Reviewerly/exHarmony huggingface dataset:
Is This a Violation? Learning and Understanding Norm Violations in Online Communities

Using norms to guide and coordinate interactions has gained tremendous attention in the multi-agent community. However, new challenges arise as the interest moves towards dynamic socio-technical systems, where human and software agents interact, and interactions are required to adapt to human's changing needs. For instance, different agents (human or software) might not have the same understanding of what it means to violate a norm (e.g., what characterizes hate speech), or their understanding of a norm might change over time (e.g., what constitutes an acceptable response time). The challenge is to address these issues by learning the meaning of a norm violation from limited interaction data. For this, we use batch and incremental learning to train an ensemble of classifiers. Ensemble learning and data-sampling handle the imbalanced class distribution of the interaction stream. At the same time, the training approaches use different strategies to ensure that the ensemble models reflect the latest community view on the meaning of norm violation. Batch learning uses weight assignment, while incremental learning continuously updates the ensemble models as community members interact. Here, we extend our previous work by creating a different balance strategy for online learning and integrating interpretability to understand norm violations. Additionally, we evaluate the proposed approaches in the context of Wikipedia article edits, where interactions revolve around editing articles, and the norm in question is prohibiting vandalism. Lastly, we conduct ablation studies to compare the ensemble's performance against a single model approach and to examine the behavior of two data sampling techniques. Results indicate that the different machine learning frameworks can learn the meaning of a norm violation in a setting with data imbalance and concept drift, although with significant differences.
Multi-modal anchor adaptation learning for multi-modal summarization

In this paper, we focus on analyzing the relationship between the input of source text and source image, and then through the integration and generalization of the multi-modal information (e.g., texts and images), outputs the multi-modal summarization including text and image. However, existing multi-modal summarization methods face several challenges: (1) Different modalities exist in different semantic spaces, and expressing the multi-modal information of source modalities in a similar semantic representation space is crucial. (2) For the result of text summarization, it is crucial to explore how to capture both the differences and similarities within the source modalities, which can reduce redundancy to improve the quality of text summarization. In order to overcome the challenge above, Multi-Modal Anchor Adaptation Learning for Multi-Modal Summarization (MA-Sum) has been proposed. Specifically, MA-Sum employs a novel and highly efficient image anchor selection method, which selects the object sample containing the richest image information as the image anchor. Simultaneously, it carefully selects the text sentence closely intertwined with the image semantics to serve as the language anchor. Therefore, the multi-modal anchor can be seen as a bridge for multi-modal alignment to alleviate the semantic gap between textual and visual. Moreover, based on the distance between the anchors and the semantic information in the respective modal, the positive and negative semantic information of each modal will be distinguished. Based on negative semantic information, the counterfactual learning mechanism is constructed to optimize the result of multi-modal summarization. Finally, the process of multi-modal features interaction is optimized by image summary which is chosen by using multi-modal anchors. According to the experimental results, compared with the state-of-art multi-modal summarization, our proposed MA-Sum can be optimized in terms of summarization consistency and completeness, so as to obtain the optimal multi-modal summarization metric.
Spatial unity for the apperception engine

We develop a logical approach for computational agents to spatially explore their environment, extending on the Kant-inspired logical unification methods of the Apperception Engine. Evaluating models of the Regional Connection Calculus as Alexandroff Topologies, we axiomatise connectedness and unity of space. We further define dimensionality for tolerance spaces and prove that locally verifiable properties guarantee global consistency of space as the existence of isomorphisms to grids, turbands or tori. Finally, we provide a generally competent and explainable computational agent that unifies intuition in space, the Spatial Apperception Engine.
BDACD: Blockchain-based decentralized auditing supporting ciphertext deduplication

Public auditing enables data owners to entrust a third-party auditor (TPA) to perform auditing tasks periodically. To resist malicious TPAs, a plethora of blockchain-based public auditing mechanisms have been proposed. However, existing schemes cannot effectively mitigate single point of failure and collusion between TPAs and miners. These schemes are burdened by significant wastage of storage resources due to the presence of redundant data. In this paper, we propose a blockchain-based decentralized auditing scheme supporting ciphertext deduplication (BDACD). BDACD leverages a decentralized blockchain, which takes over the role traditionally played by a centralized TPA. A decentralized autonomous organization is incorporated to enhance resistance against collusion attacks. By integrating advanced ciphertext deduplication techniques, BDACD effectively mitigates storage overhead. Furthermore, we introduce a novel concept, T-Merkle hash tree, along with a corresponding search algorithm. This innovation significantly enhances blockchain storage utilization and enables efficient half-interval searches within a block. To ensure accountability and fairness, a smart contract is employed to establish an arbitration mechanism. This mechanism serves a dual purpose: penalizing any malevolent actions by cloud servers and providing adequate compensation to data owners whose data integrity has been compromised. Detailed security analysis and performance evaluation demonstrate BDACD has desirable security and efficiency.
T2FM: A novel hashtable based type-2 fuzzy frequent itemsets mining

Association rule mining (ARM) is an important research issue in the field of data mining that aims to find relations among different items in binary databases. The conventional ARM algorithms consider the frequency of the items in binary databases, which is not sufficient for real time applications. In this paper, a novel hash table based Type-2 fuzzy mining algorithm (T2FM) with an efficient pruning strategy is presented for discovering multiple fuzzy frequent itemsets from quantitative databases. The algorithm employs a hash table based structure for efficient storage and retrieval of item/itemset which reduces the search efficiency to O(1) or constant time. Previously, type-2 based Apriori and FP-growth based fuzzy frequent itemsets mining were proposed, which required large amounts of computation and a greater number of candidate generation and processing. Meanwhile, the proposed approach reduces a huge amount of computation by finding the common keys before the actual intersection operation takes place. An efficient pruning strategy is proposed to avoid unpromising candidates in order to speed up the computations. Several experiments are carried out to verify the efficiency of the approach in terms of runtime and memory for different minimum support threshold and the results show that the designed approach provides better performance compared to the state-of-the-art algorithms.
Constraint-based high utility mobile trajectory pattern mining for security systems

In security system, high utility pattern mining of a large number of users mobile trajectories is helpful to analyze user behavior patterns, and enhance the internal prevention of the security system.Currently, the frequent pattern mining for mobile trajectory in security systems do not take into account the differences in the safety levels between staying points and the frequency of their occurrence. Existing high utility mobile trajectory pattern mining methods are unable to discover staying points that meet specific user requirements. To solve these problems, a constraint-based high utility mobile trajectory mining algorithm is proposed (names as HUIM-ILC-ACO). It takes into account the user’s stay time, stay frequency, and the safety level of each stopping point and calculates the utility value of each staying point by incorporating these factors through weighted computation. Based on this, the algorithm combines ant colony optimization with length constraints and item constraints to construct a method for mining high utility mobile trajectory patterns that better align with user interests. Experimental results on real datasets and a target mobile trajectory RFID dataset show that proposed algorithm is efficient in terms of runtime and pattern quantity, and it can effectively mine a pattern set that is closer to user interests.
Multi-scale cross-layer feature interaction GAN for underwater image enhancement

Due to the intricate underwater environment, underwater images frequently exhibit issues such as low contrast, detail loss, and color distortion, which significantly affect underwater operations. To alleviate these concerns and improve image quality, multi-scale cross-layer feature interaction GAN for underwater image enhancement model (MCFIGAN) is proposed in this paper. In the proposed model, we have designed a cross-layer feature interaction module (CLFIM) that fosters the fusion and transmission of features at various levels. This approach aims to extract more comprehensive feature information and enhance the robustness of the network model. Additionally, we have constructed a multi-scale feature module (MSFEM) to extract features at different levels and scales, enabling the model to gain a more comprehensive understanding of the object. By utilizing CLFIM and MSFEM, the generator is capable of producing a generated image that contains a wealth of feature information. Then, an enhanced discriminator module (EDM) is designed to improve the model's ability to identify generated images, thereby encouraging the generator network to generate more realistic underwater images. Finally, several training sessions utilizing multiple joint losses yield underwater images with characteristics of high contrast and rich detail. Through simulation experiments conducted on EUVP dataset and UIEB dataset, our proposed model is found to be superior to related models in terms of visual effect and quantitative index, particularly in contrast enhancement, detail recovery, and color restoration.
A machine-learning based approach to predict facies associations and improve local and regional stratigraphic correlations

The geological record has challenged stratigraphers through time. Many depositional, tectonic and paleobiological events require stratigraphic positioning to determine temporal relationships among such events. This task is complicated and challenging, especially in sedimentary succession with scarcity or lack of paleontological content bearing biostratigraphic value and radiometric ages. Therefore, subjective personal criteria adopted during correlations and field mapping activities make stratigraphic correlations more complex and confusing. New methodological approaches are necessary to test human expertise in recognizing stratigraphic units with environmental significance and to contribute to stratigraphic correlations based on quantitative data. We used cores obtained by coal drilling campaigns during the 1970s and 1980s on the southern border of the Paraná Basin, southern Brazil, to generate a quantitative database. Data obtained from gamma spectrometry in the cores of Carboniferous to Permian age measured total count (cps), potassium (K), uranium (U) and thorium (Th). We used machine learning (ML) to predict facies associations and to confront the quantitative database with the three facies associations mapped through time based on qualitative geological mapping criteria. The k-nearest neighbors (KNN) algorithm reached maximum accuracy values of 86.0%% and f1-score of 90.0%, 73.0% and 91.0% for facies associations 1, 2 and 3, respectively, using exponential moving average and normalized data. This KNN-based method using gamma spectrometric data opened new possibilities to perform local and regional stratigraphic correlations using quantified data. New tests must be performed to improve this promising correlation method in other regional settings and related basins.
A Relation Embedding Assistance Networks for Multi-hop Question Answering

Multi-hop Knowledge Graph Question Answering aims at finding an entity to answer natural language questions from knowledge graphs. When humans perform multi-hop reasoning, people tend to focus on specific relations across different hops and confirm the next entity. Therefore, most algorithms choose the wrong specific relation, which makes the system deviate from the correct reasoning path. The specific relation at each hop plays an important role in multi-hop question answering. Existing work mainly relies on the question representation as relation information, which cannot accurately calculate the specific relation distribution. In this article, we propose an interpretable assistance framework that fully utilizes the relation embeddings to assist in calculating relation distributions at each hop. Moreover, we employ the fusion attention mechanism to ensure the integrity of relation information and hence to enrich the relation embeddings. The experimental results on three English datasets and one Chinese dataset demonstrate that our method significantly outperforms all baselines. The source code of REAN will be available at https://github.com/2399240664/REAN
Transfer learning approach to reduce similar IOT sensor data for industrial applications

The Industrial Internet of Things (IIoT) is a developing technology that has the potential to advance industrial intelligence, achieve targeted effectiveness, and lower manufacturing costs. Data fusion, a procedure that enables the collection, analysis, and processing of the enormous amounts of Internet of Things (IoT) information created by industrialized machinery and applications, is essential to IIoT's advancement and improvement of industrial products and applications. A real-time, efficient, and private information image fusion mechanism is required by the IIoT. Moreover, the current efforts call for such training of multiple systems in data analysis that also fall short of meeting real-time IIoT needs. The ineffectiveness of internal defenses and the challenge of finding the right balance between system stability and data confidentiality, however, undermine the efficiency and privacy preservation of a data fusion process. In this paper, we propose a Transfer learning-based Secure Data Fusion (TSDF) for the IIoT to address the aforementioned issues. In addition to the proposed network, we developed and deployed a defect estimation method that makes use of sensor data fusion to evaluate the manufacturing plant's operational state. The efficiency outcomes of evaluating the conceptual model are included in the original study, including a comparison to a conventional deep neural network structure. The study's findings demonstrate that TSDF may provide privacy, preserving information fusion in diverse IIoT application environments while reaching higher system capacity and low latency.
Sea-Pix-GAN: Underwater image enhancement using adversarial neural network

In the last decade, the exploration of deep-sea ecosystems has surged, offering exciting prospects for discovering untapped resources such as medical drugs, food and energy sources, and renewable energy products. Consequently, research in underwater image processing has witnessed substantial growth. However, underwater imaging poses significant challenges, particularly without sophisticated, specialized cameras. Traditional cameras are impacted by absorption and scattering in the aquatic environment, producing hazy images with a blue–green tint. This phenomenon holds implications for marine research and other disciplines that rely on underwater imaging. While hardware advancements have been made over the years, image processing remains a valuable, cost-effective, and practical approach for underwater enhancement. Despite the existence of state-of-the-art techniques for underwater enhancement and restoration, their performance is often inconsistent. While some methods excel in contrast restoration, color restoration remains a pervasive challenge. In this paper, we introduce Sea-Pix-GAN, a Generative Adversarial Network (GAN)-based model that addresses these issues in underwater image enhancement. We redefine the problem as an image-to-image translation task and tailor the objective and loss functions to achieve color, content, and style transfer. The model is trained on a large dataset of underwater scenes, encompassing the diverse color dynamics of underwater subjects. Sea-Pix-GAN demonstrates promising results in restoring color, contrast, texture, and saturation. To validate its effectiveness, we compare the performance of Sea-Pix-GAN quantitatively based on metrics like PSNR, SSIM, and UIQM and qualitatively against several existing techniques.
Monocular endoscopy images depth estimation with multi-scale residual fusion

Monocular depth estimation plays a fundamental role in clinical endoscopy surgery. However, the coherent illumination, smooth surfaces, and texture-less nature of endoscopy images present significant challenges to traditional depth estimation methods. Existing approaches struggle to accurately perceive depth in such settings. To overcome these challenges, this paper proposes a novel multi-scale residual fusion method for estimating the depth of monocular endoscopy images. Specifically, we address the issue of coherent illumination by leveraging image frequency domain component space transformation, thereby enhancing the stability of the scene's light source. Moreover, we employ an image radiation intensity attenuation model to estimate the initial depth map. Finally, to refine the accuracy of depth estimation, we utilize a multi-scale residual fusion optimization technique. To evaluate the performance of our proposed method, extensive experiments were conducted on public datasets. The structural similarity measures for continuous frames in three distinct clinical data scenes reached impressive values of 0.94, 0.82, and 0.84, respectively. These results demonstrate the effectiveness of our approach in capturing the intricate details of endoscopy images. Furthermore, the depth estimation accuracy achieved remarkable levels of 89.3 % and 91.2 % for the two models' data, respectively, underscoring the robustness of our method. Overall, the promising results obtained on public datasets highlight the significant potential of our method for clinical applications, facilitating reliable depth estimation and enhancing the quality of endoscopy surgical procedures.
Efficient Syndrome Decoder for Heavy Hexagonal QECC via Machine Learning

Error syndromes for heavy hexagonal code and other topological codes such as surface code have typically been decoded by using Minimum Weight Perfect Matching– (MWPM) based methods. Recent advances have shown that topological codes can be efficiently decoded by deploying machine learning (ML) techniques, in particular with neural networks. In this work, we first propose an ML-based decoder for heavy hexagonal code and establish its efficiency in terms of the values of threshold and pseudo-threshold for various noise models. We show that the proposed ML-based decoding method achieves ~ 5 × higher values of threshold than that for MWPM. Next, exploiting the property of subsystem codes, we define gauge equivalence for heavy hexagonal code, by which two distinct errors can belong to the same error class. A linear search-based method is proposed for determining the equivalent error classes. This provides a quadratic reduction in the number of error classes to be considered for both bit flip and phase flip errors and thus a further improvement of ~ 14% in the threshold over the basic ML decoder. Last, a novel technique based on rank to determine the equivalent error classes is presented, which is empirically faster than the one based on linear search.
Residual networks for text-independent speaker identification: Unleashing the power of residual learning

The human voice, a dynamic signal, conveys valuable information for speaker identification, encompassing gender, age, emotions, and language. In the biometrics industry, identifying voices in real-time amidst diverse accents, tones, and noisy backgrounds is a challenging task. Voice biometry, a complex aspect of speaker identification, is gaining importance in various applications, such as user authentication, attendance systems, forensics, and banking operations, as it eliminates the need for traditional credentials like cards or passwords. Recent advancements in Human–Computer Interaction technology have made conversational tasks technically feasible. Deep Neural Learning approaches, especially Convolutional Deep Neural Networks (CDNN), have emerged as a powerful tool in the field of speech processing, surpassing traditional Speaker Identification methods. This paper introduces a novel approach using 1-Dimensional Convolutional Residual Blocks for audio classification and Speaker Identification, specifically focusing on speaker recognition from spoken Hindi language. The proposed Residual architecture significantly enhances speaker identification, even in low Signal Noise Ratio environments, achieving an impressive accuracy rate of 86.02%. This outperforms traditional Gaussian Mixture Model (GMM) and Feed Forward Back-propagation Network (FFBN) model for the same set of speakers. Future research directions may explore the classification of audio and speaker identification using various acoustic features derived from speech signals.
Investigation on evaluation of education effect based on deep learning algorithm

With the rapid development of society today, in the context of the impact of education on personal future development, people have increasingly attached importance to education. Various types of online and offline education institutions have mushroomed. However, the factors that affect the effectiveness of education are numerous and complex. As a very important part of early childhood learning, preschool education should receive more attention. The deep learning algorithm can reasonably classify and plan the factors affecting preschool education through massive data calculations to ensure the reasonable allocation of resources and save the time and energy costs wasted in allocating educational resources. The educational effectiveness of preschool education institutions and organizations can be reasonably evaluated. This article applied deep learning algorithms to the evaluation of preschool education effectiveness. Data related to the effectiveness of preschool education in preschool education institutions using convolutional neural networks and not using algorithms were compared. The experimental results showed that the expert recognition rates of the convolutional neural network algorithm and traditional manual measurement data were 98.8% and 92.55%, respectively. At the time level of teaching quality estimation, five algorithms and 100 sets of relevant experimental data were compared. In terms of the accuracy of feature search, the average accuracy of the convolutional neural network algorithm was 97.96%, while the accuracy of the traditional manual search was 53.9%. Therefore, the application of the convolutional neural network algorithm in deep learning to the evaluation of preschool education effectiveness was more efficient and efficient and could improve the evaluation effect from various aspects.