Reviewerly/exHarmony huggingface dataset:
Energy Compaction-Based Image Compression Using Convolutional AutoEncoder

Image compression has been an important research topic for many decades. Recently, deep learning has achieved great success in many computer vision tasks, and its use in image compression has gradually been increasing. In this paper, we present an energy compaction-based image compression architecture using a convolutional autoencoder (CAE) to achieve high coding efficiency. Our main contributions include three aspects: 1) we propose a CAE architecture for image compression by decomposing it into several down(up)sampling operations; 2) for our CAE architecture, we offer a mathematical analysis on the energy compaction property and we are the first work to propose a normalized coding gain metric in neural networks, which can act as a measurement of compression capability; 3) based on the coding gain metric, we propose an energy compaction-based bit allocation method, which adds a regularizer to the loss function during the training stage to help the CAE maximize the coding gain and achieve high compression efficiency. The experimental results demonstrate our proposed method outperforms BPG (HEVC-intra), in terms of the MS-SSIM quality metric. Additionally, we achieve better performance in comparison with existing bit allocation methods, and provide higher coding efficiency compared with state-of-the-art learning compression methods at high bit rates.
Guided Hybrid A-star Path Planning Algorithm for Valet Parking Applications

Throughout the last decades, the Robotics Community has influenced the Autonomous Vehicles field in multiple different areas ranging from Scene Understanding and Decision Making to Vehicle Control and Optimal Path Planning. Existing path planning algorithms such as A-star, Dijkstra and Graph-based approaches, although providing good optimal path approximations, are suffering from high-runtimes to convergence. This paper presents an innovative and computationally efficient approach of fusing the well-known Hybrid A-star search engine with the Visibility Diagram planning to find the shortest possible non-holonomic path in a hybrid (continuous-discrete) environment for valet parking. The primary novelty of our method stems from two points: at first, we use the Visibility Diagram to introduce an improved and application-aware cost function for the Hybrid A-star algorithm, and then the derived shortest path is used to provide the correct waypoints for the Hybrid A-star to plan the optimum path in regard to the non-holonomic constraints. The method has been extensively tested and proven to up to 40% (20% in average) faster than Hybrid A-star algorithm.
A Saliency-Based Convolutional Neural Network for Table and Chart Detection in Digitized Documents

Within the realm of information extraction from documents, detection of tables and charts is particularly needed as they contain a visual summary of the most valuable information contained in a document. For a complete automation of the visual information extraction process from tables and charts, it is necessary to develop techniques that localize them and identify precisely their boundaries. In this paper we aim at solving the table/chart detection task through an approach that combines deep convolutional neural networks, graphical models and saliency concepts. In particular, we propose a saliency-based fully-convolutional neural network performing multi-scale reasoning on visual cues followed by a fully-connected conditional random field (CRF) for localizing tables and charts in digital/digitized documents. Performance analysis, carried out on an extended version of the ICDAR 2013 (with annotated charts as well as tables) dataset, shows that our approach yields promising results, outperforming existing models.
Prognostic models will be victims of their own success, unless…

Abstract Predictive analytics have begun to change the workflows of healthcare by giving insight into our future health. Deploying prognostic models into clinical workflows should change behavior and motivate interventions that affect outcomes. As users respond to model predictions, downstream characteristics of the data, including the distribution of the outcome, may change. The ever-changing nature of healthcare necessitates maintenance of prognostic models to ensure their longevity. The more effective a model and intervention(s) are at improving outcomes, the faster a model will appear to degrade. Improving outcomes can disrupt the association between the model’s predictors and the outcome. Model refitting may not always be the most effective response to these challenges. These problems will need to be mitigated by systematically incorporating interventions into prognostic models and by maintaining robust performance surveillance of models in clinical use. Holistically modeling the outcome and intervention(s) can lead to resilience to future compromises in performance.
Two-Stream Deep Hashing With Class-Specific Centers for Supervised Image Search

Hashing has been widely used for large-scale approximate nearest neighbor search due to its storage and search efficiency. Recent supervised hashing research has shown that deep learning-based methods can significantly outperform nondeep methods. Most existing supervised deep hashing methods exploit supervisory signals to generate similar and dissimilar image pairs for training. However, natural images can have large intraclass and small interclass variations, which may degrade the accuracy of hash codes. To address this problem, we propose a novel two-stream ConvNet architecture, which learns hash codes with class-specific representation centers. Our basic idea is that if we can learn a unified binary representation for each class as a center and encourage hash codes of images to be close to the corresponding centers, the intraclass variation will be greatly reduced. Accordingly, we design a neural network that leverages label information and outputs a unified binary representation for each class. Moreover, we also design an image network to learn hash codes from images and force these hash codes to be close to the corresponding class-specific centers. These two neural networks are then seamlessly incorporated to create a unified, end-to-end trainable framework. Extensive experiments on three popular benchmarks corroborate that our proposed method outperforms current state-of-the-art methods.
Learning binary code for fast nearest subspace search

Subspace is widely used to represent objects under different viewpoints, illuminations, identities, and more. Due to the growing amount and dimensionality of visual contents, fast search in a large-scale database with high-dimensional subspaces is an important task in many applications, such as image retrieval, clustering, video retrieval, and visual recognition. This can be facilitated by approximate nearest subspace (ANS) search which requires effective subspace representation. All existing methods for this problem represent a subspace by a point in the Euclidean or the Grassmannian space before applying the approximate nearest neighbor (ANN) search. However, the efficiency of these methods is not guaranteed because the subspace representation step can be very time consuming when coping with high-dimensional data. Moreover, the subspace to point transforming process may cause subspace structural information loss which influences the search accuracy. In this paper, we present a new approach for hashing-based ANS search which can directly binarize a subspace without transforming it into a vector. The proposed method learns the binary codes for subspaces following a similarity preserving criterion, and simultaneously leverages the learned binary codes to train matrix classifiers as hash functions. Experiments on face and action recognition and video retrieval applications show that our method outperforms several state-of-the-art methods in both efficiency and accuracy. Moreover, we also compare our method with vector-based hashing methods. Results also show the superiority of our subspace matrix based search scheme.
Detection of Lung Cancer Lymph Node Metastases from Whole-Slide Histopathologic Images Using a Two-Step Deep Learning Approach

The application of deep learning for the detection of lymph node metastases on histologic slides has attracted worldwide attention due to its potentially important role in patient treatment and prognosis. Despite this attention, false-positive predictions remain problematic, particularly in the case of reactive lymphoid follicles. In this study, a novel two-step deep learning algorithm was developed to address the issue of false-positive prediction while maintaining accurate cancer detection. Three-hundred and forty-nine whole-slide lung cancer lymph node images, including 233 slides for algorithm training, 10 slides for validation, and 106 slides for evaluation, were collected. In the first step, a deep learning algorithm was used to eliminate frequently misclassified noncancerous regions (lymphoid follicles). In the second step, a deep learning classifier was developed to detect cancer cells. Using this two-step approach, errors were reduced by 36.4% on average and up to 89% in slides with reactive lymphoid follicles. Furthermore, 100% sensitivity was reached in cases of macrometastases, micrometastases, and isolated tumor cells. To reduce the small number of remaining false positives, a receiver-operating characteristic curve was created using foci size thresholds of 0.6 mm and 0.7 mm, achieving sensitivity and specificity of 79.6% and 96.5%, and 75.5% and 98.2%, respectively. A two-step approach can be used to detect lung cancer metastases in lymph node tissue effectively and with few false positives.
AdaGraph: Unifying Predictive and Continuous Domain Adaptation Through Graphs

The ability to categorize is a cornerstone of visual intelligence, and a key functionality for artificial, autonomous visual machines. This problem will never be solved without algorithms able to adapt and generalize across visual domains. Within the context of domain adaptation and generalization, this paper focuses on the predictive domain adaptation scenario, namely the case where no target data are available and the system has to learn to generalize from annotated source images plus unlabeled samples with associated metadata from auxiliary domains. Our contribution is the first deep architecture that tackles predictive domain adaptation, able to leverage over the information brought by the auxiliary domains through a graph. Moreover, we present a simple yet effective strategy that allows us to take advantage of the incoming target data at test time, in a continuous domain adaptation scenario. Experiments on three benchmark databases support the value of our approach.
Flexible attribute-based proxy re-encryption for efficient data sharing

An increasing number of people are sharing their data through third-party platforms. Attribute-based encryption (ABE) is a promising primitive that allows enforcing fine-grained access control on the data to be shared. An issue in ABE is that a priori access policies should be determined during the system setup or encryption phase, but these policies will become obsolete over time. Another issue is that the decryption of ABE generally requires complicated and expensive computations, which may be unaffordable for resource-limited users (e.g., mobile-device users). To address these issues, we propose a new paradigm called hybrid attribute-based proxy re-encryption (HAPRE). In HAPRE, a semitrusted proxy can be authorized to convert ciphertexts of an ABE scheme into ciphertexts of an identity-based encryption (IBE) scheme without letting the proxy know the underlying messages. With these features, HAPRE enables resource-limited users to efficiently access the data previously encrypted by ABE. We construct two HAPRE schemes by utilizing a compact IBE scheme and a key rerandomization technique, and then we prove that the schemes are semantically secure and collusion resistant. Theoretical and experimental analyses demonstrate the efficiency of the HAPRE schemes.
Operation-aware Neural Networks for user response prediction

User response prediction makes a crucial contribution to the rapid development of online advertising system and recommendation system. The importance of learning feature interactions has been emphasized by many works. Many deep models are proposed to automatically learn high-order feature interactions. Since most features in advertising systems and recommendation systems are high-dimensional sparse features, deep models usually learn a low-dimensional distributed representation for each feature in the bottom layer. Besides traditional fully-connected architectures, some new operations, such as convolutional operations and product operations, are proposed to learn feature interactions better. In these models, the representation is shared among different operations. However, the best representation for each operation may be different. In this paper, we propose a new neural model named Operation-aware Neural Networks (ONN) which learns different representations for different operations. Our experimental results on two large-scale real-world ad click/conversion datasets demonstrate that ONN consistently outperforms the state-of-the-art models in both offline-training environment and online-training environment.
2D Pose-Based Real-Time Human Action Recognition With Occlusion-Handling

Human Action Recognition (HAR) for CCTV-oriented applications is still a challenging problem. Real-world scenarios HAR implementations is difficult because of the gap between Deep Learning data requirements and what the CCTV-based frameworks can offer in terms of data recording equipments. We propose to reduce this gap by exploiting human poses provided by the OpenPose, which has been already proven to be an effective detector in CCTV-like recordings for tracking applications. Therefore, in this work, we first propose ActionXPose: a novel 2D pose-based approach for pose-level HAR. ActionXPose extracts low- and high-level features from body poses which are provided to a Long Short-Term Memory Neural Network and a 1D Convolutional Neural Network for the classification. We also provide a new dataset, named ISLD, for realistic pose-level HAR in a CCTV-like environment, recorded in the Intelligent Sensing Lab. ActionXPose is extensively tested on ISLD under multiple experimental settings, e.g. Dataset Augmentation and Cross-Dataset setting, as well as revising other existing datasets for HAR. ActionXPose achieves state-of-the-art performance in terms of accuracy, very high robustness to occlusions and missing data, and promising results for practical implementation in real-world applications.
Generation of 3D Brain MRI Using Auto-Encoding Generative Adversarial Networks

As deep learning is showing unprecedented success in medical image analysis tasks, the lack of sufficient medical data is emerging as a critical problem. While recent attempts to solve the limited data problem using Generative Adversarial Networks (GAN) have been successful in generating realistic images with diversity, most of them are based on image-to-image translation and thus require extensive datasets from different domains. Here, we propose a novel model that can successfully generate 3D brain MRI data from random vectors by learning the data distribution. Our 3D GAN model solves both image blurriness and mode collapse problems by leveraging $$\alpha $$ -GAN that combines the advantages of Variational Auto-Encoder (VAE) and GAN with an additional code discriminator network. We also use the Wasserstein GAN with Gradient Penalty (WGAN-GP) loss to lower the training instability. To demonstrate the effectiveness of our model, we generate new images of normal brain MRI and show that our model outperforms baseline models in both quantitative and qualitative measurements. We also train the model to synthesize brain disorder MRI data to demonstrate the wide applicability of our model. Our results suggest that the proposed model can successfully generate various types and modalities of 3D whole brain volumes from a small set of training data.
A Novel S-Box Design Algorithm Based on a New Compound Chaotic System

Substitution-boxes (S-Boxes) are important non-linear components in block cryptosystem, which play an important role in the security of cryptosystems. Constructing S-Boxes with a strong cryptographic feature is an important step in designing block cipher systems. In this paper, a novel algorithm for constructing S-Boxes based on a new compound chaotic system is presented. Firstly, the new chaotic system, tent–logistic system, is proposed, which has better chaotic performance and wider chaotic range than the tent and logistic system, and can not only increase the randomness of the chaotic sequences but also expand the key space of cryptosystems. Secondly, a novel linear mapping is employed to construct the initial S-Box. Then, the permutation operation on the initial S-Box is performed by using chaotic sequence generated with the tent–logistic system, which improves the cryptographic features of the S-Box. The idea behind the proposed work is to make supplementary safe S-box. Detail tests for cryptographic strength of the proposed S-Box are performed by using different standard benchmarks. The test results and performance analysis show that our proposed S-Box has very smaller values of linear probability (LP) and differential probability (DP) and a satisfactory average value of nonlinearity compared with other S-Boxes, showing its excellent application potential in block cipher system.
Learning to Rank Query Graphs for Complex Question Answering over Knowledge Graphs

In this paper, we conduct an empirical investigation of neural query graph ranking approaches for the task of complex question answering over knowledge graphs. We propose a novel self-attention based slot matching model which exploits the inherent structure of query graphs, our logical form of choice. Our proposed model generally outperforms other ranking models on two QA datasets over the DBpedia knowledge graph, evaluated in different settings. We also show that domain adaption and pre-trained language model based transfer learning yield improvements, effectively offsetting the general lack of training data.
Data — from objects to assets

How did data get so big? Through political, social and economic interests, shows Sabina Leonelli, in the fourth essay on how the past 150 years have shaped the science system, marking Nature's anniversary. How did data get so big? Through political, social and economic interests, shows Sabina Leonelli.
GP-BPR

Owing to the recent advances in the multimedia processing domain and the publicly available large-scale real-world data provided by online fashion communities, like the IQON and Chictopia, researchers are enabled to investigate the automatic clothing matching solutions. In a sense, existing methods mainly focus on modeling the general item-item compatibility from the aesthetic perspective, but fail to incorporate the user factor. In fact, aesthetics can be highly subjective, as different people may hold different clothing preferences. In light of this, in this work, we attempt to tackle the problem of personalized compatibility modeling from not only the general aesthetics but also the personal preference perspectives. In particular, we present a personalized compatibility modeling scheme GP-BPR, comprising of two essential components: general compatibility modeling and personal preference modeling, which characterize the item-item and user-item interactions, respectively. In particular, due to the concern that both the modalities (e.g., the image and context description) of fashion items can deliver important cues regarding user personal preference, we present a comprehensive personal preference modeling method. Moreover, for evaluation, we create a large-scale dataset, IQON3000, from the online fashion community IQON. Extensive experiment results on IQON3000 verify the effectiveness of the proposed scheme. As a byproduct, we have released the dataset, codes, and involved parameters to benefit other researchers.
Personalized Content Extraction and Text Classification Using Effective Web Scraping Techniques

Web scraping is a technique to extract information from various web documents automatically. It retrieves the related contents based on the query, aggregates and transforms the data from an unstructured format into a structured representation. Text classification becomes a vital phase to summarize the data and in categorizing the webpages adequately. In this article, using effective web scraping methodologies, the data is initially extracted from websites, then transformed into a structured form. Based on the keywords from the data, the documents are classified and labeled. A recursive feature elimination technique is applied to the data to select the best candidate feature subset. The final data-set trained with standard machine learning algorithms. The proposed model performs well on classifying the documents from the extracted data with a better accuracy rate.
A new multi-objective differential evolution approach for simultaneous clustering and feature selection

Today’s real-world data mostly involves incomplete, inconsistent, and/or irrelevant information that causes many drawbacks to transform it into an understandable format. In order to deal with such issues, data preprocessing is a proven discipline in data mining. One of the typical tasks in data preprocessing, feature selection aims to reduce the dimensionality in the data and thereby contributes to further processing. Feature selection is widely used to enhance the performance of a supervised learning algorithm (e.g., classification) but is rarely used in unsupervised tasks (e.g., clustering). This paper introduces a new multi-objective differential evolution approach in order to find relatively homogeneous clusters without the prior knowledge of cluster number using a smaller number of features from all available features in the data. To analyze the goodness of the introduced approach, several experiments are conducted on a various number of real-world and synthetic benchmarks using a variety of clustering approaches. From the analyzes through several different criteria, it is suggested that our method can significantly improve the clustering performance while reducing the dimensionality at the same time.