Reviewerly/exHarmony huggingface dataset:
Autonomic active learning strategy using cluster-based ensemble classifier for concept drifts in imbalanced data stream

Systems are becoming autonomous nowadays. In real-world scenarios, the involvement of experts in generating a new decision has to be paid a certain amount of cost. Thus, in these conditions, creating a system with the least expert involvement and costs is the main destination. As a solution, an autonomic active learning strategy with a cluster-based ensemble classifier for concept drifts in imbalanced data stream (AACE-DI) is proposed in this paper. Initially, a cluster-based initializing formula is considered to select the most informative instances for labeling a dynamically changing ensemble classifier. Next, the automatically adjusting uncertainty strategy is introduced to prioritize the most uncertain, representative, and minority class data from imbalanced data stream, which also ensures the least labeling cost. This automatic formula not only outperforms the system but also makes it autonomic. Experimental evaluations are organized using 6 real-world data streams and 16 synthetic data streams with different types of concept drift and imbalanced ratios. To measure performance, evaluators, such as the prequential area under curve, recall, and geometric mean, are considered. Moreover, the proposed AACE-DI method achieves expected outcomes with the least cost as compared with other state-of-the-art learning methods.
RFPNet: Reorganizing feature pyramid networks for medical image segmentation

Medical image segmentation is a crucial step in clinical treatment planning. However, automatic and accurate medical image segmentation remains a challenging task, owing to the difficulty in data acquisition, the heterogeneity and large variation of the lesion tissue. In order to explore image segmentation tasks in different scenarios, we propose a novel network, called Reorganization Feature Pyramid Network (RFPNet), which uses alternately cascaded Thinned Encoder-Decoder Modules (TEDMs) to construct semantic features in various scales at different levels. The proposed RFPNet is composed of base feature construction module, feature pyramid reorganization module and multi-branch feature decoder module. The first module constructs the multi-scale input features. The second module first reorganizes the multi-level features and then recalibrates the responses between integrated feature channels. The third module weights the results obtained from different decoder branches. Extensive experiments conducted on ISIC2018, LUNA2016, RIM-ONE-r1 and CHAOS datasets show that RFPNet achieves Dice scores of 90.47%, 98.31%, 96.88%, 92.05% (Average between classes) and Jaccard scores of 83.95%, 97.05%, 94.04%, 88.78% (Average between classes). In quantitative analysis, RFPNet outperforms some classical methods as well as state-of-the-art methods. Meanwhile, the visual segmentation results demonstrate that RFPNet can excellently segment target areas from clinical datasets.
Open quantum random walks and quantum Markov Chains on trees II: the recurrence

The present paper is a continuation of earlier appeared work, where we have constructed Quantum Markov Chains (QMC) associated with Open Quantum Random Walks and discussed a phase transition phenomena within the proposed scheme. In the current work, we define the recurrence and accessibility of within QMC over trees. Moreover, the recurrence property of QMC associated with Open Quantum Random Walks is investigated.
CNN-LSTM Model for Recognizing Video-Recorded Actions Performed in a Traditional Chinese Exercise

Identifying human actions from video data is an important problem in the fields of intelligent rehabilitation assessment. Motion feature extraction and pattern recognition are the two key procedures to achieve such goals. Traditional action recognition models are usually based on the geometric features manually extracted from video frames, which are however difficult to adapt to complex scenarios and cannot achieve high-precision recognition and robustness. We investigate a motion recognition model and apply it to recognize the sequence of complicated actions of a traditional Chinese exercise (ie, Baduanjin). We first developed a combined convolutional neural network (CNN) and long short-term memory (LSTM) model for recognizing the sequence of actions captured in video frames, and applied it to recognize the actions of Baduanjin. Moreover, this method has been compared with the traditional action recognition model based on geometric motion features in which Openpose is used to identify the joint positions in the skeletons. Its performance of high recognition accuracy has been verified on the testing video dataset, containing the video clips from 18 different practicers. The CNN-LSTM recognition model achieved 96.43% accuracy on the testing set; while those manually extracted features in the traditional action recognition model were only able to achieve 66.07% classification accuracy on the testing video dataset. The abstract image features extracted by the CNN module are more effective on improving the classification accuracy of the LSTM model. The proposed CNN-LSTM based method can be a useful tool in recognizing the complicated actions.
Performance Analysis of the CHAID Algorithm for Accuracy

The chi-squared automatic interaction detector (CHAID) algorithm is considered to be one of the most used supervised learning methods as it is adaptable to solving any kind of problem at hand. We are keenly aware of the non-linear relationships among CHAID maps, and they can empower predictive models with stability. However, we do not precisely know how high its accuracy. To determine the perfect scope the CHAID algorithm fits into, this paper presented an analysis of the accuracy of the CHAID algorithm. We introduced the causes, applicable conditions, and application scope of the CHAID algorithm, and then highlight the differences in the branching principles between the CHAID algorithm and several other common decision tree algorithms, which is the first step towards performing a basic analysis of CHAID algorithm. We next employed an actual branching case to help us better understand the CHAID algorithm. Specifically, we used vehicle customer satisfaction data to compare multiple decision tree algorithms and cited some factors that affect the accuracy and some corresponding countermeasures that are more conducive to obtaining accurate results. The results showed that CHAID can analyze the data very well and reliably detect significantly correlated factors. This paper presents the information required to understand the CHAID algorithm, thereby enabling better choices when the use of decision tree algorithms is warranted.
Identifying Tidal Disruption Events with an Expansion of the FLEET Machine-learning Algorithm

We present an expansion of FLEET, a machine learning algorithm optimized to select transients that are most likely to be tidal disruption events (TDEs). FLEET is based on a random forest algorithm trained on the light curves and host galaxy information of 4,779 spectroscopically classified transients. For transients with a probability of being a TDE, \ptde$>0.5$, we can successfully recover TDEs with a $\approx40$\% completeness and a $\approx30$\% purity when using the first 20 days of photometry, or a similar completeness and $\approx50$\% purity when including 40 days of photometry. We find that the most relevant features for differentiating TDEs from other transients are the normalized host separation, and the light curve $(g-r)$ color during peak. Additionally, we use FLEET to produce a list of the 39 most likely TDE candidates discovered by the Zwicky Transient Facility that remain currently unclassified. We explore the use of FLEET for the Legacy Survey of Space and Time on the Vera C. Rubin Observatory (\textit{Rubin}) and the \textit{Nancy Grace Roman Space Telescope} (\textit{Roman}). We simulate the \textit{Rubin} and \textit{Roman} survey strategies and estimate that $\sim 10^4$ TDEs could be discovered every year by \textit{Rubin}, and $\sim200$ TDEs per year by \textit{Roman}. Finally, we run FLEET on the TDEs in our \textit{Rubin} survey simulation and find that we can recover $\sim 30$\% of those at a redshift $z <0.5$ with \ptde$>0.5$. This translates to $\sim3,000$ TDEs per year that FLEET could uncover from \textit{Rubin}. FLEET is provided as a open source package on GitHub https://github.com/gmzsebastian/FLEET
Revocation in attribute-based encryption for fog-enabled internet of things: A systematic survey

Attribute-based encryption (ABE) is regarded as a powerful cryptographic approach for providing fine-grained access control and confidentiality security services, well suited for cloud-based applications where one-to-many encryption is a must. Revocation in ABE, the ability to revoke or cancel the access privileges of attribute possessors, has been a critical problem for the practical usage of ABE-based security schemes. This work presents a systematic literature review of ABE schemes that provide revocation mechanisms published until December 2022. As a distinctive, we focus on the fog-enabled internet of things (IoT) application domain, which is more commonly found in cloud-based scenarios (honest-but-curious) such as health and industrial domains. Although revocation has already been addressed in cloud-based IoT, it has yet to be widely studied in fog-enabled IoT, where systems exhibit constraints and features that impose specific revocation requirements. We survey, discuss, and provide a taxonomy of existing revocation approaches in the context mentioned above, including attribute and user revocation. Furthermore, we explore how the fog is exploited in the reviewed schemes, provide a qualitative comparison of the surveyed works, and present a quantitative comparison of the associated costs of different revocation approaches. Finally, we present some areas of opportunity for improving revocable ABE schemes for fog-enabled IoT and discuss some of the challenges currently faced by these systems.
The radiation shielding proficiency and hyperspectral-based spatial distribution of lateritic terrain mapping in Irikkur block, Kannur, Kerala

The practice of identifying the potential zones for mineral exploration in a speedy and low-cost method includes the use of satellite imagery analysis as a part of remote sensing techniques. It is challenging to explore the iron mineralization of a region through conventional methods which are a time-consuming process. The current study utilizes the Hyperion satellite imagery for mapping the iron mineralization and associated geological features in the Irikkur region, Kannur, Kerala. Along with the remote sensing results, the field study and laboratory-based analysis were conducted to retrieve the ground truth point and geochemical proportion to verify the iron ore mineralization. The MC simulation showed for shielding properties indicate an increase in the linear attenuation coefficient with raising the Fe2O3+SiO2 concentrations in the investigated rocks where it is varied at 0.662 MeV in the range 0.190 cm−1- 0.222 cm−1 with rising the Fe2O3+SiO2 content from 57.86 wt.% to 71.15 wt.%. The analysis also revealed that when the γ-ray energy increased from 0.221 MeV to 2.506 MeV, sample 1 had the largest linear attenuation coefficient, ranging from 9.33 cm−1 to 0.12 cm−1. Charnockite rocks were found to have exceptional shielding qualities, making them an excellent natural choice for radiation shielding applications.
The attitudes of high school students and teachers toward mobile apps for learning English: A Q methodology study

This study aims to find out the attitudes of high school students and teachers toward mobile apps for learning English and how the opinions of the two groups relate to or differ from each other. As there is an E-learning trend worldwide, the findings of this research can help English learners gain a broader view of language learning, identify their own demands, and choose applications in a wiser way. Meanwhile, with the support of our findings, it is believed that application developers could review and polish the existing functions of their apps and develop new ones to meet the needs of student users. The research adopted the Q methodology to provide a more comprehensive understanding of the attitudes varied in different subjects. Interviews were conducted among students and teachers in Chinese high schools. Our study investigated three prominent opinions from the students and two from the teachers. PQMethod and Ken-Q Analysis were used for data analysis. Three factors of attitudes were found among students: 1) learning outcome-oriented; 2) function-oriented; 3) motivation-oriented. Two factors of attitudes were found among teachers: a) learning outcome-oriented; b) platform-oriented. This study is a new attempt to apply the Q methodology in related language research. Remarkably, the Q methodology offers comprehensive and deep insights into the subjective attitudes of students and teachers. Based on the findings, it is recommended that mobile application developers attach more importance to the achievement and encouragement of students in the design; students should take advantage of the mobile apps to achieve their expected outcome under the suggestions from their teachers; English learning apps can be promoted as a helpful auxiliary tool for language learning.
Understanding computational dialogue understanding

In this paper, we first explain why human-like dialogue understanding is so difficult for artificial intelligence (AI). We discuss various methods for testing the understanding capabilities of dialogue systems. Our review of the development of dialogue systems over five decades focuses on the transition from closed-domain to open-domain systems and their extension to multi-modal, multi-party and multi-lingual dialogues. From being somewhat of a niche topic of AI research for the first 40 years, it has made newspaper headlines in recent years and is now being discussed by political leaders at events such as the World Economic Forum in Davos. We ask whether large language models are super-parrots or a milestone towards human-like dialogue understanding and how they relate to what we know about language processing in the human brain. Using ChatGPT as an example, we present some limitations of this approach to dialogue systems. Finally, we present some lessons learned from our 40 years of research in this field about system architecture principles: symmetric multi-modality, no presentation without representation and anticipation feedback loops. We conclude with a discussion of grand challenges such as satisfying conversational maxims and the European Language Equality Act through massive digital multi-linguality-perhaps enabled by interactive machine learning with human trainers. This article is part of a discussion meeting issue 'Cognitive artificial intelligence'.
Understanding user‐generated questions in social Q&amp;A: A goal‐framing approach

Abstract In social Q&amp;A, user‐generated questions can be viewed as goal expressions shaping the responses. Several studies have identified askers' goals from questions. However, it remains unclear how questions set goals for responders. To fill this gap, this research applies goal‐framing theory. Goal‐frames influence responses by attracting responders' attention to different goals. Eight question cues are used to identify gain, hedonic and normative goal‐frames. A total of 14,599 posts are collected. To investigate the influence of goal‐frames, response networks are constructed. Results reveal that gain goal‐frames attract interactions with questions, while hedonic, and normative goal‐frames promote interactions among responses. Further, topic types influence the effects of goal‐frames. Gain goal‐frames increase interactions with questions in Science, Technology, Engineering, and Mathematics (STEM) topics while hedonic and normative goal‐frames attract interactions in non‐STEM topics. This research leverages responders' perspectives to explain responses to questions, which are influenced by the goals set up by question cues. Beyond that, our findings enrich the empirical knowledge of social Q&amp;A topics, revealing that the influence of questions varies across STEM and non‐STEM topics because the question cues for specifying goals are different in the two topics. Our research opens new directions to investigate questions from responders' perspectives.
DyVGRNN: DYnamic mixture Variational Graph Recurrent Neural Networks

Although graph representation learning has been studied extensively in static graph settings, dynamic graphs are less investigated in this context. This paper proposes a novel integrated variational framework called DYnamic mixture Variational Graph Recurrent Neural Networks (DyVGRNN), which consists of extra latent random variables in structural and temporal modelling. Our proposed framework comprises an integration of Variational Graph Auto-Encoder (VGAE) and Graph Recurrent Neural Network (GRNN) by exploiting a novel attention mechanism. The Gaussian Mixture Model (GMM) and the VGAE framework are combined in DyVGRNN to model the multimodal nature of data, which enhances performance. To consider the significance of time steps, our proposed method incorporates an attention-based module. The experimental results demonstrate that our method greatly outperforms state-of-the-art dynamic graph representation learning methods in terms of link prediction and clustering.2.
Data security crisis in universities: identification of key factors affecting data breach incidents

The extremely complex and dynamic digital environments of universities make them highly vulnerable to the risk of data breaches. This study empirically investigated the factors influencing data breach risks in the context of higher education, according to crime opportunity theory and routine activity theory. The data consisted of university samples from China and were collected mainly from the Chinese Education Industry Vulnerability Reporting Platform. After applying Poisson regression for the estimation, increased public disclosure of vulnerabilities was found to escalate the frequency of data breaches, whereas cross-border data flow decreased the number of data breaches. Furthermore, the mechanism by which academic strength affects data breaches was examined through the two mediators of cross-border data flow and vulnerability disclosure. In addition, cloud adoption reduced data breaches, and public clouds were determined to be relatively more secure than private clouds. Cloud adoption also acted as a moderator between the negative impact of vulnerabilities and the positive impact of cross-border data flow on data breaches. The estimation and robustness findings revealed the underlying mechanisms that impacted university data security, clarifying the understanding of data breaches and suggesting practical implications for universities and other institutes to improve information security. The findings of this study provide insights and directions for future research.
Mechanism Design for Blockchain Storage Sustainability

In a blockchain system, ensuring storage sustainability is crucial for the long-term feasibility of the system's operation. This article presents a comprehensive review of the state-of-the-art mechanisms designed to address the storage sustainability problem. We first introduce technological mechanisms aimed at reducing miners' storage costs. Next, we discuss incentive mechanisms that encourage users to pay adequate transaction fees to cover storage costs. Finally, we discuss future challenges and open problems in this field.
Dual-Modality Space-Time Memory Network for RGBT Tracking

RGBT tracking is rapidly developing due to its complementary advantages of RGB and thermal frames. Existing methods with high accuracy track at a lower speed, and do not make full use of the hierarchical information in the feature extraction and the historical information of the sequences. To address these issues, a novel dual-modality space-time memory (DMSTM) network is proposed for robust RGBT tracking. Specifically, DMSTM is divided into three modules. The first module is the dual-modality backbone that utilizes both shallow and deep information by aggregating feature maps of dimensional changes during downsampling. Another module is the space-time memory reader with bimodal fusion. It aggregates features of historical and current frames to share information in the time domain. The last module is the siamese head network, which computes the predicted loss sum of the two modalities and back-propagates it. This avoids degrading the tracking performance due to sequence frame pairs where the training targets are not perfectly aligned. Extensive experiments on three RGBT benchmark datasets show that the performance and efficiency of the proposed DMSTM exceed that of state-of-the-art methods while running at 27.6 FPS.
Few-Shot Learning Meets Transformer: Unified Query-Support Transformers for Few-Shot Classification

The goal of Few-shot classification (FSL) is to identify unseen classes with very limited samples has attracted more and more attention. Usually, it is formulated as a metric learning problem. The core issue of few-shot classification is how to learn (1) consistent representations for images in both support and query sets and (2) effective metric learning for images between support and query sets. In this paper, we show that the two challenges can be well modeled simultaneously via a unified Query-Support TransFormer (QSFormer) model. To be specific, the proposed QSFormer involves global query-support sample Transformer (sampleFormer) branch and local patch Transformer (patchFormer) learning branch. sampleFormer aims to capture the dependence of samples in support and query sets for image representation. It adopts the Encoder, QS-Decoder and Cross-Attention to respectively model the Support, Query (image) representation and Metric learning for few-shot classification task. Also, as a complementary to global learning branch, we adopt a local patch Transformer to extract structural representation for each image sample by capturing the long-range dependence of local image patches. In addition, we introduce a novel Cross-scale Interactive Feature Extractor (CIFE) to extract and fuse different scale CNN features as an effective backbone module for the proposed few-shot learning method. We integrate these into a unified framework and train it in an end-to-end way. A large number of experiments are conducted on four popular datasets to validate the superiority and effectiveness of the proposed QSFormer.