Reviewerly/exHarmony huggingface dataset:
Scene Description Using Keyframe Extraction and Image Captioning

Multimedia content has evolved drastically over the past few years and has necessitated study into audio-video content. Although visual media is widely available, the accessibility for individuals with visual impairments is inadequate. Most successful attempts at improving scene understanding require laborious manual work as automation is minimal. In recent years, there has been significant research in the field of generating descriptions for videos and the processing of images. Our primary goal is to generate accurate descriptions of the scenes in a given video and to present a framework for automating the generation of these descriptions. Through our framework, we were able to obtain better METEOR scores than previous automated implementations with a METEOR score of 0.46 with the MSVD dataset and 0.134 with the MPII Movie Description Dataset. The implementation divides the process into many incremental steps to generate descriptions for all the individual scenes in a given video. The framework involves preprocessing the given video, followed by a process known as keyframe extraction. This generates an output of the most relevant frames in the given video. This is fed as input to the image captioning algorithm that generates a caption for every single keyframe. Following this, we use a summarisation method to obtain a description of every scene in a given video. Our framework provides the ability to separate the task into many steps or modules, each of which can be separately improved. This allows the framework the scope to constantly evolve as more research and breakthroughs are achieved in the relevant fields.
Classification of Corporate Tax Compliance in Indonesia Based on k-Nearest Neighbors Algorithm

This study demonstrates the application of machine learning for classifying the tax compliance level of manufacturing companies in Indonesia. The k-nearest neighbor (kNN) algorithm was utilized to develop the machine learning model. A dataset was collected through a survey conducted directly with finance personnel in charge of the manufacturing companies. Data collection proved challenging as not all companies were willing to participate, resulting in 209 data points. Accountants analyzed the collected data and identified three classes of tax compliance levels. The developed machine learning model successfully achieved a classification accuracy of 92.86%.
Quantum Machine Learning for Security Data Analysis

In this paper, we apply Quantum Machine Learning to analyze security datasets. We compare cross-models, Quantum Machine Learning (QML) against Classical Machine Learning (CML), performance with increasing data size, and performance with increasing iteration numbers using commonly used machine learning techniques such as Neural Networks (NN), Support Vector Machines (SVM), and Logistic Regression (LR). Our study focuses on assessing the accuracy of QML and CML approaches on real-world security datasets. The results provide light on the advantages and disadvantages of both QML and CML methodologies, with implications for their use in security data analysis. The experimental findings provide useful information on the applicability of QML and CML for security-related applications. The study contributes to the growing field of quantum machine learning research, particularly in the context of security data analysis, and offers helpful advice for academics and practitioners working in this area.
Abstractive Text Summarization Based on Long-Short Transformer

With the advent of the era of Big Data, Internet transmission has become more convenient and rapid, and the textual information published and received by each person every day is exploding. Text summarization technology can automatically summarize long texts to obtain more concise summary information. However, constructing a summary model that can generate long texts with fluent semantics is still a complex research problem, which usually suffers from long-distance dependency problems, unregistered words problems, and poor readability problems. In this paper, we proposes a Long-Short Transformer based text Summarization model (LSTS) that captures both local and global information of the input document while using a pointer network to alleviate the problem of unlabeled words and introducing an attention mechanism in the Decoder to alleviate the problem of repeated words. We conducted sufficient experiments in the two large-scale datasets to verify the model’s effectiveness.
Data Mining Algorithms Predict DAT1 and COMT Dopamine Genotypes Based on Reinforcement Learning Task

Reinforcement learning implies learning from positive and negative feedback when an agent interacts with its environment. Studies mapped feedback-based teaching signals to how the human brain processes information using dopamine (among other neurotransmitters). Two main dopamine genes; DAT1 and COMT, have a key role in regulating dopamine levels in the brain. Each gene has two well-studied variations that modulate reinforcement learning differently, thus creating four DAT1-COMT interaction patterns. Every human being carries one out of the four patterns. Extracting genotype variations via biological sampling is costly and time-consuming. Here, we introduce a machine learning alternative for effectively predicting the DAT1-COMT genotype variations by training classifiers on data from a reinforcement learning task, namely: (1) . We k-nearest neighbor, random forest, and neural classifiers on reinforcement learning task data from 146 subjects who also provided blood samples for genotyping. The results showed that random forest has the best performance in predicting individual gene variations, while neural networks showed the highest performance for predicting the DAT1-COMT combined genotypes compared to biological sampling. Our approach opens a new direction for using machine learning in the form of reinforcement learning tasks and classifiers to infer key biological information.
Object on Use Case Description: Sequence Diagram Conformance based on Step Performed using Text Pre-Processing on Sipranta Application SRS

In the Use Case Diagram, a Step Performed in Tabulation contains an object's explanation flow. A sequence diagram attempts to depict in detail the interaction of objects in a system, the messages or commands sent, and the implementation time. In other words, the Sequence Diagram, like the Use Case Diagram, encapsulates an object's explanation flow in a graphical diagram. The Step Performed and the Sequence Diagram should be related, yet the Step Performed and Sequence Diagram diverge while creating the Sipranta Application SRS. By adopting Text Pre-processing, this research intends to quantify the value of conformance between the Sequence Diagram and the Step Performed in Use Case Description. This study identifies the Step Performed and the Sequence Diagram using 22 documents, 11 of which are Step Performed and 11 of which are Sequence Diagrams. In addition, the Text Analysis process produces two documents with the highest conformity: d1 with d12 of 0.933461. Cohen Cappa Score produces a validation value of 0.29195, which is included in the "Fair Agreement" category. While the reliability results from 2 groups of experts, which is 0.8275, so it is included in the "Substantial agreement" category. The results differ between the two methods, which is 0.536.
Sentiment Analysis of Public Opinion about the Omnibus Law on Twitter

The purpose of this study is to analyze public opinion on the Omnibus Law on Twitter using the KNN, Naive Bayes, and Random Forest methods. The dataset used is a collection of tweets related to the Omnibus Law, collected and processed using pre-processing algorithms to clean the data of irrelevant information. Then, three classification methods, namely KNN, Naive Bayes, and Random Forest, were used to analyze the sentiment of the tweets. The study results show that all three classification methods have a high level of accuracy in classifying the sentiment of tweets related to the Omnibus Law. However, Random Forest provides better results compared to ANN and Naive Bayes. Furthermore, sentiment analysis shows that the majority of tweets related to the Omnibus Law are negative, with emotions such as disagreement, frustration, and distrust towards government policies. The central themes of these tweets are workers' rights, environmental protection, and government responsibility. These results can provide valuable insights for policymakers and practitioners in managing public opinion about the Omnibus Law.
HSAM: Hybrid Sentiment Analysis Model for COVID-19 Contact Tracing Applications

To understand the public’s perception of COVID-19 tracing applications, previous studies were primarily based on exploratory research, surveys or machine learning methods, which are semantically weak and time-consuming. To increase the reliability of this analytical methodology, hybrid-based Twitter sentiment analysis can be applied. In this paper, we propose a hybrid model for sentiment analysis by using Valence Aware Dictionary for Sentiment Reasoning (VADER) + Support Vector Machine (SVM). We demonstrate from the numerical analysis that a VADER and SVM-based hybrid model provides the best performance with 82.3% accuracy, 0.84 precision, 0.83 recall and 0.82 F1-score. The use of hybrid-based methods is shown to be effective in analysing the public’s perception towards COVID-19 contact tracing applications using tweets collected from the UK, USA and India. Positive responses clearly outweighed negatives responses towards contact tracing, but this was contradicted by the low uptake of apps in all three nations. Our analysis, however, showed that neutral responses were 52% of the collected tweets; these tweets did not express positive or negative opinions, and subsequent tweets from the same users could not be verified, thus limiting the number of analyzed tweets available.
An Empirical Study of Alexa Skill System from Malicious Skill Developers

Amazon Alexa is a cloud-based voice service that allows users to control other devices, such as smart lights, using voice commands. It relies on different voice applications, known as "skills", that run on a cloud backend and respond to user voice requests. However, this process is not flawless and there are several places where issues could happen. This paper examines issues in the Alexa skill ecosystem from a malicious skill developer’s perspective, including the ability to override other skills during the skill invocation process and the threats of malicious scripts running on skill description pages through cross-site scripting (XSS) vulnerabilities. We are the first to identify an XSS vulnerability in the Alexa skill description page, and our finding was confirmed by Amazon. Our research aims to bring attention to these issues in the Alexa skill ecosystem and improve the security and reliability of voice applications.
Sentiment Classification of Indonesian Emotion Related to Vaccination Event using LSTM

Since 2021, Indonesians had access to the covid-19 vaccination. International programs help to fund the vaccination program in Indonesia. Subsequently, the Indonesian government organizes vaccination distribution events in stages. Vaccination began with President Joko Widodo's first vaccination, followed by the vaccination of the elderly (more than 60 years old), then the vaccination of children (6 to 12 years old), and booster vaccination. In every event of vaccination, Indonesians respond to this news. The amount of information on social media expands, so does the number of reacting tweets. This study took 1000 rows of data about vaccination events from Twitter. Then, we pre-processed the data before labeling it based on psychological emotion using Text2emotion. Finally, we classified the tweets data using Long Short Term Memory (LSTM) algorithm in Python. In conclusion, we could summarize the best LSTM model in training: testing 80:20, epoch = 20, batch size = 100, results accuracy of 98,12% for first vaccination, 99,25% for elderly vaccination, 99,62% for children vaccination, and 99,25% for booster vaccination.
Machine Learning Models for Histopathological Breast Cancer Image Classification

Breast cancer is among the most common forms of cancer, representing 30% of cancer cases in the United States per year and is the leading cause of cancer-induced mortality. Diagnosis is determined through biopsies followed by analysis of histopathological images. Machine Learning (ML) proves to be an essential image classification tool, with Convolutional Neural Networks (CNN) providing high accuracy, but long training time. To provide optimal ML models for medical use and evaluate model performance, unstructured images are converted to structured data. Singular Value Decomposition extracted features from 277,524 histopathological images and Recursive Feature Selection reduced the dimensionality of the resultant dataset. Logistic Regression, Decision Tree, XGBoost, K-Nearest neighbours and Multi-layer Perceptron ML models were trained and evaluated. K-Nearest neighbours had the highest accuracy of 77.94%. While these models do not have optimal accuracy for real world applications, their exceptional speed provides support for further improvement and exploration.
Serverless Stream-Based Processing for Real Time Credit Card Fraud Detection Using Machine Learning

Advancement in technology has exponentially increased the number of digital transactions occurring every day. In order to make accurate decisions in a situation, we use various ML algorithms to identify hidden patterns in the data. However, the dynamic nature of a customer’s behavior and adaptive nature of fraudsters makes it difficult for accurate predictions by fraud detection systems. Additionally, the redactions (due to security reasons) in the publicly available credit card fraud datasets make it difficult for an in-depth analysis. This paper studies the various data imbalance handling techniques such as Oversampling, UnderSampling, Synthetic Minority OverSampling and Adasyn and its effects on the accuracy of the machine learning algorithms for credit card fraud detection. For an in-depth analysis, four machine learning algorithms like Logistic Regression, Random Forest, XGBoost and Decision Trees have been chosen for comparison. Each of these algorithms were tuned for optimal hyperparameters before building the models. As an additional feature, the benefits of feature engineering have been studied and a real-time fraud detection system has been developed and deployed on Google Cloud Platform. XGBoost with oversampling provided the best results with an ROC Score of 0.97 and is considered the best model. Therefore, the XGBoost model was deployed on Google Cloud and feature engineering was studied on this model. The model built with feature engineering had an ROC score of 0.9880 which is higher in comparison to the model without feature engineering 0.9659. Therefore, we can conclude that feature engineering significantly improves the accuracy of the model. Finally, a dashboard was built and deployed on Google Cloud Dashboards for incorporating analytics into the fraud detection system.
Genre Classification of Bangla Poem Using Machine Learning and Deep Learning Techniques

The computational analysis of the Bangla poems is a challenging task due to the diverse linguistic, stylistic, and semantic features of the Bangla language. In this work, we prepared a dataset of 1311 Bangla poems of two separate categories: Love and Miscellaneous poem, which contain 500 and 811 poems respectively. We used word or semantic-based features to classify Bangla poems using the TF-IDF feature techniques. We used Logistic Regression, Naïve Bayes (NB), and Support Vector Machine (SVM) models for classification through machine learning, and we used Bayesian optimization techniques for hyperparameters tuning of these three models. We also used LSTM, CNN, and transformer models for this research. For the performance evaluation of the classification models, we used four evaluation metrics of precision, recall, F1-score, and accuracy. We also used the ROC-AUC curve to distinguish between all the machine learning and deep learning models. The experimental results expressed that, the transformer model achieved the highest accuracy compared to all the typical machine learning and deep learning models with an accuracy of 87%.
A Comparative Study of Machine Learning Classifiers for Speaker’s Accent Recognition

Recognizing a speaker’s accent is crucial in speech technology, natural language processing, and forensic linguistics, especially in English. With the English language having a diverse range of accents and being the most widely spoken language globally, it is vital to develop accurate accent recognition systems for speech recognition and language learning platforms. Our dataset comprises six different accents, including French English, Spanish English, German English, Italian English, UK English, and US English. Our proposed model utilizes Mel-Frequency Cepstral Coefficients (MFCCs) to generate numerical data from raw audio and predicts the speaker’s accent from our classification categories. This paper presents a comparative study of machine learning classifiers for speakers’ accent recognition. We conducted multi-class classification on our dataset and implemented necessary data preprocessing techniques to address the class imbalance and improve data quality. Eight different machine learning models, including Support Vector Machine, Random Forest, and XGBoost algorithm, were trained and compared for their performance. Random Forest achieved the best performance with an accuracy of 95.28%, and we provided a detailed analysis of each model’s strengths and weaknesses and their performance in each accent class.
YOLO-LCD: A Lightweight Algorithm for Crosswalk Detection Based on Improved YOLOv5s

Intelligent recognition and location of crosswalks is a crucial element of autonomous driving. Achieving accurate real-time crosswalk detection with limited computational resources and multiple interference scenes has been troubling researchers. To address these issues, a lightweight convolutional neural network based on YOLOv5s, namely YOLO-LCD, is proposed. First, a lightweight network incorporating a coordinate attention(CA) mechanism is proposed as a replacement for the backbone network of YOLOv5s, which leads to a significant reduction in the parameter count of YOLOv5s by using depthwise convolution operations. Secondly, for better extraction of crosswalk features, Reparameterized Generalized-FPN(RepGFPN) is adopted as the neck of YOLO-LCD. Finally, for better localization of crosswalks, we use a dynamic label assignment strategy to better define positive anchors and use the EIoU loss rather than the CIoU loss. Additionally, the detection algorithm that we propose is verified on the crosswalk dataset containing complex scenarios. The experimental results demonstrate that the YOLO-LCD algorithm obtains a surprisingly higher F1 score of 97.8% with 80.2% fewer parameters and 82.9% fewer FLOPs than YOLOv5s, and the FPS on an old CPU device is 186.4% faster. This study provides a solution for accurate real-time crosswalk detection under limited computing resources.
Task-Oriented Communications for NextG: End-to-end Deep Learning and AI Security Aspects

Communications systems to date are primarily designed with the goal of reliable transfer of digital sequences (bits). Next generation (NextG) communication systems are beginning to explore shifting this design paradigm to reliably executing a given task, such as in task-oriented communications. In this article, wireless signal classification is considered as the task for the NextG Radio Access Network (RAN), where edge devices collect wireless signals for spectrum awareness and communicate with the NextG base station (gNodeB) that needs to identify the signal label. Edge devices may not have sufficient processing power and may not be trusted to perform the signal classification task, whereas the transfer of signals to the gNodeB may not be feasible due to stringent delay, rate, and energy restrictions. Task-oriented communications is considered by jointly training the transmitter, receiver, and classifier functionalities as an encoder-decoder pair for the edge device and the gNodeB. This approach improves the accuracy compared to the separated case of signal transfer followed by classification. Adversarial machine learning poses a major security threat to the use of deep learning for task-oriented communications. A major performance loss is shown when backdoor (Trojan) and adversarial (evasion) attacks target the training and test processes of task-oriented communications.
Wireless Semantic Transmission via Revising Modules in Conventional Communications

Semantic communication has become a widely researched area due to its high spectrum efficiency and error correction capabilities. Some studies used deep learning to extract semantic features, typically resulting in end-to-end semantic communication systems that are difficult to adapt to varying wireless environments. Therefore, new semantic-based coding methods and performance metrics were investigated. Designed semantic systems incorporate various modules similar to those for traditional communication but with enhanced functionality. This study discusses recent advancements in state-of-the-art semantic communication, utilizing conventional modules in wireless systems. Moreover, through two examples, this study shows that traditional hybrid automatic repeat request and modulation methods can be re-purposed for novel semantic coding and metrics to further improve the performance of wireless semantic communication. Lastly, this study identifies some open research questions.
Time series fragmental variation trend anomaly detection method based on a temporal sequential modeling approach

Time series fragmentation anomalies are a common phenomenon that occurs when industrial systems undergo state transitions, such as the degradation of energy systems and changes in UAV trajectories. Thus, by detecting the fragmental variation trend, it is possible to maintain and control industrial systems. However, since most time series fragmentation anomalies are characterized by slow initial changes and long duration, it is difficult to detect them by the commonly used point prediction-based anomaly detection methods. Therefore, this paper proposes an anomaly detection method based on a temporal sequential predicting approach. In this paper, a statistical-based threshold generation approach is researched to improve the accuracy of the threshold. At the same time, a gated recurrent unit sequence-to-sequence (GRU-Seq2Seq) model is established to predict the trend of abnormal fragments, and the residual between the predicted fragments and the real fragments is used for anomaly detection to enhance the slowly changing fault characteristics and improve the detection rate. Finally, the experimental results based on real distributed antenna system (DAS) monitored data illustrate that show that the method proposed in this paper can effectively detect the fragmentation trend of time series.