Reviewerly/exHarmony huggingface dataset:
Image Captioning for Visual Surveillance System

Closed Circuit Television (CCTV) for surveillance has evolved from a passive manual system to an automated, integrated intelligent system. However, image processing in this system is generally based on detection and classification, so the resulting output does not give as good an impression as the human language description. In this research, we implemented Image Captioning with Bahasa output on office area CCTV data. This model used pre-trained EfficientNetV2s as the image extractor and GRU as the language generator. We explored the best architecture by adding attention blocks, dropout, and batch normalization layers. We also searched for the best hyperparameter values for the optimizer, activation function, weight initialization, and batch size. The best model evaluated and produced a moderate average score for BLEU-1 of 0.5710 and a Meteor score of 0.3733. In general, the output description is acceptable. However, there are still many captions that have correct grammar but have semantic errors. In the future, it is necessary to develop a more reliable model to be implemented as a new generation monitoring system that produces output in a more varied and natural language.
How the Representation Similarity Between Layers Affects the Random Vector Functional Link Neural Network?

Random vector functional link neural network (RVFLNN) is a fast learning model for single-hidden layer neural networks. The optimal output weight vector of RVFLNN is determined by the pseudo-inverse of the concatenation of the input matrix and enhancement matrix; however, how the representation similarity of the input layer and enhancement layer affects the random vector functional link neural network is still unknown in the literature. To this end, in this paper, we conduct a comprehensive investigation of the relationship between the input layer and enhancement layer. Moreover, by using the canonical correlation analysis (CCA) to define the statistical similarity of the input layer and the enhancement layer, we propose a pretraining method for RVFLNN. The main idea is to adjust the network weights connecting the input layer and enhancement layer to decrease the similarity between layers using the gradient method. Simulations are conducted on ten UCI classification datasets to verify the effectiveness of the proposed pretraining algorithms.
Bridging the Gap: A Messaging App Simulation for Comprehending Differential Privacy

Differential privacy is a mathematical framework that guarantees privacy while permitting meaningful data analysis. However, comprehending complex ideas like differential privacy can be difficult, especially for non-technical audiences. This paper presents a novel web application that simulates a messaging scenario to illustrate the concept and practical implementation of differential privacy. Our application demonstrates how differential privacy can protect individual user privacy while still enabling aggregate data analysis. We also explain the key aspects of differential privacy, such as epsilon, noise addition, and Laplace mechanism, and how they affect the privacy-utility trade-off.
Explainable Prediction Model of Logging Lithology Classification Based on XGBoost and SHAP

Lithology identification is a key and difficult problem in geological interpretation of reservoirs. To accurately predict the rock types in the target area of oil and gas exploration and provide correct reservoir lithology, an explainable model of lithology classification combining XGBoost and SHAP has been proposed. The model can achieve both good classification and explanation ability. Firstly, the lithologic classification model is established by the limit gradient lifting (XGBoost) algorithm, and the K-fold cross-validation (K-fold CV) and grid search algorithm are used to find the optimal hyperparameter combination of the model. Then, the predicted value of the optimized XGBoost model is compared with the random forest algorithm and support vector machine algorithm by using different evaluation indexes, and the overall and local explanation of the predicted result of the XGBoost model is given by using the SHAP method. The experimental results show that the lithologic identification model established by the XGBoost method optimized by hyperparameter adjustment has achieved good effects in the actual data test, and the SHAP method can effectively explain the output of the XGBoost model.
Research on Big Data Mining and Application Based on Roll Up and Drill Down Techniques

To improve the efficiency of big data mining while ensuring the accuracy of mining results. Propose a big data mining method based on roll up and drill down techniques. By using roll up techniques to achieve feature aggregation, reduce the number of features, improve time efficiency, and analyze the mining results to determine the target clustering features. Drill down on the target clustering features to ensure the accuracy of the mining results. The experiment uses the independently created Mongolian medicine formula datasets as a case study to provide specific applications. The experimental results show that the time efficiency and efficiency improvement rate of mining result generation have been improved to a certain extent under various expansion data multiples, and the three error rate indicators of mining results have also been improved. Therefore, this method can not only improve the efficiency of big data mining, but also ensure the accuracy of mining results.
A Heuristic Repelling Strategy for Accelerating the Global Convergence of Intelligent Optimization Algorithms - A Case Study on Differential Evolution Algorithm

This work proposed a heuristic-based strategy to improve the global convergence of Intelligent Optimization Al-gorithms (IOAs) by scrutinizing their evolutionary mechanisms. Specifically, according to Gaussian and Cauchy distribution functions, six potential individuals were generated primarily based on the current best, worst, and farthest one from the best in the population. Then, they replaced the six least effective individuals, and the rest remained unchanged to form a new population to continue evolving. This process was repeated until termination. A case study on the Differential Evolution (DE) algorithm was detailed. Simulations on 21 benchmark functions, including 13 high-dimensional ones, demonstrated improved accuracy and reduced computational effort compared to some existing methods. The parameter settings also became more lenient, and the strategy showed potential for applicability to other swarm intelligence-like algorithms.
Cancer Classification in Imbalance Data Using Double Filtering Approach

Cancer, also known as malignant neoplasm, is a complex and potentially fatal disease characterized by uncontrolled and abnormal cell growth in the body. The main problems with microarray cancer studies are the high curse of dimensionality and small sample size caused by redundant and irrelevant genes. To deal with the number of features that exceed the amount of data, this research purposed double filtering method Lasso-GA, Lasso is used to select the features based on feature correlation while Genetic Algorithm is used to optimize the most important features with accuracy traditional machine learning as its fitness function. The results show how effective the suggested method is; in breast cancer, the linear SVC model achieves excellent accuracy (0.93), precision (0.94), recall (0.94), and F1 score (0.94), while in lung cancer, the linear SVC, random forest, and logistic regression models perform well (accuracy: 0.95, precision: 0.92, recall: 1, F1 score: 0.95). Logistic regression is the most effective method for bladder cancer, with an accuracy of 0.82, precision of 0.77, recall of 1, and F1 score of 0.87.
FusNet: A Deep Information Fusion Network for Medical Image Segmentation

In recent years, significant progress has been achieved in medical image segmentation by leveraging deep neural networks based on the U-Net architecture and skip connections. While widely used in medical image segmentation, convolutional neural networks (CNNs) have limitations in learning global semantic information due to their localized convolutional operations. Additionally, fusion methods like element-wise addition or concatenation in encoder-decoder architectures often introduce unnecessary information, leading to the loss of local details. To address these challenges, we introduce FusNet, an innovative information fusion network. FusNet combines features from Swin Transformer and Res2Net, enhancing global dependency relationships and low-level spatial details. A fundamental subtraction unit (DIV) is used to eliminate redundant information in each layer, reducing high-level up-sampling-induced redundancy. Finally, FusNet aggregates features from various layers to produce segmentation results in the decoder. Evaluation on four diverse medical image datasets, including polyps, eye diseases, cell nuclei, and breast cancer, demonstrates FusNet's remarkable segmentation performance and robustness, surpassing alternative methods. FusNet holds significant potential for improving medical image segmentation tasks. The source code of FusNet is freely available at https://github.com/HaoWuLab-Bioinformatics/FusNet
MIMRD: An Enhanced Masked Image Modeling Reverse Distillation for Anomaly Detection

Anomaly detection is a meaningful and important research topic. Currently, unsupervised methods for anomaly detection demonstrate exceptional performance. Reverse distillation (RD) is an effective approach that ensures minimal latency while demonstrating overly strong generalization ability, causing abnormal features to be generalized as normal features in anomaly detection tasks. However, there may be instances where the model's generalization ability becomes overly dominant. To address this problem, we propose an enhanced masked image modeling reverse distillation (MIMRD). In this paper, we establish a state-of-the-art benchmark on the challenging MVTec dataset for both identifying and localizing anomalies. This model reconstructs the features with pseudo anomalies and enhance the difference in anomalousness through utilization of projection layer. Then, the student model regresses to the normal features generated by the teacher model. From the experimental results, our method has achieved competitive results without a significant increase in inference time.
Compressing Neural Networks with Two-Layer Decoupling

The single-layer decoupling problem has recently been used for the compression of neural networks. However, methods that are based on the single-layer decoupling problem only allow the compression of a neural network to a single flexible layer. As a result, compressing more complex networks leads to worse approximations of the original network due to only having one flexible layer. Having the ability to compress to more than one flexible layer thus allows to better approximate the underlying network compared to compression into only a single flexible layer. Performing compression into more than one flexible layer corresponds to solving a multilayer decoupling problem. As a first step towards general multilayer decoupling, this work introduces a method for solving the two-layer decoupling problem in the approximate case. This method enables the compression of neural networks into two flexible layers.
Requirement-to-Code Traceability in MVC Architecture Using Property Listing Task

The smooth traceability between software artifacts indicates successful software development. However, performing manual tracing is expensive. This research advances our knowledge of PLT's capabilities and potential applications in real-world software engineering scenarios. The previously developed PLT concept is tailored to MVC characteristics. This article proposes a novel requirement-to-code traceability in the MVC architecture. It uses a Property Listing Task to extract and represent information from each software artifact. Then, three rules are applied in finding the trace links of user stories and three separation concerns of MVC. A medium-sized industrial project was used to confirm the performance of the proposed method against the ground truth. Results show that it performs well with overall precision, recall, and F-measure each 89%, 92%, and 90%. The results of this case study can provide invaluable insights into the practical applicability and impact of our traceability solution in real-world software development scenarios.
Digital Image Enhancement Using MirNet and Zero-Deep Curve Estimation (Zero-DCE)

Increasing public concern about security due to the lack of CCTV monitoring and lighting at night. The problem is that the quality of CCTV is low if the room does not have normal lighting or even tends to be dark. Images produced from cameras that have poor performance produce dark images so that the resulting information cannot be used for the next process. In this research, the image improvement results of the MIRNet and Zero-DCE methods were compared, from this comparison the best method was obtained. To improve the quality of low/dark images, deep learning-based approaches such as MIRNet and Zero-Deep Curve Estimation (Zero-DCE) have received great attention. This study aims to compare the performance of both approaches in the context of image enhancement in low-light conditions. This research uses Low-Light (LoL) and real CCTV datasets. The data is divided into training and testing data. Then the data was analyzed using MIRNet and Zero-DCE. The test results using MIRNet have a PSNR of 27.86 dB and an MSE of 10.595. Meanwhile, Zero-DCE has average PSNR and MSE values of 28.055 dB and 10.204. The PSNR and MSE results show that Zero-DCE has the best average results between the two existing methods.
Smooth QR Decomposition of Polynomial Matrices

This paper presents a novel algorithm for determining a compact order QR decomposition of a polynomial matrix, where both the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{Q}$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{R}$</tex> factors themselves are approximated by polynomial matrices. The QR factorisation is subject to an allpass ambiguity; existing time domain methods can lead to factorisations of high order. The proposed algorithm performs the conventional QR decomposition the discrete Fourier transform domain. Subsequently, it establishes phase coherence between adjacent bins through a phase smoothing procedure, aimed at obtaining compact-order factors. The method is validated through experiments over an ensemble of randomized polynomial matrices and shown to outperform state-of-the-art algorithms.
Indonesian Dewey Decimal Classification System Using Support Vector Machine and Neural Network Algorithms

The Dewey Decimal Classification (DDC) system is used to classify books, allowing books to be categorized according to their respective fields in the library. However, knowledge and understanding of the DDC classification system are not widely known among the general public but are primarily known by librarians. Therefore, an automated DDC classification system is needed to simplify the classification system and recognize the different types of books based on their classifications. Given this problem statement, the researchers were interested in studying the DDC classification system using the support vector machine (SVM) and neural network (NN) approaches. This research compares SVM and NN, for algorithms. The study utilizes a dataset of books from one of the universities in Indonesia. Subsequently, the model is tested under various scenarios by varying the dataset, vectorization methods, and classification algorithms. The results indicate that the SVM model with a combination of TF-IDF feature extraction techniques provides the best performance, reaching 0.79. The research also demonstrates that the addition of keyword features to the title can improve performance.
Multi-Target Tracking with Transferable Convolutional Neural Networks

Multi-target tracking (MTT) is a classical signal processing task, where the goal is to estimate the states of an unknown number of moving targets from noisy sensor measurements. In this paper, we revisit MTT from a deep learning perspective and propose a convolutional neural network (CNN) architecture to tackle it. We represent the target states and sensor measurements as images and recast the problem as an image-to-image prediction task. Then we train a fully convolutional model at small tracking areas and transfer it to much larger areas with numerous targets and sensors. This transfer learning approach enables MTT at a large scale and is also theoretically supported by our novel analysis that bounds the generalization error. In practice, the proposed transferable CNN architecture outperforms random finite set filters on the MTT task with 10 targets and transfers without re-training to a larger MTT task with 250 targets with a 29% performance improvement.
Development of Chatbot Features for Stunting Education Using Artificial Neural Network Algorithm

Stunting is a global public health problem that continues to cause concern, especially in developing countries like Indonesia. The importance of education about stunting, especially for pregnant women and parents of toddlers, cannot be ignored. This is due to the high prevalence of stunting in Indonesia. Based on these problems, educational media is needed that is in accordance with technological developments. This research aims to develop a website-based stunting education chatbot application using the Artificial Neural Network algorithm. The method used in this research is NLP Pipeline. The results showed that the ANN algorithm succeeded in achieving an accuracy of 99.33% and a loss of 1.17%. Testing of the chatbot is done by entering 5 questions into the application, and the results of the chatbot can provide answers correctly.
Solutions of the Graph Matching Problem Using Graph Signals

The problem of graph matching involves finding a node correspondence between two unlabeled graphs with known topologies, which has applications in various fields such as social network analysis and species identification. In this paper, we tackle this problem without prior knowledge of the underlying graphs and using only observations of graph signals. We assume that these signals are generated by applying graph filters to graph signal excitations. We construct sample covariance matrices from the graph signals and match the nodes based on the selected eigenvectors of the sample covariance matrices. Numerical results demonstrate that our proposed algorithm outperforms the existing method that matches two estimated underlying graphs learned from the graph signals.
Feature Attribution-Guided Contrastive Learning: Mitigating Lexical Bias in Toxic Speech Detection

Automatic detection methods for toxic speech can potentially curb the dissemination of offensive, abusive, hateful, and other toxic speech on social media. However, these methods often show bias by overreacting to words such as identity terms, profanity, and swear words that occur in non-toxic speech and erroneously classifying them as toxic speech. Recent research has attempted to regularise relevant biased words in predefined lexicons to mitigate their impact on model classification. However, this approach faces two challenges. Firstly, words such as pro-fanity and swear words in biased words play a crucial role in the model's ability to identify toxic speech, and excessive suppression of these words using regularization methods can detrimentally affect the performance of the classification model. Secondly, due to the limitations of regularization techniques, existing methods rely on manually constructed biased word dictionaries, which can only mitigate bias associated with identity-related terms. This bias should not significantly impact hate speech prediction. It is challenging to encompass lexical biases such as profanity and swear words that are specific to different datasets beyond identity terms To address the above challenges, this paper proposes a novel feature attribution-guided contrastive learning method. The method consists of two repeated steps across epochs. In each epoch, first identifies keywords in sentences that are crucial for predicting toxicity through feature attribution. Then it applies contrastive learning to separate samples that have common toxic keywords but different labels. Experiments show that our approach can mitigate lexical bias in toxic speech detection without any data augmentation or prior knowledge and achieve competitive performance gains.
Robust Identification of Graph Structure

Partial correlations (PCs) and the related inverse covariance matrix adopted by graphical lasso, are widely applicable tools for learning graph connectivity given nodal observations. The resultant estimators however, can be sensitive to outliers. Robust approaches developed so far to cope with outliers do not (explicitly) account for nonlinear interactions possibly present among nodal processes. This can hurt the identification of graph connectivity, merely due to model mismatch. To overcome this limitation, a novel formulation of robust PC is introduced based on nonlinear kernel functions. The proposed scheme leverages robust ridge regression techniques, spectral Fourier feature based kernel approximants, and robust association measures. Numerical tests on synthetic and real data illustrate the potential of the novel approach.
Outside-Knowledge Visual Question Answering for Visual Impaired People

Outside-knowledge Visual Question Answering (VQA) is a challenging and promising task with broad applications. It requires models to accumulate external knowledge, acquire cross-modality scene understanding, and develop reasoning capabilities. A good VQA system can serve as the “eyes” of visually impaired people, enabling them to see the world around them. For example, it can assist in reading road signs, identifying directions, and recognizing objects. However, most of the existing VQA systems are based on classification fashion, which is limited in its ability to handle answers that have not been encountered in the training set. In contrast, generative VQA systems would naturally perform better in real-world scenarios. Nonetheless, research on such methods is still in its initial stages. Furthermore, in real-world scenarios, visual question answering often requires the utilization of extensive outside knowledge. And existing methods are often based on explicit knowledge from fixed knowledge bases, which is difficult to provide sufficient knowledge. To address the above two issues, this paper proposes a novel VQA system based on encoder-decoder generative models that fuses both implicit multimodal knowledge and implicit textual knowledge. Its absolute improvement of at least 5.69% over a range of baselines on the OKVQA dataset verifies the effectiveness of the proposed method.
Research on Speech Recognition Algorithm Based on Embedded System Architecture

In order to improve the application value of speech recognition algorithm in embedded system, the speech recognition algorithm is optimized and designed using the embedded system structure. Under the embedded system structure model, the hardware equipment is used to collect the voice signal, and the initial voice signal is preprocessed through filtering, windowing and framing, endpoint detection and other steps. From the two dimensions of cepstrum domain and time domain, the speech signal features are extracted, and the speech recognition results are obtained by matching with the language template features. Compared with the traditional recognition algorithm, it is concluded that the speech recognition error rate of the optimized design algorithm is reduced by about 1.21% and 0.87% respectively, and the recognition dropout rate is reduced by 1.38% and 0.66% respectively in the environment with and without noise, which proves that the optimized design algorithm has higher recognition performance.