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Stimulating creativity and grit of high school students with creative STEM activities: an RCT with noncompliance

Quality and Quantity 2024年第1期59卷 605-634页

作者： Ballerini, Veronica Dominici, Alice Ferracane, Martina Francesca Menchetti, Fiammetta Noirjean, Silvia Department of Statistics Computer Science Applications “G. Parenti” University of Florence Florence Italy Department of Economics European University Institute Fiesole Italy Dondena Centre for Research on Social Dynamics and Public Policy Bocconi University Milan Italy School of Computing Engineering & amp Digital Technologies Teesside University Middlesbrough United Kingdom Robert Schuman Centre for Advanced Studies European University Institute Fiesole Italy

Creativity and grit are widely recognized as vital components for the success and prosperity of individuals, even more so for younger people who will have to deal with the complex challenges connected to the digital era. Yet, whether these skills can be learnt or are innate traits is still subject to debate, which is further complicated by the difficulty of defining and, in turn, assessing these concepts. In this study, we challenge the idea that creativity and grit cannot be learnt and show that creative pedagogy activities, such as those offered by FabLabs, are powerful tools to enhance these non-cognitive skills. We conduct a randomized controlled trial in which 710 students from five Italian high schools are randomly assigned to creative STEM courses. The courses are delivered by FabLabs, small-scale workshops that offer access to tools for digital fabrication and employ a hands-on pedagogical approach expected to impact students’ creativity and grit positively. Assignment to the courses is at the class level: only students in classes randomly selected to join the Fablab activities can participate, and they may decide whether to enrol or not on a voluntary basis. We address noncompliance by adopting an instrumental variable approach. We use two modalities to measure creativity: a self-assessment through the Short Scale of Creative Self and an assessment made by an independent expert using an index of creativity developed by the authors. While the effect on self-assessed creativity is not significant, results show that the external, independent assessment of students’ creativity can capture a significant effect of Fablabs’ courses. It also emerges that FabLab activities have a positive significant effect on students’ grit, measured through the Duckworth scale. The encouraging results obtained for a relatively small sample of students should prompt a replication of the experiment on a broader scale. © The Author(s) 2024.

关键词： Causal inference Creative pedagogy FabLabs Instrumental variables Noncompliance

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Improving Energy Efficiency using Colliding Bodies Optimization based Routing Protocol for Wireless Sensor Networks 2

Improving Energy Efficiency using Colliding Bodies Optimizat...

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2nd International Conference on Electronics and Renewable Systems, ICEARS 2023

作者： Prabakaran, P. Shalini, R. Hariharan, N. Marieswari, S. Kumar, Kallakunta Ravi Apoorva, Maturi Gayatri Lakshmi Vels Institute of Science Technology and Advanced Studies School of Computing Sciences Department of Computer Applications Chennai India Vels Institute of Science Technology and Advanced Studies School of Computing Sciences Department of Bca and It Chennai India R.M.K. College of Engineering and Technology Science and Humanities - Eee Thiruvallur601206 India P. S. R. Engineering College Biomedical Engineering India Koneru Lakshmaiah Education Foundation Department of Ece Vaddeswaram India Amrita School of Engineering Ece Department India

ISBN: (纸本)9798350346640

Wireless sensor network (WSN) contains millions of small, low-power gadgets with a lack of resources are memory and battery power. Such gadgets were deployed in a dispersed manner and were often utilized to monitor and sense applications. Owing to constraints and limited resources of WSNs, routing can be a great difficulty. Routing in WSN was the process of choosing the optimal path for data to travel from a source node to a destination node. The goal of routing in WSN offer dependable and effective transmission while minimizing network overhead and energy consumption. This study designs an Energy Efficient Colliding Bodies Optimization based Routing Protocol (EECBO-RP) for WSN. The end goal of the EECBO-RP algorithm lies in the optimal election of routes to a destination. For achieving reasonable results for the minimization of energy exploitation, the EECBO-RP system derives the fitness function using the following variables: residual energy, distance to BS, and node degree. The EECBO-RP technique chooses the relay nodes so that a way that the overall efficiency of the WSN gets maximized. The simulation values of the EECBO-RP system are tested under several dimensions and the outcomes pointed out the betterment of the EECBO-RP technique. © 2023 IEEE.

关键词： Energy efficiency

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Secure Data Embedding in Digital Images: Enhancing Covert Communication with LSB-Based Techniques

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Procedia Computer Science 2025年 258卷 2091-2100页

作者： Tina Babu Rekha R Nair Rajesh T M Kishore S Department of Computer Science and Engineering School of Advanced Computing Alliance University Bengaluru India Department of Computer Science and Engineering Dayananda Sagar University Bengaluru India Department of Computer Applications Nitte (Deemed to be University) Nitte Institute of Professional Education Mangalore Karnataka India

Information security and the ability to convey secret messages have been necessities since the dawn of civilization. Various media forms, such as photos, audio files, and videos, offer a conducive environment for concealing information that is invisible to the human eye in the current digital era by taking advantage of superfluous or unused data bits. This hidden information can be used for secret communication or as a way to include more messages into a single file, which will minimize the quantity of data that needs to be sent. In order to encode and store messages within the RGB pixels of a cover image (canvas image) without causing visual distortion, two methods are shown in this study. Both approaches split the message into separate segments, which are then translated into 8-bit binary values. These values are then encrypted using a straightforward symmetric exclusive OR (XOR) encryption key, and the message is encoded in the canvas image by changing the pixel value’s least significant bit (LSB). The first approach encodes the secret message from top to bottom and left to right by sequentially storing it beginning with the top-left pixel. By encoding the message bits into the cover image pixels at random, the second technique reduces the susceptibility of the concealment to analysis. By using redundant data in digital media, these approaches can hide information while preserving the original visual integrity. This makes them an effective and safe way to communicate covertly or embed data into a single file, which maximizes data transmission.

关键词： Steganography Least Significant Bit (LSB) Data Encryption Covert Communication Digital Media XOR Encryption

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Custom Transformer-Based Approach for Enhanced Bengali Automatic Speech Recognition 1

Custom Transformer-Based Approach for Enhanced Bengali Autom...

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1st International Conference on Innovations in Communications, Electrical and Computer Engineering, ICICEC 2024

作者： Deepa, R. Jayalakshmi, V. Thilakavathy, P. Manikandan, G. Surendran, R. SRM Institute of Science and Technology School of Computing Department of Computing Technologies Chennai India SRM Institute of Science and Technology Department of Computer Science and Applications Ramapuram Chennai India Vels Institute of Science and Engineering Vels Institute of Science Technology and Advanced Studies Chennai India Anna University St. Joseph's College of Engineering Department of Artificial Intelligence and Machine Learning Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798350376517

ISBN: (纸本)9798350376517

Automated speech recognition (ASR) systems struggle with Bengali, which is the fifth most spoken language. Bengali has a varied morphology, many dialects, and limited high-quality annotated voice data. Traditional voice recognition techniques work well however struggle with Bengali phonetics and syntax. This method eliminates the need for large amounts of tagged data and the difficulties of differentiating sounds in different languages, both issues are addressed by this procedure. The research in this paper offers an Intellectual Bengali Speech Recognition System using Deep Learning Techniques (IBSRS-DLT). The model in this system uses transformer architecture and attention processes to manage Bengali speech pattern long-range dependencies. The IBSRS-DLT outperforms standard models in recognition accuracy using a pre-trained transformer model modified for phonetic and lexical correctness and fine-tuned on Bengali datasets. After training on a vast Bengali speech corpus, real-world datasets and rigorous simulations confirm the system. IBSRS-DLT can be used for automatic transcription, voice-controlled devices, and Bengali virtual assistants, such applications are efficient and accessible. The simulation showed a considerable boost in recognition speed and a decrease in keyword mistakes compared to current methods. This paper shows that deep learning can solve linguistic issues in ASR systems, enabling scalable regional language detection. © 2024 IEEE.

关键词： Deep learning

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Recurrent Neural for Time Series Prediction in Finance

Recurrent Neural for Time Series Prediction in Finance

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IT Innovation and Knowledge Discovery (ITIKD), International Conference on

作者： Rekha R Nair Tina Babu S Kishore Department of Computer Science and Engineering Alliance School of Advanced Computing Alliance University Bengaluru India Department of Computer Applications name of organization Nitte Institute of Professional Education Nitte (Deemed to be University) Mangalore India

ISBN: (数字)9798350355468

ISBN: (纸本)9798350355475

Overcoming risk is crucial when predicting financial time series, such as stock prices, due to market volatility influenced by factors like economic reports, political events, and investor sentiment. Linear models like ARIMA handle linear dependencies well but fail to capture nonlinear relationships, which are key in financial forecasting. While Long Short-Term Memory (LSTM) models excel at capturing temporal dependencies, they struggle with identifying significant market events, potentially contaminating predictions. This paper introduces an Attention RNN with Sentiment Integration, which combines Recurrent Neural Networks (RNN) with an attention mechanism for sentiment analysis of financial news and social media. The model considers relevant time steps and market sentiment, improving context-awareness. Results demonstrate that this hybrid approach outperforms traditional methods by providing a more accurate and flexible forecasting model, addressing the complexities of financial markets. Additionally, sentiment data helps the model capture both stock-specific and overall market sentiment, particularly during periods of heightened uncertainty or volatility, offering a more comprehensive approach to stock price prediction. Keywords: Risk, Financial Time Series, ARIMA, LSTM, Attention RNN, Sentiment Analysis, Market Sentiment, Stock Prices, Forecasting, Volatility.

关键词： Sentiment analysis Technological innovation Recurrent neural networks Attention mechanisms Uncertainty Biological system modeling Time series analysis Predictive models Forecasting Long short term memory

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Image analysis and fine-tuned ResNet50 model for effective malware detection

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Procedia Computer Science 2025年 260卷 1098-1106页

作者： Sanjeev Kumar Yudhishthira Sapru Jitendra Kumar Sugandha Sapru Cyber Security Technology Division Centre for Development of Advanced Computing Mohali India Indian Computer Emergency Response Team CERT-In Delhi India Department of Mathematics Bioinformatics and Computer Applications MANIT Bhopal India Department of Mathematics PGGC Panjab University Chandigarh India

With the rapid growth of connected assets to the Internet, malware attacks have increased exponentially, often causing substantial damage to these connected assets. Early detection of evolving malware attacks can avoid such damage to the Internet ecosystem. However, it requires an efficient and robust approach with little reliance on existing signature-based approaches, as these are no longer effective in detecting obfuscated and complex malware attacks. This study presents a new malware detection and classification approach using image analysis and a fine-tuned ResNet50 convolution neural network (CNN) model through transfer learning. The raw binary executable programs are transformed into grayscale images and resized to 224 × 224 × 1 image size to be fed to the deep CNN model for feature extraction. The pre-trained ResNet50 model is customized to extract the textural features by retaining the learned weight and bias from the source ImageNet data and making the last fully connected layer non-trainable. After that, a fully connected dense layer is added for dimension reduction of the feature map into N × 128, with N as the number of malware files. Two public datasets are used in experiments - MalImg and Microsoft BIG. This study uses comprehensive performance metrics to evaluate the model and obtained an accuracy of 98.50% for the MalImg dataset and 94.85% for the Microsoft BIG data set. The experiment results show that the proposed methodology can handle malware obfuscated by standard polymorphic techniques and is better than similar methods in the literature.

关键词： Cybersecurity Convolution neural network ResNet Malware classification Transfer learning

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MSSThreD: Multitask Social Media Sentiment Analysis with Sarcasm for Public Security Threat Detection

MSSThreD: Multitask Social Media Sentiment Analysis with Sar...

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International Conference on Information Technology, Computer and Electrical Engineering (ICITACEE)

作者： Mohd Suhairi Md Suhaimin Mohd Hanafi Ahmad Hijazi Ervin Gubin Moung Department of Polytechnic and Community Politeknik Kota Bharu College Education Kota Bharu Kelantan Malaysia Data Technology and Applications Research Group Creative Advanced Machine Intelligence Research Centre Faculty of Computing and Informatics Universiti Malaysia Sabah Sabah Malaysia Data Technology and Applications Research Group Faculty of Computing and Informatics Universiti Malaysia Sabah Sabah Malaysia

ISBN: (数字)9798350389302

ISBN: (纸本)9798350389319

Public security threat detection is critical for maintaining safety of citizens from possible natural or non-natural disasters. Social media, with its vast and diverse content, serves as a significant source for communication and information dissemination. Early detection of threats through social media analysis can greatly benefit in prevention before the event, or response and rescue during the event. However, the noisy textual with linguistic nature of social media content presents substantial challenges for sentiment analysis. Coupling with sarcasm can reverse the polarity of sentiments, complicating threat detection efforts. To address these challenges, we introduce an application framework for public security threat detection that embeds multitask learning, incorporating sentiment analysis and sarcasm detection. This application utilized preprocessing and feature engineering techniques based on Natural Language Processing to capture the nuance of sentiment and sarcasm, with deep learning network model of Bidirectional Long Short-Term Memory and Gated Recurrent Unit combination. The sentiment filtering and identifying threat-related keywords, producing monitoring reports that highlight threats earlier than traditional web news sources, as evidenced by our case study results. This application demonstrates the practical utility and impact of the framework in enhancing public security through timely threat detection.

关键词： Sentiment analysis Social networking (online) Filtering Prevention and mitigation Threat assessment Public security Noise measurement Monitoring Long short term memory Videos

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An Active Learning Model Production Method for Electric Power Inspection Image Multi-target Detection

An Active Learning Model Production Method for Electric Powe...

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IEEE Information Technology and Mechatronics Engineering Conference (ITOEC)

作者： Jiangqi Chen Bo Wang Xi Zhang Kunlun Gao Peng Wu State Grid Laboratory of Grid Advanced Computing and Applications Beijing China State Grid Smart Grid Research Institute Co. Ltd Beijing China

With the development of intelligent electric power inspection technology, electric power companies use UAVs, cameras, robots and other equipment for power transmission line inspections, and take a large number of inspection images. In recent years, the image multi-target detection model has been maturely applied in the intelligent inspection of electric power. The current image intelligent detection model mainly relies on a large number of image sample labels. Power companies have a small number of labeled image samples, and a large number of unlabeled. But manual data annotation is very expensive, and cost lots of time and money. Moreover, there will be certain errors in manual annotation, so it is necessary to repeat the annotation and verification to reduce the error. But this will also increase the cost. This article proposes an active learning model production method for electric power inspection image multi-target detection. We select samples for annotation according to some strategies, and select labeled samples for verification. This method can reduce the cost of sample annotation, and improve the model. productivity. Compared with the traditional random labeling strategy, our method can achieve the same accuracy with only 20% of the annotation cost. With 50% of the annotation cost, the mAP index will increase by 12%.

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IoT based Monitoring of Mushroom 2

IoT based Monitoring of Mushroom

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2nd International Conference on Sustainable computing and Data Communication Systems, ICSCDS 2023

作者： Eben, J. Lysa Kaur, Chamandeep Thelly, Mathews T Parimita Vels Institute of Science Technology and Advanced Studies School of Computing Sciences Department of Computer Applications Chennai India Jazan University Department of Computer Science and Information Technology Jazan Saudi Arabia Kuriakose Elias College Department of Botany Mannanam India Sam Higginbottom University of Agriculture Technology & Sciences Agricultural Engineering Prayagraj India

ISBN: (纸本)9781665491990

Most of India's population relies on farming for their livelihood. Every year brings new advances in agricultural technology. The use of wireless sensors in contemporary agriculture is crucial. The employment of wireless sensors across a wide range of agricultural applications has a profoundly favourable effect on crop productivity and economic efficiency. The mushroom business is still relatively young and modest compared to other sectors of India's agricultural market. The white button mushroom is India's most commercially significant fungus and is a global favourite. It may be grown anywhere with a suitable climate and soil, but most of the world's supply comes from North India in the winter. For mycelia to flourish, the ideal temperature range is between 22 and 25 degrees Celsius. In contrast, the sweet spot for developing fruit bodies is between 14 and 18 degrees Celsius with high relative humidity. Equipment for maintaining a steady temperature and pasteurising the mushrooms, should be present in the rooms used for growing. The primary objective of this study is to automate the mushroom production plant and keep track of the crop room's environmental state to reduce the amount of human care required for the mushroom plant. The Internet of Things has also modernized agriculture industries, creating an environmental monitoring and regulating system that is used in the mushroom farm in this work. Using the Speak internet platform, users may monitor ecological conditions in a mushroom farm on their Android mobile, including the ambient temperature, relative humidity, carbon dioxide level, and light intensity. The Arduino prototype platform is employed as the sensors station's controller, ensuring that the plant parameters are within the user-specified range. Using a pair of low-power ESP8266 as Wi-Fi modems, the current parameter status is relayed to a distant monitoring station. The integrated Arduino uses the programming environment to write the controller's cod

关键词： Controllers

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CNN-based Plant Leaf Disease Detection: A Key Solution for Enhancing Agricultural Productivity

CNN-based Plant Leaf Disease Detection: A Key Solution for E...

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Advancement in Technology (ICONAT), International Conference for

作者： Deepika Nayak Rekha R Nair TinaBabu S Kishore Department of Computer Science and Engineering Alliance School of Advanced Computing Alliance University Bengaluru India Department of Computer Applications Nitte Institute Of Professional Education Nitte(Deemed to be University) Mangaluru Karnataka India

ISBN: (数字)9798350354171

ISBN: (纸本)9798350354188

Agriculture plays a pivotal role in our lives and holds significant importance in our economy. Proper management of agricultural practices is essential for maximizing profits in agricultural production. However, many farmers lack expertise in identifying and managing plant leaf diseases, leading to reduced crop yields. Since agricultural productivity directly impacts the profitability of farming, efficient disease detection and management are crucial. To address this issue, Convolutional Neural Networks (CNN) emerge as a viable solution for leaf disease detection and classification. The primary objective of this research is to develop a robust CNN-based system capable of detecting and classifying leaf diseases in various crops such as apple, grape, corn, potato, tomato, and more. In this research, the CNN algorithm was utilised, and the accuracy was $\mathbf{9 7. 5 8 \%}$. The system’s application will enable farmers to monitor large fields of crops, facilitating early detection and treatment of diseases. The significance of plant leaf disease detection spans across multiple sectors, including Biological Research and Agriculture Institutes. Detecting diseases promptly can help implement timely medical treatments, thereby mitigating the negative impacts on crop health and productivity. Moreover, such a system can assist in monitoring crop health on a larger scale, benefiting the agricultural sector as a whole.

关键词： Productivity Accuracy Profitability Plants (biology) Crops Agriculture Real-time systems Convolutional neural networks Monitoring Diseases

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