Reviewerly/exHarmony huggingface dataset:
Implementation of SMOTE and whale optimization algorithm on breast cancer classification using backpropagation

Breast cancer, which is characterized by uncontrolled cell growth, is the primary cause of mortality among women worldwide. The unchecked proliferation of cells leads to the formation of a mass or tumor. Generally, the absence of timely and efficient treatment contributes to this phenomenon. To prevent breast cancer, one of the strategies involves the classification of malignant and non-malignant types. For this particular investigation, the Breast Cancer Wisconsin dataset (original) comprising 699 instances with 11 classes and 1 target attribute was utilized. Synthetic Minority Oversampling (SMOTE) was employed to balance the dataset, with the Backpropagation classification algorithm and the Whale Optimization Algorithm (WOA) serving as optimization techniques. The main objectives of this study were to analyze the impact of the backpropagation method and SMOTE, examine the effect of the backpropagation method in conjunction with WOA, and assess the outcome of using the backpropagation method and SMOTE after incorporating WOA. The evaluation of the study's findings was performed using a confusion matrix and the Area Under the Curve (AUC) metric. The research outcomes based on the application of backpropagation yielded an accuracy rate of 96%, precision of 94%, recall of 95%, and an AUC of 96%. Subsequently, upon implementing SMOTE and WOA, the performance of the backpropagation method improved, resulting in an accuracy rate of 99%, precision of 97%, recall of 97%, and an AUC of 98%. This notable enhancement in performance suggests that the utilization of SMOTE and WOA effectively enhances accuracy. However, it is important to note that the observed improvements are relatively modest in nature.
Feature Selection Using Firefly Algorithm With Tree-Based Classification In Software Defect Prediction

Defects that occur in software products are a universal occurrence. Software defect prediction is usually carried out to determine the performance, accuracy, precision and performance of the prediction model or method used in research, using various kinds of datasets. Software defect prediction is one of the Software Engineering studies that is of great concern to researchers. This research was conducted to determine the performance of tree-based classification algorithms including Decision Trees, Random Forests and Deep Forests without using feature selection and using firefly feature selection. And also know the tree-based classification algorithm with firefly feature selection which can provide better software defect prediction performance. The dataset used in this study is the ReLink dataset which consists of Apache, Safe and Zxing. Then the data is divided into testing data and training data with 10-fold cross validation. Then feature selection is performed using the Firefly Algorithm. Each ReLink dataset will be processed by each tree-based classification algorithm, namely Decision Tree, Random Forest and Deep Forest according to the results of the firefly feature selection. Performance evaluation uses the AUC value (Area under the ROC Curve). Research was conducted using google collab and the average AUC value generated by Firefly-Decision Tree is 0.66, the average AUC value generated by Firefly-Random Forest is 0.77, and the average AUC value generated by Firefly-Deep Forest is 0, 76. The results of this study indicate that the approach using the Firefly algorithm with Random Forest classification can work better in predicting software damage compared to other tree-based algorithms. In previous studies, tree-based classification with hyperparameter tuning on software defect prediction datasets obtained quite good results. In another study, the classification performance of SVM, Naïve Bayes and K-nearest neighbor with firefly feature selection resulted in improved performance. Therefore, this research was conducted to determine the performance of a tree-based algorithm using the firefly selection feature.
LSTM and Bi-LSTM Models For Identifying Natural Disasters Reports From Social Media

Natural disaster events are occurrences that cause significant losses, primarily resulting in environmental and property damage and in the worst cases, even loss of life. In some cases of natural disasters, social media has been utilized as the fastest information bridge to inform many people, especially through platforms like Twitter. To provide accurate categorization of information, the field of text mining can be leveraged. This study implements a combination of the word2vec and LSTM methods and the combination of word2vec and Bi-LSTM to determine which method is the most accurate for use in the case study of news related to disaster events. The utility of word2vec lies in its feature extraction method, transforming textual data into vector form for processing in the classification stage. On the other hand, the LSTM and Bi-LSTM methods are used as classification techniques to categorize the vectorized data resulting from the extraction process. The experimental results show an accuracy of 70.67% for the combination of word2vec and LSTM and an accuracy of 72.17% for the combination of word2vec and Bi-LSTM. This indicates an improvement of 1.5% achieved by combining the word2vec and Bi-LSTM methods. This research is significant in identifying the comparative performance of each combination method, word2vec + LSTM and word2vec + Bi-LSTM, to determine the best-performing combination in the process of classifying data related to earthquake natural disasters. The study also offers insights into various parameters present in the word2vec, LSTM, and Bi-LSTM methods that researchers can determine.
Characterizing and understanding deep neural network batching systems on GPUs

As neural network inference demands are ever-increasing in intelligent applications, the performance optimization of model serving becomes a challenging problem. Dynamic batching is an important feature of contemporary deep learning serving systems, which combines multiple requests of model inference and executes them together to improve the system’s throughput. However, the behavior characteristics of each part in deep neural network batching systems as well as their performance impact on different model structures are still unknown. In this paper, we characterize the batching system by leveraging three representative deep neural networks on GPUs, performing a systematic analysis of the performance effects from the request batching module, model slicing module, and stage reorchestrating module. Based on experimental results, several insights and recommendations are offered to facilitate the system design and optimization for deep learning serving.
A Computable Phenotype for the Identification of Sexual and Gender Minorities in Electronic Health Records.

Sexual gender minorities, including lesbian, gay, and bisexual (LGB) individuals face unique challenges due to discrimination, stigma, and marginalization, which negatively impact their well-being. Electronic health record (EHR) systems present an opportunity for LGB research, but accurately identifying LGB individuals in EHRs is challenging. Our study developed and validated a rule-based computable phenotype (CP) to identify LGB individuals and their subgroups using both structured data and unstructured clinical narratives from a large integrated health system. Validating against a sample of 537 chart-reviewed patients, our three best performing CP algorithms balancing different performance metrics, each achieved sensitivity of 1.000, PPV of 0.982, and F1-score of 0.875 in identifying LGB individuals, respectively. Applying the three best-performing CPs, our study also found that the LGB population is younger and experiences a disproportionate burden of adverse health outcomes, particularly mental health distress.
Challenges and Opportunities for Professional Medical Publications Writers to Contribute to Plain Language Summaries (PLS) in an AI/ML Environment - A Consumer Health Informatics Systematic Review.

Professional medical publications writers (PMWs) cover a wide range of biomedical writing activities that recently includes translation of biomedical publications to plain language summaries (PLS). The consumer health informatics literature (CHI) consistently describes the importance of incorporating health literacy principles in any natural language processing (NLP) app designed to communicate medical information to lay audiences, particularly patients. In this stepwise systematic review, we searched PubMed indexed literature for CHI NLP-based apps that have the potential to assist PMWs in developing text based PLS. Results showed that available apps are limited to patient portals and other technologies used to communicate medical text and reports from electronic health records. PMWs can apply the lessons learned from CHI NLP-based apps to supervise development of tools specific to text simplification and summarization for PLS from biomedical publications.
Impacto da pandemia da covid-19 no estilo de vida dos idosos: uma revisão integrativa

Objective: this study aimed to analyze, through the literature, the impacts generated by the pandemic on the lifestyle of this population.Methodology: this is an integrative literature review, which aims to search, critically evaluate, and synthesize the results of research on the topic in question.Thirteen articles were selected according to the established inclusion criteria.Results: after detailed reading, it was observed that the main impacts evidenced in the literature on the lifestyle of the elderly caused by the pandemic of COVID-19 are: poor eating habits, not taking medications correctly, sedentary lifestyle, increased use of electronics, increased anxiety, depression, sleep disorders, stress, increased weight, muscle weakness, increased smoking and alcoholism.Conclusion: in view of the studies approached and analyzed, it was noticed that the changes found are of negative and harmful effects on the lifestyle of the elderly.It was also found that the literature is in agreement about the theme and that it is convenient to conduct more studies on the subject, especially nationally.
Moving Beyond the Stigma: Understanding and Overcoming the Resistance to the Acceptance and Adoption of Artificial Intelligence Chatbots

Artificial intelligence chatbots may fundamentally transform academic research, automate mundane tasks, and enhance productivity. However, the integration of artificial intelligence chatbots (AIc) is impeded by a complex stigma deeply rooted in individuals’ misconceptions and apprehension, including concerns about academic integrity, job displacement, data privacy, and algorithmic bias. The aim of this study was to scrutinize the origins and impacts of the stigma associated with artificial intelligence chatbots within the realm of academic research and to propose strategies to mitigate such stigmas. This study draws parallels between the reception of artificial intelligence chatbots and previous transformative technologies, presenting case studies illustrating the spectrum of responses to the integration of artificial intelligence chatbots into academic research. This study identifies the need for a shift in mindset from perceiving artificial intelligence chatbots as threats to recognizing them as facilitators of efficiency and innovation. It also underscores the importance of understanding these models as tools that aid researchers but do not replace the need for human expertise and judgment. We further highlighted the role of education, transparency, regulation, and ethical guidelines in overcoming the stigma associated with artificial intelligence chatbots. Given how adaptable people are, the surrounding stigma will likely fade with time. We support a cooperative strategy with continuing education and discussion to maximize the benefits of artificial intelligence chatbots while minimizing their drawbacks, hopefully paving the way for their ethical and successful application in scholarly research.
Speech Recognition Interface for Updating Electronic Medical Records with Automatic Itemization

In this study, we propose a voice-based interface designed to enhance the efficiency of populating electronic medical records by healthcare professionals. The interface utilizes advanced voice recognition technology to interpret the verbal input of medical personnel, then leverages a large-scale language model to correctly format the recognized sentences to match for the corresponding sections of the patient's electronic medical record. This approach should expedite the process of updating electronic medical records by eliminating the need for manual data entry. Three variations of our interface, with various strengths and weaknesses are proposed.
Automatic Speech Recognition Using Linguistic and Verbal/Non-Verbal Information

This study proposes an automatic speech recognition (ASR) model that simultaneously recognizes linguistic information and nine types of verbal/non-verbal (VNV) phenomena (fillers, laughter, small voice, interrogative ascending tones, end of speech, pronunciation errors, word fragments, speech related to the flow of conversation and speech in dialects or foreign languages). Although ASR technology has rapidly advanced in recent years, most ASR systems are only able to recognize linguistic information, and are still unable to detect VNV phenomena. However, recognition of VNV phenomena is important because speakers often convey their intentions during spontaneous speech using these types of utterances. We achieve VNV recognition by applying VNV phenomena tags to the transcription of the speech used to train the ASR model. Our experimental results when using data from the Corpus of Everyday Japanese Conversation revealed that speech recognition accuracy is improved when VNV phenomena are also recognized. We also observed that the best recognition performance was achieved when the VNV phenomena tags appeared before the linguistic information in the speech transcription used for model training.
Implementing Quantization to Indonesian BERT Language Model

In last several years, BERT has gain its own popularity and become widely adopted for various task and language, including Indonesian. Despite its effectiveness, the humongous size and the costly attention mechanism of BERT yield various efficiency challenges related to latency and model size. Many efforts have been introduce to improve the efficiency of BERT models, but unfortunately, there are very limited efforts to evaluate the model's performance in Indonesian. To address this issue, this paper evaluates compression techniques through quantization for an Indonesian version of the BERT model, namely IndoBERT. Experiments on 7 downstream tasks show a similar trend reported in the previous work where the quantized model achieves slightly degraded performance compared to the full precision model. However, we observe a huge degradation in extreme cases such as 4-bit quantization and sequence labeling tasks when using post-training quantization. While quantization aware training is found to be effective and efficient regardless of the bit depth. Furthermore, our experimental results show that the quantized model can achieve in terms of size and inference speed compared to the full precision model. Furthermore, our experimental results show that the quantized model can achieve in terms of size and inference speed compared to the full precision model. The analysis presented in this work provides that quantization can perform well in Indonesian model, which is an important avenue for bringing better efficiency to various Indonesian language tasks.
Learning to Prompt for Vision-Language Emotion Recognition

In the realm of vision-language tasks, the selection of prompts plays a crucial role in determining model performance, particularly in complex tasks such as emotion recognition. Despite the promise shown by models like CLIP and CoOp, their performance can exhibit significant variability, contingent upon the selection and adaptability of prompts. Addressing this challenge, this paper introduces an innovative method that exploits the philosophy of learning to learn. This novel approach facilitates the design of an emotion recognition model capable of dynamically optimizing prompt selection according to the specific demands of a given task. We empirically demonstrate that our approach outperforms established models such as CLIP and CoOp in both few-shot and zero-shot settings across three datasets, indicating its potential to enhance the generalization and adaptation capabilities of vision-language emotion recognition models.
A Teacher and Student Facial Expression Recognition Model Based on Classroom Teaching Videos

“Dear teacher make students believe his way”. Related literature research shows that positive emotions of teachers’ and students have a greater impact on the effectiveness of classroom teaching, and help to improve the learning efficiency of the students. After summarizing the domestic and foreign research status, this paper focuses on the realization of face recognition technology in AI evaluation of teacher teaching. The main research work is as follows. Firstly, this paper preprocesses the obtained classroom teaching video, including framing, removing invalid frames, etc.; followed, based on the Roboflow labeling tool, self-construct a teacher’s and student’s classroom teaching facial expression data set; then analyzes the characteristics of the general emotion data set; moreover, selects the general data set FER2013 as the training set; and the teacher’s facial expressions are divided into three categories: positive, negative, and neutral, which is exported as a verification set subsequently; Finally, a teacher and student expression recognition algorithm is designed based on deep convolutional network. This article mainly uses the classic VGGNet as the main body of the network, and simultaneously completes feature extraction and expression classification. Simulation experiments show that the model has a high accuracy in teacher’s and student’s FER.
Domain Adaptation with a Non-Parallel Target Domain Corpus

The accuracy of conventional ASR systems depends largely on the amount of speech and associated transcription data available in the target domain for model training. However, preparing parallel training data each time a model is built for a new domain is costly and time-consuming. To solve this problem, we propose a method of domain adaptation that does not require the use of a large amount of parallel data from the target domain since most of the data used for model training is not from the target domain that uses only non-parallel speech and text (data in which the two contents do not correspond to each other), which are relatively inexpensive to collect. We performed domain adaptation in two steps. 1) A pre-trained wav2vec 2.0 model is further pre-trained with a large amount of target-domain speech data and then fine-tuned with a large amount of non-target-domain speech and its transcriptions. 2) Density Ratio Approach (DRA) on the language model trained with target domain text during inference. In an evaluation experiment, an ASR model trained using the proposed method obtained a CER approximately 9 pts lower than the same ASR model trained with a non-target-domain corpus when target-domain speech was used in the test set, a 33.6 % reduction in relative CER.
Intelligent agents driven data analytics using Large Language Models

In the contemporary era, the surge in data-driven decision-making underscores the significance of extracting actionable insights from intricate data sources. Large Language Models (LLMs) emerge as transformative entities reshaping data analytics paradigms. These models not only facilitate data insight generation but also enhance data visualization when integrated with specialized tools. With their extensive parameters, LLMs possess an innate ability to discern nuanced intricacies and patterns within data, transcending conventional analytic methods. Nonetheless, challenges arise, including the potential for generating non-factual information and navigating complex multi-step procedures. We introduces an approach to enhance LLMs analytic capacities. The main contribution is an agent-based graph where multiple agents collaboratively address data analytics challenges. These agents encompass data sources, Python libraries for data visualization, and even other LLMs for data digestion and summarization. Notably, this methodology enable LLMs as inquisitive agents, actively seeking clarity and engaging other agents for task completion. This dynamic, graph search, inter-agent communication framework promises a more comprehensive data analytics process with reduced human feedback.
Domain Adaptation Using Density Ratio Approach and CTC Decoder for Streaming Speech Recognition

This study proposes a method of streaming automatic speech recognition (ASR) for Japanese speech using domain adaptation. The development of ASR technology has led to its use in various applications, however it has been observed that recognition accuracy declines when there is a mismatch between the domains of the training data and the speech to be recognized (i.e., the target data). In particular, the accuracy of online speech recognition, also known as streaming ASR, which is becoming popular in real-world applications, is degraded by such differences. Since creating custom training data for specialized applications is costly, domain adaptation is often used to transform existing training corpora. In this study, we propose a method of domain adaptation is often used to transform existing training corpora. In this study, we propose a method of domain adaptation for streaming ASR of Japanese speech, utilizing Density Ratio Approach (DRA), which involves the use of Language Models (LMs) trained with large amounts of text data. To facilitate recognition of streaming speech data, a CTC decoder is used to successively replace linguistic information on a frame-by-frame basis. Recognition results are then obtained through greedy searches. Taking into consideration CTC's assumption of conditional independence, we used 1-gram, 2-gram and 3-gram LMs for the subtraction and addition of language information during domain adaptation with DRA, for replacement of the selected linguistic information. Experimental results confirm that the proposed method enhances speech recognition accuracy when there is a mismatch between the data in the training and target domains.
Natural Language Generation for Socially Competent Task-Oriented Agent

Conversational Artificial Intelligence has been used in many industrial use cases, especially in customer service. While emotion is often associated with chit-chat and open-domain interactions, it is still a core aspect of human interaction. Though arguably less frequent, a customer’s emotional and social displays are very meaningful in official task-oriented dialogue. Hence why it is crucial for vocal dialogue systems to learn how to properly detect and adapt their behaviour to respond to this social context. This context can be represented by textual and semantic content, but it can also be multimodal, using prosody elements extracted from speech analysis. This research aims to investigate a method to take these social and emotional components for conditional generation, in order to develop a socially competent, task-oriented conversational agent. In this Doctoral Consortium paper, I introduce my PhD subject, its scope, and the main research questions we tackle. I give an overview of the related works, before presenting the methodology, the work done, and the avenues we intend on exploring in order to respond to our established research objectives.