Reviewerly/exHarmony huggingface dataset:
Instagram for student learning and library promotions: a quantitative study using the 5E Instructional Model

Purpose Libraries worldwide, including Hong Kong, increasingly use social media tools to introduce and promote their services and resources to users. Instagram, in particular, is used to target younger users. This study investigates the effectiveness of Instagram in promoting library services and university students' perceptions of the value of Instagram as a learning support tool. Design/methodology/approach A major academic library in Hong Kong was chosen for this study. Library users' habits, perceptions, preferences, and views on Instagram's effectiveness as a learning support tool were compared in two age groups. The data were collected using a survey based on the 5E Instructional Model. Findings Despite the significantly higher frequency of Instagram use by younger students, the results showed that Instagram was probably an ineffective promotion platform for either age group because of low user engagement, relatively neutral perception of Instagram as a learning support tool, and notably low user acceptance of Instagram as a promotional tool. Originality/value Studies of student perspectives on various social media tools have increased; however, few have explored the use of Instagram, especially in Hong Kong or Asia. This study provides researchers and librarians with practical insights into current Instagram users' engagement, perceptions, and preferences and their view of its effectiveness as a learning support tool. The study also provides suggestions for improving the current situation.
Robust Zero-Shot Cross-Domain Slot Filling with Example Values

Task-oriented dialog systems increasingly rely on deep learning-based slot filling models, usually needing extensive labeled training data for target domains.Often, however, little to no target domain training data may be available, or the training and target domain schemas may be misaligned, as is common for web forms on similar websites.Prior zero-shot slot filling models use slot descriptions to learn concepts, but are not robust to misaligned schemas.We propose utilizing both the slot description and a small number of examples of slot values, which may be easily available, to learn semantic representations of slots which are transferable across domains and robust to misaligned schemas.Our approach outperforms state-ofthe-art models on two multi-domain datasets, especially in the low-data setting.
PDRCNN: Precise Phishing Detection with Recurrent Convolutional Neural Networks

Through well-designed counterfeit websites, phishing induces online users to visit forged web pages to obtain their private sensitive information, e.g., account number and password. Existing antiphishing approaches are mostly based on page-related features, which require to crawl content of web pages as well as accessing third-party search engines or DNS services. This not only leads to their low efficiency in detecting phishing but also makes them rely on network environment and third-party services heavily. In this paper, we propose a fast phishing website detection approach called PDRCNN that relies only on the URL of the website. PDRCNN neither needs to retrieve content of the target website nor uses any third-party services as previous approaches do. It encodes the information of an URL into a two-dimensional tensor and feeds the tensor into a novelly designed deep learning neural network to classify the original URL. We first use a bidirectional LSTM network to extract global features of the constructed tensor and give all string information to each character in the URL. After that, we use a CNN to automatically judge which characters play key roles in phishing detection, capture the key components of the URL, and compress the extracted features into a fixed length vector space. By combining the two types of networks, PDRCNN achieves better performance than just using either one of them. We built a dataset containing nearly 500,000 URLs which are obtained through Alexa and PhishTank. Experimental results show that PDRCNN achieves a detection accuracy of 97% and an AUC value of 99%, which is much better than state-of-the-art approaches. Furthermore, the recognition process is very fast: on the trained PDRCNN model, the average per URL detection time only cost 0.4 ms.
A Closer Look At Feature Space Data Augmentation For Few-Shot Intent Classification

New conversation topics and functionalities are constantly being added to conversational AI agents like Amazon Alexa and Apple Siri.As data collection and annotation is not scalable and is often costly, only a handful of examples for the new functionalities are available, which results in poor generalization performance.We formulate it as a Few-Shot Integration (FSI) problem where a few examples are used to introduce a new intent.In this paper, we study six feature space data augmentation methods to improve classification performance in FSI setting in combination with both supervised and unsupervised representation learning methods such as BERT.Through realistic experiments on two public conversational datasets, SNIPS, and the Facebook Dialog corpus, we show that data augmentation in feature space provides an effective way to improve intent classification performance in fewshot setting beyond traditional transfer learning approaches.In particular, we show that (a) upsampling in latent space is a competitive baseline for feature space augmentation (b) adding the difference between two examples to a new example is a simple yet effective data augmentation method.
Factors Influencing the Development of Digital Competence in Teachers: Analysis of the Teaching Staff of Permanent Education Centres

The technological development of recent decades has had an impact on the lifestyle of adults.As a result, it has been necessary for citizens to adapt and learn how to use technological resources, an essential requirement to foster employability.This requirement acquires a special nuance within Permanent Education, which mainly involves adults who would like to cover some training need.The aims of this study were to analyze the digital competence of Permanent Education teachers, and to determine the factors that have an impact on its development.Therefore, a quantitative methodology has been used, whereby a sample of Permanent Education teachers in the region of Andalusia, Spain, were asked to complete a questionnaire on digital competence (n = 140).The results confirmed the low digital competence of teachers, and how some factors such as age, kind of centre, prior training on ICT, degree, teaching experience and professional category affect the development of digital competence.The last part of the study discussed the potential explanations for the study findings and suggestions for future research.
Ensemble Distribution Distillation

Ensembles of models often yield improvements in system performance. These ensemble approaches have also been empirically shown to yield robust measures of uncertainty, and are capable of distinguishing between different forms of uncertainty. However, ensembles come at a computational and memory cost which may be prohibitive for many applications. There has been significant work done on the distillation of an ensemble into a single model. Such approaches decrease computational cost and allow a single model to achieve an accuracy comparable to that of an ensemble. However, information about the diversity of the ensemble, which can yield estimates of different forms of uncertainty, is lost. This work considers the novel task of Ensemble Distribution Distillation (EnD^2) - distilling the distribution of the predictions from an ensemble, rather than just the average prediction, into a single model. EnD^2 enables a single model to retain both the improved classification performance of ensemble distillation as well as information about the diversity of the ensemble, which is useful for uncertainty estimation. A solution for EnD^2 based on Prior Networks, a class of models which allow a single neural network to explicitly model a distribution over output distributions, is proposed in this work. The properties of EnD^2 are investigated on both an artificial dataset, and on the CIFAR-10, CIFAR-100 and TinyImageNet datasets, where it is shown that EnD^2 can approach the classification performance of an ensemble, and outperforms both standard DNNs and Ensemble Distillation on the tasks of misclassification and out-of-distribution input detection.
CEFL: Online Admission Control, Data Scheduling, and Accuracy Tuning for Cost-Efficient Federated Learning Across Edge Nodes

With the proliferation of Internet of Things (IoT), zillions of bytes of data are generated at the network edge, incurring an urgent need to push the frontiers of artificial intelligence (AI) to network edge so as to fully unleash the potential of the IoT big data. To materialize such a vision which is known as edge intelligence, federated learning is emerging as a promising solution to enable edge nodes to collaboratively learn a shared model in a privacy-preserving and communication-efficient manner, by keeping the data at the edge nodes. While pilot efforts on federated learning have mostly focused on reducing the communication overhead, the computation efficiency of those resource-constrained edge nodes has been largely overlooked. To bridge this gap, in this article, we investigate how to coordinate the edge and the cloud to optimize the system-wide cost efficiency of federated learning. Leveraging the Lyapunov optimization theory, we design and analyze a cost-efficient optimization framework CEFL to make online yet near-optimal control decisions on admission control, load balancing, data scheduling, and accuracy tuning for the dynamically arrived training data samples, reducing both computation and communication cost. In particular, our control framework CEFL can be flexibly extended to incorporate various design choices and practical requirements of federated learning, such as exploiting the cheaper cloud resource for model training with better cost efficiency yet still facilitating on-demand privacy preservation. Via both rigorous theoretical analysis and extensive trace-driven evaluations, we verify the cost efficiency of our proposed CEFL framework.
Blockchain-Based Privacy-Aware Content Caching in Cognitive Internet of Vehicles

The Cognitive Internet of Vehicles (CIoV) introduces a cognitive engine in the traditional Internet of Vehicles, which can realize more intelligent functions such as vehicle deployment and resource allocation. Especially in terms of content caching, the cognitive engine can perceive the content requirements of users and match the content providers and content requesters to improve the caching hit rate. However, during this process, users may worry that their privacy data may be leaked. Content requesters need to submit data about points of interest in contents, which is part of their sensitive information. In addition, in terms of rapid speed of vehicles, the connection time is limited, such as vehicle-to-vehicle and vehicle-to-roadside unit (RSU), which leads to limited transaction time during obtaining contents. In order to solve these problems, in this article, we propose a blockchain-based privacy-aware content caching architecture in CIoV. In general, when vehicles need contents, in order to protect the privacy of vehicles, it is no longer necessary to submit a request to an RSU, but through broadcasting contents from an RSU or surrounding vehicles, where vehicles can selectively obtain contents. To improve the cache hit rate, the cognitive engine will perceive content requirements and recommend relevant content requirements to an RSU or content-providing vehicles based on machine learning or deep learning methods. However, this method of content acquisition will connect different vehicles, which brings the untrusted problem. Both content requesters and providers may worry about untrusted users connected with them. To this end, we adopt the blockchain technology to record the completed content transactions, which are written into the block after the consensus mechanism is completed, thus solving the problem of distrust between vehicles. Experiments demonstrate the privacy-aware content caching architecture based on blockchains effectiveness.
Image Classification using SVM and CNN

On the surface, teaching a computer to do something like image classification seemed very intriguing to us. Moreover, there are countless real-world applications of this concept. It is in light of these reasons that we decided to work on Image Classification. Thankfully though, this topic has been well-researched by the scientific community and we didn’t break a sweat finding resources to learn from. So naturally, we perused a bunch of research papers that dealt with image classification, each from a different perspective. We then decided to implement image classification on a small-scale with the limited hardware we were in possession of. As difficult as it was, we started with SVM and a very small dataset to achieve an accuracy of 93%. Although SVM is a very strong technique, achieving such a high accuracy is still an anomaly. We realized that our results boasted such a high accuracy due to the lack of a large enough dataset. So, using data augmentation, we more than tripled the size of our dataset. On performing SVM now, we achieved an accuracy of 82%, a significant decrease. Unsatisfied with the results, we decided to move to other deep learning techniques. This quest led us to Neural Networks and, CNN. On successfully implementing CNN, we achieved an accuracy of a staggering 93.57% on the very same dataset. This stands as a testimony to the increased potential of deep learning techniques over the more traditional machine learning techniques.
Resource-Efficient Chemistry on Quantum Computers with the Variational Quantum Eigensolver and the Double Unitary Coupled-Cluster Approach

Applications of quantum simulation algorithms to obtain electronic energies of molecules on noisy intermediate-scale quantum (NISQ) devices require careful consideration of resources describing the complex electron correlation effects. In modeling second-quantized problems, the biggest challenge confronted is that the number of qubits scales linearly with the size of the molecular basis. This poses a significant limitation on the size of the basis sets and the number of correlated electrons included in quantum simulations of chemical processes. To address this issue and enable more realistic simulations on NISQ computers, we employ the double unitary coupled-cluster (DUCC) method to effectively downfold correlation effects into the reduced-size orbital space, commonly referred to as the active space. Using downfolding techniques, we demonstrate that properly constructed effective Hamiltonians can capture the effect of the whole orbital space in small-size active spaces. Combining the downfolding preprocessing technique with the variational quantum eigensolver, we solve for the ground-state energy of H2, Li2, and BeH2 in the cc-pVTZ basis using the DUCC-reduced active spaces. We compare these results to full configuration-interaction and high-level coupled-cluster reference calculations.
Feature Interaction for Streaming Feature Selection

Traditional feature selection methods assume that all data instances and features are known before learning. However, it is not the case in many real-world applications that we are more likely faced with data streams or feature streams or both. Feature streams are defined as features that flow in one by one over time, whereas the number of training examples remains fixed. Existing streaming feature selection methods focus on removing irrelevant and redundant features and selecting the most relevant features, but they ignore the interaction between features. A feature might have little correlation with the target concept by itself, but, when it is combined with some other features, they can be strongly correlated with the target concept. In other words, the interactive features contribute to the target concept as an integer greater than the sum of individuals. Nevertheless, most of the existing streaming feature selection methods treat features individually, but it is necessary to consider the interaction between features. In this article, we focus on the problem of feature interaction in feature streams and propose a new streaming feature selection method that can select features to interact with each other, named Streaming Feature Selection considering Feature Interaction (SFS-FI). With the formal definition of feature interaction, we design a new metric named interaction gain that can measure the interaction degree between the new arriving feature and the selected feature subset. Besides, we analyzed and demonstrated the relationship between feature relevance and feature interaction. Extensive experiments conducted on 14 real-world microarray data sets indicate the efficiency of our new method.
Vector-Quantized Autoregressive Predictive Coding

Autoregressive Predictive Coding (APC), as a self-supervised objective, has enjoyed success in learning representations from large amounts of unlabeled data, and the learned representations are rich for many downstream tasks. However, the connection between low self-supervised loss and strong performance in downstream tasks remains unclear. In this work, we propose Vector-Quantized Autoregressive Predictive Coding (VQ-APC), a novel model that produces quantized representations, allowing us to explicitly control the amount of information encoded in the representations. By studying a sequence of increasingly limited models, we reveal the constituents of the learned representations. In particular, we confirm the presence of information with probing tasks, while showing the absence of information with mutual information, uncovering the model's preference in preserving speech information as its capacity becomes constrained. We find that there exists a point where phonetic and speaker information are amplified to maximize a self-supervised objective. As a byproduct, the learned codes for a particular model capacity correspond well to English phones.
SPICE: Semantic Pseudo-Labeling for Image Clustering

The similarity among samples and the discrepancy among clusters are two crucial aspects of image clustering. However, current deep clustering methods suffer from inaccurate estimation of either feature similarity or semantic discrepancy. In this paper, we present a Semantic Pseudo-labeling-based Image ClustEring (SPICE) framework, which divides the clustering network into a feature model for measuring the instance-level similarity and a clustering head for identifying the cluster-level discrepancy. We design two semantics-aware pseudo-labeling algorithms, prototype pseudo-labeling and reliable pseudo-labeling, which enable accurate and reliable self-supervision over clustering. Without using any ground-truth label, we optimize the clustering network in three stages: 1) train the feature model through contrastive learning to measure the instance similarity; 2) train the clustering head with the prototype pseudo-labeling algorithm to identify cluster semantics; and 3) jointly train the feature model and clustering head with the reliable pseudo-labeling algorithm to improve the clustering performance. Extensive experimental results demonstrate that SPICE achieves significant improvements (~10%) over existing methods and establishes the new state-of-the-art clustering results on six balanced benchmark datasets in terms of three popular metrics. Importantly, SPICE significantly reduces the gap between unsupervised and fully-supervised classification; e.g. there is only 2% (91.8% vs 93.8%) accuracy difference on CIFAR-10. Our code is made publicly available at https://github.com/niuchuangnn/SPICE.
Measuring Reading Comprehension with the Lexile Framework

Abstract Implicit in the idea of measurement is the concept of objectivity. When we measure the temperature using a thermometer, we assume that the measurement we obtain is not dependent on the conditions of measurement, such as which thermometer we use. Any functioning thermometer should give us the same reading of, for example, 75 °F. If one thermometer measured 40 °, another 250 and a third 150, then the lack of objectivity would invalidate the very idea of accurately measuring temperature.
Outlier Detection in Wearable Sensor Data for Human Activity Recognition (HAR) Based on DRNNs

Wearable sensors provide a user-friendly and non-intrusive mechanism to extract user-related data that paves the way to the development of personalized applications. Within those applications, human activity recognition (HAR) plays an important role in the characterization of the user context. Outlier detection methods focus on finding anomalous data samples that are likely to have been generated by a different mechanism. This paper combines outlier detection and HAR by introducing a novel algorithm that is able both to detect information from secondary activities inside the main activity and to extract data segments of a particular sub-activity from a different activity. Several machine learning algorithms have been previously used in the area of HAR based on the analysis of the time sequences generated by wearable sensors. Deep recurrent neural networks (DRNNs) have proven to be optimally adapted to the sequential characteristics of wearable sensor data in previous studies. A DRNN-based algorithm is proposed in this paper for outlier detection in HAR. The results are validated both for intra- and inter-subject cases and both for outlier detection and sub-activity recognition using two different datasets. A first dataset comprising 4 major activities (walking, running, climbing up, and down) from 15 users is used to train and validate the proposal. Intra-subject outlier detection is able to detect all major outliers in the walking activity in this dataset, while inter-subject outlier detection only fails for one participant executing the activity in a peculiar way. Sub-activity detection has been validated by finding out and extracting walking segments present in the other three activities in this dataset. A second dataset using four different users, a different setting and different sensor devices is used to assess the generalization of results.
Resilient Color Image Watermarking Using Accurate Quaternion Radial Substituted Chebyshev Moments

In this work, a new quaternion-based method for color image watermarking is proposed. In this method, a novel set of quaternion radial substituted Chebyshev moments (QRSCMs) is presented for robust geometrically invariant image watermarking. An efficient computational method is proposed for highly accurate, fast, and numerically stable QRSCMs in polar coordinates. The proposed watermarking method consists of three stages. In the first stage, the Arnold transform is used to improve the security of the watermarking scheme by scrambling the binary watermark. In the second stage, the proposed accurate and stable QRSCMs of the host color image are computed. In the third stage, the encrypted binary watermark is embedded into the host image by employing the quantization technique on selected-magnitude QRSCMs where the watermarked color image is obtained by adding the original host color image to the compensation image. Then, the binary watermark can be extracted directly without using the original image from the magnitudes of QRSCMs. Numerical experiments are performed where the performance of proposed method is compared with the existing quaternion moment-based watermarking methods. The comparison clearly shows that the proposed method is very efficient in terms of the visual imperceptibility capability and the robustness under different attacks compared to the existing quaternion moment-based watermarking algorithms.