Reviewerly/exHarmony huggingface dataset:
HetConv: Heterogeneous Kernel-Based Convolutions for Deep CNNs

We present a novel deep learning architecture in which the convolution operation leverages heterogeneous kernels. The proposed HetConv (Heterogeneous Kernel-Based Convolution) reduces the computation (FLOPs) and the number of parameters as compared to standard convolution operation while still maintaining representational efficiency. To show the effectiveness of our proposed convolution, we present extensive experimental results on the standard convolutional neural network (CNN) architectures such as VGG and ResNet. We find that after replacing the standard convolutional filters in these architectures with our proposed HetConv filters, we achieve 3X to 8X FLOPs based improvement in speed while still maintaining (and sometimes improving) the accuracy. We also compare our proposed convolutions with group/depth wise convolutions and show that it achieves more FLOPs reduction with significantly higher accuracy.
DNNRec: A novel deep learning based hybrid recommender system

We propose a novel deep learning hybrid recommender system to address the gaps in Collaborative Filtering systems and achieve the state-of-the-art predictive accuracy using deep learning. While collaborative filtering systems are popular with many state-of-the-art achievements in recommender systems, they suffer from the cold start problem, when there is no history about the users and items. Further, the latent factors learned by these methods are linear in nature. To address these gaps, we describe a novel hybrid recommender system using deep learning. The solution uses embeddings for representing users and items to learn non-linear latent factors. The solution alleviates the cold start problem by integrating side information about users and items into a very deep neural network. The proposed solution uses a decreasing learning rate in conjunction with increasing weight decay, the values cyclically varied across epochs to further improve accuracy. The proposed solution is benchmarked against existing methods on both predictive accuracy and running time. Predictive Accuracy is measured by Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and R-squared. Running time is measured by the mean and standard deviation across seven runs. Comprehensive experiments are conducted on several datasets such as the MovieLens 100 K, FilmTrust, Book-Crossing and MovieLens 1 M. The results show that the proposed technique outperforms existing methods in both non-cold start and cold start cases. The proposed solution framework is generic from the outperformance on four different datasets and can be leveraged for other ratings prediction datasets in recommender systems.
Universal Perturbation Attack Against Image Retrieval

Universal adversarial perturbations (UAPs), a.k.a. input-agnostic perturbations, has been proved to exist and be able to fool cutting-edge deep learning models on most of the data samples. Existing UAP methods mainly focus on attacking image classification models. Nevertheless, little attention has been paid to attacking image retrieval systems. In this paper, we make the first attempt in attacking image retrieval systems. Concretely, image retrieval attack is to make the retrieval system return irrelevant images to the query at the top ranking list. It plays an important role to corrupt the neighbourhood relationships among features in image retrieval attack. To this end, we propose a novel method to generate retrieval-against UAP to break the neighbourhood relationships of image features via degrading the corresponding ranking metric. To expand the attack method to scenarios with varying input sizes or untouchable network parameters, a multi-scale random resizing scheme and a ranking distillation strategy are proposed. We evaluate the proposed method on four widely-used image retrieval datasets, and report a significant performance drop in terms of different metrics, such as mAP and mP@10. Finally, we test our attack methods on the real-world visual search engine, i.e., Google Images, which demonstrates the practical potentials of our methods.
Lightweight Face Recognition Challenge

Face representation using Deep Convolutional Neural Network (DCNN) embedding is the method of choice for face recognition. Current state-of-the-art face recognition systems can achieve high accuracy on existing in-the-wild datasets. However, most of these datasets employ quite limited comparisons during the evaluation, which does not simulate a real-world scenario, where extensive comparisons are encountered by a face recognition system. To this end, we propose two large-scale datasets (DeepGlint-Image with 1.8M images and IQIYI-Video with 0.2M videos) and define an extensive comparison metric (trillion-level pairs on the DeepGlint-Image dataset and billion-level pairs on the IQIYI-Video dataset) for an unbiased evaluation of deep face recognition models. To ensure fair comparison during the competition, we define light-model track and large-model track, respectively. Each track has strict constraints on computational complexity and model size. To the best of our knowledge, this is the most comprehensive and unbiased benchmarks for deep face recognition. To facilitate future research, the proposed datasets are released and the online test server is accessible as part of the Lightweight Face Recognition Challenge at the International Conference on Computer Vision, 2019.
A novel design of differential evolution for solving discrete traveling salesman problems

Differential evolution is one of the most powerful and popular evolutionary algorithms, which is primarily used to solve continuous-based optimization problems. Although differential evolution has been considered unsuitable for solving many permutation-based real-world combinatorial problems, several efforts for designing an efficient discrete version of the differential evolution algorithm have occurred in recent years. This paper introduces a novel discrete differential evolution algorithm for improving the performance of the standard differential evolution algorithm when it is used to solve complex discrete traveling salesman problems. In this approach, we propose to use a combination of, 1) an improved mapping mechanism to map continuous variables to discrete ones and vice versa, 2) a k-means clustering-based repairing method for enhancing the solutions in the initial population, 3) an ensemble of mutation strategies for increasing the exploration capability of the algorithm. Finally, for improving the local capability of the proposed algorithm when solving discrete problems, two well-known local searches have also been adapted. To judge its performance, the proposed algorithm is compared with those of 27 state-of-the-art algorithms, for solving 45 instances of traveling salesman problems, with different numbers of cities. The experimental results demonstrated that our technique significantly outperforms most of the comparative methods, in terms of the average errors from the best-known solutions, and achieved very competitive results with better computational time than others.
JPEG Artifacts Reduction via Deep Convolutional Sparse Coding

To effectively reduce JPEG compression artifacts, we propose a deep convolutional sparse coding (DCSC) network architecture. We design our DCSC in the framework of classic learned iterative shrinkage-threshold algorithm. To focus on recognizing and separating artifacts only, we sparsely code the feature maps instead of the raw image. The final de-blocked image is directly reconstructed from the coded features. We use dilated convolution to extract multi-scale image features, which allows our single model to simultaneously handle multiple JPEG compression levels. Since our method integrates model-based convolutional sparse coding with a learning-based deep neural network, the entire network structure is compact and more explainable. The resulting lightweight model generates comparable or better de-blocking results when compared with state-of-the-art methods.
Semantic-Aware Knowledge Preservation for Zero-Shot Sketch-Based Image Retrieval

Sketch-based image retrieval (SBIR) is widely recognized as an important vision problem which implies a wide range of real-world applications. Recently, research interests arise in solving this problem under the more realistic and challenging setting of zero-shot learning. In this paper, we investigate this problem from the viewpoint of domain adaptation which we show is critical in improving feature embedding in the zero-shot scenario. Based on a framework which starts with a pre-trained model on ImageNet and fine-tunes it on the training set of SBIR benchmark, we advocate the importance of preserving previously acquired knowledge, e.g., the rich discriminative features learned from ImageNet, to improve the model's transfer ability. For this purpose, we design an approach named Semantic-Aware Knowledge prEservation (SAKE), which fine-tunes the pre-trained model in an economical way and leverages semantic information, e.g., inter-class relationship, to achieve the goal of knowledge preservation. Zero-shot experiments on two extended SBIR datasets, TU-Berlin and Sketchy, verify the superior performance of our approach. Extensive diagnostic experiments validate that knowledge preserved benefits SBIR in zero-shot settings, as a large fraction of the performance gain is from the more properly structured feature embedding for photo images.
Classification of white blood cells using capsule networks

While the number and structural features of white blood cells (WBC) can provide important information about the health status of human beings, the ratio of sub-types of these cells and the deformations that can be observed serve as a good indicator in the diagnosis process of some diseases. Hence, correct identification and classification of the WBC types is of great importance. In addition, the fact that the diagnostic process that is carried out manually is slow, and the success is directly proportional to the expert's skills makes this problem an excellent field of application for computer-aided diagnostic systems. Unfortunately, both the ethical reasons and the cost of image acquisition process is one of the biggest obstacles to the fact that researchers working with medical images are able to collect enough data to produce a stable model. For that reasons, researchers who want to perform a successful analysis with small data sets using classical machine learning methods need to undergo their data a long and error-prone pre-process, while those using deep learning methods need to increase the data size using augmentation techniques. As a result, there is a need for a model that does not need pre-processing and can perform a successful classification in small data sets. WBCs were classified under five categories using a small data set via capsule networks, a new deep learning method. We improved the model using many techniques and compared the results with the most known deep learning methods. Both the above-mentioned problems were overcame and higher success rates were obtained compared to other deep learning models. While, convolutional neural networks (CNN) and transfer learning (TL) models suffered from over-fitting, capsule networks learned well training data and achieved a high accuracy on test data (96.86%). In this study, we briefly discussed the abilities of capsule networks in a case study. We showed that capsule networks are a quite successful alternative for deep learning and medical data analysis when the sample size is limited.
Biobjective Task Scheduling for Distributed Green Data Centers

The industry of data centers is the fifth largest energy consumer in the world. Distributed green data centers (DGDCs) consume 300 billion kWh per year to provide different types of heterogeneous services to global users. Users around the world bring revenue to DGDC providers according to actual quality of service (QoS) of their tasks. Their tasks are delivered to DGDCs through multiple Internet service providers (ISPs) with different bandwidth capacities and unit bandwidth price. In addition, prices of power grid, wind, and solar energy in different GDCs vary with their geographical locations. Therefore, it is highly challenging to schedule tasks among DGDCs in a high-profit and high-QoS way. This work designs a multiobjective optimization method for DGDCs to maximize the profit of DGDC providers and minimize the average task loss possibility of all applications by jointly determining the split of tasks among multiple ISPs and task service rates of each GDC. A problem is formulated and solved with a simulated-annealing-based biobjective differential evolution (SBDE) algorithm to obtain an approximate Pareto-optimal set. The method of minimum Manhattan distance is adopted to select a knee solution that specifies the Pareto-optimal task service rates and task split among ISPs for DGDCs in each time slot. Real-life data-based experiments demonstrate that the proposed method achieves lower task loss of all applications and larger profit than several existing scheduling algorithms. Note to Practitioners-This work aims to maximize the profit and minimize the task loss for DGDCs powered by renewable energy and smart grid by jointly determining the split of tasks among multiple ISPs. Existing task scheduling algorithms fail to jointly consider and optimize the profit of DGDC providers and QoS of tasks. Therefore, they fail to intelligently schedule tasks of heterogeneous applications and allocate infrastructure resources within their response time bounds. In this work, a new method that tackles drawbacks of existing algorithms is proposed. It is achieved by adopting the proposed SBDE algorithm that solves a multiobjective optimization problem. Simulation experiments demonstrate that compared with three typical task scheduling approaches, it increases profit and decreases task loss. It can be readily and easily integrated and implemented in real-life industrial DGDCs. The future work needs to investigate the real-time green energy prediction with historical data and further combine prediction and task scheduling together to achieve greener and even net-zero-energy data centers.
Evolutionary multi-task optimization for parameters extraction of photovoltaic models

As the demand for solar energy increases dramatically, the optimization and control of photovoltaic systems become increasingly important, accurate and reliable parameter identification of photovoltaic models is always required, which proposes an urgent need for accurate and robust algorithms. To this end, many heuristic algorithms have been proposed to extract the parameters of different photovoltaic models. However, they only extract the parameters of one model in a single run, which is inconsistent with the human ability to solve multiple tasks simultaneously and ignores the useful information derived from different models. Therefore, in this paper an evolutionary multi-task optimization algorithm is proposed to extract the parameters of multiple different photovoltaic models simultaneously. To be specific, the helpful information found by the population is transferred through the cross-task crossover to improve the performance in terms of solution quality and convergence rate of the population. The proposed algorithm is evaluated by extracting the parameters of three different models simultaneously, i.e., single diode, double diode, and photovoltaic module model. Comprehensive results demonstrate that the proposed algorithm has better performance with respect to the accuracy and robustness in comparison with other state-of-the-art algorithms.
Evaluating Sequence-to-Sequence Models for Handwritten Text Recognition

Encoder-decoder models have become an effective approach for sequence learning tasks like machine translation, image captioning and speech recognition, but have yet to show competitive results for handwritten text recognition. To this end, we propose an attention-based sequence-to-sequence model. It combines a convolutional neural network as a generic feature extractor with a recurrent neural network to encode both the visual information, as well as the temporal context between characters in the input image, and uses a separate recurrent neural network to decode the actual character sequence. We make experimental comparisons between various attention mechanisms and positional encodings, in order to find an appropriate alignment between the input and output sequence. The model can be trained end-to-end and the optional integration of a hybrid loss allows the encoder to retain an interpretable and usable output, if desired. We achieve competitive results on the IAM and ICFHR2016 READ data sets compared to the state-of-the-art without the use of a language model, and we significantly improve over any recent sequence-to-sequence approaches.
Unsupervised Pre-Training of Bidirectional Speech Encoders via Masked Reconstruction

We propose an approach for pre-training speech representations via a masked reconstruction loss. Our pre-trained encoder networks are bidirectional and can therefore be used directly in typical bidirectional speech recognition models. The pre-trained networks can then be fine-tuned on a smaller amount of supervised data for speech recognition. Experiments with this approach on the LibriSpeech and Wall Street Journal corpora show promising results. We find that the main factors that lead to speech recognition improvements are: masking segments of sufficient width in both time and frequency, pre-training on a much larger amount of unlabeled data than the labeled data, and domain adaptation when the unlabeled and labeled data come from different domains. The gain from pre-training is additive to that of supervised data augmentation.
Rapid Characterization of the July 2019 Ridgecrest, California, Earthquake Sequence From Raw Seismic Data Using Machine‐Learning Phase Picker

Abstract The two principle earthquakes of the July 2019 Ridgecrest, California, earthquake sequence, M W 6.4 and 7.1, and their immediate foreshocks and thousands of aftershocks present a challenging environment for rapid analysis and characterization of this sequence as it unfolded. In this study, we analyze the first 6 days of the sequence using continuous data from available seismic networks to detect and locate earthquakes associated with the earthquake sequence. We build a high‐precision earthquake catalog using a deep‐neural‐network‐based picker—PhaseNet and a sequential earthquake association and location workflow. Without prior information, we automatically detect and locate more than twice as many earthquakes as the routine catalog. Our high‐precision earthquake catalog reveals detailed spatiotemporal evolution of the earthquake sequence and clearly defines multiple faults activated during the sequence. Our study demonstrates that it is possible to characterize earthquake sequences from raw seismic data using a well‐trained machine‐learning picker and our workflow.
Introduction to digital pathology and computer-aided pathology

Digital pathology (DP) is no longer an unfamiliar term for pathologists, but it is still difficult for many pathologists to understand the engineering and mathematics concepts involved in DP. Computer-aided pathology (CAP) aids pathologists in diagnosis. However, some consider CAP a threat to the existence of pathologists and are skeptical of its clinical utility. Implementation of DP is very burdensome for pathologists because technical factors, impact on workflow, and information technology infrastructure must be considered. In this paper, various terms related to DP and computer-aided pathologic diagnosis are defined, current applications of DP are discussed, and various issues related to implementation of DP are outlined. The development of computer-aided pathologic diagnostic tools and their limitations are also discussed.
The Role of Eye Gaze in Security and Privacy Applications: Survey and Future HCI Research Directions

For the past 20 years, researchers have investigated the use of eye tracking in security applications. We present a holistic view on gaze-based security applications. In particular, we canvassed the literature and classify the utility of gaze in security applications into a) authentication, b) privacy protection, and c) gaze monitoring during security critical tasks. This allows us to chart several research directions, most importantly 1) conducting field studies of implicit and explicit gaze-based authentication due to recent advances in eye tracking, 2) research on gaze-based privacy protection and gaze monitoring in security critical tasks which are under-investigated yet very promising areas, and 3) understanding the privacy implications of pervasive eye tracking. We discuss the most promising opportunities and most pressing challenges of eye tracking for security that will shape research in gaze-based security applications for the next decade.
The Capacity of <i>T</i>-Private Information Retrieval With Private Side Information

We consider the problem of T-Private Information Retrieval with private side information (TPIR-PSI). In this problem, N replicated databases store K independent messages, and a user, equipped with a local cache that holds M messages as side information, wishes to retrieve one of the other K - M messages. The desired message index and the side information must remain jointly private even if any T of the N databases collude. We show that the capacity of TPIR-PSI is (1+ T/N + ⋯ +(T/N) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K-M</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> ) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> . As a special case obtained by setting T = 1, this result settles the capacity of PIR-PSI, an open problem previously noted by Kadhe et al. We also consider the problem of symmetric-TPIR with private side information (STPIR-PSI), where the answers from all N databases reveal no information about any other message besides the desired message. We show that the capacity of STPIR-PSI is 1 - T/N if the databases have access to common randomness (not available to the user) that is independent of the messages, in an amount that is at least T/N -T bits per desired message bit. Otherwise, the capacity of STPIR-PSI is zero.
Edge-Guided Non-Local Fully Convolutional Network for Salient Object Detection

Fully Convolutional Neural Network (FCN) has been widely applied to salient object detection recently by virtue of high-level semantic feature extraction, but existing FCN-based methods still suffer from continuous striding and pooling operations leading to loss of spatial structure and blurred edges. To maintain the clear edge structure of salient objects, we propose a novel Edge-guided Non-local FCN (ENFNet) to perform edge-guided feature learning for accurate salient object detection. In a specific, we extract hierarchical global and local information in FCN to incorporate non-local features for effective feature representations. To preserve good boundaries of salient objects, we propose a guidance block to embed edge prior knowledge into hierarchical feature maps. The guidance block not only performs feature-wise manipulation but also spatial-wise transformation for effective edge embeddings. Our model is trained on the MSRA-B dataset and tested on five popular benchmark datasets. Comparing with the state-of-the-art methods, the proposed method performance well on five datasets.