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Difference in Software Development: Novice and Expert

Difference in Software Development: Novice and Expert

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Big data, Cloud computing, data Science & Engineering (BCD)

作者： Simon Xu School of Computer Science and Technology Algoma University Canada

ISBN: (数字)9798350394191

ISBN: (纸本)9798350394207

In this talk, we explore the profound distinctions in the cognitive processes and methodologies between experienced and beginner software developers. This presentation will delve into current research findings, highlighting how experts' approaches to problem-solving, design, code comprehension, and implementation contrast with those of novices. We will discuss empirical methods and cognitive models that have been instrumental in uncovering these differences. Additionally, the speech will consider the implications of these insights for educational practices and tool development, offering a vision for future research directions that could further bridge the gap between novice and expert programmers.

关键词： Computer science Software development management Software cognitive processes Big data Problem-solving Instruments data science data mining Codes

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Machine Learning for Talent Analytics: Unveiling Competency Indicators in Live Streamer

Machine Learning for Talent Analytics: Unveiling Competency ...

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cognitive computing and complex data (ICCD), international conference on the

作者： Caisheng Liao Yuki Todo Minyu Wang Yingwei Pan Division of Electrical Information and Communication Engineering Kanazawa University Kanazawa Japan Faculty of Electrical Information and Communication Engineering Kanazawa University Kanazawa Japan College of Traditional Chinese Medicine Fujian University of Traditional Chinese Medicine Fuzhou China School of Hotel Tourism and Catering Guangdong Vocational College of Hotel Management Dongguan China

ISBN: (数字)9798350352894

ISBN: (纸本)9798350352900

The burgeoning live-streaming industry has become a transformative force in business, prompting a reevaluation of competency requirements for live streamers. However, there is still a lack of studies that focus on the objective assessment of live streamer competency indicators. This study aims to determine the key competencies of live streamers by utilizing machine learning algorithms for topic modeling. We identified seven core competency indicators for live streamers from 8,743 job advertisements from China's leading job search website, Kanzhun Limited. The top three competencies are "Interaction and Adaptability", "Marketing and Planning Skills", and "Talents and Specialties". The results also indicate that large companies prioritize operational and review abilities, while small companies focus on resource development. Moreover, part-time roles value talents and specialties, whereas full-time positions emphasize marketing and planning skills alongside operational and review abilities. This research provides theoretical insights into competency modeling within the live-streaming context and offers practical implications for talent acquisition and development in the industry. The findings suggest that organizations should tailor their recruitment strategies to align with the specific competencies required for different roles and organizational sizes.

关键词： Industries Machine learning algorithms Reviews Force Companies Machine learning data models Planning Recruitment Context modeling

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complex Semantic Tabular Interpretation using SDD-Gen

Complex Semantic Tabular Interpretation using SDD-Gen

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international conference on Semantic computing

作者： Matthew Johnson Jeanette A. Stingone Sofia Bengoa James Masters Deborah L. McGuinness Tetherless World Constellation Rensselaer Polytechnic Institute Troy USA Department of Epidemiology Columbia University New York USA Department of Environmental Medicine and Public Health Icahn School of Medicine at Mount Sinai New York USA Scientific Computing and Data Icahn School of Medicine at Mount Sinai New York USA Dept. of Computer Science Tetherless World Constellation Rensselaer Polytechnic Institute Troy USA

Knowledge graphs have become an essential technology for both businesses and governments. They enable a wide variety of critical tasks, such as aligning diverse datasets, improving the capabilities of search engines, supporting error checking, and generating explanations using inference engines. However, populating, augmenting, and/or validating a knowledge graph can be challenging because developers need domain knowledge to understand their data and experience in ontology modeling to align concepts properly as well as experience with conflict detection and truth maintenance tools. Previous efforts have explored automatically integrating simple tabular data into knowledge graphs to lower the barrier to entry. These methods heavily rely on named entity overlap and require that tables are similar to relational tables in third normal form. While these methods have been successful under competition, these limitations make them impractical for general usage. In this paper, we introduce the semantic data dictionary generator (SDD-Gen), an algorithm that aligns complex tabular data to ontological terms for knowledge graph generation. Our methodology leverages context information from data dictionaries to make alignments, enabling us to align complex tables with few named entities and multiple subject columns.

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cognitive computing in English Language Classrooms

Cognitive Computing in English Language Classrooms

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Cybernetics, Cognition and Machine Learning Applications (ICCCMLA), ieee international conference on

作者： Iliyas Ali Shaik Ansar Hussain Shaik P Athahar B Rasheeda Begum School of Technology The Apollo University Chittoor India Department of Humanities & Sciences Annamacharya University Rajampeta India Department of English & Foreign Languages Madanapalle Institute of Technology and Sciences Madanapalle India Department of Humanities and Science KSRMCE Kadapa Andhra Pradesh India

ISBN: (数字)9798331505790

ISBN: (纸本)9798331505806

A paradigm shift has occurred in the field of English language instruction with the advent of cognitive and machine learning technologies. This study delves into how cognitive science and machine learning come together to create cutting-edge software and language-learning tools for English language courses. It explores the theoretical underpinnings of language learning cognition, surveys the state-of-the-art machine learning tools used in language education, and then looks at how these tools are put to use in language classrooms and on language learning software.

关键词： Training Surveys Collaboration data protection Machine learning Learning (artificial intelligence) Software Cognition cognitive science cognitive systems

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Discrete Randomized Smoothing Meets Quantum computing

Discrete Randomized Smoothing Meets Quantum Computing

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Quantum computing and Engineering (QCE), ieee international conference on

作者： Tom Wollschläger Aman Saxena Nicola Franco Jeanette Miriam Lorenz Stephan Günnemann School of Computation Information & Technology Technical Univ. of Munich Germany Fraunhofer Institute for Cognitive Systems IKS Munich Germany

ISBN: (数字)9798331541378

ISBN: (纸本)9798331541385

Breakthroughs in machine learning (ML) and advances in quantum computing (QC) drive the interdisciplinary field of quantum machine learning to new levels. However, due to the susceptibility of ML models to adversarial attacks, practical use raises safety-critical concerns. Existing Randomized Smoothing (RS) certification methods for classical machine learning models are computationally intensive. In this paper, we propose the combination of QC and the concept of discrete randomized smoothing to speed up the stochastic certification of ML models for discrete data. We show how to encode all the perturbations of the input binary data in superposition and use Quantum Amplitude Estimation (QAE) to obtain a quadratic reduction in the number of calls to the model that are required compared to traditional randomized smoothing techniques. In addition, we propose a new binary threat model to allow for an extensive evaluation of our approach on images, graphs, and text.

关键词： Threat modeling Quantum computing Smoothing methods Computational modeling Stochastic processes Machine learning data models Robustness Amplitude estimation Certification

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Message from the Technical Program Chairs: ISES 2024

Message from the Technical Program Chairs: ISES 2024

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ieee international Symposium on Smart Electronic Systems (iSES)

ISBN: (数字)9798331533229

ISBN: (纸本)9798331533236

It is a great pleasure for us to welcome you to the 1 01h ieee international Symposium on SmartElectronic Systems (ieee iSES, https://***) in New Delhi, the capital city of India. Theprimary objective of iSES 2024 is to provide a platform for both hardware and software researchersto interact under one umbrella for further development of smart electronic systems. The symposiumaddresses key technologies and enabling technologies for Smart Systems as well as solutions forenergy, agriculture, health, biomedical, industry, housing, sustainability, social cohesion, andmobility. Smart systems entail architectures, systems software, and algorithms as key ***, other technologies such as the internet, data analytics, security, and cognitive sciencejoin as enabling technologies. Smart systems adapt to changing conditions such as context andapplication and may possess their awareness and cater to the needs of efficient sensing, storage,software, and computing. Big data which are large, complex data sets, are now a part of the Internetworld. The storage and processing needs of enormous amounts of structured and unstructured dataare becoming increasingly challenging. At the same time, the Internet of Things (loT) and cyberphysicalsystems (CPS) have been evolving with simultaneous development of hardware andsoftware and span across everyday consumer electronics. The performance and efficiency of thevarious generations of computing and information processing systems depend on advances inhardware and software.

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Quadratic Advantage with Quantum Randomized Smoothing Applied to Time-Series Analysis

Quadratic Advantage with Quantum Randomized Smoothing Applie...

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Quantum computing and Engineering (QCE), ieee international conference on

作者： Nicola Franco Marie Kempkes Jakob Spiegelberg Jeanette Miriam Lorenz Fraunhofer Institute for Cognitive Systems IKS Munich Germany Volkswagen Group Innovation Volkswagen AG Wolfsburg Germany

ISBN: (数字)9798331541378

ISBN: (纸本)9798331541385

As quantum machine learning continues to develop at a rapid pace, the importance of ensuring the robustness and efficiency of quantum algorithms cannot be overstated. Our research presents an analysis of quantum randomized smoothing, how data encoding and perturbation modeling approaches can be matched to achieve meaningful robustness certificates. By utilizing an innovative approach integrating Grover's algorithm, a quadratic sampling advantage over classical randomized smoothing is achieved. This strategy necessitates a basis state encoding, thus restricting the space of meaningful perturbations. We show how constrained k-distant Hamming weight perturbations are a suitable noise distribution here, and elucidate how they can be constructed on a quantum computer. The efficacy of the proposed framework is demonstrated on a time series classification task employing a Bag-of-Words pre-processing solution. The advantage of quadratic sample reduction is recovered especially in the regime with large number of samples. This may allow quantum computers to efficiently scale randomized smoothing to more complex tasks beyond the reach of classical methods.

关键词： Smoothing methods Quantum algorithm Perturbation methods Time series analysis Qubit Noise Phase estimation Encoding Robustness Standards

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Remote Sensing Identification and Evaluation Method for Oyster Resources in Marine Pasture Rafting

Remote Sensing Identification and Evaluation Method for Oyst...

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cognitive computing and complex data (ICCD), international conference on the

作者： Guangping Zhang Chenxiao Zhang Jinzhan Wei School of Computer Science and Engineering Guilin University of Aerospace Technology Guilin China College of Food Engineering Beibu Gulf University Qinzhou China College of Electronic Information and Automation Guilin University of Aerospace Technology Guilin China

ISBN: (数字)9798350352894

ISBN: (纸本)9798350352900

In response to the problems of large sea areas, scattered breeding points and areas, time-consuming and labor-intensive manual statistical methods, and low accuracy in current marine ranch aquaculture, a set of artificial intelligence remote sensing recognition and evaluation methods is proposed to quickly and accurately evaluate the resources of oysters raised on large, scattered, and small target marine pasture rafts. Based on machine learning object recognition technology, a detection box generation method for rotation angle elements is proposed, and a pixel based intersection and union method is used to calculate the PIoU loss value of the target. The OysterRE model is proposed to comprehensively calculate and evaluate the resources of oyster farming in ocean pastures. The total resources of oyster farming in ocean pastures are calculated by multiplying the number of oyster farms by the biomass of individual oyster farms. The results indicate that: 1) A method for generating detection boxes based on rotation angle elements has been proposed, which enables the detection boxes to better match irregular targets under complex backgrounds; 2) Propose a pixel based intersection union method to calculate the IoU of the target, making the loss value sensitive to the size, position, and rotation angle of the annotated box, which is more conducive to non-maximum suppression control of the selection of detection boxes; 3) The AVG Recall of the OysterRE model is 0.73, which is 0.12 higher than the average recall of the YOLOv5 model of 0.61. This shows that the proposed model has better recognition performance for oyster farming rafts and consistent with the actual field investigation.

关键词： YOLO Target recognition Biological system modeling Computational modeling Oceans data models Biomass Remote sensing Farming Aquaculture

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LLM4HIN: Discovering Meta-path with Large Language Model for Reasoning on complex Heterogeneous Information Networks

LLM4HIN: Discovering Meta-path with Large Language Model for...

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ieee/ACM Int'l conference on & Int'l conference on Cyber, Physical and Social computing (CPSCom) Green computing and Communications (GreenCom)

作者： Haoxiang Cheng Shixuan Liu Changjun Fan Kuihua Huang Hua He Xianghan Wang Zhong Liu Laboratory for Big Data and Decision National University of Defense Technology Changsha China

ISBN: (数字)9798350351637

ISBN: (纸本)9798350351644

Heterogeneous Information Networks (HINs) are complex structures hosting various types of entity nodes and diverse relationships. Meta-paths are crucial in HINs for relaying detailed information and enhancing knowledge transfer. However, their discovery is challenged by the expansive meta-path space inherent in complex HINs. To address this, we introduce LLM4HIN, an innovative meta-path discovery framework that utilizes Large Language Models (LLMs) for efficient meta-path discovery. Through the use of LLMs, LLM4HIN bypasses challenges including computational limitations, semantic inconsistency, and path selection optimization traditionally associated with meta-path reasoning. LLM4HIN could efficiently discovers meta-paths by utilizing LLMs to generate these paths from a minimal subset of HIN samples. Experimental evaluation on large-scale HINs attests to the enhanced performance of the meta-paths generated by LLM4HIN in link prediction tasks compared to state-of-the-art methods.

关键词： Social computing Large language models Computational modeling Semantics Refining Cognition Natural language processing data mining Optimization Knowledge transfer

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Development of Low Resource Machine Learning Models for Child cognitive Ability Assessments 4

Development of Low Resource Machine Learning Models for Chil...

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4th international conference on Advancements in computing, ICAC 2022

作者： Kahawanugoda, Adithya Gnanarathna, Kulanika Meegoda, Nilan Monarawila, Randika Samarasinghe, Pradeepa Lindamulage, Asiri Gawesha Department of Computer Science and Software Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Information Technology Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350398090

Automated cognitive assessment tools are state-of-the-art in assessing cognition development. Due to the low availability of resources, building automated cognitive ability evaluation tools is challenging. This study focuses on developing machine learning models using a limited amount of data to assess Reasoning IQ, Knowledge IQ, Mental Chronometry and Attention-levels of Sinhala-speaking children between the age of 7 to 9 years. Our solution includes Sinhala speech recognition systems, image classification models, gaze estimation, blink count detection and facial expression recognition models to evaluate the above four cognitive ability measuring factors. Open domain speech recognition has been used to evaluate complex Sinhala child verbal responses and limited vocabulary responses were assessed using an end-to-end speech recognition system, respectively achieving 40.1% WER and 97.14% accuracy. Additionally, the image classification models for handwritten Sinhala letter recognition and two shape recognition models have gained 97%, 89% and 99% accuracy. The linear regression model for attention level evaluation that utilizes the inputs from a combination of eye-gaze estimation, facial expression recognition and blink rate detection models has gained 85% accuracy. © 2022 ieee.

关键词： Image classification

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