In order to improve the analysis effect of the international competitiveness of China’s new energy vehicle industry, this paper combines the improved AHP and grey relational analysis method to analyze the international competitiveness of China’s new energy vehicle industry. This paper uses the quantitative information analysis algorithm of new energy vehicle competitiveness to process the signal data of China’s new energy vehicle industry’s international competitiveness. Moreover, this paper collects data through various channels, and applies the improved AHP and grey relational analysis to the analysis of the international competitiveness of China’s new energy vehicle industry. In addition, this paper proposes an improved SP & SPWVD combined time-frequency distribution algorithm. It combines the long-window short-time Fourier transform (STFT) with better frequency resolution and the short-window STFT with better time resolution to obtain a combined window spectrogram with better time-frequency aggregation. The research results show that when the improved AHP and grey relational analysis method are used in the analysis of the international competitiveness of China’s new energy vehicle industry, the analysis effect of the international competitiveness of China’s new energy vehicle industry can be effectively improved.
With the development of artificial intelligence technology, university lecturers are experiencing a series of reactions that may occur after changing traditional teaching methods. This study uses 26 Chinese university lecturers as a sample, based on the grounded theory of qualitative research, and uses NVivo 14 and fsQCA 3.0 to explore the differentiated antecedent configuration pathways of job burnout and job insecurity among university lecturers of different genders under the influence of AI. The study found that male university lecturers with weaker adaptability to AI tend to develop stronger negative awareness of AI, making them more prone to job burnout. In contrast, female university lecturers with stronger adaptability to AI are more likely to develop positive awareness of AI, yet they also experience higher levels of job burnout. Male lecturers who are highly adaptable to AI but have negative awareness of it are more likely to feel insecure about their jobs for job insecurity. Nevertheless, the effect of female lecturers' adaptability and awareness of AI on their job insecurity seems minimal. Based on the different configuration pathways formed by the antecedent conditional variables, this study explains the combination of factors that significantly affect lecturers’ job insecurity and job burnout to help universities pay attention to and take effective strategies to alleviate the negative impact of these factors, reasonably allocate limited resources, and assist university lecturers of different genders to understand and manage job insecurity and burnout more rationally.
Arpit Guleria, Harshan Jagadeesh, Ranjitha Prasad
et al.
Abstract Class imbalance in training datasets can lead to bias and poor generalization in machine learning models. While pre-processing of training datasets is an efficient way to address both these issues in centralized learning environments, it is challenging to detect and address these issues in a distributed learning environment such as federated learning. In this paper, we propose FLICKER, a privacy preserving framework to address issues related to global class imbalance in federated learning. At the heart of our contribution lies the popular Cheon-Kim-Kim-Song (CKKS) homomorphic encryption scheme, which is used by the clients to privately share their data attributes, and subsequently balance their datasets before implementing the federated learning scheme. Extensive experimental results show that our proposed method improves the federated learning accuracy numbers by up to 8 $$\%$$ when used along with popular datasets and relevant baselines.
Information technology, Electronic computers. Computer science
Sonia Jacqueline Tigua Moreira, Edison Luis Cruz Navarrete, Diana Lucia Tigua Moreira
et al.
Protected areas, such as the 6078-hectare Cerro Blanco Protected Forest of Guayaquil, often struggle with inefficient wildlife information management, including previously physical and inefficient methods of fauna control, which can hinder effective conservation. Comprehensive digital resources are needed to improve data accessibility for informed decision-making regarding conservation activities. This project addressed this opportunity by developing and evaluating a digital platform specifically designed to facilitate the collection, organization, and dissemination of wildlife-based resources. While the consideration of digital approaches in conservation is rapidly increasing, there are minimal articles focused on successful encompassing digital platforms, and none that assess their real-world effect via plithogenic analysis. Therefore, this digital application, which includes functional modules for security, fauna information, a digital catalog, and incident reporting, was implemented in the Cerro Blanco Protected Forest. Its real-world effect was subsequently assessed using plithogenic analysis, a method chosen to provide quantifiable evidence of its success. The results indicate a highly significant positive sentiment from users toward the post-application functionality and the standardization of faunal resources. In addition, the plithogenic analysis produced more statistically significant emergent trends and occurrences due to the information being housed in one collected location. Thus, this study contributes theoretically to the literature by acknowledging the digital application as a practical integration model for wildlife resource management, and practically it applies the application itself as an effective tool for any protected area to achieve higher resource management efficiency.
Hybrid collaboration, where co-located and remote learners work together using online tools, is becoming increasingly relevant to education due to its high degree of flexibility. There is extensive research contrasting face-to-face (F2F) to remote collaboration, but much less research on hybrid learning formats. The scarce available research on hybrid collaboration suggests that the remote learners in such settings often feel less related to their peers than the co-located learners. In general, research on socio-affective factors in hybrid learning is particularly lacking, even though factors such as the learners' perceived relatedness and social presence play a crucial role for learners’ interaction and the acceptance of hybrid collaborative learning.In this paper, we experimentally compare the different perceptions of learners in hybrid, F2F and remote collaboration. In a laboratory study conducted in the context of higher education, N = 180 students rated their socio-affective state after participating in one of the three participation modes (F2F, remote, hybrid). The study revealed statistically significant differences between the three conditions in terms of learners’ perceived relatedness, social presence, enjoyment as well as their willingness to collaborate again. Based on our findings, we discuss key issues of hybrid collaboration that should be addressed in future research.
Thomas Schilling, Rebecca Müller, Thomas Ellwart
et al.
Many organizations use decision support systems (DSS) to support DSS users in their daily work demands (e.g., high workload, insufficient information, ambiguous situations). A key question regarding their interaction is how the decision-control is divided between the DSS and the user, represented by the system's level of automation (LoA). To investigate the need for an adaptable DSS where users can manually adjust the LoA across situations, we used a vignette design to examine whether users prefer different LoA in different situations (i.e., six situational criteria, each manipulated by two specifications; e.g., low vs. high workload). In the twelve vignettes, the 116 participants should imagine working in an emergency control-center—a setting they were familiar with from previous experiments. Our results showed significant differences between the two corresponding vignettes, indicating that users prefer different LoA across situations. However, after controlling for the participants' overall preference for a situation-independent baseline LoA, the significant differences between all paired vignettes disappear. Our results have implications for whether situational or individual criteria are more important regarding LoA preferences, adaptable DSS, and for human-centered design based on user profiles. We discuss our findings in relation to the broader literature on trust and acceptance of DSS.
BackgroundThe electronic health record (EHR) has been widely implemented internationally as a tool to improve health and healthcare delivery. However, EHR implementation has been comparatively slow amongst hospitals in the Arabian Gulf countries. This gradual uptake may be linked to prevailing opinions amongst medical practitioners. Until now, no systematic review has been conducted to identify the impact of EHRs on doctor-patient relationships and attitudes in the Arabian Gulf countries.ObjectiveTo understand the impact of EHR use on patient-doctor relationships and communication in the Arabian Gulf countries.DesignA systematic review of English language publications was performed using PRISMA chart guidelines between 1990 and 2023.MethodsElectronic database search (Ovid MEDLINE, Global Health, HMIC, EMRIM, and PsycINFO) and reference searching restricted to the six Arabian Gulf countries only. MeSH terms and keywords related to electronic health records, doctor-patient communication, and relationship were used. Newcastle-Ottawa Scale (NOS) quality assessment was performed.Results18 studies fulfilled the criteria to be included in the systematic review. They were published between 1992 and 2023. Overall, a positive impact of EHR uptake was reported within the Gulf countries studied. This included improvement in the quality and performance of physicians, as well as improved accuracy in monitoring patient health. On the other hand, a notable negative impact was a general perception of physician attention shifted away from the patients themselves and towards data entry tasks (e.g., details of the patients and their education at the time of the consultation).ConclusionThe implementation of EHR systems is beneficial for effective care delivery by doctors in Gulf countries despite some patients' perception of decreased attention. The use of EHR assists doctors with recording patient details, including medication and treatment procedures, as well as their outcomes. Based on this study, the authors conclude that widespread EHR implementation is highly recommended, yet specific training should be provided, and the subsequent effect on adoption rates by all users must be evaluated (particularly physicians). The COVID-19 Pandemic showed the great value of EHR in accessing information and consulting patients remotely.
Aiming at the deficiency that blockchain technology is too sensitive to the binary-level changes of high resolution remote sensing (HRRS) images, we propose a new subject-sensitive hashing algorithm specially for HRRS image blockchains. To implement this subject-sensitive hashing algorithm, we designed and implemented a deep neural network model MultiRes-RCF (richer convolutional features) for extracting features from HRRS images. A MultiRes-RCF network is an improved RCF network that borrows the MultiRes mechanism of MultiResU-Net. The subject-sensitive hashing algorithm based on MultiRes-RCF can detect the subtle tampering of HRRS images while maintaining robustness to operations that do not change the content of the HRRS images. Experimental results show that our MultiRes-RCF-based subject-sensitive hashing algorithm has better tamper sensitivity than the existing deep learning models such as RCF, AAU-net, and Attention U-net, meeting the needs of HRRS image blockchains.
Network Intrusion Detection Systems (NIDS) represent a crucial component in the security of a system, and their role is to continuously monitor the network and alert the user of any suspicious activity or event. In recent years, the complexity of networks has been rapidly increasing and network intrusions have become more frequent and less detectable. The increase in complexity pushed researchers to boost NIDS effectiveness by introducing machine learning (ML) and deep learning (DL) techniques. However, even with the addition of ML and DL, some issues still need to be addressed: high false negative rates and low attack predictability for minority classes. Aim of the study was to address these problems that have not been adequately addressed in the literature. Firstly, we have built a deep learning model for network intrusion detection that would be able to perform both binary and multiclass classification of network traffic. The goal of this base model was to achieve at least the same, if not better, performance than the models observed in the state-of-the-art research. Then, we proposed an effective refinement strategy and generated several models for lowering the FNR and increasing the predictability for the minority classes. The obtained results proved that using the proper parameters is possible to achieve a satisfying trade-off between FNR, accuracy, and detection of the minority classes.
Early closure of cranial vault sutures, defined as craniosynostosis is a relatively common condition with somehow specific head and face abnormality for each subtype. Early diagnosis results in a better prognosis but pediatricians and primary care providers are not so familiar with these abnormalities while taking 3D CT scan of skull, predisposes the growing brain to harmful effects of radiation. Thus, developing a user-friendly and accurate diagnostic system would be helpful. This study aimed to diagnose simple suture synostosis by using machine learning based methods in digital photographs of child head. Digital photos of 145 craniosynostosis infants, operated in Mofid children hospital (Tehran, Iran) are used in this study. Head border is identified by GrabCut algorithm segmentation method and then several anthropometric indices such as cranial index (CI), cranial vault asymmetry index (CVAI), anterior–midline width ratio (AMWR) and anterior–posterior width ratio (APWR) and left–right height ratio (LRHR) are calculated. Moreover, statistical pattern matching indices (Chi-square (CS), Hu moment invariants (HuMI), absolute difference of white pixels probability (AbsDifWPP) and pixel intensity (PI)) are calculated and compared to anthropometric indices. The classification results for statistical pattern matching indices varied in the range of 85%–92% which is statistically higher than hand crafted indices. Our proposed approach could diagnose and classify common subtypes of single suture craniosynostosis and could help pediatricians and parents in early diagnosis and follow-up of this disorder.
Shaimaa Safaa Ahmed Alwaisi, Maan Nawaf Abbood, Luma Fayeq Jalil
et al.
The amount of data in our world has been rapidly keep growing from time to time. In the era of big data, the efficient processing and analysis of big data using machine learning algorithm is highly required, especially when the data comes in form of streams. There is no doubt that big data has become an important source of information and knowledge in making decision process. Nevertheless, dealing with this kind of data comes with great difficulties; thus, several techniques have been used in analyzing the data in the form of streams. Many techniques have been proposed and studied to handle big data and give decisions based on off-line batch analysis. Today, we need to make a constructive decision based on online streaming data analysis. Many researchers in recent years proposed some different kind of frameworks for processing the big data streaming. In this work, we explore and present in detail some of the recent achievements in big data streaming in term of contributions, benefits, and limitations. As well as some of recent platforms suitable to be used for big data streaming analytics. Moreover, we also highlight several issues that will be faced in big data stream processing. In conclusion, it is hoped that this study will assist the researchers in choosing the best and suitable framework for big data streaming projects.
The Kmeans clustering and spectral clustering are two popular clustering methods for grouping similar data points together according to their similarities. However, the performance of Kmeans clustering might be quite unstable due to the random initialization of the cluster centroids. Generally, spectral clustering methods employ a two-step strategy of spectral embedding and discretization postprocessing to obtain the cluster assignment, which easily lead to far deviation from true discrete solution during the postprocessing process. In this paper, based on the connection between the Kmeans clustering and spectral clustering, we propose a new Kmeans formulation by joint spectral embedding and spectral rotation which is an effective postprocessing approach to perform the discretization, termed KMSR. Further, instead of directly using the dot-product data similarity measure, we make generalization on KMSR by incorporating more advanced data similarity measures and call this generalized model as KMSR-G. An efficient optimization method is derived to solve the KMSR (KMSR-G) model objective whose complexity and convergence are provided. We conduct experiments on extensive benchmark datasets to validate the performance of our proposed models and the experimental results demonstrate that our models perform better than the related methods in most cases.
Frank Phillipson, Peter Langenkamp, Reinder Wolthuis
Bayesian Belief Networks are used in many fields of application. Defining the conditional dependencies via conditional probability tables requires the elicitation of expert belief to fill these tables, which grow very large quickly. In this work, we propose two methods to prepare these tables based on a low number of input parameters using specific structures and one method to generate the table using probability tables of each relation of a child node with a certain parent. These tables can be used further as a starting point for elicitation.
This paper investigates second-order representations in the sense of Kawamura
and Cook for spaces of integrable functions that regularly show up in analysis.
It builds upon prior work about the space of continuous functions on the unit
interval: Kawamura and Cook introduced a representation inducing the right
complexity classes and proved that it is the weakest second-order
representation such that evaluation is polynomial-time computable. The first
part of this paper provides a similar representation for the space of
integrable functions on a bounded subset of Euclidean space: The weakest
representation rendering integration over boxes is polynomial-time computable.
In contrast to the representation of continuous functions, however, this
representation turns out to be discontinuous with respect to both the norm and
the weak topology. The second part modifies the representation to be continuous
and generalizes it to Lp-spaces. The arising representations are proven to be
computably equivalent to the standard representations of these spaces as metric
spaces and to still render integration polynomial-time computable. The family
is extended to cover Sobolev spaces on the unit interval, where less basic
operations like differentiation and some Sobolev embeddings are shown to be
polynomial-time computable. Finally as a further justification quantitative
versions of the Arzel\`a-Ascoli and Fr\'echet-Kolmogorov Theorems are presented
and used to argue that these representations fulfill a minimality condition. To
provide tight bounds for the Fr\'echet-Kolmogorov Theorem, a form of
exponential time computability of the norm of Lp is proven.
Kebutuhan masyarakat atas penggunaan jasa internet semakin meluas dan mencakup berbagai bidang kehidupan hal ini mengakibatkan turut meningkatkannya kejahatan dalam dunia internet. Keamaanan aplikasi yang tidak baik mengakibatkan data penting dan kerahasiaan pengguna menjadi terancam. Hal ini tentu saja merugikan bagi pihak pengguna maupun penyelenggara. Untuk itu perlu dikedepankan metode–metode terbaik dalam keamanan berinternet dan untuk menentukan tindakan penanggulangan yang tepat terlebih dahulu harus dipahami cara kerja dari jenis kejahatan tersebut. Cross Site Scripting merupakan jenis serangan injection terhadap situs dengan mengandalkan kelemahan dari target atau pengguna internet. Penyerang akan memanfaatkan kelemahan pengguna melalui ajakan atau bujukan untuk mengikuti arahan menuju suatu kondisi tertentu yang telah dimuati oleh usaha untuk pencurian data, kerahasiaan atau perintah tertentu melalui code scripting oleh penyerang.Dalam penelitian ini dilakukan Sejumlah Eksperimen untuk mencari pengaruh perlakuan tertentu terhadap yang lain dalam kondisi yang terkendalikan, yaitu dengan menanamkan metacharacter kedalam code scripting sebelum dan sesudah proses berjalan. Tujuan yang hendak dicapai melalui penelitian ini adalah pembuktian terhadap fungsi metacharacter dalam menutup celah kerentanan keamanan aplikasi