Reviewerly/exHarmony huggingface dataset:
Entity relation joint extraction model combining pointer network and attention mechanism based on relative position embedding

Authors:
raw_author_name: Wei Liu
raw_affiliation_string: Changchun Univ. of Science and Technology (China)
raw_author_name: Jun Xu
raw_affiliation_string: Changchun Univ. of Science and Technology (China)
raw_author_name: Chunsheng Xu
raw_affiliation_string: Changchun Univ. of Science and Technology (China)

Abstract: Extracting entity relations from unstructured text is an important step in constructing a knowledge graph, but most current methods are ineffective in dealing with the complex problem of overlapping entities. In this paper, we propose a novel entity relation joint extraction model. It extracts subjects through pointer annotation, fuses the extracted subjects with the sentence vector, then inputs them into an attention layer (Attention Mechanism Based on Relative Position Embedding, AMBRPE) to enhance feature expression ability. Under predefined relation conditions, the model extracts objects corresponding to the extracted subjects to generate relation triplets. And the model can effectively solve the problem of overlapping entities through hierarchical pointer annotation. In addition, we introduce an Adversarial Training Component (ATC) into the model, which generates adversarial samples for training and acts as a text data augmentation method to improve model generalization ability. On the public datasets NYT and WebNLG, we conducted extensive experiments and the results show that our model outperforms the cascaded binary tagging framework (CasRel) by 2.1 and 0.9 percentage points, respectively. Moreover, the effectiveness of the proposed model is verified through ablation experiments.
Efficiency study of VGG networks in autonomous driving tasks

Authors:
raw_author_name: Junhua Qi
raw_affiliation_string: Univ. of Science and Technology Beijing (China)

Abstract: In this study, we aim to evaluate the applicability of the VGG 19 architecture-based neural network for autonomousdriving tasks. The objective is to demonstrate the effectiveness of employing this neural network to solve the problemand to compare the identification quality. Experimental methodology is utilized to compare the efficiency of different neural networks. During the experimentation, we observed a disparity in the efficiency of VGG with and without theinclusion of the attention mechanism. Presently, a variety of machine learning methods are increasingly used to addressdiverse problems. Image recognition holds particular significance among these challenges. The practical problemunderinvestigation in this study pertains to the identification of roads with varying curvatures and their correspondence tothevehicle’s steering angles for the purpose of autonomous vehicle control. This paper explores multiple types of neural networks commonly used for image recognition. Previous research has demonstrated the high efficiency of these neural networks in solving various tasks, while this evaluation focuses on the applicability of neural networks in solvingtheautonomous driving problems and a performance comparison of these methods. The experimental results indicatethat VGG 19 achieves the highest accuracy of all neural networks.
Factors Affecting the Happiness Index

Authors:
raw_author_name: Qingyuan Liu

Abstract: The happiness index is a system of measuring collective happiness as well as the well-being of a nation. The instrument measures life satisfaction and the condition of life among citizens because happiness is an indispensable human goal. A country’s happiness provides an important insight into its citizen’s well-being. For the citizens to be happy, the government must offer good governance supporting their well-being. A number of factors have a negative or positive impact on the happiness index of any given country. The factors that positively impact the happiness index include Gross Domestic Product, generosity, high life expectancy, generosity, and social support. Corruption negatively impacts the happiness index of a country. The government must ensure that they minimize the cases of corruption in the government so that citizens can have hope in the government, which increases their happiness. To ensure citizens are happy, the government ought to have a consistent gathering of happiness data on a large scale because it helps policymakers recognize deliverables that must be formed to enhance citizens’ well-being.
Research on brand differentiation strategies that can be implemented for e-commerce transformation - Using PDD as a case study

Authors:
raw_author_name: Mingyue Shi

Abstract: In order to promote the transformation of China’s e-commerce industry, this study proposes a feasible strategy for brand differentiation based on the current status of China’s e-commerce, which is transitioning from high-speed growth to high-quality development. Under the guidance of a brand differentiation strategy, a brand differentiation strategy design system is constructed through case summaries and development analysis of PDD. Based on the theory of brand differentiation, this enables PDD to emerge as a leading company in a saturated and competitive market. When the market is highly homogenized, showcasing unique brand characteristics and differentiated features is the key to success. Through the theory of brand differentiation, combined with PDD’s brand development journey and differentiation strategy, we can identify the unique characteristics of the brand. This provides innovative ideas and serves as a reference for the transformation of China’s e-commerce.
Exploring Effective Models of Leadership in the Age of AI within Private Higher Education Institutions in England

Authors:
raw_author_name: Sarwar Khawaja
raw_affiliation_string: SK HUB LTD

Abstract: Artificial intelligence (AI) has gained significant interest and application in various fields and the impacts are evident in teaching, learning, and assessment. On one hand, AI can bring the much-needed revolution in higher education by assisting leaders and management to offer personalized teaching approaches that meet specific student needs, it can also enhance efficient feedback and the automation of administrative functions including assessment and grading. These capabilities make AI an important instrument in promoting efficiency in higher education and creating room for institutions to redirect their focus on other equally important aspects such as curriculum development. However, despite these advantages, AI comes with significant setbacks that adversely impact the quality of education. Particularly, various platforms allow students to do their assignments automatically thus negating their responsibility to research and learn. As such, institutions must put in place effective models of leadership and approaches that will ensure the use of AI in an ethical manner without compromising the quality of education.
Pengembangan Literacy Digital Anak Usia Dini Melalui Pembelajaran Computer Kids

Authors:
raw_author_name: Mila Faila Shofa
raw_affiliation_string: Universitas Islam Negeri Raden Mas Said Surakarta

Abstract: The purpose of this study is to determine the developing digital literacy in early childhood through Computer Kids learning. This research uses descriptive qualitative methods. Data collection techniques through interviews, documentation, and observation. Data validity techniques use source triangulation and triangulation methods. Data analysis uses interactive data analysis, presentation of data, data reduction, and drawing conclusions. The results showed that Computer Kids learning at PAUD Inclusion Saymara was carried out once a week in the multimedia room. Before carrying out learning, educators make plans related to the material to be delivered. The learning materials for Computer kids include getting to know the hardware, the parts of the computer, turning the computer on and off. In addition, software that is suitable for early childhood is also introduced such as interactive games, Microsoft Paint, Microsoft Word where children can recognize shapes, letters, and numbers. Through this Computer Kids learning, children have the knowledge and skills to utilize digital media wisely, intelligently, meticulously, and precisely according to their age development.
A Novel Training Approach in Deep Spiking Neural Network Based on Fuzzy Weighting and Meta-heuristic Algorithm

Authors:
raw_author_name: Melika Hamian
raw_affiliation_string: Department of Computer Engineering, North Tehran Branch, Islamic Azad University, Tehran, Iran
raw_author_name: Karim Faez
raw_affiliation_string: Department of Computer Engineering, North Tehran Branch, Islamic Azad University, Tehran, Iran
raw_author_name: Malihe Sabeti
raw_affiliation_string: Department of Computer Engineering, North Tehran Branch, Islamic Azad University, Tehran, Iran

Abstract: Abstract The challenge of supervised learning in spiking neural networks (SNNs) for digit classification from speech signals is examined in this study. Meta-heuristic algorithms and a fuzzy logic framework are used to train SNNs. Using gray wolf optimization (GWO), the features obtained from audio signals are reduced depending on the dispersion of each feature. Then, it combines fuzzy weighting system (FWS) and spike time-dependent flexibility (STDP) approach to implement the learning rule in SNN. The FWS rule produces a uniformly distributed random weight in the STDP flexibility window, so that the system requires fewer training parameters. Finally, these neurons are fed data to estimate the training weights and threshold values of the neurons using wild horse algorithm (WHO). With the parameters given, these rule weights are applied to appropriately display the class's share in extracting the relevant feature. The suggested network can classify speech signals into categories with 97.17% accuracy. The dataset was obtained using neurons operating at sparse biological rates below 600 Hz in the TIDIGITS test database. The suggested method has been evaluated on the IRIS and Trip Data datasets, where the classification results showed a 98.93% and 97.36% efficiency, respectively. Compared to earlier efforts, this study's results demonstrate that the strategy is both computationally simpler and more accurate. The accuracy of classification of digits, IRIS and Trip Data has increased by 4.9, 3.46 and 1.24%, respectively. The principal goal of this research is to improve the accuracy of SNN by developing a new high-precision training method.
A First Approach to Quantum Logical Shape Classification Framework

Authors:
raw_author_name: Marvin Kahra
raw_author_name: Michael Breuß

Abstract: Quantum logic is a well-structured theory, which has recently received some attention because of its fundamental relation to quantum computing. However, the complex foundation of quantum logic borrowing concepts from different branches of mathematics as well as its peculiar settings have made it a non-trivial task to device suitable applications. This article aims to propose for the first time an approach to use quantum logic in image processing at hand of a process for shape classification. We show how to make use of the principal component analysis to realize quantum logical propositions. In this way we are able to assign a concrete meaning to the rather abstract quantum logical concepts, and we are able to compute a probability measure from the principal components. For shape classification we consider encrypting given point clouds of different objects by making use of specific distance histograms. This enables to initiate the principal component analysis. At hand of experiments, we explore the possibility to distinguish between different geometrical objects and discuss the results in terms of quantum logical interpretation.
PRODIGY: Probing Resilience Of Domain-wall Interactions at Gauge Theory

Authors:
raw_author_name: Matthew James Stephenson

Abstract: We delve into the axion-dilaton model within the supergravity framework, with a specific focus on the intricacies of domain wall construction and stability. Employing holographic vitrification, we unravel the dynamics of domain wall formation in gauge theories featuring periodic vacuum structures. Our model, incorporating a QCD-like axion term and a stabilizing dilaton, undergoes scrutiny for conductivity variations under weak disorder. The investigation reveals the model's resilience, manifesting near-perfect conductivity under mild disorder conditions. However, the rigorous mathematical motivation for the holographic setup demands further elucidation. The scattered nature of our results prompts the necessity for a more systematic interpretation of QCD phenomena and conductivity transitions. This study contributes to the mathematical understanding of the axion-dilaton model's behavior, highlighting the imperative for a refined holographic framework and a more coherent interpretation of observed phenomena.
Image alignment-based patch distribution framework for anomaly detection

Authors:
raw_author_name: Yue Liu
raw_affiliation_string: Zhengzhou Univ. (China)
raw_author_name: Ling Ma
raw_affiliation_string: Zhengzhou Univ. (China)
raw_author_name: Huiqin Jiang
raw_affiliation_string: Zhengzhou Univ. (China)

Abstract: Surface quality inspection is a crucial step in industrial manufacturing. However, it is challenging to collect an adequate quantity of abnormal samples in practice. Supervised methods require sample annotation, which is costly, so unsupervised methods that are high-speed and low-cost are more suitable for industrial applications. Among the current unsupervised methods, embedding-similarity methods haven shown excellent performance, but most of them do not preprocess the images and directly use convolutional neural networks to extract image features. While in actual scenarios, image can have some degree of offset or rotation due to machine variations. Therefore, this paper proposes a new patch distribution framework for anomaly detection, specifically a novel image alignment module is proposed to enhance the utility of the model. Image alignment reduces the dense distance between pixels during training, enabling more precise learning of the feature distribution of normal samples and reduce false positives during testing. In addition, in the feature extraction stage, the middle layer of the network is selected to extract features and establish embedding connections. This not only enhances the model’s precision but also reduces its memory requirements. Experiments on the publicly available datasets MVTec AD and BeanTech AD show that our proposed new framework achieves better performance than other baseline models.
Attention-guided hybrid transformer-convolutional neural network for underwater image super-resolution

Authors:
raw_author_name: Chaofeng Li
raw_affiliation_string: Shanghai Maritime University, Institute of Logistics Science and Engineering, Shanghai, China
raw_author_name: Yuqi Zhang
raw_affiliation_string: Shanghai Maritime University, Institute of Logistics Science and Engineering, Shanghai, China

Abstract: Underwater images suffer from localized distortion and blurred degradation of edge structures due to light absorption and scattering by water. However, existing super-resolution (SR) methods for underwater images cannot effectively solve the above problems and encounter model sizes that are too large. To this end, we propose an attention-guided hybrid transformer-CNN network (AHTCN) to improve the SR reconstruction of underwater images through the interaction of local and multiscale global information, as well as the long-range dependencies modeling capability. Specifically, AHTCN mainly consists of several cascaded transformer-CNN feature extraction blocks (TCFEB) and an image reconstruction module. In TCFEB, the designed attention-based channel separation mechanism can adaptively separate the weighted features while reducing the number of model parameters and then extract the local details and global structural information at different scales through the dual-stream structure. Moreover, we replace the feedforward layer in the transformer with the blueprint separable convolutional feedforward layer and propose an enhanced pyramid pooling transformer layer, which helps to strengthen the feature perception of the model. Experimental results demonstrate that AHTCN outperforms the state-of-the-art algorithms in terms of both subjective visual effects and objective quality assessment, while requiring fewer parameters.
Exploring ASR fine-tuning on limited domain-specific data for low-resource languages

Authors:
raw_author_name: Avashna Govender
raw_author_name: Jaco Badenhorst

Abstract: The majority of South Africa’s eleven languages are low-resourced, posing a major challenge to Automatic Speech Recognition (ASR) development. Modern ASR systems require an extensive amount of data that is extremely difficult to find for low-resourced languages. In addition, available speech and text corpora for these languages predominantly revolve around government, political and biblical content. Consequently, this hinders the ability of ASR systems developed for these languages to perform well especially when evaluating data outside of these domains. To alleviate this problem, the Icefall Kaldi II toolkit introduced new transformer model scripts, facilitating the adaptation of pre-trained models using limited adaptation data. In this paper, we explored the technique of using pre-trained ASR models in a domain where more data is available (government data) and adapted it to an entirely different domain with limited data (broadcast news data). The objective was to assess whether such techniques can surpass the accuracy of prior ASR models developed for these languages. Our results showed that the Conformer connectionist temporal classification (CTC) model obtained lower word error rates by a large margin in comparison to previous TDNN-F models evaluated on the same datasets. This research signifies a step forward in mitigating data scarcity challenges and enhancing ASR performance for low-resourced languages in South Africa.
Advanced Machine Learning Techniques in Gomoku: Strategy,Implementation, and Analysis

Authors:
raw_author_name: Jiajun Han

Abstract: The strategic board game Gomoku has become a compelling domain for artificial intelligence (AI) research, particularly in developing and applying machine learning techniques. This paper comprehensively analyzes advanced machine learning strategies in Gomoku, focusing on logistic regression for board evaluation, neural networks for pattern recognition, and reinforcement learning for strategic gameplay. We discuss integrating these techniques in creating a sophisticated AI capable of high-level play and adaptability. Through this exploration, we highlight the potential of AI in strategic decision-making and its broader applications beyond board games.
A Comprehensive Review of Text Sentiment Analysis: A Survey of Traditional Methods and Deep Learning Approaches

Authors:
raw_author_name: Ruichen Xi

Abstract: Text sentiment analysis is a crucial aspect within the realm of natural language processing. This paper, incorporating the latest research advancements, systematically reviews and summarizes mainstream methods in text sentiment analysis. It covers a spectrum of traditional text analysis techniques, including rule-based and dictionary-based methods, as well as machine learning approaches. The paper further delves into an in-depth discussion of text sentiment analysis methods based on deep learning. This encompasses Recurrent Neural Networks and their enhanced structures, Convolutional Neural Networks and their extended techniques, along with applications involving pre-trained models and transfer learning.
A Machine Learning Classification of Individuals with Mild Cognitive Impairment into Variants from Writing

Authors:
raw_author_name: Hana Kim
raw_affiliation_string: University of South Florida Tampa Campus: University of South Florida

Abstract: Introduction: Individuals with Mild Cognitive Impairment (MCI), a transitional stage between cognitively healthy aging and dementia, are characterized by subtle neurocognitive changes. Clinically, they can be grouped into two main variants, namely into patients with amnestic MCI (aMCI) and non-amnestic MCI (naMCI). The distinction of the two variants is known to be clinically significant as they exhibit different progression rates to dementia. However, it has been particularly challenging to classify the two variants robustly. Recent research indicates that linguistic changes may manifest as one of the early indicators of pathology. Therefore, we focused on MCI’s discourse-level writing samples in this study. We hypothesized that a written picture description task can provide information that can be used as an ecological, cost-effective classification system between the two variants. Methods: We included one hundred sixty-nine individuals diagnosed with either aMCI or naMCI who received neurophysiological evaluations in addition to a short-written picture description task. Natural Language Processing (NLP) and BERT pre-trained Language Models were utilized to analyze the writing samples. Results: We showed that the written picture description task provided 90% overall classification accuracy for the best classification models, which performs better than cognitive measures. Discussion: Written discourses analyzed the AI models can automatically assess individuals with aMCI and naMCI and facilitate diagnosis, prognosis, therapy planning, and evaluation.
Research on extractive text abstract generation method for graph model based on TextRank

Authors:
raw_author_name: Mingsong Dong
raw_affiliation_string: Changchun Univ. of Science and Technology (China)

Abstract: The traditional TextRank algorithm is based on a graph model, and the extracted abstract generated by it does not deviate from the source text, but there may be issues such as polysemy. The BERT model can capture contextual semantics well. It can directly convert sentences in text into vector form, which can better solve the problem of polysemy. BiLSTM can obtain further information, IDCNN can better handle overfitting problems compared to traditional convolutional neural networks, and CRF can better solve the problem of sentence coherence. The model proposed in this article has been compared with other extraction based text summarization methods on the TTNews dataset, and experiments have shown that the method proposed in this article can obtain meaningful text summaries with significant optimization.
Predictive analytics and the collective dimensions of data protection

Authors:
raw_author_name: Rainer Mühlhoff
raw_affiliation_string: Institute for Philosophy &amp; Institute for Cognitive Science, University of Osnabrück, Osnabrück, Germany
raw_author_name: Hannah Ruschemeier
raw_affiliation_string: Department of Law, University of Hagen, Hagen, Germany

Abstract: This paper takes an interdisciplinary approach, combining legal studies, ethics and technical insights to shed light on the complex issues surrounding the regulation of predictive analytics. We argue that the individualised concept of regulation, shaped by the dogma of fundamental rights, is unable to adequately capture the implications of predictive analytics. We show that predictive analytics is a problem of collective privacy and informational power asymmetries, and conceptualise the form of data power at work in predictive analytics as ‘prediction power’. The unregulated prediction power of certain actors poses societal risks, especially if this form of informational power asymmetry is not normatively represented. The article analyses this legal lacuna in the light of recent case law of the European Court of Justice and new legislation at the EU level. To address these challenges, we develop the concept of ‘predictive privacy’ as a protected good based on collective interests.