Reviewerly/exHarmony huggingface dataset:
One Step Is Not Enough: A Multi-Step Procedure for Building the Training Set of a Query by String Keyword Spotting System to Assist the Transcription of Historical Document

Digital libraries offer access to a large number of handwritten historical documents. These documents are available as raw images and therefore their content is not searchable. A fully manual transcription is time-consuming and expensive while a fully automatic transcription is cheaper but not comparable in terms of accuracy. The performance of automatic transcription systems is strictly related to the composition of the training set. We propose a multi-step procedure that exploits a Keyword Spotting system and human validation for building up a training set in a time shorter than the one required by a fully manual procedure. The multi-step procedure was tested on a data set made up of 50 pages extracted from the Bentham collection. The palaeographer that transcribed the data set with the multi-step procedure instead of the fully manual procedure had a time gain of 52.54%. Moreover, a small size training set that allowed the keyword spotting system to show a precision value greater than the recall value was built with the multi-step procedure in a time equal to 35.25% of the time required for annotating the whole data set.
Exploring multiple diversification strategies for academic citation contexts recommendation

Purpose Citation contexts have been found useful in many scenarios. However, existing context-based recommendations ignored the importance of diversity in reducing the redundant issues and thus cannot cover the broad range of user interests. To address this gap, the paper aims to propose a novelty task that can recommend a set of diverse citation contexts extracted from a list of citing articles. This will assist users in understanding how other scholars have cited an article and deciding which articles they should cite in their own writing. Design/methodology/approach This research combines three semantic distance algorithms and three diversification re-ranking algorithms for the diversifying recommendation based on the CiteSeer X data set and then evaluates the generated citation context lists by applying a user case study on 30 articles. Findings Results show that a diversification strategy that combined “word2vec” and “Integer Linear Programming” leads to better reading experience for participants than other diversification strategies, such as CiteSeer X using a list sorted by citation counts. Practical implications This diversifying recommendation task is valuable for developing better systems in information retrieval, automatic academic recommendations and summarization. Originality/value The originality of the research lies in the proposal of a novelty task that can recommend a diversification context list describing how other scholars cited an article, thereby making citing decisions easier. A novel mixed approach is explored to generate the most efficient diversifying strategy. Besides, rather than traditional information retrieval evaluation, a user evaluation framework is introduced to reflect user information needs more objectively.
XlanV Model with Adaptively Multi-Modality Feature Fusing for Video Captioning

The dynamic feature extracted by the 3D convolutional network and the static feature extracted by CNN are proved to be beneficial for video captioning. We adaptively fuse these two kinds of features in the X-Linear Attention Network Video and propose XlanV model for video captioning. However, we notice that the dynamic feature is not compatible with vision-language pre-training techniques when the frame length distribution and average pixel difference of training video and test video biases. Consequently, we directly train the XlanV model on the MSR-VTT dataset without pre-training on the GIF dataset in this challenge. The proposed XlanV model reaches the 1st place in the pre-training for video captioning challenge, which shows that substantially exploiting the dynamic feature is more effective than vision-language pre-training in this challenge.
SpatialGAN

The image generation model based on generative adversarial networks has recently received significant attention and can produce diverse, sharp, and realistic images. However, generating high-resolution images has long been a challenge. In this paper, we propose a progressive spatial recursive adversarial expansion model(called SpatialGAN) capable of producing high-quality samples of the natural image. Our approach uses a cascade of convolutional networks to progressively generate images in a part-to-whole fashion. At each level of spatial expansion, a separate image-to-image spatial adversarial expansion network (conditional GAN) is recursively trained based on context image generated by previous GAN or CGAN. Unlike other coarse-to-fine generative methods that constraint on generative process either by multi-scale resolution or by hierarchical feature, the SpatialGAN decomposes image space into multiple subspaces and gradually resolves uncertainties in the local-to-whole generative process. The SpatialGAN greatly stabilizes and speeds up the training, which allows us to produce images of high quality. Based on visual Inception Score and Fréchet Inception Distance, we demonstrate that the quality of images generated by SpatialGAN on several typical datasets is better than that of images generated by GANs without cascading and comparative with the state of art methods with cascading.
Discriminative Sparse Filtering for Multi-Source Image Classification

Distribution mismatch caused by various resolutions, backgrounds, etc. can be easily found in multi-sensor systems. Domain adaptation attempts to reduce such domain discrepancy by means of different measurements, e.g., maximum mean discrepancy (MMD). Despite their success, such methods often fail to guarantee the separability of learned representation. To tackle this issue, we put forward a novel approach to jointly learn both domain-shared and discriminative representations. Specifically, we model the feature discrimination explicitly for two domains. Alternating discriminant optimization is proposed to obtain discriminative features with an l2 constraint in labeled source domain and sparse filtering is introduced to capture the intrinsic structures exists in the unlabeled target domain. Finally, they are integrated in a unified framework along with MMD to align domains. Extensive experiments compared with state-of-the-art methods verify the effectiveness of our method on cross-domain tasks.
Photo Stream Question Answer

Understanding and reasoning over partially observed visual clues are often regarded as a challenging real-world problem even for human beings. In this paper, we present a new visual question answering (VQA) task -- Photo Stream QA, which aims to answer the open-ended questions about a narrative photo stream. Photo Stream QA is more challenging and interesting than the existing VQA tasks, since the temporal and visual variance among photos in the stream is huge and hard to observe. Therefore, instead of learning simple vision-text mappings, the AI algorithms must fill these variance gaps with more recollection, reasoning, even the knowledge from our daily experiences. To tackle the problems in Photo Stream QA, we propose an end-to-end baseline (E-TAA) with a novel Experienced Unit (E-unit) and Three-stage Alternating Attention (TAA). E-unit yields a better visual representation which captures the temporal semantic relation among visual clues in the photo stream, while TAA creates three levels of attention that gradually refines visual features by using the textual representation from the question as the guidance. Experimental results on our developed dataset demonstrate that, as the first attempt at the Photo Stream QA task, E-TAA provides promising results outperforming all the other baseline methods.
Re-training and parameter sharing with the Hash trick for compressing convolutional neural networks

As an ubiquitous technology for improving machine intelligence, deep learning has largely taken the dominant position among nowadays most advanced computer vision systems. To achieve superior performance on large-scale datasets, convolutional neural networks (CNNs) are often designed as complex models with millions of parameters. This limits the deployment of CNNs in embedded intelligent computer vision systems, such as intelligent robots that are resource-constrained with real-time computing requirement. This paper proposes a simple and effective model compression scheme to improve the real-time sensing of the surrounding objects. In the proposed framework, the Hash trick is first applied to a modified convolutional layer, and the compression of the convolutional layer is realized via weight sharing. Subsequently, the Hash index matrix is introduced to represent the Hash function, and its relaxation regularization is introduced into the fine-tuned loss function. Through the dynamic retraining of the index matrix, the Hash function can be updated. We evaluate our method using several state-of-the-art CNNs. Experimental results showed that the proposed method can reduce the number of parameters in AlexNet by 24× with no accuracy loss. In addition, the compressed VGG16 and ResNet50 can achieve a more than 60× increased speed, which is significant.
Stable Video Style Transfer Based on Partial Convolution with Depth-Aware Supervision

As a very important research issue in digital media art, neural learning based video style transfer has attracted more and more attention. A lot of recent works import optical flow method to original image style transfer framework to preserve frame-coherency and prevent flicker. However, these methods highly rely on paired video datasets of content video and stylized video, which are often difficult to obtain. Another limitation of existing methods is that while maintaining inter-frame coherency, they will introduce strong ghosting artifacts. In order to address these problems, this paper has following contributions: (1).presents a novel training framework for video style transfer without dependency on video dataset of target style; (2).firstly focuses on the ghosting problem existing in most previous works and uses partial convolution-based strategy to utilize inter-frame context and correlation, together with additional depth loss as a constrain to the generated frames to suppress ghosting artifacts and preserve stability at the same time. Extensive experiments demonstrate that our method can produce natural and stable video frames with target style. Qualitative and quantitative comparisons also show that the proposed approach outperforms previous works in terms of overall image quality and inter-frame stability. To facilitate future research, we publish our experiment code at \urlhttps://github.com/Huage001/Artistic-Video-Partial-Conv-Depth-Loss.
Resource conversion between operational coherence and multipartite entanglement in many-body systems

Abstract We establish a set of resource conversion relationships between quantum coherence and multipartite entanglement in many-body systems, where the operational measures of resource cost and distillation are focused. Under the multipartite incoherent operation, the initial coherence of single-party system bounds the maximal amounts of corresponding operational entanglement in an arbitrary bipartite partition as well as the genuine multipartite entanglement (GME) in many-body systems. Furthermore, the converted multipartite entanglement can be transferred to its subsystems and restored to coherence of a single party by means of local incoherent operations and classical communication, which constitutes a protocol of resource interconversion within the full incoherent operation scenario. As an example, we present a scheme for cyclic interconversion between coherence and GME in three-qubit systems without loss. Moreover, we analyze the property of bipartite and multipartite genuine multi-level entanglement by the initial coherence and investigate multipartite resource dynamics in the conversion.
H-AT: Hybrid Attention Transfer for Knowledge Distillation

Knowledge distillation is a widely applicable technique for supervising the training of a light-weight student neural network by capturing and transferring the knowledge of a high-capacity trained teacher neural network. However, the distillation performance is very sensitive to how the distilled knowledge and the distillation loss are defined. In this paper, we propose a novel channel-wise attention transfer loss with a corresponding channel selection method. Further, we develop a hybrid attention transfer loss. Extensive experiments on four public datasets demonstrate that the proposed channel-wise transfer method shows a complementary effect with other attention based methods, and the hybrid attention transfer achieves state-of-the-art performance on knowledge distillation tasks. Code for our experiments is available at https://github.com/somone23412/H-AT.
Mineral systems prospectivity modelling for gold and nickel in the Halls Creek Orogen, Western Australia

Geodynamic models, geological-geophysical interpretations, and isotope analysis illustrate that there are links between the nickel and gold mineral systems in the Halls Creek Orogen, Western Australia. Whole-rock Nd and 40Ar/39Ar analysis of rocks throughout the region, when compared with the geodynamic models suggest that nickel and gold mineralization in the Halls Creek Orogen can be related to basin development and subsequent basin closure during the convergence of North Australian Craton and Kimberley Craton, respectively. Whole-rock Nd analysis confirmed the input of juvenile melts in the centre of the orogen before the 1835–1805 Ma Halls Creek Orogeny, supporting the upwelling of decompression mantle melts during the basin development. These analyses also revealed the spatial links between nickel and gold mineralization and lithological units with positive εNd values. Spatially the link between these mineral systems appears to be related to the presence of deep-seated shear zones that formed early in the history of the orogen and were later reactivated. The results of geodynamic models, geophysical interpretation, and isotopic analysis are used to understand the critical processes in the gold and nickel mineralization, which are presented by predictor maps. The GIS-based knowledge-driven fuzzy logic method used to integrate the predictor maps and create the prospectivity maps. Herein we show that mafic-ultramafic units prospective for nickel mineralization formed by upwelling of decompression mantle melt during crustal thinning and extension during basin development, and typically consist of the most juvenile magmas in the region. Whereas, gold deposits formed during the compressional regime and basin closure, and are located along a major shear zone separating two terranes. This deep crustal-scale shear zone is implied to be the site of multiple stages of deformation that acted as fluid migration pathways during basin closure and subsequent collision. Another critical element that appears to be related to gold prospectivity is the presence of lithologies with a juvenile signature. In contrast to nickel analyses which are closely related to mafic-ultramafic units, the source component seems less influential when attempting to target orogenic gold deposits.
A Personalised Blood Pressure Prediction System using Gaussian Mixture Regression and Online Recurrent Extreme Learning Machine

Hypertension is an epidemic restricted not only to the developing but also to the developed nations. It is triggered by various lifestyle choices that depend on each individual based on their personal physiology and lifestyle. Early diagnosis is possible, but it requires continuous blood pressure monitoring. Various machine learning methods have been proposed for early diagnosis of hypertension by predicting blood pressure and detecting high spikes in the values. However, these solutions are built upon the generic guidelines which may not be applicable for every patient. Most of these solutions incorporate batch learning and require all data to be present before prediction and do not support any online learning mechanism. This leads to potentially outdated models. Furthermore, there is also a lack of an intelligent approach to handling incomplete time series while training the model. This paper presents a personalized approach to estimate blood pressure that eliminates the need for continuous monitoring based on the Online recurrent extreme learning machine (OR-ELM). The missing values are imputed using Gaussian mixture models. The prediction model learns from the historical data and learns online as more data becomes available. The proposed scheme is developed and deployed on a mobile application for secured prediction results. The method is used to predict blood pressure in Malaysian population and compared with existing batch-learning and online learning methods. The results show that OR-ELM based model outperforms the existing online techniques such as the Online sequential extreme learning machine and batch learning technique such as Extreme learning machine.
Spatiotemporal retrieval of dynamic video object trajectories in geographical scenes

Abstract Current studies on video trajectory retrieval focus on the retrieval and analysis of image content, neglecting the gap between the spatiotemporal continuity of retrieval conditions and the spatiotemporal discontinuity of multi‐camera video trajectories. In this study, we propose a method for the spatiotemporal retrieval of dynamic video object trajectories in geographic scenes. Based on the camera calibration, the proposed method organizes the scene, cameras, and trajectories, constructs the spatiotemporal constraints, and queries the trajectories using two measures: camera‐by‐camera retrieval and global trajectory retrieval. The proposed method was verified through experiments, and the results demonstrate that both measures can query trajectories effectively and reduce the spatiotemporal video review range under different spatiotemporal constraints. Furthermore, compared with camera‐by‐camera retrieval, global trajectory retrieval can reduce the spatiotemporal video review range further and return more accurate results. The proposed method may provide support for the spatial analysis and understanding of surveillance video data.
Ethical challenges in argumentation and dialogue in a healthcare context

As the average age of the population increases, so too do the number of people living with chronic illnesses. With limited resources available, the development of dialogue-based e-health systems that provide justified general health advice offers a cost-effective solution to the management of chronic conditions. It is however imperative that such systems are responsible in their approach. We present in this paper two main challenges for the deployment of e-health systems, that have a particular relevance to dialogue and argumentation: collecting and handling health data, and trust. For both challenges, we look at specific issues therein, outlining their importance in general, and describing their relevance to dialogue and argumentation. Finally, we go on to propose six recommendations for handling these issues, towards addressing the main challenges themselves, that act both as general advice for dialogue and argumentation research in the e-health domain, and as a foundation for future work on this topic.
Elastic Provisioning of Hadoop Clusters on OpenStack Private Cloud

Big Data Processing refers to processing, analyzing, and extracting information from a large amount of data. The traditional processing techniques of big data are no longer viable. Therefore this large volume of data is processed by distributed computing frameworks like Apache Hadoop. MapReduce frameworks are an excellent way to process larger datasets on the cloud platform. The automation of Hadoop is achieved on OpenStack based private cloud. Auto-scaling refers to the dynamic addition of nodes to or removal of the nodes from the existing cluster to use the resources effectively. The Quality of Service(QoS) has to be maintained while auto-scaling the resources on-demand. There is a requirement for auto-scaling of the Hadoop cluster when the load increases to adhere to the Service Level Agreements(SLAs). The paper presents the auto-scaling method of Hadoop clusters using predictive algorithms. To effectively implement auto-scaling, the prediction techniques are employed. The proposed model obtains the CPU utilization metrics and scales up or scales down the cluster based on the time series analysis. The experimental result using the OpenStack-based private cloud testbed reveals the effectiveness of the proposed mechanism.
Highly accurate 3D reconstruction based on a precise and robust binocular camera calibration method

The precision of the camera calibration is one of the key factors that affect attitude measurement accuracy in many computer vision tasks. This study proposes a new calibration approach for binocular cameras. Firstly, based on singular value decomposition, the best transformation matrix to the essential matrix is approximated as the initial guess, which is solved in using the Frobenius norm. Secondly, the initial guess is refined through maximum likelihood estimation. A new calculating expression is derived for computing the relative position matrix of the binocular cameras. The Levenberg–Marquardt algorithm is then implemented to refine the initial guess. Large sets of synthesised and real point correspondences were tested to demonstrate the validity of the proposed method. Extensive experiments demonstrated that the proposed method outperforms the state-of-the-art methods. The error rate of the proposed method was 0.5% for the length test and about 1% for the angle test at a range of 1 m. This method can advance three-dimensional (3D) computer vision one additional step from laboratory environments to real-world use.
Feature Extraction and Feature Selection for Emotion Recognition using Facial Expression

Facial expressions play a significant role in describing the emotions of a person. Due to its applicability to a wide range of applications, such as human-computer interaction, driver status monitoring, etc. Facial Expression Recognition (FER) has received substantial attention among the researchers. According to the earlier studies, a small feature set is used for the extraction of facial features for FER system. To date, a systematic comparison of the facial features does not exist. Therefore, in the current research, we identified 18 different facial features (cardinality of 46,352) by reviewing 25 studies and implemented them on the publicly available Extended-Cohn-Kanade (CK+) dataset. After extracting facial features, we performed Feature Selection (FS) using Joint Mutual Information (JMI), Conditional Mutual Information Maximization (CMIM) and MaxRelevance Min-Redundancy (MRMR) and explain the systematic comparison between them, and for classification, we applied various machine learning techniques. The Bag of Visual Words (BoVW) model approach results in significantly higher classification accuracy over the formal approach. Also, we found that the optimal classification accuracy for FER can be obtained by using only 20% of the total identified features. Grey comatrix and haralick features were explored for the first time for the FER and grey comatrix feature outperformed several most commonly used features Local Binary Pattern (LBP) and Active Appearance Model (AAM). Histogram of Gradients (HOG) turns out to be the most significant feature for FER followed Local Directional Positional Pattern (LDSP) and grey comatrix.