Reviewerly/exHarmony huggingface dataset:
Software Risk Prediction at Requirement and Design Phase : An Ensemble Machine Learning Approach

Software development is a highly structured process that involves the creation and maintenance of a particular system, ranging from simple applications to complex enterprise software. Despite following a well-defined process, unforeseen events can occur at any stage of the SDLC that may impact the software development process, leading to losses or failures in software development. Software projects inherently involve risks, and no software development project is immune to these risks. Identifying and predicting such risks accurately is a challenge in software project development. To address this challenge, this study aims to develop a software risk prediction model using homogenous ensemble machine learning algorithms. These algorithms were selected due to their proven effectiveness in handling complex datasets and their ability to achieve high prediction accuracy. We have used an experimental research methodology to develop a software risk prediction model. The methodology involved collecting datasets related to requirements and design from publicly available websites such as zenodo and Harvard education dataset. These datasets were then used to train and validate the performance of the machine learning algorithms. Our study has achieved impressive prediction scores of 98.67%, 97.3%, 96.0%, and 96.0% for the algorithms Gradient Boost, Random Forest, AdaBoost, and bagging algorithms with their homogenous decision tree respectively. Using the four different homogeneous ensemble machine learning algorithms we develop software risk predictive models. Ultimately, Gradient Boost was selected as the algorithm to construct our risk predictive model due to its superior performance and ability to handle complex data. By employing this model, software development organizations can improve their ability to identify and mitigate risks, thereby improving the quality and reliability of their software products.
Minimum Error Entropy High-Order Extended Kalman Filter

The conventional extended Kalman filter(EKF) is proposed under the criterion of minimum mean square error(MMSE) and omits high-order information. This leads to poor estimation of the EKF in non-Gaussian and strongly nonlinear systems. In this paper, for a class of nonlinear non-Gaussian systems in polynomial form, Pseudo-linearizing the system while preserving the high-order information, a new filter is proposed based on the minimum error entropy(MEE) criterion called the minimum error entropy high-order extended Kalman filter (MEE-HEKF). In the MEE-HEKF algorithm, the state estimation problem is transformed into a recursive solution problem in the form of a Kalman filter by a fixed-point iterative approach based on the MEE cost function. The proposed algorithm has better robustness under strong nonlinearity and non-Gaussian noise. Finally, the effectiveness of the new algorithm is illustrated by an example.
Reliable Region Mining for Weakly Supervised Medical Image Segmentation

Class Activation Map (CAM) is a common tool to address image-level weakly supervised image segmentation (WSIS) tasks, which mainly extract the most discriminative regions as pseudo-labels for the segmentation networks. However, it is difficult to accurately locate the salient regions since similar image intensities and low-contrast in the medical image field. In our work, we put in a novel WSIS model to exploit visual detail features to enhance the semantic expression ability of the object target. The model consists of two branches: the classification branch and the segmented branch. The classification branch includes two subbranches: local classification and global classification. Differing from existing local-global models, we employ expansion convolution to increase the receptive field for the global classification branch, while designing an affinity-aware module in the local network to obtain the original context information of the local image. The global and local classification branches work together to provide pseudo-labels for the segmentation branch. The experimental results show that our model outperforms other state-of-the-art methods with a DSC of 46.7% on the publicly available MSD dataset.
An Improved AdaBoost Method in Imbalanced Data Learning

Learning from imbalanced datasets has recently received increasing research attention. Despite the remarkable results of AdaBoost in balanced situation, the imbalance problem remains to be solved. To address this, this paper proposes a novel method to improve the AdaBoost using a new error factor and a weighted voting parameter for a few classes and weak classifiers. Our method can weaken the dominant role of the majority class in iterative training by improving the classification ability of the minority class. The superiority of the proposed algorithm is finally proved theoretically and practically. Several real-world imbalance datasets in experiments as specific support, and our method outperforms previous algorithms, especially in the F-1 metric.
Class Imbalanced Semi-Supervised Learning With Meta-Learning

In recent years, in order to solve the lack of labeled data faced by deep learning, semi-supervised learning (SSL) algorithms based on pseudo-label methods and consistency regularization methods have achieved remarkable results. These algorithms can achieve performance comparable to supervised learning with only a small amount of data labels, but they perform poorly on class-imbalanced datasets in practical applications. On the one hand, the class imbalance problem will cause the model classification to shift towards the majority class, and on the other hand, it will cause pseudo-labels noise generated by the SSL algorithm. These two challenges together harm the performance of these algorithms on class-imbalanced datasets. We propose a meta sample reweighting algorithm to simultaneously address the above two challenges. We introduce two meta networks to adaptively weight labeled and unlabeled data respectively, use meta-learning framework to learn the parameters of the meta networks, and propose a meta learning algorithm that alternately updates the parameters of the meta-network and the classifier network. Experiments prove that this method can effectively alleviate the performance degradation of state-of-the-art SSL algorithms affected by the class imbalance problem.
A Data-driven Neural Network Used on Maneuvering Target Tracking

Maneuvering target tracking is a key issue in the field of target tracking. The target tracking based on the motion model is hard to accurately predict and track maneuvering target. This article design a data-driven neural network to complete the maneuvering target tracking. A maneuver trajectory generator and trajectory mapper are designed to generate enough trajectory sample, providing data-driven support for training the neural network. The DTN network was trained based on the trajectory sample and the trained DTN network is able to effectively track the maneuvering target. Simulation result demonstrates that the DTN has high accuracy and efficiency than the traditional model prediction method on maneuvering target tracking.
Intention Recognition Trustworthiness Measurement using a Spatiotemporal Knowledge Graph

The current intent recognition method has insufficient generalization ability, and the evaluation of confidence level of intent recognition is the key to its popularization and application in the high-reliability military and civilian fields. The spatiotemporal Knowledge Graph can uniformly represent and efficiently organize entities containing spatial location information and temporal information. Battlefield situational objectives and their relationships can be abstracted as a triple composed of two specific types of spatiotemporal entities and combat intent relationships. Using typical confrontation scene data to train neural networks, and fusing the confidence levels of the entity and Knowledge Graph, the confidence level evaluation results of the intent triple for the existing spatiotemporal knowledge base are calculated. The simulation test results show that the method can effectively evaluate the confidence level of the intent recognition results, and the accuracy rate is improved compared with the current mainstream methods. This method breaks the "closure" assumption of the Knowledge Graph update and completion algorithm from the algorithm idea level, and completes the last link in the popularization and application of the intent recognition method
Detecting Fraud in Motor Insurance Claims Using XGBoost Algorithm with SMOTE

Fraudulent claims in motor insurance policies con-tinue to be a big menace to insurance companies. Fraudsters are devising new tactics of fabricating claims to make them appear valid. This makes insurance companies register huge losses in billions of money every year. The insurance policyholders bear these losses through increased premiums thus having negative social and economic ramifications. Numerous approaches have been proposed and applied in detecting and preventing fraudu-lent claims. The traditional approaches have become complex, time-consuming, and with low success ratio. To improve on fraud detection, the existing historical data can be used to train prediction models. To optimize the performance, this data require feature engineering to ensure only relevant features are used and handling of class imbalance. In this paper, we propose a model that is built on XGBoost algorithm. In data preparation, we propose to handle class imbalance by oversampling, using SMOTE. We aim at comparing the effect of class imbalance and oversampling on the performance of our model. The results obtained reveals that XGBoost performs well with SMOTE compared to imbalanced training dataset and also compared to other algorithms. Once the model is deployed, insurance companies will be able to detect and identify perpetrators of fraud and take necessary action. This will reduce their loss adjustment expenses and thus increase their profits.
Research on human pose estimation algorithm for occlusion scene

This paper proposes a human pose estimation algorithm for obscured scenes with poor prediction results caused by self or external objects. Firstly, a lightweight upsampling operator CARAFE is introduced to optimize the traditional nearest neighbor interpolation to reduce the localization error caused by interpolation and thus improve the localization accuracy of key points. Secondly, by building an improved multiscale feature fusion module WASP, the network can fully utilize and fuse different resolution features to ensure the adequacy of feature information. The experimental results show that the detection accuracy AP of this algorithm can reach 76.7 %, which is 0.8 % higher than the original algorithm, and effectively improves the performance of human key point detection in occlusion scenes.
Deep CNN with Dense and Many-to-Many Connections for Image Super-Resolution

The deep convolution neural network (CNN) has attracted continually increasing attention in the field of machine learning. The deep CNN is applied in image super-resolution (SR) fields and has obtained good performance. This paper proposes a new deep CNN model (SR2DCNN) with dense and many-to-many connections to reconstruct the SR image. Our model uses a deep CNN with DenseNet and many-to-many connections, which makes it faster and performs well. This structure efficiently addresses the issue of gradient disappearance while greatly reducing the number of parameters. In addition, we research the preprocessing and chose a more effective method. The proposed model can improve the applicability of the algorithm and reduce the requirements of the operating environment drastically. Specifically, the qualities of SR image can be improved by the SR2DCNN. Finally, extensive simulation experiments demonstrate the effectiveness of SR2DCNN
Three-Way Causal Attribute Partial Order Structure Analysis

As an emerging concept cognitive learning model, partial order formal structure analysis (POFSA) has been widely used in the field of knowledge processing. In this paper, we propose a method named three-way causal attribute partial order structure (3WCAPOS) to evolve the POFSA from set coverage to causal coverage in order to increase the interpretability and classification performance of the model. First, the concept of causal factor (CF) is proposed to evaluate the causal correlation between attributes and decision attributes in the formal decision context. Then, combining CF with attribute partial order structure, the concept of causal attribute partial order structure is defined and makes set coverage evolve into causal coverage. Finally, combined with the idea of three-way decision, 3WCAPOS is formed, which makes the purity of nodes in the structure clearer and the changes between levels more obviously. In addition, experiments are carried out from the classification ability and the interpretability of the structure through six datasets. Through these experiments, it is concluded the accuracy of 3WCAPOS is improved by 1%-9% compared with classification and regression tree, and more interpretable and the processing of knowledge is more reasonable compared with attribute partial order structure.
Multi-Label Contrastive Semantics Preserving Based Cross-Modal Hashing

Thanks to the efficient storage and retrieval capabilities provided by hashing techniques, coupled with the highly discriminative feature extraction capabilities of deep neural networks, the field of deep cross-modal hashing has made significant progress in realizing efficient cross-modal retrieval tasks in recent years. Nonetheless, a majority of current supervised cross-modal hashing techniques typically rely on a solitary label to gauge the semantic similarity between cross-modal paired instances. This approach tends to overlook the fact that numerous cross-modal datasets encompass extensive semantic information across multiple labels. To solve this challenge, we propose a novel multi-label contrastive semantics preserving based cross-modal hashing (MCSPH). MCSPH firstly utilizes the multiple labels of instances and calculates the semantic similarity matrix of raw data through cosine similarity, and secondly uses supervised contrastive loss to make our method have better performance. Thorough experimental evaluations conducted on two benchmark datasets substantiate the superiority of the MCSPH method over prominent baseline approaches.
A Distributed Message Queuing System Based On Hbase-like K-v Databases

Message queues, as a low-latency, high-concurrency, and easily accessible data processing middleware, can improve the reliability and efficiency of data processing, and have become an indispensable part of large data and computing systems. For some users, the demand for data security is more prominent, and this article provides a message queue implementation method based on an hbase-like key-value database, which uses the database as a foundation to implement a distributed message queue that can freely control consumer progress and ensure temporal consistency. The safety and performance of this method are equivalent to that of the db components used, and different security levels of databases can be flexibly used as the basic component. In terms of the read-write mechanism of the message, this article has also designed it in detail to achieve optimal performance.
A Real-Time Obstacle Detection and Classification System For Assisting Blind and Visually Impaired People Based On Yolo Model

Computer vision is one of the state-of-the-art technologies for object detection problems. Accurate detection of obstacles could assist blind and visually impaired people to n avigate safely while they walk. However, object detection and t racking are some of the challenging tasks in computer vision. I n video analysis, there are basic steps to be done: detection of target objects in consecutive frames, and analysis of objects to understand their behavior. Recently, several methods of object detection based convolutional neural networks have improved performance undue different conditions like speed and accurac y. However, most of these methods have slow recognition speed that limits their use in real-time situations. Recently, a unified object detection model You Only Look Once (YOLO) was prop osed, which could directly regress from input image to object cl ass scores and positions. In this paper, we applied YOLOv2 to our prepared datasets of three different classes namely pothole, garbage bin, and pole and we proposed a technique called Short-Term Memory, which considers information between every f rame, to reinforce the detection capability of YOLO in video st reaming by including obj ect location and size estimation tasks. Through this, we achieved a mean average precision of 60.17% accuracy with an average detection speed of 34.6 fps.
AIEPU at ALQAC 2023: Deep Learning Methods for Legal Information Retrieval and Question Answering

Given the widespread integration of Artificial Intelligence across various domains, the exploration of its applications in the legal field remains understudied, emphasizing the critical need for the development of effective deep learning approaches. This paper describes on our approaches to legal information retrieval (Task 1) and legal question answering (Task 2) in the Automated Legal Question Answering Competition (ALQAC 2023). Specifically, we employed the Paraformer model for information retrieval and leveraged large language models for answering questions. In the competition, we achieved the 1 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> place for Task 2. In this task, our findings demonstrate that fine-tuning appropriate prompts helps the large language models to achieve better performance. Our prompts are available at https://github.com/DucLong06/Legal-Prompts
Hadiyyissa Automatic Speech Recognition Using Deep Learning Approach

Speech Recognition focuses on developing advanced technologies that can recognize and translate spoken language into text. Automatic Speech Recognition (ASR) is an Artificial Intelligence (AI) technology that captures human voice from a mi-crophone and analyzes it based on spoken words to comprehend natural language. In Ethiopia, ASR models have been developed for specific local languages such as Amharic, Tigrigna, Wolaytta, and Afaan Oromo, but there has been a lack of research on ASR models for the Hadiyyisa language, which is also spoken in Ethiopia. The research study designed and developed an ASR model for the Hadiyyisa language. The researchers collected data from native speakers of the Hadiyyisa language and used Python programming to design and develop the deep learning model for the Hadiyissa ASR model. Different methods and techniques were used in the study to achieve the research objectives and complete the entire research process. The proposed model utilized deep learning algorithms including RNN, LSTM, BiLSTM, and CNN, along with suitable feature extraction techniques such as Mel Frequency Cepstral Coefficients (MFCC). The accuracy of the proposed ASR model was evaluated through experimentation, which demonstrated that the BiLSTM model outperformed the other deep learning models with an overall accuracy of 90.95 % Based on the experimental evidence, the proposed model was found to be better suited for ASR for the Hadiyyisa language.
Reliable Code-Based Post-Quantum Cryptographic Algorithms through Fault Detection on FPGA

Code-based cryptography is a promising post-quantum cryptographic solution against attacks enabled by classical and quantum computers. The Niederreiter cryptosystem is a well-known code-based cryptographic algorithm that has been traditionally scrutinized due to its key size and its computational requirements. However, with the advent of quantum computers, the Niederreiter cryptosystem is becoming more popular as it is thought that code-based cryptography is quantum resistant. Nevertheless, security does not mean reliability, and the finite field operational blocks used in some code-based algorithms are vulnerable to fault injection. This paper derives error detection mechanisms based on normal and interleaved parity for code-based cryptosystems. Moreover, such techniques are applied to the Key Generator of the Niederreiter cryptosystem and implemented on Xilinx field-programmable gate array (FPGA) family Kintex UltraScale+ (device xcku5p-ffvd900-1-i). This is done to assess the overheads and the decrease in performance when the derived fault detection techniques are included to the original designs.
Semi-Supervised Learning with Dense Target Producer for End-to-End Lightweight Polyp Detection

Semi-Supervised Object Detection (SSOD) can greatly enhance the performance of object detectors by leveraging a vast amount of unlabeled data. Despite great successes, most existing SSOD methods are based on pseudo-boxes, which require subsequent postprocessing steps with many hyperparameters to be tuned. This paper proposes a novel effective semi-supervised method called Dense Target Producer (DTP) for heatmap-based end-to-end detectors. Unlike conventional SSOD methods, our method directly uses dense prediction of a teacher model as pseudo-labels without the need for any postprocessing or target assigner steps. Moreover, we also introduce Threshold Epoch Adaptor (TEA), a dynamic thresholding procedure to adaptively filter unreliable pseudo labels based on the learning status of the models. Finally, we propose a lightweight heatmap-based end-to-end detector, namely CenterNet++, that enhances the performance relative to the baseline CenterNet. Experiments conducted on a large PolypsSet dataset across different settings showcase our method's superior performance over existing SSOD methods. The experimental results show that our DTP significantly increases the AP50 score by 16.7, 3.2, and 2.8 compared to the supervised baseline model when using 1%, 5%, and 10% of the training set as labeled data, respectively.
Two Novel Variants of ECDH Key Exchange Protocol Using Schnorr Zero-Knowledge Proof

The Elliptic Curve Diffie-Hellman (ECDH) protocol is a key exchange protocol that is widely used in information technology systems. However, the ECDH protocol still has inherent weaknesses due to the lack of a user authentication mechanism and is affected by man-in-the-middle attacks. In this paper, we propose two novel variants of the ECDH protocol using Schnorr Zero-Knowledge Proof (ZKP) for Client-Server network model. We aim to integrate the user authentication mechanism into the key exchange process for the Client-Server network model with higher security than applying the original ECDH protocol. To achieve this goal, we compare and analyze the network model utilizing the original ECDH protocol versus the proposed protocols. Therefore, we draw some conclusions about the advantages, disadvantages, and feasibility of the proposed solution.