Reviewerly/exHarmony huggingface dataset:
Noisy Tree Data Structures and Quantum Applications

We suggest a new technique for developing noisy tree data structures. We call it a “walking tree”. As applications of the technique we present a noisy Self-Balanced Binary Search Tree (we use a Red–Black tree as an implementation) and a noisy segment tree. The asymptotic complexity of the main operations for the tree data structures does not change compared to the case without noise. We apply the data structures in quantum algorithms for several problems on strings like the string-sorting problem and auto-complete problem. For both problems, we obtain quantum speed-up. Moreover, for the string-sorting problem, we show a quantum lower bound.
Inferring Complementary and Substitutable Products Based on Knowledge Graph Reasoning

Complementarity and substitutability between products are essential concepts in retail and marketing. To achieve this, existing approaches take advantage of knowledge graphs to learn more evidence for inference. However, they often omit the knowledge that lies in the unstructured data. In this research, we concentrate on inferring complementary and substitutable products in e-commerce from mass structured and unstructured data. An improved knowledge-graph-based reasoning model has been proposed which cannot only derive related products but also provide interpretable paths to explain the relationship. The methodology employed in our study unfolds through several stages. First, a knowledge graph refining entities and relationships from data was constructed. Second, we developed a two-stage knowledge representation learning method to better represent the structured and unstructured knowledge based on TransE and SBERT. Then, the relationship inferring problem was converted into a path reasoning problem under the Markov decision process environment by learning a dynamic policy network. We also applied a soft pruning strategy and a modified reward function to improve the effectiveness of the policy network training. We demonstrate the effectiveness of the proposed method on standard Amazon datasets, and it gives about 5–15% relative improvement over the state-of-the-art models in terms of NDCG@10, Recall@10, Precision @10, and HR@10.
Image Annotation Model Using Visual Aligning Attention of Crucial Data in the Grapics

Image captioning is a challenging endeavor that blends the written and visual worlds. To create sentences in human languages, picture captioning algorithms need to be able to comprehend the content of the input images. The picture caption research has made extensive use of the attention technique, which can offer more exact visual input for deep sequential model training. This paper provides the solution for image captioning with visual aligning attention which will be useful in crucial data. This paper will introduce approach to give image annotation model with better precision and accuracy in terms of more accurate image information
Assessment of public information understanding using plain language and data visualization

Understanding is an essential quality in the presentation of information, mainly to promote democracy, transparency, and citizen participation in open government data (OGD). Data visualizations are common in open data portals and public services, but there is still a lack of consolidated practices for their publication and evaluations aimed at understanding users. Above all, add the Plain Language technique, which helps to make information more understandable for everyone and reduce barriers to the inclusion of a poorly literate public. This research evaluated the impact of the practices of this language gathered in a usage Guide for creating graphics understandable to the public in relation to other visualizations. The study was carried out with users of public health visualization in the context of the COVID-19 pandemic. The results showed that the use of the Guide improved understanding in tasks with fewer variables and data involved. An important insight for the development of dashboards and public portals with visualizations accessible to all. However, with challenges to be overcome mainly in the size and complexity of the various practices to be followed, making it difficult for users to fully use the Guide. Investing in researching a user support system can ensure higher quality graphics.
Maximizing the value delivered of chatbots in e-Gov using the incremental funding method

The society has increasingly demanded efficiency and agility from its governments. Artificial Intelligence, when oriented to serve the citizen, to facilitate the use and access to Information and Communication Technologies (ICT) applications, is one of the priorities of governments digitalization. This research aimed to create a method that would allow optimizing the value delivered of Chatbots in e-Gov, a critical tool for governments digitalization. The approach was based on the Incremental Funding Method (IFM) method and the prioritization of intangibles through the Analytic Hierarchy Process (AHP) technique, considering the peculiar characteristics of Chatbot and Machine Learning projects. It was possible to show that government services of an INSS (Institute of Social Security and Pension of Brazil) Application Example implemented from Chatbot could be self-financing and deliver benefits more quickly to the population using this customized optimization delivery sequencing method. The research is more relevant when the nature of the solution, Chatbots, can improve inclusion of more citizens in the digital government transformation.
Design of an ontology to represent the elaboration of the annual operational plan in the area of public sector planning

The Semantic Web helps to represent knowledge in such a way that it can be easily processed by machines. Ontologies are a tool the Semantic Web uses to define concepts, establish their hierarchy, outline attributes, and determine relationships, which capture knowledge within a specific domain. In the public sector, institutions generate a substantial volume of information. To foster transparency and openness, such information is published on the web. However, in many cases, it is necessary to incorporate semantic content that helps to organize the information so that it can be processed automatically, by government authorities and interested users. One of the fundamental activities in public institutions is planning, which includes a set of processes that contribute to the achievement of previously defined objectives. In this article, we design an ontology whose domain is the elaboration of the Annual Operational Plan in Ecuador. An operational plan is a process that belongs to the Planning area of the Decentralized Autonomous Governments of Ecuador. We propose a set of activities and their respective tasks for developing the ontology based on the Methontology methodology. After its development, we formalized the proposed ontology using Protégé. The main theoretical contribution of this paper is the definition of an ontology in a specific government area. In addition, from a practical perspective, we developed a tool that facilitates the analysis and processing of data for budget planning in Ecuador.
Stereotypes in ChatGPT: an empirical study

ChatGPT is rapidly gaining interest and attracts many researchers, practitioners and users due to its availability, potentials and capabilities. Nevertheless, there are several voices and studies that point out the flaws of ChatGPT such as its hallucinations, factually incorrect statements, and potential for promoting harmful social biases. Being the focus area of this contribution, harmful social biases may result in unfair treatment or discrimination of (a member of) a social group. This paper aims at gaining insight into social biases incorporated in ChatGPT language models. To this end, we study the stereotypical behavior of ChatGPT. Stereotypes associate specific characteristics to groups and are related to social biases. The study is empirical and systematic, where about 2300 stereotypical probes in 6 formats (like questions and statements) and from 9 different social group categories (like age, country and profession) are posed to ChatGPT. Every probe is a stereotypical question or statement where a word is masked and ChatGPT is asked to fill in the masked word. Subsequently, as part of our analysis, we map the suggestions of ChatGPT to positive and negative sentiments to get a measure of stereotypical behavior of a language model of ChatGPT. We observe that ChatGPT stereotypical behavior differs per social group category, for some categories the average sentiment is largely positive (e.g., for religion), while for others it is negative (e.g., for political). Further, our work empirically affirms the previous claims that the formats of probing affect the sentiments of the stereotypical outcomes of ChatGPT. Our results can be used by practitioners and policy makers to devise societal interventions to change the image of a category or a social group, as captured in ChatGPT language model(s), and/or to decide to appropriately influence the stereotypical behavior of such language models.
Research on a Random Mask Infection Countermeasure against Double Fault Attacks

The infection countermeasure, in which the main idea is to prevent adversaries from exploiting faulty ciphertexts to break the key by spreading the induced fault, is a very effective countermeasure against fault attacks. However, most existing infection countermeasures struggle to defend against double-fault attacks effectively due to the single-fault assumption. By analyzing the principle of infection mechanism and adding different random Boolean masks in the two encryption paths, this paper proposes a measure called a random mask infection countermeasure to defend against double-fault attacks. In addition, the multiplication mask is used to randomize the fault diffusion to further resist single-byte fault attacks. The experimental results indicate that the random mask infection countermeasure proposed can perform fault diffusion effectively when the cryptographic circuit suffers double-fault attacks, and the fault diffusion shows randomness, and can effectively defend against these fault attacks.
FGT-SAMK-NN: impact of the right to be forgotten using a lazy algorithm in data stream learning

“Right to Be Forgotten" is guaranteed by new international regulations on personal management data. This means that individuals can request the erasure of their data from third-party tools and services. However, this poses a challenge for machine learning estimators, who will need to forget parts of their knowledge. This paper examines the impact of learning and forgetting policies in Data Stream Learning. Storing data or retraining learning models from scratch in data stream mining is usually not feasible due to the large volume of instances. Therefore, more efficient solutions are necessary to deal with the dynamic nature of online machine learning. To address this issue, we implemented FGT-SAMK-NN, an incremental version of one of the most knowledgeable algorithms in Data Stream lazy algorithms: The SAMK-NN classifier. FGT-SAMK-NN can erase its past data, and we investigate the impact of data forgetting on predictive performance. Our proposal is compared to the original SAMK-NN algorithm using four non-stationary stream datasets. Our results demonstrate that evaluation metrics did not undergo significant changes, which may support the idea that has a good architecture for adaptations of the proposed nature. However, it was also noted that the processing time is very high for cases involving more forgettings, which may indicate that the high complexity of the model creates conflicts if the pattern of data streams, where the algorithm is used, involves a high forgetfulness rate.
New Interval Improved Fuzzy Partitions Fuzzy C-Means Clustering Algorithms under Different Distance Measures for Symbolic Interval Data Analysis

Symbolic interval data analysis (SIDA) has been successfully applied in a wide range of fields, including finance, engineering, and environmental science, making it a valuable tool for many researchers for the incorporation of uncertainty and imprecision in data, which are often present in real-world scenarios. This paper proposed the interval improved fuzzy partitions fuzzy C-means (IIFPFCM) clustering algorithm from the viewpoint of fast convergence that independently combined with Euclidean distance and city block distance. The two proposed methods both had a faster convergence speed than the traditional interval fuzzy c-means (IFCM) clustering method in SIDA. Moreover, there was a problem regarding large and small group division for symbolic interval data. The proposed methods also had better performance results than the traditional interval fuzzy c-means clustering method in this problem. In addition, the traditional IFCM clustering method will be affected by outliers. This paper also proposed the IIFPFCM algorithm to deal with outliers from the perspective of interval distance measurement. From experimental comparative analysis, the proposed IIFPFCM clustering algorithm with the city block distance measure was found to be suitable for dealing with SIDA with outliers. Finally, nine symbolic interval datasets were assessed in the experimental results. The statistical results of convergence and efficiency on performance revealed that the proposed algorithm has better results.
DECORAIT - DECentralized Opt-in/out Registry for AI Training

We present DECORAIT; a decentralized registry through which content creators may assert their right to opt in or out of AI training and receive rewards for their contributions. Generative AI (GenAI) enables images to be synthesized using AI models trained on vast amounts of data scraped from public sources. Model and content creators who may wish to share their work openly without sanctioning its use for training are thus presented with a data governance challenge. Further, establishing the provenance of GenAI training data is important to creatives to ensure fair recognition and reward for their such use. We report a prototype of DECORAIT, which explores hierarchical clustering and a combination of on/off-chain storage to create a scalable decentralized registry to trace the provenance of GenAI training data to determine training consent and reward creatives who contribute that data. DECORAIT combines distributed ledger technology (DLT) with visual fingerprinting, leveraging the emerging C2PA (Coalition for Content Provenance and Authenticity) standard to create a secure, open registry through which creatives may express consent and data ownership for GenAI.
A Compact and Semantic Latent Space for Disentangled and Controllable Image Editing

Recent advances in the field of generative models and in particular generative adversarial networks (GANs) have lead to substantial progress for controlled image editing. Despite their powerful ability to apply realistic modifications to an image, these methods often lack properties such as disentanglement (the capacity to edit attributes independently). In this paper, we propose an auto-encoder which re-organizes the latent space of StyleGAN, so that each attribute which we wish to edit corresponds to an axis of the new latent space, and furthermore that the latent axes are decorrelated, encouraging disentanglement. We work in a compressed version of the latent space, using Principal Component Analysis, meaning that the parameter complexity of our autoencoder is reduced, leading to short training times (∼ 45 mins). Qualitative and quantitative results demonstrate the editing capabilities of our approach, with greater disentanglement than competing methods, while maintaining fidelity to the original image with respect to identity. Our autoencoder architecture is simple and straightforward, facilitating implementation.
Attribute-Based Intelligent Data Management for Industrial Application

In the area of industrial engineering, a product is designed and refined, step by step, from different respects promoted by various autonomous roles, such as design engineers, process engineers, manufacturing engineers, maintenance engineers, product users, etc. As a result, the dataset of a product is often generated and shared in a cross-platform way. It is a typical distributed product evolutionary process associated with various data versions. It often challenges the consistency of product data management (PDM) that accompanies a product’s lifecycle. With this scenario, an effective integrated environment or platform is indispensable for harmonizing various product data’s versions produced at different stages. In view of this challenge, a blockchain-driven method for product data’s version management in the industrial metaverse is investigated in this chapter. In our method, an industrial metaverse is used to coordinate the various roles for strengthening the aggregation among more engineering scenes in a virtual way, and a smart contract of blockchain is used to guarantee the consistency of the data version. Concretely speaking, multi-authority attribute-based encryption is adopted to ensure the security of shared version data. Moreover, blockchain is taken advantage of to provide a platform for data sharing under the execution of several smart contracts. Consortium blockchain is deployed in an experimental environment, which validates the performance of our method. At last, the version management method presented in this chapter has been deployed in a real product data management system.
User Data Privacy Protection in Industrial Application

With the rapid development and widespread adoption of Internet of Things (IoT) technology, an increasing number of devices and objects are now interconnected, resulting in a substantial volume of data, especially the location data. Common components of a location-based service (LBS) include “location,” “content,” “timestamp,” and “range.” When it comes to safeguarding personal information, the location attribute and the content property are crucial. While most current privacy protection measures primarily focus on safeguarding the location attribute, they tend to overlook the content component of the LBS system. To tackle this challenge, we introduce a novel approach called content property privacy (CPP), which specifically leverages the content aspect to enhance privacy protection. To hide private information, the CPP approach makes use of k-anonymity to create fake content characteristics. An analysis of real-world data showed that the CPP technique may really improve privacy protection.
A software test bed for sharing and evaluating color transfer algorithms for images and 3D objects

Over the past decades, an overwhelming number of scientific contributions have been published related to the topic of color transfer, where the color statistic of an image is transferred to another image. Recently, this idea was further extended to 3D point clouds. Due to the fact that the results are normally evaluated subjectively, an objective comparison of multiple algorithms turns out to be difficult. Therefore, this paper introduces the ColorTransferLab, a web based test bed that offers a large set of state-of-the-art color transfer implementations. Furthermore, it allows users to integrate their implementations with the ultimate goal of providing a library of state-of-the-art algorithms for the scientific community. This test bed can manipulate both 2D images, 3D point clouds and textured triangle meshes, and it allows us to objectively evaluate and compare color transfer algorithms by providing a large set of objective metrics. As part of ColorTransferLab, we are introducing a comprehensive dataset of freely available images. This dataset comprises a diverse range of content with a wide array of color distributions, sizes, and color depths which helps in appropriately evaluating color transfer. Its comprehensive nature makes it invaluable for accurately evaluating color transfer methods.
Federated Adversarial Training Strategies for Achieving Privacy and Security in Sustainable Smart City Applications

Smart city applications that request sensitive user information necessitate a comprehensive data privacy solution. Federated learning (FL), also known as privacy by design, is a new paradigm in machine learning (ML). However, FL models are susceptible to adversarial attacks, similar to other AI models. In this paper, we propose federated adversarial training (FAT) strategies to generate robust global models that are resistant to adversarial attacks. We apply two adversarial attack methods, projected gradient descent (PGD) and the fast gradient sign method (FGSM), to our air pollution dataset to generate adversarial samples. We then evaluate the effectiveness of our FAT strategies in defending against these attacks. Our experiments show that FGSM-based adversarial attacks have a negligible impact on the accuracy of global models, while PGD-based attacks are more effective. However, we also show that our FAT strategies can make global models robust enough to withstand even PGD-based attacks. For example, the accuracy of our FAT-PGD and FL-mixed-PGD models is 81.13% and 82.60%, respectively, compared to 91.34% for the baseline FL model. This represents a reduction in accuracy of 10%, but this could be potentially mitigated by using a more complex and larger model. Our results demonstrate that FAT can enhance the security and privacy of sustainable smart city applications. We also show that it is possible to train robust global models from modest datasets per client, which challenges the conventional wisdom that adversarial training requires massive datasets.
Deep Optimization for Storage and Computation Over Industrial Internet

The Industrial Internet has revolutionized the way businesses operate by integrating Internet technology with industrial processes. With the advent of the Industrial Internet, the integration of data and automation has become possible, creating new opportunities for businesses to optimize their operations and improve their efficiency. Intelligent technologies, including deep learning, attentive model compression, computation sharing, and deep optimization, are driving a transformation in the Industrial Internet. These technologies empower businesses with data-driven insights, enabling them to make informed decisions and streamline their workflows. Deep learning has played a significant role in optimizing storage and computational resources. With the development of deep learning techniques such as neural network compression and quantization, deep learning models can be deployed in industrial equipment in a smaller and more efficient manner without sacrificing accuracy. This allows for more efficient use of storage space and computational resources, which is crucial in industrial applications where resource efficiency is essential. In addition, techniques such as transfer learning allow for the reuse of pre-trained models, further reducing the need for extensive computational resources. Overall, deep learning has helped to make intelligent industrial applications more efficient and cost-effective by optimizing the use of storage and computational resources.