Reviewerly/exHarmony huggingface dataset:
Twitter Opinion Sentiment Analysis Based on New Student Admission Zoning Issues Using the Naïve Bayes and TensorFlow Methods

Education is a reflection of the quality of a nation. Therefore, this matter is highly prioritized. The Indonesian government is currently implementing a zoning policy for new student admission with the aim of equal distribution of educational resources. Government policy regarding zoning has been a controversial topic. Twitter has become a platform for people to express their views and opinions. As a result of this zoning issue, pros and cons arise regarding implementing this policy. Therefore, researchers conducted sentiment analysis to understand in depth the opinions of the Twitter user community, which can be used as material for evaluating government policies related to education. Qualitative data collection techniques: data resulting from the process of crawling tweets from Twitter social media users. Next, data preprocessing is carried out. The preprocessing data was analyzed for sentiment using the Naïve Bayes Classifier Method and the TensorFlow Machine Learning Framework by producing three sentiment analysis classifications, namely positive, negative, and neutral, and measuring the accuracy of implementing the performance of these two methods. Apart from providing an overview of public opinion regarding zoning policies, this research also provides an overview of the effectiveness of the two methods in Sentiment Analysis. From the results of this research, the results of the sentiment classification of PPDB zoning problems related to public opinion tend to be negative. A comparison of the application of the method between the TensorFlow Machine Learning Framework and the Naïve Bayes Classifier is that the accuracy obtained by the TensorFlow Machine Learning Framework is 0.78, and the accuracy obtained by the Naïve Bayes classifier is 0.95. From the accuracy results, it is known that the Naïve Bayes method is better than the TensorFlow Machine Learning Framework in terms of Sentiment Analysis.
Voice Pathology Detection Using Decision Tree Classifier

Nowadays, the systems of voice disorder detection obtained considerable attention due to the high importance of this field. However, the assessment of voice pathology requires certain tools and well-trained doctors. Moreover, this assessment can be identified by a group of professionals who listen to a patient in order to assess the patient's speech to identify whether the patient's voice is pathological or normal. Nevertheless, this assessment is based on the listener’s experience. Consequently, machine learning is the most suitable technique for the detection of voice pathology, where this technique is a cost-effective and non-invasive method. Therefore, this paper presents Decision Tree (DT) algorithm based on the Mel-Frequency Cepstral Coefficient (MFCC) technique for the detection of voice pathology. The voice samples for pathological and healthy classes are collected and taken from the Saarbrucken Voice Database (SVD). The performance of the DT algorithm is assessed in terms of many evaluation measurements such as accuracy, sensitivity, precision, G-mean, F-measure, and specificity.
Entropy-weighted Voting Method for Diffusion-based Semantic Segmentation

Recently, semantic segmentation methods leveraging image generation models have garnered significant attention. In particular, approach based on Diffusion models (DDPM) that utilize mid-level activations from the diffusion network with a majority voting of distributions from several light multi-layer perceptron (MLP) have shown better performance compared to GAN-based approaches. However, utilizing a simple majority voting system is suboptimal. In this paper, we propose a novel voting method for DDPM-based semantic segmentation. Our method introduces a weighted sum of distributions, where the weights are determined by the entropy of the class prediction results obtained from each MLP model. We conduct experiments on various datasets, including LSUN-Bedroom, FFHQ-256, LSUN-Cat, and LSUN-Horse. The results demonstrate that our proposed method achieves better mean Intersection over Union (mIoU) scores compared to previous work.
Quantization-based Optimization with Perspective of Quantum Mechanics

Statistical and stochastic analysis based on thermodynamics has been the main analysis framework for stochastic global optimization. Recently, with the appearance of quantum annealing or quantum tunneling algorithms for global optimization, we require a new research framework for global optimization algorithms. In this paper, we provide the analysis for quantization-based optimization based on the Schrödinger equation to reveal what property in quantum mechanics enables global optimization. We present that the tunneling effect derived by the Schrödinger equation in quantization-based optimization enables to escape of a local minimum. Additionally, we confirm that this tunneling effect is the same property included in quantum mechanics-based global optimization. Experiments with standard multi-modal benchmark functions represent that the proposed analysis is valid.
An Analysis of the Impact of Dataset Characteristics on Data-Driven Reinforcement Learning for a Robotic Long-Horizon Task

Data-driven reinforcement learning (RL) is a cost-effective method for training agents without online interaction with the real-world environment. This approach involves collecting and storing data from various sources such as expert demonstrations or random policies, and learning from these datasets without further online interaction with the environment. However, learning an optimal behavioral model from offline data is challenging as it may not cover the entire state-action space. The paper discusses an experimental study analyzing how dataset characteristics impact the performance on a long-horizon robot manipulation task using a robotic arm. The goal of the paper is to provide guidance on strategically organizing datasets for training agents via data-driven RL.
Recursive inference for individual’s identification in video data based on the description

This study aims to enhance the Textual and Visual Representations for the identification of the same individuals within video descriptions. The character inference and ID generation for identification of individuals based on Transformer. A scene of a video visually represents that ‘who’ acts ‘what’ and ‘how’ in ‘where’ and ‘when’. Among entities composing a scene, a person plays a crucial role in representing the context of the scene. Thus, in the video description problem, various ways to identify the persons in the scene have been studied. This paper deals with the problem of ‘fill-in-the characters’ that aims to predict the local IDs of characters that appeared through several consecutive scenes. In this task, it is demanded to predict local IDs of character that it is not required to recognize each character globally (in an entire movie), but locally (within a set of 5 clips). Due to the restriction of the problem definition, global identifications of characters cannot be obtained with contemporary methods while it is often required to deploy them in services and applications. To resolve this problem, we propose the method of recursive inference of local ID. Additionally, we propose optimizing Bert embedding for mask tokens from video descriptions to infer character’s local IDs. According to the experimental results, the recursive process allows the acquisition of coherent representations among unique individuals.
Enhancing Passenger Comfort and Safeness in the Local Path Planning Strategy for Autonomous Road Vehicles Operating in New Capital City of Indonesia

The Indonesian government plans to move the capital city to East Kalimantan in 2024, focusing on developing a modern capital. Its implementation includes using autonomous vehicles to improve the efficiency and effectiveness of urban transportation. This study proposes a strategy to enhance passenger comfort and safeness in the local path planning for autonomous vehicles operating in urban environments. This approach is based on running the path planning algorithm in the configuration time space. Enhancing the safeness in the local path planning can be obtained because path planning in the configuration time-space involves predicting the movement of other vehicles, involving factors of uncertainty and variability, and considering safe distances from other vehicles. Enhancing comfort in local path planning can be obtained because it involves limiting speed and acceleration by the level of passenger comfort and smoothing to produce smooth acceleration and deceleration. Our path planning algorithm is the hybridization of Rapidly-exploring Random Tree with Ant Colonies Systems (RRT-ACS) and bidirectional rule templates. Path Planning in the Configuration Time-Space (PP+CTS) is the name of this proposed method. Several experiments were carried out to validate this proposed method. The experimental results show that the PP+CTS method can produce faster, safer, and more comfortable results. Therefore, this method is suitable for autonomous vehicles operating in a dynamic environment and can contribute to developing a more efficient future transportation system for the new Indonesian capital.
Federated Learning with Differential Privacy for Intrusion Detection in Internet of Flying Things: A Robust Approach

In recent times, Federated Learning (FL) has emerged as a decentralized framework for intelligent knowledge sharing, demonstrating a degree of privacy preservation in safeguarding users’ sensitive information across various cyber-physical networks like the Internet of Flying Things (IoFT) network. Nevertheless, there exists a vulnerability wherein adversaries can deduce clients’ gradient or parameter updates to compromise privacy. This vulnerability is particularly concerning due to the clients’ utilization of cybersecurity models aimed at securing the network against cyber intrusions and attacks. This study investigates the utilization of Gaussian and Laplace differential privacy (DP) mechanisms by clients during local training to obfuscate their model parameters, thereby mitigating the risk of data leakage. Extensive simulations utilizing the edge-IIoT dataset, validate the effectiveness of perturbing client models, in terms of upholding privacy and enhancing global model performance. Thus, demonstrating significant global model accuracy of 90%, specifically with the introduction of Laplace noise outperforming an unperturbed global model in a scalable network.
Two-Stage Image Restoration: A Shield against Adversarial Attacks on Face Identification

Various methods have been proposed to defend face identification systems against adversarial attacks by eliminating adversarial perturbations from target face images. However, it is challenging to eliminate such perturbations while preserving the crucial facial features within the images. In this paper, we propose a novel purification method for an effective defense against adversarial attacks on target face images. Our method incorporates a two-stage image restoration utilizing diffusion, consisting of sequential super-resolution-based image restoration followed by colorization-based image restoration. The experimental results demonstrate the effectiveness of our method in eliminating perturbations while preserving the identity of the facial features.
Security Analysis for Applying the Proof-of-Stake Consensus Algorithm to IoET Network in Antarctica

In Antarctica, many studies in various fields are conducted every year. Some of these studies use sensors to collect relevant data for scientific research. However, Antarctica's lack of communication resources makes it difficult to automate this data collection. In most cases, this collection is done manually and the time and space of the study are limited. Over the past few years, several alternatives for deploying remote wireless sensor networks in Antarctica have been explored. Therefore, in this paper, the analysis of the delay-tolerant network was performed based on the partial Δ-synchronized proof-of-stake (PoS) blockchain model. 82% (=1-1/(2e)) of active honest nodes must satisfy the quorum to ensure the security of the proof-of-stake blockchain. If the adversary has less than 18% of the total stake and sets the overall growth rate low, the longest chain protocol is secure. However, as Nakamoto argues in proof-of-stake blockchain protocols, an adversary cannot secure the longest-chain protocol at less than 50% of total stake. In this study, we analyzed how this network delay and balance attack could affect the proof-of-stake consensus protocol in order to find a way for an adversary to secure the longest-chain protocol at less than 50% of the total stake.
Keyword Extraction from Scientific Publications Using Local Features and Embedding Model

In the field of natural language processing (NLP), keywords are crucial for enhancing information retrieval (IR) and content summarization, as well as for optimizing search engines and organizing documents. As the volume of generated information increases, identifying keywords manually from large documents becomes more challenging and no longer feasible. Therefore, automatic keyword extraction is necessary as a cost-effective method to handle large documents and to provide scalable solutions for various applications in NLP and information management. In the academic domain, automatic keyword extraction simplifies the process of finding and categorizing scientific publications, enabling paper repositories to optimize their IR and document organizing systems. However, many methods of keyword extraction use either global semantic features based on pre-trained embedding models or local statistical features separately, which yields low results. Since a good keyword must be identified by both external knowledge and local statistical features, this paper proposes a method to improve the performance of keyword extraction from scientific publications by combining local statistical features with embedding models. The proposed method outperforms the baseline methods with an F-score of 0.70 on the SemEval2017 dataset using the SciBERT model and SVM classifier. This research confirms that both local statistical information and contextualized semantic information are important to identify keywords.
Natural Language Processing for Marketing

Promotion is one effort to increase consumers. The use of the Internet as a promotional medium has been widely used by companies because it can save costs in reaching consumers. Social media can be a platform for marketing, one of the social media that is widely used today is Twitter. However, promotions often fail if they are given to the wrong consumers. So it is necessary to identify consumers correctly when carrying out promotions. This research aims to identify users who are looking for a place to study, by analyzing messages sent via social media. In this research, the dataset from Twitter is processed first so that it is free from noise. The initial stages carried out in the research were deleting messages in the form of advertisements, filtering so that the data was clean of hashtags, links and symbols other than letter characters; tokenizing; stemming; remove stop words so that the data is clean of words that are felt to be unrelated to the topic. As a result of the initial stage, the dataset was then subjected to feature extraction using TF-IDF. The results of data set feature extraction are fed into machine learning to determine which users are looking for campuses and which users are just talking about campuses. The final result of this research is to get usernames from social media which will be promotional targets. The testing carried out in this research is more about testing the accuracy of machine learning to determine the right users who will be potential customers from campus. In this research, cross validation is used. The accuracy of Support Vector Machine (SVM) and Naive Bayes (NBC) reaches 96%. Even though in terms of accuracy it is very good, the recall and precision values are 0%. So the research objectives were not achieved. This happens because the dataset is unbalanced. Therefore, in future research it is necessary to consider the imbalance dataset problem.
Exploring Uncertainty-aware Class-wise Thresholds for Recognition Model’s Uncertainty Detection

This study addresses the current limitations in artificial intelligence models, specifically their ability to accurately predict uncertainty, a crucial aspect for improving trustworthiness and safety. We introduce a technique for estimating uncertainty in zero-shot image classification using the vision-language model, CLIP. The standard method for quantifying uncertainty in classification models is to measure the entropy from normalized output value, which is then compared to a predetermined threshold to ascertain the certainty of the model’s output. We propose a method for adapting this approach for vision-language model-based zero-shot image classification to enhance uncertainty prediction through class-wise entropy thresholding. Experimental results reveal that class-wise entropy thresholding surpasses the previous approach. Additionally, it is demonstrated that our approach is successful for detecting out-of-distribution instances.
A study on visual attention for measuring responsiveness in online performance environment

This paper is a study to measure the audience's responsiveness by visual attention in an online performance environment. Due to the nature of online performances, it is not possible to know the direct reaction of the audience, so an indirect method of measuring the level of responsiveness is needed. However, since responsiveness is a complex emotion, it is not easy to measure. Recently, studies on selection and concentration using visual attention are in progress. Therefore, the effect of visual attention on responsiveness was investigated through experiments. The experimental results indicate that visual attention is relatively related to the measure of responsiveness.
RBBA: ResNet - BERT - Bahdanau Attention for Image Caption Generator

In recent years, the topic of image caption generators has gained significant attention. Several successful projects have emerged in this field, showcasing notable advancements. Image caption generators automatically generate descriptive captions for images through the encoder and decoder mechanisms. The encoder leverages computer vision models, while the decoder utilizes natural language processing models. In this study, we aim to assess a comprehensive set of seven distinct methodologies, including six existing methods from prior research and one newly proposed. These methods are trained and evaluated with bilingual evaluation (BLEU) on the Flickr8K dataset. In our experiments, the proposed ResNet50 – BERT – Bahdanau Attention model outperforms the other models in terms of the BLEU-1 score of 0.532143 and BLEU-4 score of 0.126316.
Research on the Application of Artificial Intelligence Personal Training Terminal in Assisting Double Cycle Talent Training in Dawan District

The development of higher vocational and technical colleges in Guangdong, Hong Kong and Macao Bay Area has serious homogeneity problem in the field of AI. The focus is on "industrial robotics". This will limit the diversification and sustainability of AI in future directions. Combined with the actual situation of the Greater Bay Area, the overall design idea and related technical operation of the system are carried out. VS2013 is the design platform of the system, ASP. NETMVC is the architecture model of the system, C# is the language of the system, and SQL Server 2018 R2 is the background database of the system. A modified random forest algorithm is used to establish a model to evaluate the quality of talent cultivation. The function, module and process of the talent training system in the Greater Bay Area are studied in detail. The structure of a network interconnection equipment and the establishment of a simulation training system are designed and developed. Application examples show that the proposed method can effectively evaluate training quality and improve the accuracy of training quality evaluation.
Deep Learning-Based Power Generation Prediction for Photovoltaic Buildings

Photovoltaic integration in buildings is an important application area for photovoltaic technology, with the advantages of low cost, easy installation, and non-pollution. The introduction of photovoltaic (PV) technologies has enabled the transformation of buildings into state-of-the-art renewable energy complexes. However, photovoltaic power generation is sensitive to environmental and meteorological influences that can easily affect the reliable supply and economic distribution of energy on the local grid. Therefore, in this work, a deep learning model ConvGRU-Attention is developed to achieve the prediction of energy production from PV buildings, which contributes to the economic efficiency and security of grid operation. First of all, this work first analyses the meteorological factors influencing the energy production of PV buildings using the Pearson correlation coefficient and identifies the main meteorological factors that mainly affect energy production; A DBSCAN clustering algorithm is then used to cluster and filter the historical PV data to remove both isolated points and noisy points from the data; Finally, given the large amount of PV generation data and the large variation in the characteristics of electricity generation under different weather conditions, a focus mechanism will be put in place to improve the ConvGRU network for PV generation forecasting. The improved ConvGRU-Attention model in this paper is experimentally compared and analysed with the mainstream network model. The experimental results show that the MAE of the improved network model in this paper is 0.307, MSE is 0.231, R2 is 0.981, RMSE is 0.481, which has better prediction accuracy and stability compared with other models.
Detecting encrypted traffic activities and patterns in ZigBee network Data

With the increase in data transmissions and network traffic over the years, there has been an increase in concerns about protecting network data and information from snooping. With this concern, encryptions are incorporated into network protocols. From wireless protocols to web and phone applications, systems that handle the going and coming of data on the network have applied different kinds of encryptions to protect the confidentiality and integrity of their data transfers. The addition of encryptions poses a new question. What will be observed from encrypted traffic data? This work in progress research delivers an in-depth overview of the ZigBee protocol and analyzes encrypted ZigBee traffic on the ZigBee network. From our analysis, we developed possible strategies for ZigBee traffic analysis. Adopting the proposed strategy makes it possible to detect encrypted traffic activities and patterns of use on the ZigBee network. To the best of our knowledge, this is the first work that tries to understand encrypted ZigBee traffic. By understanding what can be gained from encrypted traffic, this work will benefit the security and privacy of the ZigBee protocol.