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Proceedings ArticleDOI
Minghui Zhou, Audris Mockus 
07 Nov 2010
90 Citations
By studying developer fluency we contribute by determining dimensions along which developer expertise is acquired, finding ways to measure them, and quantifying the trajectories of developer learning.
It does indeed appear that Python has something to offer as an introductory programming language.
Open accessProceedings ArticleDOI
Brian A. Malloy, James F. Power 
09 Nov 2017
19 Citations
Python developers are confining themselves to a language subset, governed by the diminishing intersection of Python 2, which is not under development, and Python 3, which is under development with new features being introduced as the language continues to evolve.
Due to its ease of use, the library is also an ideal candidate for inclusion in Python educational courses.
Open accessJournal ArticleDOI
Oscar Karnalim, Mewati Ayub 
30 Oct 2017
7 Citations
Finally, based on student perspectives, Python Tutor is a helpful tool positively affecting the students.
Results show which modules of Python are the most appropriate for students to study for their future professional teaching activities.
The results show strong evidence that the students feel that Python is a suitable language.

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1.How do SMMEs in developing countries perceive cybercrime?
5 answers
SMMEs in developing countries perceive cybercrime as a significant threat due to their vulnerability resulting from the adoption of digital technologies without adequate cybersecurity measures. The increase in cybercrime in developing nations over the past two decades has highlighted the lack of resources and appropriate reactions to cyber threats, leading to a conducive environment for cybercriminal activities. Additionally, the exposure of SMEs to online cybersecurity threats underscores the importance of addressing these challenges to protect businesses that are crucial for employment and economic growth in developing countries. Furthermore, the pervasiveness of cybercriminal activities in countries like Ghana has raised concerns about the lack of confidence in law enforcement to combat cybercrime effectively.
How KOLs Influence Consumer Purchasing Decisions on Green Fashion Products?
5 answers
Key Opinion Leaders (KOLs) significantly impact consumer purchasing decisions on green fashion products. Consumers' sustainable purchase decisions are influenced by the sustainable fashion environment, product features, and consumption awareness. Ethical concerns and ethical consumption play a crucial role in shaping consumer purchase decisions, with factors like Ethical Knowledge, Environmental Concern, Personal Values, and Price Factor affecting these decisions. Luxury brands aiming for sustainable fashion face challenges as customers show mixed attitudes towards green products, influenced by factors like Interdependent-self, independent-self, and perceived personal relevancy. Additionally, factors such as credibility, attractiveness, product suitability with personality, and celebrity recognition impact consumer purchase intentions of green fashion products. Overall, KOLs play a vital role in influencing consumer purchasing decisions on green fashion products through various factors and considerations.
How does airbnb features increase user experience?
5 answers
Airbnb features increase user experience by focusing on key aspects such as listing, host, location, and overall experience features, as highlighted in various studies. The analysis of online reviews reveals that factors like cleanliness, location, stay, host, and neighborhood significantly impact user satisfaction and positive sentiments. Additionally, attributes like atmosphere, flexibility, special amenities, and humanized service contribute to guests' positive experiences, while issues such as cleanliness expectations, shared amenities, noisy hosts, and pressure for positive reviews can lead to negative sentiments. Understanding these aspects through sentiment analysis and review patterns helps Airbnb hosts and platforms optimize their services to enhance user satisfaction and decision-making, ultimately improving the overall quality of the Airbnb experience.
What is Long Short-Term Memory?
4 answers
Long Short-Term Memory (LSTM) is a type of recurrent neural network (RNN) extensively utilized for historical time series prediction. LSTM networks are equipped with memory cells that enable the model to make long-term predictions, crucial for capturing complex changes in historical time series data. Hyperparameter optimization is a key challenge in maximizing LSTM performance, especially for users at varying expertise levels. Researchers have proposed innovative methods like incorporating additional memory cells and utilizing optimization algorithms such as the whale optimization algorithm (WOA) to enhance LSTM models for tasks like short-term load forecasting. These approaches aim to improve the accuracy and efficiency of LSTM-based predictions by addressing data processing, hyperparameter selection, and model optimization challenges.
What are the main regulatory risks that businesses face today compared to 10 years ago?
5 answers
Businesses today face a myriad of regulatory risks compared to a decade ago. The current regulatory environment is increasingly aggressive, with UK companies ill-equipped to handle growing risks, lacking confidence in their risk management processes. The rise of digitization and internet usage has introduced new challenges, with corporates venturing into these realms without full consideration of the expanding regulatory regime, leading to potential regulatory consequences due to loss of control over architecture and security. Additionally, proposals to regulate Big Tech akin to past sectoral regulations have been cautioned against, emphasizing the negative outcomes of such approaches in the past. Overall, businesses today must navigate complex regulatory landscapes, proactively managing risks to avoid severe penalties and maintain a competitive edge.
How to define the optimal pricing and credit granting strategy for low-income people?
5 answers
To define the optimal pricing and credit granting strategy for low-income individuals, a combination of differentiated subsidies on commodities and credit-based congestion pricing (CBCP) can be considered. By offering travel credits to low-income users to offset toll payments, and implementing subsidies on goods consumed in higher proportion by low-income households, a more equitable system can be established. Additionally, leveraging peer groupings among low-income earners can help pool resources, improve risk mitigation, and enhance credit quality levels. Partnering with innovative citizen sector organizations can also aid in effectively reaching low-income markets and maximizing social impact. By integrating these approaches, a comprehensive strategy can be formulated to address the pricing and credit needs of low-income populations.
What are some of the barriers to instructional technology integration specifically in the integration of AIED?
4 answers
Barriers to instructional technology integration, particularly in the integration of Artificial Intelligence in Education (AIED), include technophobia, lack of time, absence of planning, lack of incentives, lack of evaluation, work saturation, intermittent power supply, lack of skills to use technologies, intermittent Internet connectivity, simplification leading to behaviorism, information cocoon from algorithmic recommendations, teachers' AI anxiety, ethical concerns, and emotional deficiencies. These barriers hinder the effective adoption and utilization of AIED in educational settings, emphasizing the need for addressing these challenges to enhance the integration of technology in teaching and learning processes.
What are the most commonly used methods for detecting and preventing cyberbullying?
5 answers
The most commonly used methods for detecting and preventing cyberbullying include traditional machine learning models, deep learning approaches, and natural language processing techniques. Traditional machine learning models have been widely employed in the past, but they are often limited to specific social networks. Deep learning models, such as Long Short Term Memory (LSTM) and 1DCNN, have shown promising results in detecting cyberbullying by leveraging advanced algorithms and embeddings. Additionally, the integration of Natural Language Processing (NLP) with Machine Learning (ML) algorithms, like Random Forest, has proven effective in real-time cyberbullying detection on platforms like Twitter. These methods aim to analyze social media content, language, and user interactions to identify and prevent instances of cyberbullying effectively.
What is the taxonomic classification of bamboo leaves?
5 answers
Bamboo leaves used in products can be taxonomically classified to the genera Phyllostachys and Pseudosasa from the temperate "woody" bamboo tribe (Arundinarieae). The temperate bamboos, part of the Bambusoideae subfamily, are morphologically diverse and have a complex taxonomy, with the Arundinaria clade being a significant lineage within this group. Additionally, a hierarchical classification approach utilizing the K nearest neighbor algorithm has been proposed for effective discrimination of bamboo species, which can have implications for the conservation of Giant Pandas. Molecular phylogenetic analyses have been conducted to understand the relationships among temperate woody bamboo species, emphasizing the importance of chloroplast DNA markers and complete plastomes in determining taxonomic classifications within this group.
How the channel can be estimated in irs-assisted mmWave multiuser MIMO system?
5 answers
Channel estimation in IRS-assisted mmWave multiuser MIMO systems can be achieved through various innovative approaches. One method involves leveraging deep learning for two-stage channel estimation, where the sparsity of the mmWave massive MIMO channel in the angular domain is exploited using a convolutional neural network, followed by channel reconstruction through a least squares problem. Another technique utilizes a machine learning-based channel predictor to estimate and predict user-IRS channels efficiently, reducing training pilot signals and enhancing data rates. Additionally, a peak detection-message passing algorithm can estimate angle, delay parameters, and channel gain by exploiting the array steering vector properties, particularly effective in low SNR scenarios. These methods showcase the diverse strategies available for accurate and efficient channel estimation in IRS-assisted mmWave multiuser MIMO systems.
How to improve the accuracy of LLM model?
5 answers
To enhance the accuracy of Large Language Models (LLMs), several strategies have been proposed. One approach involves utilizing a human evaluation framework to assess model answers across various dimensions like factuality, comprehension, reasoning, possible harm, and bias. Additionally, instruction prompt tuning has been introduced as a parameter-efficient method to align LLMs to new domains, showing improvements in comprehension, knowledge recall, and reasoning with model scale. Another method includes implementing a Selection-Inference (SI) framework that leverages pre-trained LLMs for logical reasoning tasks, resulting in significant performance enhancements without fine-tuning. Moreover, employing a natural approach in multiple-choice question answering tasks, along with ensuring high multiple choice symbol binding (MCSB) ability in LLMs, has shown promising results in improving accuracy and closing the gap with the state of the art.