Reviewerly/exHarmony huggingface dataset:
A Disaggregated Cloud Architecture for Edge Computing

The use of a disaggregated cloud architecture would allow companies and institutions to deploy their own edge computing infrastructure and services by combining their on-premises cloud infrastructure with multiple highly dispersed edge nodes leased from third-party providers.
A Robust AUC Maximization Framework With Simultaneous Outlier Detection and Feature Selection for Positive-Unlabeled Classification

The positive-unlabeled (PU) classification is a common scenario in real-world applications such as healthcare, text classification, and bioinformatics, in which we only observe a few samples labeled as “positive” together with a large volume of “unlabeled” samples that may contain both positive and negative samples. Building robust classifiers for the PU problem is very challenging, especially for complex data where the negative samples overwhelm and mislabeled samples or corrupted features exist. To address these three issues, we propose a robust learning framework that unifies area under the curve maximization (a robust metric for biased labels), outlier detection (for excluding wrong labels), and feature selection (for excluding corrupted features). The generalization error bounds are provided for the proposed model that give valuable insight into the theoretical performance of the method and lead to useful practical guidance, e.g., to train a model, we find that the included unlabeled samples are sufficient as long as the sample size is comparable to the number of positive samples in the training process. Empirical comparisons and two real-world applications on surgical site infection (SSI) and EEG seizure detection are also conducted to show the effectiveness of the proposed model.
Enhancing Joint Entity and Relation Extraction with Language Modeling and Hierarchical Attention

Both entity recognition and relation extraction can benefit from being performed jointly, allowing them to enhance each other. However, existing methods suffer from the sparsity of relevant labels and strongly rely on external natural language processing tools, leading to error propagation. To tackle these problems, we propose an end-to-end joint framework for entity recognition and relation extraction with an auxiliary training objective on language modeling, i.e., learning to predict surrounding words for each word in sentences. Furthermore, we incorporate hierarchical multi-head attention mechanisms into the joint extraction model to capture vital semantic information from the available texts. Experiments show that the proposed approach consistently achieves significant improvements on joint extraction task of entities and relations as compared with strong baselines.
A Framework for the Construction of Upper Bounds on the Number of Affine Linear Regions of ReLU Feed-Forward Neural Networks

We present a framework to derive upper bounds on the number of regions that feed-forward neural networks with ReLU activation functions are affine linear on. It is based on an inductive analysis that keeps track of the number of such regions per dimensionality of their images within the layers. More precisely, the information about the number regions per dimensionality is pushed through the layers starting with one region of the input dimension of the neural network and using a recursion based on an analysis of how many regions per output dimensionality a subsequent layer with a certain width can induce on an input region with a given dimensionality. The final bound on the number of regions depends on the number and widths of the layers of the neural network and on some additional parameters that were used for the recursion. It is stated in terms of the L1-norm of the last column of a product of matrices and provides a unifying treatment of several previously known bounds: Depending on the choice of the recursion parameters that determine these matrices, it is possible to obtain the bounds from Montúfar et al. [1] (2014), Montúfar [2] (2017), and Serra et al. [3] (2017) as special cases. For the latter, which is the strongest of these bounds, the formulation in terms of matrices provides new insight. In particular, by using explicit formulas for a Jordan-like decomposition of the involved matrices, we achieve new tighter results for the asymptotic setting, where the number of layers of the same fixed width tends to infinity.
Analysis of the Effect of QoS on Video Conferencing QoE

Network service providers tend to focus on the quality of service (QoS) they provide to their customers. This entails analysis of various QoS metrics (such as bandwidth, packet loss and jitter) in order to be able to improve their services. This is a single-dimensional approach to a problem that needs to be analyzed not only from a business improvement perspective but also from a customer satisfaction perspective. QoS metrics do not directly translate to customer experience, which is more qualitative than quantitative. Thus, it is necessary to correlate qualitative metrics that customers relate to with quantitative metrics that can be analyzed and improved upon by service providers. This is a non-trivial problem that needs deeper exploration. In this paper, we attempt to correlate video conferencing QoE (Quality of Experience) with network QoS. In order to do this, we developed a novel Docker image called Lime, to be able to automate the experiments and emulate the network environment. We performed 144 separate video conferences under predefined network handicaps (scenarios). We discovered that bandwidth is directly proportional to the perceived quality of the video implying that higher bandwidth is preferred. On the other hand, frequently fluctuating bandwidth quickly reduced the user-opinion, and also resulted in slower subsequent climb in opinion after a period of high fluctuation. This indicated that steady bandwidth is preferred over irregularly increasing bandwidth. Jitter and packet loss were found to contribute to negative user-opinion as well as low bandwidth. Conversely, increasing jitter and packet loss was mostly forgiven if the bandwidth stayed stable and high. Lime is shown to be a novel tool to fulfill requirements related to video conferencing experiments under pre-defined network scenarios.
Iterative Manifold Embedding Layer Learned by Incomplete Data for Large-Scale Image Retrieval

Existing manifold learning methods are not appropriate for image retrieval tasks, because most of them are unable to process query images and they have much greater computational cost especially for large-scale database. Therefore, we propose the iterative manifold embedding (IME) layer, of which the weights are learned offline by an unsupervised strategy, to explore the intrinsic manifolds by incomplete data. On the large-scale database that contains 27 000 images, the IME layer is more than 120 times faster than other manifold learning methods to embed the original representations at query time. We embed the original descriptors of database images that lie on manifold in a high-dimensional space into manifold-based representations iteratively to generate the IME representations in an offline learning stage. According to the original descriptors and the IME representations of database images, we estimate the weights of the IME layer by ridge regression. In the online retrieval stage, we employ the IME layer to map the original representation of a query image with an ignorable time cost (2 ms per image). We experiment on five public standard datasets for image retrieval. The proposed IME layer significantly outperforms the related dimension reduction methods and manifold learning methods. Without postprocessing, our IME layer achieves a boost in the performance of state-of-the-art image retrieval methods with postprocessing on most datasets, and needs less computational cost. The code is available at https://github.com/XJhaoren/IME_layer.
HyperNOMAD

The performance of deep neural networks is highly sensitive to the choice of the hyperparameters that define the structure of the network and the learning process. When facing a new application, tuning a deep neural network is a tedious and time-consuming process that is often described as a “dark art.” This explains the necessity of automating the calibration of these hyperparameters. Derivative-free optimization is a field that develops methods designed to optimize time-consuming functions without relying on derivatives. This work introduces the HyperNOMAD package, an extension of the NOMAD software that applies the MADS algorithm [7] to simultaneously tune the hyperparameters responsible for both the architecture and the learning process of a deep neural network (DNN). This generic approach allows for an important flexibility in the exploration of the search space by taking advantage of categorical variables. HyperNOMAD is tested on the MNIST, Fashion-MNIST, and CIFAR-10 datasets and achieves results comparable to the current state of the art.
Flexible High-Dimensional Unsupervised Learning with Missing Data

The mixture of factor analyzers (MFA) model is a famous mixture model-based approach for unsupervised learning with high-dimensional data. It can be useful, inter alia, in situations where the data dimensionality far exceeds the number of observations. In recent years, the MFA model has been extended to non-Gaussian mixtures to account for clusters with heavier tail weight and/or asymmetry. The generalized hyperbolic factor analyzers (MGHFA) model is one such extension, which leads to a flexible modelling paradigm that accounts for both heavier tail weight and cluster asymmetry. In many practical applications, the occurrence of missing values often complicates data analyses. A generalization of the MGHFA is presented to accommodate missing values. Under a missing-at-random mechanism, we develop a computationally efficient alternating expectation conditional maximization algorithm for parameter estimation of the MGHFA model with different patterns of missing values. The imputation of missing values under an incomplete-data structure of MGHFA is also investigated. The performance of our proposed methodology is illustrated through the analysis of simulated and real data.
High efficiency postprocessing for continuous-variable quantum key distribution: using all raw keys for parameter estimation and key extraction

High efficiency postprocessing of continuous-variable quantum key distribution (CV-QKD) system has a significant impact on the secret key rate and transmission distance of the system. For a quantum channel with unknown prior characteristics, we have to sacrifice part (typically half) of the raw keys to estimate the channel parameters, which is used to assist in error correction and estimate the secret key rate. This introduces a trade-off between the secret key rate and the accuracy of parameter estimation when considering the finite-size effect. In this paper, we propose a high efficiency postprocessing method which uses all the raw keys for both parameter estimation and key extraction. The correcting party uses the extra data (used for phase compensation and synchronization etc.) to roughly estimate the quantum channel parameters, or she can use the results of the last block, then she selects a suitable code to correct the errors between them, finally she recovers the raw keys of the other side by a reverse mapping function after the success of error correction. Then, she uses all the raw keys of both sides for parameter estimation. We show that this method improves the accuracy of parameter estimation and the secret key rate of CV-QKD system.
Distributed access control solution for the IoT based on multi-endorsed attributes and smart contracts

The ever-growing world of the Internet of Things (IoT) is yet to agree on an effective and practical access control solution. To overcome challenges such as limited resources, or unreliable connectivity, a number of schemes offload heavy computations onto a central entity, thus creating a single point of failure. Our contribution consists in the construction of a distributed attribute-based access control mechanism, relying on the blockchain technology to dynamically manage multi-endorsed attributes and trust anchors. The originality of our proposal is multifold. First, it enables the integration of multiple security domains into a single resilient access control system. Second, its focus on attributes offers flexibility, expressiveness, and user-centricity, accommodating the dynamic addition of subjects. Third, our attribute endorsement is open, scalable, and flexible, enabling multiple administrators without sacrificing ease of management. Finally, the final access control decision is taken by the device and only requires local connection to its gateway.
Fast Cosine Similarity Search in Binary Space with Angular Multi-Index Hashing

Given a large dataset of binary codes and a binary query point, we address how to efficiently find <inline-formula><tex-math notation="LaTeX">$K$</tex-math></inline-formula> codes in the dataset that yield the largest cosine similarities to the query. The straightforward answer to this problem is to compare the query with all items in the dataset, but this is practical only for small datasets. One potential solution to enhance the search time and achieve sublinear cost is to use a hash table populated with binary codes of the dataset and then look up the nearby buckets to the query to retrieve the nearest neighbors. However, if codes are compared in terms of cosine similarity rather than the Hamming distance, then the main issue is that the order of buckets to probe is not evident. To examine this issue, we first elaborate on the connection between the Hamming distance and the cosine similarity. Doing this allows us to systematically find the probing sequence in the hash table. However, solving the nearest neighbor search with a single table is only practical for short binary codes. To address this issue, we propose the angular multi-index hashing search algorithm which relies on building multiple hash tables on binary code substrings. The proposed search algorithm solves the exact angular <inline-formula><tex-math notation="LaTeX">$K$</tex-math></inline-formula> nearest neighbor problem in a time that is often orders of magnitude faster than the linear scan baseline and even approximation methods.
Context-Aware Authentication: State-of-the-Art Evaluation and Adaption to the IIoT

Authentication is an important and non-trivial topic for the security of the tremendously growing industrial Internet of Things. Classical authentication methods often do not meet the requirements of IoT networks, where computing power and bandwidth are usually constrained. This overview paper therefore turns its attention to context-aware authentication, a method that uses features of a shared or otherwise known context to mutually authenticate devices. First the idea of context-aware authentication and the state of the art in research is introduced. The work done so far is then evaluated, with a focus on the examined context features and authentication mechanisms. Afterwards it is discussed how context-awareness can be transferred from user-centric towards machine-to-machine authentication in the industrial IoT. The specific requirements are discussed together with use-cases for Smart Logistics and Industry 4.0.
Stochastic Gradient Descent With Hyperbolic-Tangent Decay on Classification

Learning rate scheduler has been a critical issue in the deep neural network training. Several schedulers and methods have been proposed, including step decay scheduler, adaptive method, cosine scheduler and cyclical scheduler. This paper proposes a new scheduling method, named hyperbolic-tangent decay (HTD). We run experiments on several benchmarks such as: ResNet, Wide ResNet and DenseNet for CIFAR-10 and CIFAR-100 datasets, LSTM for PAMAP2 dataset, ResNet on ImageNet and Fashion-MNIST datasets. In our experiments, HTD outperforms step decay and cosine scheduler in nearly all cases, while requiring less hyperparameters than step decay, and more flexible than cosine scheduler. Code is available at https://github.com/BIGBALLON/HTD.
Effect of Ubiquitous Fraction App on Mathematics Learning Achievements and Learning Behaviors of Taiwanese Students in Authentic Contexts

This paper aimed to investigate the effect of a mobile app on mathematics learning in authentic contexts. Authentic contexts contain rich resources wherein students can use authentic objects as aid in advanced educational technology-assisted mathematics learning. We designed Ubiquitous-Fraction (U-Fraction), a mobile application that helps students to learn fraction in authentic contexts and that integrates fraction concepts into daily contexts. This mixed study involved 80 fifth-grade Taiwanese students. We compared the learning achievements of students who used U-Fraction (experimental group) with those of students who used web-based tool and the traditional learning mode (control groups). We studied the students' behaviors toward the use of U-Fraction in their learning activities and analyzed the relationship of students' learning behavior with their learning achievements. Furthermore, we collected students' perception of U-Fraction. Results demonstrated that the experimental group significantly outperformed the control groups in terms of learning achievements. In terms of learning behaviors, fraction concept/comparison learning with authentic photo taking and fraction addition/subtraction learning with authentic photo taking were most significantly correlated with learning achievement. It indicated that fraction can be learned better in an authentic environment. Furthermore, regression analysis showed that the assignment scores could significantly predict learning achievements. We found that most participants had positive perception toward U-Fraction and that they enjoyed using the photo-taking feature to solve fraction problems in their surroundings. Therefore, we suggest that fraction learning activities should be designed in a way that students are encouraged to apply through photo taking in authentic environments and the knowledge they have learned.
Anomaly Detection Using GANs for Visual Inspection in Noisy Training Data

The detection and the quantification of anomalies in image data are critical tasks in industrial scenes such as detecting micro scratches on product. In recent years, due to the difficulty of defining anomalies and the limit of correcting their labels, research on unsupervised anomaly detection using generative models has attracted attention. Generally, in those studies, only normal images are used for training to model the distribution of normal images. The model measures the anomalies in the target images by reproducing the most similar images and scoring image patches indicating their fit to the learned distribution. This approach is based on a strong presumption; the trained model should not be able to generate abnormal images. However, in reality, the model can generate abnormal images mainly due to noisy normal data which include small abnormal pixels, and such noise severely affects the accuracy of the model. Therefore, we propose a novel anomaly detection method to distort the distribution of the model with existing abnormal images. The proposed method detects pixel-level micro anomalies with a high accuracy from $$1024\times {1024}$$ high resolution images which are actually used in an industrial scene. In this paper, we share experimental results on open datasets, due to the confidentiality of the data.
Rough extreme learning machine: A new classification method based on uncertainty measure

Extreme learning machine (ELM) is a new single hidden layer feedback neural network. The weights of the input layer and the biases of neurons in hidden layer are randomly generated; the weights of the output layer can be analytically determined. ELM has been achieved good results for a large number of classification tasks. In this paper, a new extreme learning machine called rough extreme learning machine (RELM) was proposed. RELM uses rough set to divide data into upper approximation set and lower approximation set, and the two approximation sets are utilized to train upper approximation neurons and lower approximation neurons. In addition, an attribute reduction is executed in this algorithm to remove redundant attributes. The experimental results showed, comparing with the comparison algorithms, RELM can get a better accuracy and a simpler neural network structure on most data sets; RELM cannot only maintain the advantages of fast speed, but also effectively cope with the classification task for high-dimensional data.
Mesh Saliency via Weakly Supervised Classification-for-Saliency CNN

Recently, effort has been made to apply deep learning to the detection of mesh saliency. However, one major barrier is to collect a large amount of vertex-level annotation as saliency ground truth for training the neural networks. Quite a few pilot studies showed that this task is difficult. In this work, we solve this problem by developing a novel network trained in a weakly supervised manner. The training is end-to-end and does not require any saliency ground truth but only the class membership of meshes. Our Classification-for-Saliency CNN (CfS-CNN) employs a multi-view setup and contains a newly designed two-channel structure which integrates view-based features of both classification and saliency. It essentially transfers knowledge from 3D object classification to mesh saliency. Our approach significantly outperforms the existing state-of-the-art methods according to extensive experimental results. Also, the CfS-CNN can be directly used for scene saliency. We showcase two novel applications based on scene saliency to demonstrate its utility.
Neural Multi-scale Image Compression

This study presents a new lossy image compression method that utilizes the multi-scale features of natural images. Our model consists of two networks: multi-scale lossy autoencoder and parallel multi-scale lossless coder. The multi-scale lossy autoencoder extracts the multi-scale image features to quantized variables, and the parallel multi-scale lossless coder enables rapid and accurate lossless coding of the quantized variables via encoding/decoding the variables in parallel. Our proposed model achieves comparable performance to the state-of-the-art model on Kodak and RAISE-1k dataset images, and it encodes a PNG image of size $$768 \times 512$$ in 70 ms with a single GPU and a single CPU process and decodes it into a high-fidelity image in approximately 200 ms.
Consensus-based cubature information filtering for sensor networks with incomplete measurements

Consensus-based distributed filtering is an effective technique for state estimation or data fusion over sensor networks. However, nonlinearity of systems as well as network-induced incomplete information problems are the main obstacles on the way. In this paper, we deal with the distributed filtering of nonlinear systems over sensor networks with incomplete measurements. A perception of credibility evaluation on nodes’ estimations is presented, followed by a novel credibility weight design method. The weights are redistributed by nodes’ estimations in a normal fashion at each time step. The convergence of data fusion with the weights is proved. Further the credibility weight method is integrated into the general consensus-based cubature information filtering algorithm to handle various incomplete measurement problems. Numerical experiments, in the case of unknown noise statistics, measurement interference and measurement missing, demonstrate the effectiveness and robustness of the proposed algorithm.