Reviewerly/exHarmony huggingface dataset:
Quantifying User Experience Through Self-reporting Questionnaires: A Systematic Analysis of the Sentence Similarity Between the Items of the Measurement Approaches

Standardized questionnaires are a common way to collect self-reported data about users. User experience (UX) questionnaires are metrics with multi-dimensional factors. These factors represent specific UX qualities based on items measuring the user’s subjective perception. However, UX questionnaires vary in factor sets, factors names, or measurement items. This study examined 705 items of such factors from 27 popular UX questionnaires. This study aimed to identify the underlying semantic similarity of UX factors on the measurement item level. Augmented SBERT was applied to measure the sentence similarity between the items. Highly similar items were then grouped based on their cosine similarity to identify similarity clusters by the Fast Clustering approach. As a result, 14 similarity clusters could be identified.
Personalized Cybersecurity Education: A Mobile App Proof of Concept

Online users are confronted with many privacy and security decisions every day. However, users often fail to understand online privacy, security risks and the importance of security solutions. Instead of using a “one-size-fits-all” approach in educating people about the security and privacy implications of their technology use to stay safe online as they often vary widely in their level of IT knowledge, personality traits etc., we developed an Android application that provides personalized cybersecurity training using a machine learning algorithm. The app recommends a variety of cybersecurity materials (e.g., videos, articles, tips) for each individual based on their personality traits and IT expertise. The project is a proof of concept, but has the potential to be extended for use by schools, government organizations, and businesses to personalize their cybersecurity training programs.
FUNgi: An Interactive Toy Designed for Young Children Based on e-Textile Material

Electronic toys are almost necessary in young child’s childhood as a companion. However, more and more electronic toys in the market can provide a richer sensory experience for children but still not soft and skin-friendly enough, and allow limited ways for children to interact with them. Eletronic textile, as a new type of intelligent material, has both characteristics of traditional fabric and the ability of communication. In this research, we want to create a soft toy with eletronic textiles. We focus on the textile characteristic of e-textiles, and utilize different colors and textures of them to guide children play naturally with their full body and obtain organic and synesthetic feedback. The research provides an alternative solution to improve the common hard shell electronic toys for young children, and extend the interactive modes of e-textiles.
Identification of the Types of Strategies for the Development of Social Competencies in Virtual Environments of Management Students in Colombian Universities

The present study is directed towards the identification of the types of strategies for the development of social competences in virtual environments of administration students at Colombian universities. The sample is taken from a group of 25 teachers, who teach in the business administration program of a private university located in the city of Barranquilla, Colombia. A questionnaire consisting of a total of 15 items applied to this sample, which was validated by experts and its reliability was determined by means of the Cronbach's Alpha coefficient, yielding a value of 0.84. Thus, the results show the indicators of the dimension Types of strategies for the development of social competence, which in turn are organized into indicators as follows: Pre-instructional Strategies, Co-instructional Strategies and Post-instructional Strategies. Each of these indicators obtained a respective weighting of 2.89, 3.35 and 2.40. In relation to the teaching strategies for the development of social competence, which constitute the object of study of the research presented, low frequencies were obtained for this variable in its two dimensions, which reveals that the teachers in the sample exhibit low levels of application of these strategies in the classroom. This situation highlights the need to train students in this type of competencies, given their impact on the achievement of school success.
A Context-Free Grammar Associated with Fibonacci and Lucas Sequences

We introduce a context-free grammar <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>G</mi> <mo>=</mo> <mfenced open="{" close="}" separators="|"> <mrow> <mi>s</mi> <mo>⟶</mo> <mi>s</mi> <mo>+</mo> <mi>d</mi> <mo>,</mo> <mi>d</mi> <mo>⟶</mo> <mi>s</mi> </mrow> </mfenced> </math> to generate Fibonacci and Lucas sequences. By applying the grammar <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mi>G</mi> </math> , we give a grammatical proof of the Binet formula. Besides, we use the grammar <math xmlns="http://www.w3.org/1998/Math/MathML" id="M3"> <mi>G</mi> </math> to provide a unified approach to prove several binomial convolutions about Fibonacci and Lucas numbers, which were given by Hoggatt, Carlitz, and Church. Meanwhile, we also obtain some new binomial convolutions.
Automated Nonverbal Cue Detection in Political-Debate Videos: An Optimized RNN-LSTM Approach

This study proposes a computational-video-analysis pipeline using OpenPose for keypoint detection, the RNN-LSTM network for constructing 12 gesture classifiers, and data augmentation and epoch early-stopping techniques for performance optimization. Through the measurement of accuracy, precision, recall, and F1 scores, this study compares three approaches (the vanilla approach, data-augmentation approach, and epoch-optimization approach), which gradually increase the model performance for all gesture features. The study suggests that a combination of data augmentation and epoch early-stopping techniques can effectively solve the imbalanced dataset problem faced by customized datasets and substantially increase the accuracy and F1 scores by 10–20%, achieving a satisfying accuracy of 70%–90% for most gesture detections.
Harnessing Online Knowledge Transfer for Enhanced Search and Rescue Decisions via Multi-Agent Reinforcement Learning

In the rapidly evolving domain of the Internet of Things (IoT), devices play an instrumental role in high-stakes scenarios like search and rescue (SAR) operations. Traditional decision-making processes within SAR missions often struggle to cope with the dynamic and unpredictable nature of such environments, leading to inefficiencies and delayed responses. This paper aims to explore the potential of multi-agent reinforcement learning (MARL) to improve the decision-making process within SAR operations underpinned by IoT. Functional, current methods are limited by their static decision frameworks and inability to adapt in real time to the chaotic variables present in SAR situations. We introduced a novel MARL framework and compared its performance against benchmark strategies, specifically the multi-agent deep deterministic policy gradient (MADDPG) approach. Uniquely enhanced by online knowledge transfer, the framework leverages the capabilities of the deep deterministic policy gradient (DDPG) method. The preliminary findings underscore the proposed framework’s superior efficiency and speed in SAR contexts. Our research highlights MARL’s transformative potential, positing it as a groundbreaking strategy for IoT-based decision making in high-pressure SAR environments with suggestions for further studies in varied real-world scenarios.
New Trends Based on 4.0 Technologies for the Study of Juvenile Crime: A Bibliometric Study

The present study is directed towards identifying the new scientific trends based on 4.0 technologies for the study of juvenile crime in the Scopus database. For the development of this research, a descriptive and quantitative method is proposed, based on bibliometric tools that allow to effectively measure the scientific production of the variables juvenile crime and technology 4.0 within the Scopus database. This process then requires the approach of a search equation that covers the crucial elements of the study, resulting in the following command: (TITLE-ABS-KEY (“Youth in conflict with the law”) OR TITLE-ABS-KEY (“Juvenile delinquency”) OR TITLE-ABS-KEY ( youth AND crime) OR TITLE-ABS-KEY (underprivileged AND youth) AND TITLE-ABS-KEY (big AND data) OR TITLE-ABS-KEY (data AND mining) OR TITLE-ABS-KEY (4.0) OR TITLE-ABS-KEY (intelligent AND systems) OR TITLE-ABS-KEY (machine AND learning) OR TITLE-ABS-KEY (artificial AND intelligence) OR TITLE-ABS-KEY (iot) OR TITLE-ABS-KEY (“internet of things”)). From an initial search, a total of 85 documents published between 2011 and 2023 are observed, of which more than 58,8% have been submitted from 2020 to date. The leaders in terms of publication in the area are University of Pittsburgh and King Khalid University with five publications each.
On the Relationship between Feature Selection Metrics and Accuracy

Feature selection metrics are commonly used in the machine learning pipeline to rank and select features before creating a predictive model. While many different metrics have been proposed for feature selection, final models are often evaluated by accuracy. In this paper, we consider the relationship between common feature selection metrics and accuracy. In particular, we focus on misorderings: cases where a feature selection metric may rank features differently than accuracy would. We analytically investigate the frequency of misordering for a variety of feature selection metrics as a function of parameters that represent how a feature partitions the data. Our analysis reveals that different metrics have systematic differences in how likely they are to misorder features which can happen over a wide range of partition parameters. We then perform an empirical evaluation with different feature selection metrics on several real-world datasets to measure misordering. Our empirical results generally match our analytical results, illustrating that misordering features happens in practice and can provide some insight into the performance of feature selection metrics.
Employing online modalities in the universities within the post-Covid-19 and 4IR Era

The psychological, behavioral, and technological impacts of adopting online modalities in the Universities within the post-COVID-19 and 4IR era were assessed in this paper. The current researcher obtained informed consent from the 125 academic staff members from five universities in four provinces—the University of Pretoria, University of Johannesburg, Durban University of Technology, University of Western Cape, and North-West University—through a cross-sectional survey methodology. This study's researcher promoted voluntary participation and ensured ethical standards were respected. In total, 116 surveys were located and completed. Statistical Packages for Social Sciences (SPSS) version 29 was used to clean and analyze the data. This study illustrates how willingness to use online modalities inside universities during the post-COVID-19 and Fourth Industrial Revolution (4IR) is highly influenced by a readiness to change, innovative work behavior, and technological competence. This study demonstrated that adopting online modalities in South African universities during the post-COVID-19 and 4IR periods was significantly influenced by technological proficiency, innovative behavior, and willingness to change.
Sample efficient graph classification using binary Gaussian boson sampling

We present a variation of a quantum algorithm for the machine learning task of classification with graph-structured data. The algorithm implements a feature extraction strategy that is based on Gaussian boson sampling (GBS), a near-term model of quantum computing. However, unlike the currently proposed algorithms for this problem, our GBS setup requires only binary (light or no light) detectors, as opposed to photon-number-resolving detectors. Binary detectors are technologically simpler and can operate near room temperature, making our algorithm much less complex and costly to implement physically. We also investigate the connection between graph theory and the Torontonian matrix function which characterizes the probabilities of binary GBS detection events.
Exploring ququart computation on a transmon using optimal control

Contemporary quantum computers encode and process quantum information in binary qubits $(d=2)$. However, many architectures include higher energy levels that are left as unused computational resources. We demonstrate a superconducting ququart $(d=4)$ processor and combine quantum optimal control with efficient gate decompositions to implement high-fidelity ququart gates. We distinguish between viewing the ququart as a generalized four-level qubit and an encoded pair of qubits, and characterize the resulting gates in each case. In randomized benchmarking experiments we observe gate fidelities $\ensuremath{\ge}95%$ and identify coherence as the primary limiting factor. Our results validate ququarts as a viable tool for quantum information processing.
Adaptive low-depth quantum algorithms for robust multiple-phase estimation

This paper is an algorithmic study of quantum phase estimation with multiple eigenvalues. We present robust multiple-phase estimation (RMPE) algorithms with Heisenberg-limited scaling. The proposed algorithms improve significantly from the idea of single-phase estimation methods by combining carefully designed signal processing routines and an adaptive determination of runtime amplifying factors. They address both the integer-power model, where the unitary $U$ is given as a black box with only integer runtime accessible, and the real-power model, where $U$ is defined through a Hamiltonian $H$ by $U=exp(\ensuremath{-}2\ensuremath{\pi}\mathrm{i}H)$ with any real runtime allowed. These algorithms are particularly suitable for early fault-tolerant quantum computers in the following senses: (1) a minimal number of ancilla qubits are used, (2) an imperfect initial state with a significant residual is allowed, (3) the prefactor in the maximum runtime can be arbitrarily small given that the residual is sufficiently small and a gap among the dominant eigenvalues is known in advance. Even if the eigenvalue gap does not exist, the proposed RMPE algorithms can achieve the Heisenberg limit while maintaining (1) and (2).
OpenApePose, a database of annotated ape photographs for pose estimation

Because of their close relationship with humans, non-human apes (chimpanzees, bonobos, gorillas, orangutans, and gibbons, including siamangs) are of great scientific interest. The goal of understanding their complex behavior would be greatly advanced by the ability to perform video-based pose tracking. Tracking, however, requires high-quality annotated datasets of ape photographs. Here we present OpenApePose , a new public dataset of 71,868 photographs, annotated with 16 body landmarks of six ape species in naturalistic contexts. We show that a standard deep net (HRNet-W48) trained on ape photos can reliably track out-of-sample ape photos better than networks trained on monkeys (specifically, the OpenMonkeyPose dataset) and on humans ( COCO ) can. This trained network can track apes almost as well as the other networks can track their respective taxa, and models trained without one of the six ape species can track the held-out species better than the monkey and human models can. Ultimately, the results of our analyses highlight the importance of large, specialized databases for animal tracking systems and confirm the utility of our new ape database.
Quantifying the impact of uninformative features on the performance of supervised classification and dimensionality reduction algorithms

Machine learning approaches have become critical tools in data mining and knowledge discovery, especially when attempting to uncover relationships in high-dimensional data. However, researchers have noticed that a large fraction of features in high-dimensional datasets are commonly uninformative (too noisy or irrelevant). Because optimal feature selection is an NP-hard task, it is essential to understand how uninformative features impact the performance of machine learning algorithms. Here, we conduct systematic experiments on algorithms from a wide range of taxonomy families using synthetic datasets with different numbers of uninformative features and different numbers of patterns to be learned. Upon visual inspection, we classify these algorithms into four groups with varying robustness against uninformative features. For the algorithms in three of the groups, we find that when the number of uninformative features exceeds the number of data instances per pattern to be learned, the algorithms fail to learn the patterns. Finally, we investigate whether increasing the distinguishability of patterns or adding training instances can mitigate the effect of uninformative features. Surprisingly, we find that uninformative features still cause algorithms to suffer big losses in performance, even when patterns should be easily distinguishable. Analyses of real-world data show that our conclusions hold beyond the synthetic datasets we study systematically.
Unveiling Ethereum’s Future: LSTM-Based Price Prediction and a Systematic Blockchain Analysis

Cryptocurrency has emerged as a revolutionary innovation that has been replacing traditional finances and enthralling the worldwide technology landscape. This has gained a lot of popularity worldwide for its potential to enable peer-to-peer transactions and offer opportunities for investment and novelty. Nevertheless, it gives rise to issues concerning regulatory adherence, instability, and security apprehensions, turning them into a topic of continuous evaluation and investigation within the fields of finance and technology. This research paper presents a comprehensive exploration of the historical evolution of “Ethereum” as one of the leading blockchain platforms, with a primary focus on price prediction using a long-short-term memory (LSTM) machine learning model. The study includes various critical aspects of Ethereum, starting from its historical evolution to its potential future scope in scaling solutions and payments, and also covering the insights of Ethereum’s tokenomics, utility, and beyond. In addition, the methodology involves using the LSTM model to analyze data from Ethereum. The accuracy of price predictions is assessed by evaluating error metrics and further improved by visualizing the data through graphs that show indicators. This paper gives an in-depth perspective for anyone who is seeking a holistic understanding of cryptocurrencies, mainly concentrated on Ethereum, and also provides valuable guidance to investors, developers, and enthusiasts, encouraging them to make knowledgeable decisions in the everchanging blockchain ecosystem.
Multipurpose optimization of yield production in agriculture using fuzzy self-reproducing automata theory

Abstract In determining the irrigation depth of barley, it is inevitable to find the best periods to increase the efficiency of water consumption and also to achieve the highest yield of the product. A multi-objective optimization model has been presented to improve irrigation planning and the allowable amount of irrigation during the growth period using genetic algorithm based on nondominated sorting (NSGAII) and cellular automata. Under this concept, the structure of the optimal water supply allocation model is included in the form of two main objective functions. Therefore, the first objective function is to minimize water allocation and the second objective function is to maximize the total income from the cultivation pattern compared to its costs. The latest data related to the cultivation pattern and economic information such as product sales price and production costs in the planting and harvesting stages were collected for 1 year of study. The daily data of river flow, rainfall and climatic data of Hulunbuir district in Inner Mongolia province were converted into 10-day periods. It shows the optimal irrigation planning results of winter barley in three different scenarios. In ten periods of growth, the rainfall is enough to provide most of the plants’ water needs.
Door-Opening Technologies: Search for Affordable Assistive Technology

To the authors’ knowledge, currently, there is no review covering the different technologies applied to opening manual doors. Therefore, this review presents a summary of the various technologies available on the market as well as those under research and development for opening manual doors. Four subtopics—doorknob accessories, wheelchair-mounted door-opening accessories, door-opening robots, and door-opening drones—were used to group the various technologies for manually opening doors. It is evident that opening doors is a difficult process, and there are different ways to solve this problem in terms of the technology used and the cost of the end product. The search for an affordable assistive technology for opening manual doors is ongoing. This work is an attempt to provide wheelchair users and their healthcare providers with a one-stop source for door-opening technologies. At least one of these door-opening solutions could prove beneficial to the elderly and some wheelchair users for increased independence. The ideal option would depend on an individual’s needs and capabilities, and occupational therapists could assess and recommend the right solutions.
A Bi-directional Attention Based End to End Mispronunciation Detection and Diagnosis of Mandarin

Abstract An increasing number of individuals are acquiring proficiency in Mandarin, signifying the growing significance of employing computer-assisted pronunciation training systems for Mandarin learners. One pivotal component within these systems is the technique for identifying and addressing mispronunciations, known as Mispronunciation Detection and Diagnosis (MDD). Recently, certain end-to-end techniques have tried to fuse features of prompt text and acoustic features into the model for training and have shown good results. However, previous approaches have fused acoustic features with prompt text features by a simple attention mechanism. In this paper, we posit that the impact of text features varies significantly when mapped to distinct acoustic characteristics. Furthermore, we propose that the prompt text can lead the model towards achieving an integrated text-audio representation, thereby enhancing the inference quality. Hence, this article presents a model aimed at detecting and diagnosing mispronunciations. The model utilizes a bidirectional attention mechanism to integrate acoustic and prompt text features. Good results were achieved by conducting experiments on a self-built dataset of short Mandarin read-aloud texts.