Reviewerly/exHarmony huggingface dataset:
Do we know and do we care? Algorithms and Attitude towards Conversational User Interfaces: Comparing Chatbots and Voice Assistants

As conversational user interfaces (CUIs) are increasingly integrated into daily life, ethical and societal concerns about integrated content filtering algorithms emerge. In addressing these concerns, it is essential to know how aware and knowledgeable society is of the algorithms it encounters using CUIs and the extent to which this impacts the attitude towards these technologies. In this survey study, we made a first attempt to measure and compare participants' algorithm awareness of chatbots and voice assistants. Further, we assessed the effect of algorithm literacy on the attitude towards CUIs and possible interaction effects with technology acceptance. Lastly, we compared previous and future usage purposes for chatbots and voice assistants. We found higher algorithm awareness for voice assistants than for chatbots. No correlation between algorithm literacy and attitude towards either chatbots or voice assistants was found. An additional personal-level factor, technology acceptance, did not affect this relationship. The results show that participants preferred using voice assistants for task completion and social purposes over chatbots, while getting information was equally preferred between chatbots and voice assistants. Considering its societal relevance, we want to encourage more research on algorithmic awareness and understanding in the field of CUI and its cognitive and behavioral effects.
USING MICROSOFT EXCEL IN RECAPITULATING STUDENT SCORES FOR PRIMARY TEACHERS

The purpose of implementing the Community Prescription was to increase the knowledge and skills of teachers in SD N 1 and 2 Setu using Microsoft Excel to recapitulate student data and values so that this is a solution for teachers and admin staff. They no longer use manual methods. The implementation of this activity is carried out in three stages: preparation, performance, and evaluation. The implementation stage starts with the basics of using Microsoft Excel. At the preparation stage, the community service team was surveyed to determine the conditions and analyse the places to use. Next, the team prepares the material to be given in providing training to community service participants. This service activity involves presentations, lectures with questions and answers, and practice. While delivering the material, the community service team conducted a question-and-answered session with the participants, who were expected to interact with the servants and the participants. The last stage is the evaluation stage. The community service team gives the participants time to make examples of inputting data and student grades in the rankings.
An efficient algorithm to update non-flat and incremental attributes in morphological trees

• An efficient algorithm to update non-flat and incremental attributes in morphological trees. • Concepts and properties about updating non-flat and incremental attributes in morphological trees. • Adaptations of filter rules to compute additional structures for updating non-flat and incremental attributes. • Our algorithm is suitable for improving computation of composition of attribute filters Attribute Filters are powerful image simplification operators that have very good contour-preservation properties. They can be efficiently computed using morphological trees. The most common strategy to compute attribute filters using morphological trees is based on three steps: ( i ) tree construction: the tree is built along with its attributes; ( i i ) filtering: we simplify the tree by removing some of its nodes based on some attribute and a filtering rule defined on a threshold value; and ( i i i ) image reconstruction: this is the final phase where the tree is converted back to an image leading to an attribute filter. However, after filtering a few attributes in tree may change (we call them as non-flat attributes) and they must be updated, when other attribute filters are applied to the simplified tree again, with either a different or the same threshold value. In this paper we present an efficient algorithm to update non-flat and incremental attributes in morphological trees with low memory consumption and fast computation.
Efficient Floating-point Division Quantum Circuit using Newton-Raphson Division

Abstract The development of quantum algorithms is facilitated by quantum circuit designs. A floating-point number can represent a wide range of values and is extremely useful in digital signal processing. A quantum circuit model to implement the floating-point division problem using the Newton-Raphson division algorithm is proposed in this paper. The proposed division circuit offers a significant savings in T-gates and qubits used in the circuit design when correlated with the state of art works proposed on fast division algorithms. The qubits savings are estimated around 17% and 20%, T-count savings are around 59.03% and 20.31%. Similarly, T-depth savings is estimated around 77.45% and 24.33% over the existing works.
Performance evaluation of MDKF based on state estimations for path tracking applications

Abstract One of the most difficult tasks for tracking in control systems in the presence of uncertainty is estimating the accuracy and efficiency of hidden variables. For tracking applications, the Kalman filter is the most extensively used estimation algorithm. However, tracking several objects remains a difficult effort in order to improve prediction and corrective results. For tracking several objects, a multi-dimensional Kalman filter (MDKF) based on state estimations is offered as a solution. This research also includes a performance analysis of the MDKF for tracking paths by modifying co-efficients of the steady-state and covariance equations. For linear dynamic systems, MDKF is evaluated. Path tracking using the Kalman filter and MDKF is also investigated. The true and filtered responses of MDKF filtering algorithm for path tracking are observed. After four trials, the output covariance yields steady state values. The simulation results reveal that our proposed filtering algorithm performs 2x better than a typical Kalman filter for objects moving in linear motion, indicating that it is suitable for real-time implementation.
Implementation of Quantum Key Distribution network simulation in Quantum Channel

Abstract The aim is to analyze the efficiency of a communication method, Quantum key distribution. This newly popularized method of communication uses the principles of quantum mechanics. The simulation environment allows researchers to create complex network topologies and a high degree of control and repeatable experiments, allowing them to conduct studies and verify their outcomes. It would be costly to implement QKD in reality, which would need optical and Internet connections between network nodes and the verification of a certain network method or protocol. Thus, the performance analysis here is done in open-source software called NS-3 or Network Simulator- 3, which has an in-built module called QKD netsim for creating the quantum channel.
Samplets: Construction and scattered data compression

We introduce the concept of samplets by transferring the construction of Tausch-White wavelets to scattered data. This way, we obtain a multiresolution analysis tailored to discrete data which directly enables data compression, feature detection and adaptivity. The cost for constructing the samplet basis and for the fast samplet transform, respectively, is O(N), where N is the number of data points. Samplets with vanishing moments can be used to compress kernel matrices, arising, for instance, in kernel based learning and scattered data approximation. The result are sparse matrices with only O(Nlog⁡N) remaining entries. We provide estimates for the compression error and present an algorithm that computes the compressed kernel matrix with computational cost O(Nlog⁡N). The accuracy of the approximation is controlled by the number of vanishing moments. Besides the cost efficient storage of kernel matrices, the sparse representation enables the application of sparse direct solvers for the numerical solution of corresponding linear systems. In addition to a comprehensive introduction to samplets and their properties, we present numerical studies to benchmark the approach. Our results demonstrate that samplets mark a considerable step in the direction of making large scattered data sets accessible for multiresolution analysis.
Development of Ontology for Knowledge of Traditions Common Culture of Countries in the Greater Mekong Subregion

The development of ontology is one important research area in the digital humanities. This study aims at creating a semantic search system for traditions common culture in the Greater Mekong Subregion (GMS) to solve problems in semantic gaps. This paper presents the second phase of the main research. It will present how to develop ontologies for the traditions and common culture in the GMS to gain a perspicuous understanding of the traditions and common culture of those countries in the region. A theoretical concept of seven steps for ontology development was applied by using an ontology editor called Hozo Ontology Editor. The main ontology found in this study included 15 main classes: common culture, history, belief, purpose, location, ritual, activity, literature, values, place, time, principle, person, equipment, and ethnic group. Traditions common culture is a subclass of common culture classes that were found to be related to all classes in ontology. This ontology will be useful for developing a semantic search system of the traditions common culture of the GMS in the next steps of the main study.
Engaging Stakeholders for Designing a FAIR Energy Data Management Tool: The Horizon 2020 EnerMaps Project

Energy transition deals with and starts from data and information, which are relevant for decision making and strategy implementation. Several stakeholders who deal with planning, energy management, and policy-making processes need findable, accessible, interoperable, and reusable (FAIR) data to solve professional issues. The Horizon 2020 (H2020) EnerMaps project contributes to providing FAIR data management. It aims to improve data availability, data quality, and data management for industry (especially renewable technology industry), energy planners, energy utilities, energy managers, energy consultants, public administration officers operating in the energy sector, policy decision makers, and social innovation experts. We apply a flow of methods to engage stakeholders for designing and operating a data management tool in the energy field—the EnerMaps Data Management Tool (EDMT). The methodologies applied include: stakeholder analysis, social network investigation, and semi-structured interviews to assemble user stories and needs. Far from being obvious, this type of analysis is capable of addressing the needs and challenges in the data sector, proposing an innovative tool. In this case, the main issues emerging are data quality (inclusive data normalization), the acquisition of datasets, and the deep understanding of data tools operation. In contrast, concerning the user needs inquiry, a number of topics emerge, such as the need to access datasets related to energy consumption and production, and several software-related needs, such as the possibility of normalizing and harmonizing the data.
Comparing QUBO Models of the Magic Square Problem for Quantum Annealing

QUBO (Quadratic Unconstrained Binary Optimization) has become the modeling language for quantum annealing and quantum-inspired annealing solvers. We present different modeling in QUBO of the Magic Square problem, which can be modeled by linear equations and a permutation constraint over integer variables. Different ways of encoding integers by Booleans in QUBO amounts to models that have very different performance. Experiments performed on the Fixstars Amplify Annealer Engine, a quantum-inspired annealing solver, show that using unary encoding for integers performs much better than using the classical one-hot encoding.
Multi-sensor target data detection method based on improved deep learning

Aiming at the problems of low detection accuracy and long detection time in the traditional multi-sensor target data detection method, a multi-sensor target data detection method based on improved deep learning is proposed. A regional nomination network model is built to locate all rectangular areas where multi-sensor targets may exist. A sample acquisition model is established by fusing multi-sensor target data in this area in combination with the storage structure of multi-sensor target data to collect multi-sensor target data and filter the collected target data. The histogram feature method is used to extract the processed multi-sensor target data features and the extracted multi-sensor target data features are input into the neural network training samples. The square error function is used to calculate the sample deviation and the deviation is corrected by the deviation gradient sinking method. Finally, the multi-sensor target data detection results are output. Simulation results show that the proposed method has high accuracy, low false detection rate and short detection time along with applicability.
The circular quantile residual

Circular-linear regression is often used to model the relationship between a circular dependent variable and a set of linear predictor variables. It is used in many areas such as meteorology, biology, and medicine. For checking model adequacy, it is desirable to use residuals that are approximately standard normally distributed. Most of the residuals used in circular regression models do not meet this requirement and are used especially for outlier identification. Other residuals are limited to the von Mises regression models. An asymptotically standard normally distributed residual that can be used for any parametric circular-linear regression model is introduced. Monte Carlo simulation studies suggest that the distribution of this residual is well approximated by the standard normal distribution in small samples. To study the behavior of this residual, two regression models are introduced, and two applications are used to show that the proposed residual can detect model misspecification.
DETEXA: Declarative Extensible Text Exploration and Analysis

Metadata enrichment through text mining techniques is becoming one of the most significant tasks in digital libraries. Due to the pandemic increase of open access publications, several new challenges have emerged. Raw data are usually big, unstructured, and come from heterogeneous data sources. In this paper, we introduce a text analysis framework which is implemented in extended SQL and exploits the scalability characteristics of modern database management systems. The purpose of this framework is to provide the opportunity to build performant end-to-end text mining pipelines which includes data harvesting, cleaning, processing, and text analysis at once. SQL is selected due to its declarative nature which offers fast experimentation and the ability to build APIs, so that domain experts can edit text mining workflows via easy-to-use graphical interfaces. Our experimental analysis demonstrates that the proposed framework is very effective and achieves significant speedup in common use cases compared to other popular approaches.
Design and Evaluation of a Mobile Application for an Italian UNESCO Site: Padova Urbs Picta

This paper presents the design, development and a first evaluation of a smartphone application aimed at promoting a cultural heritage site, Padua's fourteenth-century fresco cycle, which has been registered in the UNESCO World Heritage List under the lettering Padua Urbs Picta. The evaluation have been realized through a questionnaire submitted to a number of master students.
Holistic Graph-Based Representation and AI for Digital Library Management

The traditional record-based approach used in Digital Libraries has gone as far as it could. To support the needs and activities of different kinds of users, we propose a graph-based 'holistic' representation of DL knowledge, describing the documents' metadata, physical aspects, content, context and lifecycle. We also propose its implementation based on a framework in which Formal Ontologies play the role of data schemas for the LPG-based DBs. This enables automated reasoning on the DL data, and we propose the use of multiple inference strategies.
Enriching the Greek National Cultural Aggregator with Key Figures in Greek History and Culture: Challenges, Methodology, Tools and Outputs

Since 2015, SearchCulture.gr, the Greek cross-domain Cultural Data Aggregator, a service developed by the National Documentation Centre in Greece (EKT), has collected a growing number of 800.000 digitised Cultural Heritage Objects (CHOs) from 73 cultural institutions. Addressing metadata heterogeneity in order to be able to provide advanced search, browsing and filtering options to users has been a key target from the start. Controlled linked data vocabularies for item types, historical periods and themes were developed over the course of the past years and are being used for the semantic enrichment of the CHOs' metadata. In the current paper we present the challenges, the methodology and tools used over the past 2 years for the process of enriching the aggregated CHOs' metadata with person entities from a Linked Data vocabulary comprising over 8.200 entries concerning Greek persons that left some mark in history, society, science, letters and art that we created for that purpose. This latest development allows all the works relating to a person to be interlinked and semantically enriched, adds significant browse and search functionalities to the portal and, therefore, opens new horizons for Greek SSH research.
Conducting the Opera: The Evolution of the RDA Work to the Share-VDE Opus and BIBFRAME Hub

This paper examines recent developments in the use of Resource Description and Access Work, BIBFRAME Hub, and Share-VDE Opus (referred to collectively as Opera) in bibliographic description. These Opera will be discussed to capture the current state of developments in this area, but also make recommendations for a path forward as we reach a confluence with these parallel developments. With a new version of the Official RDA, and many libraries working towards BIBFRAME implementation this is a good point of reflection, where scientific analysis of conceptions of Opera have been further developed through initiatives at the Library of Congress (LC), and Share-VDE. Past discussions comparing the RDA and BIBFRAME models have focused on compatibility issues, but this paper will attempt at framing the differences as part of a developmental trajectory.
Got 404s? Crawling and Analyzing an Institution’s Web Domain

Link rot - disappearance of web resources - is detrimental to an institution's web presence, which is commonly used to communicate, for example, research highlights and organizational news. Organizations, especially taxpayer-funded ones such as the Los Alamos National Laboratory (LANL), therefore put emphasis on the availability and authenticity of their institutional record on the web. We conducted a web crawl of the lanl.gov domain and investigated the scale of missing resources and the ratio of resources recovered from public web archives. We found a noticeable number of special cases of link rot (soft404s) and transient errors, and had little success in recovering resources from web archives. We argue that, as an institution, we could become a better steward of our web content by establishing an institutional web archive to improve the availability and authenticity of web resources.
Overview Visualizations for Large Digitized Correspondence Collections: A Design Study

Overview visualization is a useful alternative to search engines in digital libraries. We describe such a tool in the context of a large correspondence collection: the Godin-Moret archive (20,000 letters). After a review of previous works, we describe our interface design and how it has been derived with the help of an online co-creation workshop. The interface is organized with a specific representation called the correspondence matrix, coordinated with more standard visualizations like tag clouds, maps and bar graphs.
Analyzing the Web: Are Top Websites Lists a Good Choice for Research?

The web has been a subject of research since its beginning, but it is difficult if not impossible to analyze the whole web, even if a database of all URLs would be freely accessible. Hundreds of studies have used commercial top websites lists as a shortcut, in particular the Alexa One Million Top Sites list. However, apart from the fact that Amazon decided to terminate Alexa, we question the usefulness of such lists for research as they have several shortcomings. Our analysis shows that top sites lists miss frequently visited websites and offer only little value for language-specific research. We present a heuristic-driven alternative based on the Common Crawl host-level web graph while also taking language-specific requirements into account.
Simulating User Querying Behavior Using Embedding Space Alignment

Simulation is used as a cost-efficient and repeatable means of experimentation to support Information Retrieval (IR) systems and digital libraries with more realistic directives when user interaction data is lacking. While simulation has been mainly used for the study of user search behaviours, we argue that the potential of using it for generating querying behavior is in its infancy. In this paper, we describe a pilot study to simulate query sessions via embedding alignment techniques and assess its capability of transferring knowledge from one source to the other. Besides, we validate the simulated query sessions using a cost and benefit evaluation task and investigate whether they can reproduce properties of real query sessions. We found that Skip-thought vectors are well suited for query embedding. In particular, using Skip-thought with vocabulary expansion as a support for embedding alignment techniques is effective in transferring contextualized embeddings that represent a user querying behavior. While the results of the performance evaluation are encouraging and comparable to related research, it is still challenging to simulate interactive query reformulations.
PH-Remix Prototype

Born in the complex and interdisciplinary scenario of digital culture, PH-Remix is a prototype of a web platform granting access and reuse of a vast amount of clips extracted from videos through AI techniques. The paper focuses both on the contribution of AI with the use of multiple machine learning algorithms specialized in the extraction of information from videos and on the possibilities derived from the use of a NoSQL database that plays a key role in the microservices architecture developed.