Reviewerly/exHarmony huggingface dataset:
Data Analysis Model for the Evaluation of the Factors That Influence the Teaching of University Students

Currently, the effects of the pandemic caused by the Coronavirus disease discovered in 2019 are the subject of numerous studies by experts in labor, psychological issues, educational issues, etc. The universities, for their continuity, have implemented various technological tools for the development of their activities, such as videoconference platforms, learning management systems, etc. This experience has led the educational sector to propose new educational models, such as hybrid education, that focus on the use of information technologies. To carry out its implementation, it is necessary to identify the adaptability of students to a technological environment and what the factors are that influence learning. To do this, this article proposes a data analysis framework that identifies the factors and variables of a hybrid teaching environment. The results obtained allow us to determine the level of influence of educational factors that affect learning by applying data analysis algorithms to profile students through a classification based on their characteristics and improve learning methodologies in these educational models. The updating of educational systems requires a flexible process that is aligned with the needs of the students. With this analysis framework, it is possible to create an educational environment focused on students and allows for efficient change with the granular analysis of the state of the learning.
Dirichlet process mixture models for non-stationary data streams

In recent years we have seen a handful of work on inference algorithms over non-stationary data streams. Given their flexibility, Bayesian non-parametric models are a good candidate for these scenarios. However, reliable streaming inference under the concept drift phenomenon is still an open problem for these models. In this work, we propose a variational inference algorithm for Dirichlet process mixture models. Our proposal deals with the concept drift by including an exponential forgetting over the prior global parameters. Our algorithm allows to adapt the learned model to the concept drifts automatically. We perform experiments in both synthetic and real data, showing that the proposed model outperforms state-of-the-art variational methods in density estimation, clustering and parameter tracking.
Few-shot Partial Multi-label Learning with Data Augmentation

Partial multi-label learning (PML) models the scenario where each training sample is annotated with a set of candidate labels, but only a subset of them corresponds to the ground-truths. The key challenge for PML is how to minimize the negative impact of incorrect labels concealed within the candidate ones. Most existing PML solutions require abundant samples to train a noise-robust multi-label predictor. However, due to privacy, safety or ethic issues, we more often have a handful of training samples for the target task. In this paper, we propose an approach named FsPML-DA (Few-shot Partial Multi-Label Learning with Data Augmentation) to simultaneously estimate label confidence, perform data augmentation and induce multilabel classifier. Specifically, FsPML-DA disambiguates the label confidence vector of each PML sample by jointly modeling the feature and semantic similarity, label credibility of other samples and label co-occurrence. Next, FsPML-DA introduces a synthetic feature network to generate more training samples from pairs of given samples with label confidence values. FsPML-DA then leverages original and generated samples to train a noise-tolerant multi-label classifier. Extensive experiments on benchmark datasets show that FsPML-DA performs better than recent competitive PML baselines and few-shot solutions. FsPML-DA can dislodge noisy labels by mining PML data in a sensible way and the proposed data augmentation strategy effectively combats with the scarcity of few-shot training samples.
MSDN: A Multi-Subspace Deviation Net for Anomaly Detection

General anomaly detection techniques have always received a lot of attention. Current detection methods usually focus solely on representation learning or anomaly judgment. This paper proposes a Multi-Subspace Deviation Network (MSDN) framework to build a model combining feature learning with anomaly score learning under the condition that a small number of labeled anomalies can be observed. Concretely, our framework combines a feature learner with two specific projectors: a self-supervised projector and an anomaly score learner. We utilize random affine transformations to map the raw data to multiple subspaces and train a classifier to predict the transformation label in the self-supervised module. Anomaly scores are then obtained directly from a deviation network, where the contrastive loss is used to amplify the gap in the anomaly scores between normal objects and anomalies. Extensive experiments on eight datasets show that our proposed method achieves higher detection accuracy than previous schemes with fewer observed anomalies.
A Bi-directional Recommender System for Online Recruitment

Most existing recommendation research has been concentrated on unidirectional recommendation, i.e. only recommending items to users. However, in many real-world scenarios, the platform needs to achieve bi-directional recommendation. For example, in an online recruitment scenario, the recommender system not only needs to recommend positions to candidates, but also recommend candidates to enterprises. In this paper, we first formalize a new recommendation problem called bi-directional recommendation and contribute a new bidirectional recommendation model named BiROR (Bi-directional Recommendation for Online Recruitment). In BiROR, an encoder component is utilized to learn the text embeddings, and a graph learning component is designed to learn the graph embeddings. In addition, a multi-task learning framework is designed to achieve bi-directional recommendation. In the multi-task learning framework, we share the text embeddings and graph embeddings to alleviate the problems of data sparsity and data asymmetry in online recruitment. Extensive experiments in a real-world task show that BiROR outperforms the state-of-the-art methods, verifying the effectiveness of the designs of our model.
Deep Spatial Domain Generalization

Spatial autocorrelation and spatial heterogeneity widely exist in spatial data, which make the traditional machine learning model perform badly. Spatial domain generalization is a spatial extension of domain generalization, which can generalize to unseen spatial domains in continuous 2D space. Specifically, it learns a model under varying data distributions that generalizes to unseen domains. Although tremendous success has been achieved in domain generalization, there exist very few works on spatial domain generalization. The advancement of this area is challenged by: 1) Difficulty in characterizing spatial heterogeneity, and 2) Difficulty in obtaining predictive models for unseen locations without training data. To address these challenges, this paper proposes a generic framework for spatial domain generalization. Specifically, We develop the spatial interpolation graph neural network <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> that handles spatial data as a graph and learns the spatial embedding on each node and their relationships. The spatial interpolation graph neural network infers the spatial embedding of an unseen location during the test phase. Then the spatial embedding of the target location is used to decode the parameters of the downstream-task model directly on the target location. Finally, extensive experiments on ten real-world datasets demonstrate the proposed method’s strength. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> https://github.com/dyu62/Deep-domain-generalization
Backpropagation-free Graph Neural Networks

We propose a class of neural models for graphs that do not rely on backpropagation for training, thus making learning more biologically plausible and amenable to parallel implementation in hardware. The base component of our architecture is a generalization of Gated Linear Networks which allows the adoption of multiple graph convolutions. Specifically, each neuron is defined as a set of graph convolution filters (weight vectors) and a gating mechanism that, given a node and its topological context, selects the weight vector to use for processing the node’s attributes. Two different graph processing schemes are studied, i.e., a message-passing aggregation scheme where the gating mechanism is embedded directly into the graph convolution, and a multi-resolution one where neighbouring nodes at different topological distances are jointly processed by a single graph convolution layer. We also compare the effectiveness of different alternatives for defining the context function of a node, i.e., based on hyper-planes or on prototypes. A theoretical result on the expressiveness of the proposed models is also reported. We experimented our backpropagation-free graph convolutional neural architectures on commonly adopted node classification datasets, and show competitive performances compared to the backpropagation-based counterparts.
Research on Enhancing Vocational Competence in Computer and Information Engineering Talents Based on Big Data Analysis

Through collecting the employment situation of computer and information majors in our university and analyzing the employment data, we found that the employment quality of graduates with various vocational certifications is generally better. Therefore, our university takes the implementation of the national "1+X" certificate system as an opportunity to conduct research on the cultivation of engineering talents in computer and information majors. In the research process, we adopt the CDIO concept, fully consider the needs of engineering talents cultivation, and gradually realize the integration of professional teaching standards and professional skill level certificate standards, curriculum system integration, and the integration of "Web front-end development" professional skill level certificate standards. The integration of professional teaching standards and professional skill level certificate standards, the integration of curriculum system and practical training system, the construction of a good channel for the cultivation of engineering talents, and the improvement of graduates’ vocational ability.
Query-Efficient Target-Agnostic Black-Box Attack

Adversarial attacks have recently been proposed to scrutinize the security of deep neural networks. Most blackbox adversarial attacks, which have partial access to the target through queries, are target-specific; e.g., they require a well-trained surrogate that accurately mimics a given target. In contrast, target-agnostic black-box attacks are developed to attack any target; e.g., they learn a generalized surrogate that can adapt to any target via fine-tuning on samples queried from the target. Despite their success, current state-of-the-art target-agnostic attacks require tremendous fine-tuning steps and consequently an immense number of queries to the target to generate successful attacks. The high query complexity of these attacks makes them easily detectable and thus defendable. We propose a novel query-efficient target-agnostic attack that trains a generalized surrogate network to output the adversarial directions iv.r.t. the inputs and equip it with an effective fine-tuning strategy that only fine-tunes the surrogate when it fails to provide useful directions to generate the attacks. Particularly, we show that to effectively adapt to any target and generate successful attacks, it is sufficient to fine-tune the surrogate with informative samples that help the surrogate get out of the failure mode with additional information on the target’s local behavior. Extensive experiments on CIFAR10 and CIFAR-100 datasets demonstrate that the proposed target-agnostic approach can generate highly successful attacks for any target network with very few fine-tuning steps and thus significantly smaller number of queries (reduced by several order of magnitudes) compared to the state-of-the-art baselines.
HP-GMN: Graph Memory Networks for Heterophilous Graphs

Graph neural networks (GNNs) have achieved great success in various graph problems. However, most GNNs are Message Passing Neural Networks (MPNNs) based on the homophily assumption, where nodes with the same label are connected in graphs. Real-world problems bring us heterophily problems, where nodes with different labels are connected in graphs. MPNNs fail to address the heterophily problem because they mix information from different distributions and are not good at capturing global patterns. Therefore, we investigate a novel Graph Memory Networks model on Heterophilous Graphs (HP-GMN) to the heterophily problem in this paper. In HP-GMN, local information and global patterns are learned by local statistics and the memory to facilitate the prediction. We further propose regularization terms to help the memory learn global information. We conduct extensive experiments to show that our method achieves state-of-the-art performance on both homophilous and heterophilous graphs. The code of this paper can be found at https://github.com/junjie-xu/HP-GMN.
Unsupervised Deep Subgraph Anomaly Detection

Effectively mining anomalous subgraphs in networks is crucial for many application scenarios, such as disease outbreak detection, financial fraud detection, and activity monitoring in social networks. Identifying anomalous subgraphs is extremely challenging due to their complex topological structures and high-dimensional attributes, various notions of anomalies, and the exponentially large subgraph space in a given graph. Existing classical shallow models typically rely on handcrafted anomaly measure functions, which cannot handle common situations when such prior knowledge is unavailable. Recently, deep learning-based methods provide an end-to-end way that learns the anomaly measure functions. However, although they have achieved great success in detecting node-level, edge-level, and graph-level anomalies, detecting anomalous at the subgraph level has been largely under-explored due to enormous difficulties in subgraph representation learning, supervision, and end-to-end anomaly quantification. To circumvent the above mentioned challenges, this paper proposes a novel deep framework named Anomalous Subgraph Autoencoder (AS-GAE) to extract the anomalous subgraphs in an unsupervised and weakly supervised manner. Specifically, we first develop a location-aware graph auto-encoder to uncover the anomalous areas in the given graph according to the mismatch during the reconstruction. Then a supermodular graph scoring function module is proposed to assign reasonable anomaly scores to the subgraphs in the extracted anomalous areas. The superiority of our proposed method was demonstrated through extensive experiments on two synthetic datasets and nine real-world datasets.
NeuCEPT: Learn Neural Networks’ Mechanism via Critical Neurons with Precision Guarantee

Despite recent studies on understanding deep neural networks (DNNs), there exists numerous questions on how DNNs generate their predictions. Especially, given similar predictions on different inputs, are the underlying mechanisms generating those predictions the same? In this work, we propose NeuCEPT, a method to identify critical neurons that are important to the model’s local predictions and learn their underlying mechanisms. We first formulate a critical neurons identification problem as maximizing a sequence of mutual-information objectives and provide a theoretical framework to efficiently solve for critical neurons while keeping the precision under control. NeuCEPT next heuristically learns different model’s mechanisms in an unsupervised manner. Our experiments and case studies show that neurons identified by NeuCEPT not only have strong influence on the model’s predictions but also hold meaningful information about model’s mechanisms.
Fast Stochastic Recursive Momentum Methods for Imbalanced Data Mining

Standard deep learning models have been mainly designed for balanced data mining tasks and use accuracy to evaluate the classifier. However, in many real-world applications, the distribution of data is skewed. If the standard models, which are designed to optimize the accuracy, are applied to the imbalanced data, the prediction performance could be poor because the model bias towards the majority class. To address the imbalanced data mining problem, areas under precision-recall curves (AUPRC) was proposed as a good measure to evaluate the performance of prediction models on imbalanced data sets, and shows excellent capability in identifying the models with high predictive power. To improve the performance of models, researchers recently design methods to directly optimize AUPRC for imbalanced data mining. However, these approaches suffer from a high iteration complexity and efficient methods are desired. In this paper, we propose a faster stochastic method (i.e., ROAP) for maximizing the AURPC based on the momentum-based variance reduced technique. Our new method is based on the maximization of non-parametric averaged precision (AP), which is a popular unbiased point estimator of AUPRC, and the optimization objective in this paper can be converted into a sum of dependent compositional functions, where the inner functions rely on random variables of both inner and outer levels. Compared to previous methods, our ROAP algorithm can achieve a lower iteration complexity of $O(\epsilon^{-3})$ for finding an ϵ-stationary solution. Furthermore, we extend our method to an adaptive version (i.e., AROAP) with the same iteration complexity of $O(\epsilon^{-3})$. To the best of our knowledge, this paper is the first work showing that the variance reduction method can be incorporated into maximizing the AURPC for efficient data mining on imbalanced datasets. Finally, we conduct extensive experiments on various imbalanced data sets with different models to demonstrate the efficiency of our new algorithms.
A Generic Graph Sparsification Framework using Deep Reinforcement Learning

The interconnectedness and interdependence of modern graphs are growing ever more complex, causing enormous resources for processing, storage, communication, and decision-making of these graphs. In this work, we focus on the task of graph sparsification: an edge-reduced graph of a similar structure to the original graph is produced while various user-defined graph metrics are largely preserved. Existing graph sparsification methods are mostly sampling-based, which introduce high computation complexity in general and lack of flexibility for a different reduction objective. We present SparRL, the first generic and effective graph sparsification framework enabled by deep reinforcement learning. SparRL can easily adapt to different reduction goals and promise graph-size-independent complexity. Extensive experiments show that SparRL outperforms all prevailing sparsification methods in producing high-quality sparsified graphs concerning a variety of objectives.
Designing Low-Power and High-Speed FPGA-Based Binary Decision Tree Hardware Accelerators

With the rapid development of Internet-of-Things (IoT) technologies and edge computing, identifying efficient solutions for data mining tasks such as classification is becoming crucial. Binary Decision Trees (DT) are one of the most used classifiers, due to their ability to handle a large amount of data with a high accuracy. More important, inference of DTs is well suited to be accelerated through custom hardware designs that allow enabling parallel and power-efficient computations with respect to the software-based counterpart. This is very important especially in the context of edge computing, where the computation is performed locally to the data acquisition with a reduced hardware-power budget. This paper describes a novel approach to design low-power and high-speed FPGA-based hardware accelerators of DTs. It is based on the idea of a novel hardware processing element, the so-called Super PE, that can be configured to execute in parallel the operation of three nodes belonging to two consecutive levels of the tree: a parent and its two children nodes. When implemented on the FPGA platform hosted on the Xilinx Zynq XC7Z020-1CLG400C heterogeneous System on Chip (SoC), the new design has shown a dynamic power reduction of up to 41%, in comparison with previously published designs based on the conventional PE. Moreover, compared to a pure software-based DT implementation, the proposed hardware accelerator, integrated into a complete embedded system, has shown an execution speed-up of about 21 $$\times $$ .
Television Programs Classification via Deep Learning Approach Using SSMI-CNN

The classification of television programs and their identification is a fundamental point for streaming multimedia services, real-time, social communities, video sharing platforms (Youtube, Twitch, Facebook), and in particular television monitoring systems. The objective of the proposed approach is to use a CNN (Convolutional Neural Network) with an SSIM (Structural Similarity Index Measure) to classify perfectly the start and end of programs and the contents present to detect time overruns during a transmission, easily adaptable for multiple systems. A database of discriminatory images used for the interception Title Sequence of the program is built. The few data of a title sequence and its possible change over time would result in a scarcity of data and new training for a neural network. Presenting the proposed approach, the paper provides an overview of the encouraging experimental results and results obtained from CNN training on five sports classes. 95% accuracy levels and long-lasting video ratings are achieved.
‘Just a little hack’: Investigating cultures of content moderation circumvention by Facebook users

As social media platforms adapt their rules to limit the presence, spread, and amplification of harmful content on their services, users develop strategies to circumvent content moderation policies. To better understand cultures of content moderation circumvention, including the types of rules that Facebook users seek to circumvent, we analysed a sample of YouTube videos and Reddit threads in which users discuss content moderation circumvention. We show how Facebook users turn to others across platforms to obtain information about circumvention methods. We observe that these users often discuss overcoming Facebook’s content moderation policies in terms that downplay the significance of their intended actions. We suggest that where Facebook’s policies and enforcement measures fail to deter rule violations that may facilitate harm, Facebook should consider new culture-driven approaches to platform governance that foster prosocial environments and engender compliance with platform rules.
Interacting with the News Through Voice User Interfaces

A key application area for voice user interfaces (VUIs) is news consumption, and there is a need to explore how such interactions are performed in practice. This paper presents a study exploring how novice users navigate the news through VUIs and what interactions point to users’ expectations beyond the VUIs’ current capabilities. A field trial with seven participants in five households and follow-up in-depth interviews with six of the participants were conducted. The analysis provides a detailed picture of how such interactions are practically accomplished and offers insights into the participants’ perspectives on their experiences. The participants had some expectations when interacting with the news through VUIs, but many of these were not met. This paper identifies three key challenges: 1) lack of interactional guidance in news contexts, 2) limited navigation capabilities in news sources, and 3) insufficient support for in-depth exploration of the news, with accompanying design implications for more engaging interactions with VUIs for news purposes.