Reviewerly/exHarmony huggingface dataset:
A New Closeness Centrality Measure based on Minimum Weight Spanning Tree

This paper proposes a simple new measure for closeness centrality for undirected and connected graphs with edge weights. The new closeness centrality measure is based on minimum weight spanning trees and consists of three steps. In the first step, when calculating the absolute closeness centrality, distances from the source vertex to all other vertices are taken as a summation of the edge weights along the shortest path. In the second step, the minimum weight spanning tree is found. In the final step, the normalized closeness measure of a vertex is computed by dividing the total weight of the minimum weight spanning tree by the absolute closeness value (summation of distances of this vertex to other vertices, which is obtained in the first step). This closeness measure is also intrinsically normalized, always giving a value between ‘0’ (lowest closeness value) and ‘1’ (highest closeness value). As this closeness measure is normalized, considers the edge weights, and is based on the minimum weight spanning tree, it is useful for real-world applications.
Unsupervised Log Anomaly Detection Based on Pre-training

With the continuous development of software technology, the size and complexity of software systems and hardware systems are growing rapidly. Any failure can cause huge losses. It is very important to ensure the stability and reliability of the system. Logs are a rich, important and valuable source of information that can help debug the system and analyze the root cause. Transfer learning emerges as a possible solution to the problem of insufficient training data by learning from a source domain with abundant data knowledge or patterns and then applying them to different but related target domains to reduce the demand for training data. However, existing transfer learning-based log anomaly detection methods require the labeled datasets for fine-tuning. Therefore, unsupervised Log Anomaly Detection based on pre-training (LogUPT), is proposed to address the problem of fast and low-cost log anomaly detection among different software systems of the same architecture. The source system log data has labels, and the target system log has no labels. The source system logs are used to train the model as a pre-training; the target logs with higher confidence are filtered by the pre-training with unsupervised clustering method to construct pseudo-labels; the target logs with pseudo-labels are used to fine-tune (Fine-Tuning) the pre-training model to make the model more adaptive to the target system. Experiments on real datasets PageRank and WordCount show that when the number of log messages is only 8194, the F1 score of this method still reaches 85.5%.
An Anomaly Detection Method for Nonlinear Industrial Process Using Sparse Stacked Denoising Autoencoder

With the increasing complexity of industrial systems, new challenges are posed to the monitoring of industrial process data, which often appear to be characterized by nonlinear and strong feature correlation. Therefore, a sparse stacked denoise autoencoder(SSDAE) based anomaly detection model is proposed in the paper, which uses the autoencoder model to capture the nonlinear feature structure in the industrial data, and we extract the feature space and the residual space to build a statistic to capture the system changes, and finally use the KDE to determine the threshold to detect the anomalies. In this paper, using the Tennesse-Eastman dataset, method validation is carried out and compared with algorithms such as PCA, DAE and LRAE, which verifies the effectiveness of the algorithm, improves the detection rate of faults, and is able to identify more faults.
FpRE:Front-prompt based dependency enhanced Few-shot Relation Extraction

Prompt-tuning has been a highly successful method for Natural Language Processing tasks, particularly in large language models. However, prompt-tuning based large language models can be difficult to interpret due to their complex architectures, various parameters, and sensitivity for training data and hyperparameters. In few-shot setting, there are very few labeled examples available, which makes it challenging for the model to learn to recognize and extract new relationships. To address this difficulties, we propose an approach called Front Prompt-tuning based dependency enhanced Few-shot Relation Extraction, which enhances the dependency relation into prompt-tuning for few-shot relation extraction. Our approach includes (1) incorporate the front template into the automating prompt with dependency-based method;(2)employ group-wise collaborative optimization to further enhance the performance of FpRE by jointly optimizing the prompt representation and the structured constraints in a synergistic manner;(3)design Adaptive focal loss to balance inter-group sample diversity. We showcase the efficacy of our approach in few-shot scenarios by comparing several different front-templates across three datasets.
A Radial-Based Mixed sampling method for imbalanced data classification

Mitigating the impact of imbalanced data on classifiers is a challenging task in machine learning. Oversampling, a well-known approach, modifies the data distribution and generates minority class samples to alleviate the influence of imbalanced data on classifiers. However, many existing oversampling methods often involve k-nearest neighbors (KNN) techniques, and changes in the KNN parameter values can lead to noticeable instability. The recently proposed Radial-Based Oversampling (RBO) addresses this issue, but it tends to introduce a significant amount of redundant samples and noise after sampling. In this study, we propose a Radial-Based Mixed Sampling method for imbalanced data classification. Firstly, we employ RBO to oversample a portion of the minority class samples. Then, we use the concept of potential to identify redundant samples and noise in the dataset. Finally, Radial-Based Undersampling (RBU) is employed as a data cleaning strategy to eliminate redundant samples and noise from the majority class samples. Extensive comparative experiments are conducted on 10 imbalanced datasets from KEEL, two different classifiers, and five sampling methods. The results indicate that our approach helps classifiers achieve higher F <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> scores and G-means. This suggests a significant improvement in the classifier’s ability to identify minority class samples while considering majority class samples. Additionally, statistical analysis shows higher Friedman Ranking for our method.
CALP-FD: Cross Attention-Based Facial Landmarks Prompt for Face De-Morphing

Face morphing attacks have been demonstrated to present a significant risk to current Automatic Border Control systems. Despite research on morphing attack detection has made certain progress, the forensic analysis and restoration of accomplices’ facial images remain in the initial stage. Currently, face de-morphing methods based on deep learning technology usually ignore the role of facial landmarks, which are crucial to the structure of facial images. Therefore, the paper proposes CALP-FD, a novel method for face de-morphing using cross-attention-based facial landmarks prompt. By integrating facial landmarks, CALP-FD pays more attention to the features around the facial features and assigns greater weight to pixels around the landmarks. In addition, in order to effectively separate the identities of accomplices, CALP-FD applies cross-attention mechanism for representation interaction in the separation network to improve the restoration accuracy and image quality. In the experimental section, the paper conducts a comprehensive evaluation on HNU_FM, and the experimental results unequivocally demonstrate the superior restoration accuracy and robustness of our method compared to existing approaches.
Point Cloud Pose Alignment Based on Improved Nearest Point Iteration

The ICP alignment algorithm requires high initial position of the point cloud to be aligned, and the alignment process is time-consuming, so a point cloud positional alignment algorithm based on improved nearest point iteration is proposed. For the error problem of mismatch removal, the adaptive threshold of mismatch removal is proposed, and its threshold is determined by the mean and standard deviation of the point distance set. To address the problem of underutilization of point cloud features by the cost function, a cost function based on the covariance matrix of the point distribution is established using the processing idea of probability theory. This cost function can also avoid nonlinear computation. The experiments show that this algorithm avoids the ICP alignment algorithm from falling into local optimum due to the initial position pose, and improves the alignment efficiency and accuracy at the same time.
Reinforcement Learning based End-to-End Control of Bimanual Robotic Coordination

To optimize the motion planning of multi robotic arms for a single task execution, traditional methods rely on manually planning the end effector’s trajectory, which is time-consuming and labor-intensive. Reinforcement learning offers an alternative, where the robotic arm explores actions within its environment, receiving feedback through rewards, and learns control strategies for task completion. This research encompasses three primary areas: practical operation, simulation environment creation, and reinforcement learning algorithm training. For practical operations, the UR3 robotic arm model is programmed using a teaching device, facilitating motion planning via manual instruction. In simulation, a MuJoCo-based environment with a physics engine is developed to mirror the robotic arm’s real-world movements. This simulation controls the arm through a mocap data stream, guiding the end effector’s position and posture. Reinforcement learning training involves crafting various reward functions within the environment to achieve typical tasks, such as reaching targets and manipulating objects. Some preliminary results reveal that such a framework would facilitate the self-learning of multi agents in achieving the global target.
Meta Learning Framework for Improved Long-tail Item Recommendation

It is very common to have long tail distribution of items in recommender systems. However, due to the limited interaction records of long-tail items, recommending them to users significantly affects the model’s performance. In the existing methods, the features of the head item are transferred to the tail item, thereby ignoring the difference between them, and fail to produce a satisfactory recommendation effect. To address this issue, we propose a novel recommendation model based on meta-learning, called MLFRec. First, The MLFRec obtains the initial parameters from the head items through meta-learning, and then fine-tunes the model parameters in the learning process of the features of tail items; Second, it adopts attention graph convolutional network (AGCN) to enhance tail items, thereby reducing the difference between head and tail items. Through above two steps, the model takes advantage of the abundant data of the head items that help to address the issue of sparse data of the tail items, and improves the recommendation quality. Extensive experiments were conducted on three real-world datasets, and the results indicate that our proposed MLFRec model outperforms other state-of-the-art baselines for the tail items recommendation.
An Integrated Long Short-Term Memory Based Online Log Anomaly Detection

System logs record noteworthy information and become a valuable resource for tracking and investigating the status of a system. Detecting anomalies from logs as fast as possible can enhance quality of service. Although many deep learning algorithms have achieved good results in the field of log anomaly detection, we find that they assume that data is acquired and trained in bulk and may have high training time, while in reality log data streams are constantly arriving. Facing these challenges, this paper proposes a Long Short-Term Memory network (LSTM) based online log anomaly detection. We use transfer learning to train weak learners and integrate weak learners as strong learners for the constantly arriving log. We compare the performance of the methods using a public log dataset and experimentally validate the effectiveness of each module.
WZ Algorithms from AS Kind to MD One Attempted to Predict Solar Minimum

Solar minimum has occurred multiple times since recorded by humans. In this study, we utilize four well-known solar minimums as known data to predict the upcoming solar minimum. We employ the 3-input Weng-Zhang (WZ) algorithms of addition-subtraction (AS) frequency (WZA-ASF) method to forecast the start and end years of the ongoing solar minimum. Additionally, we creatively propose a modification of WZA-ASF method called Weng-Zhang algorithms of multiplication-division (MD) frequency (WZA-MDF) method and apply it to predict the start and end years of the upcoming solar minimum as an attempt. Finally, we present a tentative prediction with a start year of 2017 (or even as early as 2010) and potential end year of 2080, 2101, or 2110 for the ongoing solar minimum.
Adaptive Ensemble with Density Peak and Random Forest for Class-Imbalance Learning

Classifying imbalanced data poses a significant challenge in machine learning. Employing an ensemble strategy stands out as a competitive solution, enhancing imbalanced classification performance through multi-classifier voting. In this study, we introduce an adaptive ensemble method, leveraging density peak clustering and random forest, to tackle imbalanced classification issues. Initially, the imbalanced dataset's majority class samples undergo density peak clustering. We calculate the ρ and δ values for each majority class sample point, establishing a sequence by descending order of ρ as the primary order and δ as the secondary order. This sequence is then equitably split, identifying the first half as the core sample set and the second half as the boundary sample set. Subsequently, a dynamic function is employed to control the weight of boundary samples, adjusting sampling weight based on the iterations of the integrated classifier. This yields an undersampled majority class sample set, combined with the minority class sample set to form a new training subset. Finally, the classifier learns from diverse training subsets, generating subclassifiers that exhibit balance and variability. A voting mechanism is employed to integrate all subclassifiers, resulting in the final classifier. Our proposed method is compared with eight established approaches across twelve benchmark datasets. The results demonstrate its superior performance on most datasets, particularly in terms of F1 and Gmean.
Spectral Clustering Augmentation Pseudo-Label Conditional Generation Adversarial Imputation Networks for Multivariate Time-Series Missing Data

Electroencephalogram (EEG) signal data, as a type of multivariate time-series data, finds widespread applications in real-life scenarios such as drug effect diagnosis, seizure symptom prediction, and emotion classification. However, data collection for these signals often results in missing data due to equipment and network issues. Imputation of missing data is a commonly used technique, and existing methods employing Generative Adversarial Networks (GANs) for imputation typically model the entire time-series data distribution with a generator, minimizing imputation errors in the non-missing portions. Yet, they overlook latent category features among time-series data samples. Addressing this issue, we propose Spectral Clustering Augmentation Pseudo-label Conditional Generation Adversarial Imputation Networks (SCAPC-GAIN) for imputing time-series data. The training process involves two stages: pre-training and formal training. In the pre-training stage, a small portion of missing data is imputed, and pseudo-labels are synthesized using spectral clustering for pre-training an auxiliary classifier. In the formal training stage, the classifier is integrated into the network framework to provide category features. A Missingness Augmentation method is then employed during training to reinforce the generator and ensure the correctness of the pseudo-labels. Experimental results on EEG datasets demonstrate that the proposed model outperforms other GAN-based imputation methods.
Material Touch: Diffracted from Wool — Stich Sisters Collective

Material Touch documents a performance lecture by the Stitch Sisters. The session was a sensory material entanglement of art with discussions on sustainability in the warp, weft and stitching of wool that wove and knitted words and being together, attempting to make sense of our future at a time of great urgency. A discussion followed the performance lecture.
A Survey of Graph-based Clustering Techniques

Graph clustering is a powerful technique used to identify and group similar nodes within a complex network structure. This procedure involves segmenting the graph into distinct groups, with the nodes in each group having strong interconnections or similar characteristics. In this survey, we will highlight a variety of existing approaches of graph clustering, including spectral clustering, modularity optimization and hierarchical clustering, to efficiently discover meaningful clusters, facilitating analysis and decision-making in diverse fields ranging from data science and network analysis to social sciences and bioinformatics.
Towards Improving Mathematics Learning Using the Deep Learning Models

Nowadays, the majority of intelligent tutoring systems (ITS) have significantly advanced in terms of capabilities and technologies. These systems have evolved to provide personalized and adaptive learning experiences for students. In mathematics, ITS can provide hints or messages to facilitate problem solving, but they do not provide feedback on educational concepts useful for resolving (such as theorems, definitions, etc.). Our methodology enables the generation of feedback in human learning environments. It makes it possible to predict the didactic concepts relevant to solving a given question Q(i) in an exercise based on existing or previously demonstrated data (Q(i-1), Q(i-2),…) using the Math-Bridge ontology. To extract knowledge from mathematical text, we use vectorization with SBERT models to calculate the semantic similarity between ontology concepts and exercises. We tested our approach on a set of exercises from mathematics teaching platforms and compared the number of concepts generated by our approach with those of experts. The results highlight the encouraging potential of our exercise solution method.
Directional interpolation using bilateral filter and modified Akima Interpolation

Recently, image interpolation has became necessary and useful for all computer vision applications. Directional interpolation provide very high quality resolution image in the output and suppress the noise and aliasing in the original image. This paper present a new method of image reconstruction based on directional image interpolation using the bilateral filter. This involves estimating edge directions using the bilateral filter, followed by interpolating the missing pixel along the detected edge direction using a modified Akima interpolation. We start the simulation by downsamling the gray high-resolution image by a factor of two to obtain a low-resolution image. Then the preprocessed image is reconstructed by using the proposed method. To evaluate this method, we provide an implementation in Matlab, we testing this method to several gray images, and we comparing to other interpolation algorithms. Extensions simulation and comparison with the other state-of-the-art methods show that our algorithm achieves competition performance, with finer details and sharp edges.
Deep Neural Collaborative Filtering for Personalized Arabic Documents Recommendation in Smart Campus

Although the act of reading online has brought benefits to individuals and societies, the digital revolution of the Internet of Things (IoT) has raised an overload of shared information in a smart campus. Faced to this issue, textual documents recommendation has become a growing research topic of artificial intelligence and natural language processing. By cons, traditional collaborative filtering methods ignored the inherent connection between user's preference and time. In this paper, Real-Time based Deep Neural Collaborative Filtering (RT-DNCF) is proposed for documents recommendation in Arabic smart campus. To deal with data sparsity and scalability problems, the Apache Kafka supported firstly scaling out for distributed data processing in fewer time. Seeing that Arabic language has received less attention compared to others because of its analysis complexity, collaborative filtering approach is thereafter introduced based on the potential of incremental and deep learning. It was beneficial to avoid the need to reprocess all the historical data every time a new user interaction is observed. Experiments are conducted on BRAD1.0 dataset and results demonstrated the effectiveness of the proposed approach compared to state-of-the-art methods.
A Situational Awareness Tool using Open-Source Intelligence (OSINT) and Artificial Intelligence (AI)

Social media platforms have become a means for users to share information, interests, and events with friends, among others. This shift has led to a surge in shared content related to events, such as posts that reflect some people's concerns about real-life occurrences. As a result, significant progress has been made in developing situational awareness systems that offer valuable insights derived from data collected from various sources, especially social media platforms. However, few studies in the literature focus on the South African landscape. Additionally, these studies are mainly based on Twitter data, overlooking the use of information shared by media publishers. Furthermore, most research analyses posts only after civil unrest has occurred. This study proposes a situational awareness tool that uses open-source intelligence (OSINT) and machine learning algorithms to predict civil unrest events. It uses data from Twitter and news media platforms such as SABC News, Eyewitness News (EWN), and News24, in addition to information from the Armed Conflict Location & Event Data Project (ACLED). We employ natural language processing (NLP) to process and explore the data to obtain insights and train supervised learning models, including logistic regression, support vector machines, decision trees, and random forest classifiers. These models are evaluated using CountVectorizer, term frequency-inverse document frequency (TF-IDF) and LabelEncoder for data normalisation. Experimental evaluations reveal that the logistic regression model, paired with TF-IDF, outperforms the other models in predicting instances of social unrest with the highest accuracy. Predicting such events helps law enforcement agencies, organisations, and individuals understand and anticipate these occurrences, allowing proactive measures to protect peace, public safety, and economic stability.