Reviewerly/exHarmony huggingface dataset:
Neural nonnegative matrix factorization for hierarchical multilayer topic modeling

Abstract We introduce a new method based on nonnegative matrix factorization, Neural NMF, for detecting latent hierarchical structure in data. Datasets with hierarchical structure arise in a wide variety of fields, such as document classification, image processing, and bioinformatics. Neural NMF recursively applies NMF in layers to discover overarching topics encompassing the lower-level features. We derive a backpropagation optimization scheme that allows us to frame hierarchical NMF as a neural network. We test Neural NMF on a synthetic hierarchical dataset, the 20 Newsgroups dataset, and the MyLymeData symptoms dataset. Numerical results demonstrate that Neural NMF outperforms other hierarchical NMF methods on these data sets and offers better learned hierarchical structure and interpretability of topics.
Path Planning Using a Flow of Pedestrian Traffic in an Unknown Environment

This paper outlines a path planning method for autonomous rovers navigating urban environments without prior mapping, with a particular focus on addressing the Tsukuba Challenge. Our approach utilizes observations of pedestrian and robot movement trajectories to construct path graphs for global path planning. We provide a detailed overview of the autonomous rover’s hardware and software system, as well as a comprehensive description of the path planning algorithm. Our methodology entails extracting and continuously tracking dynamic objects from LiDAR data, resulting in the creation of a path graph based on their observed trajectories. Subsequently, a path aligned with the desired direction is selected. Notably, in indoor experimental settings, our approach proves effective, as the rover successfully generates a path to the goal by closely monitoring and tracking pedestrian movements. In conclusion, this paper introduces a promising path planning methodology and suggests potential areas for further research in autonomous mobility within uncharted environments.
Quantization of the Energy

Important experiments in the early 1900s illustrated us how energy is quantized. These experiments are important milestones in the history of science because they laid the foundation for new physics. Quantum mechanics is a broad subject that explains phenomena that are hard to understand using common sense. Yet it embodies classical mechanics. This chapter introduces concepts and principles related to the energy in quantum mechanics, which are required to describe the wave-particle nature of matter and the energy quantization in the microscopic world. With quantum mechanics, we can rethink old ideas and develop a new philosophy to understand the world.
DNA Classification Using Machine Learning

Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL Copy DOI
Fixed-Wing UAV Formation Path Planning Based on Formation Control: Theory and Application

Formation path planning is a significant cornerstone for unmanned aerial vehicle (UAV) swarm intelligence. Previous methods were not suitable for large-scale UAV formation, which suffered from poor formation maintenance and low planning efficiency. To this end, this paper proposes a novel millisecond-level path planning method appropriate for large-scale fixed-wing UAV formation, which consists of two parts. Instead of directly planning paths independently for each UAV in the formation, the proposed method first introduces a formation control strategy. It controls the chaotic UAV swarm to move as a single rigid body, so that only one planning can obtain the feasible path of the entire formation. Then, a computationally lightweight Dubins path generation method with a closed-form expression is employed to plan feasible paths for the formation. During flight, the aforementioned formation control strategy maintains the geometric features of the formation and avoids internal collisions within the formation. Finally, the effectiveness of the proposed framework is exemplified through several simulations. The results show that the proposed method can not only achieve millisecond-level path planning for the entire formation but also excellently maintain formation during the flight. Furthermore, simple formation obstacle avoidance in a special case also highlights the application potential of the proposed method.
Large Language Models Reasoning and Reinforcement Learning

Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL Copy DOI
MetaLiDAR: Automated metamorphic testing of LiDAR‐based autonomous driving systems

Abstract Recent advances in artificial intelligence technology and perception components have promoted the rapid development of autonomous vehicles. However, as safety‐critical software, autonomous driving systems often make wrong judgments, seriously threatening human and property safety. LiDAR is one of the most critical sensors in autonomous vehicles, capable of accurately perceiving the three‐dimensional information of the environment. Nevertheless, the high cost of manually collecting and labeling point cloud data leads to a dearth of testing methods for LiDAR‐based perception modules. To bridge the critical gap, we introduce MetaLiDAR, a novel automated metamorphic testing methodology for LiDAR‐based autonomous driving systems. First, we propose three object‐level metamorphic relations for the domain characteristics of autonomous driving systems. Next, we design three transformation modules so that MetaLiDAR can generate natural‐looking follow‐up point clouds. Finally, we define corresponding evaluation metrics based on metamorphic relations. MetaLiDAR automatically determines whether source and follow‐up test cases meet the metamorphic relations based on the evaluation metrics. Our empirical research on five state‐of‐the‐art LiDAR‐based object detection models shows that MetaLiDAR can not only generate natural‐looking test point clouds to detect 181,547 inconsistent behaviors of different models but also significantly enhance the robustness of models by retraining with synthetic point clouds.
Smart <i>h</i>‐Chain: A blockchain based healthcare framework with insurance fraud detection

Abstract In today's smart applications, very frequently used data like electronic health records (EHR's) are highly sensitive and soft target to the unauthorized agencies. Data management, integrity, and security issues plague EHR systems. Insurance firms are prohibited by law and corporate privacy from sharing patient data, but because the data are not linked and synchronized among insurance providers, there has been a rise in healthcare fraud. A crucial component of an e‐health system is safe communication between patients, healthcare providers, and insurance companies. Building a system to securely manage and track insurance operations by combining data from all sources is required to combat health insurance fraud. Over time, blockchain grew in popularity, and the healthcare sector's interest in it led to the development of design concepts that addressed security issues. In this study, a blockchain‐based security architecture has been presented to protect the EHR and provide a secure method for patients, their caregivers, physicians, and insurance agents to access the clinical data of the patients. By using off‐chain storage and cryptographic key exchange through blockchain our approach also covers the scalability, integrity and access control issue that a healthcare system in general is facing. A crucial component of an e‐health system is safe communication between patients, healthcare providers, and insurance companies. A blockchain‐based security architecture has been presented to protect the EHR and provide a secure method for patients, caregivers, physicians, and insurance agents to access the clinical data of the patients.
Recommender Systems: Techniques, Effects, and Measures Toward Pluralism and Fairness

Abstract Recommender systems are widely used in various applications, such as online shopping, social media, and news personalization. They can help systems by delivering only the most relevant and promising information to their users and help people by mitigating information overload. At the same time, algorithmic recommender systems are a new form of gatekeeper that preselects and controls the information being presented and actively shapes users’ choices and behavior. This becomes a crucial aspect, as, if unaddressed and not safeguarded, these systems are susceptible to perpetuate and even amplify existing biases, including unwanted societal biases, leading to unfair and discriminatory outcomes. In this chapter, we briefly introduce recommender systems, their basic mechanisms, and their importance in various applications. We show how their outcomes and performance are assessed and discuss approaches to addressing pluralism and fairness in recommender systems. Finally, we highlight recently emerging directions within recommender systems research, pointing out opportunities for digital humanism to contribute interdisciplinary expertise.
Sign Language Recognition

Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL Copy DOI
Artificial Intelligence &amp;amp; Data Obfuscation: Algorithmic Competition in Digital Ad Auctions

Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL Copy DOI
Survey on Machine Learning Biases and Mitigation Techniques

Machine learning (ML) has become increasingly prevalent in various domains. However, ML algorithms sometimes give unfair outcomes and discrimination against certain groups. Thereby, bias occurs when our results produce a decision that is systematically incorrect. At various phases of the ML pipeline, such as data collection, pre-processing, model selection, and evaluation, these biases appear. Bias reduction methods for ML have been suggested using a variety of techniques. By changing the data or the model itself, adding more fairness constraints, or both, these methods try to lessen bias. The best technique relies on the particular context and application because each technique has advantages and disadvantages. Therefore, in this paper, we present a comprehensive survey of bias mitigation techniques in machine learning (ML) with a focus on in-depth exploration of methods, including adversarial training. We examine the diverse types of bias that can afflict ML systems, elucidate current research trends, and address future challenges. Our discussion encompasses a detailed analysis of pre-processing, in-processing, and post-processing methods, including their respective pros and cons. Moreover, we go beyond qualitative assessments by quantifying the strategies for bias reduction and providing empirical evidence and performance metrics. This paper serves as an invaluable resource for researchers, practitioners, and policymakers seeking to navigate the intricate landscape of bias in ML, offering both a profound understanding of the issue and actionable insights for responsible and effective bias mitigation.
Block designs from self-dual codes obtained from Paley designs and Paley graphs

In 2002, P. Gaborit introduced two constructions of self-dual codes using quadratic residues, so-called pure and bordered construction, as a generalization of the Pless symmetry codes. In this paper, we further study conditions under which the pure and the bordered construction using Paley designs and Paley graphs yield self-dual codes. Special attention is given to the binary and ternary codes. Further, we construct \(t\)-designs from supports of the codewords of a particular weight in the binary and ternary codes obtained.
A Method for Extracting Photovoltaic Panels from High-Resolution Optical Remote Sensing Images Guided by Prior Knowledge

The extraction of photovoltaic (PV) panels from remote sensing images is of great significance for estimating the power generation of solar photovoltaic systems and informing government decisions. The implementation of existing methods often struggles with complex background interference and confusion between the background and the PV panels. As a result, the completeness and edge clarity of PV panel extraction results are compromised. Moreover, most previous studies have overlooked the unique color characteristics of PV panels. To alleviate these deficiencies and limitations, a method for extracting photovoltaic panels from high-resolution optical remote sensing images guided by prior knowledge (PKGPVN) is proposed. Firstly, aiming to address the problems related to missed extractions and background misjudgments, a Photovoltaic Index (PVI) based on visible images in the three-band is constructed to serve as prior knowledge to differentiate between PV panels and non-PV panels. Secondly, in order to strengthen information interaction between shallow features and deep features and enhance the accuracy and integrity of results, a Residual Convolution Hybrid Attention Module (RCHAM) is introduced into the skip-connection of the encoding–decoding structure. Finally, for the purpose of reducing the phenomenon of blurred edges, a multilevel Feature Loss (FL) function is designed to monitor the prediction results at different scales. Comparative experiments are conducted with seven methods, including U-Net, on publicly available datasets. The experimental results show that our PKGPVN achieves superior performance in terms of evaluation metrics such as IoU (above 82%), Precision (above 91%), Recall (above 89%), and F1-score (above 90%) on the AIR-PV dataset. Additionally, the ablation experiments illustrate the effectiveness of our key parts. The proposed method reduces the phenomena of missed extractions and background misjudgments effectively while producing highly accurate results with clear boundaries.
La <i>Legenda aurea </i>volgare «sobrevità ridotta et in rima»: stadi traduttivi e versificazione nella silloge canterina di Cristofano Guidini

This paper proposes an analysis of in-verse reworking of vernacular Legenda aurea by Cristofano Guidini. In his cantari for the festivities of the liturgical year, we can find a rich interlacing of textual sources, historical records, oral or written testimonies. Here we want to demonstrate how the translating rewriting by Guidini works, starting from its performed destination and from didactic usefulness of legendary narrations.
Integrating Knowledge Graphs into Distribution Grid Decision Support Systems

Distribution grids are complex networks containing multiple pieces of equipment. These components are interconnected, and each of them is described by various attributes. A knowledge graph is an interesting data format that represents pieces of information as nodes and relations between the pieces as edges. In this paper, we describe the proposed vocabulary used to build a distribution system knowledge graph. We identify the concepts used in such graphs and a set of relations to represent links between concepts. Both provide a semantically rich representation of a system. Additionally, we offer a few illustrative examples of how a distributed system knowledge graph can be utilized to gain more insight into the operations of the grid. We show a simplified analysis of how outages can influence customers based on their locations and how adding DERs can influence/change it. These demonstrative use cases show that the graph-based representation of a distribution grid allows for integrating information of different types and how such a repository can be efficiently utilized. Based on the experiments with distribution system knowledge graphs presented in this article, we postulate that graph-based representation enables a novel way of storing information about power grids and facilitates interactive methods for their visualization and analysis.
Educational Intervention in The Subject Hydraulic Engineering I of the Universidad Técnica De Manabí, Ecuador

Objective: Propose a methodological structure that helps the teacher apply new strategies that enhance educational work.&#x0D; &#x0D; Methods: The inductive-deductive method and the bibliographic review method were applied.&#x0D; &#x0D; Results: The subject is developed in a traditional way where the teacher occupies the center of the process through the dissertation of master classes, whose contents are the theoretical foundations of the topics, followed by the resolution of practical exercises with the use of the blackboard or through of a projector, a scenario in which the student is only a passive actor in the process.&#x0D; &#x0D; Conclusions: A methodology was developed that can cause difficulties in the incorporation of teachers into the learning community; but with this, substantive changes are achieved in the teaching and learning of the subject hydraulic engineering I.
CNL2ASP: Converting Controlled Natural Language Sentences into ASP

Abstract Answer set programming (ASP) is a popular declarative programming language for solving hard combinatorial problems. Although ASP has gained widespread acceptance in academic and industrial contexts, there are certain user groups who may find it more advantageous to employ a higher-level language that closely resembles natural language when specifying ASP programs. In this paper, we propose a novel tool, called CNL2ASP, for translating English sentences expressed in a controlled natural language (CNL) form into ASP. In particular, we first provide a definition of the type of sentences allowed by our CNL and their translation as ASP rules and then exemplify the usage of the CNL for the specification of both synthetic and real-world combinatorial problems. Finally, we report the results of an experimental analysis conducted on the real-world problems to compare the performance of automatically generated encodings with the ones written by ASP practitioners, showing that our tool can obtain satisfactory performance on these benchmarks.
Bridging the Gap: A Survey on Integrating (Human) Feedback for Natural Language Generation

Abstract Natural language generation has witnessed significant advancements due to the training of large language models on vast internet-scale datasets. Despite these advancements, there exists a critical challenge: These models can inadvertently generate content that is toxic, inaccurate, and unhelpful, and existing automatic evaluation metrics often fall short of identifying these shortcomings. As models become more capable, human feedback is an invaluable signal for evaluating and improving models. This survey aims to provide an overview of recent research that has leveraged human feedback to improve natural language generation. First, we introduce a taxonomy distilled from existing research to categorize and organize the varied forms of feedback. Next, we discuss how feedback can be described by its format and objective, and cover the two approaches proposed to use feedback (either for training or decoding): directly using feedback or training feedback models. We also discuss existing datasets for human-feedback data collection, and concerns surrounding feedback collection. Finally, we provide an overview of the nascent field of AI feedback, which uses large language models to make judgments based on a set of principles and minimize the need for human intervention. We also release a website of this survey at feedback-gap-survey.info.
The language and its misuse into through social communications

Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL Copy DOI
Parts-of-Speech (PoS) Analysis and Classification of Various Text Genres

Abstract Natural language processing (NLP) has made significant leaps over the past two decades due to the advancements in machine learning algorithms. Text classification is pivotal today due to a wide range of digital documents. Multiple feature classes have been proposed for classification by numerous researchers. Genre classification tasks form the basis for advanced techniques such as native language identification, readability assessment, author identification etc. These tasks are based on the linguistic composition and complexity of the text. Rather than extracting hundreds of variables, a simple premise of text classification using only the text feature of parts-of-speech (PoS) is presented here. A new dataset gathered from Project Gutenberg is highlighted in this study. PoS analysis of each text in the created dataset was carried out. Further grouping of these texts into fictional and non-fictional texts was carried out to measure their classification accuracy using the artificial neural networks (ANN) classifier. The results indicate an overall classification accuracy of 98 and 35 % for the genre and sub-genre classification, respectively. The results of the present study highlight the importance of PoS not only as an important feature for text processing but also as a sole text feature classifier for text classification.
Granting Legal Personality To Artificial Intelligences In Brazil’s Legal Context: A Possible Solution To The Copyright Limbo

Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL Copy DOI
<i>Tereza Batista cansada de guerra</i> de Jorge Amado em italiano Uma análise paratextual

This paper aims to analyse and present the Italian paratexts of the Brazilian novel Tereza Batista cansada de guerra (1972) by Jorge Amado. Paratexts are a valuable instrument, sometimes even better than the actual translation, in order to verify how the source cultural and literary systems are represented. In this case, by examining the paratextual elements, the representation of Brazilian women and of Brazil in general is presented, as well as the status of the translation and the translation agents involved in the process, the rewarded translatress Giuliana Segre Giorgi and the publishing houses.
The Impact of Indiscriminate Policing Policy on Social Media Content Contribution: (How) Does Blocking Content "Work"?

Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL Copy DOI
Enterprise Large Language Models: Knowledge Characteristics, Risks and Organizational Activities

Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL Copy DOI
Content Analysis of Library Websites/Webpages of National Importance of Karnataka: An Evaluation

Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL Copy DOI
Zero-Knowledge Proofs and OAuth 2.0 for Anonymity and Security in Distributed Systems

Abstract—This paper investigates the integration of Zero-Knowledge Proofs (ZKP) and OAuth 2.0 to enhance anonymity and security in multi-agent distributed systems. We propose an approach that allows agents to authenticate and prove possession of specific data without revealing the underlying details. Additionally, we outline a potential access control strategy using ZKP for anonymity, allowing agents to validate their access rights without identity exposure. When combined with OAuth 2.0, this mechanism provides a framework for secure data access. While the proposed methods offer promising solutions to security, privacy, and anonymity challenges in multi-agent systems, they also highlight the need for further research and validation to confirm their effectiveness.