Reviewerly/exHarmony huggingface dataset:
MIDAS at SemEval-2019 Task 6: Identifying Offensive Posts and Targeted Offense from Twitter

In this paper, we present our approach and the system description for Sub-task A and Sub Task B of SemEval 2019 Task 6: Identifying and Categorizing Offensive Language in Social Media.Sub-task A involves identifying if a given tweet is offensive or not, and Sub Task B involves detecting if an offensive tweet is targeted towards someone (group or an individual).Our models for Sub-task A is based on an ensemble of Convolutional Neural Network, Bidirectional LSTM with attention, and Bidirectional LSTM + Bidirectional GRU, whereas for Sub-task B, we rely on a set of heuristics derived from the training data and manual observation.We provide a detailed analysis of the results obtained using the trained models.Our team ranked 5th out of 103 participants in Sub-task A, achieving a macro F1 score of 0.807, and ranked 8th out of 75 participants in Sub Task B achieving a macro F1 of 0.695.
Prostate Segmentation with Encoder-Decoder Densely Connected Convolutional Network (Ed-Densenet)

Prostate cancer is a leading cause of mortality among men. Prostate segmentation of Magnetic Resonance (MR) images plays a critical role in treatment planning and image guided interventions. However, manual delineation of prostate is very time-consuming and subjects to large inter-observer variations. To deal with this problem, we proposed a novel Encoder-Decoder Densely Connected Convolutional Network (ED-DenseNet) to segment prostate region automatically. Our model consists of two interconnected pathways, a dense encoder pathway, which learns discriminative high-level image features and a dense decoder pathway, which predicts the final segmentation in the pixel level. Instead of using the convolutional network as the basic unit in the encoder-decoder framework, we utilize Densely Connected Convolutional Network (DenseNet) to preserve the maximum information flow among layers by a densely-connected mechanism. In addition, a novel loss function that jointly considers the encoder-decoder reconstruction error and the prediction error is proposed to optimize the feature learning and segmentation result. Our automatic segmentation result shows high agreement (DSC 87.14%) to the clinical segmentation results by experienced radiation oncologists. In addition, comparison with state-of-the-art methods shows that our ED-DenseNet model is superior in segmentation performance.
Encrypted Databases for Differential Privacy

Abstract The problem of privatizing statistical databases is a well-studied topic that has culminated with the notion of differential privacy. The complementary problem of securing these differentially private databases, however, has—as far as we know—not been considered in the past. While the security of private databases is in theory orthogonal to the problem of private statistical analysis (e.g., in the central model of differential privacy the curator is trusted) the recent real-world deployments of differentially-private systems suggest that it will become a problem of increasing importance. In this work, we consider the problem of designing encrypted databases (EDB) that support differentially-private statistical queries. More precisely, these EDBs should support a set of encrypted operations with which a curator can securely query and manage its data, and a set of private operations with which an analyst can privately analyze the data. Using such an EDB, a curator can securely outsource its database to an untrusted server (e.g., on-premise or in the cloud) while still allowing an analyst to privately query it. We show how to design an EDB that supports private histogram queries. As a building block, we introduce a differentially-private encrypted counter based on the binary mechanism of Chan et al. ( ICALP , 2010). We then carefully combine multiple instances of this counter with a standard encrypted database scheme to support differentially-private histogram queries.
The subfield codes of hyperoval and conic codes

Hyperovals in PG(2,GF(q)) with even q are maximal arcs and an interesting research topic in finite geometries and combinatorics. Hyperovals in PG(2,GF(q)) are equivalent to [q+2,3,q] MDS codes over GF(q), called hyperoval codes, in the sense that one can be constructed from the other. Ovals in PG(2,GF(q)) for odd q are equivalent to [q+1,3,q−1] MDS codes over GF(q), which are called oval codes. In this paper, we investigate the binary subfield codes of two families of hyperoval codes and the p-ary subfield codes of the conic codes. The weight distributions of these subfield codes and the parameters of their duals are determined. As a byproduct, we generalize one family of the binary subfield codes to the p-ary case and obtain its weight distribution. The codes presented in this paper are optimal or almost optimal in many cases. In addition, the parameters of these binary codes and p-ary codes seem new.
A Degeneracy Framework for Scalable Graph Autoencoders

In this paper, we present a general framework to scale graph autoencoders (AE) and graph variational autoencoders (VAE). This framework leverages graph degeneracy concepts to train models only from a dense subset of nodes instead of using the entire graph. Together with a simple yet effective propagation mechanism, our approach significantly improves scalability and training speed while preserving performance. We evaluate and discuss our method on several variants of existing graph AE and VAE, providing the first application of these models to large graphs with up to millions of nodes and edges. We achieve empirically competitive results w.r.t. several popular scalable node embedding methods, which emphasizes the relevance of pursuing further research towards more scalable graph AE and VAE.
Balanced Clustering: A Uniform Model and Fast Algorithm

Clustering is a fundamental research topic in data mining and machine learning. In addition, many specific applications demand that the clusters obtained be balanced. In this paper, we present a balanced clustering model that is to minimize the sum of squared distances to cluster centers, with uniform regularization functions to control the balance degree of the clustering results. To solve the model, we adopt the idea of the k-means method. We show that the k-means assignment step has an equivalent minimum cost flow formulation when the regularization functions are all convex. By using a novel and simple acceleration technique for the k-means and network simplex methods our model can be solved quite efficiently. Experimental results over benchmarks validate the advantage of our algorithm compared to the state-of-the-art balanced clustering algorithms. On most datasets, our algorithm runs more than 100 times faster than previous algorithms with a better solution.
Elastic RSS

Meeting Service-Level Objectives (SLOs) for workloads in today's datacenter environments places stringent demands on end-host servers: work conservation, tolerance to varying request service time distributions, high throughput, and CPU efficiency. Beginning with Receive Side Scaling (RSS), various schedulers have been proposed to steer packets to cores while preserving locality. However, these techniques are either too inflexible (randomly steering traffic at the NIC) or slow (bottlenecked by a central CPU-based scheduler).
Investigating the environmental antecedents of organizations’ intention to adopt agile software development

Purpose Agile software development (ASD) has emerged as an active research area due to its enormous growth in popularity. The innovative differences between ASD and traditional development include the use of fundamentally different procedures, organizational structures and people, yet organizations still attempt to embrace ASD. Apparently, the underlying factors arousing organizations’ intent to adopt ASD are not well known and have not been well explained in the extant literature. The purpose of this paper is to fill this gap for which the authors propose a conceptual framework based on the business process management (BPM) perspective to identify the environmental stimuli that affect an organization’s ASD adoption. Design/methodology/approach Proposition for a methodology approach is used to construct a theoretical framework based on existing literature and theories in BPM. Findings The framework recognizes external and internal environmental stimuli, including institutional isomorphic forces and interior enablers, such as top management championship, the culture type and resource readiness, which affect organizational ASD adoption decisions. Originality/value This paper consolidates both the internal and external environmental aspects of the stimuli that lead to ASD adoption and offers insight into creating a suitable context for ASD adoption.
Looking Beyond Label Noise: Shifted Label Distribution Matters in Distantly Supervised Relation Extraction

In recent years there is a surge of interest in applying distant supervision (DS) to automatically generate training data for relation extraction (RE).In this paper, we study the problem what limits the performance of DS-trained neural models, conduct thorough analyses, and identify a factor that can influence the performance greatly, shifted label distribution.Specifically, we found this problem commonly exists in real-world DS datasets, and without special handing, typical DS-RE models cannot automatically adapt to this shift, thus achieving deteriorated performance.To further validate our intuition, we develop a simple yet effective adaptation method for DS-trained models, bias adjustment, which updates models learned over the source domain (i.e., DS training set) with a label distribution estimated on the target domain (i.e., test set).Experiments demonstrate that bias adjustment achieves consistent performance gains on DS-trained models, especially on neural models, with an up to 23% relative F1 improvement, which verifies our assumptions.
Supporting Programmable Autoscaling Rules for Containers and Virtual Machines on Clouds

With the increasing utilization of cloud computing and container technologies, orchestration is becoming an important area on both cloud and container levels. Beyond resource allocation, deployment and configuration, scaling is a key functionality in orchestration in terms of policy, description and flexibility. This paper presents an approach where the aim is to provide a high degree of flexibility in terms of available monitoring metrics and in terms of the definition of elasticity rules to implement practically any possible business logic for a given application. The aim is to provide a general interface for supporting programmable scaling policies utilizing monitoring metrics originating from infrastructure, application or any external components. The paper introduces a component, called Policy Keeper performing the auto-scaling based on user-defined rules, details how this component is operating in the auto-scaling framework, called MiCADO and demonstrates a deadline-based scaling use case.
Optimal Estimation of Wavelet Decomposition Level for a Matching Pursuit Algorithm

In this paper, we consider the application of the matching pursuit algorithm (MPA) for spectral analysis of non-stationary signals. First, we estimate the approximation error and the performance time for various MPA modifications and parameters using central processor unit and graphics processing unit (GPU) to identify possible ways to improve the algorithm. Next, we propose the modifications of discrete wavelet transform (DWT) and package wavelet decomposition (PWD) for further use in MPA. We explicitly show that the optimal decomposition level, defined as a level with minimum entropy, in DWT and PWD provides the minimum approximation error and the smallest execution time when applied in MPA as a rough estimate in the case of using wavelets as basis functions (atoms). We provide an example of entropy-based estimation for optimal decomposition level in spectral analysis of seismic signals. The proposed modification of the algorithm significantly reduces its computational costs. Results of spectral analysis obtained with MPA can be used for various signal processing applications, including denoising, clustering, classification, and parameter estimation.
The Effect of a Virtual-Reality Full-Body Illusion on Body Representation in Obesity

Background. The effective illusory ownership over an artificial body in modulating body representations in healthy and eating disorders population has been repeatedly reported in recent literature. In this study, we extended this research in the field of obesity: specifically, we investigated whether ownership over a virtual body with a skinny abdomen might be successfully experienced by participants affected by obesity. Methods. Fifteen participants with obesity and fifteen healthy-weight participants took part at this study in which the VR-Full-Body Illusion was adopted. The strength of illusion was investigated through the traditional Embodiment Questionnaire, while changes in bodily experience were measured through a body size estimation task. Results. Participants with obesity as well as healthy-weight participants reported to experience the illusion. About the body size estimation task, both groups reported changes only in the estimation of the abdomen's circumference after the experimental condition, in absence of any another difference. Discussion. Participants with obesity reported to experience the illusion over a skinny avatar, but the modulation of the bodily experience seems controversial. Future lines of research exploiting this technique for modulating body representations in obesity, specifically in terms of potential therapeutic use, were discussed.
Interpretations are useful: penalizing explanations to align neural networks with prior knowledge

For an explanation of a deep learning model to be effective, it must provide both insight into a model and suggest a corresponding action in order to achieve some objective. Too often, the litany of proposed explainable deep learning methods stop at the first step, providing practitioners with insight into a model, but no way to act on it. In this paper, we propose contextual decomposition explanation penalization (CDEP), a method which enables practitioners to leverage existing explanation methods in order to increase the predictive accuracy of deep learning models. In particular, when shown that a model has incorrectly assigned importance to some features, CDEP enables practitioners to correct these errors by directly regularizing the provided explanations. Using explanations provided by contextual decomposition (CD) (Murdoch et al., 2018), we demonstrate the ability of our method to increase performance on an array of toy and real datasets.
MSN: Modality separation networks for RGB-D scene recognition

RGB-D image based indoor scene recognition is a challenging task due to the complex scene layouts and cluttered objects. Although the depth modality can provide extra geometric information, how to better learn the multi-modal features is still an open problem. Considering this, in this paper we propose the modality separation networks to extract the modal-consistent and modal-specific features simultaneously. The motivations of this work are from two aspects: 1) The first one is to learn what is unique to each modality and what is common between the two modalities explicitly; 2) The second one is to explore the relationship between global/local features and modal-specific/consistent features. To this end, the proposed framework contains two branches of submodules to learn the multi-modal features. One branch is used to extract the individual characteristics of each modality by minimizing the similarity between two modalities. Another branch is to learn the common information between two modalities by maximizing the correlation term. Moreover, with the spatial attention module, our method can visualize the spatial positions where different submodules focus on. We evaluate our method on two public RGB-D scene recognition datasets, and new state-of-the-art results are achieved with the proposed framework.
Infinite Mixtures of Infinite Factor Analysers

Factor-analytic Gaussian mixtures are often employed as a modelbased approach to clustering high-dimensional data.Typically, the numbers of clusters and latent factors must be fixed in advance of model fitting.The pair which optimises some model selection criterion is then chosen.For computational reasons, having the number of factors differ across clusters is rarely considered.Here the infinite mixture of infinite factor analysers (IMIFA) model is introduced.IMIFA employs a Pitman-Yor process prior to facilitate automatic inference of the number of clusters using the stick-breaking construction and a slice sampler.Automatic inference of the cluster-specific numbers of factors is achieved using multiplicative gamma process shrinkage priors and an adaptive Gibbs sampler.IMIFA is presented as the flagship of a family of factor-analytic mixtures.Applications to benchmark data, metabolomic spectral data, and a handwritten digit example illustrate the IMIFA model's advantageous features.These include obviating the need for model selection criteria, reducing the computational burden associated with the search of the model space, improving clustering performance by allowing cluster-specific numbers of factors, and uncertainty quantification.
LAM: Remote Sensing Image Captioning with Label-Attention Mechanism

Significant progress has been made in remote sensing image captioning by encoder-decoder frameworks. The conventional attention mechanism is prevalent in this task but still has some drawbacks. The conventional attention mechanism only uses visual information about the remote sensing images without considering using the label information to guide the calculation of attention masks. To this end, a novel attention mechanism, namely Label-Attention Mechanism (LAM), is proposed in this paper. LAM additionally utilizes the label information of high-resolution remote sensing images to generate natural sentences to describe the given images. It is worth noting that, instead of high-level image features, the predicted categories’ word embedding vectors are adopted to guide the calculation of attention masks. Representing the content of images in the form of word embedding vectors can filter out redundant image features. In addition, it can also preserve pure and useful information for generating complete sentences. The experimental results from UCM-Captions, Sydney-Captions and RSICD demonstrate that LAM can improve the model’s performance for describing high-resolution remote sensing images and obtain better S m scores compared with other methods. S m score is a hybrid scoring method derived from the AI Challenge 2017 scoring method. In addition, the validity of LAM is verified by the experiment of using true labels.
Design Research and Object-Oriented Ontology

Abstract In this paper we recount several research projects conducted at ImaginationLancaster ( http://imagination.lancs.ac.uk) a Design-led research laboratory, all of which consider Object-Oriented Ontology (OOO). The role OOO plays in these projects is varied: as a generative mechanism contributing to ideation; as a framework for analysis; and as a constituent in developing new design theory. Each project’s focus is quite unique—an app, a board game, a set of Tarot cards, a kettle and a living room—however they are all concerned with developing new understandings relating to the ubiquitous, networked, ‘smart’ technologies which are often referred to as the Internet of Things (IoT). Through our reflexive account of these research projects the aim of this paper is to provide insights into, and promote new discussion about, the relevance and scope for OOO in socio-technical Design Research, and beyond.
Quantum information preserving computational electromagnetics

We present a computational framework for canonical quantization in arbitrary inhomogeneous dielectric media by incorporating quantum electromagnetic effects into complex solutions of quantum Maxwell's equations. To do so, the proposed algorithm integrates and performs (1) numerical computation of normal modes and (2) evaluation of arbitrary products of ladder operators acting on multimode Fock states. The former is associated with Hermitian-Helmholtz linear systems using finite-element or finite-difference methods; consequently, the complete set of numerical normal modes diagonalizes the Hamiltonian operators up to floating-point precision. Its Hermiticity is retained, allowing its quantization. Then, we perform quantum numerical simulations of two-photon interference occurring in a 50:50 beam splitter to observe the Hong-Ou-Mandel effect. Our prototype model is useful for numerical analyses on various narrow-band quantum-optical multiphoton systems such as quantum metasurfaces, quantum-optical filters, and quantum electrodynamics in open optical cavities.
Camera-Based Micro-Vibration Measurement for Lightweight Structure Using an Improved Phase-Based Motion Extraction

Video cameras are capable to collect high-density spatial data as each pixel is a sensor. An issue is that the video data is difficult to convert into the motion information, such as displacement. This paper presents a novel camera-based vibration measurement methodology for the lightweight structure. First, the phase-based motion extraction method is improved by directly transforming the phase variations into the displacement without the computation of phase gradient. The displacement is then converted into acceleration information to describe the vibration. Moreover, the proposed vibration measurement is investigated for a lightweight structure without attached sensors to change the physical properties. The measurements of the acceleration signals from camera and accelerometer are compared for verification. The experimental results for the effect of mass-loading, different camera parameters and down-sampling on the measurement validate the effectiveness of the proposed method.
Large-Scale Analysis of the Docker Hub Dataset

Docker containers have become a prominent solution for supporting modern enterprise applications due to the highly desirable features of isolation, low overhead, and efficient packaging of the execution environment. Containers are created from images which are shared between users via a Docker registry. The amount of data Docker registries store is massive; for example, Docker Hub, a popular public registry, stores at least half a million public images. In this paper, we analyze over 167 TB of uncompressed Docker Hub images, characterize them using multiple metrics and evaluate the potential of file-level deduplication in Docker Hub. Our analysis helps to make conscious decisions when designing storage for containers in general and Docker registries in particular. For example, only 3% of the files in images are unique, which means file-level deduplication has a great potential to save storage space for the registry. Our findings can motivate and help improve the design of data reduction, caching, and pulling optimizations for registries.