-Krasovskii functional. In what follows, an attempt has been made to propose the linear matrix inequality based sufficient conditions for designing the robust H∞ filter from the filtering error system attains exponential stability with the appropriate filtering gain matrices. Eventually, the theoretical conclusion is substantiated through a numerical example and the simulation outcomes reveal the applicability and efficiency of the proposed filter scheme.
Smartphone-Based COVID-19 Contact Tracing Apps – Insights from New Zealand

In the COVID-19 pandemic, even as vaccines became more accessible, contact tracing remained important [@fetzer2021measuring; @ferguson2020impact; @pierucci2020joint]. Huge resources have been used to expand testing and tracing capabilities, including smartphone applications. This paper builds on and extends an analysis of New Zealand's QR code-based application New Zealand COVID Tracer up to November 2021 (Howell \& Potgieter 2022). Because New Zealand is one of the last countries in the world to experience entrenched widespread community transmission of COVID-19, we can explore the effects of a number of independent variables on the uptake and use of the application. Rather than focusing on the effects of the application on infection rates, this paper focuses on the nexus between policy settings, pandemic state and application performance, using a new framework. NZCT likely had no effect on infection transmission in New Zealand. Contact tracing and location-based applications like NZCT cannot scale up effectively as infection rates rise. New Zealand offers a live experiment in which an ill-suited application remained mandatory long after it was of any use. End users realized this quickly and when mandatory use was dropped, scanning usage collapsed.
An Intelligent Tutoring System for Adult Literacy Learners: Lessons for Practitioners

This article is written by two researchers and a teacher involved with the development and implementation of a web-based intelligent tutoring system for adults reading at elementary levels.A description of
A Qualitative Study on Elementary Teachers' Perceptions and Experiences Regarding Dokdo Education

This study was conducted to understand the current educational situation of the Dokdo islands (Dokdo) with regard to its educational introduction in elementary schools and to find ways to enhance and revitalize Dokdo education for elementary school students for the future. To this end, eight elementary school teachers, who are the implementers of Dokdo education, were interviewed in-depth to analyze the perception and experience of elementary school teachers regarding Dokdo education. As a result of the study, it was confirmed that the necessity of Dokdo education recognized by elementary school teachers and the practice of Dokdo education were appropriate for the direction pursued by the Ministry of Education. However, practical limitations such as teacher-dependent classes at the level of civic education, quality limitations of class materials, alienation in the school curriculum, and lack of class hours and depth of education were obstacles to Dokdo education. Therefore, this study suggests that it is necessary to develop and distribute modular Dokdo specialized curriculum, expand hands-on experience opportunities for teachers and students, and establish a teacher cooperation network as a way to overcome these limitations and enhance Dokdo education.
Learning Neighbor User Intention on User-Item Interaction Graphs for Better Sequential Recommendation

The task of Sequential Recommendation aims to predict the user’s preference by analyzing the user’s historical behaviours. Existing methods model item transitions through leveraging sequential patterns. However, they mainly consider the target user’s own behaviours and dynamic characteristics, while often ignore the high-order collaborative connections when modelling user preferences. Some recent works try to use graph-based methods to introduce high-order collaborative signals for Sequential Recommendation, but they have two main problems. One is that the sequential patterns cannot be effectively mined, and the other is that their way of introducing high-order collaborative signals is not very suitable for Sequential Recommendation. To address these problems, we propose to fully exploit sequence features and model high-order collaborative signals for Sequential Recommendation. We propose a N eighbor user I ntention based S equential Rec ommender, namely NISRec, which utilizes the intentions of high-order connected neighbor users as high-order collaborative signals, in order to improve recommendation performance for the target user. To be specific, NISRec contains two main modules: the neighbor user intention embedding module (NIE) and the fusion module. The NIE describes both the long-term and the short-term intentions of neighbor users and aggregates them separately. The fusion module uses these two types of aggregated intentions to model high-order collaborative signals in both the embedding process and the user preference modelling phase for recommendation of the target user. Experimental results show that our new approach outperforms the state-of-the-art methods on both sparse and dense datasets. Extensive studies further show the effectiveness of the diverse neighbor intentions introduced by NISRec.
Using Performance Metrics to Guide the Selection of a Website Content Management System -The Case of Joomla, Drupal and WordPress

The most recent trend in website development is the usage of Content Management Systems (CMS).These systems provide user-friendly interactive interface for persons without prior computer coding knowledge to utilize them to create websites.Different kinds of content management system technologies are now available, hence, it becomes challenging for users/developers to decide on which one is best to work with.This study, researched into three popular CMS's namely, Joomla, Drupal and WordPress to explore which of these proposed three will be ideal for developing efficient website in terms of performance.This study resulted in researching into details the features common to all the three CMSs.These features were evaluated based on four performance criteria.The method this study used was a quantitative method.Practically, the researcher developed three websites using the three CMSs, and tested them against performance criteria namely, page load time, page size , number of cascading style sheet file and number of JavaScript files.The results or data collected through the page performance exercise was then tabulated, compared, and later analyzed.The study found that Joomla CMS created much JavaScript files as compared to all, and as a result was very interactive, WordPress also created much CSS file, as a result gives the best web layout.The researcher concluded that each of these CMS performed well in different criteria.As a result, the best CMS will depend on the interest of the developer for his or her site.These researched done will help readers and the academia in general to acquire knowledge that reveals the characteristics of the three CMS in terms of strengths and shortfalls.
Design and research of introducing language information based on neural machine translation model

Compared with the traditional machine translation model, the neural machine translation model can use bilingual alignment data for training without using additional language information for guidance, but the bilingual data are mainly compiled by manual translation, the scale is relatively small, and the parameter model of neural network is relatively large. This conflict will affect the application effect of the existing neural machine translation model, resulting in that the generated vector representation can not contain sufficient language information, and the incomplete language representation will also produce translation problems. For example, in the process of translation, the accuracy of local word information is insufficient, which will lead to problems such as missing translation, over translation, wrong translation. Based on the current neural machine translation model, different language information is introduced to restrict and guide the training and generation of neural machine translation model, and these problems can be solved, which will help to improve the accuracy and integrity of translation results and ensure the quality of neural machine translation.
Mendefinisikan ulang keterampilan dan kompetensi pustakawan untuk mendukung ekosistem investasi di Indonesia: studi kasus di perpustakaan BSN

The Indonesian Government has made various efforts to increase investment, mainly through deregulation. One of the implementations of these deregulation efforts is the development of the One-Stop Service Center (PTSP) at the Ministry of Investment/BKPM as an integrated service center for licensing and non-licensing activities related to investment, Foreign Investment (PMA), and Domestic Investment (PMDN). Badan Standardisasi Nasional (BSN) took part in providing service counters at the Central PTSP to provide services in the field of Standardization and Conformity Assessment (SPK). The implication is that BSN requires competent human resources to support these services. Currently, the personnel assigned to the Central PTSP is a librarian. This study aims to identify the skills and competencies needed by librarians to support the investment ecosystem in Indonesia through Central PTSP services. This study was designed by qualitative research with a case study method. Data collection methods using literature study and in-depth interview. This paper focused on the case of a BSN librarian as an informant who was assigned as a liaison officer at the Central PTSP. Data analysis was carried out using a descriptive method. Findings in this study highlight that librarians in special libraries need to improve and adopt 11 (eleven) skills and competencies that are relevant to the duties and functions of the institution, as well as an investment ecosystem to produce optimal librarian performance for the parent institution and society in general. The conclusions of the study include; First, special libraries have different resource tendencies from one institution to another which have an impact on different needs for skills and competencies. Second, special libraries can synergize with institutions outside the parent institution to meet the information needs of segmented users. Third, librarians in special libraries are very important to be subject specialists in their institutional environment. Fourth, the skills and competencies experienced by librarians in special libraries can be overcome by various strategic steps.
OLD CURRENCY COIN RECOGNITION USING ALEXNET MODEL

The recognition of old currency coins is a challenging task due to their physical degradation over time.In this research paper, we present a solution for old currency coin recognition using a deep convolutional neural network (DCNN) based on the AlexNet architecture.The model was trained on a dataset of images of old coins collected from various sources and accurately classified them into their respective categories.The results show that the AlexNet-based DCNN model outperforms traditional computer vision techniques and is able to recognize old coins with high accuracy, even in cases of significant physical deterioration.The proposed approach has potential applications in fields such as archaeology, numismatics, and historical research.In conclusion, this work demonstrates the efficacy of deep learning methods for solving the problem of old currency coin recognition and provides a promising starting point for future research in this area.
PENGENDALIAN INTERNAL DALAM SUATU ORGANISASI

The role of accounting as a source of financial information for a company is very useful for evaluating or making decisions on the performance of a company.Within its scope, internal control consists of preventive, detective and corrective controls with the results of organizational discussions required to have commitment and integrity, real and clear in the treatment of organizational authorization and minimal organizational risk in developing a qualified organization that is reliable and has structured internal controls and well awake.
CC-GNN: A Community and Contraction-based Graph Neural Network

Graph Neural Networks (GNNs) have attracted much research interest due to their successful applications on graph-structured data. Despite the effectiveness, due to the data dependency, GNNs are confronted with the neighborhood explosion and over-smoothing problems. Many sampling-based and level-down methods have been proposed to solve the neighborhood explosion problem to boost efficiency. However, they suffer from either poor accuracy or considerable runtime overheads. Moreover, the over-smoothing problem prevents GNNs from exploring more distant neighborhoods effectively. In this paper, we present a Community-and-Contraction-based Graph Neural Network (CC-GNN), which leverages community and contraction to boost the time and space efficiency of GNNs. Specifically, CC-GNN first performs contraction and retrieves the communities as super nodes, and connects them using a tailored similarity function to obtain an informative community contracted graph (CC-Graph). CC-GNN then learns the representations of the super nodes in the CC-Graph, which are used to reconstruct the representations of the original nodes. Finally, CC-GNN explores more distant neighborhoods without additional convolution layers, implicitly alleviating the over-smoothing problem. Since CC-GNN conducts the costly training on a much smaller contracted graph, the efficiency is boosted significantly. Most importantly, we have proved that the information loss of node representations caused by the CC-Graph is bounded. Extensive experimental studies verify the efficiency boost and the effectiveness.