Reviewerly/exHarmony huggingface dataset:
Ethnographic study of a commercially available augmented reality HMD app for industry work instruction

Industrial applications of Augmented Reality (AR) are becoming increasingly commonplace but there are only a small number of published user studies examining the use of commercially available AR technologies, like AR HMDs, with real workers in real industry settings. This paper presents ethnographic research of an industry task that includes the context of the industry procedure, pain-points with current methods and a user experience study of an HMD-delivered AR application for delivering work instructions to support engineers performing the procedure. The AR application is delivered to engineers with different levels of experience through a commercially-available AR HMD (the DAQRI Smart Glasses®). Engineers (users) were observed and video recorded by researchers as they performed the procedure in the real-world setting of a sprinkler room of a hospital in the Netherlands. Engineers who used AR were found to deviate less from the correct procedure in comparison to an engineer who performed sprinkler maintenance using the current industry method, without AR instruction. Errors made by engineers on the procedure, together with semi-structured interview responses, shed light on customer pain points that AR can alleviate, useful UX/UI design considerations, barriers to adoption and insights for informing larger scale user evaluations of industry AR from maintenance to manufacturing.
Trust Mends Blockchains: Living up to Expectations

At the heart of Blockchains is the trustless leader election mechanism for achieving consensus among pseudo-anonymous peers, without the need of oversight from any third party or authority whatsoever. So far, two main mechanisms are being discussed: proof-of-work (PoW) and proof-of-stake (PoS). PoW relies on demonstration of computational power, and comes with the markup of huge energy wastage in return of the stake in cyrpto-currency. PoS tries to address this by relying on owned stake (i.e., amount of crypto-currency) in the system. In both cases, Blockchains are limited to systems with financial basis. This forces non-crypto-currency Blockchain applications to resort to "permissioned" setting only, effectively centralizing the system. However, non-crypto-currency permisionless blockhains could enable secure and self-governed peer-to-peer structures for numerous emerging application domains, such as education and health, where some trust exists among peers. This creates a new possibility for valuing trust among peers and capitalizing it as the basis (stake) for reaching consensus. In this paper we show that there is a viable way for permisionless non-financial Blockhains to operate in completely decentralized environments and achieve leader election through proof-of-trust (PoT). In our PoT construction, peer trust is extracted from a trust network that emerges in a decentralized manner and is used as a waiver for the effort to be spent for PoW, thus dramatically reducing total energy expenditure of the system. Furthermore, our PoT construction is resilient to the risk of small cartels monopolizing the network (as it happens with the mining-pool phenomena in PoW) and is not vulnerable to sybils. We evluate security guarantees, and perform experimental evaluation of our construction, demonstrating up to 10-fold energy savings compared to PoW without trading off any of the decentralization characteristics, with further guarantees against risks of monopolization.
Review of Camera Calibration Algorithms

Camera calibration is used to establish a mathematical model and solve the parameters of the camera through the correspondence between a series of scene points and pixel points. How to establish this mapping relationship is a key issue that needs to be solved in camera calibration. Various algorithms of calibration have been proposed by domestic and foreign scholars, including traditional visual calibration algorithm, camera self-calibration algorithm, and active-vision-based calibration algorithm. This paper focuses on some of the most widely used camera calibration algorithms and compares them.
Learning a Deep Convolutional Network for Speckle Noise Reduction in Underwater Sonar Images

Underwater sonar imaging system has been widely utilized to detect and identify the submerged objects of interest. However, imaging quality often suffers from the undesirable signal-dependent speckle noise during signal acquisition and transmission. The speckle noise will restrict the practical applications, such as object detection, tracking and recognition, etc. To enhance the sonar imaging performance, we propose a deep learning approach to directly estimate the speckle noise in logarithmic domain based on the convolutional neural network. Once the speckle noise is obtained, the latent sharp image can then be easily calculated according to the image degradation model. The patch-based loss function, i.e., structural similarity metric, is adopted to preserve the important geometrical structures during speckle noise reduction. Experiments have been implemented on different noise levels to demonstrate the effectiveness of the proposed deep learning approach. Experimental results have illustrated that it outperforms several widely-used speckle noise reduction methods.
File-level socio-technical congruence and its relationship with bug proneness in OSS projects

Coordination is important in software development. Socio-Technical Congruence (STC) is proposed to measure the match between coordination requirements and actual coordination activities. The previous work of Cataldo et al. computes STC in commercial projects and finds it related to software failures. In this paper, we study the relationship between file-level STC and bug proneness in Open Source Software (OSS) projects. We apply the fundamental STC framework to the OSS data setting and present a method of computing file-level STC based on our available data. We also propose a derivative STC metric called Missing Developer Links (MDL), which is to measure the amount of coordination breakdowns. In our empirical analysis on five OSS projects, we find that MDL is more related to bug proneness than STC. Furthermore, STC or MDL can be computed based on different types of file networks and developer networks, and we find out the best file network and the best developer network via an empirical study. We also evaluate the usefulness of STC or MDL metrics in bug prediction. This work is promising to help detect coordination issues in OSS projects.
Learning to Find Unpaired Cross-Spectral Correspondences

We present a deep architecture and learning framework for establishing correspondences across cross-spectral visible and infrared images in an unpaired setting. To overcome the unpaired cross-spectral data problem, we design the unified image translation and feature extraction modules to be learned in a joint and boosting manner. Concretely, the image translation module is learned only with the unpaired cross-spectral data, and the feature extraction module is learned with an input image and its translated image. By learning two modules simultaneously, the image translation module generates the translated image that preserves not only the domain-specific attributes with separate latent spaces but also the domain-agnostic contents with feature consistency constraint. In an inference phase, the cross-spectral feature similarity is augmented by intra-spectral similarities between the features extracted from the translated images. Experimental results show that this model outperforms the state-of-the-art unpaired image translation methods and cross-spectral feature descriptors on various visible and infrared benchmarks.
Edge Based Image Steganography: Problems and Solution

Steganography means hiding secrete message in cover object in a way that no suspicious from the attackers, the most popular steganography schemes is image steganography. A very common questions that asked in the field are: 1- what is the embedding scheme used?, 2- where is (location) the secrete messages are embedded?, and 3- how the sender will tell the receiver about the locations of the secrete message?. Here in this paper we are deal with and aimed to answer questions number 2 and 3. We used the popular scheme in image steganography which is least significant bits for embedding in edges positions in color images. After we separate the color images into its components Red, Green, and Blue, then we used one of the components as an index to find the edges, while other one or two components used for embedding purpose. Using this technique we will guarantee the same number and positions of edges before and after embedding scheme, therefore we are guaranteed extracting the secrete message as it's without any loss of secrete messages bits.
Crowd counting using a self‐attention multi‐scale cascaded network

Recent developments of crowd analysis and behaviour prediction have attracted much attention. Crowd counting, as the essential and challenging task in crowd analysis, is riddled with many issues, such as large scale variations, serious occlusion, and so on. In this study, a self‐attention‐based multi‐scale cascaded network called SAMC‐Net to estimate density map for crowd counting, especially for high congested scene, is proposed. The proposed SAMC‐Net consists of two components: a classification sub‐network for density estimation and an end‐to‐end multi‐scale convolution neural network for crowd counting. In order to reduce the negative effect of multi‐scale issue on crowd counting task, the main network is designed as a multi‐scale structure similar to U‐Net. In order to enhance the crowd feature representation, this study proposes a self‐attention‐based crowd feature extraction way and uses it in the proposed SAMC‐Net . Extensive experiments demonstrate the feasibility, effectiveness and robustness of the proposed SAMC‐Net .
Bidirectional-GRU Based on Attention Mechanism for Aspect-level Sentiment Analysis

Aspect-level sentiment analysis is a fine-grained natural language processing task. For traditional deep learning models, they cannot accurately construct the aspect-level sentiment features. Such as, for the sentence of "the movie is very funny, but the seats in the theater is uncomfortable." For the movie, the polarity is positive, but it is negative for seats. To deal with this problem, we propose a bidirectional gated recurrent units neural network model that integrates the attention mechanism to solve the task of aspect-level sentiment analysis. The attention mechanism can focus on the different parts of a sentence when the sentence has several different aspects. Because we use a bidirectional gated recurrent unit, we can get independent context semantic information and get the deeper aspect sentiment information from the front and back, so that we can deal with the specific aspect sentiment polarity. Finally, we experiment on SemEval-2014 dataset and twitter dataset, the result of experiments verified the effectiveness of attention-based bidirectional gated recurrent unit on the aspect sentiment analysis. The model achieves good performance at different datasets and has further improvement comparing to previous models.
(U-Th)/He-dating of ferruginous duricrust: Insight into laterite formation at Boddington, WA

Geochronological characterization of pisolites and nodules in ferruginous duricrust from laterite profiles exposed near the Boddington gold mine (BGM) in Western Australia was undertaken by application of (UTh)/He dating. Optical microscopy, Raman spectroscopy, X-ray diffraction (XRD) analysis and TESCAN Integrated Mineral Analyser (TIMA) mineral mapping identified areas in pisolites and nodules in pisolitic and fragmental duricrust suitable for (UTh)/He-dating. (UTh)/He dates of 5.8–1.3 Ma for pisolites and fragmental duricrust nodules indicate late-Miocene/Pliocene to possible early-Pleistocene ages as a minimum age for the formation of pisolites and nodules and a maximum age for formation of the BGM duricrust. Averaged (UTh)/He ages suggest at least two main episodes in the evolution of pisolite formation, the oldest occurring at 5.8–5.2 Ma and a younger episode at 3.9–3.2 Ma, with indication of an even later episode of pisolite formation at 2.3–2.0 Ma. These results are comparable with (UTh)/He data for pisolitic duricrust approximately 50 km to the east of Perth (Toodyay), and are consistent with other age estimates for lateritization in Western Australia, suggesting that processes of regolith formation and/or modification in the Darling Range, to the east and south-west of Perth, were regionally synchronous. The significant difference in age of the rim (2.0 ± 0.2 Ma) compared to the core (3.9 ± 0.5 Ma) of a single pisolite, in conjunction with a difference in mineralogy and textural evidence, suggest that ancient, episodic bushfires may have affected or reset the (UTh)/He system of iron oxides. Despite this, geochronological characterization can provide quantitative constraints on the age and relative timing of regolith formation.
Arabic Text Classification: A Comparative Approach Using a Big Dataset

Text classification is the process of categorizing text documents to a predefined group of categories. This paper aims to provide experimental evaluations of six well-known classification models in classifying a large Arabic corpus. These models are Nave Bayes (NB), Support Vector Machine (SVM), Random Forest, Logistic Regression, Decision Tree (DT), and Stochastic Gradient Descent (SGD). We used a corpus consisting of 111,728 Arabic documents that fall into five categories: sport, culture, economy, news, and diverse. Three performance metrics were applied to evaluate the experimental results of each model. The experimental results show that the Logistic Regression model scores the highest weighted F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> score, followed by SGD, SVM, NB, Random Forest, and DT. The experiments also show that both feature size and corpus size have high impacts on the performance of the models.
Timesaving phase retrieval approach based on difference map normalization and fast iterative algorithm

To achieve high measurement accuracy with less computational time in phase shifting interferometry, a random phase retrieval approach based on difference map normalization and fast iterative algorithm (DN&FIA) is proposed, it doesn't need pre-filtering, and has the advantage of the iterative algorithms-high accuracy, moreover, it also has the advantage of non-iterative algorithms-timesaving, it only needs three randomly phase shifted interferograms, and the initial phase shifts of the iteration can be random, last but not least, it is effective for the circular, straight or complex fringes. The simulations and experiments verify the correctness and feasibility of DN&FIA.
SOD‐CED: salient object detection for noisy images using convolution encoder–decoder

During the last decade, there has been profound progress in the field of visual saliency. However, there still exist various major challenges that hinder the detection performance for scenes with complex composition, presence of additive noise, objects of diverse scale and rotations etc. Generally, images with additive noise have low spatial resolution and blurred edges, which affects the learning capability of the network and causes inaccurate detection. In order to address these issues, in this study, the authors propose a fully convolutional neural network which jointly denoise the input maps by learning edges and contrast details, followed by learning of residing salient details via colour spatial maps in an end‐to‐end fashion. Their framework employs convolutional layers that use gradient and contrast details of images to denoise the areas with high edge density. After denoising, the denoised images are subjected to salient object detection (SOD) using convolutional layers. The effectiveness of the proposed network is evaluated on benchmark datasets. The experimental results demonstrate the significant performance improvement of the proposed method over state‐of‐the‐art detection techniques.
Learning Temporal Specifications from Imperfect Traces Using Bayesian Inference

Verification is essential to prevent malfunctioning of software systems. Model checking allows to verify conformity with nominal behavior. As manual definition of specifications from such systems gets infeasible, automated techniques to mine specifications from data become increasingly important. Existing approaches produce specifications of limited lengths, do not segregate functions and do not easily allow to include expert input. We present BaySpec, a dynamic mining approach to extract temporal specifications from Bayesian models, which represent behavioral patterns. This allows to learn specifications of arbitrary length from imperfect traces. Within this framework we introduce a novel extraction algorithm that for the first time mines LTL specifications from such models.
LQ Non-Gaussian Regulator With Markovian Control

This letter concerns the linear quadratic non-Gaussian (LQnG) sub-optimal control problem when the input signal travels through an unreliable network, namely a Gilbert-Elliot channel. In particular, the control input packet losses are modeled by a two-state Markov chain with known transition probability matrix, and we assume that the moments of the non-Gaussian noise sequences up to the fourth order are known. By mean of a suitable rewriting of the system through an output injection term, and by considering an augmented system with the second-order Kronecker power of the measurements, a simple solution is provided by substituting the Kalman predictor of the LQG control law with a quadratic optimal predictor. Numerical simulations show the effective ness of the proposed method.
Explore and Exploit. Dictionary Expansion with Human-in-the-Loop

Many Knowledge Extraction systems rely on semantic resources - dictionaries, ontologies, lexical resources - to extract information from unstructured text. A key for successful information extraction is to consider such resources as evolving artifacts and keep them up-to-date. In this paper, we tackle the problem of dictionary expansion and we propose a human-in-the-loop approach: we couple neural language models with tight human supervision to assist the user in building and maintaining domain-specific dictionaries. The approach works on any given input text corpus and is based on the explore and exploit paradigm: starting from a few seeds (or an existing dictionary) it effectively discovers new instances (explore) from the text corpus as well as predicts new potential instances which are not in the corpus, i.e. “unseen”, using the current dictionary entries (exploit). We evaluate our approach on five real-world dictionaries, achieving high accuracy with a rapid expansion rate.
An embedded framework enabling access of elderly and disabled persons to IP-based emergency communications

Being able to perform promptly and accurate emergency calls is crucial for every human life. However, people with impairments feel precluded when there is need to have access to emergency centers. The main reason is the absence of IP-based emergency systems triggered by assistive devices and body area networks. To guarantee that anyone can benefit from emergency systems, it is essential to enable embedded devices to trigger calls to emergency centers and support IP-based real time communication. To achieve this, the present paper extends SEEK framework (Andriopoulou et al., 2018), an embedded framework consisting of environmental, body sensors and a haptic device. SEEK framework enables elderly and disabled people who live independent to perform emergency calls. Sensors embedded in SEEK, aggregate environmental data and vital signs that monitor user's current health status 7and automatically trigger emergency calls when an emergency occurs, providing continuous real-time sensor data. The haptic device provides further modalities to the end user (disabled person or elderly) supporting audio, media and text communication with first responders and healthcare providers at emergency centers. A prototype implementation and initial results are presented as a proof of concept for deaf and speech impaired people enabling them to report emergency events and establish real time communication with first responders. The results depict that even lower capability hardware platforms may be used in order to minimize the cost and increase usability by minimizing power consumption of such an embedded framework.
Robust Learning with Implicit Residual Networks

In this effort, we propose a new deep architecture utilizing residual blocks inspired by implicit discretization schemes. As opposed to the standard feed-forward networks, the outputs of the proposed implicit residual blocks are defined as the fixed points of the appropriately chosen nonlinear transformations. We show that this choice leads to the improved stability of both forward and backward propagations, has a favorable impact on the generalization power and allows to control the robustness of the network with only a few hyperparameters. In addition, the proposed reformulation of ResNet does not introduce new parameters and can potentially lead to a reduction in the number of required layers due to improved forward stability. Finally, we derive the memory-efficient training algorithm, propose a stochastic regularization technique and provide numerical results in support of our findings.
Cyber Social Media Analytics and Issues: A Pragmatic Approach for Twitter Sentiment Analysis

Social media analytics is one of the most important forms of data analytics in current scenario of cyber world. To leverage the use of social media analytics for branding and image building and its negative impact on misuse of such data for scamming and defamation activities, it is necessary to understand the content posted on social media and adopt comprehensive analytical techniquesSharma, Sanur to predict and evaluate relevant information. This paperJain, Anurag discusses social media analytics at data level, its challenges and issues that arise in analysis of social media data at large. It also presents a study on analysis of twitter data, where tweets are collected in real time and machine learning and lexicon-based techniques are applied to understand the sentiments and emotions behind the text that is posted on social media. The results efficiently classify the social media users opinions into positive, negative and neutral to better understand their perspectives.
Automatic Assertion Generation from Natural Language Specifications Using Subtree Analysis

We present an approach to generate assertions from natural language specifications by performing semantic analysis of sentences in the specification document. Other techniques for automatic assertion generation use information found in the design implementation, either by performing static or dynamic analysis. Our approach generates assertions directly from the specification document, so bugs in the implementation will not be reflected in the assertions. Our approach parses each sentence and examines the resulting syntactic parse trees to locate subtrees which are associated with important phrases, such as the antecedent and consequent of an implication. Formal assertions are generated using the information inside these subtrees to fill a set of assertion templates which we present. We evaluate the effectiveness of our approach using a set of statements taken from a real specification document.