Reviewerly/exHarmony huggingface dataset:
Weakly-Supervised Cross-Domain Road Scene Segmentation via Multi-Level Curriculum Adaptation

Semantic segmentation, which aims to acquire pixel-level understanding about images, is among the key components in computer vision. To train a good segmentation model for real-world images, it usually requires a huge amount of time and labor effort to obtain sufficient pixel-level annotations of real-world images beforehand. To get rid of such a nontrivial burden, one can use simulators to automatically generate synthetic images that inherently contain full pixel-level annotations and use them to train a segmentation model for the real-world images. However, training with synthetic images usually cannot lead to good performance due to the domain difference between the synthetic images (i.e., source domain) and the real-world images (i.e., target domain). To deal with this issue, a number of unsupervised domain adaptation (UDA) approaches have been proposed, where no labeled real-world images are available. Different from those methods, in this work, we conduct a pioneer attempt by using easy-to-collect image-level annotations for target images to improve the performance of cross-domain segmentation. Specifically, we leverage those image-level annotations to construct curriculums for the domain adaptation problem. The curriculums describe multi-level properties of the target domain, including label distributions over full images, local regions and single pixels. Since image annotations are “weak” labels compared to pixel annotations for segmentation, we coin this new problem as weakly-supervised cross-domain segmentation. Comprehensive experiments on the GTA5→ Cityscapes and SYNTHIA→ Cityscapes settings demonstrate the effectiveness of our method over the existing state-of-the-art baselines.
Low Light Video Enhancement Using Synthetic Data Produced with an Intermediate Domain Mapping

Advances in low-light video RAW-to-RGB translation are opening up the possibility of fast low-light imaging on commodity devices (e.g. smartphone cameras) without the need for a tripod. However, it is challenging to collect the required paired short-long exposure frames to learn a supervised mapping. Current approaches require a specialised rig or the use of static videos with no subject or object motion, resulting in datasets that are limited in size, diversity, and motion. We address the data collection bottleneck for low-light video RAW-to-RGB by proposing a data synthesis mechanism, dubbed SIDGAN, that can generate abundant dynamic video training pairs. SIDGAN maps videos found ‘in the wild’ (e.g. internet videos) into a low-light (short, long exposure) domain. By generating dynamic video data synthetically, we enable a recently proposed state-of-the-art RAW-to-RGB model to attain higher image quality (improved colour, reduced artifacts) and improved temporal consistency, compared to the same model trained with only static real video data.
Attribute-based proxy re-signature from standard lattices and its applications

We introduce Attribute-based Proxy Re-Signatures (ABPRS), an extension of Proxy Re-Signatures (PRS), which allows a semi-trusted proxy to transform a signature of one entity into a signature of another, without revealing any signing key and information about the signer. The proposed primitive combines features of PRS and Attribute-based Signatures (ABS), which is useful for many applications (we show how to apply ABPRS to three such applications). We propose a unidirectional ABPRS construction based on the hardness of the Inhomogeneous Small Integer Solution (ISIS) problem in standard lattices. We show that the proposed unidirectional ABPRS construction is multi-hop, where the transformation can be performed multiple times on any signature. Furthermore, we prove that the proposed unidirectional ABPRS construction satisfies the security notion of no-signature-query unforgeability and (perfect) privacy, under the ISIS assumption. Finally, we show how to obtain a multi-hop unidirectional ABPRS scheme that satisfies a stronger security notion of co-selective unforgeability by using a generic transformation technique.
Regional registration of whole slide image stacks containing major histological artifacts

Abstract Background High resolution 2D whole slide imaging provides rich information about the tissue structure. This information can be a lot richer if these 2D images can be stacked into a 3D tissue volume. A 3D analysis, however, requires accurate reconstruction of the tissue volume from the 2D image stack. This task is not trivial due to the distortions such as tissue tearing, folding and missing at each slide. Performing registration for the whole tissue slices may be adversely affected by distorted tissue regions. Consequently, regional registration is found to be more effective. In this paper, we propose a new approach to an accurate and robust registration of regions of interest for whole slide images. We introduce the idea of multi-scale attention for registration. Results Using mean similarity index as the metric, the proposed algorithm (mean ± SD $$0.84 \pm 0.11$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mn>0.84</mml:mn><mml:mo>±</mml:mo><mml:mn>0.11</mml:mn></mml:mrow></mml:math> ) followed by a fine registration algorithm ( $$0.86 \pm 0.08$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mn>0.86</mml:mn><mml:mo>±</mml:mo><mml:mn>0.08</mml:mn></mml:mrow></mml:math> ) outperformed the state-of-the-art linear whole tissue registration algorithm ( $$0.74 \pm 0.19$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mn>0.74</mml:mn><mml:mo>±</mml:mo><mml:mn>0.19</mml:mn></mml:mrow></mml:math> ) and the regional version of this algorithm ( $$0.81 \pm 0.15$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mn>0.81</mml:mn><mml:mo>±</mml:mo><mml:mn>0.15</mml:mn></mml:mrow></mml:math> ). The proposed algorithm also outperforms the state-of-the-art nonlinear registration algorithm (original: $$0.82 \pm 0.12$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mn>0.82</mml:mn><mml:mo>±</mml:mo><mml:mn>0.12</mml:mn></mml:mrow></mml:math> , regional: $$0.77 \pm 0.22$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mn>0.77</mml:mn><mml:mo>±</mml:mo><mml:mn>0.22</mml:mn></mml:mrow></mml:math> ) for whole slide images and a recently proposed patch-based registration algorithm (patch size 256: $$0.79 \pm 0.16$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mn>0.79</mml:mn><mml:mo>±</mml:mo><mml:mn>0.16</mml:mn></mml:mrow></mml:math> , patch size 512: $$0.77 \pm 0.16$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mn>0.77</mml:mn><mml:mo>±</mml:mo><mml:mn>0.16</mml:mn></mml:mrow></mml:math> ) for medical images. Conclusion Using multi-scale attention mechanism leads to a more robust and accurate solution to the problem of regional registration of whole slide images corrupted in some parts by major histological artifacts in the imaged tissue.
Optimisation of cyber insurance coverage with selection of cost effective security controls.

Nowadays, cyber threats are considered among the most dangerous risks by top management of enterprises. One way to deal with these risks is to insure them, but cyber insurance is still quite expensive. The insurance fee can be reduced if organisations improve their cyber security protection, i.e., reducing the insured risk. In other words, organisations need an investment strategy to decide the optimal amount of investments into cyber insurance and self-protection. In this work, we propose an approach to help a risk-averse organisation to distribute its cyber security investments in a cost-efficient way. What makes our approach unique is that next to defining the amount of investments in cyber insurance and self-protection, our proposal also explicitly defines how these investments should be spent by selecting the most cost-efficient security controls. Moreover, we provide an exact algorithm for the control selection problem considering several threats at the same time and compare this algorithm with other approximate algorithmic solutions.
Multi-Modal Explicit Sparse Attention Networks for Visual Question Answering

Visual question answering (VQA) is a multi-modal task involving natural language processing (NLP) and computer vision (CV), which requires models to understand of both visual information and textual information simultaneously to predict the correct answer for the input visual image and textual question, and has been widely used in smart and intelligent transport systems, smart city, and other fields. Today, advanced VQA approaches model dense interactions between image regions and question words by designing co-attention mechanisms to achieve better accuracy. However, modeling interactions between each image region and each question word will force the model to calculate irrelevant information, thus causing the model’s attention to be distracted. In this paper, to solve this problem, we propose a novel model called Multi-modal Explicit Sparse Attention Networks (MESAN), which concentrates the model’s attention by explicitly selecting the parts of the input features that are the most relevant to answering the input question. We consider that this method based on top-k selection can reduce the interference caused by irrelevant information and ultimately help the model to achieve better performance. The experimental results on the benchmark dataset VQA v2 demonstrate the effectiveness of our model. Our best single model delivers 70.71% and 71.08% overall accuracy on the test-dev and test-std sets, respectively. In addition, we also demonstrate that our model can obtain better attended features than other advanced models through attention visualization. Our work proves that the models with sparse attention mechanisms can also achieve competitive results on VQA datasets. We hope that it can promote the development of VQA models and the application of artificial intelligence (AI) technology related to VQA in various aspects.
The OpenCitations Data Model

A variety of schemas and ontologies are currently used for the machine-readable description of bibliographic entities and citations. This diversity, and the reuse of the same ontology terms with different nuances, generates inconsistencies in data. Adoption of a single data model would facilitate data integration tasks regardless of the data supplier or context application. In this paper we present the OpenCitations Data Model (OCDM), a generic data model for describing bibliographic entities and citations, developed using Semantic Web technologies. We also evaluate the effective reusability of OCDM according to ontology evaluation practices, mention existing users of OCDM, and discuss the use and impact of OCDM in the wider open science community.
A comprehensive comparison of end-to-end approaches for handwritten digit string recognition

Over the last decades, most approaches proposed for handwritten digit string recognition (HDSR) have resorted to digit segmentation, which is dominated by heuristics, thereby imposing substantial constraints on the final performance. Few of them have been based on segmentation-free strategies where each pixel column has a potential cut location. Recently, segmentation-free strategies has added another perspective to the problem, leading to promising results. However, these strategies still show some limitations when dealing with a large number of touching digits. To bridge the resulting gap, in this paper, we hypothesize that a string of digits can be approached as a sequence of objects. We thus evaluate different end-to-end approaches to solve the HDSR problem, particularly in two verticals: those based on object-detection (e.g., Yolo and RetinaNet) and those based on sequence-to-sequence representation (CRNN). The main contribution of this work lies in its provision of a comprehensive comparison with a critical analysis of the above mentioned strategies on five benchmarks commonly used to assess HDSR, including the challenging Touching Pair dataset, NIST SD19, and two real-world datasets (CAR and CVL) proposed for the ICFHR 2014 competition on HDSR. Our results show that the Yolo model compares favorably against segmentation-free models with the advantage of having a shorter pipeline that minimizes the presence of heuristics-based models. It achieved a 97%, 96%, and 84% recognition rate on the NIST-SD19, CAR, and CVL datasets, respectively.
Supporting the teaching of design thinking techniques for requirements elicitation through a recommendation tool

The development of systems with different features becomes increasingly challenging, given the actual context, implying on the search for new approaches for requirements elicitation. One of these approaches is design thinking (DT), a process of encouraging innovation used by designers, which presents itself as fundamental in the success of new products. This scenario motivates the importance of knowing DT concepts and their techniques, which can support requirements elicitation. In this context, the authors present DTA4RE (DT assistant for requirements elicitation), a tool that suggests DT techniques for requirements elicitation, which can be used by both students and professionals who would like to adopt DT in their projects. The DTA4RE is composed of a set of 27 techniques that could be suggested to the user through a recommendation questionnaire as well as an open repository with material to support the application of these techniques. Results from the authors’ two empirical studies with software engineering undergraduate and graduate students and industry professionals indicate that DTA4RE has helped in the selection of and in the learning of DT techniques when considering real problems. Most participants considered the questions from the recommendation questionnaire easy to answer, and the techniques suggested by the tool useful.
Linguistic Steganography: From Symbolic Space to Semantic Space

Previous works about linguistic steganography such as synonym substitution and sampling-based methods usually manipulate observed symbols explicitly to conceal secret information, which may give rise to security risks. In this letter, in order to preclude straightforward operation on observed symbols, we explored generation-based linguistic steganography in latent space by means of encoding secret messages in the selection of implicit attributes (semanteme) of natural language. We proposed a novel framework of linguistic semantic steganography based on rejection sampling strategy. Concretely, we utilized controllable text generation model for embedding and semantic classifier for extraction. In experiments, a model based on CTRL and BERT is implemented for further quantitative assessment. Results reveal that our approach is able to achieve satisfactory efficiency as well as nearly perfect imperceptibility. Our code is available at https://github.com/YangzlTHU/Linguistic-Steganography-and-Steganalysis/tree/master/Steganography/Linguistic-Semantic-Steganography.
Neural collapse with unconstrained features

Neural collapse is an emergent phenomenon in deep learning that was recently discovered by Papyan, Han and Donoho. We propose a simple unconstrained features model in which neural collapse also emerges empirically. By studying this model, we provide some explanation for the emergence of neural collapse in terms of the landscape of empirical risk.
ICFHR 2020 Competition on Offline Recognition and Spotting of Handwritten Mathematical Expressions - OffRaSHME

This paper presents the competition on Offline Recognition and Spotting of Handwritten Mathematical Expressions (OffRaSHME) held at the 17th International Conference on Frontiers in Handwriting Recognition (ICFHR 2020). Handwritten Mathematical Expression Recognition (HMER) has wide potential applications and is capturing increasing attention in recent years. Previous HMER competitions mainly focused on online datasets or offline datasets that are converted from online data. In this competition, we have collected a dataset of offline handwritten mathematical expressions by scanning papers that contain expressions. Moreover, we labeled the offline dataset at symbol level, i.e., the bounding boxes of each symbol are also provided, to facilitate the research of HMER. At last, 19,749 offline handwritten mathematical expressions are collected for training, and 2,000 ones are provided for evaluating the participating systems. In the competition, 7 teams submitted 8 systems for the task of offline HMER, among which 5 systems only use the provided datasets without any extra data while 3 systems use extra data. The winner team achieved a recognition accuracy of 79.85% (without extra data) and 81.85% (with extra data) on the offline formula recognition task.
A Closed-Form Estimator for Bearings-Only Fusion of Heterogeneous Passive Sensors

Bearings-only target motion analysis with fusion of heterogeneous passive sensors with and without propagation delay attempts to estimate a target trajectory by fusing bearings from two sensors: a sensor whose measurements have negligible propagation delay and a sensor with measurements that have non-negligible propagation delay. This problem is highly nonlinear and challenging, particularly due to the fact that the propagation delay is unknown and is highly coupled with the unknown target states. This paper proposes a novel closed-form instrumental variable based batch estimator for this problem that is computationally fast. The estimator is derived by writing pseudolinear type equations for the error vector-difference between the true target state and a nominal solution. A theoretical bias and mean square error analysis of the proposed estimator is carried out which provides insight into the performance of this estimator. Results indicate that this is a computationally efficient algorithm that is able to produce comparable performance to the maximum likelihood estimator and achieves the Cramer Rao lower bound.
‘It’s not a romantic relationship’: Stories of Adoption and Abandonment of Smart Speakers at Home

Smart speakers become increasingly ubiquitous in our homes. Consequently, we need to study how smart speakers affect the members of a household. Understanding the adoption of a smart speaker can assure it does not negatively influence the social dynamics within a household and create opportunities for further assistance. We deployed an Amazon Echo dot in nine households with 20 inhabitants who were new smart speaker users. We conducted multiple interviews, inquiring how a smart speaker was integrated into a household from day one. We investigated the development of social rules around using the device and how the smart speaker was appropriated. Users developed different strategies of using the device which altered social behaviours in some households. Further, we identified barriers and unmet requirements in introducing smart speakers to home environments. Our work contributes to an understanding of ubiquitous assistance for user groups at home.
Using geostatistics and maximum entropy model to identify geochemical anomalies: A case study in Mila Mountain region, southern Tibet

Separating geochemical anomalies from background values is crucial for the processing of geochemical data. In the present study, a workflow for identifying geochemical anomalies was constructed by using the direct sampling algorithm of multi-point geostatistics, the maximum entropy model, and local singularity analysis. The smoothing effect and the uncertainty of the unsampled point value in the traditional interpolation method were taken into consideration in this workflow. Based on the statistic of singular exponential distribution of each element with equal probability, the geochemical anomaly probability distribution of each element was obtained (Ag、Cd、Cu、Pb、Zn). Based on the five anomaly probability distributions, the maximum entropy model was used to establish a comprehensive perspective of geochemical anomaly uncertainty evaluation. The validity of the method was verified by analyzing geochemical data of stream sediment samples from the Mila Mountain region in Tibet. The results showed that the prospectivity map of copper deposits generated by the maximum entropy model can effectively link the probability of multivariate geochemical anomalies with the known positions of copper deposits and greatly increase the precision of the potential exploration areas for copper deposits.
Python 3 types in the wild: a tale of two type systems

Python 3 is a highly dynamic language, but it has introduced a syntax for expressing types with PEP484. This paper explores how developers use these type annotations, the type system semantics provided by type checking and inference tools, and the performance of these tools. We evaluate the types and tools on a corpus of public GitHub repositories. We review MyPy and PyType, two canonical static type checking and inference tools, and their distinct approaches to type analysis. We then address three research questions: (i) How often and in what ways do developers use Python 3 types? (ii) Which type errors do developers make? (iii) How do type errors from different tools compare?
Autonomous Landing of UAV on Moving Platform: A Mathematical Approach

Recently, the demand for Unmanned Aerial Vehicles (UAVs) has substantially escalated amidst the current unprecedented events and the global pandemic, as they play an essential role in both the security and health sectors, through surveillance, extracting and conveying test samples, transportation of crucial assets and providing temporary services. Nevertheless, the process of designing and producing such aerial vehicles is suppressed by the internal and external constraints that pose serious challenges. One of the key operations during flight is landing, especially, the autonomous landing of UAV on a moving platform, which is a scientifically complicated engineering problem. Typically, executing a successful automatic landing on a moving platform requires accurate localization of the landing zone, swift flying trajectory planning, and robust control configuration. To accomplish such goals, intense observations of the data concerning the autonomous landing approach such as the intersection point between the two moving bodies, the position of the platform/UAV and the inclination angle required to land are significantly crucial. In this paper, a mathematical approach to this problem is presented in the X-Y plane based on the inclination angle and state of UAV during the landing procedure. Moreover, the experimental results depict the accurate position of the UAV, the intersection between the UAV and the moving platform and inclination angle in the landing process, allowing for prediction of the intersection point.
Visual Question Answering on Image Sets

We introduce the task of Image-Set Visual Question Answering (ISVQA), which generalizes the commonly studied single-image VQA problem to multi-image settings. Taking a natural language question and a set of images as input, it aims to answer the question based on the content of the images. The questions can be about objects and relationships in one or more images or about the entire scene depicted by the image set. To enable research in this new topic, we introduce two ISVQA datasets – indoor and outdoor scenes. They simulate the real-world scenarios of indoor image collections and multiple car-mounted cameras, respectively. The indoor-scene dataset contains 91,479 human-annotated questions for 48,138 image sets, and the outdoor-scene dataset has 49,617 questions for 12,746 image sets. We analyze the properties of the two datasets, including question-and-answer distributions, types of questions, biases in dataset, and question-image dependencies. We also build new baseline models to investigate new research challenges in ISVQA.