Search

Basic computer skills are essential for authors writing research papers as it has the potential to make the task easier for a researcher. This article provides a glimpse about the essential software programs for a novice author writing a research paper. These software applications help streamline the writing process, improve the quality of work, and ensure that papers are formatted correctly. It covers word processing software, grammar correction software, bibliography management software, paraphrasing tool, writing tools, and statistical software. All of the tools described are free to use. Hence, it would help researchers from resource-limited settings or busy physicians who get lesser time for research writing. We presume this review paper would help provide valuable insights and guidance for novice authors looking to write a high-quality research paper. [ABSTRACT FROM AUTHOR]

This paper describes the possibilities of variable selection in large-scale industrial systems. It introduces knowledge-based, data-based and model-based methods for this purpose. As an example, Case-Based Reasoning application for the evaluation of the web break sensitivity in a paper machine is introduced. The application uses Linguistic Equations approach and basic Fuzzy Logic. The indicator combines the information of on-line measurements with expert knowledge and provides a continuous indication of the break sensitivity. The web break sensitivity defines the current operating situation at the paper mill and gives new information to the operators. Together with information of the most important variables this prediction gives operators enough time to react to the changing operating situation. [ABSTRACT FROM AUTHOR]

In the past decades, a significant increase has been observed in cyberattacks on the web-based systems used for financial purposes. Such individual systems often contain security weaknesses, called vulnerabilities that can be exploited for malicious purposes. The exploitation of such vulnerabilities can result in disclosure and manipulation of sensitive data as well as have destructive effects. To protect such systems, security testing is required on a periodic basis. Various detection and assessment techniques have been suggested by developers and researchers to address these security issues. In this paper, we survey the contributions of academia in the field of security testing for software applications and communication systems. A comprehensive review and in-depth analysis of the existing literature testing approaches has been performed to analyze their effectiveness and applicability under various scenarios. Further, we discuss various techniques used for conducting various security assessments. We follow the widely used method by Kitchenham and Charters for conducting a comprehensive systematic literature review process. Also, we propose a taxonomy for security testing techniques consisting of three main categories (Identification, Testing, and Reporting) and 17 subcategories consisting of specific security testing techniques (e.g., Black-box testing, risk assessment). Further, we assign a distinctive category from our taxonomy to each published paper in the security testing area, based on the material presented/discussed in the paper. [ABSTRACT FROM AUTHOR]

Highlights: [•] Granular computing aspects of natural computing. [•] Review of different granular soft computing research. [•] Biological motivation, design principles, application areas, open research problems and challenging issues of these models. [Copyright &y& Elsevier]

The development of the education industry benefits from the continuous progress and updates of internet technology, and the organic integration of the two can further promote the emergence of the LMS (Learning Management System) system. Based on the application characteristics of the LMS system and the characteristics of the Software as a Service online education management system, a Software as a Service online education management system based on the extended LMS system was constructed. The service mode of the system was analysed, and a solution to the problem of paper formation and an analysis of the automatic paper formation algorithm in the system were provided. In addition, further research was conducted on the scores of the credibility evaluation of the system's services under different attribute parameters. Through the correlation analysis between different attributes and the credibility evaluation scores of the Software as a Service system, it can be seen that among the selected attribute parameters, Response Time and Latency show a negative correlation with the credibility evaluation, while other parameters show a positive correlation. The Software as a Service system constructed in this article provides relevant technical solutions, which have great practical application value and have reference significance for other similar applications. It provides more support for the application and promotion of the Software as a Service (SaaS) online education management system. [ABSTRACT FROM AUTHOR]

Highlights: [•] We deposited IZO by DC Sputtering on heat sensitive substrates such as paper. [•] The materials displayed high electric conductivity and optical transparency. [•] Electrochromic materials were deposited using screen-printing. [•] Solid-state electrochromic devices were assembled, showing an excellent performance. [ABSTRACT FROM AUTHOR]

Abstract: Planning is motivated by acting. Most of the existing work on automated planning underestimates the reasoning and deliberation needed for acting; it is instead biased towards path-finding methods in a compactly specified state-transition system. Researchers in this AI field have developed many planners, but very few actors. We believe this is one of the main causes of the relatively low deployment of automated planning applications. In this paper, we advocate a change in focus to actors as the primary topic of investigation. Actors are not mere plan executors: they may use planning and other deliberation tools, before and during acting. This change in focus entails two interconnected principles: a hierarchical structure to integrate the actorʼs deliberation functions, and continual online planning and reasoning throughout the acting process. In the paper, we discuss open problems and research directions toward that objective in knowledge representations, model acquisition and verification, synthesis and refinement, monitoring, goal reasoning, and integration. [Copyright &y& Elsevier]

Abstract: This paper proposes the concept and the technology of social networking federation as a paradigm where information on various social network systems can be seamlessly integrated in order to provide users a uniform and semantic view of their social connections. Such a uniformly fused social network provides a single point of access where all information with respect to one’s social networks can be queried and reasoned about. The goal of the research is to establish a foundation of integrating and assimilating information within multiple social network systems. We designed a reference model of social networking federation system in this paper, as well as some prototype application to demonstrate its paradigm. We believe our work would provide a novel vision of future online social network. [Copyright &y& Elsevier]

A computer is a vital tool for UUM community in doing routine jobs and activities. Spending many hours using a computer without breaks can cause bad effect on users' health. Inappropriate computer use can cause muscle and joint pain, overuse injuries of the shoulder, arm, wrist or hand, and eyestrain. Fortunately, this pain and misery can be avoided with only a few simple routines. Therefore, a short break from using a computer is necessary. The break could be filled with a short and simple exercise. Physical exercise can provide many benefits to individual and organization. Besides improving the productivity of the organization, physical exercise also can help staff and students to be more focused, stay alert, feel motivated, energetic, and have less stress. In response to this, this paper introduces a digital health application to create health awareness among the UUM community. The methods employed in the study are a preliminary investigation, requirement gathering and analysis, prototype development, and prototype validation. From the requirement analysis phase, 11 criteria were gathered and applied in designing the Digital Healthy Lifestyle Application prototype. Then it was validated by the content and user interface experts. The experts responded positively towards the application. [ABSTRACT FROM AUTHOR]

Linear Logic is a versatile framework with diverse applications in computer science and mathematics. One intriguing fragment of Linear Logic is MultiplicativeAdditive Linear Logic (MALL), which forms the exponential-free component of the larger framework. Modifying MALL, researchers have explored weaker logics such as Noncommutative MALL (Bilinear Logic, BL) and Cyclic MALL (CyMALL) to investigate variations in commutativity. In this paper, we focus on Cyclic Nonassociative Bilinear Logic (CyNBL), a variant that combines noncommutativity and nonassociativity. We introduce a sequent system for CyNBL, which includes an auxiliary system for incorporating nonlogical axioms. Notably, we establish the cut elimination property for CyNBL. Moreover, we establish the strong conservativeness of CyNBL over Full Nonassociative Lambek Calculus (FNL) without additive constants. The paper highlights that all proofs are constructed using syntactic methods, ensuring their constructive nature. We provide insights into constructing cut-free proofs and establishing a logical relationship between CyNBL and FNL. [ABSTRACT FROM AUTHOR]

This paper discusses the author's interest in proving that it matters whether someone builds a new application using HCI principles. This is an important issue to discuss because many HCI professionals and researchers think that the conclusion is obvious i.e. that of course it matters and that we should build an application with an end user in mind and then test it with a subset of said users. However, most application developers disagree and actually build their applications using a 'field of dreams approach' i.e. if I build it. they will come. This issue/disagreement has become especially important to discuss given the advent of mobile applications for the i.phone and other quickly programmed applications. [ABSTRACT FROM AUTHOR]

Introduction: Electronic sick leave certificates, called e-ZLA, were being implemented nationwide in Poland from 1 January 1 2016 until 30 November 30 2018. During the time of implementation doctors in Poland could issue sick leave certificates alternatively in electronic or in the paper form. Since 1 December 2018 sick leave certificates are to be issued in the whole Poland electronically only. Practice change: Paper form certificates in Medical and Diagnostic Centre (CMD) - an integrated care organization located in Siedlce (Poland) used to be prepared with the help of the registration or nurse staff. The average work time consumed for that process was calculated in CMD for 13 minutes per one paper form certificate. CMD is serving approximately 85000 patients and issues about 1500 sick leaves certificates every month. Therefore, CMD made an effort of rapid implementation of electronic sick leave certificates issuing. Aim and theory of change: The vendor of computer application for managing of health care related processes introduced in February 2018 in CMD's client application a new functionality of issuing electronic sick leave certificates. The personnel of CMD was trained adequately. Timeline: The first e-ZLA in the CMD was issued on 20February 2018. After short period of time when the correctness of sickleaves certificates issued via client application was being verified, the change was implemented in the whole organization. Highlights: The concept of having an e-certificate uploaded and stored within the system was highly appreciated by CMD staff. The average time of e-ZLA issuing and its online transmission is 2-3 min compared to 13 min for managing paper-form sick leave certificates. Between 20 February 2018 and 30 November 2018, CMD doctors used to issue about 1200 e-ZLAs monthly. The necessity of writing multiple sick leave certificates for patients having more than one employer was also eliminated. This system allows also for review the history of e-ZLAs received by the patient and thus prevent potential abuses. Conclusions: As a result of e-ZLA introduction, the registration staff and nurses were relieved from paper work related to issuing paper-form certificates and additional administrative tasks related to providing copies of sick leave certificates to health insurer branches. Decrease of paper work burden resulted in saving of time which could be sacrificed for patients. Enabling the release of electronic sick leave certificates was an important milestone on the way to better integrated care. [ABSTRACT FROM AUTHOR]

With the development of the brake-by-wire system, more and more advanced driver assistance systems have been applied to automobiles. The brake-by-wire system can collect the driver's braking intention through the displacement sensor and thus realize accurate braking by the motor. Based on the brake-by-wire system, we design an algorithm that can realize the vehicle Comfort Stop Technology (CST) in this paper. The CST can control the drop and rise of brake fluid pressure during the braking stop of the vehicle, and therefore reduce the sharp feeling of front and back pitching during the braking stop. Finally, through real car verification, the functional algorithm designed in this paper can improve the nodding feeling of the vehicle by reducing the deceleration of the vehicle during braking. [ABSTRACT FROM AUTHOR]

Objective: This paper explores machine learning methods for exercise-induced laryngeal obstruction (EILO) diagnostics. Traditional diagnostic approaches like CLE scoring face subjectivity, limiting precise objective assessments. Machine learning is introduced as a theoretical solution to potentially overcome these limitations and improve diagnostic precision. Methods: A narrative review was conducted to explore the integration of machine learning techniques in the diagnostics of EILO. Result: Three machine learning methods for the segmentation of laryngeal images were discovered: fully convolutional network, Mask R-CNN, and 3D VOSNet. Our findings reveal that the integration of machine learning with EILO diagnostics remains a largely untapped research domain, providing significant room for further exploration. Conclusions: The integration of ML techniques for EILO diagnostics has the potential to be a helpful tool for clinicians. The application of computer vision ML methods, such as image segmentation, to delineate laryngeal structures paves the way for a more objective assessment. While challenges persist, especially in differences in patients' laryngeal anatomy, the synergy of ML and medical expertise is an important field to explore in the years to come. [ABSTRACT FROM AUTHOR]

This paper introduces a stereoscopic image and depth dataset created using a deep learning model. It addresses the challenge of obtaining accurate and annotated stereo image pairs with irregular boundaries for deep learning model training. Stereoscopic image and depth dataset provides a unique resource for training deep learning models to handle irregular boundary stereoscopic images, which are valuable for real-world scenarios with complex shapes or occlusions. The dataset is created using monocular depth estimation, a state-of-the-art depth estimation model, and it can be used in applications like rectifying images, estimating depth, detecting objects, and autonomous driving. Overall, this paper presents a novel dataset that demonstrates its effectiveness and potential for advancing stereo vision and developing deep learning models for computer vision applications. [ABSTRACT FROM AUTHOR]

Maritime transport helps the development of the economy of countries. Improving the current situation in this type of transport requires the application of modern software tools for assessment, analysis and forecasting. The aim of this paper is to suggest an approach for an in-depth analysis of marine traffic near to independent ports. This approach is tested and validated for the Varna and Constanta ports for the period 2004–2021. Data from Eurostat are used. This paper proposes a new methodology for an in-depth analysis and forecasting of marine traffic of independent nearby ports using public data. Correlations, multiple regression, graphical methods, seasonality and trendlines are used to test and validate the proposed methodology. The results show that the proposed methodology may be applied for other independent ports and periods. The results show some interesting facts about the analyzed ports of Varna and Constanta. Our initial assumptions that these two independent ports have similar seasonality is rejected. The novelty of the paper refers to a new methodology for the in-depth analysis and forecasting of marine traffic of independent nearby ports using public data. Using the methodology in this paper (for an in-depth analysis of marine traffic of independent nearby ports) similar research may be done for other nearby ports and periods. Other research may focus on finding the specific types of cargo for each port influencing the differences in seasonality. Nearby ports with separate management may use the proposed methodology for better cargo planning and investment planning. [ABSTRACT FROM AUTHOR]

Shrimp farming has traditionally served as a crucial source of seafood and revenue for coastal countries. However, with the rapid development of society, conventional small-scale manual shrimp farming can no longer meet the increasing demand for rapid growth. As a result, it is imperative to continuously develop automation technology for efficient large-scale shrimp farming. Smart shrimp farming represents an innovative application of advanced technologies and management practices in shrimp aquaculture to expand the scale of production. Nonetheless, the use of these new technologies is not without difficulties, including the scarcity of public datasets and the high cost of labeling. In this paper, we focus on the application of advanced computer vision techniques to shrimp farming. To achieve this objective, we first establish a high-quality shrimp dataset for training various deep learning models. Subsequently, we propose a method that combines unsupervised learning with downstream instance segmentation tasks to mitigate reliance on large training datasets. Our experiments demonstrate that the method involving contrastive learning outperforms the direct fine-tuning of an instance segmentation model for shrimp in instance segmentation tasks. Furthermore, the concepts presented in this paper can extend to other fields that utilize computer vision technologies. [ABSTRACT FROM AUTHOR]

The article offers information on the people involved in the IEEE transactions on industry applications.

Over the past two decades, advances in remote sensing methods and technology have enabled larger and more sophisticated datasets to be collected. Due to these advances, the need to effectively and efficiently communicate and visualize data is becoming increasingly important. We demonstrate that the use of mixed- (MR) and virtual reality (VR) systems has provided very promising results, allowing the visualization of complex datasets with unprecedented levels of detail and user experience. However, as of today, such visualization techniques have been largely used for communication purposes, and limited applications have been developed to allow for data processing and collection, particularly within the engineering–geology field. In this paper, we demonstrate the potential use of MR and VR not only for the visualization of multi-sensor remote sensing data but also for the collection and analysis of geological data. In this paper, we present a conceptual workflow showing the approach used for the processing of remote sensing datasets and the subsequent visualization using MR and VR headsets. We demonstrate the use of computer applications built in-house to visualize datasets and numerical modelling results, and to perform rock core logging (XRCoreShack) and rock mass characterization (EasyMineXR). While important limitations still exist in terms of hardware capabilities, portability, and accessibility, the expected technological advances and cost reduction will ensure this technology forms a standard mapping and data analysis tool for future engineers and geoscientists. [ABSTRACT FROM AUTHOR]

This paper proposes a novel algorithm that combines symbolic execution and data flow testing to generate test cases satisfying multiple coverage criteria of critical software applications. The coverage criteria considered are data flow coverage as the primary criterion, software safety requirements, and equivalence partitioning as sub-criteria. black The characteristics of the subjects used for the study include high-precision floating-point computation and iterative programs. The work proposes an algorithm that aids the tester in automated test data generation, satisfying multiple coverage criteria for critical software. The algorithm adapts itself and selects different heuristics based on program characteristics. The algorithm has an intelligent agent as its decision support system to accomplish this adaptability. Intelligent agent uses the knowledge base to select different low-level heuristics based on the current state of the problem instance during each generation of genetic algorithm execution. The knowledge base mimics the expert's decision in choosing the appropriate heuristics. black The algorithm outperforms by accomplishing 100% data flow coverage for all subjects. In contrast, the simple genetic algorithm, random testing and a hyper-heuristic algorithm could accomplish a maximum of 83%, 67% and 76.7%, respectively, for the subject program with high complexity. black The proposed algorithm covers other criteria, namely equivalence partition coverage and software safety requirements, with fewer iterations. black The results reveal that test cases generated by the proposed algorithm are also effective in fault detection, with 87.2% of mutants killed when compared to a maximum of 76.4% of mutants killed for the complex subject with test cases of other methods. [ABSTRACT FROM AUTHOR]

Identifying individual pigs is crucial for efficient breeding, health management, and disease control in modern farming. Traditional animal face identification methods are labor-intensive and prone to inaccuracies, while existing CNN-based pig face recognition models often struggle with high computational demands, large sizes, and reliance on extensive labeled data, which limit their practical application. This paper addresses these challenges by proposing a novel, decoupled approach to pig face recognition that separates detection from identification. This strategy employs a detection model as a pre-processing step, significantly reducing the need for extensive re-annotation for new datasets. Additionally, the paper introduces a method that integrates offline knowledge distillation with a lightweight pig face recognition model, aiming to build an efficient and embedding-friendly system. To achieve these objectives, the study constructs a small-scale, high-quality pig face detection dataset consisting of 1500 annotated images from a selection of 20 pigs. An independent detection model, trained on this dataset, then autonomously generates a large-scale pig face recognition dataset with 56 pig classes. In the face recognition stage, a robust teacher model guides the student model through a distillation process informed by a knowledge distillation loss, enabling the student model to learn relational features from the teacher. Experimental results confirm the high accuracy of the pig face detection model on the small-scale detection dataset and the ability to generate a large-scale dataset for pig face recognition on unlabeled data. The recognition experiments further verify that the distilled lightweight model outperforms its non-distilled counterparts and approaches the performance of the teacher model. This scalable, cost-effective solution shows significant promise for broader computer vision applications beyond agriculture. [ABSTRACT FROM AUTHOR]

In order to explore the application of network music education system, this paper proposes an application of computer network technology in the network music education system. This method explores the application of network music education systems through the key technical problems and solutions of information recommendation based on computer network technology. The research shows that the efficiency of network music education system based on computer network technology is about 30% higher than that of traditional methods. This paper implements the system according to the layered mode and tests the operation of the software system. The test results show that, through the use case test of each functional module of the foreground and background, the system can execute the issued commands smoothly and normally. Finally, it is concluded that the average risk occurrence rate is 2.02 hours/hour, and the probability of high risk is 17.4%. Conclusion. After testing, the operation of the software system has achieved the expected goal. [ABSTRACT FROM AUTHOR]

In many applications, top-k query is an important operation to return a set of interesting points in a potentially huge data space. It is analyzed in this paper that the existing algorithms cannot process top- k query on massive data efficiently. This paper proposes a novel table-scan-based T2S algorithm to efficiently compute top-k results on massive data. T2S first constructs the presorted table, whose tuples are arranged in the order of the round-robin retrieval on the sorted lists. T2S maintains only fixed number of tuples to compute results. The early termination checking for T2S is presented in this paper, along with the analysis of scan depth. The selective retrieval is devised to skip the tuples in the presorted table which are not top-k results. The theoretical analysis proves that selective retrieval can reduce the number of the retrieved tuples significantly. The construction and incremental-update/batch-processing methods for the used structures are proposed in this paper. The extensive experimental results, conducted on synthetic and real-life data sets, show that T2S has a significant advantage over the existing algorithms. [ABSTRACT FROM AUTHOR]

Gesture recognition is crucial in computer vision-based applications, such as drone control, gaming, virtual and augmented reality (VR/AR), and security, especially in human–computer interaction (HCI)-based systems. There are two types of gesture recognition systems, i.e., static and dynamic. However, our focus in this paper is on dynamic gesture recognition. In dynamic hand gesture recognition systems, the sequences of frames, i.e., temporal data, pose significant processing challenges and reduce efficiency compared to static gestures. These data become multi-dimensional compared to static images because spatial and temporal data are being processed, which demands complex deep learning (DL) models with increased computational costs. This article presents a novel triple-layer algorithm that efficiently reduces the 3D feature map into 1D row vectors and enhances the overall performance. First, we process the individual images in a given sequence using the MediaPipe framework and extract the regions of interest (ROI). The processed cropped image is then passed to the Inception-v3 for the 2D feature extractor. Finally, a long short-term memory (LSTM) network is used as a temporal feature extractor and classifier. Our proposed method achieves an average accuracy of more than 89.7%. The experimental results also show that the proposed framework outperforms existing state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Spiking Neural Networks have gained significant attention due to their potential for energy efficiency and biological plausibility. However, the reduced number of user-friendly tools for designing, training, and visualizing Spiking Neural Networks hinders widespread adoption. This paper presents the SNNtrainer3D v1.0.0, a novel software application that addresses these challenges. The application provides an intuitive interface for designing Spiking Neural Networks architectures, with features such as dynamic architecture editing, allowing users to add, remove, and edit hidden layers in real-time. A key innovation is the integration of Three.js for three-dimensional visualization of the network structure, enabling users to inspect connections and weights and facilitating a deeper understanding of the model's behavior. The application supports training on the Modified National Institute of Standards and Technology dataset and allows the downloading of trained weights for further use. Moreover, it lays the groundwork for future integration with physical memristor technology, positioning it as a crucial tool for advancing neuromorphic computing research. The advantages of the development process, technology stack, and visualization are discussed. The SNNtrainer3D represents a significant step in making Spiking Neural Networks more accessible, understandable, and easier for Artificial Intelligence researchers and practitioners. [ABSTRACT FROM AUTHOR]

This paper presents a novel segmentation algorithm specially developed for applications in 3D point clouds with high variability and noise, particularly suitable for heritage building 3D data. The method can be categorized within the segmentation procedures based on edge detection. In addition, it uses a graph-based topological structure generated from the supervoxelization of the 3D point clouds, which is used to make the closure of the edge points and to define the different segments. The algorithm provides a valuable tool for generating results that can be used in subsequent classification tasks and broader computer applications dealing with 3D point clouds. One of the characteristics of this segmentation method is that it is unsupervised, which makes it particularly advantageous for heritage applications where labelled data is scarce. It is also easily adaptable to different edge point detection and supervoxelization algorithms. Finally, the results show that the 3D data can be segmented into different architectural elements, which is important for further classification or recognition. Extensive testing on real data from historic buildings demonstrated the effectiveness of the method. The results show superior performance compared to three other segmentation methods, both globally and in the segmentation of planar and curved zones of historic buildings. [ABSTRACT FROM AUTHOR]

Computer-supported collaborative learning (CSCL) is a learning strategy that gathers students together on campus through mobile application software on intelligent handheld devices to carry out creative exploration learning activities and social interaction learning activities. Learning resource diffusion is a very important constituent part of CSCL mobile software. However, learners will receive or forward a large number of learning resources such that short video, images, or short audio which will increase the energy consumption of forwarding nodes and reduce the message delivery success rate. How to improve the message delivery success rate is an urgent problem to be solved. To solve the aforementioned problem, this paper mainly studies the diffusion of learning resources in campus opportunistic networks based on credibility for CSCL. In campus opportunistic networks, learners who participate in collaborative learning can obtain the desired learning resources through the distribution and sharing of learning resources. Learning resource diffusion depends on the credibility of learners who participate in collaborative learning. However, the existing classical algorithms do not take into account the credibility between learners. Firstly, the concept of credibility in campus opportunistic networks is proposed, and the calculation method of credibility is also presented. Next, the problem of node initialization starvation is solved in this paper. The node initialization starvation phase of collaborative learning is defined and resolved in campus opportunistic networks. Based on the information of familiarity and activity between nodes formed in the process of continuous interaction, a learning resource diffusion mechanism based on node credibility is proposed. Finally, the paper proposes a complete learning resource diffusion algorithm based on credibility for computer-supported collaborative learning (LRDC for short) to improve the delivery success rate of learning resources on the campus. Extensive simulation results show that the average message diffusion success rate of LDRC is higher than that of classical algorithms such as DirectDeliver, Epidemic, FirstContact, and SprayAndWait under the different transmission speed, buffer size, and initial energy, which is averagely improved by 46.83%, 44.43%, and 45.6%, respectively. The scores of LRDC in other aspects are also significantly better than these classical algorithms. [ABSTRACT FROM AUTHOR]

Currently, due to the proliferation of smart appliances and the rapid development of wireless communication technologies, there is a huge shortage of spectrum resources. Cognitive radio utilizes technologies that dramatically increase the utilization of free frequency bands, detecting the environment in real time and maximizing the reallocation of spectrum resources by sensing and reallocating the licensed free frequency bands. The frequency and accuracy required to determine the free bands is an important basis for determining the spectrum awareness in all aspects of a sensing radio system. Therefore, for the more complex network environment in reality and the intrusion of malicious users, this paper uses Fast K-medoids and Mean-shift methods for information fusion and an iterative approach to overcome malicious use to fuse data and finally obtain realistic feature statistics. The results show that the collaborative identification strategy proposed in this paper has good results. [ABSTRACT FROM AUTHOR]

This paper presents a bibliometric study of the articles and citations given in the KELPRO Bulletin: A Bi-Annual Journal of Kerala Library Professionals’ Organisation published during 1997-2014. This paper includes year-wise distribution of articles, authorship pattern, geographical distribution of articles, subject coverage of articles, rank list of authors of KELPRO Bulletin articles of a particular period, etc. The analysis reveals that the Kelpro Bulletin has published an average of 15 articles per year, the maximum number of contributions are from single authors with 50.00%, and the analysis also shows that the majority of the contributions are from India with 98.89%. The majority of contributions (77= 28.52%) appeared under Computer Applications in library activities and services. The rank wise distribution of authors reveals that S. Humayoon Kabir occupies the top rank by contributing the highest number of articles (10) which has been followed by Manoj Kumar Sinha by contributing seven (7) research papers. The majority of the articles (44.81%) have a length of 6 to 10 pages. Most of the contributors preferred scholarly journals as the main source of information for writing their research papers/articles contributing to the highest number of citations (59.01%). Annals of Library and Information Studies is the most cited and top-ranking journal with altogether 106 citations. [ABSTRACT FROM AUTHOR]

Non-fungible tokens (NFTs) are individual tokens with valuable information stored inside them over blockchain technology. They can be purchased and sold like other physical and virtual art pieces because their worth is mostly determined by the market and demand. The unique data of NFTs render it simple to verify and authenticate their ownership and transfer of tokens between owners. However, in Pakistan, developers cannot acquire different licences to accomplish their projects not because they cannot afford it, but because they cannot invest in every piece of software to accomplish each new sensitive task. Rather, they can render the product platform independent. Considering this technology, this paper provides IT professionals with a new NFT approach and business policies that solely belong to the information technology domain. In addition, this paper also introduces how NFT tokens can hold software applications. Since we can store files, we can let NFTs also store complete applications to help developers in further utilising virtuality and having the metaverse at their fingertips. Whenever they succeed in a project, they never receive rewards, and their skills only pay the bills. In a nutshell, this paper presents a prototype of NFTs that would be further polished to save and utilise applications in a decentralised manner while rewarding the developers. [ABSTRACT FROM AUTHOR]

This paper describes a digital application designed for learning Space Groups of Symmetry (SGS). It teaches how to recognize the operations performed by the symmetry elements, both point (or 2D) operators (proper and improper rotations, including mirroring, inversion, and other rotoinversions), and space (or 3D) operators (screw axes and glide planes), as well as their combinations with the translations of the lattice. The software applies a 3D space vision to identify the elements of symmetry compatible with the proposed structural models. The symbols of internationally accepted representation are used. The system, class, and space group of the crystal that agree with the proposed model are solved. Two settings are taken into account in the Monoclinic system. The application self-evaluates and assesses the knowledge acquired, allowing each exercise to be re-done until it is correctly completed with the appropriate recommendations. This application constitutes a useful and easy-to-use tool for SGS learning. It is aimed at beginner students of crystallography, with elementary knowledge about elements of symmetry, Bravais lattices, crystal classes, and wallpaper groups. [ABSTRACT FROM AUTHOR]

A chatbot, or else a conversational agent (CA), is a technology that is used in order to imitate the process of a conversation between a human being and a software application for supporting specific services. The utilization of this technology has been increasing considerably over the past five years, particularly in education where CAs are mostly utilized as teaching assistants that provide educational content. This paper aims to contribute to the existing body of knowledge by systematically reviewing the support provided by chatbots both to educational institutions and their students, investigating their capabilities in further detail, and highlighting the various ways that this technology could and should be used in order to maximize its benefits. Emphasis is given to analyzing and synthesizing the emerging roles of CAs, usage recommendations and suggestions, student's desires, and challenges recorded in the literature. For this reason, a systematic literature review (SLR) was carried out using the PRISMA framework in order to minimize the common biases and limitations of SLRs. However, we must note that the SLR presented has specific limitations, namely using only Scopus as a search engine, utilizing a general search query, and selecting only journal articles published in English in the last five years. [ABSTRACT FROM AUTHOR]

Copyright of FME Transactions is the property of University of Belgrade, Faculty of Mechanical Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Cooperative information systems (CIS) are at the core of a digital economy that is dedicated to providing hyper-connected citizens with access to intuitive and sophisticated technology. No longer are commercial applications the only driver of development activities, but wider societal and individual deployment areas are now explored. CIS researchers have for more than two decades studied the nature and requirements of cooperative systems linking latest technologies with hybrid user bases. However, the current opportunity-rich landscape of digital technologies such as mobile and social computing, advanced data analytics, robotic process automation or smart devices has triggered entire new, exciting research questions. In light of this setting, the 23rd International Conference on Cooperative Information Systems took place within the context of the 14th OnTheMove Federated Conferences and Workshops in Rhodes, Greece, in October 2015. This Special Issue features the carefully selected and comprehensively reviewed best papers from this event. [ABSTRACT FROM AUTHOR]

The most significant invention made in recent years to serve various applications is software. Developing a faultless software system requires the software system design to be resilient. To make the software design more efficient, it is essential to assess the reusability of the components used. This paper proposes a software reusability prediction model named Flexible Random Fit (FRF) based on aging resilience for a Service Net (SN) software system. The reusability prediction model is developed based on a multilevel optimization technique based on software characteristics such as cohesion, coupling, and complexity. Metrics are obtained from the SN software system, which is then subjected to min-max normalization to avoid any saturation during the learning process. The feature extraction process is made more feasible by enriching the data quality via outlier detection. The reusability of the classes is estimated based on a tool called Soft Audit. Software reusability can be predicted more effectively based on the proposed FRF-ANN (Flexible Random Fit - Artificial Neural Network) algorithm. Performance evaluation shows that the proposed algorithm outperforms all the other techniques, thus ensuring the optimization of software reusability based on aging resilient. The model is then tested using constraint-based testing techniques to make sure that it is perfect at optimizing and making predictions. [ABSTRACT FROM AUTHOR]

Today, blockchain technology is playing a vital role in the professional world as one of the most important discoveries and creative developments. Blockchain technology is continuously advancing and revolutionizing our society. An overview of blockchain technology is presented in this paper. The blockchain is a distributed ledger technology that allows different transactions and operations to be recorded in a chain of blocks without requiring a third party. A growing number of businesses and industrial communities are exploring blockchain technology as a technological option. In the last decade, blockchain technology has completely changed the paradigm of computer applications. Blockchain was created with the vision of creating an immutable, confidential, open-source, decentralized P2P network located on a decentralized network to facilitate data sharing. An overview of blockchain applications in different areas is presented. We provide a timely summary of blockchain research for individuals and organizations interested in this field. This paper presents an overview of blockchain technology, listing all the key characteristics, benefits, and features which make blockchain technology both superior and unique. We also discuss popular consensus protocols and taxonomy for blockchain systems. [ABSTRACT FROM AUTHOR]

In most Computer Vision applications, Deep Learning models achieve state-of-the-art performances. One drawback of Deep Learning is the large amount of data needed to train the models. Unfortunately, in many applications, data are difficult or expensive to collect. Data augmentation can alleviate the problem, generating new data from a smaller initial dataset. Geometric and color space image augmentation methods can increase accuracy of Deep Learning models but are often not enough. More advanced solutions are Domain Randomization methods or the use of simulation to artificially generate the missing data. Data augmentation algorithms are usually specifically designed for single images. Most recently, Deep Learning models have been applied to the analysis of video sequences. The aim of this paper is to perform an exhaustive study of the novel techniques of video data augmentation for Deep Learning models and to point out the future directions of the research on this topic. [ABSTRACT FROM AUTHOR]

Featured Application: This software design pattern can be used in OOP programming in order to promote conceptual clarity, reduce coupling, and facilitate system scalability. Object-oriented programming (OOP) has long been a dominant paradigm in software development, but it is not without its challenges. One major issue is the problem of tight coupling between objects, which can hinder flexibility and make it difficult to modify or extend code. Additionally, the complexity of managing inheritance hierarchies can lead to rigid and fragile designs, making it hard to maintain and evolve the software over time. This paper introduces a software development pattern that seeks to offer a renewed approach to writing code in object-oriented (OO) environments. Addressing some of the limitations of the traditional approach, the Puzzle Pattern focuses on extreme modularity, favoring writing code exclusively in building blocks that do not possess a state (e.g., Java interfaces that support concrete methods definitions in interfaces starting from version 8). Concrete classes are subsequently assembled through the implementation of those interfaces, reducing coupling and introducing a new level of flexibility and adaptability in software construction. The highlighted pattern offers significant benefits in software development, promoting extreme modularity through interface-based coding, enhancing adaptability via multiple inheritance, and upholding the SOLID principles, though it may pose challenges such as complexity and a learning curve for teams. [ABSTRACT FROM AUTHOR]

This is an expansion and modiÖcation of the paper from [6]. We discuss the convergence of locus in the paper [5], which originated from a practice problem for the Chinese college entrance exam. Next, we extended some results in [6] from 2D to 3D. We are interested in the limit of a recursive sequence of loci that is built on a special convex combination of vectors involving curves or surfaces. We shall see many interesting graphs of uniform convergence of sequences generated by parametric curves and surfaces, which we hope to inspire many applications in computer graphics, and other related disciplines. [ABSTRACT FROM AUTHOR]

Gaussian filtering, being a convolution with a Gaussian kernel, is a widespread technique in image analysis and computer vision applications. It is the traditional approach for noise reduction. In some cases, performing the exact convolution can be computationally expensive and time-consuming. To address this problem, approximations of the convolution are often used to achieve a balance between accuracy and computational efficiency, such as with running sums, Bell blur, Deriche approximation, etc. At the same time, modern computing devices support data parallelism (vectorization) via Single Instruction Multiple Data (SIMD) and can process integer numbers faster than floating-point approaches. In this paper, we describe several methods for approximating a Gaussian filter, implement the SIMD and quantized versions, and compare them in terms of speed and accuracy. The experiments were performed on central processing units with a x86_64 architecture using a family of SSE SIMD extensions and an ARMv8 architecture using the NEON SIMD extension. All the optimized approximations demonstrated 10–20× speedup while maintaining the accuracy in the range of 1 × 10 − 5 or higher. The fastest method is a trivial Stack blur with a relatively high error, so we recommend using the second-order Vliet–Young–Verbeek filter and quantized Bell blur and running sums as more accurate and still computationally efficient alternatives. [ABSTRACT FROM AUTHOR]

Introduction: Liver cancer presents a growing global health concern, necessitating advanced approaches for intervention. This review investigates the use and effectiveness of software navigation in interventional radiology for liver tumour procedures. Methods: In accordance with Preferred Reporting Items for Systematic reviews and Meta‐Analyses extension for Scoping Reviews (PRISMA‐ScR) guidelines, a scoping review was conducted of the literature published between 2013 and 2023 sourcing articles through MEDLINE, Scopus, CINAHL and Embase. Eligible studies focused on liver cancer, utilised cone‐beam computed tomography (CBCT), and employed software for intervention. Twenty‐one articles were deemed eligible for data extraction and analysis. Results: Categorised by type, software applications yielded diverse benefits. Feeder detection software significantly enhanced vessel identification, reducing non‐target embolisation by up to 43%. Motion correction software demonstrated a 20% enhancement in image quality, effectively mitigating breathing‐induced motion artefacts. Liver perfusion software facilitated efficient tumour targeting while simultaneously reducing the occurrence of side effects. Needle guide software enabled precise radiofrequency ablation needle placement. Additionally, these software applications provided detailed anatomical simulations. Overall, software integration resulted in shorter procedures, reduced radiation exposure and decreased contrast media usage. Conclusion: This scoping review highlights the innovative yet relatively underexplored role of software navigation for liver tumour procedures. The integration of software applications not only enhances procedural efficiency but also bolsters operator confidence, and contributes to improved patient outcomes. Despite the current lack of uniformity and standardisation, these software‐driven advancements hold significant promise for transforming liver tumour interventions. To realise these benefits, further research is needed to explore the clinical impact and optimal utilisation of software navigation tools in interventional radiology. [ABSTRACT FROM AUTHOR]

Salient object detection is an increasingly popular topic in the computer vision field, particularly for images with complex backgrounds and diverse object parts. Background information is an essential factor in detecting salient objects. This paper suggests a robust and effective methodology for salient object detection. This method involves two main stages. The first stage is to produce a saliency detection map based on the dense and sparse reconstruction of image regions using a refined background dictionary. The refined background dictionary uses a boundary conductivity measurement to exclude salient object regions near the image's boundary from a background dictionary. In the second stage, the CascadePSP network is integrated to refine and correct the local boundaries of the saliency mask to highlight saliency objects more uniformly. Using six evaluation indexes, experimental outcomes conducted on three datasets show that the proposed approach performs effectively compared to the state-of-the-art methods in salient object detection, particularly in identifying the challenging salient objects located near the image's boundary. These results demonstrate the potential of the proposed framework for various computer vision applications. [ABSTRACT FROM AUTHOR]

In graph theory, the weighted Laplacian matrix is the most utilized technique to interpret the local and global properties of a complex graph structure within computer vision applications. However, with increasing graph nodes, the Laplacian matrix's dimensionality also increases accordingly. Therefore, there is always the "curse of dimensionality"; In response to this challenge, this paper introduces a new approach to reducing the dimensionality of the weighted Laplacian matrix by utilizing the Gershgorin circle theorem by transforming the weighted Laplacian matrix into a strictly diagonal domain and then estimating rough eigenvalue inclusion of a matrix. The estimated inclusions are represented as reduced features, termed GC features; The proposed Gershgorin circle feature extraction (GCFE) method was evaluated using three publicly accessible computer vision datasets, varying image patch sizes, and three different graph types. The GCFE method was compared with eight distinct studies. The GCFE demonstrated a notable positive Z-score compared to other feature extraction methods such as I-PCA, kernel PCA, and spectral embedding. Specifically, it achieved an average Z-score of 6.953 with the 2D grid graph type and 4.473 with the pairwise graph type, particularly on the E_Balanced dataset. Furthermore, it was observed that while the accuracy of most major feature extraction methods declined with smaller image patch sizes, the GCFE maintained consistent accuracy across all tested image patch sizes. When the GCFE method was applied to the E_MNSIT dataset using the K-NN graph type, the GCFE method confirmed its consistent accuracy performance, evidenced by a low standard deviation (SD) of 0.305. This performance was notably lower compared to other methods like Isomap, which had an SD of 1.665, and LLE, which had an SD of 1.325; The GCFE outperformed most feature extraction methods in terms of classification accuracy and computational efficiency. The GCFE method also requires fewer training parameters for deep-learning models than the traditional weighted Laplacian method, establishing its potential for more effective and efficient feature extraction in computer vision tasks. [ABSTRACT FROM AUTHOR]