Reviewerly/exHarmony huggingface dataset:
An Idiom Reading Comprehension Model Based on Multi-Granularity Reasoning and Paraphrase Expansion

Idioms are a unique class of words in the Chinese language that can be challenging for Chinese machine reading comprehension due to their formal simplicity and the potential mismatch between their literal and figurative meanings. To address this issue, this paper adopted the “2 + 2” structure as the representation model for idiom structure feature extraction. According to the linguistic theory of idioms, to enhance the model’s learning ability for idiom semantics, we propose a two-stage semantic expansion method that leverages semantic knowledge during the pre-training stage and extracts idiom interpretation information during the fine-tuning stage to improve the model’s understanding of idioms. Moreover, with the consideration of the inferential interaction between global and local information during context fusion, which is neglected by all current works, we propose a method that utilizes a multi-headed attention mechanism to fully extract global and local information by selecting three attention patterns in the fine-tuning stage. This enables the fusion of semantic information extracted by the model at different granularities, leading to improved accuracy. The experimental results demonstrated that our proposed BERT-IDM model outperformed the baseline BERT model by achieving a 4.1% accuracy improvement.
A space‐time model with temporal cyclostationarity for probabilistic forecasting and simulation of solar irradiance data

To establish a more sustainable future, Saudi Arabia is striving to reduce its dependency on fossil fuels and promote renewable energies. Solar energy is a major resource in Saudi Arabia because of the country's geographical location with year‐round clear skies and plentiful sunlight. However, although solar energy is a clean and safe renewable energy resource, it can be quite unpredictable. Therefore, solar irradiance needs to be forecasted and simulated as accurately as possible on both regional and national scales in the Kingdom to assist in planning for reserve usage, switching sources and short‐term power purchases. Based on an hourly solar diffuse horizontal irradiance (DHI) dataset from 45 different Saudi Arabian solar monitoring stations, this study proposes a novel spatio‐temporal model. We identify the temporal dependency of DHI as cyclostationary and incorporate this key observation in the model. This feature helps yield accurate (i.e. significantly close to the ground truth and with narrow confidence bands) probabilistic forecasts and realistic simulations in near‐future time points and at new locations. Then, the proposed model is compared with a stationary model that fails to recognize the cyclostationarity structure in the data. The proposed model produces significantly better predictions and simulations than the stationary model. Further, we calculate the photovoltaic power outputs using the simulated samples from both the models and the original observations and then compare them. The simulated samples from the proposed model afford photovoltaic power output estimates that are significantly closer to the original observed data than those from the stationary model, and therefore, they can better assist photovoltaic power output operators in assessing solar energy production and operational pre‐planning to mitigate impacts by the uncertain nature of solar irradiance.
Multi-Order-Content-Based Adaptive Graph Attention Network for Graph Node Classification

In graph-structured data, the node content contains rich information. Therefore, how to effectively utilize the content is crucial to improve the performance of graph convolutional networks (GCNs) on various analytical tasks. However, current GCNs do not fully utilize the content, especially multi-order content. For example, graph attention networks (GATs) only focus on low-order content, while high-order content is completely ignored. To address this issue, we propose a novel graph attention network with adaptability that could fully utilize the features of multi-order content. Its core idea has the following novelties: First, we constructed a high-order content attention mechanism that could focus on high-order content to evaluate attention weights. Second, we propose a multi-order content attention mechanism that can fully utilize multi-order content, i.e., it combines the attention mechanisms of high- and low-order content. Furthermore, the mechanism has adaptability, i.e., it can perform a good trade-off between high- and low-order content according to the task requirements. Lastly, we applied this mechanism to constructing a graph attention network with structural symmetry. This mechanism could more reasonably evaluate the attention weights between nodes, thereby improving the convergence of the network. In addition, we conducted experiments on multiple datasets and compared the proposed model with state-of-the-art models in multiple dimensions. The results validate the feasibility and effectiveness of the proposed model.
Citation Gecko technology for research and entrepreneurship

Purpose This study aims to investigate how Citation Gecko technology might help libraries provide better research support services. The paper explains specifically how Citation Gecko can help libraries provide more precise and thorough references, save time and enable methodical literature reviews and scoping studies amid the constantly evolving nature of research domains. The paper also considers how businesses and libraries can gain from implementing the technology. Design/methodology/approach The paper’s conceptual study of the function of librarians in assisting researchers – particularly in the setting of exhaustive literature reviews and scoping studies – forms the foundation of the piece. The authors talk about the difficulties in guaranteeing the authenticity and comprehensiveness of the literature review process and the significance of adopting tools like Citation Gecko to help with this process. The possible adoption of Citation Gecko in libraries is also forecasted using the technological adoption model paradigm. Findings Libraries that use Citation Gecko technology provide better research support services by providing accurate and comprehensive references, saving time and assisting systematic literature surveys and scoping studies. The tool is simple to use and does not require professional training due to its user-friendly interface and ability to produce references fast. Using Citation Gecko technology can enhance libraries’ capacities to support researchers, faculty and students in their research projects by making a significant contribution to systematic surveys and referencing efforts. Originality/value This study adds to the continuing discussion about how libraries may use technology to improve the services they provide to users who need research support. It had never been looked into how citation Gecko might help libraries with their reference and systematic review study offerings.
Cyber security in university libraries and implication for library and information science education in Nigeria

Purpose Technological expansion and adoption in university libraries have precipitated cybercrimes and the need to equip library personnel with the required knowledge to combat this menace. Consequently, this study aims to examine cyber security in university libraries and its implication for Library and Information Science education. Design/methodology/approach The study adopted descriptive research design, while questionnaire and interview were used to elicit data from library personnel and heads of library schools, respectively. A total of 134 responses were elicited through structured questionnaire (administered online due to the closure of universities) while six heads of library schools were interviewed, one from each of the six geopolitical zones in Nigeria. Findings The data from the questionnaire which were descriptively analysed revealed that the perceived knowledge of cyber security among the librarians was moderately low. Also, the university libraries were exposed to various cyber threats, with cyber security/guideline been one of the critical measures to combat cybercrime. Also, the result showed that librarians displayed high level of adherence to cyber ethics. However, the disposition of library management towards cyber security issues was revealed to be the main challenge to the deployment of cyber security in university libraries, follow by poor password management. Majority of the librarians possess basic knowledge of cyber security, though with serious interest to learn more about it. They were not taught cyber security in library school and they indicated enthusiasm to learn about it. The result of the interview with heads of library schools showed majority of these schools do not offer cyber security course due to dearth in skilled manpower. Originality/value The study presents cybercrime as a menace, if not tackled, would affect the university libraries’ sustainability as information institution, compromising their ability to deliver quality services.
Chorus in the Cacophony: Dissent and Policy Communication of India’s Monetary Policy Committee

ABSTRACTABSTRACTUsing minutes of consecutive Monetary Policy Committee (MPC) meetings of the Indian central bank, we have constructed two novel measures of implicit dissent at the individual level as well as across groups. We have used VADER sentiment analysis to arrive at the proposed measures and investigated their influence on anchoring Indian growth and inflation forecasts. Our empirical findings show discordance among members increases forecast accuracy. This implies promoting an environment that supports nuanced opinions could improve policy outcomes.KEYWORDS: Monetary policydissentNLPsupply shocklinear regression modelJEL CLASSIFICATION: E52E58C22 AcknowledgementsWe are immensely grateful to the anonymous reviewer for his/her valuable suggestions that led to substantial improvement of our work.Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementThe data that support the findings of this study are openly available and can be accessed via the following: https://www.rbi.org.in/scripts/Annualpolicy.aspx
The Influence of Online Liberal Arts English on College Students’ Intercultural Interest

The goal of the current study is to determine whether online liberal arts English classes can improve students' cultural awareness and interest. The online English learning environment has drawn a lot of attention from EFL educators as a result of the COVID-19 epidemic. There are several factors that have an impact on the culture aspects of EFL learning. Yet, there is scant research showing how certain features of culture learning can be effectively applied in the Korean EFL context. This study investigates the links between English language proficiency, pre-interest, and interactions with instructors to determine whether aspects of online liberal arts English classes have a favorable impact on students' interest in foreign cultures. Participants came from 84 Listening, Reading, Speaking I and II classes, totaling 1,421. With the use of Microsoft Teams, the classes were delivered online for 15 weeks. After that, a survey was used to measure students’ pre-interest in English language learning, language achievement, interactions with professors, and cultural understanding. The results of that survey—which used the Naver Office Form—were evaluated using SPSS version 26 to examine any relevant statistics. The findings identified three factors that contribute to students' cultural understanding and interest in online liberal arts English classes. Among these factors, interactions with instructors had the most significant impact, followed by pre-interest and language proficiency. The data suggests that interactions with professors during online EFL classes play a more critical role in promoting students' understanding and interest in other cultures than factors such as English language achievement and pre-interest in English. The effect was particularly pronounced in speaking I and II classes. Therefore, it is crucial for educators to utilize various methods and technologies to encourage interactions between students and professors in online EFL classes. This study makes a significant contribution to the literature by analyzing the specific factors that influence students' cultural understanding and interest and highlighting the critical role of interactions with professors in online liberal arts English classes.
A Study on the Operation of College Liberal Arts Education for the Improvement of Quantitative Reasoning Competency as Data Literacy

The goal of this study is to suggest the potential and significance of quantitative reasoning education as a college liberal arts program and to come up with methods to improve it. To do so, this study analyzed the case of &lt;Quantitative Reasoning and Computational Thinking&gt; at S National University, presenting its achievements, weaknesses, and improvement plans. From the first semester in 2020, &lt;Quantitative Reasoning and Computational Thinking&gt; at S National University has been offered as a semi-compulsory subject with the purpose of cultivating quantitative reasoning based on quantitative relationships and computational thinking for everyday algorithms. In the class, instructors refrain from using mere problem solving using mathematical and/or scientific formulas, and instead an activity-oriented learning approach is emphasized. This allows for the students to interpret quantitative data based on logical algorithms. According to a survey about this subject, most students were satisfied with its contents and reported that it had helped them solve problems creatively based on quantitative and computational thinking. Nevertheless, various problematic issues such as the low enrollment of students in the humanities and social sciences, the dualization of subject operation between classes, and the difficulty in securing lecturers, were raised. To address these issues, it is necessary for us to expand awareness among students that &lt;Quantitative Reasoning and Computational Thinking&gt; aims to promote thinking ability through the understanding and interpretation of quantitative data. Also, it is important that we have faculties that operate on a consistent basis, so that the courses offered are able to meet the goals of the subject. By applying these improvements, the effort to develop liberal arts subjects that enhance students’ data-driven scientific thinking should be continued.
A Discriminative Multi-Channel Noise Feature Representation Model for Image Manipulation Localization

Noise feature modules are commonly used in image manipulation localization. However, different noise learning modules can only target limited tampering methods. In actual image tampering localization tasks, the tampering methods are unknown; it is difficult for a single noise feature module to match all tampering methods. In this paper, we explore the ability of different noise feature modules to localize different manipulation types. Furthermore, we propose a Multi-Channel Noise Feature (MCNF) representation model to describe the noise feature of the tampered images. MCNF contains multiple noise feature modules, which can automatically discriminate the noise feature modules that are practical for localization results. Experiments on several standard image tampering datasets show that our MCNF model achieves state-of-the-art performance compared to alternative methods.
Joint Training of Hierarchical GANs and Semantic Segmentation for Expression Translation

Manipulating images by changing only specific attributes has been a long-standing research problem. Existing methods that rely solely on a global generator often suffer from changing unwanted attributes along with the desired attributes. Although hierarchical networks consisting of global and local networks have shown success, they extract local regions using bounding boxes and are non-differential, inaccurate, and unrealistic. As a result, the solution becomes suboptimal and introduces unwanted artifacts. A recent study has shown a strong correlation between facial attributes and local regions. To exploit this correlation, we have designed a unified architecture that combines semantic segmentation and hierarchical GANs. One advantage of our end-to-end differential framework is that the segmentation network conditions the GANs during the forward pass, and gradients from the GANs are propagated to the segmentation network during the backward pass, allowing both architectures to benefit from each other. We evaluated our method on two challenging expression translation benchmarks, AffectNet and RaFD, and a segmentation benchmark, CelebAMask-HQ, validating its effectiveness over existing methods.
A Topic-Enhanced Approach for Emotion Distribution Forecasting in Conversations

Emotion Forecasting in Conversations (EFC), the task aims to predict the emotion of next utterance (yet to come), has received more and more attention in recent years. However, this task ignores the one-to-many feature of dialogue and its prediction target is emotion label, which is flawed in most cases. In this work, we propose a new task: Emotion Distribution Forecasting in Conversations (EDFC), which aims to predict the emotion distribution of next utterance. Although this task is more reasonable in real applications, it can only learn using emotion labels in most cases because of the difficulty in obtaining emotion distribution. To address it, we explore the positive role of topic in this task and propose a topic-enhanced approach. Specifically, we first obtain the topic-based emotion distribution prior through topic model and emotion generation model, and then use the emotion distribution prior to enhance original label learning model. To effectively evaluate the distribution prediction results, we construct two datasets for this task, and the experimental results prove the feasibility of the EDFC task as well as the effectiveness of our approach.
Intermediate Fine-Tuning Using Imperfect Synthetic Speech for Improving Electrolaryngeal Speech Recognition

Research on automatic speech recognition (ASR) systems for electrolaryngeal speakers has been relatively unexplored due to small datasets. When training data is lacking in ASR, a large-scale pre-training and fine tuning framework is often sufficient to achieve high recognition rates; however, in electrolaryngeal speech, the domain shift between the pretraining and fine-tuning data is too large to over-come, limiting the maximum improvement of recognition rates. To resolve this, we propose an intermediate fine-tuning step that uses imperfect synthetic speech to close the domain shift gap between the pretraining and target data. Despite the imperfect synthetic data, we show the effectiveness of this on electrolaryngeal speech datasets, with improvements of 6.1% over the baseline that did not use imperfect synthetic speech. Results show how the intermediate fine-tuning stage focuses on learning the high-level inherent features of the imperfect synthetic data rather than the low-level features such as intelligibility.
Revisit Sampling Theory of Bandlimited Graph Signals: One Bridge Between GSP and DSP

Sampling of bandlimited (BL) graph signals is one fundamental problem in graph signal processing (GSP), whose underlying kernel is an irregular graph rather than regular 1-D time-series kernel in classical discrete signal processing (DSP). Though there were amounts of sampling objectives and algorithms proposed for BL graph signals, the essential relationship between those sampling objectives in GSP and Nyquist sampling theorem in DSP is still undiscovered. In this paper, we bridge this gap by revisiting sampling theory in GSP thoroughly. In specific, we first figure out that the minimal sample size used in GSP for unique recovery can derive exact Nyquist sampling frequency in DSP when eigenvector matrix is discrete Fourier transform (DFT) matrix. Then, we propose a graph sampling objective for directed cyclic graph as one bridge, which leads to uniform sampling pattern in DSP and has the same optimal solution as other popular graph sampling objectives in GSP, thus connecting GSP and DSP closely via sampling. Finally, we present simulations to demonstrate potential applications inspired by our study, such as fast reconstruction of BL graph signals.
Bagging R-CNN: Ensemble for Object Detection in Complex Traffic Scenes

Generic object detection methods have achieved preferable results, but it is still challenging to detect objects from complicated traffic scenes like extreme illumination and adverse weather. The existing methods are not robust enough to be extended to new complex traffic scenes. To address this issue, we leverage the idea of ensemble learning for strong robustness and propose a novel Bagging R-CNN framework. Specially, we design a bagging classification branch that uses adaptive sampling to train base learners and make them different from each other. The final predictions are the ensemble of the base learners, achieving strong robustness to the challenging objects. For localizing more accurately, a progressive regression branch is proposed in which bounding boxes are continuously optimized for high quality. Extensive experiment results on TJU-DHD-traffic and Pascal VOC datasets show that our Bagging R-CNN achieves superior detection accuracy over state-of-the-art methods. The source code can be found at https://github.com/PungTeng/BaggingRCNN.
A Novel State Connection Strategy for Quantum Computing to Represent and Compress Digital Images

Quantum image processing draws a lot of attention due to faster data computation and storage compared to classical data processing systems. Converting classical image data into the quantum domain and state label preparation complexity is still a challenging issue. The existing techniques normally connect the pixel values and the state position directly. Recently, the EFRQI (efficient flexible representation of the quantum image) approach uses an auxiliary qubit that connects the pixel-representing qubits to the state position qubits via Toffoli gates to reduce state connection. Due to the twice use of Toffoli gates for each pixel connection still it requires a significant number of bits to connect each pixel value. In this paper, we propose a new SCMFRQI (state connection modification FRQI) approach for further reducing the required bits by modifying the state connection using a reset gate rather than repeating the use of the same Toffoli gate connection as a reset gate. Moreover, unlike other existing methods, we compress images using block-level for further reduction of required qubits. The experimental results confirm that the proposed method outperforms the existing methods in terms of both image representation and compression points of view.