Reviewerly/exHarmony huggingface dataset:
Validation of two measures for assessing English vocabulary knowledge on web-based testing platforms: long-form assessments

Abstract The goal of the current work was to develop and validate web-based measures for assessing English vocabulary knowledge. Two existing paper-and-pencil assessments, the Vocabulary Size Test (VST) and the Word Familiarity Test (WordFAM), were modified for web-based administration. In Experiment 1, participants ( n = 100) completed the web-based VST. In Experiment 2, participants ( n = 100) completed the web-based WordFAM. Results from these experiments confirmed that both tasks (1) could be completed online, (2) showed expected sensitivity to English frequency patterns, (3) exhibited high internal consistency, and (4) showed an expected range of item discrimination scores, with low frequency items exhibiting higher item discrimination scores compared to high frequency items. This work provides open-source English vocabulary knowledge assessments with normative data that researchers can use to foster high quality data collection in web-based environments.
Validation of two measures for assessing English vocabulary knowledge on web-based testing platforms: brief assessments

Abstract Two measures for assessing English vocabulary knowledge, the Vocabulary Size Test (VST) and the Word Familiarity Test (WordFAM), were recently validated for web-based administration. An analysis of the psychometric properties of these assessments revealed high internal consistency, suggesting that stable assessment could be achieved with fewer test items. Because researchers may use these assessments in conjunction with other experimental tasks, the utility may be enhanced if they are shorter in duration. To this end, two “brief” versions of the VST and the WordFAM were developed and submitted to validation testing. Each version consisted of approximately half of the items from the full assessment, with novel items across each brief version. Participants ( n = 85) completed one brief version of both the VST and the WordFAM at session one, followed by the other brief version of each assessment at session two. The results showed high test-retest reliability for both the VST ( r = 0.68) and the WordFAM ( r = 0.82). The assessments also showed moderate convergent validity (ranging from r = 0.38 to 0.59), indicative of assessment validity. This work provides open-source English vocabulary knowledge assessments with normative data that researchers can use to foster high quality data collection in web-based environments.
A novel design and simulation of a mechanical coordinate based photovoltaic solar tracking system

&lt;abstract&gt; &lt;p&gt;Various methods have been developed to increase electrical energy production gains in photovoltaic (PV) systems. These can be classified as solar tracking systems, cooling systems and methods of reducing the effect of shading. In order to maximise the PV energy yield, the PV systems must follow the sun. In this study, the effect of solar tracking systems on the energy yield gains of PV systems is investigated, and various types of solar tracking systems are discussed in detail. To ensure accuracte tracking of the postion of the sun, a new, low-cost, system has been developed that employs a global positioning system (GPS) module, compass and accelerometer. With this necessary angle information a dual-axis coordinate-based solar tracking system was designed using the Arduino Mega 2560 microcontroler with home-built control software. The system is validated by comparing it to a fixed angle system and an energy yield gain of 33–38% is found.&lt;/p&gt; &lt;/abstract&gt;
Skill hierarchy structure based skill assessment and learning paths

AbstractInstructional assessment and learning paths are essential for improving students' learning effectiveness. This paper explores enhancing learners' learning efficiency and quality from the perspective of the correlation between skills. Firstly, the concept of the skill hierarchy structure and valid skill combination is introduced, followed introducted the method of constructing a question bank based on the skill hierarchy structure. Secondly, the skill hierarchy structure is utilized to induce skill context and provide the competence base for the skill context. Finally, the method for assessing the skills mastered by the learner is provided in the case of a known knowledge state of learners, and the learning paths diagram suitable for the learner's personalized situation is constructed. Experimental results demonstrate that the competence structure derived from the induced skill context based on skill hierarchy structure meets the requirements of being well-graded. Furthermore, the proposed methods in this paper facilitate learners in effectively mastering and applying learned skills, thereby establishing a solid foundation and improving their learning efficiency.KEYWORDS: Skill hierarchy structurevalid skill combinationskill contextcompetence baseskill assessmentlearning paths Disclosure statementNo potential conflict of interest was reported by the author(s).
Wyner-Ziv Estimators for Distributed Mean Estimation with Side Information and Optimization

Communication efficient distributed mean estimation is an important primitive that arises in many distributed learning and optimization scenarios such as federated learning. Without any probabilistic assumptions on the underlying data, we study the problem of distributed mean estimation where the server has access to side information. We propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Wyner-Ziv estimators</i> , which are communication and computationally efficient and near-optimal when an upper bound for the distance between the side information and the data is known. As a corollary, we also show that our algorithms provide efficient schemes for the classic Wyner-Ziv problem in information theory. In a different direction, when there is no knowledge assumed about the distance between side information and the data, we present an alternative Wyner-Ziv estimator that uses correlated sampling. This latter setting offers <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">universal recovery guarantees</i> , and perhaps will be of interest in practice when the number of users is large and keeping track of the distances between the data and the side information may not be possible. With this mean estimator at our disposal, we revisit basic problems in decentralized optimization and compression where our Wyner-Ziv estimator yields algorithms with almost optimal performance. First, we consider the problem of communication constrained distributed optimization and provide an algorithm which attains the optimal convergence rate by exploiting the fact that the gradient estimates are close to each other. Specifically, the gradient compression scheme in our algorithm first uses half of the parties to form side information and then uses our Wyner-Ziv estimator to compress the remaining half of the gradient estimates. Finally, we apply our Wynzer-Ziv estimators to the classic Wyner-Ziv compression problem in information theory to get compression schemes that are computationally efficient and are almost optimal under much more relaxed assumptions than the standard probabilistic setting.
A kernel-free L1 norm regularized ν-support vector machine model with application

With a view to overcoming a few shortcomings resulting from the kernel-based SVM models, these kernel-free support vector machine (SVM) models are newly promoted and researched. With the aim of deeply enhancing the classification accuracy of present kernel-free quadratic surface support vector machine (QSSVM) models while avoiding computational complexity, an emerging kernel-free ν-fuzzy reduced QSSVM with L1 norm regularization model is proposed. The model has well-developed sparsity to avoid computational complexity and overfitting and has been simplified as these standard linear models on condition that the data points are (nearly) linearly separable. Computational tests are implemented on several public benchmark datasets for the purpose of showing the better performance of the presented model compared with a few known binary classification models. Similarly, the numerical consequences support the more elevated training effectiveness of the presented model in comparison with those of other kernel-free SVM models. What`s more, the presented model is smoothly employed in lung cancer subtype diagnosis with good performance, by using the gene expression RNAseq-based lung cancer subtype (LUAD/LUSC) dataset in the TCGA database.
veffChain: Enabling Freshness Authentication of Rich Queries over Blockchain Databases

With the wide adoption of blockchains in data-intensive applications, enabling verifiable queries over a blockchain database is urgently required. Aiming at reducing costs, previous solutions embed a small-sized authenticated data structure (ADS) in each block header, so that a user can verify search results without maintaining a full copy of blockchain databases. However, existing studies focus on exact queries with difficulty to guarantee the freshness of search results. In this paper, we propose two frameworks, called <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math>${\sf veffChain}$</tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math>${\sf veffChain++}$</tex-math></inline-formula> , to realize freshness authentication of rich queries over blockchain databases. Specifically, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math>${\sf veffChain}$</tex-math></inline-formula> concerns about verifiable latest- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$K$</tex-math></inline-formula> exact queries and employs RSA accumulator to generate constant-size ADSs; <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math>${\sf veffChain++}$</tex-math></inline-formula> integrates RSA accumulator into the Trie tree to further authenticate latest- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$K$</tex-math></inline-formula> fuzzy queries. For improved scalability, an adaptive keyword splitting (AKS) solution is proposed to enable ADSs to be incrementally updated. Compared with the state-of-the-art work, our frameworks have the following merits: (1) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Freshness Guarantee</i> . The user can efficiently retrieve the freshest data from a blockchain database in a verifiable way. (2) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Flexibility</i> . The user can specify different query patterns on demand to retrieve data as accurately as possible. The detailed security analysis and extensive experiments validate the practicality of our frameworks.
SegT: Separated edge-guidance transformer network for polyp segmentation

&lt;abstract&gt;&lt;p&gt;Accurate segmentation of colonoscopic polyps is considered a fundamental step in medical image analysis and surgical interventions. Many recent studies have made improvements based on the encoder-decoder framework, which can effectively segment diverse polyps. Such improvements mainly aim to enhance local features by using global features and applying attention methods. However, relying only on the global information of the final encoder block can result in losing local regional features in the intermediate layer. In addition, determining the edges between benign regions and polyps could be a challenging task. To address the aforementioned issues, we propose a novel separated edge-guidance transformer (SegT) network that aims to build an effective polyp segmentation model. A transformer encoder that learns a more robust representation than existing convolutional neural network-based approaches was specifically applied. To determine the precise segmentation of polyps, we utilize a separated edge-guidance module consisting of separator and edge-guidance blocks. The separator block is a two-stream operator to highlight edges between the background and foreground, whereas the edge-guidance block lies behind both streams to strengthen the understanding of the edge. Lastly, an innovative cascade fusion module was used and fused the refined multi-level features. To evaluate the effectiveness of SegT, we conducted experiments with five challenging public datasets, and the proposed model achieved state-of-the-art performance.&lt;/p&gt;&lt;/abstract&gt;
Progressive dilation dense residual fusion network for single‐image deraining

Abstract Rain removal is very important for many applications in computer vision, and it is a challenging problem due to its ill‐posed nature, especially for single‐image deraining. In order to remove rain streaks more thoroughly, as well as to retain more details, a progressive dilation dense residual fusion network is proposed. The entire network is designed in a cascade manner with multiple fusion blocks. The fusion block consists of a dilation dense residual block (DDRB) and a dense residual feature fusion block (DRFFB), where DDRB is created for feature extraction and DRFFB is mainly designed for feature fusion operation. Meanwhile, detail compensation memory mechanism (DCMM) is leveraged between each of two cascade modules to retain more background details. Compared with previous state‐of‐the‐art methods, extensive experiments show that the proposed method can achieve better results, in terms of rain streaks removal and background details preservation. Furthermore, the authors’ network also shows its superiority for image noise removal.
Fast, High-Fidelity Addressed Single-Qubit Gates Using Efficient Composite Pulse Sequences

We use electronic microwave control methods to implement addressed single-qubit gates with high speed and fidelity, for $^{43}{\mathrm{Ca}}^{+}$ hyperfine ``atomic clock'' qubits in a cryogenic (100 K) surface trap. For a single qubit, we benchmark an error of $1.5\ifmmode\times\else\texttimes\fi{}{10}^{\ensuremath{-}6}$ per Clifford gate (implemented using 600 ns $\ensuremath{\pi}/2$ pulses). For 2 qubits in the same trap zone (ion separation $5\text{ }\text{ }\mathrm{\ensuremath{\mu}}\mathrm{m}$), we use a spatial microwave field gradient, combined with an efficient four-pulse scheme, to implement independent addressed gates. Parallel randomized benchmarking on both qubits yields an average error $3.4\ifmmode\times\else\texttimes\fi{}{10}^{\ensuremath{-}5}$ per addressed $\ensuremath{\pi}/2$ gate. The scheme scales theoretically to larger numbers of qubits in a single register.
Hardware-Efficient Entangled Measurements for Variational Quantum Algorithms

Variational algorithms have received significant attention in recent years due to their potential to solve practical problems using noisy intermediate-scale quantum (NISQ) devices. A fundamental step of these algorithms is the evaluation of the expected value of Hamiltonians, and hence efficient schemes to perform this task are required. The standard approach employs local measurements of Pauli operators and requires a large number of circuits. An alternative is to use entangled measurements, which might introduce additional gates between physically disconnected qubits that harm the performance. To solve this problem, we propose hardware-efficient entangled measurements (HEEMs), that is, measurements that permit only entanglement between physically connected qubits. We show that this strategy enhances the evaluation of molecular Hamiltonians in NISQ devices by reducing the required circuits without increasing their depth. We provide quantitative metrics of how this approach offers better results than local measurements and arbitrarily entangled measurements. We estimate the ground-state energy of the ${\mathrm{H}}_{2}\mathrm{O}$ molecule with classical simulators and quantum hardware using the variational quantum eigensolver with HEEM.
Scale Invariant Deep Neural Multiple Feature Learning Based Boosted Support Vector Entropy Classification for Breast Cancer Diagnosis using Mammograms

An earlier diagnosis of cancer rises the living days of patients. The evolution of medical systems supports to identify the presence of cancer. In the state-of-the-art works, few methods were implemented for diagnosing breast cancer with different Deep Neural Network (DNN) support. However, the traditional DNN techniques were computationally expensive as it does provide better feature extraction performance with minimal time usage. Besides that, the misclassification error observed during the diagnosis process was higher, impacting the accuracy of early breast cancer prediction. Therefore, a novel Scale Invariant Deep Feature Learning Based Boosted Support Vector Entropy Classification (SIDFL-BSVEC) method is introduced. The SIDFL-BSVEC method is proposed to get better accuracy for the early identification of breast cancer with lesser time. The SIDFL-BSVEC method initially designs Scale Invariant Deep Neural Multiple Feature Extraction (SIDNMFE) algorithms to discover critical features in input mammograms with minimal time by working as robust to illumination variations, noise, partial occlusion, and minor viewpoint changes in mammograms images. These traits are significant for early cancer disease prediction when cells in the mammogram image have dissimilar sizes and orientations. After that, Support Vector Entropy Boosted Cancer Classifier (SVEBCC) algorithm is designed to minimize the error rate determined during the early analysis of breast cancer. The performance of the proposed CWDCMFE-MBRC technique is verified by taking the parameters such as disease diagnosis accuracy, disease diagnosis time, sensitivity, specificity and error rate along with various numbers of input images.
Detection of emotion by text analysis using machine learning

Emotions are an integral part of human life. We know many different definitions of emotions. They are most often defined as a complex pattern of reactions, and they could be confused with feelings or moods. They are the way in which individuals cope with matters or situations that they find personally significant. Emotion can also be characterized as a conscious mental reaction (such as anger or fear) subjectively experienced as a strong feeling, usually directed at a specific object. Emotions can be communicated in different ways. Understanding the emotions conveyed in a text or speech of a human by a machine is one of the challenges in the field of human-machine interaction. The article proposes the artificial intelligence approach to automatically detect human emotions, enabling a machine (i.e., a chatbot) to accurately assess emotional state of a human and to adapt its communication accordingly. A complete automation of this process is still a problem. This gap can be filled with machine learning approaches based on automatic learning from experiences represented by the text data from conversations. We conducted experiments with a lexicon-based approach and classic methods of machine learning, appropriate for text processing, such as Naïve Bayes (NB), support vector machine (SVM) and with deep learning using neural networks (NN) to develop a model for detecting emotions in a text. We have compared these models' effectiveness. The NN detection model performed particularly well in a multi-classification task involving six emotions from the text data. It achieved an F1-score = 0.95 for sadness, among other high scores for other emotions. We also verified the best model in use through a web application and in a Chatbot communication with a human. We created a web application based on our detection model that can analyze a text input by web user and detect emotions expressed in a text of a post or a comment. The model for emotions detection was used also to improve the communication of the Chatbot with a human since the Chatbot has the information about emotional state of a human during communication. Our research demonstrates the potential of machine learning approaches to detect emotions from a text and improve human-machine interaction. However, it is important to note that full automation of an emotion detection is still an open research question, and further work is needed to improve the accuracy and robustness of this system. The paper also offers the description of new aspects of automated detection of emotions from philosophy-psychological point of view.
Limits of humanoid robots based on a self-literature review of AI’s Limits

The aim of this paper is to identify some of the limitations of humanoid robots by applying the oriented self-literature review (o.SLR) methodology, which we propose in this study. Thus, three limits of humanoid robots have been identified in relation to human behavior that relate to self-interested entropy and gravity management (1), algorithms (2), and the integration of affective logic in conjunction with the replication/emulation of human senses (3). The originality of the work is embodied in the proposed o.SLR methodology, and in the updating of the limits.
Understanding Organizational Knowledge Loss (OKL): A Bibliometric Analysis

Purpose – Organizational knowledge loss (OKL) is an urgent problem in the field of knowledge management because it can have a negative impact on intellectual capital and leads to significant competitive disadvantages for the companies concerned. In academic research, this phenomenon is considered a consequence of knowledge risks, especially when experts possess valuable knowledge and leave a knowledge-intensive organization. However, a basic understanding of the conceptual structure of OKL remains elusive. Methodology – To address the existing knowledge gap, we conducted a bibliometric analysis based on a network and factor-oriented approach to identify the key themes, contexts, and patterns of OKL. For this purpose, we obtained bibliometric data from the Web of Science and the Scopus databases. Using the advanced bibliometrics package Biblioshiny in R, we conducted performance analysis and science mapping. Findings – Based on our four research questions, we present the following key findings: First, we find that annual science production in the field of OKL research over the period 2004 to 2023 peaks in 2022. Second, we found that absorptive capacity (AC) is increasingly becoming a central theme in OKL research. Third, the average citation rate per year for OKL research has declined noticeably since 2020. Fourth, the most influential publications in OKL research deal with AC in the context of strategic alliances. Research limitations – This study focuses solely on researching the conceptual structure and thus does not consider the intellectual and social structure of OKL. Practical implications – In view of the results obtained, we recommend that future research work should further investigate the relationship between OKL as a focus of organizational forgetting and AC as a learning ability in an innovation environment. Originality/value – This paper is insightful, as it reveals the conceptual structure and thematic evolution of OKL and directs research interest to the field of learning organizations.