Reviewerly/exHarmony huggingface dataset:
Multipurpose Virtual Reality Environment for Biomedical and Health Applications

Virtual reality is a trending, widely accessible, and contemporary technology of increasing utility to biomedical and health applications. However, most implementations of virtual reality environments are tailored to specific applications. We describe the complete development of a novel, open-source virtual reality environment that is suitable for multipurpose biomedical and healthcare applications. This environment can be interfaced with different hardware and data sources, ranging from gyroscopes to fMRI scanners. The developed environment simulates an immersive (first-person perspective) run in the countryside, in a virtual landscape with various salient features. The utility of the developed VR environment has been validated via two test applications: an application in the context of motor rehabilitation following injury of the lower limbs and an application in the context of real-time functional magnetic resonance imaging neurofeedback, to regulate brain function in specific brain regions of interest. Both applications were tested by pilot subjects that unanimously provided very positive feedback, suggesting that appropriately designed VR environments can indeed be robustly and efficiently used for multiple biomedical purposes. We attribute the versatility of our approach on three principles implicit in the design: selectivity, immersiveness, and adaptability. The software, including both applications, is publicly available free of charge, via a GitHub repository, in support of the Open Science Initiative. Although using this software requires specialized hardware and engineering know-how, we anticipate our contribution to catalyze further progress, interdisciplinary collaborations and replicability, with regards to the usage of virtual reality in biomedical and health applications.
Generating and Exploiting Deep Learning Variants to Increase Heterogeneous Resource Utilization in the NVIDIA Xavier

Deep learning-based solutions and, in particular, deep neural networks (DNNs) are at the heart of several functionalities in critical-real time embedded systems (CRTES) from vision-based perception (object detection and tracking) systems to trajectory planning. As a result, several DNN instances simultaneously run at any time on the same computing platform. However, while modern GPUs offer a variety of computing elements (e.g. CPUs, GPUs, and specific accelerators) in which those DNN tasks can be executed depending on their computational requirements and temporal constraints, current DNNs are mainly programmed to exploit one of them, namely, regular cores in the GPU. This creates resource imbalance and under-utilization of GPU resources when executing several DNN instances, causing an increase in DNN tasks' execution time requirements. In this paper, (a) we develop different variants (implementations) of well-known DNN libraries used in the Apollo Autonomous Driving (AD) software for each of the computing elements of the latest NVIDIA Xavier SoC. Each variant can be configured to balance resource requirements and performance: the regular CPU core implementation that can run on 2, 4, and 6 cores; the GPU regular and Tensor core variants that can run in 4 or 8 GPU's Streaming Multiprocessors (SM); and 1 or 2 NVIDIA's Deep Learning Accelerators (NVDLA); (b) we show that each particular variant/configuration offers a different resource utilization/performance point; finally, (c) we show how those heterogeneous computing elements can be exploited by a static scheduler to sustain the execution of multiple and diverse DNN variants on the same platform.
MetaBags: Bagged Meta-Decision Trees for Regression

Methods for learning heterogeneous regression ensembles have not yet been proposed on a large scale. Hitherto, in classical ML literature, stacking, cascading and voting are mostly restricted to classification problems. Regression poses distinct learning challenges that may result in poor performance, even when using well established homogeneous ensemble schemas such as bagging or boosting. In this paper, we introduce MetaBags, a novel stacking framework for regression. MetaBags learns a set of meta-decision trees designed to select one base model (i.e. expert) for each query, and focuses on inductive bias reduction. Finally, these predictions are aggregated into a single prediction through a bagging procedure at meta-level. MetaBags is designed to learn a model with a fair bias-variance trade-off, and its improvement over base model performance is correlated with the prediction diversity of different experts on specific input space subregions. An exhaustive empirical testing of the method was performed, evaluating both generalization error and scalability of the approach on open, synthetic and real-world application datasets. The obtained results show that our method outperforms existing state-of-the-art approaches.
Learning to Plan with Logical Automata

This paper introduces the Logic-based Value Iteration Network (LVIN) framework, which combines imitation learning and logical automata to enable agents to learn complex behaviors from demonstrations.We address two problems with learning from expert knowledge: (1) how to generalize learned policies for a task to larger classes of tasks, and (2) how to account for erroneous demonstrations.Our LVIN model solves finite gridworld environments by instantiating a recurrent, convolutional neural network as a value iteration procedure over a learned Markov Decision Process (MDP) that factors into two MDPs: a small finite state automaton (FSA) corresponding to logical rules, and a larger MDP corresponding to motions in the environment.The parameters of LVIN (value function, reward map, FSA transitions, large MDP transitions) are approximately learned from expert trajectories.Since the model represents the learned rules as an FSA, the model is interpretable; since the FSA is integrated into planning, the behavior of the agent can be manipulated by modifying the FSA transitions.We demonstrate these abilities in several domains of interest, including a lunchboxpacking manipulation task and a driving domain.
Proliferation Tumour Marker Network (PTM-NET) for the identification of tumour region in Ki67 stained breast cancer whole slide images

Abstract Uncontrolled proliferation is a hallmark of cancer and can be assessed by labelling breast tissue using immunohistochemistry for Ki67, a protein associated with cell proliferation. Accurate measurement of Ki67-positive tumour nuclei is of critical importance, but requires annotation of the tumour regions by a pathologist. This manual annotation process is highly subjective, time-consuming and subject to inter- and intra-annotator experience. To address this challenge, we have developed Proliferation Tumour Marker Network (PTM-NET), a deep learning model that objectively annotates the tumour regions in Ki67-labelled breast cancer digital pathology images using a convolution neural network. Our custom designed deep learning model was trained on 45 immunohistochemical Ki67-labelled whole slide images to classify tumour and non-tumour regions and was validated on 45 whole slide images from two different sources that were stained using different protocols. Our results show a Dice coefficient of 0.74, positive predictive value of 70% and negative predictive value of 88.3% against the manual ground truth annotation for the combined dataset. There were minimal differences between the images from different sources and the model was further tested in oestrogen receptor and progesterone receptor-labelled images. Finally, using an extension of the model, we could identify possible hotspot regions of high proliferation within the tumour. In the future, this approach could be useful in identifying tumour regions in biopsy samples and tissue microarray images.
A Taxonomy of Blockchain Threats and Vulnerabilities

Blockchain technology has become one of the most popular technologies for maintaining digital transactions. From the foundation of Bitcoin to the now-predominant smart contract, blockchain technology promises to induce a shift in thought-about digital transactions in many fields, such as energy, healthcare, Internet of Things, cybersecurity, financial services, and the supply chain. Despite blockchain technology offering many cryptography advantages such as immutability, digital signature, and hashing, it has suffered from several critical cybersecurity threats and vulnerabilities. In this chapter, we build upon the previous studies on vulnerabilities and investigate over 60 real cybersecurity incidents that have been happening on the blockchain networks between 2009 and 2019. We categorize those incidents against the key cybersecurity vulnerabilities in blockchain technologies and have developed a taxonomy that captures five types of cybersecurity threats and vulnerabilities based on five main players in blockchain. The outcome of this research prompted concerns and research direction in developing countermeasures to alleviate these risks. Blockchain; Cybersecurity; Attack; Threat; Vulnerability; Taxonomy.
MemCat: a new category-based image set quantified on memorability

Images differ in their memorability in consistent ways across observers. What makes an image memorable is not fully understood to date. Most of the current insight is in terms of high-level semantic aspects, related to the content. However, research still shows consistent differences within semantic categories, suggesting a role for factors at other levels of processing in the visual hierarchy. To aid investigations into this role as well as contributions to the understanding of image memorability more generally, we present MemCat. MemCat is a category-based image set, consisting of 10K images representing five broader, memorability-relevant categories (animal, food, landscape, sports, and vehicle) and further divided into subcategories (e.g., bear). They were sampled from existing source image sets that offer bounding box annotations or more detailed segmentation masks. We collected memorability scores for all 10 K images, each score based on the responses of on average 99 participants in a repeat-detection memory task. Replicating previous research, the collected memorability scores show high levels of consistency across observers. Currently, MemCat is the second largest memorability image set and the largest offering a category-based structure. MemCat can be used to study the factors underlying the variability in image memorability, including the variability within semantic categories. In addition, it offers a new benchmark dataset for the automatic prediction of memorability scores (e.g., with convolutional neural networks). Finally, MemCat allows the study of neural and behavioral correlates of memorability while controlling for semantic category.
Learning a Behavior Model of Hybrid Systems Through Combining Model-Based Testing and Machine Learning

Models play an essential role in the design process of cyber-physical systems. They form the basis for simulation and analysis and help in identifying design problems as early as possible. However, the construction of models that comprise physical and digital behavior is challenging. Therefore, there is considerable interest in learning such hybrid behavior by means of machine learning which requires sufficient and representative training data covering the behavior of the physical system adequately. In this work, we exploit a combination of automata learning and model-based testing to generate sufficient training data fully automatically. Experimental results on a platooning scenario show that recurrent neural networks learned with this data achieved significantly better results compared to models learned from randomly generated data. In particular, the classification error for crash detection is reduced by a factor of five and a similar F1-score is obtained with up to three orders of magnitude fewer training samples.
Enhancing Question Retrieval in Community Question Answering Using Word Embeddings

Community Question Answering (CQA) services have evolved into a popular way of online information seeking, where users can interact and exchange knowledge in the form of questions and answers. In this paper, we study the problem of finding historical questions that are semantically equivalent to the queried ones, assuming that the answers to the similar questions should also answer the new ones. The major challenge of question retrieval is the word mismatch problem between questions, as users can formulate the same question using different wording. Most existing methods measure the similarity between questions based on the bag-of-words (BOWs) representation capturing no semantics between words. Therefore, this study proposes to use word embeddings, which can capture semantic and syntactic information from contexts, to vectorize the questions. The questions are clustered using Kmeans to speed up the search and ranking tasks. The similarity between the questions is measured using cosine similarity based on their weighted continuous valued vectors. We run our experiments on real world data set from Yahoo! Answers in English and Arabic to show the efficiency and generality of our proposed method.
A Security Reputation Model for IoT Health Data Using S-AlexNet and Dynamic Game Theory in Cloud Computing Environment

With the rapid development of cloud computing, users are exposed to increasingly serious security threats such as data leakage and privacy exposure when using cloud platform services.Problems in data security, such as inaccurate screening of indicators, lack of scientific validation of reputation evaluation results are also existed.In order to solve the problems, based on cloud environment, a security reputation model using S-AlexNet convolutional neural network and dynamic game theory (SCNN-DGT) is proposed.And it is used to protect the privacy of health data in Internet of Things (IoT).Firstly, the text information of user health data is pre-classified by using S-AlexNet convolutional neural network.Then, a recommendation incentive strategy based on dynamic game theory is proposed.So that the reputation model of user health data security is built, and the evaluation system of the model is established.Finally, an experimental study is carried out to verify the validity of the model and the model index screening.It is shown by experimental results that the model can solve the problems of low reliability of health data screening index, and low accuracy of credit distinction in cloud environment.Therefore, the reliability of mobile terminals is improved, and data security and privacy protection of mobile cloud services are strengthened effectively.
Zero-shot Dependency Parsing with Pre-trained Multilingual Sentence Representations

We investigate whether off-the-shelf deep bidirectional sentence representations (Devlin et al., 2018) trained on a massively multilingual corpus (multilingual BERT) enable the development of an unsupervised universal dependency parser.This approach only leverages a mix of monolingual corpora in many languages and does not require any translation data making it applicable to low-resource languages.In our experiments we outperform the best CoNLL 2018 language-specific systems in all of the shared task's six truly low-resource languages while using a single system.However, we also find that (i) parsing accuracy still varies dramatically when changing the training languages and (ii) in some target languages zero-shot transfer fails under all tested conditions, raising concerns on the 'universality' of the whole approach.
Program-mandering

Privilege separation is an effective technique to improve software security. However, past partitioning systems do not allow programmers to make quantitative tradeoffs between security and performance. In this paper, we describe our toolchain called PM. It can automatically find the optimal boundary in program partitioning. This is achieved by solving an integer-programming model that optimizes for a user-chosen metric while satisfying the remaining security and performance constraints on other metrics. We choose security metrics to reason about how well computed partitions enforce information flow control to: (1) protect the program from low-integrity inputs or (2) prevent leakage of program secrets. As a result, functions in the sensitive module that fall on the optimal partition boundaries automatically identify where declassification is necessary. We used PM to experiment on a set of real-world programs to protect confidentiality and integrity; results show that, with moderate user guidance, PM can find partitions that have better balance between security and performance than partitions found by a previous tool that requires manual declassification.
Abstract Diagrammatic Reasoning with Multiplex Graph Networks

Abstract reasoning, particularly in the visual domain, is a complex human ability, but it remains a challenging problem for artificial neural learning systems. In this work we propose MXGNet, a multilayer graph neural network for multi-panel diagrammatic reasoning tasks. MXGNet combines three powerful concepts, namely, object-level representation, graph neural networks and multiplex graphs, for solving visual reasoning tasks. MXGNet first extracts object-level representations for each element in all panels of the diagrams, and then forms a multi-layer multiplex graph capturing multiple relations between objects across different diagram panels. MXGNet summarises the multiple graphs extracted from the diagrams of the task, and uses this summarisation to pick the most probable answer from the given candidates. We have tested MXGNet on two types of diagrammatic reasoning tasks, namely Diagram Syllogisms and Raven Progressive Matrices (RPM). For an Euler Diagram Syllogism task MXGNet achieves state-of-the-art accuracy of 99.8%. For PGM and RAVEN, two comprehensive datasets for RPM reasoning, MXGNet outperforms the state-of-the-art models by a considerable margin.
Learning to Index for Nearest Neighbor Search

In this study, we present a novel ranking model based on learning neighborhood relationships embedded in the index space. Given a query point, conventional approximate nearest neighbor search calculates the distances to the cluster centroids, before ranking the clusters from near to far based on the distances. The data indexed in the top-ranked clusters are retrieved and treated as the nearest neighbor candidates for the query. However, the loss of quantization between the data and cluster centroids will inevitably harm the search accuracy. To address this problem, the proposed model ranks clusters based on their nearest neighbor probabilities rather than the query-centroid distances. The nearest neighbor probabilities are estimated by employing neural networks to characterize the neighborhood relationships, i.e., the density function of nearest neighbors with respect to the query. The proposed probability-based ranking can replace the conventional distance-based ranking for finding candidate clusters, and the predicted probability can be used to determine the data quantity to be retrieved from the candidate cluster. Our experimental results demonstrated that the proposed ranking model could boost the search performance effectively in billion-scale datasets.
Heterogeneous Private Information Retrieval

Private information retrieval (PIR) enables clients to query and retrieve data from untrusted servers without the untrusted servers learning which data was retrieved.In this paper, we present a new class of multi-server PIR protocols, which we call heterogeneous PIR (HPIR).In such multi-server PIR protocols, the computation and communication overheads imposed on the PIR servers are non-uniform, i.e., some servers handle higher computation/communication burdens than the others.This enables heterogeneous PIR protocols to be suitable for a range of new PIR applications.What enables us to enforce such heterogeneity is a unique PIR-tailored secret sharing algorithm that we leverage in building our PIR protocol.We have implemented our HPIR protocol and evaluated its performance in comparison with regular (i.e., homogenous) PIR protocols.Our evaluations demonstrate that a querying client can trade off the computation and communication loads of the (heterogeneous) PIR servers by adjusting some parameters.For example in a two server scenario with a heterogeneity degree of 4/1, to retrieve a 456KB file from a 0.2GB database, the rich (i.e., resourceful) PIR server will do 1.1 seconds worth of computation compared to 0.3 seconds by the poor (resourceconstrained) PIR server; this is while each of the servers would do the same 1 seconds of computation in a homogeneous settings.Also, for this given example, our HPIR protocol will impose a 912KB communication bandwidth on the rich server compared to 228KB on the poor server (by contrast to 456KB overheads on each of the servers for a traditional homogeneous design).
Multispectral and RADAR images integration for geologic, geomorphic, and structural investigation in southwestern Arabian Shield, Al Qunfudhah area, Saudi Arabia

In this article, Western Saudi Arabia underwent remote sensing investigation via Operational Land Imager (OLI), Advanced Land Observing Satellite/ Phased Array type L-band Synthetic Aperture Radar (ALOS/PALSAR), Sentinal-1 and Shuttle Radar Topography Mission (SRTM). Image-transformation and enhancement techniques such as Band ratios, principal component analysis (PCA), and Minimum Noise Fraction (MNF) were utilized. Applying Sentinel-1 and SRTM DEMs enhanced the perspective views of the terrain. The main results are highlighting of geologic rock units, geomorphic and structural features, and extraction and visualization of dominant lineaments. N–S foliations that are associated with folds N–S tight, isoclinal, and asymmetric (F1), NW-SE trending Najd system strike-slip faults and NNE–SSW, NE–SW NW–SE, and E–W Cenozoic faults are the major identified structural lineaments, which were analyzed and synthesized via rose diagrams. Visible Near Infra-Red/Short Wave Infra-Red (VNIR/SWIR) radar data are very important data set for water resources applications.
An adaptable analysis workflow for characterization of platelet spreading and morphology

The assessment of platelet spreading through light microscopy, and the subsequent quantification of parameters such as surface area and circularity, is a key assay for many platelet biologists. Here we present an analysis workflow which robustly segments individual platelets to facilitate the analysis of large numbers of cells while minimizing user bias. Image segmentation is performed by interactive learning and touching platelets are separated with an efficient semi-automated protocol. We also use machine learning methods to robustly automate the classification of platelets into different subtypes. These adaptable and reproducible workflows are made freely available and are implemented using the open-source software KNIME and ilastik.