Reviewerly/exHarmony huggingface dataset:
Convolutional Neural Network with Multi-Head Attention for Human Activity Recognition

Convolutional neural networks (CNNs) have shown great promise in human activity recognition, but long-term dependencies in time series data can be difficult to capture using standard CNNs. This study introduces a new CNN architecture that incorporates a multi-head attention mechanism (CNN-MHA) to address this challenge. This mechanism is composed of several attention heads, each independently calculating attention weights for distinct segments of the input. The attention head outputs are then concatenated and processed through a fully connected layer to produce the final attention representation. The multi-head attention mechanism allows the network to focus on relevant features and maintain long-term dependencies in the input data. The proposed model is evaluated on the physical activity monitoring for aging people dataset (PAMAP2) from the UCI machine learning repository, which is preprocessed by cleaning, normalization, segmentation, and reshaping before splitting into training, validation, and testing sets. The experimental results demonstrate that the CNN-MHA model outperforms existing models, achieving F1-score of 95.7%. Particularly, the multi-head attention mechanism significantly improves the model’s ability to recognize complex activity patterns. Furthermore, our model attained an average inference latency of 0.304 seconds, which can be crucial in real-time applications. The findings clearly demonstrate the substantial promise of the proposed CNN-MHA architecture for optimizing human activity recognition tasks, offering a powerful tool for advancing the state-of-the-art in this domain.
Cross-Domain Few-Shot Classification via Dense-Sparse-Dense Regularization

This work addresses the problem of cross-domain few-shot classification which aims at recognizing novel categories in unseen domains with only a few labeled data samples. We think that the pre-trained model contains the redundant elements which are useless or even harmful for the downstream tasks. To remedy the drawback, we introduce an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L<sup>2</sup>-SP</i> regularized dense-sparse-dense (DSD) fine-tuning flow for regularizing the capacity of pre-trained networks and achieving efficient few-shot domain adaptation. Given a pre-trained model from the source domain, we start by carrying out a conventional dense fine-tuning step using the target data. Then we execute a sparse pruning step that prunes the unimportant connections and fine-tunes the weights of sub-network. Finally, initialized with the fine-tuned sub-network, we retrain the original dense network as the output model for the target domain. The whole fine-tuning procedure is regularized by an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L<sup>2</sup>-SP</i> term. In contrast to the existing methods that either tune the weights or prune the network structure for domain adaptation, our regularized DSD fine-tuning flow simultaneously exploits the benefits of sparsity regularity and dense network capacity to gain the best of both worlds. Our method can be applied in a plug-and-play manner to improve the existing fine-tuning methods. Extensive experimental results on benchmark datasets demonstrate that our method in many cases outperforms the existing cross-domain few-shot classification methods in significant margins. Our code will be released soon.
AJAN: An Engineering Framework for Semantic Web-Enabled Agents and Multi-Agent Systems

The development of Semantic Web-enabled intelligent agents and multi-agent systems still remains a challenge due to the fact that there are hardly any agent engineering frameworks available for this purpose. To address this problem, we present AJAN, a modular framework for the engineering of agents that builds on Semantic Web standards and Behavior Tree technology. AJAN provides a web service-based execution and modeling environment in addition to an RDF-based modeling language for deliberative agents where SPARQL-extended behavior trees are used as a scripting language to define their behavior. In addition, AJAN supports the modeling of multi-agent coordination protocols while its architecture, in general, can be extended with other functional modules as plugins, data models, and communication layers as appropriate and not restricted to the Semantic Web.
Interface and Window: Imagination and Technology in Movie Interaction

This paper investigates the relationship between symbolic interaction and the audience’s imagination through text analysis and case studies of desktop movies. This paper demonstrates how the interaction between the film and the computer interface has evolved, with the film’s numerous search websites, social media platforms, and streaming platforms realistically reproducing the audience’s real-world internet interaction experience and narrowing the gap between the image and the film interface. The use of digital computing technology in filmmaking, combined with the human-computer interaction mode of modern computers, has resulted in a digital cinema image-based, database algorithm as the technical logic for visual perception-oriented art presentation. As a result, in the “image-interface” movie presentation mode, interactive interfaces are becoming increasingly important components of the digital media system.
CodeGraphSMOTE - Data Augmentation for Vulnerability Discovery

The automated discovery of vulnerabilities at scale is a crucial area of research in software security. While numerous machine learning models for detecting vulnerabilities are known, recent studies show that their generalizability and transferability heavily depend on the quality of the training data. Due to the scarcity of real vulnerabilities, available datasets are highly imbalanced, making it difficult for deep learning models to learn and generalize effectively. Based on the fact that programs can inherently be represented by graphs and to leverage recent advances in graph neural networks, we propose a novel method to generate synthetic code graphs for data augmentation to enhance vulnerability discovery. Our method includes two significant contributions: a novel approach for generating synthetic code graphs and a graph-to-code transformer to convert code graphs into their code representation. Applying our augmentation strategy to vulnerability discovery models achieves the same originally reported F1-score with less than $$20\%$$ of the original dataset and we outperform the F1-score of prior work on augmentation strategies by up to $$25.6\%$$ in detection performance.
Classification Auto-Encoder Based Detector Against Diverse Data Poisoning Attacks

Poisoning attacks are a category of adversarial machine learning threats in which an adversary attempts to subvert the outcome of the machine learning systems by injecting crafted data into training data set, thus increasing the resulting model’s test error. The adversary can tamper with the data feature space, data labels, or both, each leading to a different attack strategy with different strengths. Various detection approaches have recently emerged, each focusing on one attack strategy. The Achilles heel of many of these detection approaches is their dependence on having access to a clean, untampered data set. In this paper, we propose CAE, a Classification Auto-Encoder based detector against diverse poisoned data. CAE can detect all forms of poisoning attacks using a combination of reconstruction and classification errors without having any prior knowledge of the attack strategy. We show that an enhanced version of CAE (called CAE+) does not have to rely on a clean data set to train the defense model. The experimental results on three real datasets (MNIST, Fashion-MNIST and CIFAR-10) demonstrate that our defense model can be trained using contaminated data with up to 30% poisoned data and provides a significantly stronger defense than existing outlier detection methods. The code is available at https://github.com/Emory-AIMS/CAE
Towards Exception Handling in the SARL Agent Platform

We demonstrate how exception handling can be realized in the SARL agent platform. We see exception handling as a mechanism binding some agents, entitled to raise given exceptions, to the ones entitled to handle them. To this end, we define dedicated exception spaces through which defining the agents’ behavior in presence of exceptions.
Extended Green Cloud – Modeling Cloud Infrastructure with Green Energy Sources

Recently, cloud computing has emerged as key way of delivering computing resources. Hence, research has focused on optimizing use of cloud resources. The following contribution presents an agent-based Extended Green Cloud Simulator, motivated by the Green Edge Processing project of the cloud company, CloudFerro. The simulator serves as a digital twin for agent-enabled carbon-intelligent cloud infrastructure that incorporates renewable energy sources and facilitates the modeling and evaluation of resource management strategies. Additionally, it offers a Graphical User Interface, System Engine, and Logging System which enable capturing, visualization, and analysis of key system statistics.
A New Way to Experience Art: Experience Design and Strategies for Immersive Exhibitions

Immersive exhibitions offer a new and exciting way for people to experience art and have the potential to expand the boundaries of traditional art exhibitions. The immersive art exhibition can create a deeper understanding and appreciation of the art and the ideas behind it, by providing visitors with a more interactive and engaging way of experiencing the art. This paper proposes an immersive experience design framework, providing a structured approach with four important elements contributing to immersive experiences: story, space, technology, and interactivity. By following this framework, designers can create immersive experiences that are meaningful and engaging for the user. This paper provides two case studies of immersive art exhibitions analyzed with the immersive experience design framework. The findings of this study will be useful for curators, designers, and researchers in designing virtual art experiences in immersive exhibitions.
An Autoencoder-Based Image Anonymization Scheme for Privacy Enhanced Deep Learning

The development of deep learning (DL) technology is dependent on the availability of large-scale image datasets to train deep neural networks (DNNs) for image classification. However, many raw image datasets contain sensitive identity feature information that prohibit entities from disclosing data due to privacy regulations. For example, an image dataset may include age or gender information that could be used to identify an individual. Furthermore, medical images may include additional disease information that could lead to patient re-identification. To address this problem, we propose an image transformation scheme using a convolutional autoencoder and multi-output classification model for privacy enhanced deep learning. The proposed scheme obfuscates image visual information while retaining useful attribute features that are required for model utility. Additionally, the proposed method enhances privacy by generating encoded images that exclude sensitive identity feature information. First, we train a multi-output convolutional neural network (CNN) to classify identity features and image attributes. Second, we use the pre-trained multi-output classifier for regularization in training a standard convolutional autoencoder to generate obfuscated versions of the original images that exclude identity feature information and preserve attribute features that are useful for classification. Our results on CelebA and Cifar-100 datasets illustrate that the proposed method successfully degrades classification accuracy of sensitive image data while maintaining model utility for non-sensitive data features.
A Comparative Study on the Implementation of Blockchain in Supply Chain Models

This research intends to investigate the existing state, prospective uses in supply chain, future scope of this technology, dangers, and obstacles that we may encounter. We discuss the importance of blockchain in supply chain in Indian context. Each customer should be assured of the quality and credibility of items they would be using. This technology is most known for its application in crypto currency. It has the potential of increasing its reach to much more domains. The blockchain network is comparatively a better option than a traditional approach as each new block of data is attached to the existing blockchain in a linear and chronological way. With each block connected to the preceding one, any bad actors wishing to tamper with data would need to decode prior blocks before reaching their desired data and rewriting the chain ahead while manipulating the rest of the nodes in a similar approach. As tampering with the data is both technologically difficult and commercially unfeasible, the security of a blockchain becomes two-fold.
BLENDED LEARNING IN THE CONTEXT OF DIGITAL TRANSFORMATION OF HIGHER EDUCATION

The integration of digital technologies with ordinary teaching and learning methods is a feature of higher education. Integration is growing rapidly, primarily due to the quick development of digital technologies and the enhancement of digital skills of both students and teachers. Digital technologies bring about powerful changes in the educational system. Expansion of an access to devices and communication increases students’ digital literacy. The development of learning environment and free online resources change the ways in which students get access to information and obtain new knowledge. Blended learning is an innovative teaching method today. The article substantiates the role of blended learning in the digital transformation of higher education, analyses the implementation of blended learning models in the educational process of a higher education institution. The advantages of using blended learning in the educational process and the key factors of an effective blended learning environment have been determined. The “Flipped Classroom” learning model has been substantiated in detail and effective methods for implementing the flipped classroom methodology have been identified. To implement a blended learning model, a teacher must possess competencies that include the digital competence of a teacher. The poll of teachers regarding their choice of blended learning models has been conducted. A wide range of digital tools, which are used by teachers during classes based on the blended learning method, have been identified. It has been summarized that the use of the mixed learning model contributes to the development of digital competence of teachers and students and the improvement of the quality of the educational process as well.
TopicAns: Topic-informed Architecture for Answer Recommendation on Technical Q&amp;A Site

Technical Q&amp;A sites, such as Stack Overflow and Ask Ubuntu, have been widely utilized by software engineers to seek support for development challenges. However, not all the raised questions get instant feedback, and the retrieved answers can vary in quality. The users can hardly avoid spending much time before solving their problems. Prior studies propose approaches to automatically recommend answers for the question posts on technical Q&amp;A sites. However, the lengthiness and the lack of background knowledge issues limit the performance of answer recommendation on these sites. The irrelevant sentences in the posts may introduce noise to the semantics learning and prevent neural models from capturing the gist of texts. The lexical gap between question and answer posts further misleads current models to make failure recommendations. From this end, we propose a novel neural network named TopicAns for answer selection on technical Q&amp;A sites. TopicAns aims at learning high-quality representations for the posts in Q&amp;A sites with a neural topic model and a pre-trained model. This involves three main steps: (1) generating topic-aware representations of Q&amp;A posts with the neural topic model, (2) incorporating the corpus-level knowledge from the neural topic model to enhance the deep representations generated by the pre-trained language model, and (3) determining the most suitable answer for a given query based on the topic-aware representation and the deep representation. Moreover, we propose a two-stage training technique to improve the stability of our model. We conduct comprehensive experiments on four benchmark datasets to verify our proposed TopicAns’s effectiveness. Experiment results suggest that TopicAns consistently outperforms state-of-the-art techniques by over 30% in terms of Precision@1.
Afreximbank: Bond Issuance Down

Africa Research Bulletin: Economic, Financial and Technical SeriesVolume 60, Issue 5 p. 24305B-24305C Policy and Practice Afreximbank: Bond Issuance Down First published: 11 July 2023 https://doi.org/10.1111/j.1467-6346.2023.11138.xRead the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume60, Issue5July 2023Pages 24305B-24305C RelatedInformation
ДО ПРОБЛЕМИ ПОСТМОДЕРНОГО ТЕЛЕБАЧЕННЯ ТА СЮЖЕТУ ПРО ГУСКУ, ЯКА ВКУСИЛА ПРОФЕСОРА ВІЛЬЯМА ГЕНРІ ХЕНКА-МОЛОДШОГО

&lt;p&gt;&lt;strong&gt;&lt;em&gt;The purpose of the research&lt;/em&gt;&lt;/strong&gt;&lt;em&gt; is to determine the key elements of the impact of postmodern phenomena on television. &lt;/em&gt;&lt;/p&gt;&lt;p&gt;&lt;strong&gt;&lt;em&gt;Research methodology.&lt;/em&gt;&lt;/strong&gt;&lt;em&gt; The following methods were used to achieve the goal. A synergistic approach to determining the role and place of postmodernism in television broadcasting; development of the scientific source base was based on the application of the analytical-synthetic method; the analytical method was used to reveal the peculiarities of the effects of convergence and cross-media on television as the consequences of postmodernism; the inductive method made it possible to carry out a general analysis of existing points of view on the analyzed problem. &lt;/em&gt;&lt;/p&gt;&lt;p&gt;&lt;strong&gt;&lt;em&gt;The results.&lt;/em&gt;&lt;/strong&gt;&lt;em&gt; Postmodernism has led to the fact that the audience for classic television has significantly decreased, and more and more audiovisual content is moving to the network under the guise of the latest product (the consequences of postmodern convergence), and (and this is already cross-media) the classic distinction between commissioned journalism and commercial journalism is losing its relevance. The postmodern media environment, emerging in the spirit of convergence and cross-media, is becoming more and more popular alongside the most modern networked Internet media, and television in this series remains an element of a holistic communication platform. &lt;/em&gt;&lt;/p&gt;&lt;p&gt;&lt;strong&gt;&lt;em&gt;Novelty.&lt;/em&gt;&lt;/strong&gt;&lt;em&gt; In identifying postmodern trends in television that, in relation to classic television, signal a break with conservative thought on the air. Television content supports the concept of fragmented content, and forms are superimposed on each other with a «collage» of current usage.&lt;/em&gt;&lt;/p&gt;&lt;p&gt;&lt;strong&gt;&lt;em&gt;The practical significance&lt;/em&gt;&lt;/strong&gt;&lt;em&gt; is that the obtained results can be used for further scientific studies of the use model of media hybridity together with audience studies. &lt;/em&gt;&lt;/p&gt;&lt;p&gt;&lt;strong&gt;&lt;em&gt;Key words:&lt;/em&gt;&lt;/strong&gt;&lt;em&gt; audience, content, convergence, cross-media, Internet, postmodernism, television.&lt;/em&gt;&lt;/p&gt;
Jane Austen in Digital Age : Sentiment Analysis of Northanger Abbey and Persuasion

Nowadays, with the development of natural language processing, emotional messages encoded in spoken or written texts can be explored with technology. While digital humanity (DH) is regarded as the crisis of the humanities, it can, on the other hand, empower and enhance humanity studies. Numerous studies about Jane Austen have been conducted, but few of them engage with the sentiments of her novels. To detect the sentimental changes in Austen's novels and relations between the novels and Austen's cultural background, this research combines several open-source tools (R packages) with custom scripts and modifications to produce sentimental trajectories of Austen's early and late novels Northanger Abbey and Persuasion. The findings of this research are fourfold. Firstly, in both novels, Austen subverts the conventional image of “beautiful heroine” and emphasizes the capabilities of normal girls in solving problems so as to encourage them stand on the center of their own life stage. Secondly, Persuasion, though written when Austen was sick, has a higher sentiment value than Northanger Abbey. Thirdly, in expressing negative emotions in Northanger Abbey, Austen uses “linguistic negations” to satirize the conventional Gothic narratives. Fourthly, the depressed emotions in Persuasion always stimulate introspection of the characters and help them become more mature.
MODEL MANAJEMEN LITERASI DI SEKOLAH DASAR

This study analyzes the effect of the completeness of facilities and human capital on the management of school literacy in SD Negeri Medan. The number of research samples is 150 respondents. It consists of school principals and teachers of public elementary schools in Medan City from 150 schools. Sampling was carried out using a two-stage cluster sampling technique by taking into account location factors and literacy programs in schools. Data analysis used multiple linear regression analysis and to test the hypothesis used the t test (t test) to determine the partial effect and the F test (F test) to determine the effect simultaneously. The results of the data analysis show that the research model is in the form of a nerganda linear regression equation Ŷ = 3.304 + 0.491X1 + 0.898X2 + e and the results of hypothesis testing prove that there is a positive and significant influence between the completeness of facilities on literacy management in public elementary schools in Medan City, there is a significant influence positive and significant relationship between human capital on literacy management in public elementary schools in Medan City and there is a positive and significant influence between the completeness of facilities and human capital on literacy management in public elementary schools in Medan city.
Pemanfaatan Desain Tata Ruang pada Ruang Baca Dinas Perpustakaan dan Kearsipan Daerah Kabupaten Sijunjung

The public library is the closest institution to the community, with a library, it is easy for the community to find learning resources that suit their needs. This research aims to determine the utilization of spatial design in reading room study to determine the use of spatial design in the reading room at the library service and the general archives of the sijunjung area and to find out which reading room complies with existing library guidelines. The method used in this study is qualitative. The data collected in this study by conducting observations, interviews, and literature studies that are directly related to the research. From the result of this study, it can be concluded that the layout design of the library service and the general archives of the sijunjung area is quite good and the lighting and color of the room look elegant so that when the user enters the room it makes the mind of the user calm and open