Reviewerly/exHarmony huggingface dataset:
An effective vehicle road scene recognition based on improved deep learning

In order to improve the recognition for autonomous vehicles with respect to road scenes, a new activation function called ReLU sigmoid is proposed based on ReLU and the sigmoid model, which resolves the issue of neuron necrosis in the ReLU model. Through an analysis of the action principle of the activation function, the key aspects of the activation function design are determined. The sigmoid and ReLU are combined in the positive and negative half axes of the semantic axis to optimize the road scene recognition model. The experimental results reveal that compared with the ReLU and LReLU models, the ReLU sigmoid model effectively improves the recognition accuracy of the convolutional neural network for the road scene, from 75.12% to 67.15%, respectively, to 83.70%. It is proved that the algorithm can improve the recognition performance of the deep learning model for the road scene and alleviate the phenomenon of neuron necrosis, along with improving the vehicle's perception of the road environment.
A many objective commercial recommendation algorithm via Game-Based core node extraction

The development of the recommendation system (RS) has been focused on improving the accuracy of algorithms. As the number of new users grows, RS suffers from the cold start problem, data sparsity and long-tail problem. In this paper, we proposed a many objective commercial recommendation algorithm that extracts the core node through the pay-off function of game theory and makes it guide the evolutionary computation. Firstly, the core aim of commercial recommendations is to maximize total profit. Profits can vary wildly with the same accuracy. Thus, the profit value as one of the goals is considered in attention to accuracy, coverage, and novelty. Besides, the current user state value is updated according to user features and rating scores matrix with the attention mechanism at different time stamps. The nodes are divided into different communities, forming a dynamic community, and recommendations are made within each community. Meanwhile, to reduce the computational complexity and runtime, the pay-off function of game theory is used to extract the core user in each community. Each contribution value and user interaction as criteria to guide the current community of users. Experiments on real datasets show that selecting core nodes can improve the performance of business RS. Compared with other existing algorithms in many experiments, our algorithm performs better.
Prompting Progress: Authorship in the Age of AI

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
Bridging Linguistic Divides: Rule-Based Marathi to Gujarati Text Translator (Part-I)

In our increasingly interconnected world, language diversity poses formidable challenges to effective communication and access to information. These challenges are particularly pronounced in culturally rich and linguistically diverse societies, such as India. This research paper presents an in-depth exploration of the development of a rule-based Marathi to Gujarati Text Translator, project sponsored by Mass Technologies. The primary objective of this project is to mitigate the language barriers that hinder effective communication and limit access to information. Through the application of rule-based Natural Language Processing (NLP) techniques, this translator aims to provide accurate and contextually relevant translations. This paper emphasizes the alignment of the project’s objectives with Mass Technologies’ organizational missions, which prioritize effective communication, cultural exchange, accessibility, and support for diverse societies. The paper delves into the intricacies of the project, including its goals, methodology, system requirements, future steps, risk management strategies, and its potential impact on society.
Federated Computing

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
Sentiment analysis of online responses in the performing arts with large language models

Opinion mining is a technique extracting and analyzing people's opinions from online communities, and sentiment analysis is a kind of opinion mining analyzing attitudes of people toward an object, whether positive, negative, or neutral. Sentiment analysis has evolved alongside natural language processing models and applied to targets such as movie reviews. However, the performing arts have not been subjected to sentiment analysis as movie reviews, despite the apparent need for it. In this study, we used the Korean Funnel Transformer11 language model to perform sentiment analysis on performing arts. This study looks at people's reactions to performing arts in online communities, not just whether they agree or disagree, and shows the problems with applying existing sentiment analysis to performing arts.
Joint Overlapping Event Extraction Model via Role Pre-Judgment with Trigger and Context Embeddings

The objective of event extraction is to recognize event triggers and event categories within unstructured text and produce structured event arguments. However, there is a common phenomenon of triggers and arguments of different event types in a sentence that may be the same word elements, which poses new challenges to this task. In this article, a joint learning framework for overlapping event extraction (ROPEE) is proposed. In this framework, a role pre-judgment module is devised prior to argument extraction. It conducts role pre-judgment by leveraging the correlation between event types and roles, as well as trigger embeddings. Experiments on the FewFC show that the proposed model outperforms other baseline models in terms of Trigger Classification, Argument Identification, and Argument Classification by 0.4%, 0.9%, and 0.6%. In scenarios of trigger overlap and argument overlap, the proposed model outperforms the baseline models in terms of Argument Identification and Argument Classification by 0.9%, 1.2%, 0.7%, and 0.6%, respectively, indicating the effectiveness of ROPEE in solving overlapping events.
Fast Kötter–Nielsen–Høholdt interpolation over skew polynomial rings and its application in coding theory

Abstract Skew polynomials are a class of non-commutative polynomials that have several applications in computer science, coding theory and cryptography. In particular, skew polynomials can be used to construct and decode evaluation codes in several metrics, like e.g. the Hamming, rank, sum-rank and skew metric. We propose a fast divide-and-conquer variant of Kötter–Nielsen–Høholdt (KNH) interpolation algorithm: it inputs a list of linear functionals on skew polynomial vectors, and outputs a reduced Gröbner basis of their kernel intersection. We show, that the proposed KNH interpolation can be used to solve the interpolation step of interpolation-based decoding of interleaved Gabidulin codes in the rank-metric, linearized Reed–Solomon codes in the sum-rank metric and skew Reed–Solomon codes in the skew metric requiring at most $${\widetilde{O}}\left( s^\omega {\mathfrak {M}}(n)\right) $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mover> <mml:mi>O</mml:mi> <mml:mo>~</mml:mo> </mml:mover> <mml:mfenced> <mml:msup> <mml:mi>s</mml:mi> <mml:mi>ω</mml:mi> </mml:msup> <mml:mi>M</mml:mi> <mml:mrow> <mml:mo>(</mml:mo> <mml:mi>n</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> </mml:mfenced> </mml:mrow> </mml:math> operations in $${\mathbb {F}}_{q^m}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>F</mml:mi> <mml:msup> <mml:mi>q</mml:mi> <mml:mi>m</mml:mi> </mml:msup> </mml:msub> </mml:math> , where n is the length of the code, $$s$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>s</mml:mi> </mml:math> the interleaving order, $${\mathfrak {M}}(n)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>M</mml:mi> <mml:mo>(</mml:mo> <mml:mi>n</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> the complexity for multiplying two skew polynomials of degree at most n , $$\omega $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>ω</mml:mi> </mml:math> the matrix multiplication exponent and $${\widetilde{O}}\left( \cdot \right) $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mover> <mml:mi>O</mml:mi> <mml:mo>~</mml:mo> </mml:mover> <mml:mfenced> <mml:mo>·</mml:mo> </mml:mfenced> </mml:mrow> </mml:math> the soft- O notation which neglects log factors. This matches the previous best speeds for these tasks, which were obtained by top–down minimal approximant bases techniques, and complements the theory of efficient interpolation over free skew polynomial modules by the bottom-up KNH approach. In contrast to the top–down approach the bottom-up KNH algorithm has no requirements on the interpolation points and thus does not require any pre-processing.
From Spoken Words to Prompt Triggers: Technical Iterations of a Semi-Intelligent Conversational Agent to Promote Early Literacy

AI technology is rapidly evolving and has vast potential for educational applications. This paper focuses on the technical iterations that took place as our project team developed a semi-intelligent conversational agent (CA) that uses speech recognition to fire spoken prompts to promote caregiver-child interaction as they read books aloud together. Situating this work in a design-based research methodology, the technical iterations reported here are part of the iterative “build” phase. (Easterday et al., 2018; Hoadley & Campos, 2022). The CA app promotes conversations between caregivers and children by listening to the human dyads as they read, matching their spoken words to marker words that would pinpoint the page of the storybook the dyads are reading, and playing a prompt corresponding to the page. The dynamic system that supports the app involves multiple components: web accessible services, data processing services, and human outputs, and it has gone through a combined seven iterations in three prototypes. Though a very small part of the DBR cycle, the technical iterations presented here have the potential to inform others interested in incorporating text-to-speech and other AI technologies into educational applications. We close with considerations for future directions.
DanceTrend: An Integration Framework of Video-Based Body Action Recognition and Color Space Features for Dance Popularity Prediction

Background: With the rise of user-generated content (UGC) platforms, we are witnessing an unprecedented surge in data. Among various content types, dance videos have emerged as a potent medium for artistic and emotional expression in the Web 2.0 era. Such videos have increasingly become a significant means for users to captivate audiences and amplify their online influence. Given this, predicting the popularity of dance videos on UGC platforms has drawn significant attention. Methods: This study postulates that body movement features play a pivotal role in determining the future popularity of dance videos. To test this hypothesis, we design a robust prediction framework DanceTrend to integrate the body movement features with color space information for dance popularity prediction. We utilize the jazz dance videos from the comprehensive AIST++ street dance dataset and segment each dance routine video into individual movements. AlphaPose was chosen as the human posture detection algorithm to help us obtain human motion features from the videos. Then, the ST-GCN (Spatial Temporal Graph Convolutional Network) is harnessed to train the movement classification models. These pre-trained ST-GCN models are applied to extract body movement features from our curated Bilibili dance video dataset. Alongside these body movement features, we integrate color space attributes and user metadata for the final dance popularity prediction task. Results: The experimental results endorse our initial hypothesis that the body movement features significantly influence the future popularity of dance videos. A comprehensive evaluation of various feature fusion strategies and diverse classifiers discern that a pre–post fusion hybrid strategy coupled with the XGBoost classifier yields the most optimal outcomes for our dataset.
The Attendant Card Set: A Research and Design Tool to Consider Perspectives of Attendants versus Users When Co-Experiencing Technology

Although many of our interactions with technology nowadays take place in public places (e.g., using a mobile phone in public transportation), research and design on Human-Computer Interaction (HCI) has paid little attention to how this kind of technology usage affects others present—and vice versa. To illustrate the perspective of the attendant, i.e., a person who is not interacting with technology themselves but co-experiencing it as listener or viewer, we developed the so-called Attendant Card Set (ACS). In two studies, an expert survey and a student workshop, we tested its practical applicability and usefulness. It showed not only that experts assess the cards positively, i.e., helpful, informative, and relevant, but also that the cards can be used with laypersons for perspective-taking, creative ideation, and discussions. Thus, analyzing and/or comparing the experience of different types with the help of the ACS provides a unique approach to the consideration of the attendant perspective in the research and development process. Limitations of the present research and opportunities for future tool applications are discussed. In addition to establishing this concept in HCI, we also see potential in the transferability to other areas and contexts such as the design of public space or non-technological products.
Measuring the Impact of Augmented Prototyping Systems in Co-Design Activities

In recent years, research reached a very high level of development and validation of augmented prototyping systems in support of collaborative design activities. However, there is still great scepticism in companies when it comes to integrating these new technologies within a consolidated working model. Among others, the main barrier to overcome concerns the lack of understanding of the impact of AR systems on the key objectives of a business, such as improving its efficiency and revenue. For this reason, this paper aims to quantify these indicators by observing the technological impact not on a single design session but on an entire product development process, during which the aspects related to its integration are also considered. Thanks to the collaboration with a design agency, it was possible to compare parameters such as the lead time, number of iterations, person-hours and costs between two similar and realistic projects in which only one was supported by projection-based AR technology.
Self-knowledge and personal identity in young learners

This article presents the relevance of self-knowledge in the formation of the personal identity of young school-age students. Efficient functioning in the social and school environment is facilitated by students' ability to self-knowledge. The difficulties of childhood and adolescence raise the issue of the need for students to acquire strategies for self-knowledge and personal development. One of the most important methods of self knowledge is the SWQT technique. It also presents the important aspects of self-knowledge, namely self-knowledge, personal skills and abilities, motivation, emotions and mechanisms of defense and adaptation, perceived self-efficacy.
Neural Network-Based Solar Irradiance Forecast for Edge Computing Devices

Aiming at effectively improving photovoltaic (PV) park operation and the stability of the electricity grid, the current paper addresses the design and development of a novel system achieving the short-term irradiance forecasting for the PV park area, which is the key factor for controlling the variations in the PV power production. First, it introduces the Xception long short-term memory (XceptionLSTM) cell tailored for recurrent neural networks (RNN). Second, it presents the novel irradiance forecasting model that consists of a sequence-to-sequence image regression NNs in the form of a spatio-temporal encoder–decoder including Xception layers in the spatial encoder, the novel XceptionLSTM in the temporal encoder and decoder and a multilayer perceptron in the spatial decoder. The proposed model achieves a forecast skill of 16.57% for a horizon of 5 min when compared to the persistence model. Moreover, the proposed model is designed for execution on edge computing devices and the real-time application of the inference on the Raspberry Pi 4 Model B 8 GB and the Raspberry Pi Zero 2W validates the results.
Decision with Belief Functions and Generalized Independence: Two Impossibility Theorems

Belief functions constitute a particular class of lower probability measures which is expressive enough to allow the representation of both ignorance and probabilistic information. Nevertheless, the decision models based on belief functions proposed in the literature are limited when considered in a dynamical context: either they drop the principle of dynamical consistency, or they limit the combination of lotteries, or relax the requirement for a transitive and complete comparison. The present work formally shows that these requirements are indeed incompatible as soon as a form of compensation is looked for. We then show that these requirement can be met in non compensative frameworks by exhibiting a dynamically consistent rule based on first order dominance.
Cautious Decision-Making for Tree Ensembles

Cautious classifiers are designed to make indeterminate decisions when the uncertainty on the input data or the model output is too high, so as to reduce the risk of making wrong decisions. In this paper, we propose two cautious decision-making procedures, by aggregating trees providing probability intervals constructed via the imprecise Dirichlet model. The trees are aggregated in the belief functions framework, by maximizing the lower expected discounted utility, so as to achieve a good compromise between model accuracy and determinacy. They can be regarded as generalizations of the two classical aggregation strategies for tree ensembles, i.e., averaging and voting. The efficiency and performance of the proposed procedures are tested on random forests and illustrated on three UCI datasets.
A Logical Framework for User-Feedback Dialogues on Hypotheses in Weighted Abduction

Weighted abduction computes hypotheses that explain input observations. It employs parameters, called weights, to output hypotheses suitable for each application. This versatility makes it applicable to plant operation, cybersecurity or discourse analysis. However, the hypotheses selected by an abductive reasoner from among possible hypotheses may be inconsistent with the user’s knowledge such as an operator’s or analyst’s expertise. In order to resolve this inconsistency and generate hypotheses in accordance with the user’s knowledge, this paper proposes two user-feedback dialogue protocols in which the user points out, either positively or negatively, properties of the hypotheses presented by the reasoner, and the reasoner regenerates hypotheses that satisfy the user’s feedback. As a minimum requirement for user-feedback dialogue protocols, we then prove that our protocols necessarily terminate under certain reasonable conditions and achieve a fixed target hypothesis if the user determines the positivity or negativity of each pointed-out property based on whether the target hypothesis has that property.
First Steps Towards a Logic of Ordered Pairs

Logical proportions are a type of propositional connector that involves four variables, expressed as a formula that encodes the conjunction of two equivalences. These equivalences refer to indicators of similarity or dissimilarity between two ordered pairs of variables, say (a, b) and (c, d). An example of a logical proportion is the analogical proportion, which is of the form: "a is to b as c is to d". The concept of logical proportion is used here to develop a logic that deals with ordered pairs of (vectors of) variables. In particular, we outline a logic of change which controls how the differences inside ordered pairs can be logically combined and propagated via a consequence relation between pairs of vectors.
Revisiting Approximate Reasoning Based on Grounded Semantics

Efficient computation of hard reasoning tasks is a key issue in abstract argumentation. One recent approach consists in defining approximate algorithms, i.e. methods that provide an answer that may not always be correct, but outperforms the exact algorithms regarding the computation runtime. One such approach proposes to use the grounded semantics, which is polynomially computable, as a starting point for determining whether arguments are (credulously or skeptically) accepted with respect to various semantics. In this paper, we push further this idea by defining various approaches to evaluate the acceptability of arguments which are not in the grounded extension, neither attacked by it. We have implemented our approaches, and we describe the result of their empirical evaluation.
Determining Preferences over Extensions: A Cautious Approach to Preference-Based Argumentation Frameworks

Preferences in abstract argumentation frameworks allow to represent the comparative strength of arguments, or preferences between values that arguments promote. In this paper, we reconsider the approach by Amgoud and Vesic, which computes the extensions of a preference-based argumentation framework by aggregating preferences and attacks into a new attack relation in a way that it favors preferred arguments in conflicts, and then simply applying Dung’s semantics to the resulting graph. We argue that this approach is too rigid in some situations, as it discards other sensible (even if less preferred) alternatives. We propose a more cautious approach to preference-based argumentation, which favors preferred arguments in attacks, but also does not discard feasible alternatives. Our semantics returns a set of extensions and a preference relation between them. It generalizes the approach by Amgoud and Vesic, in the sense that the extensions identified by their semantics will be more preferred than other extensions.
Evidential Generative Adversarial Networks for Handling Imbalanced Learning

The predictive performance of machine learning models tends to deteriorate in the presence of class imbalance. Multiple strategies have been proposed to address this issue. A popular strategy consists of oversampling the minority class. Classic approaches such as SMOTE utilize techniques like nearest neighbor search and linear interpolation, which can pose difficulties when dealing with datasets that have a large number of dimensions and intricate data distributions. As a way to create synthetic examples in the minority class, Generative Adversarial Networks (GANs) have been suggested as an alternative technique due to their ability to simulate complex data distributions. However, most GAN-based oversampling methods tend to ignore data uncertainty. In this paper, we propose a novel GAN-based oversampling method using evidence theory. An auxiliary evidential classifier is incorporated in the GAN architecture in order to guide the training process of the generative model. The objective is to push GAN to generate minority objects at the borderline of the minority class, near difficult-to-classify objects. Through extensive analysis, we demonstrate that the proposed approach provides better performance, compared to other popular methods.