Reviewerly/exHarmony huggingface dataset:
Effect of Missing Data on Test Equating Methods Under NEAT Design

In this study, it was aimed to examine the effect of missing data in different patterns and sizes on test equating methods under the NEAT design for different factors. For this purpose, as part of this study, factors such as sample size, average difficulty level difference between the test forms, difference between the ability distribution, missing data rate, and missing data mechanisms were manipulated. The effects of these factors on the equating error of test equating methods (chained-equipercentile equating, Tucker, frequency estimation equating, and Braun-Holland) were investigated. In the study, two separate sets of 10,000 dichotomous data were generated consistent with a 2-parameter logistic model. While generating data, the MCAR and MAR missing data mechanisms were used. All analyses were conducted by R 4.2.2. As a result of the study, it was seen that the RMSE of the equating methods increased significantly as the missing data rate increased. The results indicate that the RMSE of the equating methods with imputed missing data are reduced compared to equating without imputed missing data. Furthermore, the percentage of missing data, along with the difference between ability levels and the average difficulty difference between forms, was found to significantly affect equating errors in the presence of missing data. Although increasing sample size did not have a significant effect on equating error in the presence of missing data, it did lead to more accurate equating when there was no missing data present.
Micro Learning for Undergraduate Students’ Writing Ability: An Effect on Writing English Text

One of the factors that generate lack of students’ writing competence is the teaching methods used by the lecturer which is less desirable by the students. Micro learning comes as one of the alternatives to deliver the knowledge to the students which became the focus of this study on seeing the significance of the strategy in improving students’ ability to write English text. This is an experimental research involving 64 participants which was done in Universitas Muhammadiyah Sumatera Barat (UM Sumbar). The population of this study consisted of 124 students distributed across 4 classes. The sample selection was done using cluster random sampling, which resulted in one class being assigned as the experimental group and another class as the control group The data was gained by determining whether students have improved their writing scores (through writing rubric) by implementing micro teaching through pre-test and post-test. The researchers look for the statistical t-value with a significance level of &lt;0.05. Furthermore, the researchers conduct further analysis through descriptive analysis using SPSS 24. The results of this research were obtained through a t-test with a significance level of 5% (0.05), yielding an observed t-value of 2.975, while the critical t-value was 2.306. These findings indicate that students' achievement in writing texts improved. Therefore, it can be concluded that micro-learning is an effective instructional method for enhancing the writing skills of English Education students at Universitas Muhammadiyah Sumatera Barat.
Special Section Guest Editorial: Recent Advances in Multimedia Information Security

The <i>Journal of Electronic Imaging</i> (JEI), copublished bimonthly with the Society for Imaging Science and Technology, publishes peer-reviewed papers that cover research and applications in all areas of electronic imaging science and technology.
Sarcasm detection with glove and Word2Vec models

In subtle communication, such as sarcasm, the speaker expresses the antithesis of what is indicated. The ambiguity of sarcasm is one of the biggest obstacles to its detection. Satirical language is not specifically defined. The increasing number of languages is a significant problem as well. On these websites, a large number of new slang phrases are developed each day. Therefore, it's possible that sarcasm cannot be accurately detected using the current corpus of positive and negative feelings. Additionally, users are now able to employ a variety of emoticons with text thanks to recent improvements in online social networks. The sentence could become sarcastic by using these emoticons, which can flip its polarity. Sentiment analysis' accuracy can be increased by carefully analysing and comprehending sarcastic statements. Sentiment analysis is the process of determining how people feel or what they think about a certain situation or issue and detection of sarcasm has become a part of it. A two-phase structure is used for the study article. Following the implementation of two models, GloVe and Word2Vec, we came to the conclusion as to which model is more effective at detecting sarcasm and may be used in real-time applications. The first step of the algorithm gathers features linked to moods and punctuation. In the first technique, the Word2Vec model achieves accuracy of 79.38%, and in the second way, the GloVe model achieves accuracy of 82.33%.
Retracted: Construction of Economic Security Early Warning System Based on Cloud Computing and Data Mining

[This retracts the article DOI: 10.1155/2022/2080840.].
Retracted: Online Loan Default Prediction Model Based on Deep Learning Neural Network

[This retracts the article DOI: 10.1155/2022/4276253.].
IJCNN 2023: Celebrating 80 Years of Neural Networks

and Italy.The IJCNN 2023 program covers many topics in the areas of neural networks and applications including deep learning architectures, cognitive architectures, braininspired architectures, vision and robotics, forecasting, explainable AI, semantic cognition, contrastive learning, reinforcement learning, federated learning, biomedical and cyber security applications.The program consists of 108 in-person oral presentation sessions, 42 virtual oral presentation sessions and 3 poster sessions.The program features 6 keynote speeches, 2 panel discussions, 11 tutorials, 5 workshops, 18 special sessions, and 1 competition.The conference has registered more than 1000 attendees including some of the top researchers in the field of neural networks from around the world.We are fortunate to have many distinguished keynote speakers such as
Multi-contrastive Learning Recommendation Combined with Knowledge Graph

Knowledge graphs have rich semantic information and are widely used in recommender systems to alleviate the sparsity problem of user-item interaction data. Existing graph collaborative filtering models based on knowledge graphs face two challenges: i) real-world knowledge graphs usually contain a large amount of noise, which affects the accuracy of recommendation results. ii) the intrinsic association of user-item interaction data is not fully explored, which cannot effectively alleviate the data sparsity problem. To address the above two problems, we proposes a multi-contrastive learning recommendation model combined with knowledge graph (MLKG). First, a knowledge-aware aggregation enhancement method is used to derive a more robust knowledge-aware representation for items, second, the node representation is enhanced by random graph contrastive learning, and finally, the neighbor relationship of nodes is strengthened by neighborhood contrastive learning. Since MLKG makes full use of knowledge semantic information, self-supervised signal and neighborhood structure signal to learn user representation from different aspects, it can capture the real preferences of users more accurately, and solve the two key problems of data sparsity and data noise at the same time to achieve better recommendation effect. Extensive comparison experiments on three publicly available datasets demonstrate the effectiveness of the MLKG model.
A Bidirectional Tree Tagging Scheme for Joint Medical Relation Extraction

Joint medical relation extraction refers to extracting triples, composed of entities and relations, from the medical text with a single model. One of the solutions is to convert this task into a sequential tagging task. However, in the existing works, the methods of representing and tagging the triples in a linear way failed to the overlapping triples, and the methods of organizing the triples as a graph faced the challenge of large computational effort. In this paper, inspired by the tree-like relation structures in the medical text, we propose a novel scheme called Bidirectional Tree Tagging (BiTT) to form the medical relation triples into two binary trees and convert the trees into a word-level tags sequence. Based on BiTT scheme, we develop a joint relation extraction model to predict the BiTT tags and further extract medical triples efficiently. Our model outperforms the best baselines by 2.0% and 2.5% in F1 score on two medical datasets. What's more, the models with our BiTT scheme also obtain promising results in three public datasets of other domains.
Neural Architecture Search with Heterogeneous Representation Learning for Zero-Shot Multi-Label Text Classification

Zero-shot multi-label text classification has become a hot topic recently and has a wide range of applications, including assigning legal concepts to legislation, category information to goods, and disease information to patient records. Existing approaches employ a series of neural networks to represent the text and labels separately by relying on artificial experience, which can not achieve good performance. To effectively represent text data and multi-label data together, it is critical to aggregate the neighboring information with graph structure information in zero-shot multi-label text classification. To solve this problem, we propose a neural architecture search (NAS) approach with heterogeneous representation learning for the representation of text data and labels data together. We split the original search space of NAS into two heterogeneous search spaces and reformulated NAS with heterogeneous representation learning to aggregate neighboring information better and reduce unnecessary search. Besides, we design an alternating search strategy to search for suitable neural architectures for zero-shot multi-label text classification. We conduct neural architecture search and retraining experiments on ERULEX57K dataset. The results demonstrate that our method outperforms previous zero-capable methods and improves the normalized discounted cumulative gain at the top 5 predicted labels (nDCG@5) by 3.0%, 2.4%, 18.9% and 1.0% for overall, frequent, few-shot, and zero-shot labels, respectively.
Fatou Bah on the Bromley Memorial Event

Fatou Bah, master’s student in the ScholCommLab, attended the 2023 Bromley Memorial event in Washington, D.C. alongside invited graduate students from the University of Ottawa (UOttawa). Organized by the UOttawa Institute for Science, Society &amp;amp; Policy (ISSP) and the George Washington University Institute for International Science &amp;amp; Technology Policy (IISTP), the Bromley Memorial event provides an opportunity for graduate students to discuss and exchange ideas with senior science and...
Explainable Restaurant Closure Prediction through Co-Attentive Contrastive Learning

In this paper, we propose a novel approach to enhance user and restaurant representations in the context of predicting the closure of a restaurant and give an explanation based on data generated from user-restaurant interactions. In order to accurately predict the operating status of a restaurant and give a reasonable explanation, we need rich relevant information of user-restaurant interaction and reviews to model their representations. However, the interaction information between the user and the restaurant is usually sparse. To address this issue, we propose a new model, which is called the Co-Attentive Contrastive Learning (CACL) model. Our model employs a contrastive learning algorithm to deal with data sparsity and leverages co-attention mechanism to select the most relevant review information assisting the former to obtain a more accurate and granular representation. By fusing these module, we obtain rich information to perform two tasks with better quality. To demonstrate the effectiveness of our model, extensive experiments was conducted in six different cities, and the results showed that CACL was superior to the previous method in terms of prediction accuracy and explainable ability (average 4.8% improvement in prediction and 35.4% improvement in explanation).
A Novel Multi-robot Path Planning Algorithm Considering Dynamic Environmental Information

Multi-robot path planning is a vital part of Simultaneous localization and mapping (SLAM) systems. In recent years, numerous studies have been conducted in the field of multi-robot path planning. This paper presents a novel method for multi-robot path planning that addresses the challenge of dynamic environments. This method joints adaptive parameters and adaptive steps strategy to the A* algorithm, which greatly improves the operational performance. This paper presents a novel multi-robot path planning method that addresses the challenge of path planning for multiple robots in dynamic environments. In addition, a multi-objective optimization algorithm is introduced and applied to the multi-robot system. To address the issue of dynamic and unknown obstacle avoidance, the rolling window method and obstacle avoidance strategy are utilized. The simulation experiment demonstrates that the proposed algorithm outperforms the CPP-OCA and MAPP-RL algorithms in all three metrics. Additionally, the algorithm’s practical feasibility is verified by applying it to the ROS simulation environment.
The Effects of Police Officers' Emotional Labor Public Service Motivation: Focused on the Moderating Effect of Social Support

This study empirically verified the effect of emotional labor on the public service motivation of police officials and the moderating effect of social support in their relationship. Analysis Results First, in the case of internal behavior perceived by police officials, it served as a factor that increased their motivation for public service. In addition, in the case of surface behavior, it was confirmed that it acted as a factor that hindered public service motivation. Second, it has been confirmed that both the supervisor's support and the organization's support, which are sub-variables of social support, have a significant impact on the police's public service motivation. Third, as a result of verifying the moderating effect of social support in the relationship between emotional labor and public service motivation of the police, it was confirmed that the support of the boss and the support of the organization rather weakens the relationship between the police's internal behavior and public service motivation. Next, it was confirmed that the support of the boss and the support of the organization alleviates the relationship between the police's surface behavior and public service motivation. Through the results of these studies, implications were derived for how to manage emotional labor and public service motivation of police officials.
Data-Locked Knowledge Distillation with Ready-To-Use Substitutes

Knowledge Distillation (KD) becomes a popular way of knowledge transfer from a pre-trained cumbersome teacher model to a compact student model by training the student to mimic the teacher's outputs. However, the original training data of the teacher model, which are supposed to be used for distilling, are often inaccessible during the student training phase due to privacy and intellectual property issues. Several studies reported encouraging results for such kind of data-locked KD problem by collecting substitute inputs via synthesization or seeking ready-to-use samples from publicly available data. This paper aims to take a step further with the more cost-efficient selection-based method. Specifically, we propose a new In-Distribution Detection (IDD) criterion to measure the quality of a candidate sample based on both model confidence and class-relationship consistency. Immediate evaluation of IDD performance with several benchmark image datasets verifies the effectiveness of the designed measurement compared to existing ones, showing its potential for substitute selection from open resources. Experimental results on data-locked KD demonstrate that our approach consistently improves the previous selection-based approach and even outperforms the latest synthesization-based ones given a proper candidate set.
Meta-MSGAT: Meta Multi-scale Fused Graph Attention Network

In recent years, graph attention networks(GAT) have received extensive attention. According to the different attention extraction methods, GAT is mainly divided into two categories: hierarchical graph attention and structural information extraction of attention. However, they cannot achieve plug-and-play effects. Recent studies have found that the excellent performance of Transformer on different tasks lies not in the multi-head attention mechanism but in the structure itself. Inspired by the above, we propose a plug-and-play graph attention convolution structure: Meta Multi-scale Fused Graph Attention Network(Meta-MSGAT). Meta-MSGAT consists of multi-scale feature extraction, feature transformation and aggregation, and message-passing. The Multi-scale feature extraction is implemented using multiple channels. Feature transformation and aggregation consist of different convolution kernels to increase the diverse representation of features. The message-passing layer consists of a mixer that does not limit the specific network layer. To verify the effectiveness of our proposed structure, we performed extensive experiments on six datasets and achieved an improvement from 0.35% to 2.76% compared to baselines. In particular, we use MLP or GAT instead of the mixer for ablation experiments, and both achieve superior performance over baselines.
Cost-effective Modality Selection for Video Popularity Prediction

Video popularity prediction, also known as predicting the future popularity of a video, is a task that generally uses various modalities of the video, such as visual, audio, text, and metadata, to make predictions. However, the traditional approach puts all the modalities directly into a model for processing, which introduces noise and redundancy into the model. To address this issue, we propose Cost-effective Modality Selection(CeMS) for video popularity prediction, which can adaptively select cost-effective modalities as input based on the features of the metadata. Specifically, a policy network first obtains the prior information from metadata to analyze the differences in representation ability and computation cost between various modalities. Then, with the policy estimation, an optimal set of cost-effective modalities is determined, and the backbone network corresponding to the selected modalities is activated for feature extraction. Finally, the semantic information of all selected modalities is fed into the decoder to yield the predicted popularity. Experiments demonstrate that our proposed approach yields 63%-69% reduction in computation when compared to the traditional baseline that simply uses all the modalities. Additionally, we achieve consistent improvements in accuracy over the state-of-the-art methods.
Detecting Early Risk of Alzheimer’s Disease Using Self-Supervised Multimodal Representation Learning

Alzheimer’s disease is an irreversible neurogenerative disorder whose treatment is mainly focused on slowing its progression. Most studies aimed at automating the detection of this disease generally focus on a single type of data (or modality) to train AI models, which limits its massive application. This work explores the impact of integrating multiple modalities (structural and functional MRI) for classifying features of patients with Alzheimer’s disease (AD) versus normal cognition. In addition, the trained model was tested with data from individuals with Subjective Cognitive Complaint (SCC) to estimate the probability of belonging to the AD group. This could be useful to follow up SCC patients with a high risk of developing AD according to the model. Our experimental results reveal that our proposed multimodal approach outperforms the unimodal approach by approximately 2.45% AUC score.
Extracting Common and Variable Code using the LCS Algorithm for Migration to SPLE

The LCS (Longest Common Subsequence) algorithm is a well-known algorithm that finds the longest subsequence from two different strings while preserving the relative order between the characters that make up the strings. When migrating from source code to Software Product Line Engineering (SPLE), the process of identifying commonality and variability is a crucial step and many studies have applied the LCS algorithm for this process, but this algorithm can be applied only to two sources although the cases of three or more sources are common. This study proposes a method that extracts common and variable code lines from three or more sources. The proposed method consists of the preprocessing phase that divides sources into sections by reflecting the characteristics of their programming languages, and the phase for applying the LCS algorithm. To evaluate the proposed method, we applied it to ArgoUML-SPL and compared the result with the original platform of ArgoUML-SPL, which confirmed that common and variable code lines were effectively identified.
Authentic Learning Approach for Artificial Intelligence Systems Security and Privacy

The main objective of authentic learning is to offer students an exciting and stimulating educational setting that provides practical experiences in tackling real-world security issues. Each educational theme is composed of pre-lab, lab, and post-lab activities. Through the application of authentic learning, we create and produce portable lab equipment for AI Security and Privacy on Google CoLab. This enables students to access and practice these hands-on labs conveniently and without the need for time-consuming installations and configurations. As a result, students can concentrate more on learning concepts and gain more experience in hands-on problem-solving abilities.
Speak-to-Write from Multiple Perspectives, as Method

One of the practice-based research methods that excites me the most today is to work with writing as a practice, and as a practice-based research method. The technology of writing can be very misleading, especially when that writing is typed using a word processing program. When using this tool, writing looks the same whether it represents a stream of consciousness, a first draft, or a final proof. Because of this, I have found that I hold myself to the standard of the final version, which of course completely freezes me up. If we are always aiming for the final version, there is not much room for thinking, making errors, going sideways and backward and forward again. There is only the guaranteed feeling of failure.&#x0D; &#x0D; In response, graphic designer Juliette Bellocq and I have developed a set of writing exercises that address these two limitations, as I have come to know writing from my training as an academic. In this piece, we share our exercise, 1,000 Ways Home. It is a non-linear process of thinking and writing. It also offers the alchemy of communicating in the presence of another person who pays close attention. We call our process speak-to-write.
Contents list

Permissions Request permissions Contents list J. Anal. At. Spectrom., 2023, 38, 1517 DOI: 10.1039/D3JA90033A This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. You can use material from this article in other publications without requesting further permissions from the RSC, provided that the correct acknowledgement is given. Read more about how to correctly acknowledge RSC content.