Reviewerly/exHarmony huggingface dataset:
The Automatic Text Classification Method Based on BERT and Feature Union

For the traditional model based on the deep learning method most used CNN(convolutional neural networks) or RNN(Recurrent neural Network) model and is based on the dynamic character-level embedding or word-level embedding as input, so there is a problem that the text feature extraction is not comprehensive. In the development environment of the Internet of Things, A method of Automatic text classification based on BERT(Bidirectional Encoder Representations from Transformers) and Feature Fusion was proposed in this paper. Firstly, the text-to-dynamic character-level embedding is transformed by the BERT model, and the BiLSTM(Bi-directional Long-Short Term Memory) and CNN output features are combined and merged to make full use of CNN to extract the advantages of local features and to use BiLSTM to have the advantage of memory to link the extracted context features to better represent the text, so as to improve the accuracy of text classification task. A comparative study with state-of-the-art approaches manifests the proposed method outperforms the state-of-the-art methods in accuracy. It can effectively improve the accuracy of tag prediction for text data with sequence features and obvious local features.
Adaptive Similarity Embedding for Unsupervised Multi-View Feature Selection

Multi-view learning has become a significant research topic in image processing, data mining and machine learning due to the proliferation of multi-view data. Considering the difficulty in obtaining labeled data in many real applications, we focus on the multi-view unsupervised feature selection problem. Most existing multi-view feature selection introduce an identical similarity matrix among different views, which cannot preserve the specific correlation between each single view. Also, some of these methods just consider either global or local structures. In this paper, we propose an embedding method, Adaptive Similarity Embedding for Unsupervised Multi-View Feature Selection (ASE-UMFS). This method reduces the high-dimensional data to the low dimensions and unifies different views to a combination weight matrix. We also use parameters to constraint the similarity matrix for the local structure, where the regularization term is used to add a prior of uniform distribution; taking into account of the independence in projection matrix among different views, optimization of the similarity matrix is further improved. To confirm the effectiveness of ASE-UMFS, comparisons are made with benchmark algorithm on real-world data sets. The experimental results demonstrate that the proposed algorithm outperforms several state-of-the-art methods in multi-view learning.
How to conceptually engineer conceptual engineering?

Conceptual engineering means to provide a method to assess and improve our concepts working as cognitive devices. But conceptual engineering still lacks an account of what concepts are as cognitive devices and of what engineering is in the case of conceptual cognition. And without such prior understanding of its subject matter, or so it is claimed here, conceptual engineering is bound to remain useless, merely operating as a piecemeal approach, with no overall grip on its target domain. The purpose of this programmatic paper is to overcome this knowledge gap by providing some guidelines for developing the theories of concepts and of engineering (viz. 'cognitive engineering') that are presumably needed so as to make conceptual engineering an actionable method for the cognitive optimization of our conceptual devices.
Class Boundary Exemplar Selection Based Incremental Learning for Automatic Target Recognition

When adding new tasks/classes in an incremental learning scenario, the previous recognition capabilities trained on the previous training data can be lost. In the real-life application of automatic target recognition (ATR), part of the previous samples may be able to be used. Most incremental learning methods have not considered how to save the previous key samples. In this article, the class boundary exemplar selection-based incremental learning (CBesIL) is proposed to save the previous recognition capabilities in the form of the class boundary exemplars. For exemplar selection, the class boundary selection method based on local geometrical and statistical information is proposed. And when adding new classes continually, a class-boundary-based data reconstruction method is introduced to update the exemplar set. Thus, when adding new classes, the previous class boundaries could be kept complete. Experimental results demonstrate that the proposed CBesIL outperforms the other state of the art on the accuracy of multiclass recognition and class-incremental recognition.
NPMML: A Framework for Non-interactive Privacy-preserving Multi-party Machine Learning

In the recent decade, deep learning techniques have been widely adopted for founding artificial Intelligent applications, which led to successes in many data analysis tasks, such as risk assessment, medical predictions, and face recognition. Since the effectiveness of deep learning is directly proportional to the amount of data available, a large-scale collection of massive data is essential. Considering privacy and security concerns often prevent data owners from contributing sensitive data for training, researchers proposed several techniques to provide privacy guarantees of data in machine learning systems that contains multiple parties. However, all these works incurred frequent interactions between data owners during training, such that they came at a high communicational cost for data owners. To this end, in this article, we propose a new server-aid framework called non-interactive privacy-preserving multi-party machine learning (NPMML), which supports secure machine learning tasks without the participation of data owners. The NPMML framework significantly reduces data owners’ communicational overheads in multi-party machine learning. Moreover, we design a concrete construction for multi-layer neural networks based on NPMML. Finally, we evaluate the performance of NPMML by prototype implementation. The experimental result demonstrates that NPMML is communicational-efficient for data owners.
Are We <i>There</i> Yet? Comparing Remote Learning Technologies in the University Classroom

Telepresence robots can empower people to work, play, and learn along with others, despite geographic distance. To investigate the use of telepresence robots for remote attendance of university-level classes, we conducted a study in four courses at our university. We compared student experiences attending class during three distinct phases in three different ways: in person, via state-of-the-art university distance learning tools (DLT), and via a telepresence robot. The results from N = 18 student participants revealed that although class attendance method preferences were split between in-person and DLT attendance, students felt more present, self-aware, and expressive when using a telepresence robot than when using DLT. The instructors of the courses uniformly preferred in-person attendance, but they noted that for remote learning, telepresence would be preferable to DLT use. This work can help to inform telepresence robotics and higher education researchers who wish to improve distance learning technologies.
Modeling and reasoning of IoT architecture in semantic ontology dimension

The architecture for IoT is the primary foundation for designing and implementing the System of Internet of things. This paper discusses the theory, method, tools and practice of modeling and reasoning the architecture of the Internet of Things system from the dimension of semantic ontology. This paper breaks the way of static ontology modeling, and proposes an implementation framework for real-time and dynamic ontology modeling of the IoT system from the running system. According to the actual needs of the health cabin IoT system and the combination of theory and practice, the system architecture model of the semantic ontology dimension of IoT is built. Then, based on the reasoning rules of the ontology model, the model is reasoned by Pellet reasoning engine which injects the atom of the custom reasoning built-ins into the source code. In this way we have realized the automatic classification and attribute improvement of resources and behaviors of the IoT system, the real-time working state detection and fault diagnosis of the IoT system, and the automatic control of the IoT system and resources.
Depth-Wise Asymmetric Bottleneck With Point-Wise Aggregation Decoder for Real-Time Semantic Segmentation in Urban Scenes

Semantic segmentation is a process of linking each pixel in an image to a class label, and is widely used in the field of autonomous vehicles and robotics.Although deep learning methods have already made great progress for semantic segmentation, they either achieve great results with numerous parameters or design lightweight models but heavily sacrifice the segmentation accuracy.Because of the strict requirements of real-world applications, it is critical to design an effective real-time model with both competitive segmentation accuracy and small model capacity.In this paper, we propose a lightweight network named DABNet, which employs Depth-wise Asymmetric Bottleneck (DAB) and Point-wise Aggregation Decoder (PAD) module to tackle the challenging real-time semantic segmentation in urban scenes.Specifically, the DAB module creates a sufficient receptive field and densely utilizes the contextual information, and the PAD module aggregates the feature maps of different scales to optimize performance through the attention mechanism.Compared with existing methods, our network substantially reduces the number of parameters but still achieves high accuracy with real-time inference ability.Extensive ablation experiments on two challenging urban scene datasets (Cityscapes and CamVid) have proved the effectiveness of the proposed approach in real-time semantic segmentation.
Actions Speak Louder Than (Pass)words: Passive Authentication of Smartphone Users via Deep Temporal Features

Prevailing user authentication schemes on smartphones rely on explicit user interaction, where a user types in a passcode or presents a biometric cue such as face, fingerprint, or iris. In addition to being cumbersome and obtrusive to the users, such authentication mechanisms pose security and privacy concerns. Passive authentication systems can tackle these challenges by unobtrusively monitoring the user’s interaction with the device. We propose a Siamese Long Short-Term Memory (LSTM) network architecture for passive authentication, where users can be verified without requiring any explicit authentication step. On a dataset comprising of measurements from 30 smartphone sensor modalities for 37 users, we evaluate our approach on 8 dominant modalities, namely, keystroke dynamics, GPS location, accelerometer, gyroscope, magnetometer, linear accelerometer, gravity, and rotation sensors. Experimental results find that a genuine user can be correctly verified 96.47% a false accept rate of 0.1% within 3 seconds.
Adversarial Attacks on Spoofing Countermeasures of Automatic Speaker Verification

High-performance spoofing countermeasure systems for automatic speaker verification (ASV) have been proposed in the ASVspoof 2019 challenge. However, the robustness of such systems under adversarial attacks has not been studied yet. In this paper, we investigate the vulnerability of spoofing countermeasures for ASV under both white-box and black-box adversarial attacks with the fast gradient sign method (FGSM) and the projected gradient descent (PGD) method. We implement high-performing countermeasure models in the ASVspoof 2019 challenge and conduct adversarial attacks on them. We compare performance of black-box attacks across spoofing countermeasure models with different network architectures and different amount of model parameters. The experimental results show that all implemented countermeasure models are vulnerable to FGSM and PGD attacks under the scenario of white-box attack. The more dangerous black-box attacks also prove to be effective by the experimental results.
Di-DE: Depth Information-Based Differential Evolution With Adaptive Parameter Control for Numerical Optimization

Differential Evolution (DE) is a simple and effective stochastic algorithm for optimization problems, and it became much more popular in recent year because of its easy-implementation and excellent performance.Nevertheless, the performance of DE algorithm is greatly affected by the trial vector generation strategy including mutation strategy and parameter control, both of which still exist some weaknesses, e.g. the premature convergence to some local optima of a mutation strategy and the misleading interaction among control parameters.Therefore in this paper, a novel Di-DE algorithm is proposed to tackle these weaknesses.A depth information based external archive was advanced in our novel mutation strategy, which can get a better perception of the landscape of objective in an optimization.Moreover, a novel grouping strategy was also employed in Di-DE and parameters were updated separately so as to avoid the misleading among parameters.Moreover, a cooperative strategy for information interchange was also advanced aiming at improving the efficiency of the exploration behavior.By absorbing these advancements, the novel Di-DE algorithm can secure better performance in comparison with other famous optimization algorithms.The algorithm validation was conducted on CEC2013 and CEC2017 test suites, and the results revealed the competitiveness of our Di-DE algorithm in comparison with those famous optimization algorithms including Particle Swarm Optimization (PSO) variants, QUasi-Affine TRansformation Evolution (QUATRE) variants and Differential Evolution (DE) variants.
Enhancing a machine learning binarization framework by perturbation operators: analysis on the multidimensional knapsack problem

Solving combinatorial optimization problems is of great interest in the areas of computer science and operations research. Optimization algorithms and particularly metaheuristics are constantly improved in order to reduce execution times, increase the quality of solutions and address larger instances. In this work, an improvement of the binarization framework which uses the K-means technique is developed. To achieve this, a perturbation operator based on the K-nearest neighbor technique is incorporated into the framework with the aim of generating more robust binarized algorithms. The technique of K-nearest neighbors is used for improving the properties of diversification and intensification of metaheuristics in its binary version. The contribution of the K-nearest neighbors perturbation operator to the final results is systematically analyzed. Particle Swarm Optimization and Cuckoo Search are used as metaheuristic techniques. To verify the results, the well-known multidimensional knapsack problem is tackled. A computational comparison is made with the state-of-the-art of metaheuristic techniques that use general mechanisms of binarization. The results show that our improved framework produces consistently better results. In this sense, the contribution of the operator which uses the K-nearest neighbors technique is investigated finding that this operator contributes significantly to the quality of the results.
Discriminative Layer Pruning for Convolutional Neural Networks

The predictive ability of convolutional neural networks (CNNs) can be improved by increasing their depth. However, increasing depth also increases computational cost significantly, in terms of both floating point operations and memory consumption, hindering applicability on resource-constrained systems such as mobile and internet of things (IoT) devices. Fortunately, most networks have spare capacity, that is, they require fewer parameters than they actually have to perform accurately. This motivates network compression methods, which remove or quantize parameters to improve resource-efficiency. In this work, we consider a straightforward strategy for removing entire convolutional layers to reduce network depth. Since it focuses on depth, this approach not only reduces memory usage, but also reduces prediction time significantly by mitigating the serialization overhead incurred by forwarding through consecutive layers. We show that a simple subspace projection approach can be employed to estimate the importance of network layers, enabling the pruning of CNNs to a resource-efficient depth within a given network size constraint. We estimate importance on a subspace computed using Partial Least Squares, a feature projection approach that preserves discriminative information. Consequently, this importance estimation is correlated to the contribution of the layer to the classification ability of the model. We show that cascading discriminative layer pruning with filter-oriented pruning improves the resource-efficiency of the resulting network compared to using any of them alone, and that it outperforms state-of-the-art methods. Moreover, we show that discriminative layer pruning alone, without cascading, achieves competitive resource-efficiency compared to methods that prune filters from all layers.
Investigating digital doings through breakdowns: a sociomaterial ethnography of a Bring Your Own Device school

As personal digital devices have become increasingly embedded in schools, what they do tends to silently fall into the background and generally be taken for granted. In order to scrutinize the silent doings of digital devices, this paper explores how attending to breakdowns as a methodological heuristic device can be an insightful entry point to surface and analyze these doings. By conducting a sociomaterial ethnographic study in one Bring Your Own Device (BYOD) school, this paper disentangles four different breakdowns that occurred during the ethnographic study. The analysis reveals a particular mode of digital doings at school, coined as digital pedagogics, that characterizes the school space–time as plastic and poly-synchronous and curates lesson activities. Moreover, this mode is heavily conditioned by and through the school’s sociomaterial infrastructure. The paper concludes that in the wake of the breakdowns, the school’s infrastructure comes into being not merely as a bundle of cables and basic structures that make the school function, but more importantly as a complex configuration of clouds, software and interfaces, algorithms and patterns, standards, protocols and negotiations. This sets the condition for framing contemporary digitized schools within a particular techno-scholastic milieu.
Inaccurate Labels in Weakly-Supervised Deep Learning: Automatic Identification and Correction and Their Impact on Classification Performance

In data-driven deep learning-based modeling, data quality may substantially influence classification performance. Correct data labeling for deep learning modeling is critical. In weakly-supervised learning, a challenge lies in dealing with potentially inaccurate or mislabeled training data. In this paper, we proposed an automated methodological framework to identify mislabeled data using two metric functions, namely, Cross-entropy Loss that indicates divergence between a prediction and ground truth, and Influence function that reflects the dependence of a model on data. After correcting the identified mislabels, we measured their impact on the classification performance. We also compared the mislabeling effects in three experiments on two different real-world clinical questions. A total of 10,500 images were studied in the contexts of clinical breast density category classification and breast cancer malignancy diagnosis. We used intentionally flipped labels as mislabels to evaluate the proposed method at a varying proportion of mislabeled data included in model training. We also compared the effects of our method to two published schemes for breast density category classification. Experiment results show that when the dataset contains 10% of mislabeled data, our method can automatically identify up to 98% of these mislabeled data by examining/checking the top 30% of the full dataset. Furthermore, we show that correcting the identified mislabels leads to an improvement in the classification performance. Our method provides a feasible solution for weakly-supervised deep learning modeling in dealing with inaccurate labels.
Deep Semantic Reconstruction Hashing for Similarity Retrieval

Hashing has shown enormous potentials in preserving semantic similarity for large-scale data retrieval. Existing methods widely retain the similarity within two binary codes towards their discrete semantic affinity, i.e., 1 or -1. However, such a discrete reconstruction approach has obvious drawbacks. First, two unrelated dissimilar samples would have similar binary codes when both of them are the most dissimilar with an anchor sample. Second, the fine-grained semantic similarity cannot be shown in the generated binary codes among data with multiple semantic concepts. Furthermore, existing approaches generally adopt a point-wise error-minimizing strategy to enforce the real-valued codes close to its associated discrete codes, resulting in the well-learned paired semantic similarity being unintentionally damaged when performing quantization. To address these issues, we propose a novel deep hashing method with pairwise similarity-preserving quantization constraint, termed Deep Semantic Reconstruction Hashing (DSRH), which defines a high-level semantic affinity within each data pair to learn compact binary codes. Specifically, DSRH is expected to learn the specific binary codes whose similarity can reconstruct their high-level semantic similarity. Besides, we adopt a pairwise similarity-preserving quantization constraint instead of the traditional point-wise quantization technique, which is conducive to maintain the well-learned paired semantic similarity when performing quantization. Extensive experiments are conducted on four representative image retrieval benchmarks, and the proposed DSRH outperforms the state-of-the-art deep-learning methods with respect to different evaluation metrics.
A ROI-guided deep architecture for robust facial expressions recognition

This work proposes a robust facial expression recognition framework, focusing on discovering the region of interest (ROI) to train an effective face-specific of convolutional neural networks (CNN). By exploiting the relationships among ROI areas, the proposed deep architecture can improve the reliability of predicted targets. Our designed deep model is fine-tuned based on a pre-specified deep CNN instead of a new one trained from scratch. To increase the face expressions toward a robust deep CNN training, a novel data augmentation strategy called artificial face is designed. The performance of our deep architecture is evaluated on state-of-the-art databases such as CK+. To demonstrate the high generalizability of our approach, cross-database validations are conducted on the JAFFE and our own compiled Wild database. Comprehensive experiments have demonstrated the superiority of the method, i.e., achieving a recognition accuracy of 94.67% on the CK+ database, 53.77% on the JAFFE cross-database, 40.13% on the FER-2013 cross-database, and 37.25% on the Wild cross-database respectively.