This paper examines several widespread assumptions about artificial intelligence, particularly machine learning, that are often taken as factual premises in discussions on the future of patent law in the wake of '...
The infection of Plasmodium vivax is relatively less virulent than the deathliest Plasmodium falciparum. However, it still can lead to a fatal case and often induces recurring malaria due to dormant parasites in the l...
The infection of Plasmodium vivax is relatively less virulent than the deathliest Plasmodium falciparum. However, it still can lead to a fatal case and often induces recurring malaria due to dormant parasites in the liver. Thus, the research to study the drug to treat Plasmodium vivax is essential, where the enzyme dihydroorotate dehydrogenase (DHODH) has recently become a new drug target. However, the drug-enzyme interaction study has only recently been conducted in Plasmodium falciparum (pfDHODH) due to the lack of the 3D structure of enzyme DHODH from Plasmodium vivax that is crucial for the study. Therefore, this study aimed to perform the modelling study of Plasmodium vivax DHODH (PvDHODH) to create a 3D structure as a basis for drug-protein interaction study in upcoming studies. Sequence pvDHODH (Accession: SCO68359.1) was used in homology modelling using Modeller 10.2 with the crystal structure of DHODH from DHODH (Accession: 7KZY) as a template. Overall, the model generated from the homology modelling was considered a good model, and the 3D structure was close to the native state according to several parameters, including DOPE score, GA341 score, QMEAN4 value, 3D-1D score, ERRAT2 score, and ProSA score. Ramachandran plot also revealed that almost all amino acids were distributed in the desirable and allowed area (99.1%), and only 0.9% were in generously allowed regions. Superimposition of the model with the template also indicated that the model has an almost similar structure and amino acids positioning, including in the binding pocket and active site. Therefore, the model can be used for downstream analysis in drug-protein interaction studies. Nevertheless, some improvements can still be performed to upgrade the quality of the model by deleting unaligned residues on the C-terminal and realigning the residues at the doubting site.
Ancestral domain refers to the lands, territories, and resources possessed and administered collectively by indigenous peoples, such as the indigenous communities of the Philippines. Indigenous peoples who have inhabi...
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ISBN:
(纸本)9798350321302
Ancestral domain refers to the lands, territories, and resources possessed and administered collectively by indigenous peoples, such as the indigenous communities of the Philippines. Indigenous peoples who have inhabited and cared for these regions for generations attach great cultural, spiritual, and economic significance to them. Indigenous Peoples Rights Act (IPRA) of 1997 acknowledged for the first time the idea of ancestral domain in the Philippines. However, the IPRA's implementation and the preservation of ancestral properties in the Philippines have encountered obstacles. There have been instances of unlawful logging, mining, and land appropriation in ancestral domains, typically with the participation of local government officials and influential persons. This has resulted in the uprooting of indigenous populations and the devastation of their land and resources. This research aimed to analyze the data in relation to tree planting activities initiated by several advocates, especially the Father Saturnino Urios University Foundation. Which aims, helping to preserve and saving Ancestral Land. This research employs data mining activity and develops insights according to generated patterns. Descriptive analytics is the branch of data analysis that involves summarizing and describing data. It typically involves using statistics, data visualization, and other techniques to identify patterns and trends in data. Descriptive analytics can be used to understand the characteristics of data and to communicate those characteristics to others. Further, the results show that tree planting activities conducted by some advocates resulted in good results, since planted trees near the watershed have had a survival rate of 92.28 (%) percent and around 43,270 seedlings planted in 88.05 hectares. On the other hand, abaca have been planted in the area for the livelihood of IP situated in the area, have a survival rate of 96 (%) percent. This result is the output of an ongoing reh
In content creation, customer behavior insights are very important as they help creators find and create the content that drives sales. To comprehend customer needs, content creators need not just generalized informat...
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The people in the capital city of the Republic of Indonesia, Jakarta, have been living for years with air pollution. Air quality has been a concern for a long time due to the health risks it has on people, especially ...
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The people in the capital city of the Republic of Indonesia, Jakarta, have been living for years with air pollution. Air quality has been a concern for a long time due to the health risks it has on people, especially those at risk. Using classification algorithms, we would like to implement data mining for the air quality index and make predictions from our capital city, DKI Jakarta. This research aims to compare Decision Tree and Support Vector Machine in predicting Air Quality Index in DKI Jakarta in 2020. The dataset was collected from the Jakarta Open data Web, and the results of this research highlight the effectiveness of data mining techniques in predicting air quality index levels in Jakarta and emphasize the importance of selecting the appropriate classification algorithm for data mining applications. This research shows that SVM, compared to the Decision tree, has a better result with an accuracy score of 87.86%. On the other hand, Decision Tree has a score of 90.56%.
In content creation, customer behavior insights are very important as they help creators find and create the content that drives sales. To comprehend customer needs, content creators need not just generalized informat...
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This work sought to identify the interactions between persons mentioned in social media to help readers construct background knowledge of a certain topic. We propose using a rich interactive tree structure to represen...
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This work sought to identify the interactions between persons mentioned in social media to help readers construct background knowledge of a certain topic. We propose using a rich interactive tree structure to represent syntactic, contextual, and semantic information, and adopt a tree-based convolution kernel to identify segments that carry clues about personal interactions, which are then used to construct person-interaction networks. Empirical evaluations demonstrate that the proposed method is effective in detecting and extracting the interactions between persons in textual data, outperforming other existing extraction approaches. Furthermore, readers will be able to easily navigate through the network of the interactions between persons of interest that is constructed by the proposed method, and efficiently obtain insights from a massive corpus.
Sentiment analysis is crucial method in business intelligence to extract insights, which typically begin with sentiment classification. One of the latest frameworks for generating sentence embeddings for sentiment cla...
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ISBN:
(数字)9798331506407
ISBN:
(纸本)9798331506414
Sentiment analysis is crucial method in business intelligence to extract insights, which typically begin with sentiment classification. One of the latest frameworks for generating sentence embeddings for sentiment classification is LLM2Vec, which allows Transformer decoder-based models to generate sentence embeddings for text representation. Its capability is deemed language-agnostic, which, in this study, the framework is leveraged for Tokopedia tweet sentiment analysis to prove the claim. The base decoder models used in the LLM2Vec framework were Llama 3 8B, Llama 2 7B, Sheared Llama 1.3B, and Mistral 7B. Two BERT-based models, which are the Indonesian SBERT model and IndoBERT trained with the SimCSE approach, were employed as a comparison. The generated embeddings were classified using logistic regression, SVM, and MLP Classifier. Classifiers using embedding generated by LLM2Vec with Llama 3 8B and Mistral 7B achieves on-par performance with classifiers that utilize IndoBERT SimCSE embeddings, while classifiers using embeddings generated by LLM2Vec with Llama 2 7B and Sheared Llama 1.3B achieves much lower performance. Classifiers with Indonesian SBERT embeddings achieve the highest F1 score performance. Despite slightly lower performance, this study has proven the language-agnostic capability of LLM2Vec, especially with Llama 3 8B and Mistral 7B in colloquial Bahasa Indonesia sentiment analysis, since none of the base decoders were ever trained using the Bahasa Indonesia corpus.
In this paper, we investigate the economic dispatch problem of smart grids in time-varying directed networks. The EDP essentially revolves around optimizing the distribution of generation power amongst multiple genera...
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Out-of-distribution (OOD) detectors can act as safety monitors in embedded cyber-physical systems by identifying samples outside a machine learning model’s training distribution to prevent potentially unsafe actions....
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ISBN:
(数字)9798350387957
ISBN:
(纸本)9798350387964
Out-of-distribution (OOD) detectors can act as safety monitors in embedded cyber-physical systems by identifying samples outside a machine learning model’s training distribution to prevent potentially unsafe actions. However, OOD detectors are often implemented using deep neural networks, which makes it difficult to meet real-time deadlines on embedded systems with memory and power constraints. We consider the class of variational autoencoder (VAE) based OOD detectors where OOD detection is performed in latent space, and apply quantization, pruning, and knowledge distillation. These techniques have been explored for other deep models, but no work has considered their combined effect on latent space OOD detection. While these techniques increase the VAE’s test loss, this does not correspond to a proportional decrease in OOD detection performance and we leverage this to develop lean OOD detectors capable of real-time inference on embedded CPUs and GPUs. We propose a design methodology that combines all three compression techniques and yields a significant decrease in memory and execution time while maintaining AUROC for a given OOD detector. We demonstrate this methodology with two existing OOD detectors on a Jetson Nano and reduce GPU and CPU inference time by 20% and 28% respectively while keeping AUROC within 5% of the baseline.
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