Reviewerly/exHarmony huggingface dataset:
Communication through Indian Sign Language: An Approach for Data Preprocessing

People who are specially impaired with regards to speaking and hearing find it difficult to communicate with people who are not impaired. The same is true vice-versa. For this very reason, Sign Language is necessary to be known and understood by both the parties. However, there are many people who are completely unaware of Sign Language and its importance. Hence, this project is aimed at bridging this communication gap between the two parties, and eventually, have an awareness on this importance of Sign Language. The paper presents an introductory analysis for the development of the Indian Sign Language (ISL) Recognition System. The primary focus of the paper is to discuss various steps involved in the formation, preprocessing, and augmentation of the dataset. The paper also briefly discusses the various steps performed after preprocessing to train and implement the model, with the focus on the systems based on Artificial Neural Networks.
An Attention-Based Entity Linking Method for Chinese Knowledge Base Question Answering System

Entity linking for knowledge base question answering is the process of identifying entities in the question and linking them to knowledge base entities. There are some problems in entity linking of Chinese knowledge base question answering, such as missing context information, unclear word boundaries, and too many colloquialized words. We propose an attention-based entity linking method. In the candidate entity generation stage, we use various ways to generate candidate entities. In the candidate entity disambiguation stage, attention mechanism is introduced to highlight the features that are more critical for entity disambiguation. We conduct experiments on public data sets, and the results show the effectiveness of our method.
Research and implementation of customizable automated deployment methods in cloud PaaS

This paper proposes an automatic PaaS deployment method for secret-related form attachments. Block processing model is introduced to simplify vector relational data architecture. Establish a contiguous neighbor relationship with scattered data to determine the direction and spatial value of specific parts. The system can upload, register, encrypt, store, download and manage secret-related attachments. This paper completes the overall architecture, realizing capability integration, layered scheduling of multi-application types, multi-tenant and resource scheduling management, and automatic deployment of global optimization. System simulation shows that the average storage time of this scheme is better. The data space is small.
Construction of Online Teaching Quality Evaluation System in Universities Oriented by Deep Learning

In the post-epidemic era, online teaching has become the normal teaching mode. The blended teaching mode which combines online and offline efforts and integrates and complements has become the main direction of education and teaching development in colleges and universities. The new teaching model puts forward higher requirements for teaching quality assurance to improve the quality of online teaching in colleges and universities. Based on the deep learning-oriented evaluation index model and weight coefficient algorithm, this paper analyzes the online learning data and supervision feedback suggestions of the smart platform in spring 2020 and spring 2022 of our school, and constructs an online teaching quality evaluation system to accumulate experience and provide reference for colleges and universities to carry out online teaching quality evaluation reform.
User-Attention Based Product Aesthetics Evaluation with Image and Eye-Tracking Fusion Data Analysis

Users' viewing behavior could affect their perception and evaluation of design works. Taking into account users' visual attention as a subjective cognition cue, we used eye-tracking evidence to identify users' focus areas for further analysis. We conducted experiments to extract the image features of design images and the reviewers' eye-tracking data, aiming to predict the product design ranking in the competition through fusion data analysis. In particular, we collected 1,504 product design images from a design competition. Four deep convolutional neural networks were selected to explore the best aesthetics computation model. The experimental results show that using design images and eye-tracking data fusion can improve the model prediction performance. Finally, MobileNet-V3 achieves the highest classification accuracy of 74.75%. This suggests the proposed method can provide useful insights into personalized aesthetics evaluation and user-centered design perception.
Music Recommendation System Based on Collaborative Filtering Algorithm

Recommendation Systems (RS) attempt to solve the problem of information overload. The RS may offer suggestions for fresh goods, movies, and music. The music business has used recommendations algorithms to address this issue. Chinese traditional folk music is lacking in the realm of recommendations. This paper uses collaborative filtering algorithm to design a folk music recommendation system on the basis of the existing music recommendation systems. The main interface will display the sorting by heat and release time and the music recommended by users. When users click on a certain music to view the music details, the song name, singer, release time and album name of the music will be displayed, and the functions of playing, collecting, rating and commenting can be realized.
Client-Side Virtual Camera Impersonation Attacks Detection on Automatic Proctoring Exam

Online proctored exams require authentication and face verification before students access the exams. However, using an impersonation attack model, students can bypass the face verification phase using a virtual webcam. We aim to propose an approach that detects cameras based on their Hardware ID. In addition, our approach includes a log engine that collects device information, such as the total number of webcams and the number of webcams in the student device. This detection model prevents impersonation attacks on online proctoring and runs on the client-side browser. Based on our experiment, our suggested approach can detect virtual cameras on students’ devices during user verification and online-proctoring examinations by integrating client-side virtual webcam detection.
Post-Quantum Cryptography Research Landscape: A Scientometric Perspective

ABSTRACTPost-quantum cryptography (PQC) is under development to guard against the threats of quantum computers by implementing a new class of cryptosystems. In this direction, much work has been done since 2006, which has led to many publications. Hence, this study presents an overview of PQC research through scientometric analysis of the data containing 1611 publications published from 2006 to 2023, retrieved from the Scopus database. The analysis identifies growth, trends, leading countries, and significant publications, providing insights into impactful PQC research. It also demonstrates a significant rise in publications after 2015, and the United States is a highly productive country. Furthermore, this study also discusses the managerial view, which can assist technology managers in understanding its impact on global and local markets. The findings of this analysis can be a valuable resource for researchers, policymakers, and stakeholders interested in the future of cryptography and other potential impacts of quantum computing.KEYWORDS: Post-quantum cryptography (PQC)scientometricVOSviewerpublication trendsmanagerial perspective Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementThe data supporting the findings presented in this article is downloadable from Scopus database or can be made available by the corresponding author upon reasonable request.Notesa These classifications of economic status are done according to statistics prepared by the Economy Analysis and Policy Division (EAPD) of the Department of Economy and Social Affairs of the United Nations Secretariat. For more, readers are requested to refer toCitation50
Luminaire design using additive manufacturing methods

The development of industrial luminaires is often confronted with design and manufacturing challenges that traditional design, tooling, and fabrication methods are not best suited. Additive manufacturing (3D printing) offers a means to implement complex shapes and component customizations that conventional methods cannot easily support. Luminaire designs leverage greater weight saving, thermal management optimization, component reduction, and optical distribution customization when utilizing an additive approach. This enhanced flexibility in product design and deployment better supports high mix, low volume applications that are often too specialized to warrant the investment of production tooling to create an appropriate solution. As digital tools like extended/augmented reality mature and allow more precise definitions of an application environment, additive manufacturing design and production methods become increasingly important in the industrial luminaire market vertical. In this article, we seek to highlight some of the benefits of additive manufacturing in developing and deploying a custom solution to illumination of an industrial space.
Neighbor interaction-based personalised transfer for cross-domain recommendation

Mapping-based cross-domain recommendation (CDR) can effectively tackle the cold-start problem in traditional recommender systems. However, existing mapping-based CDR methods ignore data-sparse users in the source domain, which may impact the transfer efficiency of their preferences. To this end, this paper proposes a novel method named Neighbor Interaction-based Personalized Transfer for Cross-Domain Recommendation (NIPT-CDR). This proposed method mainly contains two modules: (i) an intra-domain item supplementing module and (ii) a personalised feature transfer module. The first module introduces neighbour interactions to supplement the potential missing preferences for each source domain user, particularly for those with limited observed interactions. This approach comprehensively captures the preferences of all users. The second module develops an attention mechanism to guide the knowledge transfer process selectively. Moreover, a meta-network based on users' transferable features is trained to construct personalised mapping functions for each user. The experimental results on two real-world datasets show that the proposed NIPT-CDR method achieves significant performance improvements compared to seven baseline models. The proposed model can provide more accurate and personalised recommendation services for cold-start users.
ARIAS: An AR-based interactive advertising system

In this paper, we present an interactive advertising system based on augmented reality(AR) called ARIAS, which is manipulated with gestures for displaying advertising videos. Two-dimensional markers are defined in the system. The system captures the frame data through the camera in real time, uses OpenCV library to identify the predefined markers, and calculates the pose of markers captured by the camera. With OpenGL library, a virtual cubic model is created at the position of the marker, and thus videos or images are displayed on the six faces of the cube. The virtual cube, together with the original frame data collected by the camera, is displayed in the interactive window to achieve the augmented reality effect. Customers are accessible to various advertising content by observing the marker from different positions. The system, meanwhile, supports gesture operation in order to make the customers pay attention to the content they are interested in with one hand. The MediaPipe Hand framework is used to extract the landmarks of hands, based on which, a series of gestures are designed for interactive operation. The efficiency and accuracy of the system are tested and analyzed with the result, indicating that the system has high reliability and good interactiveness. This system is open at https://github.com/wanzhuxie/ARIAS/tree/PLOS-ONE.
Effective and efficient content-based similarity retrieval of large lung CT images based on WSSLN model

The in-depth combination and application of AI technology and medical imaging, especially high- definition CT imaging technology, make accurate diagnosis and treatment possible. Retrieving similar CT image(CI)s to an input one from the large-scale CI database of labeled diseases is helpful to realize a precise computer-aided diagnosis. In this paper, we take lung CI as an example and propose progressive content-based similarity retrieval(CBSR) method of the lung CIs based on a Weakly Supervised Similarity Learning Network (WSSLN) model. Two enabling techniques (i.e., the WSSLN model and the distance- based pruning scheme) are proposed to facilitate the CBSR processing of the large lung CIs. The main result of our paper is that, our approach is about 45% more effective than the state-of-the-art methods in terms of the mean average precision(mAP). Moreover, for the retrieval efficiency, the WSSLN-based CBSR method is about 150% more efficient than the sequential scan.
How to Find Resources in Nearest Library with Address and Contact Details by using ICT Application Technology

This article is most useful for all the researcher, reader who faced the most important problem that how to find resources in nearest library. To solve this problem this article provide solution and provide nearest resources in just one click. Reader or researcher have to just search in search of the ICT application technology by searching whatever reader need as resource he will get the result of nearest library with contact details of library and librarian with address from the google map . this article is perfect answer for how find resources in nearest library with contact details as well
Evolution of quantum resources in quantum-walk-based search algorithm

Quantum walk is fundamental to designing many quantum algorithms. Here we consider the effects of quantum coherence and quantum entanglement for the quantum walk search on the complete bipartite graph. First, we numerically show the complementary relationship between the success probability and the two quantum resources (quantum coherence and quantum entanglement). We also provide theoretical analysis in the asymptotic scenarios. At last, we discuss the role played by generalized depolarizing noises and find that it would influence the dynamics of success probability and quantum coherence sharply, which is demonstrated by theoretical derivation and numerical simulation.
A Two-Stage Long Text Summarization Method Based on Discourse Structure

This paper proposes a two-stage automatic text summarization method based on discourse structure, aiming to improve the accuracy and coherence of the summary. In the extractive stage, a text encoder divides the long text into elementary discourse units (EDUs). Then a parse tree based on rhetorical structure theory is constructed for the whole discourse while annotating nuclearity information. The nuclearity terminal nodes are selected based on the summary length requirement, and the key EDU sequence is output. The authors use a pointer generator network and a coverage mechanism in the generation stage. The nuclearity information of EDUs is to update the word attention distribution in the pointer generator, which not only accurately reproduces the critical details of the text but also avoids self-repetition. Experiments on the standard text summarization dataset (CNN/DailyMail) show that the ROUGE score of the proposed two-stage model is better than that of the current best baseline model, and the summary achieves corresponding improvements in accuracy and coherence.
Laplacian-based semi-Supervised learning in multilayer hypergraphs by coordinate descent

Graph Semi-Supervised learning is an important data analysis tool, where given a graph and a set of labeled nodes, the aim is to infer the labels to the remaining unlabeled nodes. In this paper, we start by considering an optimization-based formulation of the problem for an undirected graph, and then we extend this formulation to multilayer hypergraphs. We solve the problem using different coordinate descent approaches and compare the results with the ones obtained by the classic gradient descent method. Experiments on synthetic and real-world datasets show the potential of using coordinate descent methods with suitable selection rules.
A Critical Analytical View of Control Theory and the Geopolitical and Economic Drivers Affecting Cyber Security Warfare

Nearly 1 billion emails were exposed in 1 single year affecting 20% or 1 and 5 internet users, causing cyber security warfare worldwide. Under the control theoretic approach, users can prescribe defense actions to security alerts that provide system alerts of cyber-attacks in progress when the network is compromised. This critical analytical view of the World Economic Forum statistical data surrounds the five major drivers affecting the geopolitical and economic conditions of the top 50 nations based on public motivation, government policy, education system, labor market, and population inclusivity. The statistical data illustrates how data breaches have become an ongoing business issue causing an average of $4.35 million annually per organization. Within the cyber security market spectrum, control theory can address some of the challenges associated with geopolitical and economic drivers affecting cyber security warfare by providing a framework for designing and implementing adaptive security systems.
A new surgical path planning framework for neurosurgery

Abstract Background Despite using a variety of path‐finding algorithms that use tracts, the most significant advancement in this study is considering the values of all brain areas by doing atlas‐based segmentation for a more precise search. Our motivation comes from the literature’s shortcomings in designing and implementing path‐planning methods. Since planning paths with curvatures is a complex problem that requires considering many surgical and physiological constraints, most path‐planning strategies focus on straight paths. There is also a lack of studies that focus on the complete structure of the brain with the tracks, veins, and segmented areas. Instrument dependence is another inadequacy of the methods proposed in the literature. Aims The aim of this study is to design a new surgical path planning framework that helps to plan the surgical path independently of the instrument, considers the entire structure of the brain, and allows curvilinear surgical paths. Thus, neurosurgeons can generate patient‐specific possible optimal surgical pathways before the neurosurgical procedure. Materials &amp; Methods The proposed framework includes different path‐finding algorithms (Dijkstra, A*, and their aggressive variants) that find optimal paths by taking the risk scores (surgeons assessed all the segmented regions, considering the extent of damage. In this evaluation, scores ranged from “0 to 10,” with the most critical areas receiving a score of “10,” while the least possible affected areas were assigned a score of "0") for sensitive brain areas into consideration. For the tract image processing the framework includes fractional anisotropy (FA), relative anisotropy (RA), spherical measure (SM), and linear measure (LM) methods. Results This is the first paper to handle tracts and atlas‐based segmentation of the human brain altogether under a framework for surgical path planning. The framework has a dynamic structure that gives the flexibility to add different path‐finding algorithms and generate different widths of surgical pathways. Moreover, surgeons can update the score table to guarantee minimally invasive surgery. The output file format of all the extracted surgical paths is NRRD, so it can be easily visualised, analysed, or processed over the third part software tools. Discussion In this study, we generated many possible surgical pathways then these pathways were evaluated by the surgeons the results were impressive because the framework could identify surgical pathways used in real‐world surgery that correspond to the standard pathways such as anterior transsylvian, trans sulcal, transgyral, and sub‐temporal. Conclusion This study proposes a new surgical path planning framework for neurosurgery. Moreover, in the future by adding/adopting different parameters (such as operation time, and short and long‐term complications after surgery) to the proposed framework, it would be possible to find new surgical pathways for difficult surgical conditions.
The Role of University Integration in Overcoming the Gap between Education Results and Real Needs of Labour Market

The article discusses the topic of overcoming the gap between results of professional education and actual needs of labour market in the current situation and the role of universities in the problem solution. Due to the fact that professional education provides students with a lot of important and fundamental knowledge in a certain sphere, but labour market needs change faster than the basic curriculum a gap turns up between education programs and requirements advanced to workers. As a result we can observe a situation when a qualified specialist possesses plenty of theoretical knowledge, but few practical skills necessary for work. It is an important and acute challenge for the education sphere, labour market and in the long run for society in general. The article studies key notions connected with interaction between labour market and education, proposes possible ways to cut the gap between education results and real needs of labour market and highlights potential barriers that can hinder this process. The author provides concrete examples of successful solution of the problem within the frames of implementing various partner programs of business and education both in Russia and abroad.
On Two- and Three-valued Semantics for Impure Simplicial Complexes

Simplicial complexes are a convenient semantic primitive to reason about processes (agents) communicating with each other in synchronous and asynchronous computation.Impure simplicial complexes distinguish active processes from crashed ones, in other words, agents that are alive from agents that are dead.In order to rule out that dead agents reason about themselves and about other agents, three-valued epistemic semantics have been proposed where, in addition to the usual values true and false, the third value stands for undefined: the knowledge of dead agents is undefined and so are the propositional variables describing their local state.Other semantics for impure complexes are two-valued where a dead agent knows everything.Different choices in designing a semantics produce different three-valued semantics, and also different two-valued semantics.In this work, we categorize the available choices by discounting the bad ones, identifying the equivalent ones, and connecting the non-equivalent ones via a translation.The main result of the paper is identifying the main relevant distinction to be the number of truth values and bridging this difference by means of a novel embedding from three-into two-valued semantics.This translation also enables us to highlight quite fundamental modeling differences underpinning various two-and three-valued approaches in this area of combinatorial topology.In particular, pure complexes can be defined as those invariant under the translation.