Reviewerly/exHarmony huggingface dataset:
Continental-scale controls on soil organic carbon across sub-Saharan Africa

Abstract. Soil organic carbon (SOC) stabilization and destabilization has been studied intensively. Yet, the factors which control SOC content across scales remain unclear. Earlier studies demonstrated that soil texture and geochemistry strongly affect SOC content. However, those findings primarily rely on data from temperate regions where soil mineralogy, weathering status and climatic conditions generally differ from tropical and subtropical regions. We investigated soil properties and climate variables influencing SOC concentrations across sub-Saharan Africa. A total of 1601 samples were analyzed, collected from two depths (0–20 and 20–50 cm) from 17 countries as part of the Africa Soil Information Service project (AfSIS). The data set spans arid to humid climates and includes soils with a wide range of pH values, weathering status, soil texture, exchangeable cations, extractable metals and land cover types. The most important SOC predictors were identified by linear mixed-effects models, regression trees and random forest models. Our results indicate that geochemical properties, mainly oxalate-extractable metals (Al and Fe) and exchangeable Ca, are equally important compared to climatic variables (mean annual temperature and aridity index). Together, they explain approximately two-thirds of SOC variation across sub-Saharan Africa. Oxalate-extractable metals were most important in wet regions with acidic and highly weathered soils, whereas exchangeable Ca was more important in alkaline and less weathered soils in drier regions. In contrast, land cover and soil texture were not significant SOC predictors on this large scale. Our findings indicate that key factors controlling SOC across sub-Saharan Africa are broadly similar to those in temperate regions, despite differences in soil development history.
A Novel Outlier Detection Method for Multivariate Data

Detecting anomalous objects from given data has a broad range of real-world applications. Although there is a rich number of outlier detection algorithms, most of them involve hidden assumptions and restrictions. This paper proposes a novel, yet effective outlier learning algorithm that is based on decomposing the full attributes space into different combinations of subspaces, in which the 3D-vectors, representing the data points per 3D-subspace, are rotated about the geometric median, using Rodrigues rotation formula, to construct the overall outlying score. The proposed approach is parameter-free, requires no distribution assumptions and easy to implement. Extensive experimental study and comparison are conducted on both synthetic and real-world datasets with six popular outlier detection algorithms, each from different category. The comparison is evaluated based on the precision <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">@s</i> , average precision, rank power, AUC ROC and time complexity metrics. The results show that the performance of the proposed method is competitive and promising.
Digital audio steganography: Systematic review, classification, and analysis of the current state of the art

Audio steganography is the process of hiding a message inside an audio container. This study aims to present a systematic review of audio steganography methods. Existing reviews of audio steganography suffer from a high overlap or a low segregation level among the methods reviewed due to poor classification criteria. These articles are also lacking in terms of depth of analysis and the number of articles reviewed. The data collection method includes surveying five databases, namely, Web of Science, IEEE Explore, ScienceDirect, Scopus, and Springer, resulting in a total of 134 articles. The review focuses on the embedding process of each method. The methods can be classified into several categories based on the most prominent idea in the embedding process; hence, a new classification is proposed. The proposed classification provides a scope for summarizing and understanding the most followed approaches in audio steganography. This study then critically reviews the strengths and weaknesses of each method on the basis of its embedding behavior. Other issues on the domain of embedding, carrier types, and evaluation environments are also discussed.
Edge-Variational Graph Convolutional Networks for Uncertainty-Aware Disease Prediction

There is a rising need for computational models that can complementarily leverage data of different modalities while investigating associations between subjects for population-based disease analysis. Despite the success of convolutional neural networks in representation learning for imaging data, it is still a very challenging task. In this paper, we propose a generalizable framework that can automatically integrate imaging data with non-imaging data in populations for uncertainty-aware disease prediction. At its core is a learnable adaptive population graph with variational edges, which we mathematically prove that it is optimizable in conjunction with graph convolutional neural networks. To estimate the predictive uncertainty related to the graph topology, we propose the novel concept of Monte-Carlo edge dropout. Experimental results on four databases show that our method can consistently and significantly improve the diagnostic accuracy for Autism spectrum disorder, Alzheimer's disease, and ocular diseases, indicating its generalizability in leveraging multimodal data for computer-aided diagnosis.
Stable feature selection using copula based mutual information

Feature selection is a key step in many machine learning tasks. A majority of the existing methods of feature selection address the problem by devising some scoring function while treating the features independently, thereby overlooking their interdependencies. We leverage the scale invariance property of copula to construct a greedy, supervised feature selection algorithm that maximizes the feature relevance while minimizing the redundant information content. Multivariate copula is used in the proposed copula Based Feature Selection (CBFS) to discover the dependence structure between features. The incorporation of copula-based multivariate dependency in the formulation of mutual information helps avoid averaging over multiple instances of bivariate dependencies, thus eliminating the average estimation error introduced when bivariate dependency is used between a pair of feature variables. Under a controlled setting, our algorithm outperformed the existing best practice methods in warding off the noise in data. On several real and synthetic datasets, the proposed algorithm performed competitively in maximizing classification accuracy. CBFS also outperforms the other methods in terms of its noise tolerance property.
On Modulating the Gradient for Meta-learning

Inspired by optimization techniques, we propose a novel meta-learning algorithm with gradient modulation to encourage fast-adaptation of neural networks in the absence of abundant data. Our method, termed ModGrad, is designed to circumvent the noisy nature of the gradients which is prevalent in low-data regimes. Furthermore and having the scalability concern in mind, we formulate ModGrad via low-rank approximations, which in turn enables us to employ ModGrad to adapt hefty neural networks. We thoroughly assess and contrast ModGrad against a large family of meta-learning techniques and observe that the proposed algorithm outperforms baselines comfortably while enjoying faster convergence.
NAUTILUS: A Versatile Voice Cloning System

We introduce a novel speech synthesis system, called NAUTILUS, that can generate speech with a target voice either from a text input or a reference utterance of an arbitrary source speaker. By using a multi-speaker speech corpus to train all requisite encoders and decoders in the initial training stage, our system can clone unseen voices using untranscribed speech of target speakers on the basis of the backpropagation algorithm. Moreover, depending on the data circumstance of the target speaker, the cloning strategy can be adjusted to take advantage of additional data and modify the behaviors of text-to-speech (TTS) and/or voice conversion (VC) systems to accommodate the situation. We test the performance of the proposed framework by using deep convolution layers to model the encoders, decoders and WaveNet vocoder. Evaluations show that it achieves comparable quality with state-of-the-art TTS and VC systems when cloning with just five minutes of untranscribed speech. Moreover, it is demonstrated that the proposed framework has the ability to switch between TTS and VC with high speaker consistency, which will be useful for many applications.
CNN Based Detectors on Planetary Environments: A Performance Evaluation

An essential characteristic that an exploration robot must possess is to be autonomous. This is necessary because it will usually do its task in remote or hard-to-reach places. One of the primary elements of a navigation system is the information that can be acquired by the sensors of the environment in which it will operate. For this reason, an algorithm based on convolutional neural networks is proposed for the detection of rocks in environments similar to Mars. The methodology proposed here is based on the use of a Single-Shot-Detector (SSD) network architecture, which has been modified to evaluate the performance. The main contribution of this study is to provide an alternative methodology to detect rocks in planetary images because most of the previous works only focus on classification problems and used handmade feature vectors.
Combined Spiral Transformation and Model-Driven Multi-Modal Deep Learning Scheme for Automatic Prediction of TP53 Mutation in Pancreatic Cancer

Pancreatic cancer is a malignant form of cancer with one of the worst prognoses. The poor prognosis and resistance to therapeutic modalities have been linked to TP53 mutation. Pathological examinations, such as biopsies, cannot be frequently performed in clinical practice; therefore, noninvasive and reproducible methods are desired. However, automatic prediction methods based on imaging have drawbacks such as poor 3D information utilization, small sample size, and ineffectiveness multimodal fusion. In this study, we proposed a model-driven multi-modal deep learning scheme to overcome these challenges. A spiral transformation algorithm was developed to obtain 2D images from 3D data, with the transformed image inheriting and retaining the spatial correlation of the original texture and edge information. The spiral transformation could be used to effectively apply the 3D information with less computational resources and conveniently augment the data size with high quality. Moreover, model-driven items were designed to introduce prior knowledge in the deep learning framework for multi-modal fusion. The model-driven strategy and spiral transformation-based data augmentation can improve the performance of the small sample size. A bilinear pooling module was introduced to improve the performance of fine-grained prediction. The experimental results show that the proposed model gives the desired performance in predicting TP53 mutation in pancreatic cancer, providing a new approach for noninvasive gene prediction. The proposed methodologies of spiral transformation and model-driven deep learning can also be used for the artificial intelligence community dealing with oncological applications. Our source codes with a demon will be released at https://github.com/SJTUBMEQianLab/SpiralTransform.
A Tilting-Rotor Unmanned Aerial Vehicle for Enhanced Aerial Locomotion and Manipulation Capabilities: Design, Control, and Applications

In this article, a novel multirotor unmanned aerial vehicle (UAV) is proposed for enhanced locomotion and manipulation in unstructured environments. The vehicle has a tilting-rotor architecture. Specifically, two pairs of rotors are mounted on two independently controlled tilting arms placed at two sides of the vehicle, forming an “H” configuration. Such a structure endows the vehicle with more degrees of freedom, improving the maneuverability without sacrificing energy efficiency at the cost of only two additional servos compared to traditional quadcopters. Based on this architecture, a dual-level adaptive robust control is developed to cope with inertial parametric uncertainties and uncertain nonlinearities for accurate motion tracking. To resolve the redundancy in actuation, a thrust force optimization problem minimizing power consumption while achieving the desired body force wrench is formulated and solved precisely and efficiently. With the novel design and control, the proposed aerial vehicle is capable of achieving superior maneuverability, better stability and motion accuracy in the presence of uncertainties, as well as improved power efficiency compared to traditional UAVs when performing dexterous aerial locomotion and manipulation. To demonstrate the advantage of the proposed new UAV design and control in real applications, we conduct four challenging experiments on aerial locomotion and manipulation: circular trajectory tracking, passing through a narrow tunnel, picking up an object from a cluttered shelf, and aerial hole drilling. Experimental results validate the applicability of the proposed innovation.
CryptDICE: Distributed data protection system for secure cloud data storage and computation

Cloud storage allows organizations to store data at remote sites of service providers. Although cloud storage services offer numerous benefits, they also involve new risks and challenges with respect to data security and privacy aspects. To preserve confidentiality, data must be encrypted before outsourcing to the cloud. Although this approach protects the security and privacy aspects of data, it also impedes regular functionality such as executing queries and performing analytical computations. To address this concern, specific data encryption schemes (e.g., deterministic, random, homomorphic, order-preserving, etc.) can be adopted that still support the execution of different types of queries (e.g., equality search, full-text search, etc.) over encrypted data. However, these specialized data encryption schemes have to be implemented and integrated in the application and their adoption introduces an extra layer of complexity in the application code. Moreover, as these schemes imply trade-offs between performance and security, storage efficiency, etc, making the appropriate trade-off is a challenging and non-trivial task. In addition, to support aggregate queries, User Defined Functions (UDF) have to be implemented directly in the database engine and these implementations are specific to each underlying data storage technology, which demands expert knowledge and in turn increases management complexity. In this paper, we introduce CryptDICE, a distributed data protection system that (i) provides built-in support for a number of different data encryption schemes, made accessible via annotations that represent application-specific (search) requirements; (ii) supports making appropriate trade-offs and execution of these encryption decisions at diverse levels of data granularity; and (iii) integrates a lightweight service that performs dynamic deployment of User Defined Functions (UDF) –without performing any alteration directly in the database engine– for heterogeneous NoSQL databases in order to realize low-latency aggregate queries and also to avoid expensive data shuffling (from the cloud to an on-premise data center). We have validated CryptDICE in the context of a realistic industrial SaaS application and carried out an extensive functional validation, which shows the applicability of the middleware platform. In addition, our experimental evaluation efforts confirm that the performance overhead of CryptDICE is acceptable and validates the performance optimizations for achieving low-latency aggregate queries.
Full-automatic seed point selection and initialization for digital image correlation robust to large rotation and deformation

Accurate initial guess plays a key role to realize full-automatic digital image correlation (DIC) analysis, especially when large rotational deformation presents in deformed images. In this work, an efficient, robust and full-automatic initial guess approach combining the speeded-up robust features (SURF) algorithm and the reliability-guided displacement tracking (RGDT) strategy is proposed, which can not only automatically select and update seed point, but also can effectively deal with target images with large deformation and rotation. The scale- and rotation-invariant SURF algorithm can extract and match a certain number of feature points from two images even though the significant deformation and rotation present. The Euclidean distance and deformation information (including displacement and major orientation rotation angle) of the best-matched point are used to choose the seed point and determine its initial guess, respectively. Then, the RGDT strategy is then employed to continue the DIC analysis of rest calculation points. Compared with existing path-dependent initial guess using RGDT, the proposed method not only can automatically select seed points without manual intervention, but also can provide accurate initial value estimation for the deformed images in the presence of large rotation and/or deformation. Furthermore, it has evident efficiency advantages over existing path-independent initial guess methods based on SIFT and RANSAC. The robustness and effectiveness of the proposed method are validated by numerical simulation tests and real experiments.
Data Weighted Training Strategies for Grammatical Error Correction

Recent progress in the task of Grammatical Error Correction (GEC) has been driven by addressing data sparsity, both through new methods for generating large and noisy pretraining data and through the publication of small and higher-quality finetuning data in the BEA-2019 shared task. Building upon recent work in Neural Machine Translation (NMT), we make use of both kinds of data by deriving example-level scores on our large pretraining data based on a smaller, higher-quality dataset. In this work, we perform an empirical study to discover how to best incorporate delta-log-perplexity, a type of example scoring, into a training schedule for GEC. In doing so, we perform experiments that shed light on the function and applicability of delta-log-perplexity. Models trained on scored data achieve state- of-the-art results on common GEC test sets.
Sentence Constituent-Aware Aspect-Category Sentiment Analysis with Graph Attention Networks

Aspect category sentiment analysis (ACSA) aims to predict the sentiment polarities of the aspect categories discussed in sentences. Since a sentence usually discusses one or more aspect categories and expresses different sentiments toward them, various attention-based methods have been developed to allocate the appropriate sentiment words for the given aspect category and obtain promising results. However, most of these methods directly use the given aspect category to find the aspect category-related sentiment words, which may cause mismatching between the sentiment words and the aspect categories when an unrelated sentiment word is semantically meaningful for the given aspect category. To mitigate this problem, we propose a Sentence Constituent-Aware Network (SCAN) for aspect-category sentiment analysis. SCAN contains two graph attention modules and an interactive loss function. The graph attention modules generate representations of the nodes in sentence constituency parse trees for the aspect category detection (ACD) task and the ACSA task, respectively. ACD aims to detect aspect categories discussed in sentences and is a auxiliary task. For a given aspect category, the interactive loss function helps the ACD task to find the nodes which can predict the aspect category but can’t predict other aspect categories. The sentiment words in the nodes then are used to predict the sentiment polarity of the aspect category by the ACSA task. The experimental results on five public datasets demonstrate the effectiveness of SCAN (Data and code can be found at https://github.com/l294265421/SCAN).
Detecting, Tracking and Counting People Getting On/Off a Metropolitan Train Using a Standard Video Camera

The main source of delays in public transport systems (buses, trams, metros, railways) takes place in their stations. For example, a public transport vehicle can travel at 60 km per hour between stations, but its commercial speed (average en-route speed, including any intermediate delay) does not reach more than half of that value. Therefore, the problem that public transport operators must solve is how to reduce the delay in stations. From the perspective of transport engineering, there are several ways to approach this issue, from the design of infrastructure and vehicles to passenger traffic management. The tools normally available to traffic engineers are analytical models, microscopic traffic simulation, and, ultimately, real-scale laboratory experiments. In any case, the data that are required are number of passengers that get on and off from the vehicles, as well as the number of passengers waiting on platforms. Traditionally, such data has been collected manually by field counts or through videos that are then processed by hand. On the other hand, public transport networks, specially metropolitan railways, have an extensive monitoring infrastructure based on standard video cameras. Traditionally, these are observed manually or with very basic signal processing support, so there is significant scope for improving data capture and for automating the analysis of site usage, safety, and surveillance. This article shows a way of collecting and analyzing the data needed to feed both traffic models and analyze laboratory experimentation, exploiting recent intelligent sensing approaches. The paper presents a new public video dataset gathered using real-scale laboratory recordings. Part of this dataset has been annotated by hand, marking up head locations to provide a ground-truth on which to train and evaluate deep learning detection and tracking algorithms. Tracking outputs are then used to count people getting on and off, achieving a mean accuracy of 92% with less than 0.15% standard deviation on 322 mostly unseen dataset video sequences.
Kernel two-dimensional ridge regression for subspace clustering

Subspace clustering methods have been extensively studied in recent years. For 2-dimensional (2D) data, existing subspace clustering methods usually convert 2D examples to vectors, which severely damages inherent structural information and relationships of the original data. In this paper, we propose a novel subspace clustering method, named KTRR, for 2D data. The KTRR provides us with a way to learn the most representative 2D features from 2D data in learning data representation. In particular, the KTRR performs 2D feature learning and low-dimensional representation construction simultaneously, which renders the two tasks to mutually enhance each other. 2D kernel is introduced to the KTRR, which renders the KTRR to have enhanced capability of capturing nonlinear relationships from data. An efficient algorithm is developed for its optimization with provable decreasing and convergent property in objective value. Extensive experimental results confirm the effectiveness and efficiency of our method.