Reviewerly/exHarmony huggingface dataset:
Neural Logic Rule Layers

Despite their great success in recent years, deep neural networks (DNN) are mainly black boxes where the results obtained by running through the network are difficult to understand and interpret. Compared to e.g. decision trees or bayesian classifiers, DNN suffer from bad interpretability where we understand by interpretability, that a human can easily derive the relations modeled by the network. A reasonable way to provide interpretability for humans are logical rules. In this paper we propose neural logic rule layers (NLRL) which are able to represent arbitrary logic rules in terms of their conjunctive and disjunctive normal forms. Using various NLRL within one layer and correspondingly stacking various layers, we are able to represent arbitrary complex rules by the resulting neural network architecture. The NLRL are end-to-end trainable allowing to learn logic rules directly from available data sets. Experiments show that NLRL-enhanced neural networks can learn to model arbitrary complex logic and perform arithmetic operation over the input values.
Causal Inference Under Interference And Network Uncertainty

Classical causal and statistical inference methods typically assume the observed data consists of independent realizations. However, in many applications this assumption is inappropriate due to a network of dependences between units in the data. Methods for estimating causal effects have been developed in the setting where the structure of dependence between units is known exactly, but in practice there is often substantial uncertainty about the precise network structure. This is true, for example, in trial data drawn from vulnerable communities where social ties are difficult to query directly. In this paper we combine techniques from the structure learning and interference literatures in causal inference, proposing a general method for estimating causal effects under data dependence when the structure of this dependence is not known a priori. We demonstrate the utility of our method on synthetic datasets which exhibit network dependence.
HyperNOMAD: Hyperparameter optimization of deep neural networks using mesh adaptive direct search

The performance of deep neural networks is highly sensitive to the choice of the hyperparameters that define the structure of the network and the learning process. When facing a new application, tuning a deep neural network is a tedious and time consuming process that is often described as a "dark art". This explains the necessity of automating the calibration of these hyperparameters. Derivative-free optimization is a field that develops methods designed to optimize time consuming functions without relying on derivatives. This work introduces the HyperNOMAD package, an extension of the NOMAD software that applies the MADS algorithm [7] to simultaneously tune the hyperparameters responsible for both the architecture and the learning process of a deep neural network (DNN), and that allows for an important flexibility in the exploration of the search space by taking advantage of categorical variables. This new approach is tested on the MNIST and CIFAR-10 data sets and achieves results comparable to the current state of the art.
Claim Extraction in Biomedical Publications using Deep Discourse Model and Transfer Learning

Claims are a fundamental unit of scientific discourse. The exponential growth in the number of scientific publications makes automatic claim extraction an important problem for researchers who are overwhelmed by this information overload. Such an automated claim extraction system is useful for both manual and programmatic exploration of scientific knowledge. In this paper, we introduce a new dataset of 1,500 scientific abstracts from the biomedical domain with expert annotations for each sentence indicating whether the sentence presents a scientific claim. We introduce a new model for claim extraction and compare it to several baseline models including rule-based and deep learning techniques. Moreover, we show that using a transfer learning approach with a fine-tuning step allows us to improve performance from a large discourse-annotated dataset. Our final model increases F1-score by over 14 percent points compared to a baseline model without transfer learning. We release a publicly accessible tool for discourse and claims prediction along with an annotation tool. We discuss further applications beyond biomedical literature.
Differentially private sub-Gaussian location estimators

We tackle the problem of estimating a location parameter with differential privacy guarantees and sub-Gaussian deviations. Recent work in statistics has focused on the study of estimators that achieve sub-Gaussian type deviations even for heavy tailed data. We revisit some of these estimators through the lens of differential privacy and show that a naive application of the Laplace mechanism can lead to sub-optimal results. We design two private algorithms for estimating the median that lead to estimators with sub-Gaussian type errors. Unlike most existing differentially private median estimators, both algorithms are well defined for unbounded random variables that are not even required to have finite moments. We then turn to the problem of sub-Gaussian mean estimation and show that under heavy tails natural differentially private alternatives lead to strictly worse deviations than their non-private sub-Gaussian counterparts. This is in sharp contrast with recent results that show that from an asymptotic perspective the cost of differential privacy is negligible.
Fusing Identity Management, HL7 and Blockchain into a Global Healthcare Record Sharing Architecture

Healthcare record sharing among various medical roles is a critical and challenging research problem especially in today's everchanging global IT solutions.The emergence of blockchain as a new enabling technology brought radical changes to numerous business applications, including healthcare.Blockchain is a trusted distributed ledger that forms a decentralized infrastructure.There have been several proposals regarding the sharing of critical healthcare records over blockchain infrastructure without requiring prior knowledge/trust of the parties involved (patients, service providers, and insurance companies).Another yet important issue is to securely share medical records across various countries for travelling patients to ensure an integrated and ubiquitous healthcare service.In this paper, we present a globally integrated healthcare record sharing architecture based on blockchain and HL7 client.Healthcare records are stored at the hosting country and are not stored on the blockchain.This architecture avails medical records of travelling patients temporarily and after performing necessary authentication.The actual authorisation process is performed on a federated identity management system, such as, the Shibboleth.Though there are similarities with identity management systems, our system is unique as it involves the patient in the permission process and discloses to them the identities of entities accessed their health records.Our solution also improves performance and guarantees privacy and security through the use of blockchain and identity management system.
Two-Client and Multi-client Functional Encryption for Set Intersection

We propose several functional encryption schemes for set intersection and variants on two or multiple sets. In these schemes, a party may learn the set intersection from the sets of two or more clients, without having to learn the plaintext set of each individual client. For the case of two clients, we construct efficient schemes for determining the set intersection and the cardinality of the intersection. To evaluate the cardinality of the intersection, no overhead is incurred when compared to operating on plaintext data. We also present other functionalities with a scheme for set intersection with data transfer and a threshold scheme that only discloses the intersection if both clients have at least t elements in common. Finally, we consider set intersection and set intersection cardinality schemes for the case of three or more clients from a theoretical perspective. Our proof-of-concept implementations show that the two-client constructions are efficient and scale linearly in the set sizes.
Visual Space Optimization for Zero-shot Learning

Zero-shot learning, which aims to recognize new categories that are not included in the training set, has gained popularity owing to its potential ability in the real-word applications. Zero-shot learning models rely on learning an embedding space, where both semantic descriptions of classes and visual features of instances can be embedded for nearest neighbor search. Recently, most of the existing works consider the visual space formulated by deep visual features as an ideal choice of the embedding space. However, the discrete distribution of instances in the visual space makes the data structure unremarkable. We argue that optimizing the visual space is crucial as it allows semantic vectors to be embedded into the visual space more effectively. In this work, we propose two strategies to accomplish this purpose. One is the visual prototype based method, which learns a visual prototype for each visual class, so that, in the visual space, a class can be represented by a prototype feature instead of a series of discrete visual features. The other is to optimize the visual feature structure in an intermediate embedding space, and in this method we successfully devise a multilayer perceptron framework based algorithm that is able to learn the common intermediate embedding space and meanwhile to make the visual data structure more distinctive. Through extensive experimental evaluation on four benchmark datasets, we demonstrate that optimizing visual space is beneficial for zero-shot learning. Besides, the proposed prototype based method achieves the new state-of-the-art performance.
Wavelet estimation of copula function based on censored data

In this paper, we consider wavelet analysis to obtain an estimator of a copula function based on censored data. We show that optimal convergence rates for the mean integrated squared error (MISE) of linear wavelet-based function estimators are exact under right censoring model. Moreover, we derive asymptotic formulae for MISE. Finally, the simulation results and the analysis of real data validate the proposed procedure.
Design for Healthy Horizons in a Local Community

Communities and social support are considered to be important for people's ability to cultivate a healthy lifestyle. Yet, digital technology for everyday health predominantly focuses on individual use and performance optimisation. In this paper, we present how we have designed a mobile application to support community collaboration around health based on a conceptual model aiming at bringing a horizon of connectedness with health to residents in a local community. The research is conducted as a long-term participatory design process in a neighbourhood with low socio-economic status and significant health challenges. We introduce a community perspective on everyday health and the co-designed application focused on local collaboration around physical activity. Via workshops with residents and community workers, we critically examine opportunities and challenges for the application's ability to form a horizon and facilitate community collaboration around health in the neighbourhood1.
A Novel Approach to National-level Cyber Risk Assessment Based on Vulnerability Management and Threat Intelligence

Real-time assessment of IT-related risks, performed at the national level, is very important due to the evolving nature of threats that may originate from individual hackers, organized cyber-criminal groups, as well as state activities. Evaluation of risk that is based on technical information, as well as on mutual relationships between various institutions and services, may result in very valuable situational awareness. The paper describes (in general) cyber risk analysis method which will be implemented in Polish National Cybersecurity Platform.
Vehicle tracking fusing the prior information of Kalman filter under occlusion conditions

The occlusion in traffic scenarios have posed great challenges in vehicle tracking. Establishing effective and robust vehicle tracking algorithms under occlusion conditions is a necessary issue for many traffic applications. This paper presents a novel approach of vehicle tracking by fusing the prior information of Kalman filter. The prior information of Kalman filter is used for background update, precise morphological operation and occlusion judgment. The fusion of observation and prior information is adopted to segment vehicles under occlusion. It effectively improves the robustness of the vehicle tracking algorithm in the case of occlusion. A novel vehicle description method is proposed to express vehicle shape more accurately and thus reduces the misjudgement of occlusion. Experiments show that the proposed algorithm can accurately track vehicles even under occlusion.
Phase compensation based on step-length control in continuous-variable quantum key distribution

Phase compensation is a dispensable procedure to reduce the difference between legitimate parties in continuous-variable quantum key distribution (CVQKD) because of the unavoidable phase drift of the quantum signals. However, it is a difficult task to compensate the fast drifted phase accurately. Here, we propose a novel phase compensation scheme based on an optimal iteration algorithm. Analysis shows that this scheme can make the phase compensation reach a higher precision level while simultaneously ensuring the efficiency. When the accuracy is determined, we can minimize the number of iterations by controlling the step-length to increase the algorithm efficiency. Moreover, we can improve the accuracy of phase compensation by means of changing the step-length. This work breaks the bottleneck of accuracy problem in phase compensation and contributes to the performance of the whole CVQKD system.
Modern Pedagogical Approaches in the System of Engineering Education

The article aims at studying the specifics of the psychological and pedagogical competence of future engineers in the structure of the educational process and in relation to the use of innovative and effective educational technologies. At present, the general issues of humanization and pedagogical and methodological support of the educational process are of great importance in light of the need to increase the academic mobility of university graduates who choose to work at universities. The problem of insufficient level of their psychological and pedagogical competence development arises automatically. Psychological and pedagogical competence of a graduate means his (her) ability to mastering the basic material of the psychological and pedagogical unit, the ability to operate the acquired knowledge and put it into practice, mastering acquired skills in the conditions of concrete professional and pedagogical activity. This problem has several solutions. One of the most effective is the possibility of developing and introducing the programmes of advanced psychological and pedagogical training and professional retraining for future engineers specializing in engineering pedagogy. The purpose of the article is to view the effective approaches to the development of psychological and pedagogical competences among future engineers. The authors consider using of the modern IT-technologies to be a strong motivator in this process as well as gamification (role games and business games). As the key tasks of the teaching staff at technical university the following issues are regarded: creation of conditions for the disclosure by future engineers of their professional and personal resources; obtaining by future engineers the information necessary to increase the level of their psychological and pedagogical competence; acquisition of skills necessary for knowledge application in future professional activity; expanding the range of professional competencies of future engineers in the conditions of psychological and pedagogical design. One of the most important aspects of teacher’s activity is reflection, self-evaluation. The authors present teachers’ behavior patterns for reflection and formulate the recommendations for teaching future engineering teachers. Thus, the specificity of training future engineers with the focus on increasing their psychological and pedagogical competence is a complex multi-faceted process, specially organized in time and space, involving consideration of certain psychological and pedagogical, methodological and methodical patterns and aimed at developing professional qualifications of future engineering pedagogues.
A New Weight and Sensitivity Based Variable Maximum Distance to Average Vector Algorithm for Wearable Sensor Data Privacy Protection

The problem of privacy protection of wearable devices when publishing data can be solved based on the variable-maximum distance average vector. This paper proposes a new weight and sensitivity based variable maximum distance average vector (WSV-MDAV) method aiming to solve the problems that may be contained in the existing privacy protection algorithm. The proposed approach considers the difference of the importance among all the identifiers by setting corresponding weight coefficient W. Given a specific weight to each attribute in the table, we can subsequently get a distance metric based on weight. Similarly, the different sensitivity constraint S for different sensitive attributes is also available for our proposed method. Using the WSV-MDAV algorithm we propose a new privacy protection model for the data publishing of wearable device by introducing the concept of the differential privacy. The numerical results show that the proposed WSV-MDAV algorithm improves the privacy protection performance and reduces the information loss compared to the traditional method.
Inflammatory Cells Detection in H&amp;E Staining Histology Images Using Deep Convolutional Neural Network with Distance Transformation

Inflammatory cells such as lymphocytes and neutrophils are crucial indicators in diagnosing acute inflammation from liver histology images. However, there are several challenges in detecting the inflammatory cells. The inflammatory cells have large variation and also appear similar to other cells. In an often occasion, the inflammatory cells may overlap each other. It is also unavoidable to see the clustery noise in the background. To conquer the above-mentioned problems, this paper proposes a procedure, which implements the detection-then-classification by combining the distance transformation with deep convolutional neural networks for detecting an accurate position of each cell. Then a precise image patch can be extracted for a deep convolutional neural network for classification of the cells into nuclei, lymphocyte, neutrophils and impurity (e.g. Kupffer cell). The experimental results show that the proposed approach can effectively detect the inflammatory cells from H&E Staining liver histopathological images, with an accuracy of 93.7% in inflammatory cells classification.
Estimation of Direct Normal Irradiance at Antarctica for Concentrated Solar Technology

The estimation of the average daily, monthly and annual direct normal solar irradiation(DNI) was done in the region hosting the Mario Zucchelli Station, in the bay of Terra Nova(Antarctica). Estimates are based on measurements of direct normal (DNI), horizontal global (GHI)and horizontal diffuse (Diff.HI) irradiations, observed by a solar-metric acquisition station installedduring the XXVIII scientific expedition carried out in the austral summer 2012/2013 as part of theNational Plan of Research in Antarctica (PNRA). The contemporary observations of all threeirradiations allowed the setting up of the model for the estimation of the DNI starting from thevalues of the GHI only, validated for the weather conditions of the Antarctic region. Subsequently,the long-time data reconstruction of the DNI values thanks to the availability of several years' hourlymeasurements of GHI at the Mario Zucchelli base has been carried out using the meteorologicalacquisition data installed both at the base and in places scattered around it. The final results makefeasible the estimation of solar potential for concentrated solar technology according to longmeasurements of GHI. Overall, we propose a clean technology based on a renewable power plantand a specific example with a tendency toward a decreased human carbon footprint in theatmosphere of this protected area.
Activitynet 2019 Task 3: Exploring Contexts for Dense Captioning Events in Videos

Contextual reasoning is essential to understand events in long untrimmed videos. In this work, we systematically explore different captioning models with various contexts for the dense-captioning events in video task, which aims to generate captions for different events in the untrimmed video. We propose five types of contexts as well as two categories of event captioning models, and evaluate their contributions for event captioning from both accuracy and diversity aspects. The proposed captioning models are plugged into our pipeline system for the dense video captioning challenge. The overall system achieves the state-of-the-art performance on the dense-captioning events in video task with 9.91 METEOR score on the challenge testing set.
μ-Forcing

It has been previously observed that training Variational Recurrent Autoencoders (VRAE) for text generation suffers from serious uninformative latent variables problems. The model would collapse into a plain language model that totally ignores the latent variables and can only generate repeating and dull samples. In this article, we explore the reason behind this issue and propose an effective regularizer-based approach to address it. The proposed method directly injects extra constraints on the posteriors of latent variables into the learning process of VRAE, which can flexibly and stably control the tradeoff between the Kullback-Leibler (KL) term and the reconstruction term, making the model learn dense and meaningful latent representations. The experimental results show that the proposed method outperforms several strong baselines and can make the model learn interpretable latent variables and generate diverse meaningful sentences. Furthermore, the proposed method can perform well without using other strategies, such as KL annealing.
Cascade RetinaNet: Maintaining Consistency for Single-Stage Object Detection

Recent researches attempt to improve the detection performance by adopting the idea of cascade for single-stage detectors. In this paper, we analyze and discover that inconsistency is the major factor limiting the performance. The refined anchors are associated with the feature extracted from the previous location and the classifier is confused by misaligned classification and localization. Further, we point out two main designing rules for the cascade manner: improving consistency between classification confidence and localization performance, and maintaining feature consistency between different stages. A multistage object detector named Cas-RetinaNet, is then proposed for reducing the misalignments. It consists of sequential stages trained with increasing IoU thresholds for improving the correlation, and a novel Feature Consistency Module for mitigating the feature inconsistency. Experiments show that our proposed Cas-RetinaNet achieves stable performance gains across different models and input scales. Specifically, our method improves RetinaNet from 39.1 AP to 41.1 AP on the challenging MS COCO dataset without any bells or whistles.