Reviewerly/exHarmony huggingface dataset:
Phase Extraction from Single Interferogram Including Closed-Fringe Using Deep Learning

In an optical measurement system using an interferometer, a phase extracting technique from interferogram is the key issue. When the object is varying in time, the Fourier-transform method is commonly used since this method can extract a phase image from a single interferogram. However, there is a limitation, that an interferogram including closed-fringes cannot be applied. The closed-fringes appear when intervals of the background fringes are long. In some experimental setups, which need to change the alignments of optical components such as a 3-D optical tomographic system, the interval of the fringes cannot be controlled. To extract the phase from the interferogram including the closed-fringes we propose the use of deep learning. A large amount of the pairs of the interferograms and phase-shift images are prepared, and the trained network, the input for which is an interferogram and the output a corresponding phase-shift image, is obtained using supervised learning. From comparisons of the extracted phase, we can demonstrate that the accuracy of the trained network is superior to that of the Fourier-transform method. Furthermore, the trained network can be applicable to the interferogram including the closed-fringes, which is impossible with the Fourier transform method.
On Usefulness of the Deep-Learning-Based Bug Localization Models to Practitioners

Background: Developers spend a significant amount of time and effort to localize bugs. In the literature, many researchers proposed state-of-the-art bug localization models to help developers localize bugs easily. The practitioners, on the other hand, expect a bug localization tool to meet certain criteria, such as trustworthiness, scalability, and efficiency. The current models are not capable of meeting these criteria, making it harder to adopt these models in practice. Recently, deep-learning-based bug localization models have been proposed in the literature. They show a better performance than the state-of-the-art models.
VitaBoot

VitaBoot is a footwear concept incorporating dynamic graphical patterns which indicate the wearer's activity level. Whilst shoes are an important element in fashion wear and a lot of research has focused on shoes as input devices, comparable few concepts have explored the potential for their use as an output space. We created a boot design that incorporates dynamic patterning through the use of electrochromic (EC) displays embedded in the surface material. The boot was designed and constructed from scratch, using a faux leather material, ensuring the overall aesthetic of the design, including the integration of the required control electronics and power source. The boot connects wirelessly to a chest-worn heart rate belt, and pattern changes indicate when the wearer's heart rate is above a predefined threshold. VitaBoot demonstrates the potential for dynamic shoe patterning for aesthetic or functional means, and the suitability of flexible, low-power EC display technology in this domain.
Achieving Verified Robustness to Symbol Substitutions via Interval Bound Propagation

Neural networks are part of many contemporary NLP systems, yet their empirical successes come at the price of vulnerability to adversarial attacks. Previous work has used adversarial training and data augmentation to partially mitigate such brittleness, but these are unlikely to find worst-case adversaries due to the complexity of the search space arising from discrete text perturbations. In this work, we approach the problem from the opposite direction: to formally verify a system's robustness against a predefined class of adversarial attacks. We study text classification under synonym replacements or character flip perturbations. We propose modeling these input perturbations as a simplex and then using Interval Bound Propagation -- a formal model verification method. We modify the conventional log-likelihood training objective to train models that can be efficiently verified, which would otherwise come with exponential search complexity. The resulting models show only little difference in terms of nominal accuracy, but have much improved verified accuracy under perturbations and come with an efficiently computable formal guarantee on worst case adversaries.
Automated Blood Cell Detection and Counting via Deep Learning for Microfluidic Point-of-Care Medical Devices

Abstract Automated in-vitro cell detection and counting have been a key theme for artificial and intelligent biological analysis such as biopsy, drug analysis and decease diagnosis. Along with the rapid development of microfluidics and lab-on-chip technologies, in-vitro live cell analysis has been one of the critical tasks for both research and industry communities. However, it is a great challenge to obtain and then predict the precise information of live cells from numerous microscopic videos and images. In this paper, we investigated in-vitro detection of white blood cells using deep neural networks, and discussed how state-of-the-art machine learning techniques could fulfil the needs of medical diagnosis. The approach we used in this study was based on Faster Region-based Convolutional Neural Networks (Faster RCNNs), and a transfer learning process was applied to apply this technique to the microscopic detection of blood cells. Our experimental results demonstrated that fast and efficient analysis of blood cells via automated microscopic imaging can achieve much better accuracy and faster speed than the conventionally applied methods, implying a promising future of this technology to be applied to the microfluidic point-of-care medical devices.
Fundamental Limits to Coherent Photon Generation with Solid-State Atomlike Transitions

Coherent generation of indistinguishable single photons is crucial for many quantum communication and processing protocols. Solid-state realizations of two-level atomic transitions or three-level spin-$\Lambda$ systems offer significant advantages over their atomic counterparts for this purpose, albeit decoherence can arise due to environmental couplings. One popular approach to mitigate dephasing is to operate in the weak excitation limit, where excited state population is minimal and coherently scattered photons dominate over incoherent emission. Here we probe the coherence of photons produced using two-level and spin-$\Lambda$ solid-state systems. We observe that the coupling of the atomic-like transitions to the vibronic transitions of the crystal lattice is independent of driving strength and detuning. We apply a polaron master equation to capture the non-Markovian dynamics of the ground state vibrational manifolds. These results provide insight into the fundamental limitations for photon coherence from solid-state quantum emitters, with the consequence that deterministic single-shot quantum protocols are impossible and inherently probabilistic approaches must be embraced.
TDDiscourse: A Dataset for Discourse-Level Temporal Ordering of Events

Prior work on temporal relation classification has focused extensively on event pairs in the same or adjacent sentences (local), paying scant attention to discourse-level (global) pairs.This restricts the ability of systems to learn temporal links between global pairs, since reliance on local syntactic features suffices to achieve reasonable performance on existing datasets.However, systems should be capable of incorporating cues from documentlevel structure to assign temporal relations.In this work, we take a first step towards discourse-level temporal ordering by creating TDDiscourse, the first dataset focusing specifically on temporal links between event pairs which are more than one sentence apart.We create TDDiscourse by augmenting TimeBank-Dense, a corpus of English news articles, manually annotating global pairs that cannot be inferred automatically from existing annotations.Our annotations double the number of temporal links in TimeBank-Dense, while possessing several desirable properties such as focusing on long-distance pairs and not being automatically inferable.We adapt and benchmark the performance of three stateof-the-art models on TDDiscourse and observe that existing systems indeed find discourselevel temporal ordering harder.
A Mixed Reality system for the simulation of emergency and first-aid scenarios

Simulation is a powerful learning tool, as it allows gaining direct experience in a controlled and repeatable way. However, the simulation is effective when it is able to reproduce the real conditions and when the user feels him/herself immersed and present in the situation. With the aim of improving these critical points, we propose an immersive virtual reality system for first-aid handling. Specifically, we increase the visual realism of medical mannequins and the contextualization, and we add the touch feedback by mapping the real mannequin into its virtual representation. Moreover, the interaction is performed by using a virtual representation of the users own hands by allowing a more realistic execution of tasks. The results show a good accuracy in the mapping between the real and the virtual mannequin, and a high degree of presence for both the control group and the medical one. These results and the low values of simulator sickness reported during the experiment are a good starting point for the use of the proposed mixed reality system in simulation scenarios.
ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks

Recently, channel attention mechanism has demonstrated to offer great potential in improving the performance of deep convolutional neural networks (CNNs). However, most existing methods dedicate to developing more sophisticated attention modules for achieving better performance, which inevitably increase model complexity. To overcome the paradox of performance and complexity trade-off, this paper proposes an Efficient Channel Attention (ECA) module, which only involves a handful of parameters while bringing clear performance gain. By dissecting the channel attention module in SENet, we empirically show avoiding dimensionality reduction is important for learning channel attention, and appropriate cross-channel interaction can preserve performance while significantly decreasing model complexity. Therefore, we propose a local cross-channel interaction strategy without dimensionality reduction, which can be efficiently implemented via $1D$ convolution. Furthermore, we develop a method to adaptively select kernel size of $1D$ convolution, determining coverage of local cross-channel interaction. The proposed ECA module is efficient yet effective, e.g., the parameters and computations of our modules against backbone of ResNet50 are 80 vs. 24.37M and 4.7e-4 GFLOPs vs. 3.86 GFLOPs, respectively, and the performance boost is more than 2% in terms of Top-1 accuracy. We extensively evaluate our ECA module on image classification, object detection and instance segmentation with backbones of ResNets and MobileNetV2. The experimental results show our module is more efficient while performing favorably against its counterparts.
Evaluation of Security Vulnerability Scanners for Small and Medium Enterprises Business Networks Resilience towards Risk Assessment

Risk Assessment has been identified as a critical issue in computer infrastructures, especially in medium to large scale organizations and enterprises.The goal of this research report is to provide a virtual machine based framework for testing the performance of vulnerability scanners applied to such enterprises, focused to small and medium size ones.Moreover, the purpose of this paper is to compare three of the most wellknown free vulnerability scanning solutions (Nessus, OpenVAS, Nmap Scripting Engine) in regards to how they can be used to automate the process of Risk Assessment in an organization, based on the herein presented experimental evaluation framework involving virtual machine testing.
Service contract modeling in Enterprise Architecture: An ontology-based approach

Service contracts bind parties legally, regulating their behavior in the scope of a (business) service relationship. Given that there are legal consequences attached to service contracts, understanding the elements of a contract is key to managing services in an enterprise. After all, provisions in a service contract and in legislation establish obligations and rights for service providers and customers that must be respected in service delivery. The importance of service contracts to service provisioning in an enterprise has motivated us to investigate their representation in enterprise models. We have observed that approaches fall into two extremes of a spectrum. Some approaches, such as ArchiMate, offer an opaque “contract” construct, not revealing the rights and obligations in the scope of the governed service relationship. Other approaches, under the umbrella term “contract languages”, are devoted exactly to the formal representation of the contents of contracts. Despite the applications of contract languages, they operate at a level of detail that does not match that of enterprise architecture models. In this paper, we explore and bridge the gap between these two extremes. We address the representation of service contract elements with a systematic approach: we first propose a well-founded service contract ontology, and then extend the ArchiMate language to reflect the elements of the service contract ontology. The applicability of the proposed extension is assessed in the representation of a real-world cloud service contract.
Non-Gaussianity of Stochastic Gradient Noise

What enables Stochastic Gradient Descent (SGD) to achieve better generalization than Gradient Descent (GD) in Neural Network training? This question has attracted much attention. In this paper, we study the distribution of the Stochastic Gradient Noise (SGN) vectors during the training. We observe that for batch sizes 256 and above, the distribution is best described as Gaussian at-least in the early phases of training. This holds across data-sets, architectures, and other choices.
Deep Insights into Convolutional Networks for Video Recognition

Abstract As the success of deep models has led to their deployment in all areas of computer vision, it is increasingly important to understand how these representations work and what they are capturing. In this paper, we shed light on deep spatiotemporal representations by visualizing the internal representation of models that have been trained to recognize actions in video. We visualize multiple two-stream architectures to show that local detectors for appearance and motion objects arise to form distributed representations for recognizing human actions. Key observations include the following. First, cross-stream fusion enables the learning of true spatiotemporal features rather than simply separate appearance and motion features. Second, the networks can learn local representations that are highly class specific, but also generic representations that can serve a range of classes. Third, throughout the hierarchy of the network, features become more abstract and show increasing invariance to aspects of the data that are unimportant to desired distinctions (e.g. motion patterns across various speeds). Fourth, visualizations can be used not only to shed light on learned representations, but also to reveal idiosyncrasies of training data and to explain failure cases of the system.
A Simple and Practical Single-Camera Stereo-Digital Image Correlation Using a Color Camera and X-Cube Prism

A simple and practical full-frame single-camera stereo-digital image correlation (stereo-DIC) technique for three-dimensional (3D) shape, displacement, and deformation measurements is proposed. The technique uses a compact X-cube prism-based color separation device and a color camera to capture images of blue and red colors from different optical paths, and then extracts the surface 3D shape and deformation information of a test sample by processing the captured two sub-channel color images using regular stereo-DIC algorithm. Compared with the existing full-frame single-camera stereo-DICs, the proposed one eliminates the need for a beam splitter and two bandpass filters to capture images, and offers more simple, compact, and easy-to-use optical arrangement. This novel single-camera stereo-DIC technique was validated by a series of baseline experiments involving 3D surface reconstructions, translation tests, and full-field deformation measurements, which provide a new flexible and practical avenue for measuring surface 3D shape and deformation, particularly in microscopic and high-speed applications.
Tweet Sentiment Analysis (TSA) for Cloud Providers Using Classification Algorithms and Latent Semantic Analysis

The availability and advancements of cloud computing service models such as IaaS, SaaS, and PaaS; introducing on-demand self-service, auto scaling, easy maintenance, and pay as you go, has dramatically transformed the way organizations design and operate their datacenters.However, some organizations still have many concerns like: security, governance, lack of expertise, and migration.The purpose of this paper is to discuss the cloud computing customers' opinions, feedbacks, attitudes, and emotions towards cloud computing services using sentiment analysis.The associated aim, is to help people and organizations to understand the benefits and challenges of cloud services from the general public's perspective view as well as opinions about existing cloud providers, focusing on three main cloud providers: Azure, Amazon Web Services (AWS) and Google Cloud.The methodology used in this paper is based on sentiment analysis applied to the tweets that were extracted from social media platform (Twitter) via its search API.We have extracted a sample of 11,000 tweets and each cloud provider has almost similar proportion of the tweets based on relevant hashtags and keywords.Analysis starts by combining the tweets in order to find the overall polarity about cloud computing, then breaking the tweets to find the specific polarity for each cloud provider.Bing and NRC Lexicons are employed to measure the polarity and emotion of the terms in the tweets.The overall polarity classification of the tweets across all cloud providers shows 68.5% positive and 31.5% negative percentages.More specifically, Azure shows 63.8% positive and 36.2%negative tweets, Google Cloud shows 72.6% positive and 27.4% negative tweets and AWS shows 69.1% positive and 30.9% negative tweets.
Improving Pre-Trained Multilingual Model with Vocabulary Expansion

Recently, pre-trained language models have achieved remarkable success in a broad range of natural language processing tasks.However, in multilingual setting, it is extremely resource-consuming to pre-train a deep language model over large-scale corpora for each language.Instead of exhaustively pre-training monolingual language models independently, an alternative solution is to pre-train a powerful multilingual deep language model over large-scale corpora in hundreds of languages.However, the vocabulary size for each language in such a model is relatively small, especially for low-resource languages.This limitation inevitably hinders the performance of these multilingual models on tasks such as sequence labeling, wherein in-depth token-level or sentence-level understanding is essential.In this paper, inspired by previous methods designed for monolingual settings, we investigate two approaches (i.e., joint mapping and mixture mapping) based on a pre-trained multilingual model BERT for addressing the out-of-vocabulary (OOV) problem on a variety of tasks, including part-of-speech tagging, named entity recognition, machine translation quality estimation, and machine reading comprehension.Experimental results show that using mixture mapping is more promising.To the best of our knowledge, this is the first work that attempts to address and discuss the OOV issue in multilingual settings.
Recognizing lines of code violating company-specific coding guidelines using machine learning

Abstract Software developers in big and medium-size companies are working with millions of lines of code in their codebases. Assuring the quality of this code has shifted from simple defect management to proactive assurance of internal code quality. Although static code analysis and code reviews have been at the forefront of research and practice in this area, code reviews are still an effort-intensive and interpretation-prone activity. The aim of this research is to support code reviews by automatically recognizing company-specific code guidelines violations in large-scale, industrial source code. In our action research project, we constructed a machine-learning-based tool for code analysis where software developers and architects in big and medium-sized companies can use a few examples of source code lines violating code/design guidelines (up to 700 lines of code) to train decision-tree classifiers to find similar violations in their codebases (up to 3 million lines of code). Our action research project consisted of (i) understanding the challenges of two large software development companies, (ii) applying the machine-learning-based tool to detect violations of Sun’s and Google’s coding conventions in the code of three large open source projects implemented in Java, (iii) evaluating the tool on evolving industrial codebase, and (iv) finding the best learning strategies to reduce the cost of training the classifiers. We were able to achieve the average accuracy of over 99% and the average F-score of 0.80 for open source projects when using ca. 40K lines for training the tool. We obtained a similar average F-score of 0.78 for the industrial code but this time using only up to 700 lines of code as a training dataset. Finally, we observed the tool performed visibly better for the rules requiring to understand a single line of code or the context of a few lines (often allowing to reach the F-score of 0.90 or higher). Based on these results, we could observe that this approach can provide modern software development companies with the ability to use examples to teach an algorithm to recognize violations of code/design guidelines and thus increase the number of reviews conducted before the product release. This, in turn, leads to the increased quality of the final software.