Reviewerly/exHarmony huggingface dataset:
Comprehensive Data Set for Automatic Single Camera Visual Speed Measurement

In this paper, we focus on traffic camera calibration and a visual speed measurement from a single monocular camera, which is an important task of visual traffic surveillance. Existing methods addressing this problem are difficult to compare due to a lack of a common data set with reliable ground truth. Therefore, it is not clear how the methods compare in various aspects and what factors are affecting their performance. We captured a new data set of 18 full-HD videos, each around 1 hr long, captured at six different locations. Vehicles in the videos (20 865 instances in total) are annotated with the precise speed measurements from optical gates using LiDAR and verified with several reference GPS tracks. We made the data set available for download and it contains the videos and metadata (calibration, lengths of features in image, annotations, and so on) for future comparison and evaluation. Camera calibration is the most crucial part of the speed measurement; therefore, we provide a brief overview of the methods and analyze a recently published method for fully automatic camera calibration and vehicle speed measurement and report the results on this data set in detail.
DETERMINANTS OF CLOUD COMPUTING ADOPTION BY ROMANIAN SMES IN THE DIGITAL ECONOMY

The purpose of this paper is to explore the process of Cloud Computing adoption within Romanian Small and Medium sized Enterprises in the digital age. Most of the scientific papers related to this topic examine the general outlook of cloud computing implementation in Romania and focus on the use of the afore-mentioned technologies by large scale companies. In order to identify the factors that have an influence over the choice of Romanian SMEs to adopt cloud computing technology, a conceptual model has been set forth. The model’s dependent variable is the choice of the company’s management to implement cloud computing technologies. Five different independent types of variable structures which have an effect on the adoption of cloud computing within SMEs were identified. The five categories of variables are competition &amp; government, ICT providers &amp; firm departments, employee, manager and technological factors. Each of these categories of determinants encompasses at their turn two or more constructs. An online survey tested the model based on the responses of managers and directors from 198 Romanian SMEs from the cities of Bucharest and Cluj. The validity of the model was performed by completing factor analysis and reliability tests of the data; a logistic regression analysis was deployed to test the research hypotheses. The results emphasized that the managers’ know-how on cloud computing and the perceived costs of implementing the technology represent the main components influencing the spreading of cloud computing among Romanian SMEs. This study makes a valuable contribution to the academic environment but to businesses as well. The model can be used to examine the implementation of different innovative applications. Companies which offer cloud computing services could take advantage of the outcomes of this study to expand the degree of cloud service introduction among SMEs.
On Comparing and Classifying Several Independent Linear and Non-Linear Regression Models with Symmetric Errors

In many real world problems, science fields such as biology, computer science, data mining, electrical and mechanical engineering, and signal processing, researchers aim to compare and classify several regression models. In this paper, a computational approach, based on the non-parametric methods, is used to investigate the similarities, and to classify several linear and non-linear regression models with symmetric errors. The ability of each given approach is then evaluated using simulated and real world practical datasets.
Opinion spam detection framework using hybrid classification scheme

With the advent of social networking sites, opinion-mining applications have attracted the interest of the online community on review sites to know about products for their purchase decisions. However, due to increasing trend of posting spam (fake) reviews to promote the target products or defame the specific brands of competitors, Opinion Spam detection and classification has emerged as a hot issue in the community of opinion mining and sentiment analysis. We investigate the issue of Opinion Spam detection by using different combinations of entities, features, and their sentiment scores. We enrich the feature set of a baseline Spam detection method with Spam detection features (Opinion Spam, Opinion Spammer, Item Spam). Using a dataset of reviews from the Amazon site and sentences labeled for Spam detection, we evaluate the role of spamicity-related features in detecting and classifying spam (fake) clues and distinguishing them from genuine reviews. For this purpose, we introduce a rule-based feature weighting scheme and propose a method for tagging the review sentence as spam and non-spam. Experiments results depict that spam-related features improve Spam detection in review sentences posted on product review sites. Adding a revised feature weighting scheme achieved an accuracy increase from 93 to 96%. Furthermore, a hybrid set of features are shown to improve the performance of Opinion Spam detection in terms of better precision, recall, and F-measure values. This work shows that combining spam-related features with rule-based weighting scheme can improve the performance of even baseline Spam detection method. This improvement can be of use to Opinion Spam detection systems, due to the growing interest of individuals and companies in isolating fake (spam) and genuine (non-spam) reviews about products. The outcome of this work will provide an insight into spam-related features and feature weighting and will assist in developing more advanced applications for Opinion Spam detection. In the field of Opinion Spam detection, previous state-of-the-art studies used less number of spamicity-related features and less efficient feature weighting scheme. However, we provided a revised feature selection and a revised feature weighting scheme with normalized spamicity score computation technique. Therefore, our contribution is novel to the field because it provides a significant improvement over the comparing methods.
Convolutional neural network and multi‐feature fusion for automatic modulation classification

Automatic modulation classification (AMC) lies at the core of cognitive radio and spectrum sensing. In this Letter, the authors propose a novel convolutional neural network (CNN)-based AMC method with multi-feature fusion. First, the modulation signals are transformed into two image representations of cyclic spectra (CS) and constellation diagram (CD), respectively. Then, a two-branch CNN model is developed, a gradient decent strategy is adopted and a multi-feature fusion technique is exploited to integrate the features learned from CS and CD. The proposed method is computationally efficient, benefited from its simple neural network. Experimental results show that the proposed method can achieve identical or better results with much reduced learned parameters and training time, compared with the state-of-the-art deep learning-based methods.
Classification of Phishing Attack Solutions by Employing Deep Learning Techniques: A Systematic Literature Review

Phishing is the technique by which the attacker tries to obtain confidential information from the user, with the purpose of using it fraudulently. These days, three ways to mitigate such attacks stand out: Focus based on awareness, based on blacklists, and based on machine learning (ML). However, in the last days, Deep Learning (DL) has emerged as one of the most efficient techniques of machine learning. Thus, this systematic literature review has been aimed to offer to other researchers, readers and users, an analysis of a variety of proposals of other researchers how to face these attacks, applying Deep Learning algorithms. Some of the contributions of the current study include a synthesis of each selected work and the classification of anti-phishing solutions through its approach, obtaining that the uniform resource locator (URL)-oriented approach is the most used. Furthermore, we have been able to classify the Deep Learning algorithms selected in each solution, which yielded that the most commonly used are the deep neural network (DNN) and convolutional neural network (CNN), among other fundamental data.
Can Refactoring Be Self-Affirmed? An Exploratory Study on How Developers Document Their Refactoring Activities in Commit Messages

Refactoring is a critical task in software maintenance and is usually performed to enforce best design practices, or to cope with design defects. Previous studies heavily rely on defining a set of keywords to identify refactoring commits from a list of general commits extracted from a small set of software systems. All approaches thus far consider all commits without checking whether refactorings had actually happened or not. In this paper, we aim at exploring how developers document their refactoring activities during the software life cycle. We call such activity Self-Affirmed Refactoring, which is an indication of the developer-related refactoring events in the commit messages. Our approach relies on text mining refactoring-related change messages and identifying refactoring patterns by only considering refactoring commits. We found that (1) developers use a variety of patterns to purposefully target refactoring-related activities; (2) developers tend to explicitly mention the improvement of specific quality attributes and code smells; and (3) commit messages with self-affirmed refactoring patterns tend to have more significant refactoring activity than those without.
A Spatiotemporal Convolutional Neural Network for Automatic Pain Intensity Estimation from Facial Dynamics

Devising computational models for detecting abnormalities reflective of diseases from facial structures is a novel and emerging field of research in automatic face analysis. In this paper, we focus on automatic pain intensity estimation from faces. This has a paramount potential diagnosis values in healthcare applications. In this context, we present a novel 3D deep model for dynamic spatiotemporal representation of faces in videos. Using several convolutional layers with diverse temporal depths, our proposed model captures a wide range of spatiotemporal variations in the faces. Moreover, we introduce a cross-architecture knowledge transfer technique for training 3D convolutional neural networks using a pre-trained 2D architecture. This strategy is a practical approach for training 3D models, especially when the size of the database is relatively small. Our extensive experiments and analysis on two benchmarking and publicly available databases, namely the UNBC-McMaster shoulder pain and the BioVid, clearly show that our proposed method consistently outperforms many state-of-the-art methods in automatic pain intensity estimation.
Sensors and Sensing Practices: Reworking Experience across Entities, Environments, and Technologies

This editorial examines how sensing practices are transforming through proliferating sensor technologies and altered sensing relations. Rather than engage with sensing as a project of the human mind or body as usually delineated within sensory classifications, this overview of sensors and sensing practices documents how sensing entities are emerging that are composed of shifting ensembles of multiple humans and more-than-humans, environments and technologies, politics and practices. By decoupling sensing from its exclusive human orientation, the editorial and collection demonstrate how reworked approaches to sensing make it possible to tune in to how involvement with environmental problems unfolds and endures. The collection asks how sensing practices might be crafted that attend to the distributed and accumulative inequalities of environmental problems and to speculate toward differential collectives for addressing environmental crisis and change.
Machine Learning Meets Quantitative Planning: Enabling Self-Adaptation in Autonomous Robots

Modern cyber-physical systems (e.g., robotics systems) are typically composed of physical and software components, the characteristics of which are likely to change over time. Assumptions about parts of the system made at design time may not hold at run time, especially when a system is deployed for long periods (e.g., over decades). Self-adaptation is designed to find reconfigurations of systems to handle such run-time inconsistencies. Planners can be used to find and enact optimal reconfigurations in such an evolving context. However, for systems that are highly configurable, such planning becomes intractable due to the size of the adaptation space. To overcome this challenge, in this paper we explore an approach that (a) uses machine learning to find Pareto-optimal configurations without needing to explore every configuration and (b) restricts the search space to such configurations to make planning tractable. We explore this in the context of robot missions that need to consider task timeliness and energy consumption. An independent evaluation shows that our approach results in high-quality adaptation plans in uncertain and adversarial environments.
Feature Enhancement in Medical Ultrasound Videos Using Contrast-Limited Adaptive Histogram Equalization

Speckle noise reduction algorithms are extensively used in the field of ultrasound image analysis with the aim of improving image quality and diagnostic accuracy. However, significant speckle filtering induces blurring, and this requires the enhancement of features and fine details. We propose a novel framework for both multiplicative noise suppression and robust contrast enhancement and demonstrate its effectiveness using a wide range of clinical ultrasound scans. Our approach to noise suppression uses a novel algorithm based on a convolutional neural network that is first trained on synthetically modeled ultrasound images and then applied on real ultrasound videos. The feature improvement stage uses an improved contrast-limited adaptive histogram equalization (CLAHE) method for enhancing texture features, contrast, resolvable details, and image structures to which the human visual system is sensitive in ultrasound video frames. The proposed CLAHE algorithm also considers an automatic system for evaluating the grid size using entropy, and three different target distribution functions (uniform, Rayleigh, and exponential), and interpolation techniques (B-spline, cubic, and Lanczos-3). An extensive comparative study has been performed to find the most suitable distribution and interpolation techniques and also the optimal clip limit for ultrasound video feature enhancement after speckle suppression. Subjective assessments by four radiologists and experimental validation using three quality metrics clearly indicate that the proposed framework generates superior performance compared with other well-established methods. The processing pipeline reduces speckle effectively while preserving essential information and enhancing the overall visual quality and therefore could find immediate applications in real-time ultrasound video segmentation and classification algorithms.
A Benchmark for Deep Learning Based Object Detection in Maritime Environments

Object detection in maritime environments is a rather unpopular topic in the field of computer vision. In contrast to object detection for automotive applications, no sufficiently comprehensive public benchmark exists. In this paper, we propose a benchmark that is based on the Singapore Maritime Dataset (SMD). This dataset provides Visual-Optical (VIS) and Near Infrared (NIR) videos along with annotations for object detection and tracking. We analyze the utilization of deep learning techniques and therefore evaluate two state-of-the-art object detection approaches for their applicability in the maritime domain: Faster R-CNN and Mask R-CNN. To train the Mask R-CNN including the instance segmentation branch, a novel algorithm for automated generation of instance segmentation labels is introduced. The obtained results show that the SMD is sufficient to be used for domain adaptation. The highest f-score is achieved with a fine-tuned Mask R-CNN. This is a benchmark that encourages reproducibility and comparability for object detection in maritime environments.
The Effects of Modality, Device, and Task Differences on Perceived Human Likeness of Voice-Activated Virtual Assistants

Paying attention to the rising popularity of virtual assistants (VAs) that offer unique user experiences through voice-centered interaction, this study examined the effects of modality, device, and task differences on perceived human likeness of, and attitudes toward, voice-activated VAs. To do so, a 2 (modality: voice vs. text) × 2 (device: mobile vs. laptop) × 2 (task type: hedonic vs. utilitarian) mixed factorial experimental design was employed. Findings suggest that voice (vs. text) interaction leads to more positive attitudes toward the VA system mediated by heightened perceived human likeness of the VA, but only with utilitarian (vs. hedonic) tasks. Interestingly, laptop (vs. mobile phone) interaction also enhanced perceived human likeness of the VA. This study offers theoretical and practical implications for VA research by exploring the combinational effects of modality, device, and task differences on user perceptions through human-like interactions.
Passively stable distribution of polarisation entanglement over 192 km of deployed optical fibre

Quantum key distribution (QKD) based on entangled photon pairs holds the potential for repeater-based quantum networks connecting clients over long distance. We demonstrate long-distance entanglement distribution by means of polarisation-entangled photon pairs through two successive deployed 96 km-long telecommunications fibres in the same submarine cable. One photon of each pair was detected directly after the source, while the other travelled the fibre cable in both directions for a total distance of 192 km and attenuation of 48 dB. The observed two-photon Bell state exhibited a fidelity 85% $\pm$ 2% and was stable over several hours. We employed neither active stabilisation of the quantum state nor chromatic dispersion compensation for the fibre.
Comparison between MRM simulations, CAMS and PVGIS databases with measured solar radiation components at the Methoni station, Greece

This study examines the performance of the estimated solar radiation components obtained via the Meteorological Radiation Model, satellite-based data sets (CAMS, PVGIS-CMSAF-SARAH) and reanalysis (PVGIS-ERA5) against ground measurements taken with the Sunshine-Pyranometer at Methoni station, Greece. MRM shows satisfactory simulations for the global solar irradiation (R2 = 0.97, RMSE = 11.5%, MBE = −2.5%) at 15-min time-intervals, while for the diffuse larger biases are found (R2 = 0.57, RMSE = 45%). Solar irradiation estimates via CAMS at 15-min intervals reveal RMSE values of 19.5%, 38% and 28% for the global, diffuse and direct radiations, respectively. Biases are progressively reduced for hourly, daily and monthly data sets. PVGIS databases simulate the global irradiance reasonably well (R2 = 0.82–0.92), exhibiting high uncertainties for the diffuse (R2 = 0.39–0.49) and direct (R2 = 0.75–0.87), regarding instantaneous measurements. Simulations under clear-sky conditions of all components are found to be significantly improved, from both MRM, satellite-based retrievals and reanalysis. Overcast and partially cloudy skies result in large uncertainties, especially for the diffuse and direct irradiations, since the satellite sensors may detect clouds at time intervals of unobstructed Sun disk by clouds. In addition, broken bright clouds near to the Sun's disk may increase significantly the measured diffuse irradiance, leading to large biases in the simulations from both MRM and satellite databases.
Space Food Experiences: Designing Passenger's Eating Experiences for Future Space Travel Scenarios

Given the increasing possibilities of short- and long-term space travel to the Moon and Mars, it is essential not only to design nutritious foods but also to make eating an enjoyable experience. To date, though, most research on space food design has emphasized the functional and nutritional aspects of food, and there are no systematic studies that focus on the human experience of eating in space. It is known, however, that food has a multi-dimensional and multisensorial role in societies and that sensory, hedonic, and social features of eating and food design should not be underestimated. Here, we present how research in the field of Human-Computer Interaction (HCI) can provide a user-centered design approach to co-create innovative ideas around the future of food and eating in space, balancing functional and experiential factors. Based on our research and inspired by advances in human-food interaction design, we have developed three design concepts that integrate and tackle the functional, sensorial, emotional, social, and environmental/ atmospheric aspects of "eating experiences in space". We can particularly capitalize on recent technological advances around digital fabrication, 3D food printing technology, and virtual and augmented reality to enable the design and integration of multisensory eating experiences. We also highlight that in future space travel, the target users will diversify. In relation to such future users, we need to consider not only astronauts (current users, paid to do the job) but also paying customers (non-astronauts) who will be able to book a space holiday to the Moon or Mars. To create the right conditions for space travel and satisfy those users, we need to innovate beyond the initial excitement of designing an "eating like an astronaut" experience. To do so we can draw upon prior HCI research in human-food interaction design and build on insights from food science and multisensory research, particularly research that has shown that the environments in which we eat and drink, and their multisensory components, can be crucial for an enjoyable food experience.
Scalable Mutual Information Estimation Using Dependence Graphs

The Mutual Information (MI) is an often used measure of dependency between two random variables utilized in information theory, statistics and machine learning. Recently several MI estimators have been proposed that can achieve parametric MSE convergence rate. However, most of the previously proposed estimators have high computational complexity of at least O(N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ). We propose a unified method for empirical non-parametric estimation of general MI function between random vectors in d based on N i.i.d. samples. The reduced complexity MI estimator, called the ensemble dependency graph estimator (EDGE), combines randomized locality sensitive hashing (LSH), dependency graphs, and ensemble bias-reduction methods. We prove that EDGE achieves optimal computational complexity O(N), and can achieve the optimal parametric MSE rate of O(1/N) if the density is d times differentiable. To the best of our knowledge EDGE is the first non-parametric MI estimator that can achieve parametric MSE rates with linear time complexity. We illustrate the utility of EDGE for the analysis of the information plane (IP) in deep learning. Using EDGE we shed light on a controversy on whether or not the compression property of information bottleneck (IB) in fact holds for ReLu and other rectification functions in deep neural networks (DNN).