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Breaking Barriers: Investigating the Sense of Belonging Among Women and Non-Binary Students in Software engineering 46

Breaking Barriers: Investigating the Sense of Belonging Amon...

引用

46th International Conference on Software engineering: Software engineering Education and Training, ICSE-SEET 2024

作者： Boman, Lina Andersson, Jonatan de Oliveira Neto, Francisco Gomes University of Gothenburg Dept. of Computer Science and Engineering Gothenburg Sweden Chalmers and the University of Gothenburg Dept. of Computer Science and Engineering Gothenburg Sweden

ISBN: (纸本)9798400704987

Women in computing were among the first programmers in the early 20th century and were substantial contributors to the industry. Today, men dominate the software engineering industry. Research and data show that women are far less likely to pursue a career in this industry, and those that do are less likely than men to stay in it. Reasons for women and other underrepresented minorities to leave the industry are a lack of opportunities for growth and advancement, unfair treatment and workplace culture. This research explores how the potential to cultivate or uphold an industry unfavourable to women and non-binary individuals manifests in software engineering education at the university level. For this purpose, the study includes surveys and interviews. We use gender name perception as a survey instrument, and the results show small differences in perceptions of software engineering students based on their gender. Particularly, the survey respondents anchor the values of the male software engineer (Hans) to a variety of technical and non-technical skills, while the same description for a female software engineer (Hanna) is anchored mainly by her managerial skills. With interviews with women and non-binary students, we gain insight on the main barriers to their sense of ambient belonging. The collected data shows that some known barriers from the literature such as tokenism, and stereotype threat, do still exist. However, we find positive factors such as role models and encouragement that strengthen the sense of belonging among these students. © 2024 Copyright held by the owner/author(s).

关键词： Students

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NLP Powered Restaurant Chatbot for Exceptional Customer Engagement 2

NLP Powered Restaurant Chatbot for Exceptional Customer Enga...

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2nd International Conference on Advancement in Computation and computer Technologies, InCACCT 2024

作者： Goyal, Diya Guru Prasad, M.S. Garg, Ishika Tewari, Shashank Pandu N, K. Asha, P.N. Dept. of Computer Science and Engineering Dehradun India Srinivas University Institute of Engineering & Technology Dept. of Computer Science and Engineering Surathkal India REVA University School of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350371314

Technology is very volatile in nature, in a way that it keeps on changing daily. Restaurants are no different in the effects of this changing scenario. The primary objective of this research paper is to explore the historical trajectory of restaurants, analyze the current trends shaping the industry, and delve into the potential of chatbot integration to overcome existing challenges. Chatbots can play a pivotal role in enhancing the performance of businesses as per the research done. An integrated Dialog Flow application has been proposed, incorporating Rule-based grammar matching and M.L matching algorithms. This proposed work is trying to make NLP-based chatbots which may expedite the endeavor of businesses to produce and become profitable. © 2024 IEEE.

关键词： Python

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Implementation of Blockchain and IPFS to safeguard evidentiary data

Implementation of Blockchain and IPFS to safeguard evidentia...

引用

2024 International Conference on Knowledge engineering and Communication systems, ICKECS 2024

作者： Srivastava, Supriya Kaur, GeetKiran Himank Singla, Sanjay Chandigarh University Dept. of Computer Science & Engineering Mohali140413 India Chandigarh University Dept. of Computer Science & Engineering Gharuan India

ISBN: (纸本)9798350359688

Nowadays due to increasing cases of evidence alteration the need of a robust evidence protection system is increasing rapidly. This paper presents an innovative solution that combines the InterPlanetary File System (IPFS) with blockchain technology to enhance the security, immutability, and transparency of evidential data obtained from crime *** proposed system initiates by utilizing IPFS, a decentralized and distributed file storage protocol, to securely store multimedia evidence, such as photographs. The content-addressed nature of IPFS guarantees tamper-resistant storage, making it an ideal choice for preserving the integrity of sensitive evidentiary materials. The IPFS addresses obtained from IPFS are then embedded within the blockchain, establishing a decentralized and verifiable link between the evidence and its storage location. We use multi-signature wallet to upload the data on blockchain so that we can make it more secure and monitored about who is uploading evidence state data. To verify links and integrity of evidence usage we set up a system of periodical updates of state of evidence whenever accessed. These updates serve as a mechanism to record the state of the evidence at different points in time, adding an additional layer of reliability and accountability. © 2024 IEEE.

关键词： Blockchain

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Automated Identification of Pneumonia Employing Convolution Neural Networks and the VGG-16 Model 1

Automated Identification of Pneumonia Employing Convolution ...

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1st International Conference on Computing, sciences and Communications, ICCSC 2024

作者： Srivastava, Anamika Gupta, Anuj Kaushik, Deepti Gupta, Neha Chadha, Shikha Chauhan, Rosey Noida Institute Of Engineering Technology Dept. of Computer Science & Engineering Gerater Noida India Sharda Unviersity Dept. of Computer Science & Engineering Gerater Noida India School of Engineering and Technology IIMT University Dept. of Computer Science and Engineering U.P Meerut India ABES Engineering College Dept. of Computer Science UP Ghaziabad India

ISBN: (纸本)9798350353648

Pneumonia, an inflammatory lung disease, impacts the small air sacs known as alveoli and is typically caused by the bacteria Streptococcus pneumonia. Symptoms commonly include a combination of productive or dry cough, chest pains, difficulty breathing, and fever. In India, where Covid-19 is prevalent, community-acquired pneumonia (CAP) poses a significant risk factor for mortality, accounting for one-third of fatalities, as reported by the World Health Organization (WHO).Diagnosing and predicting the outcome of pneumonia typically involves examining chest imaging radiographs of patients exhibiting clinically similar symptoms (e.g., fever, dyspnea, cough, and sputum production). Therefore, the development of an automated method for pneumonia detection could offer valuable advantages, particularly in remote regions, facilitating prompt treatment. Deep learning (DL) models enable the analysis of various chest radiographs, with Convolutional Neural Networks (CNNs) receiving considerable attention in disease classification. Leveraging features learned. © 2024 IEEE.

关键词： Convolutional neural networks

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Advanced Decision-Making for Autonomous Vehicles Utilizing Deep Q Networks

Advanced Decision-Making for Autonomous Vehicles Utilizing D...

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2025 International Conference on Emerging Technologies for Intelligent systems, ETIS 2025

作者： Britto, Akash Ij Abhishek, S. Dhanush, S. Salim, Soja Soniya, B. Sct College of Engineering Dept. of Computer Science Engineering Thiruvananthapuram India

ISBN: (纸本)9798331507541

Autonomous vehicles are poised to revolutionize the transportation industry by offering safer and more efficient navigation in dynamic environments. A critical challenge is managing interactions with other vehicles, pedestrians, and road obstacles, which complicates framing autonomous driving as a supervised learning problem. This study leverages Deep Q-Networks (DQN), a reinforcement learning technique, to control steering, throttle, and braking in autonomous vehicles. The DQN approach enables vehicles to autonomously learn and improve driving strategies, thereby enhancing their ability to navigate complex traffic scenarios effectively. Our objective is to train a model using a Deep Reinforcement Learning (DRL) algorithm, guiding the vehicle to adhere to a predetermined itinerary while adapting to real-time traffic conditions. This work emphasizes the potential of reinforcement learning for enabling continuous learning and adaptation in complex traffic scenarios, laying the groundwork for more reliable and adaptive autonomous driving systems. © 2025 IEEE.

关键词： Deep reinforcement learning

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A Machine Learning Approach for Predicting Personality Types from Text Patterns Using Natural Language Processing(NLP) 8

A Machine Learning Approach for Predicting Personality Types...

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8th International Conference on Electronics, Materials engineering and Nano-Technology, IEMENTech 2025

作者： Ganguly, Kinshuk Saha, Subhajit Mondal, Anupam Dey, Monalisa Institute of Engineering & Management Dept. of Computer Science & Engineering Kolkata India

ISBN: (纸本)9798331520762

This study explores the use of Natural Language Processing (NLP) approach to predict Myers-Briggs Type Indicator (MBTI) personality types from Twitter texts. Initially, we approached the problem as a 16-class classification task, but encountered significant challenges due to the lack of instances representing certain class labels adequately. To address this, we decomposed the MBTI types into four dimensions and trained individual classifiers for each dimension. Leveraging the best performing classifiers, we constructed a stacking ensemble model to predict personality classes corresponding to each dimension. The results demonstrate improved accuracy compared to the initial 16-class classification approach. Additionally, the performance of ensemble model is found to be better than the individual classifiers. Thus, ensemble approach paired with the separate dimension based classification method can be used for effective prediction of personality types for psychological profiling. © 2025 IEEE.

关键词： Natural language processing systems

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A Comprehensive Analysis on Skin Cancer Classification Using Transfer Learning 3

A Comprehensive Analysis on Skin Cancer Classification Using...

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3rd International Conference on Advancement in Electrical and Electronic engineering, ICAEEE 2024

作者： Renu, Israt Zerin Haque, Md. Momenul Paul, Subrata Kumer Mou, Mahmuda Shirin Rahman, Md Nahid Gupta, Sudipta Sen Paul, Rakhi Rani Rabindra Maitree University Dept. of Computer Science and Engineering Kushtia7000 Bangladesh Qadirabad Dept. of Computer Science and Engineering Dayarampur Natore6431 Bangladesh University of Rajshahi Dept. of Computer Science and Engineering Rajshahi6205 Bangladesh

ISBN: (纸本)9798350388282

Skin cancer, a prevalent and potentially life-threatening disease, necessitates accurate and timely diagnosis for effective treatment. In addressing this critical healthcare challenge, this research proposes a novel approach to skin cancer classification. The study identifies the need for enhanced diagnostic accuracy and introduces a solution by integrating the efficiency of transfer learning, specifically utilizing the EfficientNet V2 architecture. The proposed methodology leverages pre-trained convolutional neural networks (CNNs) to extract high-level features from dermatological images, which are subsequently input into a sophisticated classifier, forming a robust framework for precise skin cancer classification. The research employs a diverse dataset comprising 3297 images sourced from the publicly available Skin Cancer ISIC archive dataset, ensuring comprehensive coverage of benign and malignant skin lesions. During the training phase, the proposed EfficientNet V2 transfer learning approach outperforms conventional methods, achieving an impressive accuracy of 84%, surpassing both Inception V3 (82%) and a generic CNN model (81%). Interpretability analysis is conducted to elucidate the decision-making process of the EfficientNet V2 model, providing insights into the key features influencing its predictions. This transparency is crucial for building trust in the model's recommendations and facilitating its seamless integration into clinical workflows. The EfficientNet V2 transfer learning approach emerges as a promising and effective solution for advancing skin cancer classification systems, contributing significantly to the ongoing efforts to improve diagnostic accuracy in dermatology. © 2024 IEEE.

关键词： Decision making

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A Transformer-Based Model for Image Caption Generation with Memory Enhancement

A Transformer-Based Model for Image Caption Generation with ...

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2024 IEEE International Women in engineering (WIE) Conference on Electrical and computer engineering, WIECON-ECE 2024

作者： Ome, Shadika Afroze Azhar, Tanvir Asaduzzaman East Delta University Dept. of Computer Science & Engineering Chattogram Bangladesh Chittagong University of Engineering & Technology Dept. of Computer Science & Engineering Chattogram Bangladesh

ISBN: (纸本)9798331535476

This study proposes a convolution-free transformer-based method for generating accurate descriptions of images. A Vision Transformer is utilized as the primary encoder, replacing traditional CNN, and a Meshed Memory Transformer is integrated for generating captions. The Vision Transformer splits images into fixed-size patches for extracting visual features, which are further processed by a memory-augmented encoder to integrate prior knowledge. It ensures key features are preserved and reused and the meshed decoder combines the text embedding with the outputs from each encoding layer to generate more contextually rich and accurate captions compared to conventional approaches. The model's performance is evaluated on the Flickr30K and the MSCOCO datasets, demonstrating that the Meshed Memory architecture in our approach improves both the image encoding and caption generation step. Our model outperforms the conventional 'CNN-LSTM' models and 'CNN-Transformer' methods, achieving a 1.02% improvement in METEOR and 1.31% in SPICE on the MSCOCO dataset. Additionally, on the Flickr30k dataset, our approach exhibits superior performance across most metrics, notably a 6.37% increase in the METEOR score. © 2024 IEEE.

关键词： Encoding (symbols)

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Bidirectional Multi-Step Domain Generalization for Visible-Infrared Person Re-Identification

Bidirectional Multi-Step Domain Generalization for Visible-I...

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2025 IEEE/CVF Winter Conference on Applications of computer Vision, WACV 2025

作者： Alehdaghi, Mahdi Shamsolmoali, Pourya Cruz, Rafael M. O. Granger, Eric Livia Ills Dept. of Systems Engineering Ets Montreal Canada University of York Dept. of Computer Science United Kingdom

ISBN: (纸本)9798331510831

A key challenge in visible-infrared person re-identification (V-I ReID) is training a backbone model capable of effectively addressing the significant discrepancies across modalities. State-of-the-art methods that generate a single intermediate bridging domain are often less effective, as this generated domain may not adequately capture sufficient common discriminant information. This paper introduces Bidirectional Multi-step Domain Generalization (BMDG), a novel approach for unifying feature representations across diverse modalities. BMDG creates multiple virtual intermediate domains by learning and aligning body part features extracted from both I and V modalities. In particular, our method aims to minimize the cross-modal gap in two steps. First, BMDG aligns modalities in the feature space by learning shared and modality-invariant body part prototypes from V and I images. Then, it generalizes the feature representation by applying bidirectional multi-step learning, which progressively refines feature representations in each step and incorporates more prototypes from both modalities. Based on these prototypes, multiple bridging steps enhance the feature representation. Experiments11Our code is available at: ***/BMDG conducted on V-I ReID datasets indicate that our BMDG approach can outperform state-of-the-art part-based and intermediate generation methods, and can be integrated into other part-based methods to enhance their V-I ReID performance. © 2025 IEEE.

关键词： Multi-task learning

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Rethinking Machine Ethics - Can LLMs Perform Moral Reasoning through the Lens of Moral Theories?

Rethinking Machine Ethics - Can LLMs Perform Moral Reasoning...

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2024 Findings of the Association for Computational Linguistics: NAACL 2024

作者： Zhou, Jingyan Hu, Minda Li, Junan Zhang, Xiaoying Wu, Xixin King, Irwin Meng, Helen Dept. of Systems Engineering & Engineering Management The Chinese University of Hong Kong Hong Kong Dept. of Computer Science & Engineering The Chinese University of Hong Kong Hong Kong

ISBN: (纸本)9798891761193

Making moral judgments is an essential step toward developing ethical AI systems. Prevalent approaches are mostly implemented in a bottom-up manner, which uses a large set of annotated data to train models based on crowd-sourced opinions about morality. These approaches have been criticized for overgeneralizing the moral stances of a limited group of annotators and lacking explainability. This work proposes a flexible top-down framework to steer (Large) Language Models (LMs) to perform moral reasoning with well-established moral theories from interdisciplinary research. The theory-guided top-down framework can incorporate various moral theories. Our experiments demonstrate the effectiveness of the proposed framework on datasets derived from moral theories. Furthermore, we show the alignment between different moral theories and existing morality datasets. Our analysis exhibits the potential and flaws in existing resources (models and datasets) in developing explainable moral judgment-making systems. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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