Reviewerly/exHarmony huggingface dataset:
Robust Visual Tracking based on Adversarial Unlabeled Instance Generation with Label Smoothing Loss Regularization

Recent studies have shown that deep neural networks have pushed visual tracking accuracy to new heights, but finding more robust long-term tracking is still challenging because of the dynamic foreground and background changes. This phenomenon affects the overall performance via online training sample generation. The dense sampling strategy has been widely used for its convenience, the appearance variation is severely limited by its highly spatial overlapping mechanism. The sample candidate evaluation with a classification score metric is not always reliable throughout the entire process, therefore, tracking failure is inevitable. As an effective solution, this paper proposes a novel sample-level generative adversarial network (GAN) to enrich the training data by generating massive amounts of sample-level GAN samples. These samples are not only similar to the real-life scenarios, but also could carry more diversity of deformation and motion blur to a certain degree. For occlusion invariance, a feature-level GAN is incorporated to generate more challenging feature-level GAN data by creating random occlusion masks in deep feature space. To facilitate the online learning process, a label smoothing loss regularization is introduced to achieve model regularization and over-fitting reduction by integrating the unlabeled GAN-generated training data with the realistically labeled ones. In addition, a re-detection correlation filter conservatively trained with reliable training data is employed to integrate a classification score metric to perform reliable model updates and avoid heavy degradation. Furthermore, we also carry out the re-detection correlation filter on the candidate region proposals to handle the tracking failures. The proposed tracker has shown superior performance in comparison to the other state-of-the-art tracking approaches on the OTB-2013, OTB-100, UAV123, UAV20L, and VOT2016 benchmark datasets.
Attacking Black-Box Image Classifiers With Particle Swarm Optimization

In order to better solve the shortcomings of Deep Neural Networks (DNNs) susceptible to adversarial examples, evaluating existing neural network classification performance and increasing training sets to improve the robustness of classification models require more effective methods of the adversarial examples generation.Under the black-box condition, less information about parameters of the classification model, limited query times, and less feedback information available, it is difficult to generate adversarial examples against the black-box model.In order to further improve the efficiency of the adversarial images generation, we propose two different variants of Partial Swarm Optimization algorithm (vPSO) base on the traditional Partial Swarm Optimization for the targeted and non-targeted attack under conditions of the completely black-box.Compared with the existing of the state-of-the-art generation algorithm, the vPSO effectively reduce the number of queries to the black-box classifier and the dependence on the feedback information.The success rate of the targeted attack is up to 96.0% and the average number of queries for the black-box model is greatly reduced.Furthermore, we propose an efficient target image screening method in targeted attacks, as well as the concept of easy-to-attack and hard-to-attack images in non-targeted attacks, and give corresponding distinctions.
Non-Rigid Multi-Modal 3D Medical Image Registration Based on Foveated Modality Independent Neighborhood Descriptor

The non-rigid multi-modal three-dimensional (3D) medical image registration is highly challenging due to the difficulty in the construction of similarity measure and the solution of non-rigid transformation parameters. A novel structural representation based registration method is proposed to address these problems. Firstly, an improved modality independent neighborhood descriptor (MIND) that is based on the foveated nonlocal self-similarity is designed for the effective structural representations of 3D medical images to transform multi-modal image registration into mono-modal one. The sum of absolute differences between structural representations is computed as the similarity measure. Subsequently, the foveated MIND based spatial constraint is introduced into the Markov random field (MRF) optimization to reduce the number of transformation parameters and restrict the calculation of the energy function in the image region involving non-rigid deformation. Finally, the accurate and efficient 3D medical image registration is realized by minimizing the similarity measure based MRF energy function. Extensive experiments on 3D positron emission tomography (PET), computed tomography (CT), T1, T2, and PD weighted magnetic resonance (MR) images with synthetic deformation demonstrate that the proposed method has higher computational efficiency and registration accuracy in terms of target registration error (TRE) than the registration methods that are based on the hybrid L-BFGS-B and cat swarm optimization (HLCSO), the sum of squared differences on entropy images, the MIND, and the self-similarity context (SSC) descriptor, except that it provides slightly bigger TRE than the HLCSO for CT-PET image registration. Experiments on real MR and ultrasound images with unknown deformation have also be done to demonstrate the practicality and superiority of the proposed method.
Transformer-based Model for Single Documents Neural Summarization

We propose a system that improves performance on single document summarization task using the CNN/DailyMail and Newsroom datasets.It follows the popular encoderdecoder paradigm, but with an extra focus on the encoder.The intuition is that the probability of correctly decoding an information significantly lies in the pattern and correctness of the encoder.Hence we introduce, encodeencode -decode.A framework that encodes the source text first with a transformer, then a sequence-to-sequence (seq2seq) model.We find that the transformer and seq2seq model complement themselves adequately, making for a richer encoded vector representation.We also find that paying more attention to the vocabulary of target words during abstraction improves performance.We experiment our hypothesis and framework on the task of extractive and abstractive single document summarization and evaluate using the standard CNN/DailyMail dataset and the recently released Newsroom dataset.
Panoramic single-aperture multi-sensor light field camera

We describe a panoramic camera using one monocentric lens and an array of light field (LF) sensors to capture overlapping contiguous regions of the spherical image surface. Refractive sub-field consolidators divide the light before the image surface and concentrate the sub-images onto the optically active areas of adjacent CMOS sensors. We show the design of a 160° × 24° field-of-view (FOV) LF camera, and experimental test of a three sensor F/2.5 96° × 24° and five sensor (25 MPixel) F/4 140° × 24° camera. We demonstrate computational field curvature correction, refocusing, resolution enhancement, and depth mapping of a laboratory scene. We also present a 155° full circular field camera design compatible with LF or direct 164 MPixel sensing of 13 spherical sub-images, fitting within a one inch diameter sphere.
Automatically Discovering and Learning New Visual Categories with Ranking Statistics

We tackle the problem of discovering novel classes in an image collection given labelled examples of other classes. This setting is similar to semi-supervised learning, but significantly harder because there are no labelled examples for the new classes. The challenge, then, is to leverage the information contained in the labelled images in order to learn a general-purpose clustering model and use the latter to identify the new classes in the unlabelled data. In this work we address this problem by combining three ideas: (1) We suggest that the common approach of bootstrapping an image representation using the available labels introduces an unwanted bias, which we avoid by using self-supervised learning to train the representation from scratch on the union of labelled and unlabelled data; (2) We use rank statistics to transfer the model's knowledge of the labelled classes to the problem of clustering the unlabelled images; Finally, (3) we train the data representation by optimizing a joint objective function on the labelled and unlabelled subsets of the data, improving both the supervised classification of the labelled data, and the clustering of the unlabelled, simultaneously. We evaluate our approach on standard classification benchmarks and outperform current methods for novel category discovery by a significant margin.
Robust basketball sports recognition by leveraging motion block estimation

Recognizing human action under uncontrolled environment is significant for human–computer interaction (HCI), especially for virtual reality game, sports narrative, and video captioning. Nowadays, however, human action recognition toward basketball sports is still a challenging task because of the sophisticated backgrounds, action occlusion, and varying lighting conditions In this paper, we propose a novel recognition pipeline to recognize human action in the basketball scenery. To this end, we first collect a dataset that contains massive-scale basketball sports video clips with manually labeled narrative from YouTube. Then, we select motion regions by constructing a large affinity graph, where the entropy of the selected motion regions is calculated to extract motion block. Afterward, we leverage the GMM to calculate the feature from the motion block. Subsequently, key frames are selected by modeling the variations between adjacent frames, and the gradient histogram is used to represent the posture descriptor of the basketball actions to calculate the shape descriptor. Through the linear combination of motion and posture descriptor, we leverage the KNN algorithm to realize basketball action recognition. Comprehensive experimental results have shown the effectiveness of our proposed method.
Bio-AnswerFinder: a system to find answers to questions from biomedical texts

Abstract The ever accelerating pace of biomedical research results in corresponding acceleration in the volume of biomedical literature created. Since new research builds upon existing knowledge, the rate of increase in the available knowledge encoded in biomedical literature makes the easy access to that implicit knowledge more vital over time. Toward the goal of making implicit knowledge in the biomedical literature easily accessible to biomedical researchers, we introduce a question answering system called Bio-AnswerFinder. Bio-AnswerFinder uses a weighted-relaxed word mover's distance based similarity on word/phrase embeddings learned from PubMed abstracts to rank answers after question focus entity type filtering. Our approach retrieves relevant documents iteratively via enhanced keyword queries from a traditional search engine. To improve document retrieval performance, we introduced a supervised long short term memory neural network to select keywords from the question to facilitate iterative keyword search. Our unsupervised baseline system achieves a mean reciprocal rank score of 0.46 and Precision@1 of 0.32 on 936 questions from BioASQ. The answer sentences are further ranked by a fine-tuned bidirectional encoder representation from transformers (BERT) classifier trained using 100 answer candidate sentences per question for 492 BioASQ questions. To test ranking performance, we report a blind test on 100 questions that three independent annotators scored. These experts preferred BERT based reranking with 7% improvement on MRR and 13% improvement on Precision@1 scores on average.
Dataset of ontology competency questions to SPARQL-OWL queries translations

This data article reports on a new set of 234 competency questions for ontology development and their formalisation into a set of 131 SPARQL-OWL queries. This is the largest set of competency questions with their linked queries to date, covering several ontologies of different type in different subject domains developed by different groups of question authors and ontology developers. The dataset is focused specifically on the ontology TBox (terminological part). The dataset may serve as a manually created gold standard for testing and benchmarking, research into competency questions and querying ontologies, and tool development. The data is available in Mendeley Data. Its analysis is presented in “Analysis of Ontology Competency Questions and their formalizations in SPARQL-OWL” [15].
Demystifying the Quantum Threat: Infrastructure, Institutions, and Intelligence Advantage

In theory, a fully functional quantum computer could break the cryptographic protocols that underwrite cybersecurity everywhere, which would be disastrous for national security, global trade, and civil society. Quantum cryptography, conversely, promises an unprecedented level of security, yet this benefit comes with some danger: revisionist actors with impenetrable communications might be able to conduct surprise attacks and covert conspiracies. In reality, neither of these threat scenarios are likely. Intelligence advantage in political competition depends on the interaction of technological infrastructure with organizational institutions. Robust cryptosystems can be undermined by poor organizational coordination, and careful security policy can compensate for technical vulnerabilities. Scientific innovation in quantum technology only affects one of these dimensions while potentially complicating the other. Even if the formidable engineering challenges of quantum computing can be overcome, signals intelligence collectors will still have to analyze a vast number of decrypts and deliver timely and relevant judgments to interested decision makers. The quantum networks of tomorrow, similarly, will provide little protection for complex organizations that have weak operations security practices. In the practice of intelligence, we should expect classical politics to dominate quantum computing.
A New Feature-Based Wavelet Completed Local Ternary Pattern (Feat-WCLTP) for Texture Image Classification

LBP is one of the simplest yet most powerful feature extraction descriptors. Many descriptors based on LBP have been proposed to improve its performance. Completed Local Ternary Pattern (CLTP) is one of the important LBP variants that was proposed to overcome LBP's drawbacks. However, despite the impressive performance of CLTP, it suffers from some limitations, such as high dimensionality, thereby leading to higher computation time and may affect the classification accuracy. In this paper, a new rotation invariant texture descriptor (Feat-WCLTP) is proposed. In the proposed Feat-WCLTP descriptor, first the redundant discrete wavelet transform RDWT is integrated with the original CLTP. Then, CLTP is extracted based on the LL wavelet coefficients. Next, the mean and variance features are used to describe the magnitude information instead of using P-dimensional features as the normal magnitude components of CLTP. Reducing the number of extracted features positively affected the computational complexity of the descriptor and the dimensionality of the resultant histogram. The proposed Feat-WCLTP is evaluated using four texture datasets and compared with some well-known descriptors. The experimental results show that Feat-WCLTP outperformed the other descriptors in terms of classification accuracy. It achieves 99.66% in OuTex, 96.89% in CUReT, 95.23% in UIUC and 99.92% in the Kylberg dataset. The experimental results showed that the Feat-WCLTP not only overcomes the CLTP's dimensionality problem but also further improves the classification accuracy.
Advanced Feature Fusion Algorithm Based on Multiple Convolutional Neural Network for Scene Recognition

Scene recognition is a popular open problem in the computer vision field.Among lots of methods proposed in recent years, Convolutional Neural Network (CNN) based approaches achieve the best performance in scene recognition.We propose in this paper an advanced feature fusion algorithm using Multiple Convolutional Neural Network (Multi-CNN) for scene recognition.Unlike existing works that usually use individual convolutional neural network, a fusion of multiple different convolutional neural networks is applied for scene recognition.Firstly, we split training images in two directions and apply to three deep CNN model, and then extract features from the last full-connected (FC) layer and probabilistic layer on each model.Finally, feature vectors are fused with different fusion strategies in groups forwarded into SoftMax classifier.Our proposed algorithm is evaluated on three scene datasets for scene recognition.The experimental results demonstrate the effectiveness of proposed algorithm compared with other state-of-art approaches.
A Comprehensive and Didactic Review on Multilabel Learning Software Tools

Machine learning has become an everyday tool in so many fields that there is plenty of software to run many of these algorithms in every device, from supercomputers to embedded appliances.Most of these methods fall into the category known as standard learning, being supervised models (guided by pre-labeled examples) aimed to classify new patterns into exactly one category.This way, machine learning is in charge of getting rid of junk emails, labeling people in a picture, or detecting a fraudulent transaction when using a credit card.Aside from unsupervised learning methods, which are usually applied to group similar patterns, infer association rules and similar tasks, some non-standard supervised machine learning problems have been faced in late years.Among them, multilabel learning is arguably the most popular one.These algorithms aim to produce models in which each data pattern may be linked to several categories at once.Thus, a multilabel classifier generates a set of outputs instead of only one as a standard classifier does.However, software tools for multilabel learning tend to be scarce.This paper provides multilabel researchers with a comprehensive review of the currently available multilabel learning software.It is written following a didactic approach, focusing on how to accomplish each task rather than simply offering a list of programs and websites.The goal is to help finding the most appropriate resource to complete every step, from locating datasets and partitioning them to running many of the multilabel algorithms proposed in the literature until now.
When Is Blockchain Worth It? A Case Study of Carbon Trading

Blockchain, as an emerging technology and a disruptive innovation, has attracted attention from both academia and industry. However, there are many potential risks associated with it, such as the technical risk, the legal risk and the privacy risk. A comprehensive risk analysis is crucial for cost-effective deployment of blockchain technology. Important adoption decisions, including when to deploy blockchain, how to plan the investment, how to transfer current businesses onto blockchain, and how to price the blockchain service depend on this risk analysis. Yet very little study exists concerning the blockchain adoption planning with risks analysis. This research presents a cost-and-risk analysis framework and an adoption planning method for the case of blockchain application in carbon trading. Design requirements implied by the analysis are inferred and the architecture of a novel hybrid blockchain system is proposed. The system leverages the advantages of blockchain technology and incorporates institutional risk control framework. The optimal adoption strategy of this system is derived through modelling of users’ and the organizer’s behavior.
Formation of project competence of future environmentalists

We have gained successful experience in conducting educational projects with studentsenvironmentalists of various thematic areas. The topics of ecological projects was related to a practical, professionally oriented block of the curriculum and are intended to deepen the knowledge of individual students in a certain area in order to differentiate the learning process. Most often, project topics relate to a specific practical issue that is relevant to real life. At the same time, it requires the acquisition of students’ knowledge from different subjects stimulates systematic creative thinking, turning up the research skills and opens opportunities for effective using of ICT in the project activity.
Watermarking Framework for Authentication and Self-recovery of Tampered Colour Images

Methods of authentication and self-recovery of tampered information in digital images have been in constant development during the last years. These frameworks are developed by watermarking techniques, which generate an image digest, which is embedded inside the image that must be protected. In this paper, a method of authentication and self-recovery of tampered information in digital images is proposed. Our approach first generates the authentication watermarks, which are based on XOR operations on non-overlapping blocks, subsequently by using a halftoning technique the recovery watermark is generated. To have a higher performance, three copies of each watermark are embedded to have more chance to extract the recovery watermark. To evaluate the quality of the obtained images, the objective criterion of peak signal-to-noise ratio (PSNR) and Structural Similarity Index (SSIM) are used. The experimental results demonstrate the effectiveness of our method in comparisons with other schemes reported in the literature, where the quality of the watermarked images, the quality of the reconstruction images and the recovery rate of each scheme were evaluated.
The Schaake Shuffle Technique to Combine Solar and Wind Power Probabilistic Forecasting

One way to mitigate the variability of wind and solar power generation is to install the corresponding plants in nearby locations. For example, in Kuwait, the facility at Shagaya Renewable Energy Park is located in a desert area with both photovoltaic panels and wind turbines that allow the continuous generation of renewable energy throughout the day. The National Center for Atmospheric Research (NCAR) has developed a system to generate probabilistic wind and solar predictions for the Shagaya facility. These predictions are based on the analog ensemble technique that post-processes the wind speed and solar irradiance predictions based on a combination of multiple models including the Weather Research and Forecasting (WRF) numerical model. The ensemble forecasts have 20 members and are generated independently at each wind and solar power production facility. Here we present a method based on the Schaake Shuffle (SS) technique to pair the ensemble members from the independent systems to obtain a unique ensemble prediction of the aggregated wind and solar generation. After reordering through the SS technique, the corresponding paired solar and wind power members can be summed to build a unique ensemble of combined generation that is statistically consistent, as verified by the presented metrics.
Learning to Estimate 3D Human Pose From Point Cloud

3D pose estimation is a challenging problem in computer vision. Most of the existing neural-network-based approaches address color or depth images through convolution networks (CNNs). In this paper, we study the task of 3D human pose estimation from depth images. Different from the existing CNN-based human pose estimation method, we propose a deep human pose network for 3D pose estimation by taking the point cloud data as input data to model the surface of complex human structures. We first cast the 3D human pose estimation from 2D depth images to 3D point clouds and directly predict the 3D joint position. Our experiments on two public datasets show that our approach achieves higher accuracy than previous state-of-art methods. The reported results on both ITOP and EVAL datasets demonstrate the effectiveness of our method on the targeted tasks.