Reviewerly/exHarmony huggingface dataset:
Hyperbolic Graph Topic Modeling Network with Continuously Updated Topic Tree

Connectivity across documents often exhibits a hierarchical network structure. Hyperbolic Graph Neural Networks (HGNNs) have shown promise in preserving network hierarchy. However, they do not model the notion of topics, thus document representations lack semantic interpretability. On the other hand, a corpus of documents usually has high variability in degrees of topic specificity. For example, some documents contain general content (e.g., sports), while others focus on specific themes (e.g., basketball and swimming). Topic models indeed model latent topics for semantic interpretability, but most assume a flat topic structure and ignore such semantic hierarchy. Given these two challenges, we propose a Hyperbolic Graph Topic Modeling Network to integrate both network hierarchy across linked documents and semantic hierarchy within texts into a unified HGNN framework. Specifically, we construct a two-layer document graph. Intra- and cross-layer encoding captures network hierarchy. We design a topic tree for text decoding to preserve semantic hierarchy and learn interpretable topics. Supervised and unsupervised experiments verify the effectiveness of our model.
Counterfactual Learning on Heterogeneous Graphs with Greedy Perturbation

Due to the growing importance of using graph neural networks in high-stakes applications, there is a pressing need to interpret the predicted results of these models. Existing methods for explanation have mainly focused on generating sub-graphs comprising important edges for a specific prediction. However, these methods face two issues. Firstly, they lack counterfactual validity as removing the subgraph may not affect the prediction, and generating plausible counterfactual examples has not been adequately explored. Secondly, they cannot be extended to heterogeneous graphs as the complex information involved in such graphs increases the difficulty of generating interpretations. This paper proposes a novel counterfactual learning method, named CF-HGExplainer, for heterogeneous graphs. The method incorporates a semantic-aware attentive pooling strategy for the heterogeneous graph classifier and designs a heterogeneous decision boundaries extraction module to find the common logic for similar graphs based on the extracted graph embeddings from the classifier. Additionally, we propose to greedily perturb nodes and edges based on the distribution of node features and edge plausibility to train a neural network for heterogeneous edge weight learning. Extensive experiments on two public academic datasets demonstrate the effectiveness of CF-HGExplainer compared to state-of-the-art methods on the graph classification task and graph interpretation task.
Towards Reliable Rare Category Analysis on Graphs via Individual Calibration

Rare categories abound in a number of real-world networks and play a pivotal role in a variety of high-stakes applications, including financial fraud detection, network intrusion detection, and rare disease diagnosis. Rare category analysis (RCA) refers to the task of detecting, characterizing, and comprehending the behaviors of minority classes in a highly-imbalanced data distribution. While the vast majority of existing work on RCA has focused on improving the prediction performance, a few fundamental research questions heretofore have received little attention and are less explored: How confident or uncertain is a prediction model in rare category analysis? How can we quantify the uncertainty in the learning process and enable reliable rare category analysis?
Online Level-wise Hierarchical Clustering

Online hierarchical clustering algorithms, compared to their scalable batch setting counterparts, typically provide more limited accuracy and efficiency performance. Yet, when data is incrementally arriving, a crucial setting in many clustering applications (e.g., entity resolution and concept discovery), these batch setting algorithms do not apply. This paper presents a family of new algorithms for online hierarchical clustering that combine high quality trees and fast per-point insertion time--made possible through a limited number of parallel non-greedy tree re-arrangements. We analyze our methods under assumptions about the data and the separability of clusters. Empirically, we find that our proposed algorithms yield state-of-the-art results in hierarchical clustering dendrogram purity and in building compressed prototypes for a k-nearest representative classifier.
Voice Assistant, Buy Coffee Capsules!: Understanding the Determinants of Consumers' Intention to Use Voice Commerce

Owing to increasing adoption rates of voice assistants (VAs), integrating voice commerce (VC) as a new consumer channel is among the top objectives of businesses' current voice initiatives. VC offers consumers a new way of shopping by using VAs, which comes with benefits but also entails risks that extant literature only partially examines. Therefore, this research seeks to understand consumers' perceived benefits and risks when using VC by means of a mixed-methods design. In the first study, we conducted 30 semi-structured interviews with VA users to develop a research model of VC risks and benefits. The second study empirically validated the research model using survey data from 176 participants. According to our results, consumers perceive VC as convenient and enjoyable but also perceive risks regarding the VA's reliability, opacity, and controllability. Thus, we shed light on the determinants of channel choice in e-commerce and provide guidelines for designing VC applications.
Deep Bayesian Active Learning for Accelerating Stochastic Simulation

Stochastic simulations such as large-scale, spatiotemporal, age-structured epidemic models are computationally expensive at fine-grained resolution. While deep surrogate models can speed up the simulations, doing so for stochastic simulations and with active learning approaches is an underexplored area. We propose Interactive Neural Process (INP), a deep Bayesian active learning framework for learning deep surrogate models to accelerate stochastic simulations. INP consists of two components, a spatiotemporal surrogate model built upon Neural Process (NP) family and an acquisition function for active learning. For surrogate modeling, we develop Spatiotemporal Neural Process (STNP) to mimic the simulator dynamics. For active learning, we propose a novel acquisition function, Latent Information Gain (LIG), calculated in the latent space of NP based models. We perform a theoretical analysis and demonstrate that LIG reduces sample complexity compared with random sampling in high dimensions. We also conduct empirical studies on three complex spatiotemporal simulators for reaction diffusion, heat flow, and infectious disease. The results demonstrate that STNP outperforms the baselines in the offline learning setting and LIG achieves the state-of-the-art for Bayesian active learning.
LEA: Improving Sentence Similarity Robustness to Typos Using Lexical Attention Bias

Textual noise, such as typos or abbreviations, is a well-known issue that penalizes vanilla Transformers for most downstream tasks. We show that this is also the case for sentence similarity, a fundamental task in multiple domains, e.g. matching, retrieval or paraphrasing. Sentence similarity can be approached using cross-encoders, where the two sentences are concatenated in the input allowing the model to exploit the inter-relations between them. Previous works addressing the noise issue mainly rely on data augmentation strategies, showing improved robustness when dealing with corrupted samples that are similar to the ones used for training. However, all these methods still suffer from the token distribution shift induced by typos. In this work, we propose to tackle textual noise by equipping cross-encoders with a novel LExical-aware Attention module (LEA) that incorporates lexical similarities between words in both sentences. By using raw text similarities, our approach avoids the tokenization shift problem obtaining improved robustness. We demonstrate that the attention bias introduced by LEA helps cross-encoders to tackle complex scenarios with textual noise, specially in domains with short-text descriptions and limited context. Experiments using three popular Transformer encoders in five e-commerce datasets for product matching show that LEA consistently boosts performance under the presence of noise, while remaining competitive on the original (clean) splits. We also evaluate our approach in two datasets for textual entailment and paraphrasing showing that LEA is robust to typos in domains with longer sentences and more natural context. Additionally, we thoroughly analyze several design choices in our approach, providing insights about the impact of the decisions made and fostering future research in cross-encoders dealing with typos.
Transfer learning application of self-supervised learning in ARPES

Abstract There is a growing recognition that electronic band structure is a local property of materials and devices, and there is steep growth in capabilities to collect the relevant data. New photon sources, from small-laboratory-based lasers to free electron lasers, together with focusing beam optics and advanced electron spectrometers, are beginning to enable angle-resolved photoemission spectroscopy (ARPES) in scanning mode with a spatial resolution of near to and below microns, two- to three orders of magnitude smaller than what has been typical for ARPES hitherto. The results are vast data sets inhabiting a five-dimensional subspace of the ten-dimensional space spanned by two scanning dimensions of real space, three of reciprocal space, three of spin-space, time, and energy. In this work, we demonstrate that recent developments in representational learning (self-supervised learning) combined with k -means clustering can help automate the labeling and spatial mapping of dispersion cuts, thus saving precious time relative to manual analysis, albeit with low performance. Finally, we introduce a few-shot learning ( k -nearest neighbor) in representational space where we selectively choose one ( k = 1) image reference for each known label and subsequently label the rest of the data with respect to the nearest reference image. This last approach demonstrates the strength of self-supervised learning to automate image analysis in ARPES in particular and can be generalized to any scientific image analysis.
Combining right-angle rotation with Gray code transformation to encrypt MQIR images

With the rapid development of multimedia technology, image scrambling technology is essential for the application of information hiding and digital watermarking. However, in the field of quantum image processing, there are few researches on image scrambling. An MQIR (Multimode Quantum Image Representation) takes less qubits to store images, which has certain advantages. It is very significant to research more secure scrambling algorithms on the superior quantum images. Therefore, we propose an MQIR image encryption scheme based on right-angle rotation and Gray code transformation. We give the MQIR image encryption and decryption circuit based on right-angle rotation and Gray code transformation. In the encryption process, the combination of the NOT gate and the swap gate acts on the qubits representing the horizontal and vertical coordinates in the sub-blocks of the MQIR image, and realizes different right-angle rotations of each sub-block in the MQIR image. Further, Gray code scrambling is performed on the three-dimensional coordinates of the MQIR image, and the double scrambling of the pixel position and pixel value of the MQIR image is completed. The encryption circuit is very simple to operate, saving a certain amount of quantum gates. This encryption method is simulated on the IBM quantum cloud platform and matlab. Experimental results show that the encryption method has certain effectiveness. This method not only disrupts the histogram distribution of the original image, but also has a good scrambling effect. Finally, we give the scrambled image and histogram with the best encryption effect in the scrambling period.
Accuracy improvement of SAR image classification method using Greedy Hierarchical Learning in comparison with Support Vector Machine

We contrast the classification of SAR images using a novel greedy hierarchical learning method with a supported vector machine. 20 dataset samples in total were taken from the Kaggle learning platform. The dataset is divided into training and testing sets. In a total of 20 datasets, 10 were used for testing and 10 for training. The classification and comparison of the recognition accuracy for support vector machine and the novel greedy hierarchical learning are done using MATLAB and synthetic aperture radar images. By fixing alpha (0.05) and power (80%), the sample size is calculated using the G power. Results: Support vector machine and novel greedy hierarchical learning both achieved classification accuracy of 80.41% and 93.08% from the MATLAB simulation, respectively. The significance found using the SPSS analysis is 0.009 (p 0.05). Conclusion: The accuracy of the novel greedy hierarchical learning outperforms the support vector machine significantly for the given data set of synthetic aperture radar image classification.
Role of CISO - Cyber Security &amp; Risk Management

The data protection specialist is a Chief Information Security Officer (CISO) or Chief Security Officer (CSO), who is pioneer of creating and implementing an information security programme to safeguard an organization's data and systems. CISOs often answer to the CEO directly. The Chief Information Security Officer (CISO) be first of making sure that the data and information systems of the company are secure. To reduce possible harm to the company, a risk that comes with this obligation must be handled. In order to defend the organization's information systems and data counter to cyber-attacks, the Chief Information Security Officer (CISO) must apply a number of tactics and best practises. Cybersecurity regulations, which are crucial to safeguarding people, organisations, and governments from cyberattacks, data breaches, and other cybercrimes, are some measures proposed to lower risk in the CISO's.
Real-Time Big Data Analytics for Live Streaming Video Quality Assessment Using Deep Learning

In recent years, live streaming video has become increasingly popular, necessitating real-time big data analytics to evaluate streaming video content. In this article, it provides a framework for assessing the quality of live broadcast video using big data analytics and deep learning in real time. Our framework comprises various modules, including collection of data, preprocessing, extraction of features, selection of features, and categorization based on deep learning. To determine the quality of the streaming video, we assess a variety of video characteristics, including resolution, bit rate, frame rate, and packet loss rate. Using convolutional neural networks (CNNs), our deep learning models categorise the streaming video quality into various categories. Using real-world streaming video datasets, we evaluate our framework's performance. The results demonstrate that our proposed framework is able to efficiently analyse the quality of streaming video in real-time, outperforming other state-of-the-art frameworks. Our framework provides live streaming platforms with a tool for delivering high-quality streaming video to their consumers.
Applying Cost-Sensitive Learning Methods to Improve Extremely Unbalanced Big Data Problems Using Random Forest

In a larger part minority characterization issue, class irregularity in the dataset(s) can definitely misshape the exhibition of classifiers, creating an expectation predisposition for the greater part class. A negative (larger part) class expectation predisposition could make impeding impacts if the positive (minority) class is the gathering of interest and the application region being referred to states that a false negative is significantly more costly than a false certain. The decrease of class divergence is made more troublesome by big data because of the different and muddled design of the similarly bigger datasets. This exploration presents a wide evaluation of distributed works inside the past 8 years, zeroed in on fashionable unevenness (i.e., a greater part to-minority class proportion somewhere in the range of 100:1 and 10,000:1) in big data to survey the cutting edge in addressing ominous outcomes connected with class irregularity. In this paper we propose two methods for managing the imbalanced data grouping issue utilizing irregular backwoods. The other depends on an inspecting approach, though the first depends on cost-sensitive learning. Execution pointers like review and exactness, false-positive and false-negative rates.
IoT Based English Vocabulary Teaching

Because of the wealth of information that can be found online, today’s students are urged to take a more active role in their education. This research draws parallels between ESP (English for Specific Purposes) instruction and the IoT environment. Our focus is on the Internet of Things (IoT), and we define it, describe its features, and answer fundamental concerns regarding its potential impact on the classroom. After examining the data, we zero in on how digital tools might improve language teachers’ methods in the classroom. We provide an instructional strategy that, by using digital tools, increased students’ interest, participation, and enthusiasm in class. The paper concludes that the importance of technology in the classroom will continue to grow, but that policymakers should take an active interest in digital technologies’ potential as pedagogical tools and collaborate closely with educators and experts/developers in the field to create new methods of teaching.
Analysis of Extractive Text Summarisation Techniques for Multilingual Texts

Due to a large influx of information around us, text summarization is becoming increasingly important. Text summary helps one understand the document without having to read every line of the stack of papers. Text summarization provides a method for creating a condenses version of single or multiple articles at hand. It is an important technique for locating relevant content on the Internet or in sizable text libraries. It is also crucial to extract the information so that the user will find the article interesting. Text summarization may be performed using two methods - extractive techniques and abstractive techniques. Dividing the text into sentences and selecting the best ones based on their weight or score is a method to produce extractive summary. The essential concepts of the article are retrieved using the abstractive summarization approach, which then creates a subjective, natural-language representation of those ideas. In this paper, the goal is to provide a thorough analysis of extractive text summarizing process techniques and to provide an in-depth interpretation of extractive text summarization techniques with a summary that is less repetitious and has a high degree of coherence. A comprehensive comparison research of several extractive approaches for automated text summarization on Hindi, English and French text is presented in this work.
Second-Order Chaotic Maps With Random Coefficients to Generate Complex Chaotic Sequences for High-Security Image Encryption

Chaotic maps have been widely applied on image encryption for their complexity and sensitivity to key variation. In this work, we propose second-order chaotic maps with optimized random coefficients to generate chaotic sequences for image encryption. Two screening conditions are proposed to identify 300 candidate chaotic maps in terms of complexity indices <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> and SE. A particle swarm optimization algorithm is developed to search for the optimal chaotic maps under eight different weighting schemes. The optimal chaotic maps can achieve <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N<sub>p</sub></i> = 2, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">D<sub>KY</sub></i> = 2, CD= 2, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> > 0.9, SE> 0.9 and PE> 0.7. Key sensitivity analysis on all the system parameters and initial values confirms high security of the optimal chaotic maps. A hybrid sequence generation (HSG) scheme is also proposed to further reduce the image encryption time.
A Lightweight Authentication Framework for Fault-Tolerant Distributed WSN

The vast production of resource-constrained wireless communication devices and the development of various techniques in recent years opens a room for security concerns to overcome potential attacks.However, efficient methods are needed to reduce the trade-off between communication and computation complexities in resource-constrained wireless device communication.In this paper, we propose a lightweight fault-tolerant secure data communication framework that consists of Elliptic Curve Diffie-Hellman (ECDH) secure communication scheme, Elliptic Curve Cryptography (ECC) based secure communication scheme and Elliptic Curve Integrated Encryption Scheme (ECIES) based authentication scheme for wireless sensor network communication using Message Passing Interface (MPI) parallel program platform.Further, we have implemented the proposed framework for single sink node (scenario-1) and all sink node (scenario-2) scenarios with parallel threads using Linux Pthreads to improve the total execution time.It is observed that the overall execution time performance of ECC is better in scenario-2 whereas performance of ECDH is better in scenario-1 when number of sensors greater than 200.It is also observed that enabling Linux Pthreads in ECC implementation guarantees the parallel execution of decryption process and reduction in the overall execution time in both the scenarios.The results demonstrate the superiority of the proposed framework in terms of execution time and memory use over simulated wireless network environments, making the proposed framework suitable for fault-tolerant wireless sensor communication applications.
Prediction of Graduation Development Based on Hypergraph Contrastive Learning With Imbalanced Sampling

With the increasingly competitive job market, the employment issue for college graduates has received more and more attention. Predicting graduation development can help students understand their suitable graduation development, thus easing the pressure of finding employment after graduation. However, existing research must look into the issue of imbalance and long-tail distribution in student graduation development. This paper proposes a novel hypergraph contrastive learning model based on imbalanced sampling (IS-HGCL) that enables us to address this problem. First, construct a hypergraph using students’ school performance and social behavior. Then, our proposed imbalanced sampling strategy is applied to optimize the hypergraph structure and alleviate the imbalance issue. A self-updating hypergraph neural network is designed to optimize hyperedge representation and alleviate the long-tail distribution issue to enhance the hypergraph representation further. Finally, the structural consistency between the two optimized hypergraphs is maximized via node-level contrastive learning. Experiments on a real-world campus dataset demonstrate the superiority of the IS-HGCL model.