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ML-Driven HR System: Candidate Assessment for Enhanced Recruitment Outcomes 3

ML-Driven HR System: Candidate Assessment for Enhanced Recru...

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3rd International Conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Dixon, G.C. Srithanujan, S. Ranaweera, H.D.T.N. Ariyarathne, R.M.P.M. Weerasinghe, K.L. Gamage, N. Department of Information Technology SriLanka Institure of Information Technology New Kandy Rd Malabe Sri Lanka Department of Computer System engineering SriLanka Institure of Information Technology New Kandy Rd Malabe Sri Lanka Department of Computer Science and Software Engineering SriLanka Institure of Information Technology New Kandy Rd Malabe Sri Lanka

ISBN: (纸本)9798331532420

This study developed and implemented a machine learning-driven HR system designed to enhance recruitment outcomes, particularly in online interviews. Challenges such as candidate impersonation, interview question memorization, and deepfake manipulations were addressed. The system incorporated several critical processes utilizing advanced algorithms: pre-interview shortlisting employed Support Vector Machines (SVM) achieving 92% accuracy;cognitive skills assessment utilized adaptive testing methods powered by fine-tuned Large Language Models (LLMs) with 88% accuracy;emotional intelligence evaluation was conducted using deep learning-based emotion detection algorithms attaining 85% accuracy;face verification leveraged Convolutional Neural Networks (CNNs) for identity and deepfake detection with 90% accuracy;online video interviews were analyzed through integrated audio-visual processing techniques;language proficiency testing employed Natural Language Processing (NLP) models reaching 87% accuracy;analysis of thinking patterns was performed using sentiment analysis algorithms with 83% accuracy;and candidate grading was facilitated by ensemble learning methods ensuring consistent and reliable evaluations. The results demonstrated the system’s effectiveness in verifying candidate authenticity and assessing competence, providing a robust solution for modern recruitment challenges. © 2024 IEEE.

关键词： Support vector machines

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Improving students' cognitive abilities in online environment based on neurofeedback 14

Improving students' cognitive abilities in online environmen...

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14th IEEE Global engineering Education Conference, EDUCON 2023

作者： Jamil, Nuraini Lakas, Abderrahmane Belkacem, Abdelkader Nasreddine College of Information Technology United Arab Emirates University Department of Computer Science and Software Engineering United Arab Emirates College of Information Technology United Arab Emirates University Department of Computer and Network Engineering United Arab Emirates

ISBN: (纸本)9798350399431

The brain-computer interface (BCI) and eye-tracking technologies can potentially improve the learning environment in education. Cognitive BCIs can give a deep knowledge of brain functioning, enabling the creation of more effective learning approaches and improving brain-based abilities. This study proposes a neurofeedback strategy based on BCI and eye-tracking to collect factual data (monitoring students' brainwaves and eye movement) and analyze their cognitive capacities during online learning. This study aims to create patterns regarding students' learning behavior based on brain and eye movement responses to learning activities as part of the learning environment. As a result, teachers may adapt to new pedagogical ideas and a more flexible delivery style. © 2023 IEEE.

关键词： Brain

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An Evaluation System for Large Language Models based on Open-Ended Questions 11

An Evaluation System for Large Language Models based on Open...

引用

11th IEEE International Conference on Cyber Security and Cloud Computing, CSCloud 2024

作者： Cao, Zhiyuan Ma, Zeyu Chen, Mingang Computer and Information Engineering College Shanghai Polytechnic University Shanghai Key Laboratory of Computer Software Testing and Evaluating Shanghai Development Center of Computer Software Technology Shanghai China Shanghai Key Laboratory of Computer Software Testing and Evaluating Shanghai Development Center of Computer software Technology Mathematical and Science College Shanghai Normal University Shanghai China Shanghai Key Laboratory of Computer Software Testing and Evaluating Shanghai Development Center of Computer software Technology Shanghai China

ISBN: (纸本)9798350376982

We designed a large language model evaluation system based on open-ended questions. The system accomplished multidimensional evaluation of LLMs using open-ended questions, and it presented evaluation results with evaluation reports. Currently, the evaluation of large-scale language models often exists with two prominent limitations: (1) The evaluation methods are often single-minded, resulting in less credible results. (2) Most evaluations are based on datasets with closed-ended questions, treating generative large language models as discriminative models, which fails to adequately reflect the high output flexibility characteristic of these models. For these two limitations, we proposed an evaluation system for LLMs based on open-ended questions. Our experiments on the adapted open-source datasets demonstrated the effectiveness of this system. © 2024 IEEE.

关键词： Natural language processing systems

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Leveraging Smart Video-Based Learning to Enhance Student Engagement and Collaboration 6

Leveraging Smart Video-Based Learning to Enhance Student Eng...

引用

6th International Conference on Advancements in Computing, ICAC 2024

作者： Serasinghe, Chanukya Perera, Nashali Samarasinghe, Devindu Muthusinghe, Madusha Wijesundara, Malitha Sri Lanka Institute of Information Technology Dept. of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Humanities and Sciences Malabe Sri Lanka

ISBN: (纸本)9798331517878

The COVID-19 pandemic has expedited the shift in education to online learning, which has exposed shortcomings in virtual learning environments' personalization and engagement. This research addresses these issues by developing a web application designed to enhance student engagement and learning efficiency. The system has gamified collaboration features, video chapter segmentation, and personalized video recommendation system. The methodology comprises models for generating chapter titles and for precise video transcription, and machine learning algorithms for tailored suggestions depending on user input. Interactive Questions and Answers modules and real-time synchronized video playback are included. The results demonstrate how the system's personalized content, interactive tests, and teamwork tools increase user engagement and comprehension. The efficacy of personalized material and real-time synchronization in enhancing learning results is discussed. This innovative approach offers a comprehensive solution to the challenges of online education, creating a more immersive and interactive learning experience. © 2024 IEEE.

关键词： Federated learning

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BOAformer: Object Detection Network Based on Background-Object Attention Transformer 6

BOAformer: Object Detection Network Based on Background-Obje...

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6th International Conference on Communications, information System and computer engineering, CISCE 2024

作者： Yang, Yong Liang, Chenbin Huang, Shuying Wang, Xiaozheng School of Computer Science and Technology Tiangong University Tianjin China School of Electronics and Information Engineering Tiangong University Tianjin China School of Software Tiangong University Tianjin China School of Control Science and Engineering Tiangong University Tianjin China

ISBN: (纸本)9798350353174

At present, deep learning has achieved great success in the field of object detection. To ensure that positive samples in the image are not missed, most deep-learning object detection methods set many prediction boxes and use the same prediction operation on them. Although these methods can obtain higher predictive values for high certainty objects, they may identify objects with lower certainty, such as small or fuzzy objects, as negative samples or miss them. To address this issue, this paper presents an object detection network based on background-object attention transformer (BOAformer), which improves the accuracy of object detection by establishing the relationship between background and object features. Firstly, the existing classification model is employed to obtain scores of object prediction boxes. On this basis, a scoring mechanism is designed to filter out high scoring object prediction boxes to reduce the computational complexity of the later network. Then, to improve the accuracy of object detection, BOAformer is constructed, which obtains auxiliary classification values of objects by learning the relationship between background and object features, and adds them to the initial detection results. Finally, many experiments are conducted to verify the effectiveness of BOAformer. The experimental results show that the designed network improves the accuracy of object detection and can detect small or lost objects in the background. © 2024 IEEE.

关键词： Prediction models

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SCPSN: Spectral Clustering-based Pyramid Super-resolution Network for Hyperspectral Images 24

SCPSN: Spectral Clustering-based Pyramid Super-resolution Ne...

引用

32nd ACM International Conference on Multimedia, MM 2024

作者： Yang, Yong Zhao, Aoqi Huang, Shuying Wang, Xiaozheng Fan, Yajing School of Computer Science and Technology Tiangong University Tianjin China School of Software Tiangong University Tianjin China School of Control Science and Engineering Tiangong University Tianjin China School of Electronics and Information Engineering Tiangong University Tianjin China

ISBN: (纸本)9798400706868

Single hyperspectral image super-resolution aims to reconstruct a high-resolution hyperspectral image (HRHSI) from an observed low resolution hyperspectral image (LRHSI). Most current methods combine CNN and Transformer structures to directly extract features of all channels in LRHSI for image reconstruction, but they do not consider the interference of redundant information in adjacent bands, resulting in spectral and spatial distortions in the reconstruction results and an increase in model computational complexity. To address this issue, this paper proposes a spectral clustering-based pyramid super-resolution network (SCPSN) to progressively reconstruct HRHSI at different scales. In each image reconstruction layer, a clustering super-resolution block (CSRB) consisting of spectral clustering block (SCB), patch non local attention block (PNAB), and dynamic fusion block (DFB) is designed to achieve the reconstruction of detail features. Specifically, for the high correlation between adjacent spectral bands in LRHSI, a SCB is first constructed to achieve clustering of spectral channels and filtering of hyperchannels. This can reduce the interference of redundant spectral information and the computational complexity of the model. Then, by utilizing the non-local similarity of features within the channel, a patch non-local attention block (PNAB) is constructed to enhance the features of hyperchannels. Next, a dynamic fusion block (DFB) is designed to reconstruct the features of all channels in LRHSI by establishing correlations between enhanced hyperchannels and other channels. Finally, the reconstructed channels are upsampled and added to the corresponding channels to obtain the reconstructed HRHSI. Extensive experiments validate that the performance of SCPSN is superior to that of some other state-of-the-art (SOTA) HSSR methods in terms of visual effects and quantitative metrics. In addition, our model does not require training on large-scale datasets compared to oth

关键词： Image reconstruction

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Web Application to predict Skin Disease: An Approach Using Deep Learning Algorithm 6

Web Application to predict Skin Disease: An Approach Using D...

引用

6th International Congress on Human-computer Interaction, Optimization and Robotic Applications, HORA 2024

作者： Thayalan, Eelanila Arumugavel, Ayingaran Ganeshan, Sharujan Dalaxshan, Makenthiran Fernando, Harinda Rajapakse, Kaushalya Department of Information Technology Faculty of computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Computer Systems Engineering Faculty of computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Computer Science and Software Engineering Faculty of computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350394634

The Disease Prediction System revolutionizes healthcare with advanced machine learning techniques for early detection of skin diseases, notably focusing on skin cancer. Through image processing and Transfer Learning, it excels in identifying potential malignancies, enabling timely interventions. Its capabilities extend to benign tumors and growth disorders, utilizing the ResNet50 Model for precise identification. Additionally, it predicts skin reactions associated with various dermatological conditions, such as Urticaria Hives and Warts, leveraging the efficientnetB0 Model. Integration of the VGG16 Model enhances diagnostic accuracy for inflammatory skin conditions. This holistic approach prioritizes patient-centric care, leveraging diverse datasets and intricate pattern recognition in medical images. The system's proactive nature embodies personalized solutions for early detection, timely intervention, and improved patient outcomes. Its versatility and accuracy underscore itstransformative potential in healthcare delivery. By harnessing diverse datasets and recognizing intricate patterns within medical images, it heralds a new era of personalized healthcare solutions. In essence, the Disease Prediction System exemplifies the transformative potential of machine learning in healthcare, ensuring the highest standards of diagnostic accuracy and efficacy, while prioritizing patient well-being and quality oflife. © 2024 IEEE.

关键词： Deep learning

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MOFC: Network Embedding Based on Multi-Orders Friends Contribution for User Identification 6

MOFC: Network Embedding Based on Multi-Orders Friends Contri...

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6th International Conference on Algorithms, Computing and Artificial Intelligence, ACAI 2023

作者： Zhang, Jiajing Xu, Neng Chen, Jinlan Ma, Tinghuai School of Electronics and Information Engineering Anhui Jianzhu University China School of Mechanical and Electrical Engineering Anhui Jianzhu University China School of Computer and Software Nanjing University of Information Science & Technology China

ISBN: (纸本)9798400709203

Different social networks are independent of each other, and identifying the same person in different social networks is of great significance for cross-network information dissemination and the construction of a complete portrait of users. Existing methods for user identification via network embedding ignore the different contributions of neighbors and only consider low-order friend relationships. Aiming at these shortcomings, an algorithm of Network Embedding based on Multi-Orders Friends Contribution for User Identification (MOFC) is proposed. Firstly, the difference contribution degree of nodes to their friends is used to assign different weights to the edges between the nodes and their friends. Secondly, the embedding representation vectors of multi-orders friend contribution are used to describe the different orders friend relations of the nodes, and then the representation vectors of different orders are merged to more comprehensively represent network features. Finally, the fusion representation vectors are used as input sample of a neural network to train and build a user identification model. Experiments show that the evaluation indicators of the model have been significantly improved compared with several advanced methods. Compared with the newer and better the CSN_LINE, the accuracy rate of MOFC can be increased by 4.83%, the precision rate can be increased by 6.77%, the recall rate can be increased by 5.49%, the F1-score can be increased by 5.43%. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Network embeddings

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Enhanced Brain Tumor Detection Using Transfer Learning with Fine-Tuned Efficientnet-B3 Blocks and Graph-Based Spatial Attention Capsule Networks 21

Enhanced Brain Tumor Detection Using Transfer Learning with ...

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21st International computer Conference on Wavelet Active Media Technology and information Processing, ICCWAMTIP 2024

作者： Aggrey, Esther Stacy E.B Zhen, Qin Kodjiku, Seth Larweh Fiasem, Delali Linda Sey, Collins Ejiyi, Chukwuebuka Joseph Ntia, Nsikakabasi Williamson School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China School of Computer Science and Technology Zhejiang Gongshang University Hangzhou China College of Nuclear Technology and Automation Engineering Chengdu University of Technology Chengdu China

ISBN: (纸本)9798331519254

Brain tumors are critical medical conditions that require timely and accurate diagnosis to improve survival outcomes. This research proposes a cutting-edge framework for brain tumor classification, combining fine-tuned EfficientNet-B3 blocks with graph-based spatial attention capsule networks. By addressing challenges such as limited data availability, overfitting, and the complex nature of tumor structures, the framework employs partial transfer learning, advanced feature extraction, and attention mechanisms to effectively model spatial relationships in MRI images. Evaluated on a dataset of 3,064 contrast-enhanced brain MRI images, the model demonstrated exceptional performance, achieving 99.85% classification accuracy and notable improvements in precision, recall, and F1 scores for glioma, meningioma, and pituitary tumours. The use of advanced data augmentation techniques significantly improved the model's ability to generalize, while its computational efficiency makes it well-suited for clinical diagnostic applications. © 2024 IEEE.

关键词： Dynamic contrast enhanced MRI

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Metamorphic Relation Patterns for Metamorphic Testing, Exploration and Robustness

Metamorphic Relation Patterns for Metamorphic Testing, Explo...

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作者： Ying, Zhihao Towey, Dave Bellotti, Anthony Chua, Caslon Quan Zhou, Zhi School of Computer Science University of Nottingham Ningbo China Zhejiang Ningbo China Department of Computer Science and Software Engineering Swinburne University of Technology HawthornVIC Australia School of Computing and Information Technology University of Wollongong WollongongNSW Australia

Metamorphic testing (MT) is an effective software quality assurance method;it uses metamorphic relations (MRs) to examine the inputs and outputs of multiple test cases. Metamorphic exploration (ME) and metamorphic robustness testing (MRT) are advanced additions to the MT literature: ME can enable better understanding, and thus testing, of systems;MRT can test software robustness without an oracle. Metamorphic relation patterns (MRPs) are abstractions or templates for multiple concrete MRs. The performance of MT/ME/MRT relies on the quality of MRs, but the identification of MRs is often a manual task requiring some knowledge of MRPs, creative thinking and a good understanding of the system. In other words, the effective identification of MRs remains a challenge and requires further study. In this paper, we propose formal definitions to describe the relationship between multiple MRPs in order to classify them. We also identify a series of new MRPs and propose a new MT framework that can guide the identification and application of MRPs. Based on our definitions, we present the notion of MRP family trees and further propose two trees by classifying previously published MRPs and our proposed MRPs, in order to make it easier for users to find their target MRPs for reuse, reference or inference. Finally, we report on case studies using the MRPs and our framework to guide the identification of MRs for MT/ME/MRT and successfully detect multiple violations. © 2025 John Wiley & Sons Ltd.

关键词： software testing

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