Reviewerly/exHarmony huggingface dataset:
Starting Point: Digital Society and Media Linguistics

This textbook offers an interdisciplinary, comprehensive and state-of-the-art overview of the media linguistics approaches to explain and understand digital communication and multimodality. Linking the fields of communication studies, applied linguistics and journalism, it grounds communication practices in a deep understanding of the social and societal implications of language use in digital media. The tools to analyse multimodal texts are analysed in light of the advantages and constraints that different communication modes pose, both individually and in combination. Aimed at upper level undergraduates and graduates in applied linguistics, communication and media studies, including journalism and PR, this textbook contains case studies and professional examples highlighting the interplay between language use and digital communication and encouraging the reader to reflect on the themes covered, and put the acquired knowledge into practice. Online resources for students include videos, writing techniques, a guide to multimodal texts analysis, additional case studies and a glossary.
Quantum Circuits with Qiskit

This Jupyter notebook (pdf version) is a supplementary resource to the author’s paper Quantum Computing: An Overview. Here we discuss and provide guidance on the following: 1. IBM Quantum Experience account 2. Qiskit local environment (Windows OS) 3. One qubit circuit 3.1. Quantum Coin Game 4. Bell states circuit 4.1. Run on a quantum simulator 4.2. Run on a real quantum processor.
E-Commerce

The recommendation system works on an idea of suggesting or recommending items, products, books, movies, etc. by analyzing and using some filtering to find the user's interest. To maximize the growth of business and profit gain, users need to be recommended with products belonging to their area of interest. To fulfill this requirement, the recommendation system has been implemented. In this study, the discussion is over recommendation system and how different concepts come to work out individually as well as together for recommendation. In this analysis, the focus is on recommending the method of e-commerce. In that scenario, “cold start problem” comes into consideration. Cold start problems are also studied, and a purposed idea has also been highlighted to reduce cold start problem to some extent. ‘LCW Aspect' is going to execute and analyze user's culture, weather, local scarcity, and focused on solving recommendation problems for new emerging users.
Entrepreneurship

All production is forward looking, and the future is uncertain. The economics of prosperity, therefore, must include a sound conception of the entrepreneur, an uncertainty bearer, capitalist, and property-owning decision maker. Increased economic prosperity results from dynamic efficiency, achieved through entrepreneurial creativity and coordination. While performing the social function of bearing uncertainty, the entrepreneur coordinates diverse land, labor, and capital goods of numerous people, and directs production to its most socially valuable ends as determined by everyone in society. Entrepreneurial creativity improves everyone’s economic possibilities with a continuous flow of new ends and means. In forming their judgements, entrepreneurs make use of subjective, practical, exclusive, dispersed, and largely tacit knowledge of the particular time and place that is relevant for the personal decisions each entrepreneur must make. The entrepreneur must make judgements about the most profitable type and quality of the product, the best location in which to produce and supply the good, the optimal scale of operation the optimal time of availability for sale; and the anticipated price buyers are willing to pay for the product. Entrepreneurs make objective decisions regarding subjective values as they engage in economic calculation in which they use market monetary prices to calculate expected profit and loss. Effective economic calculation requires a monetary market economy. Sound money and voluntary exchange require maintaining social institutions that foster entrepreneurship. Economic history is replete with examples of significant entrepreneurial error or a lack of functional entrepreneurship all together resulting in both absolute and relative poverty. Neither the market division of labor nor capital accumulation nor technological improvement are autonomous. They contribute to prosperity as a multitude of entrepreneurs, each pursuing their own profit, make sound judgements about production to satisfy an uncertain future demand. Increased economic prosperity, therefore requires successful entrepreneurship.
ICTs and new scenarios for diversity

Research is a process aimed at seeking new knowledge, in this case, it will be to find alternative paths in the field of new technologies that serve to support special educational needs. Society demands these technological contributions to solve problems that contribute to inclusion, which led man to work with greater ergonomics; The school, a social institution, also needs these resources so that all students can build a teaching process of functional and meaningful learning for every student. The educational system outlines an education that attends to the educational needs of all students; from these pages, the intention is that new technologies are a path of support that assists diversity and inclusion.
RELATO DE EXPERIÊNCIA DE ESTÁGIO DE REGÊNCIA NO ENSINO DE LIBRAS

Observing the current scenario of education, it is possible to notice that there are many discussions around problems evolving educational processes.As a focus of those discussions, it is perceived the search for methodological strategies which can collaborate on the teaching and learning of both teachers and students.There are numerous challenges faced inside the classroom, one of the biggest of them is related to the processes of teaching and learning.In the face of those obstacles, many teachers become irresolute for not being able to find solutions for such issue.Considering this context and taking as a starting point my own pedagogical practice, I have opted to search strategies that could be capable of contributing to the learning of the pupils.Teaching the Curriculum Component of History, I could perceive the enormous difficulty of the students in the construction of knowledge surrounding this school subject, which is recurrently associated to the system of memorizing data, dates, and isolated facts.Thus, I have started to work with radiophonic language inside the classroom, stemming from a conception based on the existent relationship between language and imagery, which considers both image and thought as inseparable.Therefore, I am investigating how radiophonic language, through mental images can contribute for the processes of teaching and learning.This work is configured as an experience report that brings a part of the Master's Degree research accomplished in the Post-Graduation Program of Teaching, Posensino UERN/UFERSA/IFRN.As a result, this experience has evidenced a number of possibilities that the radiophone language can provide in the situations of apprenticeship, by stimulating communication, participation, and teamwork.Not to mention that through this pedagogical practice the students were able to develop various activities, especially in the areas of research, writing and orality.
Blockchain and Innovation in Services

The Encyclopedia of Services is a ground-breaking resource that offers a unique overview of what constitutes the main source of wealth and employment in our contemporary economies, namely services. This title contains one or more Open Access chapters.
DCF-MLSTM: a deep security content-based filtering scheme using multiplicative BiLSTM for movie recommendation system

Recently, the demand for online and offline recommendation systems has increased drastically. These systems are widely used in tourism, music, and video or movie recommendations. Currently, online movie streaming applications have gained huge attention. Providing better recommendations to the user is a challenging task for these applications. The content-based filtering (CBF) recommender system is a promising technique for these systems. However, traditional systems suffer from challenges such as cold-start problems, sparsity, and scalability. Consequently, we strengthen content-based recommendation algorithms by enriching the user-related and relevant product models with effective tendencies. The majority of previous work on classifiers has been in recommendation systems. To overcome these issues, we present a deep learning model that uses a deep neural network mechanism and a multiplicative BiLSTM model. This scheme uses embedding, weight updating, and preference learning processes to improve the recommendation system's performance. The performance of the proposed approach is measured in terms of MAE, MAP, Precision, Recall, and F-measure. The comparative performance shows that the proposed approach achieves better performance when compared with state-of-art movie recommendation techniques.
A Survey on Student Grievance Redressal System

Abstract: Student satisfaction is a major concern throughout the institute. However, many times students cannot express their concerns. That eventually results in student dissatisfaction. Hence, to solve this problem a student grievance cell system is useful and it deals with student issues and grievances. The student can lodge their complaints through this system which will be redressed by the institute. The student grievance cell is where students can make a complaint without fear of accomplishment and discrimination and ask for help if there is any problem. A student grievance cell is a group of people who are there to help students struggling academically and emotionally. Students can approach the cell to express their concerns regarding academic matters ,health services, libraries, and other services. It also provides support and resources to students so they can talk about their issues. The student grievance cell will also focus on harassment issues, creating a protective environment for students. The student grievance redressal system is designed to provide an easy-to-use interface for students to register their complaints and track their progress. The system is equipped with features such as notifications, reporting, and analysis to improve the overall efficiency and effectiveness of the grievance redressal process. The system is designed to be user-friendly, making it easy for students to navigate and access all the necessary information. One of the key features of the system is the ability to track and monitor the progress of each complaint.This helps to ensure that all complaints are addressed in a timely and efficient manner, and that students are kept informed of the progress of complaint slain. The system is also equipped with reporting and analysis tools to help university administrators understand the nature and frequency of complaints and make informed decisions to improve the educational experience for students
Development of secondary education after the partition of India

After the partition of India education system is ruled by the Indian Govt.and Indian Constitutions.Everyone has a right to take education without any discrimination in all stage of education.In the developmental process of human adolescence period is very crucial.In this very crucial period we stay in the secondary stage of education.So, the secondary education should be very important to develop the human resource.For developing human resource the Indian Govt.sponsored various programme to improve the total education system as well as secondary education.For developing secondary education the Indian Govt.
Digital ownership of blockchain tokens: a comparative law guideline

Digitalization's international nature leads to terminology integration, with English as the dominating language. 'Token' has become a term used in many jurisdictions, but no universal definition exists. The multi-layered meaning of the concept, depending on the context, makes it challenging to find an appropriate and sufficient legal definition or description of the term. Lichtenstein has adopted a legal definition, but many jurisdictions have not, and a transnational definition is needed. A token is a data set with no physicality, typically, but not always, stored on decentralised databases and assigned to a legal entity. Tokens can also have an economic or immaterial value. So far the classification of tokens has mainly been made from a regulatory perspective, which is largely unsuitable for property purposes. Property existence and protection of tokens depend on their connections to real-world rights. Based on a comparative analysis of Anglo-American and European jurisdictions, the author recommends a transnational regulation in the form of a UNCITRAL model law.
Focus

Making a contribution requires a lot of focus. The chapter describes two types of focus and how to reach them: (1) Focus on your work by staying concentrated and blocking out all distractions (2) Focus on important projects by choosing wisely what to work on and how to divide your time.
Finance, institutions and economic development

This chapter considers the role of institutions and finance in economic development. It develops the basic neoclassical model of economic growth and discusses the transformations associated with economic growth, particularly structural transformation. It also considers illicit financial flows from developing countries and the links between economic growth and inequality.
SLIME: State Learning in the Middle of Everything for Tool-Assisted Vulnerability Detection

Behavioural state machine models of software systems are a valuable tool for validating behaviour, but creating state machine models of existing implementations manually is highly undesirable. Fortunately, automata learning frameworks exist that completely automate the critical aspect of automata learning. However, some manual setup is usually required outside of the critical learning algorithm to create a test harness into which the system under test (SUT) and learning algorithm can function. In this paper we present a new architecture for automata learning that uses existing learning algorithms and a generic man-in-the-middle (MITM). Our architecture significantly reduces this manual setup effort. The learned state machine can be used to help uncover potential flaws in the implementation of client, server, their overall interaction and even the client-server protocol itself. These flaws can potentially be exploited by a malicious client, an impostor server, or a man-in-the-middle. Two sets of rules to automatically assist with identifying flaws in the state machine are presented, and are used to visually annotate the potential flaws in the learned model. Additionally, flaws can be detected via regression testing by comparing the learned state machine models to ones previously learned. Automatically generated and annotated state machine models of systems can be used as evidence in security, safety, and reliability assurance.
Connecting Incident Reporting Infrastructure to Election Day Proceedings

Incidents that occur during elections are alarming if not handled properly. Currently, election incident research focuses solely on reporting incidents but not on the response that resolves the incidents. This paper attempts to understand the landscape of election incidents in Idaho in order to propose new methods to solve them. Idaho election incident resolution protocols vary from county to county so, we believe there is a sufficient need to have a framework and tool in place to unify incident reporting and resolutions in Idaho. A survey was distributed to Idaho election administrators to understand current practices and the findings indicated a clear disconnect from reporting, resolving, and general protocols for incidents on election day. The survey responses helped us design the framework for a communication tool to handle election day incidents quickly and efficiently with the intention of limiting the delay in voting.
A Comparison-Based Methodology for the Security Assurance of Novel Systems

In this paper, we advocate the position that the security certification of one system should make the certification of other similar systems easier, if one can present the evidence that the second system is at least as secure as the first system. We present a development of this idea, stating the components of such comparative evidence. We stretch the idea of propagating the certification to less similar systems, if one can present a sequence of systems from the certified one to the novel one, where each system is evidenced to be at least as secure as the previous one. We apply our methodology to authentication systems, where we show that a system based on threshold cryptography is at least as secure as widely used smartcard-based systems.
SAEOn: An Ontological Metamodel for Quantitative Security Assurance Evaluation

Security assurance is a critical aspect in determining the trustworthiness of information and communication technology systems. Security assurance evaluation (SAE) is the process responsible for gathering assurance shreds of evidence to check if the defined security requirements are fulfilled. SAE can be generally categorized into qualitative and quantitative methods. As there is still a dearth of studies on the quantitative SAE, this paper intends to fill this gap by proposing an ontological quantitative security assurance evaluation metamodel (SAEOn). This ontology allows us to define the entities of the SAE and the assurance metrics separately and relate them to each other in a modular way. With the formal definition of SAE metamodel, the constructed knowledge content can be reused, shared, and exchanged over time. Moreover, the proposed metamodel is structured in a hierarchical fashion, by which we believe the ontology is sufficiently generic and highly customizable that can be applied in various application domains. In this paper, we present the proposed SAEOn ontology in detail, covering the aspects of design, implementation, and evaluation.
MVP: Model–View–Presenter

The Model-View-Presenter (MVP) pattern is a variation of the Model-View-Controller (MVC) pattern, which is widely used in iOS development. The presenter serves as a bridge between the view and the model, receiving input from the view and updating the model as needed. The view is then updated with the model's updated data. Because changes to one aspect of the app do not affect the other aspects, this separation of concerns allows for more flexible and maintainable code. Furthermore, the MVP pattern encourages the separation of concerns and makes it simpler to add new features or fix bugs.
VIPER: View–Interactor–Presenter–Entity–Router

VIPER (View, Interactor, Presenter, Entity, and Router) is an architectural pattern that allows an iOS app's code to be organized and structured in a way that separates data management, user interface, and input handling concerns. The View is in charge of displaying data to the user, the Interactor is in charge of business logic and data manipulation, the Presenter serves as a bridge between the View and the Interactor, the Entity represents the data model, and the Router is in charge of navigation. Because changes to one aspect of the app do not affect the other aspects, this separation of concerns allows for more flexible and maintainable code.
Cross-Modality Attention and Multimodal Fusion Transformer for Pedestrian Detection

Pedestrian detection is an important challenge in computer vision due to its various applications. To achieve more accurate results, thermal images have been widely exploited as complementary information to assist conventional RGB-based detection. Although existing methods have developed numerous fusion strategies to utilize the complementary features, research that focuses on exploring features exclusive to each modality is limited. On this account, the features specific to one modality cannot be fully utilized and the fusion results could be easily dominated by the other modality, which limits the upper bound of discrimination ability. Hence, we propose the Cross-modality Attention Transformer (CAT) to explore the potential of modality-specific features. Further, we introduce the Multimodal Fusion Transformer (MFT) to identify the correlations between the modality data and perform feature fusion. In addition, a content-aware objective function is proposed to learn better feature representations. The experiments show that our method can achieve state-of-the-art detection performance on public datasets. The ablation studies also show the effectiveness of the proposed components.
Privacy-Preserving Person Detection Using Low-Resolution Infrared Cameras

In intelligent building management, knowing the number of people and their location in a room are important for better control of its illumination, ventilation, and heating with reduced costs and improved comfort. This is typically achieved by detecting people using compact embedded devices that are installed on the room’s ceiling, and that integrate low-resolution infrared camera, which conceals each person’s identity. However, for accurate detection, state-of-the-art deep learning models still require supervised training using a large annotated dataset of images. In this paper, we investigate cost-effective methods that are suitable for person detection based on low-resolution infrared images. Results indicate that for such images, we can reduce the amount of supervision and computation, while still achieving a high level of detection accuracy. Going from single-shot detectors that require bounding box annotations of each person in an image, to auto-encoders that only rely on unlabelled images that do not contain people, allows for considerable savings in terms of annotation costs, and for models with lower computational costs. We validate these experimental findings on two challenging top-view datasets with low-resolution infrared images.
End to End Face Reconstruction via Differentiable PnP

This is a challenge report of the ECCV 2022 WCPA Challenge, Face Reconstruction Track. Inside this report is a brief explanation of how we accomplish this challenge. We design a two-branch network to accomplish this task, whose roles are Face Reconstruction and Face Landmark Detection. The former outputs canonical 3D face coordinates. The latter outputs pixel coordinates, i.e. 2D mapping of 3D coordinates with head pose and perspective projection. In addition, we utilize a differentiable PnP (Perspective-n-Points) layer to finetune the outputs of the two branch. Our method achieves very competitive quantitative results on the MVP-Human dataset and wins a $$3^{rd}$$ prize in the challenge.
Cross-View Nearest Neighbor Contrastive Learning of Human Skeleton Representation

Traditional self-supervised contrastive learning approaches regard different views of the same skeleton sequence as a positive pair for the contrastive loss. While existing methods exploit cross-modal retrieval algorithm of the same skeleton sequence to select positives. The common idea in these work is the following: ignore using other views after data augmentation to obtain more positives. Therefore, we propose a novel and generic Cross-View Nearest Neighbor Contrastive Learning framework for self-supervised action Representation (CrosNNCLR) at the view-level, which can be flexibly integrated into contrastive learning networks in a plug-and-play manner. CrosNNCLR utilizes different views of skeleton augmentation to obtain the nearest neighbors from features in latent space and consider them as positives embeddings. Extensive experiments on NTU RGB+D 60/120 and PKU-MMD datasets have shown that our CrosNNCLR can outperform previous state-of-the-art methods. Specifically, when equipped with CrosNNCLR, the performance of SkeletonCLR and AimCLR is improved by 0.4% $$\sim $$ 12.3% and 0.3% $$\sim $$ 1.9%, respectively.