Reviewerly/exHarmony huggingface dataset:
UCrawler: A learning-based web crawler using a URL knowledge base

Focused crawlers, as fundamental components of vertical search engines, focus on crawling the web pages related to a specific topic. Existing focused crawlers commonly suffer from the problems of low efficiency of crawling pages and subject migration. In this paper, we propose a learning-based focused crawler using a URL knowledge base. To improve the accuracy of similarity, the similarity of the topic is measured with the parent page content, anchor information, and URL content. The URL content is also learned and updated iteratively and continuously. Within the crawler, we implement a crawling mechanism based on a combination of content analysis and simple link analysis crawler strategy, which decreases computational complexity and avoids the locality problem of crawling. Experimental results show that our proposed algorithm achieves a better precision than traditional methods including the shark-search and best-first search algorithms, and avoids the local optimum problem of crawling.
Expert Performance in the Examination of Interior Surfaces in an Automobile: Virtual Reality vs. Reality

For evaluating the appearance and design language of car interiors, the surface quality and shapes are inspected by highly trained professionals. At the same time, virtual reality (VR) is making major progress, pushing the boundaries of this technology. In this paper, we evaluate the applicability of VR using head mounted displays (HMDs) in an experiment where we had experts examine the design quality of an interior in VR and compared the results with the examination on a powerwall as well as in reality. Our goal is to find out in how far current VR hardware can be used in the automotive industry and which advantages and disadvantages occur. Our results show that the experts are able to detect an amount of flaws comparable to reality with the powerwall being the superior medium in terms of flaws identified. Additionally, symptoms of cybersickness and a reduced lack of confidence was measured in the subjects using a HMD.
Sketching music: Exploring melodic similarity and contrast using a digital tabletop

In this article, we investigate the effectiveness of a purposely built Digital Tabletop Musical Instruments (DTMI) in helping novices and casual users to explore music composition. Our participants explored how melodic similarity and contrast can convey narrative through musical structure in sessions involving one participant and one tutor to guide the session. We structured the sessions as a combination of open-ended discussions and increasingly open-ended music-making exercises, culminating in the main task: Invent a short story and compose a melody to describe it. We found that the combination of a structured tutor-led activity and an approachable technology allowed our participants to explore the relationship between their ideas of similarity and contrast, the ways these concepts are manifested in melody, and the ways they can help describe a narrative. The hands-on activities provided adequate scaffolding for discussing the concepts and contextualizing them within music. Lastly, by not requiring any formal musical or instrumental training, the DTMI allowed the participants to make music while discussing similarity and contrast in a comfortable and continuous way.
Pose invariant age estimation of face images in the wild

The current work proposes a method for age estimation of face videos. To attenuate the effect of pose, our method is based on facial uv texture maps reconstructed from original frames of videos. A Wasserstein-based GAN is used to restore the full uv texture presentation. Age is then predicted from the completed uv mappings such that the proposed AgeGAN method simultaneously learns to capture the facial uv texture map and age characteristics. To train our method, we have created the UvAge dataset, the largest video dataset of face videos with age annotation (together with identity, gender, and ethnicity labels). The dataset contains videos in-the-wild from celebrities that are recorded in a variety of imaging settings. In total, we collected 6898 video segments (788,640 frames) from 516 celebrities in 57 events. Extensive experiments demonstrate that our proposed method outperforms other advanced age estimation methods.
Transitioning the information retrieval literature to a fully open access model

Almost all of the important literature on Information Retrieval (IR) is published in subscription-based journals and digital libraries. We argue that the lack of open access publishing in IR is seriously hampering progress and inclusiveness of the field. We propose that the IR community starts working on a road map for transitioning the IR literature to a fully, "diamond", open access model.
Face Detection on Pre-modern Japanese Artworks using R-CNN and Image Patching for Semi-Automatic Annotation

We propose a face detection method for semi-automatic annotation of faces on pre-modern Japanese artworks to assist art historians identify objects in the art collection. Our method is based on R-CNN, such as Faster R-CNN and Cascade R-CNN, for object detection, and image patching for taking advantage of high resolution images. Our face detectors were first trained on the KaoKore dataset to demonstrate that existing object detection models with image patching can successfully learn faces in artworks. Our face detectors were then applied to the Kouhon dataset to assist art historians create a new facial expression dataset. Finally the impact of face detection on art history research was measured by the reduction of annotation time, and it was estimated to be $1/3$ in comparison to fully manualdiscussed as the reduction of annotation time to $1/3$ in comparison to fully manual annotation.
LogiKEy workbench: Deontic logics, logic combinations and expressive ethical and legal reasoning (Isabelle/HOL dataset)

The LogiKEy workbench and dataset for ethical and legal reasoning is presented. This workbench simultaneously supports development, experimentation, assessment and deployment of formal logics and ethical and legal theories at different conceptual layers. More concretely, it comprises, in form of a dataset (Isabelle/HOL theory files), formal encodings of multiple deontic logics, logic combinations, deontic paradoxes and normative theories in the higher-order proof assistant system Isabelle/HOL. The data were acquired through application of the LogiKEy methodology, which supports experimentation with different normative theories, in different application scenarios, and which is not tied to specific logics or logic combinations. Our workbench consolidates related research contributions of the authors and it may serve as a starting point for further studies and experiments in flexible and expressive ethical and legal reasoning. It may also support hands-on teaching of non-trivial logic formalisms in lecture courses and tutorials. The LogiKEy methodology and framework is discussed in more detail in the companion research article titled "Designing Normative Theories for Ethical and Legal Reasoning: LogiKEy Framework, Methodology, and Tool Support" [5].
Convolutional neural network optimization via channel reassessment attention module

The performance of convolutional neural networks (CNNs) can be improved by adjusting the interrelationship between channels with attention mechanism. However, attention mechanism in recent advance ignores the effects of different spatial locations in feature maps on attention weights, which makes a great difference to the results of generated channel attentions. In this paper, we propose a novel network optimization module called Channel Reassessment Attention (CRA) which uses channel attentions with spatial information of feature maps to enhance representational power of networks. We employ CRA module to assess channel attentions in different channels, then adaptively refine the final features by channel multiplication between channel attentions and feature maps. CRA is a computational lightweight module and it can be embedded into any architectures of CNNs. The experiments on ImageNet, CIFAR and MS COCO datasets demonstrate that the embedding of CRA module on various networks effectively improves the performance under different evaluation standards.
A novel method based on convolutional neural network for malaria diagnosis

Malaria is one of the three major diseases with the highest mortality worldwide and can turn fatal if not taken seriously. The key to surviving this disease is its early diagnosis. However, manual diagnosis is time consuming and tedious due to the large amount of image data. Generally, computer-aided diagnosis can effectively improve doctors’ perception and accuracy. This paper presents a medical diagnosis method powered by convolutional neural network (CNN) to extract features from images and improve early detection of malaria. The image sharpening and histogram equalization method are used aiming at enlarging the difference between parasitized regions and other area. Dropout technology is employed in every convolutional layer to reduce overfitting in the network, which is proved to be effective. The proposed CNN model achieves a significant performance with the best classification accuracy of 99.98%. Moreover, this paper compares the proposed model with the pretrained CNNs and other traditional algorithms. The results indicate the proposed model can achieve state-of-the-art performance from multiple metrics. In general, the novelty of this work is the reduction of the CNN structure to only five layers, thereby greatly reducing the running time and the number of parameters, which is demonstrated in the experiments. Furthermore, the proposed model can assist clinicians to accurately diagnose the malaria disease.
Who You Are Decides How You Tell

Image captioning is gaining significance in multiple applications such as content-based visual search and chat-bots. Much of the recent progress in this field embraces a data-driven approach without deep consideration of human behavioural characteristics. In this paper, we focus on human-centered automatic image captioning. Our study is based on the intuition that different people will generate a variety of image captions for the same scene, as their knowledge and opinion about the scene may differ. In particular, we first perform a series of human studies to investigate what influences human description of a visual scene. We identify three main factors: a person's knowledge level of the scene, opinion on the scene, and gender. Based on our human study findings, we propose a novel human-centered algorithm that is able to generate human-like image captions. We evaluate the proposed model through traditional evaluation metrics, diversity metrics, and human-based evaluation. Experimental results demonstrate the superiority of our proposed model on generating diverse human-like image captions.
Digital Contact Tracing for the COVID-19 Epidemic: A Business and Human Rights Perspective

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Design Principles for Educational Mixed Reality?

Mixed reality (MR) applications are widely considered to be effective educational tools. However, the use of MR alone cannot ensure learning, and studies even suggest that the affordances of this technology could decrease the mental processes required for the acquisition of new knowledge. Like any other technological innovation, the educational possibilities of MR are closely related to the design of its contents. Despite this, there are no design recommendations for MR focused on learning. Educational psychology presents a range of empirically proven design guidelines for multimedia learning environments. This chapter reviews existing guidelines and categorizes them into principles related to the perception of information and the related essential information processing (design principles) and principles aiming at promoting generative learning (activating principles). These principles are translated to MR-learning environments.
The Business Case for Democracy

I present a business case for democracy, focusing on the impact of democracy on economic growth. This relationship is widely studied, and results are less clear for growth than many other development outcomes such as literacy or infant mortality. I discuss four factors pertaining to data quality and modelling choices, suggesting that several previous studies have underestimated the growth-benefits of democracy. I also discuss the relationship between democracy and economic crises and variation in economic performance. By mitigating abysmal economic outcomes and ensuring more stable performance, democracy is generally of benefit to risk-averse entrepreneurs, investors, workers, and consumers alike.
Tactile Sketch Saliency

In this paper, we aim to understand the functionality of 2D sketches by predicting how humans would interact with the objects depicted by sketches in real life. Given a 2D sketch, we learn to predict a tactile saliency map for it, which represents where humans would grasp, press, or touch the object depicted by the sketch. We hypothesize that understanding 3D structure and category of the sketched object would help such tactile saliency reasoning. We thus propose to jointly predict the tactile saliency, depth map and semantic category of a sketch in an end-to-end learning-based framework. To train our model, we propose to synthesize training data by leveraging a collection of 3D shapes with 3D tactile saliency information. Experiments show that our model can predict accurate and plausible tactile saliency maps for both synthetic and real sketches. In addition, we also demonstrate that our predicted tactile saliency is beneficial to sketch recognition and sketch-based 3D shape retrieval, and enables us to establish part-based functional correspondences among sketches.
Generating visual story graphs with application to photo album summarization

Making sense of ever-growing amount of visual data available on the web is difficult, especially when considered in an unsupervised manner. As a step towards this goal, this study tackles a relatively less explored topic of generating structured summaries of large photo collections. Our framework relies on the notion of a story graph which captures the main narratives in the data and their relationships based on their visual, textual and spatio-temporal features. Its output is a directed graph with a set of possibly intersecting paths. Our proposed approach identifies coherent visual storylines and exploits sub-modularity to select a subset of these lines which covers the general narrative at most. Our experimental analysis reveals that extracted story graphs allow for obtaining better results when utilized as priors for photo album summarization. Moreover, our user studies show that our approach delivers better performance on next image prediction and coverage tasks than the state-of-the-art.
Open Set Domain Adaptation with Entropy Minimization

Unsupervised domain adaptation has achieved great progress in the past few years. Nevertheless, most existing methods work in the so-called closed-set scenario, assuming that the classes depicted by the target samples are exactly the same as those of the source domain. In this paper, we tackle the more challenging scenario of open set domain adaptation with a novel end-to-end training approach, where the samples of unknown class can be present in the target domain. Our method employs entropy minimization for performing unsupervised domain adaptation, where unknown samples are aggressively used in training by forcing the classifier to output the probability of 0.5 on the unknown class. Experimental evidence demonstrates that our approach significantly outperforms the state-of-the-art in open set domain adaptation.
PatchMatch based Multiview Stereo with Local Quadric Window

Although various stereo matching methods are studied in many years, the accurate 3D reconstruction from multiview stereos in high-fidelity is still challenging due to the surface inconsistency caused by various factors such as specular illumination. In this paper, we propose an accurate PatchMatch based multiview stereo matching method with a quadric support window that efficiently captures the surface of a complex structured object. Our method takes three novel contributions. Firstly, delicate surface configurations are used for representing the complex structure of an object. By using a general 3D quadric function, the structured object surfaces can be estimated more accurately. In addition, an illumination robust framework is proposed, where the patch dissimilarities are precisely measured with disentangled representation. The matching cost is defined based on disentangled measurements of the object photometric and geometric properties, balancing the pixel intensities between images robust to illumination. Lastly, a multiview propagation method is proposed to confirm shape consistency among views. Through the disparity refinement to unify plane parameters of the views, the object surface is estimated from a global perspective. Consequently, the dense and smooth 3D shape of the object is reconstructed accurately. We evaluate our proposed method on the Middlebury stereo set and conduct comprehensive experiments on facial images. Both quantitative and qualitative results demonstrate that the proposed method shows significant improvements over state-of-the-art methods.
Multi-Person Action Recognition in Microwave Sensors

The usage of surveillance cameras for video understanding, raises concerns about privacy intrusion recently. This motivates the research community to seek potential alternatives of cameras for emerging multimedia applications. Stepping to this goal, a few researchers have explored the usage of Wi-Fi or Bluetooth sensors to handle action recognition. However, the practical ability of these sensors is limited by their frequency band and deployment inconvenience because of the separate transmitter/receiver architecture. Motivated by the same purpose of reducing privacy issues, we introduce a latest microwave sensor for multi-person action recognition in this paper. The microwave sensor works at 77GHz ~ 80GHz band, and is implemented with both transmitter and receiver inside itself, thus can be easily deployed for action recognition. Although with its advantages, two main challenging issues still remain. One is the difficulty of labelling the invisible signal data with embedding actions. The other is the difficulty of cancelling the environment noise for high-accurate action recognition. To address the challenges, we propose a novel learning framework by designed original loss functions with the considerations on weakly-supervised multi-label learning and attention mechanism to improve the accuracy for action recognition. We build a new microwave sensor data set, and conduct comprehensive experiments to evaluate the recognition accuracy of our proposed framework, and the effectiveness of parameters in each component. The experiment results show that our framework outperforms the state-of-the-art methods up to 14% in terms of mAP.
Neural3D: Light-weight Neural Portrait Scanning via Context-aware Correspondence Learning

Reconstructing a human portrait in a realistic and convenient manner is critical for human modeling and understanding. Aiming at light-weight and realistic human portrait reconstruction, in this paper we propose Neural3D: a novel neural human portrait scanning system using only a single RGB camera. In our system, to enable accurate pose estimation,we propose a context-aware correspondence learning approach which jointly models the appearance, spatial and motion information between feature pairs. To enable realistic reconstruction and suppress the geometry error, we further adopt a point-based neural rendering scheme to generate realistic and immersive portrait visualization in arbitrary virtual view-points. By introducing these learning-based technical components into the pure RGB-based human modeling framework, we can achieve both accurate camera pose estimation and realistic free-viewpoint rendering of the reconstructed human portrait. Extensive experiments on a variety of challenging capture scenarios demonstrate the robustness and effectiveness of our approach.
Fashionist: Personalising Outfit Recommendation for Cold-Start Scenarios

With the proliferation of the online fashion industry, there have been increased efforts towards building cutting-edge solutions for personalising fashion recommendation. Despite this, the technology is still limited by its poor performance on new entities, i.e. the cold-start problem. We attempt to address the cold-start problem for new users, by leveraging a novel visual preference modelling approach on a small set of input images. Additionally, we describe our proposed strategy to incorporate the modelled preference in occasion-oriented outfit recommendation. Finally, we propose Fashionist: a real-time web application to demonstrate our approach enabling personalised and diverse outfit recommendation for cold-start scenarios. Check out https://youtu.be/kuKgPCkoPy0 for demonstration.
A data encryption model based on intertwining logistic map

A data encryption model is developed using Brownian Motion based confusion and intertwining logistic map based diffusion method. In the existing encryption models, shuffling is irrelevant for the pixels with same value i.e. (all white/black/same colour) and models are prone to differential and various statistical attacks. This article investigates random data insertion to ensure the validity of shuffling engine and result in a different cipher in each execution. It makes both differential and chosen plaintext attacks infeasible and reduces the correlation among existing image pixels. Further, an intertwining logistic map is used not only for better random number distribution but also to overcome the blank window noticed in the bifurcation diagram of logistic map. Brownian motion based confusion introduced key sensitivity in the encryption model. In last, Intertwining logistic map based diffusion model is applied so that even a single bit change affects most of the pixels in the cipher. Number of pixel change rate (NPCR) and Unified average change intensity (UACI) scores support the security enhancement of the model. Randomness is supported by the NIST randomness test. Simulation results show that the model has better security level, lossless compression, resistive against chosen and known plaintext attacks.