Reviewerly/exHarmony huggingface dataset:
Multi-granularity Prompts for Topic Shift Detection in Dialogue

The goal of dialogue topic shift detection is to identify whether the current topic in a conversation has changed or needs to change. Previous work focused on detecting topic shifts using pre-trained models to encode the utterance, failing to delve into the various levels of topic granularity in the dialogue and understand dialogue contents. To address the above issues, we take a prompt-based approach to fully extract topic information from dialogues at multiple-granularity, i.e., label, turn, and topic. Experimental results on our annotated Chinese Natural Topic Dialogue dataset CNTD and the publicly available English TIAGE dataset show that the proposed model outperforms the baselines. Further experiments show that the information extracted at different levels of granularity effectively helps the model comprehend the conversation topics.
The Content Governance Dilemma

Abstract Social media content moderation faces a complex normative dilemma. The central question is: Which rules should govern online content? Private norms or democratically voted laws? If more national laws or international standards are simultaneously applicable, which law to choose? How to avoid the risk of having one single approach imperialistically imposed on the others? This dilemma exposes a tension between the risk of normative authoritarianism, imperialism and anomie. A process of ‘constitutionalisation’ of the social media environment seems to be needed. An input to this process is increasingly originating from civil society actors. Over the past few years, a significant number of ‘bills of rights’ have been proposed to articulate constitutional rights for social media. This phenomenon has been described in terms of the emergence of a movement of ‘digital constitutionalism’.
Leveraging Inter-class Differences and Label Semantics for Few-Shot Text Classification

In some few-shot text classification tasks with strong data privacy or difficult labeling, the performance of pipeline methods, which directly encode text features and perform linear classification, is limited by the feature extraction ability of models. An increasing number of studies have recognized the significance of combining text features with label semantics and achieved good results. However, these existing methods cannot be well generalized to classification tasks where the class names have weak correlations with the instance texts. In this work, we address this problem by means of an effective fusion of text-label similarity and a redesign of contrastive loss. Firstly, the semantic similarity modules of text-text and text-label are adopted for further merging to improve the feature extraction ability. Then, we introduce DLSC, an inter-class differences and label semantics contrastive loss that facilitates instance embeddings to approximate correct label semantics in vector space. Experimental results show that our approach has greatly improved F1 scores on English and Chinese datasets from six classification tasks, even in tasks where label names are not strongly correlated with texts.
Predicting the success of the government’s program of lomaya (Regional PKH) in reducing poverty

Poverty reduction is one indicator of the success of development. The form of support from the Pohuwato Regency Government through the Social Service is to organize PKH-D, which is known as LOMAYA. It is one of the implementations of the Community Movement Towards Independent Prosperity ( Gerakan Masyarakat Menuju Sejahtera Mandiri ). This research was conducted to assist the government in predicting the level of development success indicated by the satisfaction of beneficiaries of lomaya. The method employed was the Naïve Bayes method and forward feature selection. The research data was obtained from a survey of lomaya beneficiaries in the last two years. The accuracy result obtained using the Naïve Bayes algorithm was 94.19%, while Naïve Bayes with the Forward Selection feature was only 94.03%. Therefore, the Naïve Bayes algorithm method is better than the Forward Selection based Naïve Bayes algorithm. Forward selection does not improve accuracy because the selection process causes many attributes to be discarded because they are considered irrelevant. This happened because of the inaccuracy of the data after being selected for its attributes using the Forward Selection feature resulting 1 attribute only as a determinant.
FRLL-Beautified: A Dataset of Fun Selfie Filters with Facial Attributes

There is a need to assess the impact of filters on the performance of face recognition systems. For this, a standard dataset should be available with relevant filters applied. Currently, such datasets are not publicly available. Some datasets which are available with filters applied, are very low in resolution and thus not relevant for use. To mitigate these limitations, we aim to create a dataset that provides a face recognition database with filters applied to them. The proposed dataset provides high-quality images with ten different filters applied to them. These filters vary from beautification filters and AR-based filters to filters that modify facial landmarks. The wide range of filters including occlusion and beautification that has been applied to the selfies allow a more diverse set of faces to be experimented and analyzed with face recognition and other biometric systems. The dataset will contribute further to the set of facial datasets available publicly. This will allow researchers to study the impact of filters on facial features with a common public benchmark.
Vision Transformers for Galaxy Morphology Classification: Fine-Tuning Pre-trained Networks vs. Training from Scratch

In recent years, the Transformer-based deep learning architecture has become extremely popular for downstream tasks, especially within the field of Computer Vision. However, transformer models are very data-hungry, making them challenging to adopt in many applications where data is scarce. Using transfer learning techniques, we explore the classic Vision Transformer (ViT) and its ability to transfer features from the natural image domain to classify images in the galactic image domain. Using the weights of models trained on ImageNet (a popular benchmark dataset for Computer Vision), we compare the results of two distinct ViTs: one base ViT (without pre-training) and another fine-tuned ViT pre-trained on ImageNet. Our experiments on the Galaxy10 dataset show that by using the pre-trained ViT model, we can get better accuracy compared to the ViT model built from scratch and do so with a faster training time. Experimental data further shows that the fine-tuned ViT model can achieve similar accuracy to the model built from scratch while using less training data.
Perceptions and Attitudes Toward the Use of Wearable Technology in the Dance Studio Environment

Introduction: Wearable technology (WT) has become common place in sport. Increased affordability has allowed WT to reach the wrists and bodies of grassroots and community athletes. While WT is commonly used by sport populations to monitor training load, the use of WT among dancers and dance teachers is unknown. Therefore, the purpose of this study is to explore the perspectives of dancers, dance teachers, and dance parents on using WT in the dance studio environment. Methods: Dancers (aged 14+), dance teachers (aged 18+), and dance parents (with a child <18 years registered in a dance program) were recruited from local dance studios (including those offering vocational programs and/or professional training opportunities), and dance retail stores. Participants provided informed consent/assent and completed a one-time online survey about their attitudes, self-efficacy, motivations, barriers, and current practices of using WT in the studio. Results: Sixty-seven participants (19 dancers, 32 dance teachers, and 16 dance parents) completed the survey. Attitudes toward using WT were similar across all groups (mean score range = 34-38/45). Thirteen dancers (68%), 29 teachers (91%), and 7 dance parents reporting on behalf of their children (47%) were permitted to use WT in the studio. Smartwatches were the most common WT used in the studio by dancers (7/9) and teachers (13/17), while dance parents reported that their children primarily used wristband activity trackers (3/4). Among all groups, the primary reason for using WT was to track personalized training data, with calories, total duration, and heart rate being the most important perceived metrics for improving dancing. Conclusion: Across all groups, attitudes toward WT were modest. Prevalence of WT use in the dance studio varied, with wrist-based gadgets being the most common. As WT research continues in dance populations, it will be important for future studies to consider studio permissions as well as participants' existing WT use practices.
Divergences on monads for relational program logics

Abstract Several relational program logics have been introduced for integrating reasoning about relational properties of programs and measurement of quantitative difference between computational effects. Toward a general framework for such logics, in this paper, we formalize the concept of quantitative difference between computational effects as divergences on monads , then develop a relational program logic called approximate computational relational logic (acRL for short). It supports generic computational effects and divergences on them. The semantics of the acRL is given by graded strong relational liftings constructed from divergences on monads. We derive two instantiations of the acRL: (1) for the verification of various kinds of differential privacy of higher-order functional probabilistic programs and (2) the other for measuring difference of distributions of cost between higher-order functional probabilistic programs with a cost counting operator.
Evolving Multi-Output Digital Circuits Using Multi-Genome Grammatical Evolution

Grammatical Evolution is a Genetic Programming variant which evolves problems in any arbitrary language that is BNF compliant. Since its inception, Grammatical Evolution has been used to solve real-world problems in different domains such as bio-informatics, architecture design, financial modelling, music, software testing, game artificial intelligence and parallel programming. Multi-output problems deal with predicting numerous output variables simultaneously, a notoriously difficult problem. We present a Multi-Genome Grammatical Evolution better suited for tackling multi-output problems, specifically digital circuits. The Multi-Genome consists of multiple genomes, each evolving a solution to a single unique output variable. Each genome is mapped to create its executable object. The mapping mechanism, genetic, selection, and replacement operators have been adapted to make them well-suited for the Multi-Genome representation and the implementation of a new wrapping operator. Additionally, custom grammar syntax rules and a cyclic dependency-checking algorithm have been presented to facilitate the evolution of inter-output dependencies which may exist in multi-output problems. Multi-Genome Grammatical Evolution is tested on combinational digital circuit benchmark problems. Results show Multi-Genome Grammatical Evolution performs significantly better than standard Grammatical Evolution on these benchmark problems.
Attention-based localized hash retrieval for large-scale remote sensing images using deep feature splitting strategy

Remote sensing image retrieval (RSIR) frameworks encounter several issues in practical scenarios with (1) the dominating repetitive features in the final image representation, (2) inter- and intra‐class variability across objects, and (3) time complexity in exhaustively searching the large-scale remote sensing image archives. Motivated by these facts, we propose a deep feature-splitting approach that enhances a localized hashing (DFS-LHash) model for RSIR. The DFS strategy splits the fully connected (FC) layer features into equally sized blocks for a split-based localized hash learning, keeping VGG-16 as the baseline network. We incorporate effective deep clustering to improve classification performance by reducing the center-cluster loss. A block-wise softmax is applied on each block to pay more attention to the essential features with reduced dimension for object localization, which enhances retrieval performance. The center-cluster loss applied strengthens the class discriminant information and minimizes the distance between the feature descriptors belonging to similar classes to a greater extent. The classification error from the DFS strategy is reduced to learn highly discriminant and localized binary codes effectively. The proposed model provides effective search with better retrieval time and achieves state-of-the-art performance with mean average precision values of 97.42%, 96.06%, and 93.47% for the University of California- Merced, PatternNet and aerial image datasets, respectively.
Processing instruction and structured input: A critical overview

• • This is the introduction to the Special Issue.
Extending OCCI for an autonomous SLA negotiation strategy in federated clouds

Summary Each cloud service provider provides only an interface of its own cloud infrastructure for enabling clients to use its cloud resources. However, there is a number of difficulties for cloud providers to ensure proper functioning. One of the main problems of a cloud provider is the lack of resources to support a huge number of on‐demand resources provisioning. Thus, resources cannot be distributed among different cloud providers since the federation is not the basic operation of the cloud provider. The most efficient way to overcome this problem is to extend the interface's cloud provider with an automatic negotiation to dynamically form the best agreement between the different cloud providers based on the service level agreement. In this article, we propose an extension for the Open Cloud Computing Interface which is the standardized interface for the cloud computing to support the automatic negotiation between the different cloud providers. To prove the efficiency and the effectiveness of our approach, we implement a prototype to evaluate the key presented in this article.
Deep Learning Based Player Identification Via Behavioral Characteristics

Behavioral recognition in game is a fundamental topic in data analysis. With the emerging of deep learning-based method, more and more artificial neural networks are proposed to achieve higher accuracy and robustness. This paper presents a comparison between CNN and RNN-LSTM for player identification based on player behavioral characteristics in a simple game environment. The goal is to detect potential manual cheating in competitions by recognizing unidentified players (cheaters) from their behavioral patterns. We implement a basic game, record player behaviors, and then compare the accuracy of outputs from the CNN and RNN-LSTM models. The conclusion is summarized as follow. CNN obtains 87.5% accuracy and RNN-LSTM achieves 92.3% accuracy in the simulated data. Our results indicate that the RNN-LSTM model outperforms the CNN model in terms of accuracy, making it a more suitable choice for player identification in this contextt.
An Analysis of the Impact of Undergraduate Education on Students’ Physical and Psychological Growth in China

With the gradual popularization of higher education, the influence of undergraduate education on students' physical and mental development has aroused more and more attention. It is found that students' physical and mental condition is closely related to undergraduate education and has a significant influence on it. Hence, it is vital to explore the relationship between these two variables. But these potential effects have not been published in detail and systematically by some researchers. Based on the literature analysis method, this paper mainly starts with the current situation of undergraduate education in China. It first analyzes the four main parts of Chinese education, then analyzes the positive and harmful influence on students caused by these parts, and finally puts forward suggestions for future improvement. The research significance of this paper is that it hopes to provide some improvement measures for university education in China, so as to make college students get better physical and mental development.
Design of information security transmission scheme and related authentication services based on Kerberos

A plethora of cloud service companies have sprung up in response to the growing amount of data. Data security protection solutions have been proposed by service providers. As a result, a rising number of people are using the cloud to store their own data. However, as technology advances and hackers' capabilities increase, service providers' protection mechanisms become less effective, and data security cannot be guaranteed. We review the logic of the Kerberos algorithm and discuss the work of each part in this paper. This document also includes and discusses difficulties with Kerberos, as well as improvement strategies.The main focus will be on strengthening the security of the Kerberos secure communication process, which will serve as a reference for Kerberos security transmission and authentication research. It solves the problem of the symmetric encryption algorithm's relatively low security in the authentication process by replacing it with the RSA one-time encryption algorithm; in addition, the Kerberos authentication protocol is added between the client and the cloud service provider to secure the data transmission process. Furthermore, this paper explains some basic authentication principles.
A Presentation of Structures and Applications of Convolutional Neural Networks

This paper reviewed the history of convolutional neural networks, why and how they developed, and what inspired the scientists to design them. To make CNNs simpler to understand, we wrote about their characteristics and structures while introducing the basic units of convolutional neural networks, including training and modeling parameters and how they would affect the confidence and efficiency of the whole process, different kinds of layers and how they work, multiple pooling methods and loss functions with their formulas. This paper also included applications of convolutional neural networks in computer vision and natural language processing while specifying and analyzing the technologies in use to clarify this introduction. Challenges and future research directions of the convolutional neural networks were pointed out to help refine this technique.
Evaluations of Clarinet Sound Quality and Register Characteristics

The clarinet is a commonly used woodwind instrument that has a unique sound quality, making it a prominent instrument in solo performances and orchestral music. Therefore, sound quality plays an important role in the perception of clarinet sound. This study provides a review of the relevant research in the field of clarinet sound analysis and synthesis, and identifies many valuable references from previous studies. In studying the noise component of clarinet sound, this study recorded audio samples of E3, E4, and E5 and preprocessed them by slicing and performing frequency domain transformations. Using 13 representative spectral features, this paper extracted features from the spectral signals and constructed a classification model of sound quality based on the SVM algorithm of machine learning. Finally, this study analyzed the shortcomings of the SVM algorithm in high-pitch analysis and proposed future research directions of combining the SVM algorithm with different sound characteristics of clarinet registers and spectral changes throughout the entire process of playing. These results shed light on guiding further exploration of the timbre of the clarinet.
A Hybrid Text Summarization Approach Using Neural Networks and Metaheuristic Algorithms

The rapid growth of text data on the Internet requires effective automatic text summarization techniques.This study proposes a hybrid text summarization approach that combines a Multi-hidden Recurrent Neural Network and a mayfly-harmony search algorithm.The neural network generates a feature vector for each sentence.The mayflyharmony search algorithm then optimizes the feature weights to extract the most relevant sentences for the summary.This manuscript capacity provides essential information and expertise that can be effectively summarised using Efficient Abstractive Text Summarising (EATS) techniques.This project aimed to extract informative summaries from various articles by utilizing regularly utilized handcrafted elements from literature.A Multi-hidden Recurrent Neural Network (MRNN) was used to generate a feature vector, and a new feature assortment strategy called Mayfly-Harmony Search (MHS) was applied for feature extraction.The number of sentences, word frequency, title similarity, term frequency-inverse sentence frequency, sentence location, sentence length, sentencesentence similarity, sentence phrases, proper nouns, n-gram co-occurrence, and document length were the features used.By taking diverse Mayfly Algorithm explanations found from other expanses of the search space and processing them with Harmony Search, the suggested hybrid of the Mayfly Algorithm and Harmony Search was employed to produce superior results.
An improved multi-source information fusion method for IMU compensation of missile

Abstract The conventional SINS/CNS integrated navigation system equipped on ballistic missiles is typically equipped with attitude measurement, which can only estimate the gyro drift and has no effect on accelerometer bias. To address the issue, an improved multi-source information fusion method containing a new nonlinear framework called SINS/RKCNS with the indirect horizon reference and kinematic constraint is proposed, and a MAP-based modified iterated CKF is involved to increase positioning accuracy and system robustness. Furthermore, to reduce the influence of correlated noise, state augmentation is employed in the iterative process. Eventually, experiments are conducted, and the results confirm the effectiveness of the proposed approach.
The Digital Bill Ecosystem

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
Information Enhancement for Joint Extraction of Entity and Relation

Joint entity and relation extraction is an important research topic in natural language processing. However, the current work cannot clearly identify the boundaries of entity and relation when solving the triple overlapping problem where triple share the same entity. In addition, in joint entity and relation extraction, the transfer of feature information between entity recognition and relation extraction tasks is weak, which makes the ability of mutual learning between tasks low. This paper proposes a joint entity and relation extraction method based on information enhancement. By establishing the relation between entity and relation and type, the entity and relation feature of the input information is enhanced, and the entity and relation feature is cross-fused during the task progress stage. Two subtasks are carried out. Auxiliary learning and sharing feature information improves the model's ability to identify entity and relation boundaries, and at the same time enhances the model's ability to discriminate entity and relation features. The experimental result on four datasets(i.e., WebNLG, ADE, SciERC, SSUIE)show that our model is highly competitive for overlapped triples, and also verify the effectiveness of information enhancement.