Reviewerly/exHarmony huggingface dataset:
A Process Model for Service-Oriented Development of Embedded Software Systems

The concepts of service-oriented computing (SOC) have previously been used in the embedded systems domain, mostly at the device level in ad hoc manners to achieve advantages of device integration. However, SOC has not been used for the development of embedded software systems (ESS), i.e., software controlling the embedded devices. To fill this gap, a process model is proposed in this article that allows the systematic development of ESS based on SOC concepts. The proposed process consists of analysis and design phases of embedded software development. The analysis phase is concerned with the collection of system information and preparation for system design. Based on this, the service-based software architecture is developed in the design phase. The effectiveness of the proposed process model is demonstrated through its application in the Smart Home case study.
Finite-temperature quantum discordant criticality

In quantum statistical mechanics, finite-temperature phase transitions are typically governed by classical field theories. In this context, the role of quantum correlations is unclear: recent contributions have shown how entanglement is typically very short-ranged, and thus uninformative about long-ranged critical correlations. In this work, we show the existence of finite-temperature phase transitions where a broader form of quantum correlation than entanglement, the entropic quantum discord, can display genuine signatures of critical behavior. We consider integrable bosonic field theories in both two- and three-dimensional lattices, and show how the two-mode Gaussian discord decays algebraically with the distance even in cases where the entanglement negativity vanishes beyond nearest-neighbor separations. Systematically approaching the zero-temperature limit allows us to connect discord to entanglement, drawing a generic picture of quantum correlations and critical behavior that naturally describes the transition between entangled and discordant quantum matter.
Immersion Experiences in a Tablet-Based Markerless Augmented Reality Working Memory Game: Randomized Controlled Trial and User Experience Study

Background In recent years, augmented reality (AR), especially markerless augmented reality (MAR), has been used more prevalently to create training games in an attempt to improve humans' cognitive functions. This has been driven by studies claiming that MAR provides users with more immersive experiences that are situated in the real world. Currently, no studies have scientifically investigated the immersion experience of users in a MAR cognitive training game. Moreover, there is an observed lack of instruments on measuring immersion in MAR cognitive training games. Objective This study, using two existing immersion questionnaires, investigates students’ immersion experiences in a novel MAR n-back game. Methods The n-back task is a continuous performance task that taps working memory (WM) capacity. We compared two versions of n-back training. One was presented in a traditional 2D format, while the second version used MAR. There were 2 experiments conducted in this study that coordinated with 2 types of immersion questionnaires: the modified Immersive Experiences Questionnaire (IEQ) and the Augmented Reality Immersion (ARI) questionnaire. Two groups of students from two universities in China joined the study, with 60 participants for the first experiment (a randomized controlled experiment) and 51 participants for the second. Results Both groups of students experienced immersion in the MAR n-back game. However, the MAR n-back training group did not experience stronger immersion than the traditional (2D) n-back control group in the first experiment. The results of the second experiment showed that males felt deeply involved with the AR environment, which resulted in obtaining higher levels of immersion than females in the MAR n-back game. Conclusions Both groups of students experienced immersion in the MAR n-back game. Moreover, both the modified IEQ and ARI have the potential to be used as instruments to measure immersion in MAR game settings. Trial Registration UMIN Clinical Trials Registry UMIN000045314; https://upload.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000051725
UrduAI: Writeprints for Urdu Authorship Identification

The authorship identification task aims at identifying the original author of an anonymous text sample from a set of candidate authors. It has several application domains such as digital text forensics and information retrieval. These application domains are not limited to a specific language. However, most of the authorship identification studies are focused on English and limited attention has been paid to Urdu. However, existing Urdu authorship identification solutions drop accuracy as the number of training samples per candidate author reduces and when the number of candidate authors increases. Consequently, these solutions are inapplicable to real-world cases. Moreover, due to the unavailability of reliable POS taggers or sentence segmenters, all existing authorship identification studies on Urdu text are limited to the word n-grams features only. To overcome these limitations, we formulate a stylometric feature space, which is not limited to the word n-grams feature only. Based on this feature space, we use an authorship identification solution that transforms each text sample into a point set, retrieves candidate text samples, and relies on the nearest neighbors classifier to predict the original author of the anonymous text sample. To evaluate our solution, we create a significantly larger corpus than existing studies and conduct several experimental studies that show that our solution can overcome the limitations of existing studies and report an accuracy level of 94.03%, which is higher than all previous authorship identification works.
Uncertainty Calculation Method for Photodiode Pyranometers

For all measurements, a provision of uncertainty for each measurement point is good practice. So far, a comprehensive methodology for uncertainties for rotating shadowband irradiometers (RSIs) has not been established, as the typical methods for calculating uncertainty are oversimplified, too time‐consuming, or both when the instrument exhibits significant spectral dependence. The method presented herein allows the estimation of the uncertainty for each measurement provided by the RSIs. The methodology is tested on data from different climates, producing a comprehensive study focused mainly on measurements made with RSIs using photodiode pyranometers. The calculated expanded uncertainty for the RSI measurements matches the deviation from colocated thermopile sensors 95% of the time. Common uncertainty estimations of RSIs from the literature are confirmed using the comprehensive methodology in this article for the deviation and uncertainty calculations. The method is therefore well suited for the application in real measurement campaigns. In this study, photodiode pyranometers are also studied and the methodology for the RSI measurements also works for global horizontal irradiance measurements from photodiode pyranometers.
Temporal Relations Matter: A Two-Pathway Network for Aerial Video Recognition

With the increasing volume of aerial videos, the demand for automatically parsing these videos is surging. To achieve this, current researches mainly focus on extracting a holistic feature with convolutions along both spatial and temporal dimensions. However, these methods are limited by small temporal receptive fields and cannot adequately capture long-term temporal dependencies which are important for describing complicated dynamics. In this paper, we propose a novel two-pathway network to model not only holistic features, but also temporal relations for aerial video classification. More specially, our model employs a two-pathway architecture: (1) a holistic representation pathway to learn a general feature of frame appearances and short-term temporal variations and (2) a temporal relation pathway to capture multi-scale temporal relations across arbitrary frames, providing long-term temporal dependencies. Our model is evaluated on event recognition dataset, ERA, and achieves the state-of-the-art results. This demonstrates its effectiveness and good generalization capacity.
A blockchain-based approach to providing technically expressed trust in the supply chains of the fashion industry

This paper addresses the lack of research into the use of the blockchain technology to provide trust in fashion supply chain collaboration systems, which in practice leads to an empirical approach and leads to the implementation of ineffective solutions. The aim of the study is to develop an approach to build an information system for the interaction of high fashion supply chain participants based on the Hyperledger blockchain framework to ensure technically explicit trust. In the process of research, the authors have found and developed as follows: 1) Blockchain technologies can be used to provide technically explicit trust in the supply chains of the high-fashion industry through decentralization, data consistency and security, and the use of smart contracts; 2) The approach of applying Hyperledger framework in the context of supply chain processes in the fashion industry is to be justified by criteria such as open architecture, modularity, support for data access management, and the ability to manage smart contracts; 3) A conceptual model of an interaction system for fashion supply chain actors based on the Hyperledger framework has been developed; 4) A blockchain-based approach to building a blockchain-based supply chain collaboration IS for accelerating supply chain processes in the haute couture industry has been proposed.
Development guidelines for circulating technology collections

Belk Library and Information Commons at Appalachian State University began circulating technology in the early 2000s with a small set of laptops and MacBooks. This collection was augmented with the addition of approximately two dozen video and still cameras in 2006 and by 2019, the technology collection for circulation had grown to 550 devices composed of 33 types, including laptops, tablets, audio and video recorders, DSLR cameras, and headphones. Over this time, the team developed a set of guidelines to govern the development of the collection. The team developed some policies that follow traditional collection development guidelines, but many are unique to the challenges required to maintaining a useful and functioning circulation collection of technology equipment.
Face Detection and Extraction Using Viola–Jones Algorithm

In the current times, face detection by computer system has become a major field of interest. Face detection technology can be applied to various fields-including security, biometrics, law enforcement, entertainment, and personal safety—to provide surveillance and tracking of people in real time. Face detection applications use algorithms to find only the human faces within larger images. Face detection algorithms typically start by searching for human eyes, one of the easiest features to detect. The algorithm might then attempt to detect eyebrows, mouth, nose, nostrils and the iris. Once the algorithm concludes that it has found a facial region, it applies additional tests to confirm that it has, in fact, detected a face. In this report, we propose a human face detection method for colored as well as gray images. Also, we cropped the particular detected facial image and extracting and showing the individual cropped image if the input image contains many faces.
Learning a Model-Based Deep Hyperspectral Denoiser from a Single Noisy Hyperspectral Image

Hyperspectral image (HSI) denoising is a crucial preprocessing procedure to improve the quality of HSI. Model-based methods take the degradation model and the structure of underlying clean HSI into account for denoising but require a large number of numerical iterations and exhausting parameter tuning. Deep-learning-based (DL-based) methods directly learn the nonlinear transformation of clean and noisy image HSI pairs, but rely on large-scale high-quality training samples because of its “black box” denoising mechanism. In this paper, we propose a model-based DL method for HSI denoising to combine the advantages of model-based methods and DL-based methods. Specifically, we first build a HSI denoising model based on sparse representation. Then, we unfold the iterative optimization under the framework of gradient descent with momentum to yield a Gradient Momentum Sparse Coding Network (GMSC-Net) for denoising. In order to overcome the unavailability of noisy-clean HSI pairs for training, we directly learn GMSC-Net from a single HSI. The observed noisy HSI is grouped into a number of clusters containing local cubes. The cluster centers are treated as “clean” cubes and are polluted by noises, yielding a set of “noisy-clean” pairs for training. Extensive experiments show the effectiveness of our method on both synthetic and real-world datasets.
Long-Range Hand Gesture Recognition via Attention-based SSD Network

Hand gesture recognition plays an essential role in the human-robot interaction (HRI) field. Most previous research only studies hand gesture recognition in a short distance, which cannot be applied for interaction with mobile robots like unmanned aerial vehicles (UAVs) at a longer and safer distance. Therefore, we investigate the challenging long-range hand gesture recognition problem for the interaction between humans and UAVs. To this end, we propose a novel attention-based single shot multibox detector (SSD) model that incorporates both spatial and channel attention for hand gesture recognition. We notably extend the recognition distance from 1 meter to 7 meters through the proposed model without sacrificing speed. Besides, we present a long-range hand gesture (LRHG) dataset collected by the USB camera mounted on mobile robots. The hand gestures are collected at discrete distance levels from 1 meter to 7 meters, where most of the hand gestures are small and at low resolution. Experiments with the self-built LRHG dataset show our methods reach the surprising performance-boosting over the state-of-the-art method like the SSD network on both short-range (1 meter) and long-range (up to 7 meters) hand gesture recognition tasks.
UMLE: Unsupervised Multi-discriminator Network for Low Light Enhancement

Low-light image enhancement is a complex and vital task including, recovering color and texture details from low-light images. For automated driving, low-light scenarios will have severe implications for vision-based applications. To address this problem, we propose a real-time unsupervised generative adversarial network (GAN) with multiple discriminators. It includes a multi-scale discriminator, a texture discriminator, and a color discriminator to evaluate images from different perspectives. Furthermore, considering the uneven illumination distribution of images and the different information contained in the channels, we adopte a feature fusion attention module to combine channel attention with pixel attention to extract image features. Experiments show that our method outperforms state-of-the-art methods in qualitative and quantitative evaluation and provides visible improvements in SLAM localization effects.
SVD–DWT Hybrid Frequency Domain Watermarking for Gray Scale Images Using Particle Swarm Optimization

The Digital Rights Management (DRM) applications require information security measures to be applied to all kind of media-sound, image and video. The media may be available in compressed or uncompressed domain. Image watermarking is one such measure. Various soft computing techniques are employed to achieve it. These include different meta-heuristic techniques, which are found quite suitable for identifying relevant pixel coefficients for watermark embedding according to single or varying embedding scaling factors across the image. This paper proposes to use the Particle Swarm Optimization (PSO) technique to carry out embedding according to multiple scaling factor-based integration of watermark coefficients with original pixel coefficients in SVD-transform domain. Comparison of our results with prominent works in this direction shows that the proposed results outperform all other schemes.
Ranking Structured Objects with Graph Neural Networks

Graph neural networks (GNNs) have been successfully applied in many structured data domains, with applications ranging from molecular property prediction to the analysis of social networks. Motivated by the broad applicability of GNNs, we propose the family of so-called RankGNNs, a combination of neural Learning to Rank (LtR) methods and GNNs. RankGNNs are trained with a set of pair-wise preferences between graphs, suggesting that one of them is preferred over the other. One practical application of this problem is drug screening, where an expert wants to find the most promising molecules in a large collection of drug candidates. We empirically demonstrate that our proposed pair-wise RankGNN approach either significantly outperforms or at least matches the ranking performance of the naïve point-wise baseline approach, in which the LtR problem is solved via GNN-based graph regression.
Anomaly Detection and Classification in Predictive Maintenance Tasks with Zero Initial Training

The Fourth Industrial Revolution has led to the adoption of novel technologies and methodologies in factories, making these more efficient and productive. Among the new services which are changing industry, there are those based on machine learning algorithms, which enable machines to learn from their past observations and hence possibly forecast future states. Specifically, predictive maintenance represents the opportunity to understand in advance possible machine outages due to broken parts and schedule the necessary maintenance operations. However, in real scenarios predictive maintenance struggles to be adopted due to a multitude of variables and the heavy customization it requires. In this work, we propose a novel framework for predictive maintenance, which is trained online to recognize new issues reported by the operators. Our framework, tested on different scenarios and with a varying number and several kinds of sensors, shows recall levels above 0.85, demonstrating its effectiveness and adaptability.
Zero-Shot Deployment for Cross-Lingual Dialogue System

The dialogue system is widely used in many application scenarios, while the construction of the dialogue system always faces the difficulty of zero-resource training data. To alleviate that, we propose a knowledge transfer framework to build a dialogue system based on existing machine translators and training data in data-rich language. Specifically, we first generate various kinds of pseudo data with cyclic translation procedure and different data combinations. Then we propose a noise injection method and a multi-task training method for the pipeline system and end-to-end system, respectively. The noise injection method optimizes each module by incorporating machine translation noises into the pipeline process to handle the error propagation problem, thus improving the whole system's robustness. The multi-task training method combines cross-lingual dialogue, monolingual dialogue, and machine translation into the end-to-end dialogue system's training process, thus reducing the impact of noises in pseudo data. The extensive experiments on a real-world e-commerce dataset demonstrate that our methods can achieve remarkable improvements over strong baselines.
Context-Aware Selective Label Smoothing for Calibrating Sequence Recognition Model

Despite the success of deep neural network (DNN) on sequential data (i.e., scene text and speech) recognition, it suffers from the over-confidence problem mainly due to overfitting in training with the cross-entropy loss, which may make the decision-making less reliable. Confidence calibration has been recently proposed as one effective solution to this problem. Nevertheless, the majority of existing confidence calibration methods aims at non-sequential data, which is limited if directly applied to sequential data since the intrinsic contextual dependency in sequences or the class-specific statistical prior is seldom exploited. To the end, we propose a Context-Aware Selective Label Smoothing (CASLS) method for calibrating sequential data. The proposed CASLS fully leverages the contextual dependency in sequences to construct confusion matrices of contextual prediction statistics over different classes. Class-specific error rates are then used to adjust the weights of smoothing strength in order to achieve adaptive calibration. Experimental results on sequence recognition tasks, including scene text recognition and speech recognition, demonstrate that our method can achieve the state-of-the-art performance.
ArtifiVe-Potsdam: A Benchmark for Learning with Artificial Objects for Improved Aerial Vehicle Detection

In order to enable the development of powerful machine learning methods for remote sensing-based Earth observation tasks, benchmarks are needed to evaluate the methods and compare them to other methods comprehensively. We present ArtifiVe-Potsdam, a freely available dataset that is targeting vehicle detection in aerial imagery. In particular, the benchmark focuses on enriching real datasets with artificial data and quantifying the added value. The dataset aims to stimulate research on the efficient and cost-effective creation and enrichment of datasets for remote sensing since datasets with a limited number of labels are common and the collection of data and labels is time-consuming and expensive.
H.264/AVC and H.265/HEVC Codecs in the IP Environment: A Comparison Study of QoE, QoS and UX

The paper deals with the video codecs of the MPEG family. In a large-scale study, these codecs are compared with one another using a numerical tool (QoE) and a real study in which test subjects present their user experience (UX). The typical impairment parameters of the IP transport platform, i.e. packet loss and burst factor are taken into account. The encoding rates for the video content also play an important role here. In the study, the advantages and disadvantages of these video codecs are analyzed in depth on the basis of the test results, which are graphically displayed in several diagrams. Furthermore, parameterized QoS models are determined for the video codecs considered in this paper, and their effectiveness is also assessed. These QoS models are based on a protocol analysis of the video stream. They are very simple, can be carried out quickly, and are therefore very suitable for practice.
Proposal of Complex-Valued Reservoir Computing for Topographic Aspect Classification

In this paper, we propose complex-valued reservoir computing (CVRC) to deal with complex-valued images in interferometric SAR (InSAR). We conducted classification of land forms by dealing with the interferogram data as sequential pixel values with nonlinearity in the complex domain.