Reviewerly/exHarmony huggingface dataset:
Towards Interlinked FAIR Biodiversity Knowledge: The BiCIKL perspective

The Horizon 2020 project Bi odiversity C ommunity I ntegrated K nowledge L ibrary (BiCIKL) (started 1st of May 2021, duration 3 years) will build a new European community of key research infrastructures, researchers, citizen scientists and other stakeholders in biodiversity and life sciences. Together, the BiCIKL 14 partners will solidify open science practices by providing access to data, tools and services at each stage of, and along the entire biodiversity research and data life cycle (specimens, sequences, taxon names, analytics, publications, biodiversity knowledge graph) (Fig. 1, see also the BiCIKL kick-off presentation through Suppl. material 1), in compliance with the FAIR (Findable, Accessible, Interoperable and Reusable) data principles. The existing services provided by the participating infrastructures will expand through development and adoption of shared, common or interoperable domain standards, resulting in liberated and enhanced flows of data and knowledge across these domains. BiCIKL puts a special focus on the biodiversity literature. Over the span of the project, BiCIKL will develop new methods and workflows for semantic publishing and integrated access to harvesting, liberating, linking, and re-using sub-article-level data extracted from literature (i.e., specimens, material citations, sequences, taxonomic names, taxonomic treatments, figures, tables). Data linkages may be realised with different technologies (e.g., data warehousing, linking between FAIR Data Objects, Linked Open Data) and can be bi-lateral (between two data infrastructures) or multi-lateral (among multiple data infrastructures). The main challenge of BiCIKL is to design, develop and implement a FAIR Data Place (FDP), a central tool for search, discovery and management of interlinked FAIR data across different domains. The key final output of BiCIKL will the future Biodiversity Knowledge Hub (BKH), a one-stop portal, providing access to the BiCIKL services, tools and workflows, beyond the lifetime of the project.
DeepPOSE: Detecting GPS spoofing attack via deep recurrent neural network

The Global Positioning System (GPS) has become a foundation for most location-based services and navigation systems, such as autonomous vehicles, drones, ships, and wearable devices. However, it is a challenge to verify if the reported geographic locations are valid due to various GPS spoofing tools. Pervasive tools, such as Fake GPS, Lockito, and software-defined radio, enable ordinary users to hijack and report fake GPS coordinates and cheat the monitoring server without being detected. Furthermore, it is also a challenge to get accurate sensor readings on mobile devices because of the high noise level introduced by commercial motion sensors. To this end, we propose DeepPOSE, a deep learning model, to address the noise introduced in sensor readings and detect GPS spoofing attacks on mobile platforms. Our design uses a convolutional and recurrent neural network to reduce the noise, to recover a vehicle's real-time trajectory from multiple sensor inputs. We further propose a novel scheme to map the constructed trajectory from sensor readings onto the Google map, to smartly eliminate the accumulation of errors on the trajectory estimation. The reconstructed trajectory from sensors is then used to detect the GPS spoofing attack. Compared with the existing method, the proposed approach demonstrates a significantly higher degree of accuracy for detecting GPS spoofing attacks.
The fundamental architecture of the DCC

The aim of this publication is to provide an overview of the prototype digital calibration certificate. The role of calibration certificates is explained and the differences between paper-based and digital calibration certificates are described in Section 1. All specifications of the digital calibration certificate are given in Section 2; security aspects are discussed in Section 3; and finally, conclusions are drawn in Section 4. The prototype of the digital calibration certificate (DCC) was first described in the PTB-Mitteilungen [1] in 2017.
Phased Human Activity Recognition based on GPS

This paper describes an activity recognition method for Sussex-Huawei Locomotion-Transportation (SHL) recognition challenge by team TDU_BSA_BCI. The classification accuracy has been improved by switching the estimation model, depending on whether the location is available. Data, including location were classified by Deep Neural Network including LSTM layer. Data that exclude location were classified by the Gradient Boosting Decision Tree. The 2 outputs have been combined. They were optimized by applying a median filter. In the submission phase, the best F-measure obtained for the SHL validation-set was 65%.
Unconstrained Scene Generation with Locally Conditioned Radiance Fields.

We tackle the challenge of learning a distribution over complex, realistic, indoor scenes. In this paper, we introduce Generative Scene Networks (GSN), which learns to decompose scenes into a collection of many local radiance fields that can be rendered from a free moving camera. Our model can be used as a prior to generate new scenes, or to complete a scene given only sparse 2D observations. Recent work has shown that generative models of radiance fields can capture properties such as multi-view consistency and view-dependent lighting. However, these models are specialized for constrained viewing of single objects, such as cars or faces. Due to the size and complexity of realistic indoor environments, existing models lack the representational capacity to adequately capture them. Our decomposition scheme scales to larger and more complex scenes while preserving details and diversity, and the learned prior enables high-quality rendering from viewpoints that are significantly different from observed viewpoints. When compared to existing models, GSN produces quantitatively higher-quality scene renderings across several different scene datasets.
Explanatory Journeys: Visualising to Understand and Explain Administrative Justice Paths of Redress

Administrative justice concerns the relationships between individuals and the state. It includes redress and complaints on decisions of a child's education, social care, licensing, planning, environment, housing and homelessness. However, if someone has a complaint or an issue, it is challenging for people to understand different possible redress paths and explore what path is suitable for their situation. Explanatory visualisation has the potential to display these paths of redress in a clear way, such that people can see, understand and explore their options. The visualisation challenge is further complicated because information is spread across many documents, laws, guidance and policies and requires judicial interpretation. Consequently, there is not a single database of paths of redress. In this work we present how we have co-designed a system to visualise administrative justice paths of redress. Simultaneously, we classify, collate and organise the underpinning data, from expert workshops, heuristic evaluation and expert critical reflection. We make four contributions: (i) an application design study of the explanatory visualisation tool (Artemus), (ii) coordinated and co-design approach to aggregating the data, (iii) two in-depth case studies in housing and education demonstrating explanatory paths of redress in administrative law, and (iv) reflections on the expert co-design process and expert data gathering and explanatory visualisation for administrative justice and law.
Single-Input Multi-Output U-Net for Automated 2D Foetal Brain Segmentation of MR Images

In this work, we develop the Single-Input Multi-Output U-Net (SIMOU-Net), a hybrid network for foetal brain segmentation inspired by the original U-Net fused with the holistically nested edge detection (HED) network. The SIMOU-Net is similar to the original U-Net but it has a deeper architecture and takes account of the features extracted from each side output. It acts similar to an ensemble neural network, however, instead of averaging the outputs from several independently trained models, which is computationally expensive, our approach combines outputs from a single network to reduce the variance of predications and generalization errors. Experimental results using 200 normal foetal brains consisting of over 11,500 2D images produced Dice and Jaccard coefficients of 94.2 ± 5.9% and 88.7 ± 6.9%, respectively. We further tested the proposed network on 54 abnormal cases (over 3500 images) and achieved Dice and Jaccard coefficients of 91.2 ± 6.8% and 85.7 ± 6.6%, respectively.
Protocol for temperature sensing using a three-level transmon circuit

We present a method for in situ temperature measurement of superconducting quantum circuits, by using the first three levels of a transmon device to which we apply a sequence of $\pi$ gates. Our approach employs projective dispersive readout and utilizes the basic properties of the density matrix associated with thermal states. This method works with an averaging readout scheme and does not require a single-shot readout setup. We validate this protocol by performing thermometry in the range of 50 mK - 200 mK, corresponding to a range of residual populations $1\%-20 \%$ for the first excited state and $0.02\%-3 \%$ for the second excited state.
Universal control of quantum processes using sector-preserving channels

No quantum circuit can turn a completely unknown unitary gate into its coherently controlled version. Yet, coherent control of unknown gates has been realised in experiments, making use of a different type of initial resources. Here, we formalise the task achieved by these experiments, extending it to the control of arbitrary noisy channels, and to more general types of control involving higher dimensional control systems. For the standard notion of coherent control, we identify the information-theoretic resource for controlling an arbitrary quantum channel on a $d$-dimensional system: specifically, the resource is an extended quantum channel acting as the original channel on a $d$-dimensional sector of a $(d+1)$-dimensional system. Using this resource, arbitrary controlled channels can be built with a universal circuit architecture. We then extend the standard notion of control to more general notions, including control of multiple channels with possibly different input and output systems. Finally, we develop a theoretical framework, called supermaps on routed channels, which provides a compact representation of coherent control as an operation performed on the extended channels, and highlights the way the operation acts on different sectors.
Consumers’ Use of Augmented Reality Apps: Prevalence, User Characteristics, and Gratifications

This study provides a better understanding of consumers’ use of augmented reality (AR) apps by examining the (1) prevalence, (2) user characteristics, (3) gratifications, and (4) preference for AR, and (5) differences between a range of AR app types. To examine these aspects of consumer use, a survey (N = 1,978) was administered among a representative consumer sample, with participants ranging from 18 to 65 years old. This study provides an update on adoption of AR apps and an overview of the individual characteristics and gratifications that can enhance or limit (continued) use of AR apps. Findings may guide future AR research and give practitioners insights to employ AR apps that are of added value to consumers.
Generating Complex, Realistic Cloud Workloads using Recurrent Neural Networks

Decision-making in large-scale compute clouds relies on accurate workload modeling. Unfortunately, prior models have proven insufficient in capturing the complex correlations in real cloud workloads. We introduce the first model of large-scale cloud workloads that captures long-range inter-job correlations in arrival rates, resource requirements, and lifetimes. Our approach models workload as a three-stage generative process, with separate models for: (1) the number of batch arrivals over time, (2) the sequence of requested resources, and (3) the sequence of lifetimes. Our lifetime model is a novel extension of recent work in neural survival prediction. It represents and exploits inter-job correlations using a recurrent neural network. We validate our approach by showing it is able to accurately generate the production virtual machine workload of two real-world cloud providers.
Improving Scene Graph Classification by Exploiting Knowledge from Texts

Training scene graph classification models requires a large amount of annotated image data. Meanwhile, scene graphs represent relational knowledge that can be modeled with symbolic data from texts or knowledge graphs. While image annotation demands extensive labor, collecting textual descriptions of natural scenes requires less effort. In this work, we investigate whether textual scene descriptions can substitute for annotated image data. To this end, we employ a scene graph classification framework that is trained not only from annotated images but also from symbolic data. In our architecture, the symbolic entities are first mapped to their correspondent image-grounded representations and then fed into the relational reasoning pipeline. Even though a structured form of knowledge, such as the form in knowledge graphs, is not always available, we can generate it from unstructured texts using a transformer-based language model. We show that by fine-tuning the classification pipeline with the extracted knowledge from texts, we can achieve ~8x more accurate results in scene graph classification, ~3x in object classification, and ~1.5x in predicate classification, compared to the supervised baselines with only 1% of the annotated images.
Characterizing the Effect of Processing Technique and Solution Type on Cytomorphology Using Liquid-Based Cytology

Liquid-based cytology (LBC) is increasingly used for nongynecologic applications. However, the cytological preparation of LBC specimens is influenced by the processing technique and the preservative used. In this study, the influence of the processing techniques and preservatives on cell morphology was examined mathematically and statistically.Cytological specimens were prepared using the ThinPrep (TP), SurePath (SP), and AutoSmear methods, with 5 different preservative solutions. The cytoplasmic and nuclear areas of Papanicolaou-stained specimens were measured for all samples.The cytoplasmic and nuclear areas were smaller in cells prepared using the 2 LBC methods, compared to that prepared using the AutoSmear method, irrespective of the preservative used. The cytoplasmic and nuclear areas of cells prepared using the SP method were smaller than those of cells prepared using the TP method, irrespective of the preservative used. There were fewer differences among the cytoplasmic areas of cells prepared with different preservative solutions using the TP method; however, the cytoplasmic areas of cells prepared using the SP method changed with the preservative solution used.The most significant difference affecting the cytoplasmic and nuclear areas was the processing technique. The TP method increased the cytoplasmic and nuclear areas, while the methanol-based PreservCyt solution enabled the highest enlargement of the cell. LBC is a superior preparation technique for standardization of the specimens. Our results offer a better understanding of methods suitable for specimen preparation for developing precision AI-based diagnosis in cytology.
Introducing Various Semantic Models for Amharic: Experimentation and Evaluation with Multiple Tasks and Datasets

The availability of different pre-trained semantic models enabled the quick development of machine learning components for downstream applications. Despite the availability of abundant text data for low resource languages, only a few semantic models are publicly available. Publicly available pre-trained models are usually built as a multilingual version of semantic models that can not fit well for each language due to context variations. In this work, we introduce different semantic models for Amharic. After we experiment with the existing pre-trained semantic models, we trained and fine-tuned nine new different models using a monolingual text corpus. The models are build using word2Vec embeddings, distributional thesaurus (DT), contextual embeddings, and DT embeddings obtained via network embedding algorithms. Moreover, we employ these models for different NLP tasks and investigate their impact. We find that newly trained models perform better than pre-trained multilingual models. Furthermore, models based on contextual embeddings from RoBERTA perform better than the word2Vec models.
Dynamic Learning Rate in Deep CNN Model for Metastasis Detection and Classification of Histopathology Images

Diagnosis of different breast cancer stages using histopathology whole slide images (WSI) is the gold standard in determining the grade of tissue metastasis. Computer-aided diagnosis (CAD) assists medical experts as a second opinion tool in early detection to prevent further proliferation. The field of pathology has advanced so rapidly that it is possible to obtain high-quality images from glass slides. Patches from the region of interest in histopathology images are extracted and trained using artificial neural network models. The trained model primarily analyzes and predicts the histology images for the benign or malignant class to which it belongs. Classification of medical images focuses on the training of models with layers of abstraction to distinguish between these two classes with less false-positive rates. The learning rate is the crucial hyperparameter used during the training of deep convolutional neural networks (DCNN) to improve model accuracy. This work emphasizes the relevance of the dynamic learning rate than the fixed learning rate during the training of networks. The dynamic learning rate varies with preset conditions between the lower and upper boundaries and repeats at different iterations. The performance of the model thus improves and attains comparatively high accuracy with fewer iterations.
DurLAR: A High-Fidelity 128-Channel LiDAR Dataset with Panoramic Ambient and Reflectivity Imagery for Multi-Modal Autonomous Driving Applications

We present DurLAR, a high-fidelity 128-channel 3D LiDAR dataset with panoramic ambient (near infrared) and reflectivity imagery, as well as a sample benchmark task using depth estimation for autonomous driving applications. Our driving platform is equipped with a high resolution 128 channel LiDAR, a 2MPix stereo camera, a lux meter and a GNSSlINS system. Ambient and reflectivity images are made available along with the LiDAR point clouds to facilitate multi-modal use of concurrent ambient and reflectivity scene information. Leveraging DurLAR, with a resolution exceeding that of prior benchmarks, we consider the task of monocular depth estimation and use this increased availability of higher resolution, yet sparse ground truth scene depth information to propose a novel joint supervised/self-supervised loss formulation. We compare performance over both our new DurLAR dataset, the established KITTI benchmark and the Cityscapes dataset. Our evaluation shows our joint use supervised and self-supervised loss terms, enabled via the superior ground truth resolution and availability within DurLAR improves the quantitative and qualitative performance of leading contemporary monocular depth estimation approaches (RMSE = 3.639, SqRel = 0.936).
Anchor-based Collaborative Filtering

Modern-day recommender systems are often based on learning representations in a latent vector space that encode user and item preferences. In these models, each user/item is represented by a single vector and user-item interactions are modeled by some function over the corresponding vectors. This paradigm is common to a large body of collaborative filtering models that repeatedly demonstrated superior results. In this work, we break away from this paradigm and present ACF: Anchor-based Collaborative Filtering. Instead of learning unique vectors for each user and each item, ACF learns a spanning set of anchor-vectors that commonly serve both users and items. In ACF, each anchor corresponds to a unique "taste'' and users/items are represented as a convex combination over the spanning set of anchors. Additionally, ACF employs two novel constraints: (1) exclusiveness constraint on item-to-anchor relations that encourages each item to pick a single representative anchor, and (2) an inclusiveness constraint on anchors-to-items relations that encourages full utilization of all the anchors. We compare ACF with other state-of-the-art alternatives and demonstrate its effectiveness on multiple datasets.
Nonlocal Patch-Based Fully Connected Tensor Network Decomposition for Multispectral Image Inpainting

Multispectral image (MSI) inpainting plays an important role in real applications. Recently, fully connected tensor network (FCTN) decomposition has been shown the remarkable ability to fully characterize global correlation. Considering global correlation and nonlocal self-similarity (NSS) of MSIs, this letter introduces FCTN decomposition to the whole MSI and its NSS groups and proposes a novel nonlocal patch-based FCTN (NL-FCTN) decomposition for MSI inpainting. More specially, the NL-FCTN decomposition-based method, which increases tensor order by stacking similar small-sized patches to NSS groups, cleverly leverages the remarkable ability of FCTN decomposition to deal with higher-order tensors. Besides, we propose an efficient proximal alternating minimization (PAM)-based algorithm to solve the proposed NL-FCTN decomposition-based model with a theoretical convergence guarantee. Extensive experiments on MSIs demonstrate that the proposed method achieves the state-of-the-art inpainting performance among all compared methods.
Attribute-Based Access Control for Inner Product Functional Encryption from LWE

The notion of functional encryption (FE) was proposed as a generalization of plain public-key encryption to enable a much more fine-grained handling of encrypted data, with advanced applications such as cloud computing, multi-party computations, obfuscating circuits or Turing machines. While FE for general circuits or Turing machines gives a natural instantiation of the many cryptographic primitives, existing FE schemes are based on indistinguishability obfuscation or multilinear maps which either rely on new computational hardness assumptions or heuristically claimed to be secure. In this work, we present new techniques directly yielding FE for inner product functionality where secret-keys provide access control via polynomial-size bounded-depth circuits. More specifically, we encrypt messages with respect to attributes and embed policy circuits into secret-keys so that a restricted class of receivers would be able to learn certain property about the messages. Recently, many inner product FE schemes were proposed. However, none of them uses a general circuit as an access structure. Our main contribution is designing the first construction for an attribute-based FE scheme in key-policy setting for inner products from well-studied Learning With Errors (\(\mathsf {LWE}\)) assumption. Our construction takes inspiration from the attribute-based encryption of Boneh et al. from Eurocrypt 2014 and the inner product functional encryption of Agrawal et al. from Crypto 2016. The scheme is proved in a stronger setting where the adversary is allowed to ask secret-keys that can decrypt the challenge ciphertext. Doing so requires a careful setting of parameters for handling the noise in ciphertexts to enable correct decryption. Another main advantage of our scheme is that the size of ciphertexts and secret-keys depends on the depth of the circuits rather than its size. Additionally, we extend our construction in a much desirable multi-input variant where secret-keys are associated with multiple policies subject to different encryption slots. This enhances the applicability of the scheme with finer access control.