Reviewerly/exHarmony huggingface dataset:
Chain-Based Outlier Detection for Complex Data Scenarios

Outlier detection is a challenging problem due to the complexity of real-life data. Specifically, an effective outlier detection method should be able to handle (1) different types of outliers: local outliers, global outliers, and cluster outliers; (2) a lack of prior knowledge of outlier number or percentage; (3) neighboring clusters with different densities; and (4) real-time detection. Unfortunately, few algorithms can tackle all these challenges simultaneously. In this paper, we propose a chain-based theory to address these issues, where a minority of data points will be considered outliers if their distances from normal data points change abruptly. We present the parallel chaining method based on this theory. Experiments demonstrate that the proposed method exhibits comparable performance to other state-of-the-art methods in the synthetic datasets and outperform other methods in the real-life datasets.
Sign Language Interpreter via Gesture Detection

Our primary aim is to create an application that takes input from the user in the form of a photo or video, interprets and translates the sign language symbols represented in the given input and outputs the given translation in the form of text or speech. We analyze existing solutions for the communication barrier between Human Information Interactions (HII’s) and other people such as text-to-speech algorithms and aim to improve on them by providing better ease of use and accuracy. We utilize the LSTM Model for the processing and prediction of the input video frames, resulting in an output containing the descriptive textual caption of the sign-language performed.
An Improved Local Mean-Based Distance Weighted K-Nearest Neighbor with Distance Metrics

The k-Nearest Neighbor (KNN) algorithm remains a pivotal tool in classification due to its simplicity and effectiveness. In this paper, a novel KNN-based classifier is introduced, termed the local mean-based distance weighted k-Nearest Neighbor algorithm (LMWKNN), which builds upon the foundation laid by the local mean-based K-nearest neighbor (LMKNN) algorithm and the distance-weighted K-nearest neighbor (WKNN) algorithm. The LMWKNN algorithm combines the advantages of LMKNN's local mean vectors and WKNN's weighted influence, aiming to enhance classification performance. In this research, the proposed LMWKNN algorithm is rigorously evaluated against five prominent distance metrics: Euclidean, Cosine, Correlation, Spearman, and Jaccard. To validate its effectiveness, extensive experiments are conducted on eight real and representative UCI datasets. The classification error rates are meticulously measured, with a focus on comparing the performance of LMWKNN across the different distance metrics. The findings reveal that the Cosine distance model yields the lowest average error rate of 26.98%, signifying the highest accuracy among the tested distance metrics. In contrast, the Jaccard model exhibits the highest average error rate of 69.04%, reflecting its comparatively weaker performance. The performance of other distance models falls within this spectrum. The comprehensive experimental results underscore the promising potential of the LMWKNN classifier in the realm of pattern recognition. Moreover, the Cosine distance model, when compared to the traditional Euclidean distance model, demonstrates better performance.
Singular Value Decomposition (SVD) Based Optimal Image Compression Technique

Images are prevalent in the Internet and throughout the digital space today. While they are an excellent way to communicate information (an image is worth a thousand words), it is also an expensive form of data to be transported across the network. To solve this problem, compression software is often used to encode an image to a smaller physical size so it can be transported more efficiently while minimizing the perceptible quality change from its original copy. In light of this, a question that comes up is: how much could an image is compressed before it is no longer valuable in providing information to the user. In this paper we will discuss how to optimize an image compression algorithm that is based on finding the singular value de- composition of a matrix. The effectiveness of the suggested strategy in terms of compression and quality retrieval has been demonstrated through a thorough investigation. It is clear from the testing results that a compression of up to 80% can be accomplished while maintaining acceptable visual quality according to the human vision system (HVS).
Exploring Lightweight Encryption for Image Security using One-Dimensional Chaotic Maps

The modern era has witnessed an unprecedented surge in image data generation and dissemination. This proliferation is not solely attributed to advancements in social media and e-commerce but is also significantly driven by the rapid expansion of the Internet of Things (IoT). Several IoT applications, ranging from smart cities and remote surveillance to healthcare, rely significantly on visual information. Consequently, this emphasizes an urgent need for securing the sensitive and personal information embedded within the images. The conventional secure crypto primitives, primarily designed for textual and numerical data, often fail to provide robust and resource-efficient security measures for encrypting images. Contrarily, the highly random chaotic maps exhibiting considerable unpredictability are considered suitable for encrypting images. This paper explores the application of lightweight one-dimensional chaotic maps, including Logistic, Sine, Tent, and Chebyshev maps for encrypting the images. All the encryption schemes have been comprehensively evaluated in MATLAB in terms of several performance and security metrics, such as PSNR, MSE, NPCR, UACI, entropy, and execution time requirements. The findings in the paper highlight the potential of one-dimensional chaotic maps in offering balanced, computationally efficient, and optimal security solutions for safeguarding image data for diverse applications in the realm of IoT, multimedia, and beyond.
Multi-Sensor Data Fusion and Artificial Intelligence Feature Analysis

In this paper, the basic concept, working principle, characteristics and structure, data fusion method and application range of multi-sensor information fusion are described in detail. In this paper, a new multi-sensor information fusion method is established and verified by experiments. Meanwhile, a multi-source data fusion algorithm based on single D-S evidence is proposed. The upper computer software (GUI) developed in Python allows these sensors to work together. The sensor can also store data based on time stamps. The superiority of this algorithm is proved by computer simulation.
When Contrastive Learning Meets Graph Unlearning: Graph Contrastive Unlearning for Link Prediction

In today’s data-rich era, large models continuously consume vast troves of personal data, raising pertinent questions about user consent and its implications in training machine learning frameworks. With mounting calls for enhanced user privacy, the “right to be forgotten” is gaining traction in contemporary research. Parallelly, the ubiquity of graph neural networks underscores its ever-growing imprint on artificial intelligence communities, resonating in almost every facet of various applications. This study delves into enhancing the effectiveness of graph unlearning through the incorporation of graph contrastive learning. To adeptly erase edge information without compromising the performance of the subsequent link prediction task. We present a novel graph unlearning framework, Graph Contrastive Unlearning (GCU). Infusing the principles of contrastive learning into conventional graph unlearning paradigms, the GCU stands out, ensuring the absolute erasure of deleted edge data. The genius of graph contrastive unlearning lies in its dual capability: facilitating the model in discerning the deleted edges from their original counterparts while seamlessly aligning the neighbors of these edges with the original graph’s node features. Crafted meticulously, the GCU extirpates the influence of the removed elements from model parameters and neighboring representations. Yet, it ensures the sanctity and retention of the residual model knowledge post-deletion. Empirical tests underscore the GCU’s promising edge unlearning ability, revealing marked improvements in link prediction across diverse graph datasets. This not only fortifies user privacy but also streamlines the unlearning process, eliminating the cumbersome need for full model retraining even in scenarios of mass edge deletions.
Tensor Space Model-based Textual Data Augmentation for Text Classification

In this paper, we first introduce a new text representation method to convert a textual document into a tensor space model named TextCuboid, which can preserve various meanings of polysemy. Based upon the new model, we propose two novel data augmentation techniques (called Boolean augmentation and CuboidGAN) that can be directly applied to the TextCuboid model for text classification tasks. Boolean augmentation includes three simple keyword modifications: synonym replacement, synonym insertion, and random deletion. CuboidGAN is composed of two key components, style encoding, and residual regression, and it is trained in two phases to generate unambiguous and plausible concept vectors. Through intensive experiments using five commonly used datasets, we prove that our proposed methods perform better data augmentation than other conventional methods. We also show that each augmentation method component significantly contributes to text classification through ablation studies.
Application of Natural Language Processing Technology in News Event Retrieval and Classification

The news event retrieval and classification system based on natural language processing technology has been widely used internationally, but in China, its related research started late, and mainly focused on the news event retrieval and classification system in specific fields, lacking the exploration of the application of natural language processing technology in the domestic news event retrieval and classification system. This paper summarized the application status of natural language processing technology in the domestic news event retrieval and classification system, and on this basis, put forward the related research directions, including building a multi-dimensional resource database, implementing automatic extraction of event information, building an information extraction platform based on web crawlers, and further analyzing events. The results showed that natural language processing technology could improve the classification accuracy of news texts from 79.10% to 91.46% on average. This paper aimed to promote the development of relevant research in China, and also hoped to provide some reference for the international application of natural language processing technology in the domestic news event retrieval and classification system.
Advancing Recurrent Neural Networks and Generative Adversarial Networks: A Technical Framework for Enhanced Effectiveness Evaluation

This paper introduces a deep learning model that combines Recurrent Neural Networks (RNN) and Generative Adversarial Networks (GAN) for evaluating the teaching effectiveness of secondary school English teachers. To verify the effectiveness of the model, a series of experiments were designed and conducted. The results show that our model performs better than the RNN model and the base GAN model in evaluation metrics such as accuracy, precision, recall, F1 score, and AUC value. Specifically, the improved RNN+GAN model also performed excellently in terms of training time, testing time, and model complexity. These results confirm the effectiveness of deep learning in extracting useful features from teaching data, and our proposed model can more accurately evaluate teaching effectiveness. This research provides a new perspective on the use of deep learning for teaching effectiveness evaluation and demonstrates the potential of deep learning in the field of educational technology.
Multi-View Latent Diffusion

Multi-view observations potentially offer a more comprehensive understanding of real-world phenomena compared to observations acquired from a single viewpoint. Existing models that utilize multi-view data often consider that all views are available during inference, but this assumption may not hold in practical scenarios. To address this limitation, we introduce MVLD, a novel method that, by employing a deterministic autoencoder and a score-based diffusion model, is capable of imputing missing views. We finally envision MVLD being used in a communication system for image transmission.
Efficient Missing Value Imputation by Maximum Distance Likelihood

Predicting missing attribute values in data is extremely important in improving the accuracy in many applications. Existing algorithms ignore the difference between the records used for learning and predicting. The accuracy is not good enough and can be further improved. This paper proposes two solutions: (1) Maximization-based approach (MP) and (2) Distance-ratio-based approach (DP). MP and DP ensure that the incomplete records with the missed values are similar to the records used to learn the parameters as much as possible. MP and DP learn all possible parameters not only from the k nearest neighboring set (k-NN) but from the k-Sets, which are all possible combinations of k complete records. The parameters learnt from the records that are most similar to the repaired candidates of the incomplete records are chosen. Experimentally, MP and DP significantly outperform the existing approaches.
FVHNet: Homography Matrix Estimation for Virtual Camera

Robust lane detection in real-time is one of the foundations for advanced autonomous driving, which can provide substantial amounts of useful information for Autonomous Driving Systems, vehicle self-control, localization, and map construction. Since the in/extrinsic parameters of different vehicles are various which has a significant impact on the results of 3D lanes, we introduce the Virtual Camera that unifies the in/extrinsic parameters of cameras mounted on different vehicles to guarantee the consistency of the spatial relationship among cameras. It can effectively promote the learning procedure due to the unified visual space. Different from using fixed intrinsic parameters and extrinsic parameters to transform front-view images to virtual images, we train a network, termed FVHNet, that estimates the parameters of an ideal perspective transformation, conditioned on the input image. In order to train FVHNet for outputting the transformation matrix that is optimal for perspective transformation, we construct a loss function referred FVHLoss. In summary, we propose a method which can unify the in/extrinsic parameters of different vehicles with learnable homography matrix. We verify our method on the Apollo 3D Lane Synthetic Dataset and achieve competitive results.
Formal Verification of Blockchain and IoT-based E-Voting System Using SPIN

E-voting systems is great step toward maintaining democracy of countries because they are providing security, privacy, accessibility and fast countability. Blockchain and Internet of Things (IoT) increase further reliability because Blockchain makes voting more transparent and immutable. A mistake like alteration of results, miscounting and less privacy in system can cause huge loss of money, time, faith and effort of human beings. To tackle these errors our system must be correct. Therefore, we address problem related to correctness of e-voting system and try to ensure correctness using SPIN model checker by doing its formal verification. E-voting system are proposed with many different approaches in different studies and those system verifications are done using different model checking tools. But no one has used SPIN model checker in the area of e-voting system verification. In this work, we formally specify the e-voting system using the formal specification language, i.e., Process or Protocol Meta Language (PROMELA). We create a program graph for this process through the SPIN Model checker and describe safety properties using Linear Temporal Logic (LTL) in the form of a formula. The formal verification is performed to ensure correctness by giving the program graph and LTL formulas as input to the SPIN model checker to check whether the properties are satisfied with the program graph.
DRGC: A Dynamic Redundant Gradient Coding method

To solve the Straggler problem which seriously affects computational efficiency in distributed gradient descent (DGD), we design a dynamic redundant gradient coding method(DRGC) based on Reed-Solomon coding with dynamic code words and heartbeat delay-aware mechanism that can monitor the computational progress of workers. Traditional coding method ensures the straggler tolerance through a large of redundant computations, but can’t adapt to the change of cluster state. In contrast, DRGC can flexibly adapt to the change by changing the code words base on the cluster state that heartbeat delay-aware mechanism has monitored and determined. Experiments prove that compared with the RS gradient coding, DRGC can further reduce training time.
Adaptive Shrinkage Denoising and Sequential State Extraction Model for Vibration Event Recognition

Intelligent recognition of events along the fiber has become an increasingly important issue for many safety monitoring to achieve fault diagnosis and anomaly warnings. The fiber signal acquisition environment is complex, there is a lot of nonstationary noise in the signal. The traditional denoising methods are usually based on the assumption of a constant noise level, which makes it difficult to adapt to different noise situations. Besides, to address the issue of feature extraction and recognition of vibration signals, many methods have been proposed, e.g., convolution neural networks and short-time Fourier transform. However, temporal order information is ignored, which leads to poor classification of certain events that are only clearly distinguishable in the temporal domain. Therefore, this paper designs an Adaptive Shrinkage Denoising and Sequence-state Learning Vibration Detector model (ASVD) that adaptively learns soft thresholds to eliminate noise-related information and capture the temporal sequence state evolution. Specifically, we embed the adaptive soft-threshold learning module into the deep framework to enhance the discriminative feature learning ability from noised signals. Then, we incorporate the sequential state encoder into the framework to capture temporal sequence state evolution. We conducted experiments on an open dataset and achieved an accuracy of 96.5%, a false positive rate of 0.7%, and a false negative rate of 3.53% averaged across the six events.
Multimodal Sentiment Analysis based on Supervised Contrastive Learning and Cross-modal Translation under Modalities Missing<sup>*</sup>

Multimodal sentiment analysis tasks have been extensively researched in recent years. However, real-world multimodal data collection is often problematic due to missing modalities, making it suboptimal. To address these issues, this paper presents a supervised contrast learning and cross-modal translation-based multimodal sentiment analysis model that can operate even when modalities are missing. First, supervised contrast learning is employed to carry out cluster analysis on the same sample batch and establish the connection between different samples. Incomplete features of the target samples are then supplemented with the other samples. Following that, a cross-modal translation network, based on Transformer, is utilised to learn the collective representation of different modalities. A feature reconstruction network is used, employing center distance difference loss, to reduce the difference between features obtained by the model under modality-missing and modality-complete conditions. This aims to enhance the robustness of feature extraction. Experimental findings from two publicly available datasets demonstrate the model’s improved performance for varying levels of modalities missing.
A Novel Approach for Text Classification by Combining Pre-trained BERT Model with CNN Classifier

Language model pre-training has emerged as a highly effective approach for acquiring universal language representations. Among the state-of-the-art models in this field, BERT (Bidirectional Encoder Representations from Transformers) has garnered significant attention due to its remarkable performance across various language understanding tasks. With its advanced architecture and innovative techniques, BERT has consistently delivered impressive results, solidifying its position as a leading language model pre-training model. CNN (Convolutional Neural Network) is also widely used in language model training.This study presents a novel approach for text classification by combining a pre-trained BERT model with a CNN classifier. The model under consideration attains the highest level of performance currently available on the AG News and Amazon product reviews datasets. The BERT model is applied to produce contextualized embeddings for the input text data, while the CNN classifier executes the classification process. By utilizing the contextualized embeddings of BERT and the n-gram feature capturing of CNN, our model exhibits superior performance in both accuracy and F1 score when compared to the baseline models. The findings indicate the capability of our methodology in analyzing social media, determining sentiment, and classifying documents.
Label Inference Attack Based on Soft Label Towards Two-Party Split Learning

In Vertical Federated Learning, Split Learning has gained popularity as a technique where a neural network is split into two parts held by two parties. One party holds the input data, and another party holds the corresponding labels. The two parties exchange intermediate features and gradients of features to update the network parameters to preserve the privacy of the private data and network parameters. However, recent studies just utilized a small amount of auxiliary data to infer private labels. In the paper, we propose a label inference attack method based on soft label by leveraging the auxiliary data. To further enhance the performance of the attack method, we design a loss function that combines a gradient-matching loss and a model loss based on soft label. Our experimental evaluation is conducted on classification datasets, and the results demonstrate that our model has superior attack effectiveness and generalization capabilities.
Summarizing Dark Web Services with TF-IDF and LSA

Identifying individual dark web services can be challenging, but several methods exist to overcome this challenge. Automated dark web services labeling can be performed using NLP, sentiment analysis, computer vision, and deep learning techniques. These methods range from keyword-based labeling to deep learning-based labeling. The proposed work uses TF-IDF and LSA (Latent Semantic Analysis) to summarize dark web services. LSA is used to uncover latent relationships between words and concepts in a corpus of Dark Web services. In summarizing dark web services, authors can identify the most important keywords and concepts, which can provide insight into what types of services are available on the dark web. The identified key terms can potentially be the label of individual dark web services. With the help of word cloud visualization, the authors demonstrated the proposed approach. The proposed method can be particularly valuable when dealing with large volumes of unlabeled dark web text data, as it can identify patterns that would be hard to detect otherwise.