The field of music generation using Large Language Models (LLMs) is evolving rapidly, yet existing music notation systems, such as MIDI, ABC Notation, and MusicXML, remain too complex for effective fine-tuning of LLMs...
This study evaluates an agent-based reinforcement learning framework for model-based testing (MBT). The framework’s performance was assessed on three key metrics: effectiveness and efficiency in achieving model cover...
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ISBN:
(数字)9798350364538
ISBN:
(纸本)9798350364545
This study evaluates an agent-based reinforcement learning framework for model-based testing (MBT). The framework’s performance was assessed on three key metrics: effectiveness and efficiency in achieving model coverage objectives, the quantity and uniqueness of generated test cases, and code coverage. The results show improved metrics for the framework compared to traditional testers in model coverage evaluation and test case metrics. Specifically, the framework achieved higher effectiveness and efficiency, generating a higher average number of test cases with a substantial proportion of unique cases, indicating more diverse and thorough testing. Additionally, the framework, along with random and greedy testers, achieved over 70% coverage across branch, method, and line code metrics. The framework showed slightly higher values in method and line coverage compared to other testers. The evaluation highlights the use of agent-based reinforcement learning to support model-based testing in planning test case generation, exploring models using multiple strategies, and learning guided by testing metrics. Future work will focus on further refining the framework learning model, including error coverage evaluation, and testing its applicability across different software systems.
Breast cancer is an occurrence of cancer that attacks breast tissue and is the most common cancer among women worldwide, affecting one in eight women. In this modern world, breast cancer image classification simplifie...
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ISBN:
(数字)9798331539603
ISBN:
(纸本)9798331539610
Breast cancer is an occurrence of cancer that attacks breast tissue and is the most common cancer among women worldwide, affecting one in eight women. In this modern world, breast cancer image classification simplifies the process of analyzing, providing objective and accurate results. By leveraging machine learning algorithms and computer vision techniques, we developed breast cancer detection. The dataset is histopathology dataset from BreakHis and UNHAS Hospital. We chose the ConvNeXt-Tiny model then modified the classifier head as the proposed method. Before the dataset is processed by the model, we augment the images by applying random horizontal and vertical flips, adjustments to brightness, contrast, saturation, and hue using color jitter. The augmentation process simulates real-world variance and enhances the model's ability to generalize to unseen data. Our proposed model gained better performance (accuracy, F1-Score) results compared two other techniques to VGG16 and SVM. According to our experiments, the F1-Score for the ConvNeXt-Tiny model with classifier head modification is higher at 0.9516, than the gain for VGG16 at 0.9292, and the gain for the SVM at 0.83.
Motivated by ethical concerns, we build upon prior research on adult stakeholder perspectives on the potential uses of augmented reality smart glasses (ARSG) by children by obtaining the perspectives of 3-5-year-old c...
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ISBN:
(纸本)9798400714733
Motivated by ethical concerns, we build upon prior research on adult stakeholder perspectives on the potential uses of augmented reality smart glasses (ARSG) by children by obtaining the perspectives of 3-5-year-old children through play-based design activities. In the activities, children imagined a broad set of experiences with ARSG technologies, such as perceiving a range of objects, imagined beings and places, as well as altering the perception of items in the real world. We also noted that children often expected to perceive the same items as others, which facilitated social activities, but also engaged in selective disclosure when perceiving something more private. The activities we conducted provide insights on likely expectations by this age group for ARSG activities, which should be considered in future designs and ethical reflections, as well as further validation of play-based design as a method to obtain preschool children's ideas about technologies.
The article describes a new method for malware classification,based on a Machine Learning(ML)model architecture specifically designed for malware detection,enabling real-time and accurate malware *** an innovative fea...
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The article describes a new method for malware classification,based on a Machine Learning(ML)model architecture specifically designed for malware detection,enabling real-time and accurate malware *** an innovative feature dimensionality reduction technique called the Interpolation-based Feature Dimensionality Reduction Technique(IFDRT),the authors have significantly reduced the feature space while retaining critical information necessary for malware *** technique optimizes the model’s performance and reduces computational *** proposed method is demonstrated by applying it to the BODMAS malware dataset,which contains 57,293 malware samples and 77,142 benign samples,each with a 2381-feature *** the IFDRT method,the dataset is transformed,reducing the number of features while maintaining essential data for accurate *** evaluation results show outstanding performance,with an F1 score of 0.984 and a high accuracy of 98.5%using only two reduced *** demonstrates the method’s ability to classify malware samples accurately while minimizing processing *** method allows for improving computational efficiency by reducing the feature space,which decreases the memory and time requirements for training and *** new method’s effectiveness is confirmed by the calculations,which indicate significant improvements in malware classification accuracy and *** research results enhance existing malware detection techniques and can be applied in various cybersecurity applications,including real-timemalware detection on resource-constrained *** and scientific contribution lie in the development of the IFDRT method,which provides a robust and efficient solution for feature reduction in ML-based malware classification,paving the way for more effective and scalable cybersecurity measures.
To model the periodicity of beats, state-of-the-art beat tracking systems use 'post-processing trackers' (PPTs) that rely on several empirically determined global assumptions for tempo transition, which work w...
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The final decision of the educational assistance recipient in GNOTA Foundation, Jakarta is still processed manually. They usually only look at the father's occupation criteria without looking at other criteria suc...
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In the digital age, streaming platforms have revolutionized how we access and interact with music, highlighting the need for more intuitive ways to organize and categorize our ever-growing music collections. The chall...
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ISBN:
(数字)9798350391886
ISBN:
(纸本)9798350391893
In the digital age, streaming platforms have revolutionized how we access and interact with music, highlighting the need for more intuitive ways to organize and categorize our ever-growing music collections. The challenge lies in effectively classifying tracks into similar genres and styles to enhance user experience through improved music discovery and recommendation. In this context, machine learning stands out as a powerful tool. Traditional research in the field focuses on the auditory characteristics of music, such as timbre and rhythm. Nevertheless, the incorporation of spectrogram analysis introduces a richer layer of data representation, capturing the intricate musical textures that distinguish genres. This study proposes a novel approach to music genre classification, leveraging classic machine learning algorithms and the recently proposed contrastive dissimilarity method. Our methodology, which involves a detailed examination of spectrograms and the use of conventional feature extraction methods such as Local Binary Patterns (LBP), Local Phase Quantization (LPQ), Binarized Statistical Image Features (BSIF), and Oriented Basic Image Features (OBIF), combined with deep neural embeddings estimated using the contrastive dissimilarity method, offers a more comprehensive and accurate way to classify music genres. Our comparative analysis, conducted on three benchmark music genre datasets - GTZAN, Latin Music Database, and ISMIR 2004 - demonstrates promising results that approach the performance of current state-of-the-art methods.
Many individuals now trade online utilizing trading software in the digital world. Binomo is one of Indonesia's most popular trading platforms. This is because some influencers made several promises to Binomo cust...
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Many individuals now trade online utilizing trading software in the digital world. Binomo is one of Indonesia's most popular trading platforms. This is because some influencers made several promises to Binomo customers. Since many customers were deceived, this case became quite popular. This study was executed to see how Indonesians felt about the Binomo application after the case went viral. The solution taken was in the form of sentiment analysis because there had been no previous research on sentiment analysis that discussed the Binomo case. The data was scanned using Netlytic tools, a cloud-based text and social network analyzer capable of identifying any talks on social media sites such as Twitter. The sentiment analysis of Binomo trading tweets by using the Multi-Perspective Question Answering lexicon utilized the KNIME tool. But unfortunately, the accuracy of sentiment analysis results is low. Furthermore, the Support Vector Machine technique is also being conducted. The Term Frequency-Inverse Document Frequency method is applied to perform feature extraction whilst the chi-square approach is utilized to identify features that are thought to be useful for inclusion in the classification process and to exclude features that are irrelevant to the target class. The obtained accuracy is 86%. The study proposes that words from the algorithm's outputs can be utilized to improve the quality of sentiment analysis using the lexicon. As an outcome of the algorithm, positive and negative terms are added to the lexicon, increasing the accuracy of sentiment analysis using the new vocabulary from 58.984% to 71.146%.
The escalating visibility of secure direct object reference (IDOR) vulnerabilities in API security, as indicated in the compilation of OWASP Top 10 API Security Risks, highlights a noteworthy peril to sensitive data. ...
The escalating visibility of secure direct object reference (IDOR) vulnerabilities in API security, as indicated in the compilation of OWASP Top 10 API Security Risks, highlights a noteworthy peril to sensitive data. This study explores IDOR vulnerabilities found within Android APIs, intending to clarify their inception while evaluating their implications for application security. This study combined the qualitative and quantitative approaches. Insights were obtained from an actual penetration test on an Android app into the primary reasons for IDOR vulnerabilities, underscoring insufficient input validation and weak authorization methods. We stress the frequent occurrence of IDOR vulnerabilities in the OWASP Top 10 API vulnerability list, highlighting the necessity to prioritize them in security evaluations. There are mitigation recommendations available for developers, which recognize its limitations involving a possibly small and homogeneous selection of tested Android applications, the testing environment that could cause some inaccuracies, and the impact of time constraints. Additionally, the study noted insufficient threat modeling and root cause analysis, affecting its generalizability and real-world relevance. However, comprehending and controlling IDOR dangers can enhance Android API security, protect user data, and bolster application resilience.
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