Reviewerly/exHarmony huggingface dataset:
Automatic FAQ Generation Using Text-to-Text Transformer Model

Given a passage of text, the manual generation of FAQ’s (Frequently Asked Questions) is a very tedious process. The ability to automate the generation of relevant and useful FAQ’s along with appropriate answers within an acceptable time frame is an interesting problem to work upon. Due to the ever-increasing data and content on the Internet, netizens can leverage FAQ’s and their corresponding answers for better understanding, especially for technical and corporate websites. Previous research in this domain has been promising and we intend to add novel features to enhance the quality of QA (question-answer) pairs generated. In this paper, we propose an architecture that performs end-to-end processing of the input text and displays QA pairs with the help of an automated QA generation pipeline. The pipeline consists of a pre-trained Text-To-Text Transformer (T5) Model for span extraction and subsequent question-answer generation. A fine-tuned SpanBERT Model is utilized for answer generation and ranking of the QA pairs. The architecture also incorporates the simultaneous computation of the coherence factor between input sentences along with the usage of Knowledge Graph for additional domain knowledge. A web application was also developed to demonstrate the generation of a comprehensive list of FAQ’s for any user-provided passage. The proposed approach improves on existing pipelines by having BLEU scores in excess of 0.4 for answers and 0.6 for questions.
Securing Cookies/Sessions Through Non-fungible Tokens

Cookies are used as authentication tokens after successfully validating users by web applications. As they are stored on the client's side, it makes them vulnerable to hijacking, stealing, and unauthorized distribution. There are methods in the literature that are developed to protect cookies. However, cookie stealing and impersonating is still a widely adopted practice. Session cookies are used so that the user does not have to log in again and again. When an attacker accesses these cookies, he/she can join the user's active session as well; this phenomenon is called cookie hijacking. Here, we are proposing a model using the concept of blockchain, non-fungible tokens and smart contracts, which prevent the attacker from performing unauthorized tasks even when an individual gets access to the user's session cookies. The web server uses the unique identification address of the user to generate a session ticket which would represent the ownership of the verified user. Whenever, a request is made, it gets authenticated by the blockchain; thereby making the cookie verification decentralized. This method ultimately aims to prevent unauthorized users from performing tasks through a user's active session, which will decrease identity stealing and imitation through cookies.
Indexing, Clustering, and Search Engine for Documents Utilizing Elasticsearch and Kibana

With the rapidly growing progress of science and technology, a large quantity of data and information is generated utilizing computers. Traditional relational databases, such as MySQL, are becoming increasingly unable to meet the user demands for rapid retrieval. However, Elasticsearch compensates for the delayed retrieval by offering users a fast search compatibility while ensuring high quality. The paper outlines the design and development of a system for indexing, clustering, and searching scientific documents. A Java Spring web server with Bootstrap, jQuery, and Foamtree was developed, visualizing the data with Kibana, allowing an orchestration of used technologies, enabling an efficient search and analysis function. The clustering of the documents is based on the metadata of Zotero and accessed via Carrot $$^2$$ .
Understanding the cost of fitness evaluation for subset selection

With a focus on both the fitness and cost of subset selection, we study stochastic local search (SLS) heuristics in this paper. In particular, we consider subset selection problems where the cost of fitness function evaluation needs to be accounted for. Here, cost can be fitness evaluation's computation time or energy cost. We propose and study an SLS method, SLS4CFF, tailored to such problems. SLS4CFF ("SLS for costly fitness functions") is an amalgamation of several existing heuristics. We develop a homogeneous Markov chain model that explicitly represents both fitness and cost of subset selection with SLS4CFF. This Markov chain, which can be lumped or compressed for certain fitness and cost functions, enables us to better understand and analyze hyperparameter optimization in a principled manner, via expected hitting times. Studies with synthetic and real-world problems improve the understanding of SLS and demonstrate the importance of cost-awareness.
Solidity

Solidity is an object-oriented, high-level programming language that is used to construct smart contracts that automate blockchain transactions. The language was proposed in 2014 by Gavin Wood and developed by participants of the Ethereum project. Solidity was influenced by C++, Python, and JavaScript, so you will find similar language structures as in those languages. The language is primarily used to build smart contracts on the Ethereum blockchain, but it can also be used to create smart contracts on other blockchains.
Effectiveness Analysis of English Newspaper Reading Teaching Based on Deep Learning from the Perspective of Online and Offline Hybrid Teaching

In recent years, China's higher education has developed rapidly, and the overall English proficiency of college students has improved significantly. In order to improve the English proficiency of students in higher education, English newspaper reading courses are offered, many of which are elective courses. Reading English newspapers in English can help college students improve their overall English proficiency and benefit them in their future lives. Reading English newspapers can improve students' English in many ways as part of college English teaching activities. For example, it helps students to accumulate common and authentic words and phrases, improve their English reading efficiency and level, and gain a broader perspective. This paper examines the role of English newspaper reading in college English teaching, proposes an effective analysis method of teaching English newspaper reading based on deep learning from the perspective of online and offline hybrid teaching, and tests the effectiveness of the model. Finally, the specific characteristics of English newspapers are combined to propose suggestions for improving the effectiveness of English teaching activities for college students.
EXPLAINABLE AI FOR DATA DRIVEN LEARNING ANALYTICS: A HOLISTIC APPROACH TO ENGAGE ADVISORS IN KNOWLEDGE DISCOVERY

EXPLAINABLE AI FOR DATA DRIVEN LEARNING ANALYTICS: A HOLISTIC APPROACH TO ENGAGE ADVISORS IN KNOWLEDGE DISCOVERY
DESIGNING AN INNOVATIVE MOBILE-ENABLED CONTINUOUS PROFESSIONAL DEVELOPMENT SOLUTION FOR TEACHERS

Appears in: EDULEARN22 Proceedings Publication year: 2022Pages: 6862-6867ISBN: 978-84-09-42484-9ISSN: 2340-1117doi: 10.21125/edulearn.2022.1613Conference name: 14th International Conference on Education and New Learning TechnologiesDates: 4-6 July, 2022Location: Palma, Spain
CSNet-PGNet: Algorithm for Scene Text Detection and Recognition

In recent years, the detection and recognition of scene text have developed rapidly, but two difficult challenges have not been well solved. First, semantic analysis based on convolutional neural networks and powerful ImageNet pre-training incur high computational costs. Second, scene text detection with irregular shapes and irregular word order is inaccurate. Aiming at the above problems, this paper proposes a novel and lightweight network module (CSNet-PGNet) for real-time reading of a text of arbitrary shape and orientation. CSNet (Cross-Stage Cross-Scale network) is an extremely lightweight overall cross-stage and cross-scale network, which abandons the cumbersome CNN skeleton network (semantic classification) and can be trained from scratch. PGNet (Point Gathering Network) is a text detection recognizer that can detect and recognize the text of any shape, without the operation of Non-maximum Suppression (NMS) and Region of Interest (RoI), and has the advantages of end-to-end simplicity and efficiency. performance. This paper proposes the CSNet-PGNet scene curve text detection and recognition method, which is a development to more efficient and precise scene text detection of any shapes.
Some barriers are invisible

As the old saying goes, “the best geologist has seen the most rocks!” This statement superficially makes sense; if you’ve seen many rocks, you can draw on those experiences to… erm… identify and interpret more rocks. But geoscientists rely on numerous skills to determine the structure, composition, and evolution of Earth and other planets – […]
A VISION OF ESTABLISHING A COMPREHENSIVE SYSTEM OF UNIVERSITY TEACHER EDUCATION AT UNIVERSITY – A SYSTEMATIC SEARCH FOR ANSWERS

A VISION OF ESTABLISHING A COMPREHENSIVE SYSTEM OF UNIVERSITY TEACHER EDUCATION AT UNIVERSITY – A SYSTEMATIC SEARCH FOR ANSWERS
Accelerating kNN search in high dimensional datasets on FPGA by reducing external memory access

Implementing an efficient k-Nearest Neighbors (kNN) algorithm on FPGA is becoming challenging due to the fact that both the size and dimensionality of datasets that kNN is working on have been rapidly growing, which makes external memory-access a performance bottleneck. To reduce the impact of the bottleneck, in this paper we implement two kNN kernels through high-level synthesis (HLS) on FPGA by employing two data access reduction methods: low-precision data representation (LPDR) and principal component analysis based filtering (PCAF). One kernel is called MBFS-kNN (Memory-efficient Brute-Force Searching kNN) and the other is called MPCAF-kNN (Memory-efficient PCAF kNN). The two kernels are adaptive to all key parameters. By comparing them with two state-of-the-art kNN implementations on a high-end CPU server, an existing BFS-kNN kernel on FPGA, and an existing BFS-kNN kernel on GPU, our experimental results show that the two kernels substantially improve the performance by greatly reducing external memory-accesses.
What Is Cloud Computing?

The term cloud computing might be trending today, but it was first seen in books around 1996. If you think this term means computers flying into clouds in the sky, you must consider what happens when it rains. Computers are certainly not waterproof and would become useless.
Handwritten text generation and strikethrough characters augmentation

We introduce two data augmentation techniques, which, used with a Resnet-BiLSTM-CTC network, significantly reduce Word Error Rate and Character Error Rate beyond best-reported results on handwriting text recognition tasks. We apply a novel augmentation that simulates strikethrough text (HandWritten Blots) and a handwritten text generation method based on printed text (StackMix), which proved to be very effective in handwriting text recognition tasks. StackMix uses weakly-supervised framework to get character boundaries. Because these data augmentation techniques are independent of the network used, they could also be applied to enhance the performance of other networks and approaches to handwriting text recognition. Extensive experiments on ten handwritten text datasets show that HandWritten Blots augmentation and StackMix significantly improve the quality of handwriting text recognition models.
Correntropy based semi-supervised concept factorization with adaptive neighbors for clustering

Concept factorization (CF) has shown the effectiveness in the field of data clustering. In this paper, a novel and robust semi-supervised CF method, called correntropy based semi-supervised concept factorization with adaptive neighbors (CSCF), is proposed with improved performance in clustering applications. Specifically, on the one hand, the CSCF method adopts correntropy as the cost function to increase the robustness for non-Gaussian noise and outliers, and combines two different types of supervised information simultaneously for obtaining a compact low-dimensional representation of the original data. On the other hand, CSCF assigns the adaptive neighbors for each data point to construct a good data similarity matrix for reducing the sensitiveness of data. Moreover, a generalized version of CSCF is derived for enlarging the clustering application ranges. Analysis is also presented for the relationship of CSCF with several typical CF methods. Experimental results have shown that CSCF has better clustering performance than several state-of-the-art CF methods.
HyDRa: A Framework for Modeling, Manipulating and Evolving Hybrid Polystores

Data-intensive system evolution is a complex and error-prone process, as most evolution scenarios impact several interdependent artefacts such as the application code, the data structures or data instances. This process is becoming even more challenging with the emergence of heterogeneous database architectures, commonly called hybrid polystores, that rely on a combination of several, possibly overlapping relational and NoSQL databases. This paper presents HyDRa, a framework aiming to facilitate the evolution of polystores thanks to automatically generated data access APIs. For a given polystore, a conceptual API can be derived from the conceptual schema of the polystore and its correspondences with the physical schemas of the underlying databases. Applications built on top of the generated API are then protected from future schema and data reconfiguration changes applied to the polystore. Furthermore, HyDRa automatically enforces cross-database data integrity constraints and does not require developers to master multiple data models and query languages. This paper presents HyDRa and demonstrates its main features based on open-source datasets and realistic use cases.
Applying PDCA to Security, Education, Training and Awareness Programs

Security standards help to create security policies, but they are often very descriptive, especially when it comes to security awareness. Information systems security awareness is vital to maintain a high level of security. SETA programs (Security Education, Training and Awareness) increase information systems security awareness and play an important role in finding the strategic balance between the prevention and response paradigms. By reviewing the literature, we identify guidelines for designing a SETA program following a PDCA (Plan Do Check Act) cycle.
Design Principles for Coordination in the Metaverse

The Metaverse represents the next generation of the Internet that, at this instant in time, is still a concept. It is envisioned to provide interconnected experiences that are immersive and varied. This vision challenges both designers and users to understand its possible coordination architecture and develop strategies for participation. One way to understand the concept and affect its instantiation is to take a design science approach that articulates design principles that might guide the exploration of different architectures. We apply concepts related to platforms and business ecosystems as well as ideas about facilitating technologies including choreography and orchestration as ways of blending together experiences, providing transitions between virtual locations. We derive three design principles from an analysis of Metaverse scenarios: Narrative composability, social assortativity, and path discoverability. Thinking through these aspects of design leads to a discussion about the tradeoffs that will face designers of the Metaverse.
ALEGORIA: Joint Multimodal Search and Spatial Navigation into the Geographic Iconographic Heritage

In this article, we present two online platforms developed for the structuring and valorization of old geographical iconographic collections: a multimodal search engine for their indexing, retrieval and interlinking, and a 3D navigation platform for their visualization in spatial context. In particular, we show how the joint use of these functionalities, guided by geolocation, brings structure and knowledge to the manipulated collections. In the demonstrator, they consist of 54,000 oblique aerial photographs from several French providers (national archives, a museum and a mapping agency).
Attribute Based Pseudonyms: Anonymous and Linkable Scoped Credentials

Attribute-based credentials (ABCs) provide an efficient way to transfer custody of personal and private data to the final user, while minimizing the risk of sensitive data revelation and thus granting anonymity. Nevertheless, this method cannot detect whether one attribute has been used more than once without compromising anonymity when the emitter and consumer collude with one another. The protocol proposed in this article deals with this issue by using a modification of ZSS pairing-based short signatures over elliptic curves and Verheul’s self-blinded credentials scheme. Each user can generate an identifier (pseudonym) that is unique and verifiable by everyone in a given scope, without compromising anonymity. However, the identifier cannot be reused in the same scope, since such reuse would be detected.
Blockchain-based consensus algorithm for solving security issues in distributed internet of things

In the last decade, internet of things (IoT) emerged as one of the most promising fields of research. The services provided by the IoT have come as a big relief to human life due to the smartness and real-time monitoring without human intervention. IoT applications are widely used as it provides a high level of comfort, automation of the system, and efficiency. To make successful use of the ever-growing IoT applications requires addressing the security, privacy, and trust to protect IoT devices and user privacy from attackers. In this paper, initially different security and privacy challenges are identified in an IoT application. Secondly, analysis regarding blockchain technology is done which gives the idea to solve some of the IoT security challenges. Blockchain-based consensus algorithm PBFT is used to perform secure computation in a smart transportation system. Security analysis shows that the proposed protocol worked correctly in the presence of malicious or faulty nodes.
Energy efficient VM placement and task scheduling techniques to achieve green cloud environment

Continuous increase in the demand for cloud services has resulted in increased workloads. Effective distribution of these augmented tasks on a limited infrastructure for providing the services is a challenging issue. This necessitates servers to run continuously raising the power usage in the data centres. High power consumption results in releasing major amount of CO2 gas to the atmosphere, polluting our natural environment. So, there is a need to think of green cloud computing to reduce this effect of harmful gas to our environment. Designing an energy efficient data centre is a perennial challenge. Hence in this paper, optimal virtual machine placement strategy using knapsack dynamic programming technique and an efficient precedence-based task distribution approach are proposed to alleviate the power consumption and achieve the quality of service maintaining the service level agreements. The implementation results obtained have proved its power efficiency with better quality of service.
Virtual Production in Action

Virtual Production fulfills George Lucas's early dream of having an engulfing “space-opera in the sky” (1). Epic Games’ focus on realistic interactive 3D game environments using Unreal Engine, revolutionized the field of film-making, by replacing rear film projections with large format, curved, high resolution, immersive LED video screens, allowing backdrops to adapt in real time to the narrative needs of each scene by tracking the movement of the camera. Cinematographers and Art Directors are adapting to the challenges of virtual and real lighting and props, recruiting animators and new media developers who create, usually in very little time, virtual and real props, and metahuman actors and characters, enhancing the production value, optimizing and reducing costs in unparalleled ways. This poster presents the results of the first Virtual Production class offered by the Film Animation and New Media Department at the University of Tampa. In a very short time span, students working in interdisciplinary teams have seen the possibilities of these new technologies for science fiction, fantasy and experimental films that otherwise would have been impossible to create with very limited student budgets.
Age differences in visual statistical learning: Investigating the effects of selective attention and stimulus category.

A cognitive function that is of interest when investigating age-related changes is statistical learning-the ability to detect and extract regularities in sensory information from our rich, dynamic, and complex environment. A previous study has suggested that there were age differences in visual statistical learning, with older adults demonstrating visual statistical learning of attended and unattended information (due to the "hyper-binding effect"). In the present study, we were interested in investigating whether there are age differences in visual statistical learning and whether stimulus category influenced visual statistical learning of unattended information in older adults. We tested two stimulus categories: highly familiar line drawings and abstract shapes. Participants completed a selective-attention task, in which regularities were embedded into both the attended and unattended visual streams. Then, participants completed a triplet-discrimination task, which assessed their ability to extract regularities from the attended and unattended visual streams. We also implemented a 4-point confidence-rating scale in the triplet-discrimination task as an assessment of participants' awareness of these regularities. There were four key findings. First, selective attention modulates visual statistical learning, with greater visual statistical learning for attended information than for unattended information. Second, there were age differences in visual statistical learning, but these differences were only observed for visual statistical learning of attended information. Third, stimulus category did not affect visual statistical learning of unattended information in older adults. Fourth, visual statistical learning occurs with awareness of statistical regularities. Further research is warranted to investigate the age-related mechanisms underlying visual statistical learning. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
Gaussianity and the Kalman Filter: A Simple Yet Complicated Relationship

One of the most common misconceptions made about the Kalman filter when applied to linear systems is that it requires an assumption that all error and noise processes are Gaussian. This misconception has frequently led to the Kalman filter being dismissed in favor of complicated and/or purely heuristic approaches that are supposedly ``more general'' in that they can be applied to problems involving non-Gaussian noise. The fact is that the Kalman filter provides rigorous and optimal performance guarantees that do not rely on any distribution assumptions beyond mean and error covariance information. These guarantees even apply to use of the Kalman update formula when applied with nonlinear models, as long as its other required assumptions are satisfied. Here we discuss misconceptions about its generality that are often found and reinforced in the literature, especially outside the traditional fields of estimation and control.