Cosine Similarity – A Computing Approach to Match Similarity Between Higher Education Programs and Job Market Demands Based on Maximum Number of Common Words

Januzaj, Ylber; Luma, Artan

doi:10.3991/ijet.v17i12.30375

Cited by 6 publications

(5 citation statements)

References 8 publications

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“…( 2), which are calculated using the cosine similarity formula [19]. The cosine similarity, as a method of measuring the similarity between the documents, varies between positive values from 0 to 1 [20]. Two texts are virtually identical, as evidenced by a cosine similarity close to 1, indicating a small angle between them.…”

Section: B Discussionmentioning

confidence: 99%

Implementation of Cosine Similarity Algorithm on Omnibus Law Drafting

Syam,

et al. 2024

IJACSA

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Drafting of Omnibus Laws presents a complex challenge in legal governance, often involving the integration and consolidation of disparate legal provisions into a unified framework. In this context, the application of advanced computational techniques becomes crucial for streamlining the drafting process and ensuring coherence across the law's various components. Cosine similarity, a widely used measure in natural language processing and document analysis, offers a quantitative means to assess the similarity between different sections or articles within the Omnibus Law draft. By representing legal texts as high-dimensional vectors in a vector space model, cosine similarity enables the comparison of textual similarity based on the cosine of the angle between these vectors. Implementing cosine similarity in the context of omnibus law using FastAPI and Laravel can be a valuable tool for analyzing similarity between legal documents, especially in the context of omnibus law. Legal practitioners and researchers can use the cosine similarity measure to compare the textual content of different legal documents and identify similarities. This can aid in tasks such as legal document retrieval, clustering similar provisions, and detecting potential inconsistencies. The combination of FastAPI and Laravel provides a potent and efficient way to develop and deploy this functionality, contributing to the advancement of legal informatics and analysis. The dataset used is Undang-Undang (UU) which used Bahasa from 1945 to 2022, comprising a total of 1705 UU. The implemented cosine similarity yielded a recall rate of 90.10% on the law.

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Section: B Discussionmentioning

confidence: 99%

Implementation of Cosine Similarity Algorithm on Omnibus Law Drafting

Syam,

et al. 2024

IJACSA

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“…If vectors are pointing in the same direction, it means that the documents are similar; the closer they are expressed on the axis, the more similar they are. Vice versa, the farther they are expressed on the axis, the less similar they are [26].…”

Section: Meteormentioning

confidence: 99%

Combining Balancing Dataset and SentenceTransformers to Improve Short Answer Grading Performance

Wijanto,

Yong

2024

Applied Sciences

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Short-answer questions can encourage students to express their understanding. However, these answers can vary widely, leading to subjective assessments. Automatic short answer grading (ASAG) has become an important field of research. Recent studies have demonstrated a good performance using computationally expensive models. Additionally, available datasets are often unbalanced in terms of quantity. This research attempts to combine a simpler SentenceTransformers model with a balanced dataset, using prompt engineering in GPT to generate new sentences. Our recommended model also tries to fine-tune several hyperparameters to achieve optimal results. The research results show that the relatively small-sized all-distilroberta-v1 model can achieve a Pearson correlation value of 0.9586. The RMSE, F1-score, and accuracy score also provide better performances. This model is combined with the fine-tuning of hyperparameters, such as the use of gradient checkpointing, the split-size ratio for testing and training data, and the pre-processing steps. The best result is obtained when the new generated dataset from the GPT data augmentation is implemented. The newly generated dataset from GPT data augmentation achieves a cosine similarity score of 0.8 for the correct category. When applied to other datasets, our proposed method also shows an improved performance. Therefore, we conclude that a relatively small-sized model combined with the fine-tuning of the appropriate hyperparameters and a balanced dataset can provide performance results that surpass other models that require larger resources and are computationally expensive.

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“…When vectors align in the same direction, it signifies text similarity, and as the angle becomes smaller, the similarity score increases, approaching 1. Conversely, when vectors have larger angles between them, they are less similar, resulting in a similarity score closer to 0 [33].…”

Section: Compare the Similarity Of The Attack Processmentioning

confidence: 99%

Similarity Analysis of Ransomware Attacks Based on ATT&CK Matrix

Song,

Tian,

Zhang

2023

IEEE Access

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In recent years, there has been an increasingly prevalent trend of ransomware attacks, with malicious organizations employing various techniques to gain system privileges and subsequently engaging in extortion through methods such as encrypting files or leaking information. Current research predominantly focuses on the analysis of ransomware using existing features, but there has been scarce exploration of the behavioral patterns associated with ransomware attacks. In light of this situation, we propose a ransomware attack similarity analysis method based on the ATT&CK matrix. To initiate this analysis, a substantial amount of network threat intelligence is sifted through to select reliable and comprehensive ransomware attack incidents. From these incidents, we extract attack tactics, techniques, and procedural information. Subsequently, we employ the TF-IDF algorithm to calculate the keyword weights within attack descriptions. Based on these weights, we utilize the cosine similarity algorithm to compare the similarity between attack events. This approach reveals critical technical and tactical information employed by the attacking organizations, enabling researchers to gain a deeper understanding of the behavioral patterns of the attackers. Finally, we propose countermeasures corresponding to the critical attack techniques employed by these malicious organizations. These countermeasures aim to enhance network security defenses and reduce the risks associated with ransomware attacks.

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Cosine Similarity – A Computing Approach to Match Similarity Between Higher Education Programs and Job Market Demands Based on Maximum Number of Common Words

Cited by 6 publications

References 8 publications

Implementation of Cosine Similarity Algorithm on Omnibus Law Drafting

Implementation of Cosine Similarity Algorithm on Omnibus Law Drafting

Combining Balancing Dataset and SentenceTransformers to Improve Short Answer Grading Performance

Similarity Analysis of Ransomware Attacks Based on ATT&CK Matrix

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