Reviewerly/exHarmony huggingface dataset:
Wind Power Curve Data Cleaning Algorithm via Image Thresholding

Wind turbine data from the Supervisory Control And Data Acquisition (SCADA) system is very important for wind turbine conditional monitoring, wind power prediction and wind turbine performance evaluation. However, the SCADA data usually contains lots of abnormal data. This paper presents an image-based algorithm for abnormal data cleaning of wind power curve (WPC) data via image thresholding. The basic idea is to build a gray-level representation of the original binary image of WPC which is able to preserve the normal part as much as possible. Therefore, the cleaning operation is then turned into a problem of image segmentation. The proposed algorithm includes the following steps: First, the scatter data is converted into a binary image. Then the median of four distances are computed from each pixel in the image to the nearest connected domain boundary along four directions, and a gray level image is generated to strengthen the normal part, in the meantime, weaken the abnormal part. For all possible threshold t, the optimal t <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">o</inf> which makes the smallest Hu moment based dissimilarity of the segmented normal part with a reference WPC template, is finally determined. The proposed algorithm is compared with some data-based algorithms as well as an image-based mathematical morphology operation (MMO) algorithm. Experiments carried out on WPC data of 17 wind turbines from a wind farm verified the effectiveness and accuracy of the proposed method.
Accurate playground localisation based on multi-feature extraction and cascade classifier in optical remote sensing images

To address the low accuracy problem of playground detection under complex background, the accurate playground localization based on multi-feature extraction and cascade classifier is proposed in this paper. It is difficult to utilize this information to separate objects from the complex background. Therefore, we adopt multi-feature extraction method to make the playgrounds more easily to be detected. The proposed localization method is partitioned into two modules: feature extraction and classification. First, multi feature extraction method combining histogram of oriented gradients (HOG) and Haar is utilized to extract features from raw images. HOG can authentically capture the shape information, which is extracted to characterize the local region. Haar can improve the image eigenvalue calculation effectively. Afterwards, cascade classifier based on AdaBoost algorithm is adopted to classify the extracted features. Finally we conduct the experiments with our proposed methodology on a publicly accessible remote sensing images from Google Earth. The results demonstrate that the proposed framework has a better effect with achieving high levels of recall, precision and F-score compared to the state-of-the-art alternatives, without sacrificing computational soundness. What is more, the results indicate that the proposed playground 1ocalisation method has strong robustness under different complex backgrounds with high detection rate.
Practical Insights On Augmented Reality Support for Shop-Floor Tasks

The use of Augmented Reality (AR) to improve processes in production has a long history and there are various established use cases in industry and research. Nevertheless, barriers to its seamless integration in everyday shop-floor processes still remain. A holistic approach grounded on socio-technical systems theory and a practical use case in a real-world production environment serve to identify the barriers and to provide recommendations for implementing AR in shop-floor processes. As a starting point, an overview of use cases in industry and research is provided focusing on how AR is used to support shop-floor tasks. Based on this, a practical use case, where maintenance engineers were guided by a head-mounted AR device (Microsoft HoloLens), was implemented to gain insights into the current AR-technology. An experiment was carried out consisting of twelve test runs with six maintenance engineers in a real-world production setting in the automotive industry. Semi-structured interviews with the test persons and video recordings of the tests serve as a basis for data analysis. The results consist primarily of specific use case feedback from test persons and video-based observation findings. Based on these findings, we derived recommendations to seamlessly integrate AR into production processes. These results are structured around enterprise IT infrastructure, AR-technology, shop-floor process related and human-centered aspects.
Segmentation of Multimodal Myocardial Images Using Shape-Transfer GAN

Myocardium segmentation of late gadolinium enhancement (LGE) Cardiac MR images is important for evaluation of infarction regions in clinical practice. The pathological myocardium in LGE images presents distinctive brightness and textures compared with the healthy tissues, making it much more challenging to be segment. Instead, the balanced-Steady State Free Precession (bSSFP) cine images show clearly boundaries and can be easily segmented. Given this fact, we propose a novel shape-transfer GAN for LGE images, which can (1) learn to generate realistic LGE images from bSSFP with the anatomical shape preserved, and (2) learn to segment the myocardium of LGE images from these generated images. It’s worth to note that no segmentation label of the LGE images is used during this procedure. We test our model on dataset from the Multi-sequence Cardiac MR Segmentation Challenge. The results show that the proposed Shape-Transfer GAN can achieve accurate myocardium masks of LGE images.
A Makerspace Foot Pedal and Shoe Add-On for Seated Virtual Reality Locomotion

Virtual reality advances are constantly pushing the envelope of what is possible in the realm of human-computer interaction to provide more engaging and immersive experiences. Recent trends in consumer virtual reality are seeing the development of stand-alone headsets providing portable wireless experiences. However, one aspect of VR that is still in need of improvement is user-friendly, immersive, intuitive, and effective locomotion. Currently, commodity locomotion is achieved through teleportation, gamepad-based, or room-scale locomotion, unlike high-end systems featuring omnidirectional treadmills that require higher investment and specialized infrastructure. Proper virtual reality locomotion can increase immersion, presence, and allow effective interactions that can positively impact applications in health care, education, training, and safety. In this paper, we present a preliminary usability study on seated virtual reality locomotion employing consumer-level devices, teleportation, and two custom-made devices employing 3D printing and open electronics as an alternative for walking in VR.
A deformation detection method for aircraft skin on uniform pressure by using speckle image correlation technology

The fatigue cracks of aircraft skin which are caused by the pressurization for a civilian airplane during the flight process will bring potential serious safety risks. Therefore, it is necessary to study the deformation mechanism for the aircraft skin on uniform pressure. Traditional fatigue testing machines can only be used for simple tension and compression tests which cannot be applied directly. This paper proposes a deformation detection method by using speckle image correlation technology. Firstly, a fatigue testing machine simulating uniform pressure has been designed. Secondly, a speckle image correlation method by using improved KLT (Kanade-Lucas-Tomasi) algorithm is presented to obtain the coordinates of the coated speckle spots. Finally, static and dynamic experiments were implemented to verify the accuracy and effectiveness of the proposed method. In conclusion, this method can be applied in airworthiness certification to improve the testing efficiency.
ToC-RWG: Explore the Combination of Topic Model and Citation Information for Automatic Related Work Generation

Automatic related work generation is a new challenge in multi-document scientific summarization focusing on refining a related work section for a given scientific paper.In this paper, we propose a brand new framework ToC-RWG for related work generation by incorporating topic model and citation information.We present an unsupervised generative probabilistic model, called QueryTopicSum, which utilizes a LDA-style model to characterize the generative process of both the scientific paper and its reference papers.We also take advantage of citations of reference papers to identify Cited Text Spans (CTS) from reference papers.This approach provides us with a perspective of annotating the importance of the reference papers from the academic community.With QueryTopicSum and the identified CTS as candidate sentences, an optimization framework based on minimizing KL divergence is exerted to select the most representative sentences for related work generation.Our evaluation results on a set of 50 scientific papers along with their corresponding reference papers show that ToC-RWG achieves a considerable improvement over generic multi-document summarization and scientific summarization baselines.
Introduction: Blockchain in Media and Entertainment

Most people first encounter blockchain technology by way of cryptocurrencies like Bitcoin. Bitcoin came into existence during the start of the 2008 financial crisis and was first disclosed in October 2008. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Because of the timing and some clear indicators embedded into the first (genesis) block of Bitcoin, we know that a primary reason for developing cryptocurrency was a distrust of large institutions that control the world’s fiat currencies. However, what does currency have to do with media technology?
Accuracy of Automated Sentiment Analysis

Sentiment is fundamental to human communication. Countless marketing applications mine opinions from social media communication, news articles, customer feedback, or corporate communication. Various sentiment analysis methods are available and new ones have recently been proposed. Lexicons can relate individual words and expressions to sentiment scores. In contrast, machine learning methods are more complex to interpret, but promise higher accuracy, i.e., fewer false classifications. We propose an empirical framework and quantify these trade-offs for different types of research questions, data characteristics, and analytical resources to enable informed method decisions contingent on the application context. Based on a meta-analysis of 272 datasets and 12 million sentiment-labeled text documents, we find that the recently proposed transfer learning models indeed perform best, but can perform worse than popular leaderboard benchmarks suggest. We quantify the accuracy-interpretability trade-off, showing that, compared to widely established lexicons, transfer learning models on average classify more than 20 percentage points more documents correctly. To form realistic performance expectations, additional context variables, most importantly the desired number of sentiment classes and the text length, should be taken into account. We provide a pre-trained sentiment analysis model (called SiEBERT) with open-source scripts that can be applied as easily as an off-the-shelf lexicon.
Performance Comparison of Population-Based Quantum-Inspired Evolutionary Algorithms

Quantum computers are seen as the next generation computing technique with the processing power potential they have. However, currently, quantum computers are limited in terms of hardware and algorithmic capabilities. In this study, quantum-inspired methods which are formed by combining quantum computation techniques with classical algorithms are focused on. It has been emphasized in many studies that quantum-inspired methods provide advantages especially for metaheuristic methods. Different from them, in this study, the performance of population-based quantum-inspired methods are compared. The paper focuses on solving the same optimization problem by using quantum-inspired versions of the population-based optimization algorithms such as evolutionary algorithm, genetic algorithm, and differential evolution algorithm. The experimental results show that, while Quantum-inspired Evolutionary Algorithm is better at global search, Quantum-inspired Differential Evolution Algorithm is better at local search and more accurate results.
A Comparison of Hardware Based Approaches for Sign Language Gesture Recognition Systems

Sign language is a gesture-based language used worldwide by the deaf people to communicate with one another. This language comprises of different hand movements and facial expressions. Over the years, different tools and applications have been built and developed by the researchers to facilitate the deaf community in their communication with normal people. One branch of research deals with the recognition of gestures by machines, i.e. the machine is able to understand the gesture performed by a person. Many different approaches involving a variety of hardware including gloves, Microsoft Kinect, and sensors have been used for this purpose. The literature survey reveals that the most significant and advanced work in this regard has been accomplished in American Sign Language (ASL). Whereas, recently noticeable research is being conducted for the development of different Asian sign languages as well. This work presents a study of hardware-based approaches for gesture recognition in ASL and Asian sign languages.
Towards Automated Security Design Flaw Detection

Efficiency of security-by-design has become an important goal for organizations implementing software engineering practices such as Agile, DevOps, and Continuous Integration. Software architectures are (often manually) analyzed at design time for potential security design flaws, based on natural language descriptions of security weaknesses (e.g., CWE, CAPEC). The use of natural language hinders the application of such knowledge bases in an automated fashion. In this paper, we analyze an existing catalog of 19 security design flaws in order to identify conceptual, technology-independent requirements on architectural models that enable automatically detecting these flaws. This constitutes the first step towards automated assessment of design-level security. Our findings are illustrated on an IoT-based smart home system.
Vehicle Type Recognition From Surveillance Data Based on Deep Active Learning

Vehicle type recognition (VTR) from surveillance data has recently received increasing attention in both intelligent transportation and computer vision field. The deep learning techniques, e.g., convolutional neural networks (CNNs), have provided great progress in tasks with the large-scale labeled data. However, in real-world applications, manually labeling from large-scale surveillance data is time-consuming and tedious, which strongly obstructs the application of a CNN-based VTR. The existing works on VTR studied samples selection for manual annotation. Here, a VTR framework based on deep active learning that releases the burden of large-scale labeling is presented. The framework selects the most worthy samples to the manually selected, and then retrains the network with the annotated samples incrementally. Besides, the framework simultaneously takes advantage of both auto-labeled and manually-labeled data using the strategy of bi-direction entropy threshold. The proposed framework is validated on the public dataset Comprehensive Cars, and experimental results demonstrated that, with the incremental query strategy, the proposed framework could reduce annotation cost dramatically compared with random selection.
Enhancing User Privacy in IoT: Integration of GDPR and Blockchain

The development of Internet of Things (IoT) industries has raised significant questions in terms of accountability of smart devices and user privacy. The advent of European General Data Protection Regulation (GDPR) in such industries enabled users to control their collected data and be informed about the collecting devices. This paper by using blockchain technology provides the audit trail of IoT devices under GDPR rules. It translates a set of such rules into smart contracts to protect personal data in a transparent and automatic way. By proposing an abstract model and designing some business processes, the paper shows how the integration of GDPR and blockchain can appear in the design patterns of IoT devices to achieve a greater transparency of privacy.
Light-field depth estimation considering plenoptic imaging distortion

Light-field imaging can simultaneously record spatio-angular information of light rays to carry out depth estimation via depth cues which reflect a coupling of the angular information and the scene depth. However, the unavoidable imaging distortion in a light-field imaging system has a side effect on the spatio-angular coordinate computation, leading to incorrectly estimated depth maps. Based on the previously established unfocused plenoptic metric model, this paper reports a study on the effect of the plenoptic imaging distortion on the light-field depth estimation. A method of light-field depth estimation considering the plenoptic imaging distortion is proposed. Besides, the accuracy analysis of the light-field depth estimation was performed by using standard components. Experimental results demonstrate that efficiently compensating the plenoptic imaging distortion results in a six-fold improvement in measuring accuracy and more consistency across the measuring depth range. Consequently, the proposed method is proved to be suitable for light-field depth estimation and three-dimensional measurement with high quality, enabling unfocused plenoptic cameras to be metrological tools in the potential application scenarios such as industry, biomedicine, entertainment, and many others.
PixelClipper: Supporting Public Engagement and Conversation About Visualizations

In this article, we present PixelClipper, a tool built for facilitating data engagement events. PixelClipper supports conversations around visualizations in public settings through annotation and commenting capabilities. It is recognized that understanding data is important for an informed society. However, even when visualizations are available on the web, open data is not yet reaching all audiences. Public facilitated events centered around data visualizations may help bridge this gap. PixelClipper is designed to promote discussion and engagement with visualizations in public settings. It allows viewers to quickly and expressively extract visual clippings from visualizations and add comments to them. Ambient and facilitator displays attract attention by showing clippings. They function as entry points to the full visualizations while supporting deeper conversations about the visualizations and data. We describe the design goals of PixelClipper, share our experiences from deploying it, and discuss its future potential in supporting data visualization engagement events.
A New Swarm-Based Simulated Annealing Hyper-Heuristic Algorithm for Clustering Problem

Hyper-heuristic is a new generation of heuristic algorithms which include a set of heuristics that could generate a set of heuristics or automate the process of selecting a batch of heuristics to address different optimization problems. Most of the heuristic and meta-heuristic algorithms work in solution space and are highly problem-dependent and they are limited to use certain number of heuristic models only. Hyper-heuristic consists of two different sections, namely high-level heuristic and low level heuristics; to deal with optimization problems and work in heuristic spaces instead of solution spaces in selecting appropriate heuristics. In this article, we propose a new novel swarm-based simulated annealing hyper-heuristic model to solve clustering problem for analyzing data. The performance of proposed model has been evaluated by several benchmark and real data sets and compared with its state of the art in the literature. The experimental results show that the proposed algorithm provide better results than the alternative methods.
Robust GAN-Face Detection Based on Dual-Channel CNN Network

Nowadays, the identification of Generative adversarial networks (GAN) generated face images has become an important issue. Unfortunately, existing methods cannot detect these images with post-processing operations efficiently, such as image denoising and image sharpening. In this paper, the images are pre-processed by a gaussian low-pass filter, the combination of pre-processed images and the high-frequent components of original images can mitigate the influence of various image contents and can improve the detection capability against some widely-used image post-processing operations. Therefore, we carefully design a dual-channel structure based on Convolutional Neural Network (CNN) aiming to extract robust representations for detection of GAN generated face images. Extensive experiments are conducted on the public available dataset. Experimental results demonstrate that the proposed approach outperforms the state-of-the-arts with several image post-processing operations.
Breast Cancer Classification using Deep Transfer Learning on Structured Healthcare Data

Efforts to improve early identification of aggressive high grade breast cancers, which pose the greatest risk to patient health if not detected early, are hindered by the rarity of these events. To address this problem, we proposed an accurate and efficient deep transfer learning method to handle the imbalanced data problem that is prominent in breast cancer data. In contrast to existing approaches based primarily on large image databases, we focused on structured data, which has not been commonly used for deep transfer learning. We used a number of publicly available breast cancer data sets to generate a "pre-trained" model and transfer learned concepts to predict high grade malignant tumors in patients diagnosed with breast cancer at Mayo Clinic. We compared our results with state-of-the-art techniques for addressing the problem of imbalanced learning and confirmed the superiority of the proposed method. To further demonstrate the ability of the proposed method to handle different degrees of class imbalance, a series of experiments were performed on publicly available breast cancer data under simulated class imbalanced settings. Based on the experimental results, we concluded that the proposed deep transfer learning on structured data can be used as an efficient method to handle imbalanced class problems in clinical research.
Pains and Gains of Peer-Reviewing in Software Engineering

Standard Reviewing Procedures The final acceptance decision for a paper can be taken by following different alternative peer-reviewing processes (these review processes are implemented for both conferences and journals). The standard setup can be described as follows: while authors are usually not aware of the reviewers' identity (so-called blind review setting), reviewers may be aware of the authors' identity or not, resulting in single-blind or double-blind review processes, respectively. In the rare cases when the authors are aware of the reviewers' identity, the reviewing process is considered zero-blind. Other feasible alternatives are characterized by the amount of stages implemented before the acceptance decision is made (single-stage vs. multi-stage review processes) and the public visibility of review comments (open/public vs. closed review processes). The actual process of finding agreement regarding the papers' acceptance or rejection has also a certain bandwidth, ranging from delegating the responsibility of the decision-making to few people over staged committee and board setups to organizing physical/virtual meetings involving many people.