Reviewerly/exHarmony huggingface dataset:
Smart Secure: A Novel Risk based Maturity Model for Enterprise Risk Management during Global Pandemic

With the onset of the global pandemic, organizations have had to embrace digital transformation and adapt to the new normal. Digital transformation has helped global workforce to work from remote locations and ensure business continuity. However, this has also resulted in a significant surge in cyber attacks due to an increase in attack surface and rogue threat actors. Security Leaders and CFOs are facing a taxing challenge to decide on where and how to invest to secure their organizations and perform enterprise cybersecurity risk management in context of the financial constraints that most organizations are facing at the moment. We propose an efficient and risk-based cybersecurity maturity model called “Smart Secure”, for guiding security leaders to perform smart & efficient enterprise risk management. This would help in efficient distribution of available resources for information systems based on level of risk & criticality of the application, for their organization. Thus, providing a solution for efficient cybersecurity funding and prioritization to help improve overall cybersecurity posture for the organization.
A Noise Robust Micro-Range Estimation Method for Precession Cone-Shaped Targets

The estimation of micro-Range (m-R) is important for micro-motion feature extraction and imaging, which provides significant supports for the classification of a precession cone-shaped target. Under low signal-to-noise ratio (SNR) circumstances, the modified Kalman filter (MKF) will obtain broken segments rather than complete m-R tracks due to missing trajectories, and the performance of the MKF is restricted by unknown noise covariance. To solve these problems, a noise-robust m-R estimation method, which combines the adaptive Kalman filter (AKF) and the random sample consensus (RANSAC) algorithm, is proposed in this paper. The AKF, where the noise covariance is not required for the estimation of the state vector, is applied to associate m-R trajectories for higher estimation accuracy and lower wrong association probability. Due to missing trajectories, several associated segments which are parts of the m-R tracks can be obtained by the AKF. Then, the RANSAC algorithm is utilized to associate the segments and the complete m-R tracks can be obtained. Compared with the MKF, the proposed method can obtain complete m-R tracks instead of several segments, and avoids the influence of unknown noise covariance under low SNR circumstances. Experimental results based on electromagnetic simulation data demonstrate that the proposed method is more precise and robust compared with traditional methods.
Entanglement and Photon Anti-Bunching in Coupled Non-Degenerate Parametric Oscillators

We analytically and numerically show that the Hillery-Zubairy's entanglement criterion is satisfied both below and above the threshold of coupled non-degenerate optical parametric oscillators (NOPOs) with strong nonlinear gain saturation and dissipative linear coupling. We investigated two cases: for large pump mode dissipation, below-threshold entanglement is possible only when the parametric interaction has an enough detuning among the signal, idler, and pump photon modes. On the other hand, for a large dissipative coupling, below-threshold entanglement is possible even when there is no detuning in the parametric interaction. In both cases, a non-Gaussian state entanglement criterion is satisfied even at the threshold. Recent progress in nano-photonic devices might make it possible to experimentally demonstrate this phase transition in a coherent XY machine with quantum correlations.
Information Graphic Summarization using a Collection of Multimodal Deep Neural Networks

We present a multimodal deep learning framework that can generate summarization text supporting the main idea of an information graphic for presentation to a person who is blind or visually impaired. The framework utilizes the visual, textual, positional, and size characteristics extracted from the image to create the summary. Different and complimentary neural architectures are optimized for each task using crowdsourced training data. From our quantitative experiments and results, we explain the reasoning behind our framework and show the effectiveness of our models. Our qualitative results showcase text generated from our framework and show that Mechanical Turk participants favor them to other automatic and human generated summarizations. We describe the design and results of an experiment to evaluate the utility of our system for people who have visual impairments in the context of understanding Twitter Tweets containing line graphs.
A Deep Learning based Sentiment Analysis on Bang-lish Disclosure

Sentiment analysis is a field of immense possibilities and application despite being an age-old topic. Various applications of machine learning and natural language processing keep contributing to this field with innovative techniques. Variants of neural networks with attention mechanism is a well-known tool in this field. However, very few of these techniques have been applied for Bengali sentences written with English letters which is a very common scenario for this era of social networking and online e-commerce sites. As for product reviews, it is of utmost importance as it helps the companies to suggest and review their products. In social networks, it may be used to analyze the emotion of the users. Moreover, in the online platforms of Bangladesh, most people use English letters to express their reviews in the Bengali language, which becomes a major issue for further analysis to guide them. In this paper, we propose a novel attention-based CNN model to solve the problem and analyze the performances of its variants along with the least features of NLP, thus making it able to work on different platforms.
OAS-Net: Occlusion Aware Sampling Network for Accurate Optical Flow

Optical flow estimation is an essential step for many real-world computer vision tasks. Existing deep networks have achieved satisfactory results by mostly employing a pyramidal coarse-to-fine paradigm, where a key process is to adopt warped target feature based on previous flow prediction to correlate with source feature for building 3D matching cost volume. However, the warping operation can lead to troublesome ghosting problem that results in ambiguity. Moreover, occluded areas are treated equally with non occluded regions in most existing works, which may cause performance degradation. To deal with these challenges, we propose a lightweight yet efficient optical flow network, named OAS-Net (occlusion aware sampling network) for accurate optical flow. First, a new sampling based correlation layer is employed without noisy warping operation. Second, a novel occlusion aware module is presented to make raw cost volume conscious of occluded regions. Third, a shared flow and occlusion awareness decoder is adopted for structure compactness. Experiments on Sintel and KITTI datasets demonstrate the effectiveness of proposed approaches.
A Novel Feature Selection Approach based on Binary Particle Swarm Optimization and Ensemble Learning for Heterogeneous Defect Prediction

Software defect prediction is an integral part of the software development process. Defect prediction helps focus on the grey areas beforehand, thus saving the considerable amount of money that is otherwise wasted in finding and fixing the faults once the software is already in production. One of the popular areas of defect prediction in recent years is Heterogeneous Defect Prediction, which predicts defects in a target project using a source project with different metrics. Through our paper, we provide a novel feature selection based approach, En-BPSO, based on binary particle swarm optimization, coupled with majority voting ensemble classifier based fitness function for heterogeneous defect prediction. The datasets we are using are MORPH and SOFTLAB. The results show that the En-BPSO method provides the highest Friedman mean rank amongst all the feature selection methods used for comparison. En-BPSO technique also helps us dynamically determine the optimal number of features to build an accurate heterogeneous defect prediction model.
Federated Learning for Face Recognition

With the rapid development of deep learning, the accuracy of face recognition has significantly increased. However, training a face recognition model requires the collection of private data to a centralized server to obtain high performance in the desired domain. Since federated learning is a technique to train a model without collecting data to a server, it is a suitable architecture to train a face recognition model with privacy-sensitive face images held in personal smartphones. This study proposes strategies to apply federated learning to face recognition model training.
On Embodied Visual Navigation in Real Environments Through Habitat

Visual navigation models based on deep learning can learn effective policies when trained on large amounts of visual observations through reinforcement learning. Unfortunately, collecting the required experience in the real world requires the deployment of a robotic platform, which is expensive and time-consuming. To deal with this limitation, several simulation platforms have been proposed in order to train visual navigation policies on virtual environments efficiently. Despite the advantages they offer, simulators present a limited realism in terms of appearance and physical dynamics, leading to navigation policies that do not generalize in the real world. In this paper, we propose a tool based on the Habitat simulator which exploits real world images of the environment, together with sensor and actuator noise models, to produce more realistic navigation episodes. We perform a range of experiments to assess the ability of such policies to generalize using virtual and real-world images, as well as observations transformed with unsupervised domain adaptation approaches. We also assess the impact of sensor and actuation noise on the navigation performance and investigate whether it allows to learn more robust navigation policies. We show that our tool can effectively help to train and evaluate navigation policies on real-world observations without running navigation episodes in the real world.
Towards a unified ontology for IoT fabric with SDDC

The ever growing Internet of Things (IoT) paradigm aims to put people’s incorporation into the new phase of connectivity and sensor technology. While, IoT has the potential to deliver new value-added services in order to make life easier and healthier for people, there are still several issues to be addressed in order to harness the widespread dissemination and adoption of the IoT paradigm, considering its potential benefits. In this context, considering an IoT ecosystem holistically i.e. end-to-end which includes the proprietary Operational Technology (OT) comprising of software and hardware to monitor, detect and control the equipment through sensors and actuators and the general Information Technology (IT) which comprises of the data center infrastructure catering to the backend needs of IoT such as compute, storage and network, one major challenge is converging OT with IT. Such an OT/IT convergence has the potential to explore many problems that exist in the end-to-end IoT ecosystem. One such problem is related to drawing actionable insights through operational analytics. The data center paradigm which concerns this research is the Software-Defined Data Center (SDDC) which is touted as the preferred data center paradigm for IoT. While most of the research happening on IoT today deals with the data from the IoT applications and sensors, this research focuses on the plethora of the metadata contained in the end-to-end IoT ecosystem. This research demonstrates the modelling of the end-to-end IoT ecosystem using semantic-based approach such as ontology. We arrive at a cohesive unified ontology unifying the OT and the IT constituents of the IoT fabric with SDDC together with rich context aware attributes embedded in the unified ontology, thereby addressing the problem of OT/IT convergence as well as laying the foundation to explore many solutions to problems pertinent in such a converged OT/IT ecosystem.
Attribute Decomposition for Flow-Based Domain Mapping

Domain mapping aims to estimate a sophisticated mapping between source and target domains. Finding the specialized attribute in latent representation plays a key role to attain a desirable performance. However, the entangled features usually contain the mixed attribute which can not be easily decomposed in an unsupervised manner. To handle the mixed features for better generation, this paper presents an attribute decomposition based on the sequence data and carries out the flow-based image domain mapping. The latent variables, characterized by flow model, are decomposed into the attribute-relevant and attribute-irrelevant components. The decomposition is guided by multiple objectives including structural-perceptual loss, cycle consistency loss, sequential random-pair reconstruction loss and sequential classification loss where the paired training data for domain mapping are not required. Importantly, the sequential random-pair reconstruction loss is formulated by means of exchanging the attribute-relevant components within a sequence of images. As a result, the source images with the attributes of reference images can be smoothly transferred to the corresponding target images. Experiments on talking face synthesis show the merit of attribute decomposition in domain mapping.
Search-Based Software Re-Modularization: A Case Study at Adyen

Deciding what constitutes a single module, what classes belong to which module or the right set of modules for a specific software system has always been a challenging task. The problem is even harder in large-scale software systems composed of thousands of classes and hundreds of modules. Over the years, researchers have been proposing different techniques to support developers in re-modularizing their software systems. In particular, the search-based software re-modularization is an active research topic within the software engineering community for more than 20 years. This paper describes our efforts in applying search-based software re-modularization approaches at Adyen, a large-scale payment company. Adyen's code base has 5.5M+ lines of code, split into around 70 different modules. We leveraged the existing body of knowledge in the field to devise our own search algorithm and applied it to our code base. Our results show that search-based approaches scale to large code bases as ours. Our algorithm can find solutions that improve the code base according to the metrics we optimize for, and developers see value in the recommendations. Based on our experiences, we then list a set of challenges and opportunities for future researchers, aiming at making search-based software re-modularization more efficient for large-scale software companies.
Automatic Solution Summarization for Crash Bugs

The causes of software crashes can be hidden anywhere in the source code and development environment. When encountering software crashes, recurring bugs that are discussed on Q&A sites could provide developers with solutions to their crashing problems. However, it is difficult for developers to accurately search for relevant content on search engines, and developers have to spend a lot of manual effort to find the right solution from the returned results. In this paper, we present CRASOLVER, an approach that takes into account both the structural information of crash traces and the knowledge of crash-causing bugs to automatically summarize solutions from crash traces. Given a crash trace, CRASOLVER retrieves relevant questions from Q&A sites by combining a proposed position dependent similarity - based on the structural information of the crash trace - with an extra knowledge similarity, based on the knowledge from official documentation sites. After obtaining the answers to these questions from the Q&A site, CRASOLVER summarizes the final solution based on a multi-factor scoring mechanism. To evaluate our approach, we built two repositories of Java and Android exception-related questions from Stack Overflow with size of 69,478 and 33,566 questions respectively. Our user study results using 50 selected Java crash traces and 50 selected Android crash traces show that our approach significantly outperforms four baselines in terms of relevance, usefulness, and diversity. The evaluation also confirms the effectiveness of the relevant question retrieval component in our approach for crash traces.
Hardware Acceleration for GCNs via Bidirectional Fusion

Derived from the fusion of graph traversal and neural networks, graph convolutional neural networks (GCNs) have achieved state-of-the-art performance in graph learning. However, the hybrid execution pattern, caused by the opposite characteristics of graph traversal based phase and neural network based transformation phase, poses huge challenges to the efficient execution of traditional architectures. Although GCN accelerators have emerged to address these challenges, they fail to harvest both bidirectional execution and inter-phase fusion opportunities exposed by the alternate execution phases in GCNs. Previous works either concentrate on a single execution direction or exchange the execution order of phases without inter-phase fusion, hence failing to further improve performance and efficiency. Therefore, we propose a novel hardware unit named BiFusion, which can be easily applied to existing GCN accelerators with hybrid architecture in order to harvest both of the above opportunities. BiFusion enables dynamic direction selection and inter-phase fusion, and helps significantly reduce the amounts of data access and computation. Experiments show that integrating the BiFusion unit helps the state-of-the-art GCN accelerator achieve 2× speedup on average.
Negational symmetry of quantum neural networks for binary pattern classification

Although quantum neural networks (QNNs) have shown promising results in solving simple machine learning tasks recently, the behavior of QNNs in binary pattern classification is still underexplored. In this work, we find that QNNs have an Achilles’ heel in binary pattern classification. To illustrate this point, we provide a theoretical insight into the properties of QNNs by presenting and analyzing a new form of symmetry embedded in a family of QNNs with full entanglement, which we term negational symmetry. Due to negational symmetry, QNNs can not differentiate between a quantum binary signal and its negational counterpart. We empirically evaluate the negational symmetry of QNNs in binary pattern classification tasks using Google’s quantum computing framework. Both theoretical and experimental results suggest that negational symmetry is a fundamental property of QNNs, which is not shared by classical models. Our findings also imply that negational symmetry is a double-edged sword in practical quantum applications.
Generative Deep-Neural-Network Mixture Modeling with Semi-Supervised MinMax+EM Learning

Deep neural networks (DNNs) for nonlinear generative mixture modeling typically rely on unsupervised learning that employs hard clustering schemes, or variational learning with loose / approximate bounds, or under-regularized modeling. We propose a novel statistical framework for a DNN mixture model using a single generative adversarial network. Our learning formulation proposes a novel data-likelihood term relying on a well-regularized / constrained Gaussian mixture model in the latent space along with a prior term on the DNN weights. Our min-max learning increases the data likelihood using a tight variational lower bound using expectation maximization (EM). We leverage our min-max EM learning scheme for semi-supervised learning. Results on three real-world image datasets demonstrate the benefits of our compact modeling and learning formulation over the state of the art for nonlinear generative image (mixture) modeling and image clustering.
An Investigation of Using Hybrid Modeling Units for Improving End-to-End Speech Recognition System

The acoustic modeling unit is crucial for an end-to-end speech recognition system, especially for the Mandarin language. Until now, most of the studies on Mandarin speech recognition focused on individual units, and few of them paid attention to using a combination of these units. This paper uses a hybrid of the syllable, Chinese character, and subword as the modeling units for the end-to-end speech recognition system based on the CTC/attention multi-task learning. In this approach, the character-subword unit is assigned to train the transformer model in the main task learning stage. In contrast, the syllable unit is assigned to enhance the transformer’s shared encoder in the auxiliary task stage with the Connectionist Temporal Classification (CTC) loss function. The recognition experiments were conducted on AISHELL-1 and an open data set of 1200-hour Mandarin speech corpus collected from the OpenSLR, respectively. The experimental results demonstrated that using the syllable-char-subword hybrid modeling unit can achieve better performances than the conventional units of char-subword, and 6.6% relative CER reduction on our 1200-hour data. The substitution error also achieves a considerable reduction.
Ensembling Object Detectors for Image and Video Data Analysis

In this paper, we propose a method for ensembling the outputs of multiple object detectors for improving detection performance and precision of bounding boxes on image data. We further extend it to video data by proposing a two-stage tracking-based scheme for detection refinement. The proposed method can be used as a standalone approach for improving object detection performance, or as a part of a framework for faster bounding box annotation in unseen datasets, assuming that the objects of interest are those present in some common public datasets.
Fast: Feature Aggregation for Detecting Salient Object in Real-Time

This paper introduces a method named FAST for real-time salient object detection with an extremely efficient CNN architecture. Our proposed network starts from a single lightweight backbone and aggregates discriminative features through network-level and phase-level respectively. Based on the multi-scale feature propagation, FAST substantially reduces the number of parameters, but still obtains sufficient receptive field and enhances the model learning ability, which strikes a balance between the speed and performance. To better preserve object boundaries, we also explore the complementary between salient object information and edge information within our lightweight architecture. Extensive evaluations and analysis demonstrate that the proposed algorithm achieves the leading accuracy performance with real-time speed (186fps) which is significantly faster than the existing state-of-the-art methods.