Reviewerly/exHarmony huggingface dataset:
Learning Disentangled Representations with the Wasserstein Autoencoder

Disentangled representation learning has undoubtedly benefited from objective function surgery. However, a delicate balancing act of tuning is still required in order to trade off reconstruction fidelity versus disentanglement. Building on previous successes of penalizing the total correlation in the latent variables, we propose TCWAE (Total Correlation Wasserstein Autoencoder). Working in the WAE paradigm naturally enables the separation of the total-correlation term, thus providing disentanglement control over the learned representation, while offering more flexibility in the choice of reconstruction cost. We propose two variants using different KL estimators and analyse in turn the impact of having different ground cost functions and latent regularization terms. Extensive quantitative comparisons on data sets with known generative factors shows that our methods present competitive results relative to state-of-the-art techniques. We further study the trade off between disentanglement and reconstruction on more-difficult data sets with unknown generative factors, where the flexibility of the WAE paradigm leads to improved reconstructions.
Two-Dimensional EspEn: A New Approach to Analyze Image Texture by Irregularity

Image processing has played a relevant role in various industries, where the main challenge is to extract specific features from images. Specifically, texture characterizes the phenomenon of the occurrence of a pattern along the spatial distribution, taking into account the intensities of the pixels for which it has been applied in classification and segmentation tasks. Therefore, several feature extraction methods have been proposed in recent decades, but few of them rely on entropy, which is a measure of uncertainty. Moreover, entropy algorithms have been little explored in bidimensional data. Nevertheless, there is a growing interest in developing algorithms to solve current limits, since Shannon Entropy does not consider spatial information, and SampEn2D generates unreliable values in small sizes. We introduce a proposed algorithm, EspEn (Espinosa Entropy), to measure the irregularity present in two-dimensional data, where the calculation requires setting the parameters as follows: m (length of square window), r (tolerance threshold), and ρ (percentage of similarity). Three experiments were performed; the first two were on simulated images contaminated with different noise levels. The last experiment was with grayscale images from the Normalized Brodatz Texture database (NBT). First, we compared the performance of EspEn against the entropy of Shannon and SampEn2D. Second, we evaluated the dependence of EspEn on variations of the values of the parameters m, r, and ρ. Third, we evaluated the EspEn algorithm on NBT images. The results revealed that EspEn could discriminate images with different size and degrees of noise. Finally, EspEn provides an alternative algorithm to quantify the irregularity in 2D data; the recommended parameters for better performance are m = 3, r = 20, and ρ = 0.7.
International development of the SI in FAIR digital data

The International Committee for Weights and Measures has recognised the need to provide support for digital representations of the International System of Units (SI) and related metrological concepts, such as traceability and measurement uncertainty. A specialised Task Group was established to address this in 2019. This summary reports on the activities of the task group to date, which include an overarching “Grand Vision” document, sketching out an SI Digital Framework, and an international online workshop, “The SI in FAIR digital data”, held in February 2021, to engage a wide audience in discussions about a solution.
SpaceTransformers: Language Modeling for Space Systems

The transformers architecture and transfer learning have radically modified the Natural Language Processing (NLP) landscape, enabling new applications in fields where open source labelled datasets are scarce.Space systems engineering is a field with limited access to large labelled corpora and a need for enhanced knowledge reuse of accumulated design data.Transformers models such as the Bidirectional Encoder Representations from Transformers (BERT) and the Robustly Optimised BERT Pretraining Approach (RoBERTa) are however trained on general corpora.To answer the need for domainspecific contextualised word embedding in the space field, we propose SpaceTransformers, a novel family of three models, SpaceBERT, SpaceRoBERTa and SpaceSciBERT, respectively further pre-trained from BERT, RoBERTa and SciBERT on our domain-specific corpus.We collect and label a new dataset of space systems concepts based on space standards.We fine-tune and compare our domain-specific models to their general counterparts on a domain-specific Concept Recognition (CR) task.Our study rightly demonstrates that the models further pre-trained on a space corpus outperform their respective baseline models in the Concept Recognition task, with SpaceRoBERTa achieving significant higher ranking overall.
An authorized equality test on identity‐based cryptosystem for mobile social networking applications

Abstract The widespread adoption of social network applications and smart mobile devices has yielded a massive amount of data transmitted through the networks. Fortunately, social network service providers offer a wide range of storage capacity and data management functionality. Accordingly, the data can be uploaded to the social servers for processing and then accessible elsewhere. Nevertheless, considering the untrusted nature of the social servers, it becomes crucial to protect the data before uploading it to the server. Unfortunately, encryption incurs another severe problem in providing search functionality on encrypted data stored on the server. In addition, most of the existing solutions are inefficient to deploy on mobile devices due to the limited resources and computing capabilities. To address this issue, we propose an authorized equality test on identity‐based cryptosystem (AET‐IBC) scheme in this article. The scheme allows the server to test if two different ciphertexts encapsulate the same message. Besides, it supports different authorization methods to enhance the privacy. Furthermore, based on the random oracle model, we prove the security of the proposed scheme to be equivalent to the modified bilinear Diffie‐Hellman intractable assumption. Finally, the efficiency analysis shows that the AET‐IBC scheme is practicable and convenient for mobile social networking applications.
Proof-Theory and Semantics for a Theory of Definite Descriptions

This paper presents a sequent calculus and a dual domain semantics for a theory of definite descriptions in which these expressions are formalised in the context of complete sentences by a binary quantifier I. I forms a formula from two formulas. Ix[F, G] means ‘The F is G’. This approach has the advantage of incorporating scope distinctions directly into the notation. Cut elimination is proved for a system of classical positive free logic with I and it is shown to be sound and complete for the semantics. The system has a number of novel features and is briefly compared to the usual approach of formalising ‘the F’ by a term forming operator. It does not coincide with Hintikka’s and Lambert’s preferred theories, but the divergence is well-motivated and attractive.
Usability, User Comprehension, and Perceptions of Explanations for Complex Decision Support Systems in Finance: A Robo-Advisory Use Case

Robo-financial advisors are complex algorithmic decision-making systems with a high potential for mass adoption due to their low operating costs and multitasking abilities. The quantitative aspects of our study measure the efficacy and usability of explanations and qualitative aspects determine the effect of explanations on users and system usability.
Mammogram Image Segmentation Using Susan Corner Detection

Among many type of cancers in human body especially for woman in this modern world, the breast cancer is a key problem for their physical fitness. The early diagnosing of cancer for middle aged women in a major issue among us. In breast cancer detection using the images based on mammography, it's very difficult to detect exact cancer affected area due to the properties of the image. The Mammogram images occupy large size of file due to its high-resolution of graphic images. One can identify the cancer cells and the normal cells with contrast value, but the contrast difference is very thin. To identify and symbolize metastatic tumors in early stages and perform surveillance for most cancers recurrence, the volume of tumor can be measured by the use of Computed Tomography (CT) or Magnetic Resonance Imaging (MRI) and useful characterization through Positron Emission Tomography (PET) imaging. In this article, breast cancer images are taken from medical repository and cancer affected regions are detected by region of interest and the cancer affected area is classified by the Susan technique and the results are tabulated. SUSAN corner detector is a good method which yield high quality features. To use in a real-time application, this is computationally demanding. In short amount of time, this can be used to build a feature detector. This Susan corner detector outperforms current feature detectors by a large margin.
Information Reuse Attention in Convolutional Neural Networks for Facial Expression Recognition in the Wild

Unlike the constraint frontal face condition, faces in the wild have various unconstrained interference factors, such as pose variations, illumination variations and occlusion. Because of this, facial expressions recognition (FER) in the wild is a challenging task and existing methods fail to performant well. However, for occluded faces (containing occlusion caused by other objects and self-occlusion caused by head posture changes), the attention mechanism has the ability to focus on the non-occluded regions automatically. In this paper, we propose an Information Reuse Attention Module (IRAM) for Convolutional Neural Network (CNN) to extract attention-aware features from faces. Our module reduces decay information in the process of generating attention maps by reusing the information of the previous layer and not reducing the dimensionality. Sequentially, we adaptively refine the feature responses by fusing the attention maps with the feature map. The proposed method is evaluated with two in-the-wild facial expression datasets RAF-DB and FER2013 and also compared with other state-of-the-art methods.
Adaptive sampling design for multi-task learning of Gaussian processes in manufacturing

Approximation models (or surrogate models) have been widely used in engineering problems to mitigate the cost of running expensive experiments or simulations. Gaussian processes (GPs) are a popular tool used to construct these models due to their flexibility and computational tractability. The accuracy of these models is a strong function of the density and locations of the sampled points in the parametric space used for training. Previously, multi-task learning (MTL) has been used to learn similar-but-not-identical tasks together, thus increasing the effective density of training points. Also, several adaptive sampling strategies have been developed to identify regions of interest for intelligent sampling in single-task learning of GPs. While both these methods have addressed the density and location constraint separately, sampling design approaches for MTL are lacking. In this paper, we formulate an adaptive sampling strategy for MTL of GPs, thereby further improving data efficiency and modeling performance in GP. To this end, we develop variance measures for an MTL framework to effectively identify optimal sampling locations while learning multiple tasks simultaneously. We demonstrate the effectiveness of the proposed method using a case study on a real-world engine surface dataset. We observe that the proposed method leverages both MTL and intelligent sampling to significantly outperform state-of-the-art methods which use either approach separately. The developed sampling design strategy is readily applicable to many problems in various fields.
Towards real time guide wire shape extraction in fluoroscopic sequences: A two phase deep learning scheme to extract sparse curvilinear structures

Real time localization and shape extraction of guide wire in fluoroscopic images plays a significant role in the image guided navigation during cerebral and cardiovascular interventions. Given the complexity of the non-rigid and sparse characteristics of guide wire structures, and the low SNR(Signal Noise Ratio) of fluoroscopic images, traditional handcrafted guide wire tracking methods such as Frangi filter, Hessian Matrix, or open active contour usually produce insufficient accuracy with high computational cost, and may require extra human intervention for proper initialization or correction. The application of deep learning techniques to guide wire tracking is reported to produce significant improvement in guide wire localization accuracy, but the heavy calculation cost is still a concern.In this paper we propose a two phase deep learning scheme for accurate and real time guide wire shape extraction in fluoroscopic sequences. In the first phase we train a guide wire localization network to pick image regions containing guide wire structures. From the picked image regions, we train a guide wire shape extraction network in the second phase to mark the guide wire pixels.We report that our proposed method can accurately distinguish about 99% of the guide wire structure pixels, and the falsely detected pixels in the background are close to 0, the average offset from the ground truth is less than 1 pixel. For extreme cases where traditional handcrafted method may fail, our proposed method can still extract guide wire completely and accurately. The processing time for a 512 × 512 pixels image is 78 ms.Compared with the traditional filtering based method from our previous work, we show that our proposed method can achieve more accurate and stable performance. Compared with other deep learning methods, our proposed method significantly improve calculation efficiency to meet the real time requirement of clinical applications.
Quantitative matching of forensic evidence fragments utilizing <scp>3D</scp> microscopy analysis of fracture surface replicas

Silicone casts are widely used by practitioners in the comparative analysis of forensic items. Fractured surfaces carry unique details that can provide accurate quantitative comparisons of forensic fragments. In this study, a statistical analysis comparison protocol was applied to a set of 3D topological images of fractured surface pairs and their replicas to provide confidence in the quantitative statistical comparison between fractured items and their silicone cast replicas. A set of 10 fractured stainless steel samples were fractured from the same metal rod under controlled conditions and were replicated using a standard forensic casting technique. Six 3D topological maps with 50% overlap were acquired for each fractured pair. Spectral analyses were utilized to identify the correlation between topological surface features at different length scales of the surface topology. We selected two frequency bands over the critical wavelength (greater than two-grain diameters) for statistical comparison. Our statistical model utilized a matrix-variate t-distribution that accounts for overlap between images to model match and non-match population densities. A decision rule identified the probability of matched and unmatched pairs of surfaces. The proposed methodology correctly classified the fractured steel surfaces and their replicas with a posterior probability of match exceeding 99.96%. Moreover, the replication technique shows potential in accurately replicating fracture surface topological details with a wavelength greater than 20 μm, which far exceeds the feature comparison range on most metallic alloy surfaces. Our framework establishes the basis and limits for forensic comparison of fractured articles and their replicas while providing a reliable fracture mechanics-based quantitative statistical forensic comparison.
A privacy-preserving route planning scheme for the Internet of Vehicles

Internet of the Things (IoT) is being integrated into applications that are continuing to reshape many elements of our daily life. One of the major application areas is the Internet of Vehicles which can enhance existing capabilities, such as efficient vehicle route planning. Such systems usually rely on real-time traffic information that includes the temporal location feed of a vehicle. Despite offering clear advantages (such as overcoming congestion, saving fuel/energy/time, and reducing CO2 emission), privacy concerns emerge due to the use of location data. Motivated by this, a privacy-preserving vehicular location (e.g. positioning) sharing scheme is developed for edge cloud-assisted vehicles. In addition, data utility bounds are theoretically analysed, and vehicle routing efficacy is empirically analysed to evaluate the impact of the proposed scheme. Rather than sharing perturbed location on two-dimensional space, we propose a graph-based differential privacy solution for sharing location. The novelty of this work relies on translating the vehicular geospatial data to the graph-structured data for its higher applicability on the road network, designing a real-time application, and empirical analysis of privacy-efficacy optimality.
Grounding Predicates through Actions

Symbols representing abstract states such as “dish in dishwasher” or “cup on table” allow robots to reason over long horizons by hiding details unnecessary for high-level planning. Current methods for learning to identify symbolic states in visual data require large amounts of labeled training data, but manually annotating such datasets is prohibitively expensive due to the combinatorial number of predicates in images. We propose a novel method for automatically labeling symbolic states in large-scale video activity datasets by exploiting known pre- and post-conditions of actions. This automatic labeling scheme only requires weak supervision in the form of an action label that describes which action is demonstrated in each video. We use our framework to train predicate classifiers to identify symbolic relationships between objects when prompted with object bounding boxes, and demonstrate that such predicate classifiers can match the performance of those trained with full supervision at a fraction of the labeling cost. We also apply our framework to an existing large-scale human activity dataset, and demonstrate the ability of these predicate classifiers trained on human data to enable closed-loop task planning in the real world.
Deep Structured Instance Graph for Distilling Object Detectors

Effectively structuring deep knowledge plays a pivotal role in transfer from teacher to student, especially in semantic vision tasks. In this paper, we present a simple knowledge structure to exploit and encode information inside the detection system to facilitate detector knowledge distillation. Specifically, aiming at solving the feature imbalance problem while further excavating the missing relation inside semantic instances, we design a graph whose nodes correspond to instance proposal-level features and edges represent the relation between nodes. To further refine this graph, we design an adaptive background loss weight to reduce node noise and background samples mining to prune trivial edges. We transfer the entire graph as encoded knowledge representation from teacher to student, capturing local and global information simultaneously.We achieve new state-of-the-art results on the challenging COCO object detection task with diverse student-teacher pairs on both one- and two-stage detectors. We also experiment with instance segmentation to demonstrate robustness of our method. It is notable that distilled Faster R-CNN with ResNet18-FPN and ResNet50-FPN yields 38.68 and 41.82 Box AP respectively on the COCO benchmark, Faster R-CNN with ResNet101-FPN significantly achieves 43.38 AP, which outperforms ResNet152- FPN teacher about 0.7 AP. Code: https://github.com/dvlab-research/Dsig.
The 15th Multi-Agent Programming Contest

The Multi-Agent Programming Contest, MAPC, is an (almost) annual competition with international participants. The contestants develop a multi-agent system that has to compete against each other team in a series of games, requiring cooperation, collaboration, planning capabilities and much more. The contest goals are twofold: We try to find scenarios, where it pays off to use the tools of agent-oriented software engineering, and we try to encourage people to learn about or even refine those tools. We present the contest's 15th edition, its participants and results.
Characterization of hydrothermal alteration along geothermal wells using unsupervised machine-learning analysis of X-ray powder diffraction data

Abstract Zonal distribution of hydrothermal alteration in and around geothermal fields is important for understanding the hydrothermal environment. In this study, we assessed the performance of three unsupervised classification algorithms—K-mean clustering, the Gaussian mixture model, and agglomerative clustering—in automated categorization of alteration minerals along wells. As quantitative data for classification, we focused on the quartz indices of alteration minerals obtained from rock cuttings, which were calculated from X-ray powder diffraction measurements. The classification algorithms were first examined by applying synthetic data and then applied to data on rock cuttings obtained from two wells in the Hachimantai geothermal field in Japan. Of the three algorithms, our results showed that the Gaussian mixture model provides classes that are reliable and relatively easy to interpret. Furthermore, an integrated interpretation of different classification results provided more detailed features buried within the quartz indices. Application to the Hachimantai geothermal field data showed that lithological boundaries underpin the data and revealed the lateral connection between wells. The method’s performance is underscored by its ability to interpret multi-component data related to quartz indices.
Semi-supervised Image Annotation with Parallel Graph Convolutional Networks

Automatic image annotation has increasingly exerted a tremendous fascination on researchers with the development of digital imaging in recent years. First, most works exploit the sufficient labeled data to train the models and trigger the unfavorable experimental performance in semi-supervised learning. Second, some examinations solve the semi-supervised problem only by a samples graph or tags graph, limiting in improving the annotation results owing to the incomplete data structure. To this end, we propose a method called "Semi-supervised Image Annotation with Parallel Graph Convolutional Networks (SPGCN)". This algorithm combines graph convolutional networks (GCN) with image annotation to promote annotation performance under semi-supervised learning. Furthermore, SPGCN, connecting the tags graph with the samples graph, is proposed to improve annotation results, further considering tags’ distribution and features’ distribution to aggregate the features. Experiments on three benchmark image annotation datasets show that our approach outperforms other existing state-of-the-art methods.