Reviewerly/exHarmony huggingface dataset:
Concept Lens: Visually Analyzing the Consistency of Semantic Manipulation in GANs

As applications of generative AI become mainstream, it is important to understand what generative models are capable of producing, and the extent to which one can predictably control their outputs. In this paper, we propose a visualization design, named Concept Lens, for jointly navigating the data distribution of a generative model, and concept manipulations supported by the model. Our work is focused on modern vision-based generative adversarial networks (GAN), and their learned latent spaces, wherein concept discovery has gained significant interest as a means of image manipulation. Concept Lens is designed to support users in understanding the diversity of a provided set of concepts, the relationship between concepts, and the suitability of concepts to give semantic controls for image generation. Key to our approach is the hierarchical grouping of concepts, generated images, and the associated joint exploration. We show how Concept Lens can reveal consistent semantic manipulations for editing images, while also serving as a diagnostic tool for studying the limitations and trade-offs of concept discovery methods.
Poster: Maximal Rate Entanglement Swapping Schedules for Routing in the Quantum Internet

In this work, we describe a routing algebra corresponding to the problem of establishing end-to-end quantum entanglements between nodes connected through a network of first generation quantum repeaters. Our goal is to design an algorithm which can determine all the routes and swapping schedules to achieve maximal rates of end-to-end entanglement. We aim to provide an improvement in terms of complexity over an existing solution while increasing the number of routes and schedules available to offer more alternatives for the entanglement generation layer of a quantum protocol stack.
A Cross-Chain Payment Channel Network

Blockchain interoperability and throughput scalability are two crucial problems that limit the wide adoption of blockchain applications. Payment channel networks (PCNs) provide a promising solution to the inherent scalability problem of blockchain technologies, allowing off-chain payments between senders and receivers via multi-hop payment paths. This paper presents a cross-chain PCN, called XHub, that extends PfCNs to support multi-hop paths across multiple blockchains and resolves both interoperability and throughput scalability. XHub achieves service availability, transaction atomicity, and auditability. Users who correctly follow the protocols will succeed in making payments or get profits from doing the services. In addition, trustworthy information about hubs will be managed in a decentralized manner and available to all users. We conduct prototype implementation of machines that exchange Internet messages and run with two real blockchains as well as large-scale simulations based on real-world PCN topologies and transactions. The results show that XHubs has small latency for cross-chain payments and can achieve a significantly higher success rate compared to the version without hub management protocols. This work is an important step towards the big picture of a decentralized transaction system that connects a wide scope of users in different blockchains.
Performance and Energy Savings Trade-Off with Uncertainty-Aware Cloud Workload Forecasting

Cloud computing has seen widespread adoption because it increases the productivity and efficiency of industries and allows for effective scalability of their business [1]. Guaranteeing performance levels is at the core of cloud services and requires huge computational resources, especially with the latest advances in technologies such as Artificial Intelligence and the Internet of Things [2]. Typically, customers subscribe to agreements where cloud providers ensure specific levels of reliability, availability and responsiveness to systems and applications and describe penalties if the service levels are not met. At the same time, massive computational resources are a cost for providers and have a significant environmental impact, which will increase in the future. It is estimated that the energy consumption of data centres (which host cloud services) will grow from 292 TWh in 2016 to 353 TWh in 2030 [3], and greenhouse gas emissions will increase over 14% in 2040, compared to a 1-1.6% increase in the 2007–2016 [4].
The Nanoservices Framework: Co-Locating Microservices in the Cloud-Edge Continuum

Today, the microservices paradigm has emerged as one of the most widely adopted patterns to develop, package, and deploy software on a large scale. However, as they were originally designed for the cloud, the direct application of microservices DevOps practices to resource-constrained environments like the Edge may not be optimal. Specifically, deploying each piece of software as an individual microservice may result in a significant resource footprint (e.g., storage space and network bandwidth related to managing base images, CPU, and memory). In this work, we explore the concept of opportunistically grouping microservice code within the same container to reduce resource footprint when deploying multiple microservices at the Edge. To materialize this concept, we present the N anoservices framework: a framework that formalizes and provides practical means for developers to build and deploy groups of microservices on the same container (a.k.a., Nanoservices). Our early results show that with Nanoservices we can achieve a significant resource footprint reduction (base image storage, CPU, memory) with minimal effort from the developer's viewpoint.
Adaptive Hybrid Vision Transformer for Small Datasets

Recently, vision Transformers (ViTs) have achieved competitive performance on many computer vision tasks. However, vision Transformers show impaired performance on small datasets when training from scratch compared with Convolutional Neural Networks (CNNs), which is interpreted as the lack of locality inductive bias. This impedes the application of vision Transformers for small-size datasets. In this work, we propose Adaptive Hybrid Vision Transformer (AHVT) as the solution to boost the performance of vision Transformers on small-scale datasets. Specifically, on spatial dimension, we exploit a Convolutional Overlapping Patch Embedding (COPE) layer to inject desirable inductive bias in model, forcing the model to learn the local token features. On channel dimension, we insert a adaptive channel features aggregation block into vanilla feed forward network to calibrate channel responses. Meanwhile, we add several extra learnable “cardinality tokens” to patch token sequences to capture cross-channel interaction. We present extensive experiments to validate the effectiveness of our method on five small/medium datasets including CFAR101100, SVHN, Tiny-ImageNet and ImageNet-lk. Our approach attains state-of-the-art performance on above four small datasets when training from scratch.
"Now I Understand": an application to bridge the gap in communication with hearing impaired people

"Now I Understand" is a mobile application that enables bilateral communication between hearing and non-hearing people using artificial intelligence techniques. The application uses deep learning models to recognize sign language and translate it into text in real-time, while also translating text into sign language. The details of the application implementation are discussed, including data acquisition and preprocessing, selection and training of deep learning models, and the user interface. This application has the potential to revolutionize communication and accessibility for people with hearing impairments by providing a reliable and accurate tool for bilateral communication, as well as improving the general public's awareness and understanding of sign language.
Poster: Measuring Adoption of SPF, DMARC, and CAA DNS Records with Nonfederal Governments in the United States

Governments provide critical services to the residents they serve such as voting information, taxes, and health services. In turn, residents must trust information coming from their governments is legitimate. This paper examines the adoption of three DNS records, Sender Policy Framework (SPF), Domain-based Message Authentication, Reporting and Conformance (DMARC), and Certificate Authority Authorization (CAA), with governments in the United States. All three DNS records help establish legitimacy in a server's identity. From February to July 2023, SPF, DMARC, and CAA records from domains belonging to governments in the U.S. were collected, parsed, and semantically analyzed for security misconfigurations. We have developed an open source parser and analyzer to verify the syntax and security configuration of SPF and DMARC records. We found that adoption of all three records is slowly increasing, however errors and misconfigurations are disproportionately found in local, county, and state government domains.
Smart Systems for Faultless Purlieus: Leveraging AI and Machine Learning

In today’s rapidly evolving world, the imperative of efficient waste management is pivotal for fostering a sustainable future. This paper introduces a holistic solution that combines advanced hardware and software technologies to tackle the multifaceted challenges of waste classification and collection. The proposed initiative leverages state-of-the-art computer vision, robotics, and intelligent systems to revolutionize waste management processes, ultimately contributing to environmental preservation and resource optimization.Our innovative project presents an autonomous waste classification and collection system, seamlessly blending cutting-edge hardware components with sophisticated software algorithms. The primary objective is to create an integrated system that not only automates waste sorting but also optimizes resource utilization and minimizes environmental impact.
Removing Unwanted Text from Architectural Images with Multi-Scale Deformable Attention-Based Machine Learning

Dataset development for machine learning (ML) is considered a challenging and time-consuming process because of the significant resources needed for preprocessing. Automated pipelines for retrieving and preprocessing large amounts of data are not always so readily available. This article examines the benefits of using self-attention ML object detection approaches in the image preprocessing stage in an autonomous manner. We focus on the case where the required preprocessing is text (noise) removal from architectural images. The model we develop is based on the state-of-the-art Text Spotting Transformer (TESTR) framework.By using our TESTR model, we demonstrate that identification and removal of unwanted text annotations on architectural floor plan image datasets are feasible. The impact of inference threshold and image scale on the removal performance is investigated. Optimal thresholds are derived between leaving text and inadvertently removing (building) content. The lower the image scale, the worse the object detection performance. Our pipeline could be used for the preprocessing of huge image datasets for removing obsolete/unwanted annotations and features to improve performance during generative adversarial network (GAN) model training.This could boost efforts to make artificial intelligence systems automatically offer suggestions and refine the building design. The application of TESTR to the architectural image data preprocessing stage as a tool for text and numerical content removal has shown promise. The recognizer decoder of TESTR provides the ability to retain the removed content information for further labeling.
Retracted: LSTM-Based Deep Model for Investment Portfolio Assessment and Analysis

[This retracts the article DOI: 10.1155/2022/1852138.].
Retracted: Effective Graph Mining for Educational Data Mining and Interest Recommendation

[This retracts the article DOI: 10.1155/2022/7610124.].
Retracted: Deep Learning-Guided Simulated Annealing for Designing Vocational High Educational System

[This retracts the article DOI: 10.1155/2022/7187863.].
Retracted: The Influence Mechanism of BIM on Green Building Engineering Project Management under the Background of Big Data

[This retracts the article DOI: 10.1155/2022/8227930.].
Retracted: A Deep Learning Filter that Blocks Phishing Campaigns Using Intelligent English Text Recognition Methods

[This retracts the article DOI: 10.1155/2022/5036026.].
Retracted: A Research on the Establishment of a System Improving Teaching Ability of English Teachers in the Classroom Setting by Utilizing Artificial Intelligence

[This retracts the article DOI: 10.1155/2022/8371437.].
Retracted: Analysis of Sleep Quality and TCM Constitution Characteristics in 258 Outpatients: A Cross-Sectional Study Based on Outpatient Cases

[This retracts the article DOI: 10.1155/2022/2952531.].
Taken By Surprise? Evaluating how Bayesian Surprise &amp; Suppression Influences Peoples’ Takeaways in Map Visualizations

Choropleth maps have been studied and extended in many ways to counteract the many biases that can occur when using them. Two recent techniques, Surprise metrics and Value Suppressing Uncertainty Palettes (VSUPs), offer promising solutions but have yet to be tested empirically with users of visualizations. In this paper, we explore how well people can make use of these techniques in map exploration tasks. We report a crowdsourced experiment where n = 300 participants are assigned to one of Choropleth, Surprise (only), and VSUP conditions (depicting rates and Surprise in a suppressed palette). Results show clear differences in map analysis outcomes, e.g. with Surprise maps leading people to significantly higher areas of population, or VSUPs performing similar or better than Choropleths for rate selection. Qualitative analysis suggests that many participants may only consider a subset of the metrics presented to them during exploration and decision-making. We discuss how these results generally support the use of Surprise and VSUP techniques in practice, and opportunities for further technique development. The material for the study (data, study results and code) is publicly available on https://osf.io/exb95/.
Model Komunikasi Westley dan Maclean dalam Pembelajaran Kosakata Bahasa Jerman

This study aims to make the process of learning German vocabulary more interesting and easy to master. an alternative in learning vocabulary, namely using the Westley &amp; Maclean communication model. The method used in this research is descriptive qualitative. Then data collection techniques were carried out through interviews, observation, and documentation. Based on the data obtained, an analysis and interpretation of the meaning of the data is carried out. The theory used is the Westley &amp; Maclean communication model. The results of this study indicate that communication has an important role in learning German vocabulary. The proof can be seen in the communication model applied at SMA Negeri 14 Gowa. The applied learning communication has been designed systematically.
BLOCKCHAIN PLATFORM SELECTION AND SOFTWARE DEVELOPMENT FOR DECENTRALIZED EXCHANGE OF BUSINESS PROCESS MODELS

Modern organizations employing the Business Process Management (BPM) approach typically handle collections of hundreds or even thousands of business process models. Business process modeling stands as the central technology within the entire BPM methodology. In line with the BPM lifecycle, these models visually represent current organizational activities that necessitate improvement using various diagramming notations. These graphical business process models can subsequently be employed to analyze ongoing activities in the enterprise, identifying potential drawbacks or “weak spots” that hinder the company’s performance. Through business process models, organizations can modify the “virtual twins” of their organizational workflows, conduct simulations, and make informed decisions for business process improvement. Thus, business process models constitute the most valuable assets of the organization, conveying knowledge about ongoing activities and potentially encapsulating the best organizational or industry practices. The implementation of a centralized database for business process models can significantly benefit the entire organization, enhancing the efficiency of knowledge sharing and accumulation. However, centralized business process model repositories prove less efficient for inter-organizational knowledge exchange. Additionally, most business process models require significant person-hours for development and cannot be shared freely with competitors. The exchange of business process models should adhere to established mechanisms for managing valuable digital assets. Presently, Distributed Ledger Technologies (DLT), especially Blockchain, have gained enormous popularity. Therefore, we can employ the principles of Blockchain technology and the cryptocurrency industry to create software for the Decentralized Exchange (DEX) of business process models. This study explores the selection of a DLT platform and the development of software for the decentralized exchange of business process models, utilizing asset tokenization and smart contract technologies.
The invention of the “chromosound”: challenges for the development of a possible language

This article describes the Chromossonium, the project of an interactive environment conceived to host performances of Chromossonia, a synesthetic language for performance and composition. Chromossonia is based in a theoretical particle named Chromossound, which unites sound and color in a non-hierarchical way, and with which we expect creators to be able to compose scored pieces to be staged and performed in the Chromossonium. Following a description of how these elements are conceived, and which are its theoretical demands, the paper discusses the many potential directions of research the project offers, and the promises it brings. Then we discuss the technical challenges we found in the process of its implementation, choosing to generate the Chromossound as the output of a neural network, according to some technical choices and a defined architecture.
Metadata at <i>The New York Times</i> : organizing and leveraging news content from 1851 to today

The New York Times has been a leader in metadata since the birth of The New York Times Index in 1913. Today, a team of taxonomists uses natural-language processing and a human-in-the-loop model to organize, analyze, and push content to readers. Looking to the future, the taxonomy team hopes to free up resources with the help of new technologies so that the metadata practices already in place can be expanded to other areas beyond core news content.
Information provision of the sphere of education and science of Ukraine in the conditions of martial law: report materials. science and practice conf. V. Sukhomlynskyi SSEL of Ukraine, December 18, 2023, Kyiv

The collection contains materials in which the scientific reports of the participants of the reported scientific-practical conference of V. Sukhomlynskyi State Scientific and Educational Library of Ukraine "Information support of the sphere of education and science of Ukraine under martial law" held on December 18, 2023 are presented.
The Future of Virtual Reality in Indian Education: A Comprehensive Survey

Virtual Reality (VR) technology has been gaining substantial attention in the field of education as a potential tool for enhancing learning experiences. This research article presents a comprehensive survey conducted among 25,000 students in India to assess the potential of VR in Indian schooling. The study aimed to investigate the students’ perceptions, preferences, and the impact of VR on their education. Findings reveal that VR has significant potential in the Indian education system, offering a promising future for immersive learning at the schooling level. The article explores the key insights derived from this extensive survey and provides recommendations for integrating VR into Indian classrooms.
Learning Engagement of Children during the Covid-19 Period in Lalbandi Municipality: A Mixed Method

This paper analyzes the learning status of children and their access to information and communication technology (ICT) during the pandemic period. This study employs a mixed-methods design; selecting two secondary schools were purposively to cover both urban and rural areas in the Lalbandi Municipality of Madhesh Pardesh. Primary data were collected through interview guidelines with the respondents, surveys, focus group discussions, and field notes, while secondary data were collected using various sources, such as policy documents, empirical reports, theoretical literature, and internet sources. Both descriptive and analytical methods were used in data analysis and interpretation. The study shows that students learned from several modes, such as online classes using computers, mobile devices, and other electronic devices such as radio and television. Some students just self-study. It shows a gap in access to online learning between students who had access to online classes and students who studied on their own. The majority of the students preferred to study at home. Among students staying at home, the majority of them were given a separate room to study in, while others studied in a common room. Despite the variable access to online classes, most of the students performed the given assignments. The persistence of the digital divide has posed challenges to education, but the unwavering dedication of students and parents to learning emphasizes its enduring importance. This situation underscores the urgent need for a more equitable and inclusive educational system in the future.
COMPARISON OF SPELL CORRECTION IN BAHASA INDONESIA: PETER NORVIG, LSTM, AND N-GRAM

This study conducts a comprehensive comparison of spell-checking methods in Bahasa Indonesia, specifically focusing on three approaches: Peter Norvig's method, Long Short-Term Memory (LSTM), and N-gram. The primary metric for evaluation is the accuracy in correcting spelling errors. Notably, Peter Norvig's method outperforms the others, with N-gram following closely, and LSTM trailing behind. The study draws valuable insights that contribute to the enhancement of spelling correction accuracy in the Bahasa Indonesia language. To carry out the evaluation, the research employs SPECIL data (Spell Error Corpus for Indonesian Language), which includes documents with various error types such as insertion, deletion, transposition, and substitution. The testing dataset consists of 150 words, aligning with the 150-word corpus references from the 'Leipzig Corpora Collection' used for Peter Norvig's and N-gram methods. It's noteworthy that the LSTM method utilizes a reference dataset from SPECIL, comprising 150 data points and specifically focusing on insertion errors for the test data. This research provides valuable insights for researchers, developers, and language technology enthusiasts seeking to refine spell-checking techniques for the Bahasa Indonesia language. By leveraging diverse error types and a standardized testing dataset, the study aims to contribute to the continual improvement of spell-checking tools