Reviewerly/exHarmony huggingface dataset:
SMURF: SeMantic and linguistic UndeRstanding Fusion for Caption Evaluation via Typicality Analysis

The open-ended nature of visual captioning makes it a challenging area for evaluation.The majority of proposed models rely on specialized training to improve human-correlation, resulting in limited adoption, generalizability, and explainabilty.We introduce "typicality", a new formulation of evaluation rooted in information theory, which is uniquely suited for problems lacking a definite ground truth.Typicality serves as our framework to develop a novel semantic comparison, SPARCS, as well as referenceless fluency evaluation metrics.Over the course of our analysis, two separate dimensions of fluency naturally emerge: style, captured by metric SPURTS, and grammar, captured in the form of grammatical outlier penalties.Through extensive experiments and ablation studies on benchmark datasets, we show how these decomposed dimensions of semantics and fluency provide greater systemlevel insight into captioner differences.Our proposed metrics along with their combination, SMURF, achieve state-of-the-art correlation with human judgment when compared with other rule-based evaluation metrics 1 .
Digital Innovation in Times of Crisis: How Mashups Improve Quality of Education

Innovation is a crucial force underlying the organizational and societal ability to survive and thrive in crisis times, especially in education. However, not all challenges require creating novel solutions; occasionally, combining pre-existing solutions—an approach known as the mashup methodology—can provide equal benefit. Nevertheless, this approach appears to be relatively unknown. In the current case study, we provide an example of using a mashup of technologies to solve the challenge of running a remote virtual hackathon under COVID-19 social distancing regulations. Additionally, we argue that utilizing the mashup is a form of modeling, in which students learn by witnessing its use. Utilizing the Technology Acceptance Model, we show that by modeling the use of the mashup methodology in this hackathon (Study 1—case), we managed to improve students’ attitudes towards the method and increase its perceived usefulness and ease of use in their eyes. This, in turn, raised their intention to use it as an innovation tool in the future. Study 2 revealed that this effect on participants’ intentions is more significant than what would be achieved by merely teaching people about the methodology. The results of these studies add to the growing literature emphasizing the importance of using digital innovations as means of improving the quality of education, thus increasing the satisfaction and well-being of students.
Fast Blind Deblurring of QR Code Images Based on Adaptive Scale Control

Abstract With the development of 5G technology, the short delay requirements of commercialization and large amounts of data change our lifestyle day-to-day. In this background, this paper proposes a fast blind deblurring algorithm for QR code images, which mainly achieves the effect of adaptive scale control by introducing an evaluation mechanism. Its main purpose is to solve the out-of-focus caused by lens shake, inaccurate focus, and optical noise by speeding up the latent image estimation in the process of multi-scale division iterative deblurring. The algorithm optimizes productivity under the guidance of collaborative computing, based on the characteristics of the QR codes, such as the features of gradient and strength. In the evaluation step, the Tenengrad method is used to evaluate the image quality, and the evaluation value is compared with the empirical value obtained from the experimental data. Combining with the error correction capability, the recognizable QR codes will be output. In addition, we introduced a scale control parameter to study the relationship between the recognition rate and restoration time. Theoretical analysis and experimental results show that the proposed algorithm has high recovery efficiency and well recovery effect, can be effectively applied in industrial applications.
An Empirical Analysis of Deep Learning Architectures for Vehicle Make and Model Recognition

Vehicle make and model recognition is an important component of the Intelligent Transport System (ITS), which plays an essential role in the vehicle surveillance and traffic monitoring. In this paper, we evaluated the performance of the recent deep neural networks for vehicle recognition to identify 196 different types of vehicles based on their make, model, and year using Stanford Cars data. Transfer learning has been employed to reduce the training time and also added a dropout of 0.5 at the last dense layer before the output layer for improved generalization performance on an applied dataset. Different augmentation methods such as random erasing, horizontal flip, random crop, resize, rotate, shear, and sharpen were applied to the training dataset to increase the number of images. Besides, optimization methods namely mixup and cyclical learning rate were also investigated to train these deep neural networks to avoid overfitting. Finally, the proposed ensemble learning framework using k-fold cross-validation has resulted in the improved accuracy of image classification for the vehicle make and model recognition. The final achieved accuracy is 93.96% through ensembling homogeneous models of DenseNet201, which is higher than previous state-of-the-art approaches for the vehicle make and model recognition. Simultaneously, it has also improved the performance of other models where MobileNetV2 has shown the highest improvement with an increase of 2.8% in the overall accuracy using the proposed ensemble learning technique.
Hierarchical Coherence Modeling for Document Quality Assessment

Text coherence plays a key role in document quality assessment. Most existing text coherence methods only focus on similarity of adjacent sentences. However, local coherence exists in sentences with broader contexts and diverse rhetoric relations, rather than just adjacent sentences similarity. Besides, the highlevel text coherence is also an important aspect of document quality. To this end, we propose a hierarchical coherence model for document quality assessment. In our model, we implement a local attention mechanism to capture the location semantics, bilinear tensor layer for measure coherence and max-coherence pooling for acquiring high-level coherence. We evaluate the proposed method on two realistic tasks: news quality judgement and automated essay scoring. Experimental results demonstrate the validity and superiority of our work.
Sea fog detection based on unsupervised domain adaptation

Sea fog detection with remote sensing images is a challenging task. Driven by the different image characteristics between fog and other types of clouds, such as textures and colors, it can be achieved by using image processing methods. Currently, most of the available methods are data-driven and relying on manual annotations. However, because few meteorological observations and buoys over the sea can be realized, obtaining visibility information to help the annotations is difficult. Considering the feasibility of obtaining abundant visible information over the land and the similarity between land fog and sea fog, we propose an unsupervised domain adaptation method to bridge the abundant labeled land fog data and the unlabeled sea fog data to realize the sea fog detection. We used a seeded region growing module to obtain pixel-level masks from rough-labels generated by the unsupervised domain adaptation model. Experimental results demonstrate that our proposed method achieves an accuracy of sea fog recognition up to 99.17%, which is nearly 3% higher than those vanilla methods.
Unsupervised Detection of Anomalous Sound for Machine Condition Monitoring using Fully Connected U-Net

Anomaly detection in the sound from machines is an important task in machine monitoring. An autoencoder architecture based on the reconstruction error using a log-Mel spectrogram feature is a conventional approach for this domain. However, because of the non-stationary nature of some sounds from the target machine, such a conventional approach does not perform well in those circumstances. In this paper, we propose a novel approach regarding the choice of used features and a new auto-encoder architecture. We created the Mixed Feature, which is a mixture of different sound representations, and a new deep learning method called Fully-Connected U-Net, a form of autoencoder architecture. With experiments on the same dataset as the baseline system, using the same architecture for all types of machines, the experimental results showed that our methods outperformed the baseline system in terms of the AUC and pAUC evaluation metrics. The optimized model achieved 83.38% AUC and 64.51% pAUC on average overall machine types on the developed dataset and outperformed the published baseline by 13.43% AUC and 8.13% pAUC.
From Readership to Usership: Communicating Heritage Digitally Through Presence, Embodiment and Aesthetic Experience

The primary mission of cultural institutions, including heritage sites and museums, is to perform and perpetuate Cultural Heritage (CH) by ideally transforming audiences into stewards of that heritage. In recent years, these institutions have increasingly turned to Mixed Reality (MR) technologies to expand and democratize public access to Cultural Heritage—a trend that is called upon to accelerate with COVID-19—because these technologies provide opportunities for more remote outreach, and moreover, can make partial remains or ruins more relatable to the public. But as emerging evaluations indicate, existing MR intangible and tangible Digital Cultural Heritage (DCH) applications are largely proving inadequate to engaging audiences beyond an initial fascination with the immersive 3D visualization of heritage sites and artefacts owing in part to misguided storytelling or non-compelling narratives. They fail to effectively communicate the significance of Cultural Heritage to audiences and impress upon them its value in a lasting way due to their overreliance on an education-entertainment-touristic consumption paradigm. Building on the recent case made for Literature-based MR Presence, this article examines how the literary tradition of travel narratives can be recruited to enhance presence and embodiment, and further elicit aesthetic experiences in Digital Cultural Heritage applications by drawing on recent findings from the fields of Extended Reality (XR), cognitive literary science and new museology. The projected effects of this innovative approach are not limited to an increase in audience engagement on account of a greater sense of presence and embodiment. This approach is also expected to prompt a different kind of public involvement characterized by a personal valuation of the heritage owing to aesthetic experience. As the paper ultimately discusses, this response is more compatible both with MR applications’ default mode of usership, and with newly emerging conceptions of a user-centered museum (e.g., the Museum 3.0), thereby providing a narrative roadmap for future Virtual Museum (VM) applications better suited to the primary mission of transmitting and perpetuating Cultural Heritage.
Real Time Face Expression Recognition along with Balanced FER2013 Dataset using CycleGAN

Human face expression recognition is an active research area that has massive applications in medical field, crime investigation, marketing, online learning, automobile safety and video games.The first part of this research defines a deep neural network model-based framework for recognizing the seven main types of facial expression, which are found in all cultures.The proposed methodology involves four stages: (a) preprocessing the FER2013 dataset through relabeling to avoid misleading results and getting rid of non-face and non-frontal faces; (b) design of an efficient stable Cycle Generative Adversarial Network (CycleGAN), which provides unsupervised expression-to-expression translation.The CycleGAN has been designed and trained with a new cycle consistency loss.(c) Generating new images to overcome the class imbalance and finally (d) building the DNN architecture for recognizing the face sign expression, using the pretrained VGG-Face model with vggface weights.The second part encompasses the design of a GPU-accelerated face expression recognition system for real time video sequences using NVIDIAs Compute Unified Device Architecture (CUDA).OpenCV library has been compiled from scratch with CUDA and NVIDIA CUDA Deep Neural Network library, cuDNN.For face detection stage Haar Cascaded and deep learning were used and tested using both CPU and GPU as backend.Results show that the designed model run time to recognize a face sign is 0.44 seconds.Besides, the average test accuracy has been increased from 64% for the original FER2013 dataset to 91.76% for the modified balanced version using the same transfer learning model.
Scalable estimation of pure multi-qubit states

Abstract We introduce an inductive n -qubit pure-state estimation method based on projective measurements on m n + 1 separable bases or m entangled bases plus the computational basis, with m ≥ 2. The method exhibits a favorable scaling in the number of qubits compared to other estimation schemes. The use of separable bases makes our estimation method particularly well suited for applications in noisy intermediate-scale quantum computers, where entangling gates are much less accurate than local gates. Our method is also capable of estimating the purity of mixed states generated by the action of white noise on pure states. Monte Carlo simulations show that the method achieves a high estimation fidelity. Besides, the fidelity can be improved by increasing m above 2. We experimentally demonstrate the method on the IBM’s quantum processors by estimating up to 10-qubit separable and entangled states. In particular, a 4-qubit GHZ is estimated with experimental fidelity of 0.875.
Gaze-Aware Graph Convolutional Network for Social Relation Recognition

Social relation, as the basic relation in our daily life, is vital for social action analysis. However, how to learn the social feature between people is still not tackled. In this work, we propose a gaze-aware graph convolutional network (GA-GCN) for social relation recognition, which targets discovering the context-aware social relation inference with gaze-aware attention. To predict the gaze direction, we apply a convolutional network trained with gaze direction loss. Then, we build a graph convolutional inference module, which is a two-stream graph inference with both gaze-aware attention and distance-aware attention. The attention can pick up relevant context objects for context-aware representation. We further introduce additional scene features and construct a multiple feature fusion module, which can adaptively learn social relation representation from both scene feature and context-aware feature. Extensive experiments on the PISC and the PIPA datasets demonstrate that our GA-GCN can find interesting contextual objects and achieves state-of-the-art performances.
From User Stories to UML Diagrams Driven by Ontological and Production Model

The User Story format has become the most popular way of expressing requirements in Agile methods.However, a requirement does not state how a solution will be physically achieved.The purpose of this paper is to present a new approach that automatically transforms user stories into UML diagrams.Our approach aims to automatically generate UML diagrams, namely class, use cases, and package diagrams.User stories are written in natural language (English), so the use of a natural language processing tool was necessary for their processing.In our case, we have used Stanford core NLP.The automation approach consists of the combination of rules formulated as a predicate and an ontological model.Prolog rules are used to extract relationships between classes and eliminate those that are at risk of error.To extract the design elements, the prolog rules used dependencies offered by Stanford core NLP.An ontology representing the components of the user stories was created to identify equivalent relationships and inclusion use cases.The tool developed was implemented in the Python programming language and has been validated by several case studies.
Generalized Susskind–Glogower coherent states

Susskind–Glogower coherent states, whose Fock expansion coefficients include Bessel functions, have recently attracted considerable attention for their optical properties. Nevertheless, identity resolution is still an open question, which is an essential mathematical property that defines an overcomplete basis in the Fock space and allows a coherent state quantization map. In this regard, the modified Susskind–Glogower coherent states have been introduced as an alternative family of states that resolve the identity resolution. In this paper, the quantization map related to the modified Susskind–Glogower coherent states is exploited, which naturally leads to a particular representation of the su(1,1) Lie algebra in its discrete series. The latter provides evidence about further generalizations of coherent states, built from the Susskind–Glogower ones by extending the indices of the Bessel functions of the first kind and, alternatively, by employing the modified Bessel functions of the second kind. In this form, the new families of Susskind–Glogower-I and Susskind–Glogower-II coherent states are introduced. The corresponding quantization maps are constructed so that they lead to general representations of elements of the su(1,1) and su(2) Lie algebras as generators of the SU(1, 1) and SU(2) unitary irreducible representations, respectively. For completeness, the optical properties related to the new families of coherent states are explored and compared with respect to some well-known optical states.
Accessible and inaccessible quantum coherence in relativistic quantum systems

The quantum coherence of a multipartite system is investigated when some of the parties are moving with uniform acceleration and the analysis is carried out using the single-mode approximation. Due to acceleration the quantum coherence is divided into two parts as accessible and inaccessible coherence. First we investigate tripartite systems, considering both GHZ and $W$ states. We find that the quantum coherence of these states does not vanish in the limit of infinite acceleration, rather asymptoting to a nonzero value. These results hold for both single- and two-qubit acceleration. In the GHZ and $W$ states the coherence is distributed as correlations between the qubits and is known as global coherence. However, quantum coherence can also exist due to the superposition within a qubit, the local coherence. To study the properties of local coherence we investigate a separable state. The GHZ state, $W$ state, and separable states contain only one type of coherence. Next we consider the $W\overline{W}$ and star states in which both local and global coherences coexist. We find that under uniform acceleration both local and global coherence show similar qualitative behavior. Finally, we derive analytic expressions for the quantum coherence of $N$-partite GHZ and $W$ states for $n<N$ accelerating qubits. We find that the quantum coherence of a multipartite GHZ state falls exponentially with the number of accelerated qubits, whereas for multipartite $W$ states the quantum coherence decreases only polynomially. We conclude that $W$ states are more robust to Unruh decoherence and discuss some potential applications in satellite-based quantum communication and black-hole physics.
A Novel Particle Swarm Optimization With Genetic Operator and Its Application to TSP

To solve some problems of particle swarm optimization, such as the premature convergence and falling into a sub-optimal solution easily, we introduce the probability initialization strategy and genetic operator into the particle swarm optimization algorithm. Based on the hybrid strategies, we propose a improved hybrid particle swarm optimization, namely IHPSO, for solving the traveling salesman problem. In the IHPSO algorithm, the probability strategy is utilized into population initialization. It can save much more computing resources during the iteration procedure of the algorithm. Furthermore, genetic operators, including two kinds of crossover operator and a directional mutation operator, are used for improving the algorithm’s convergence accuracy and population diversity. At last, the proposed method is benchmarked on 9 benchmark problems in TSPLIB and the results are compared with 4 competitors. From the results, it is observed that the proposed approach significantly outperforms others on most the 9 datasets.
Efficiency through Diversity in Ensemble Models applied to Side-Channel Attacks

Deep Learning based Side-Channel Attacks (DL-SCA) are considered as fundamental threats against secure cryptographic implementations. Side-channel attacks aim to recover a secret key using the least number of leakage traces. In DL-SCA, this often translates in having a model with the highest possible accuracy. Increasing an attack’s accuracy is particularly important when an attacker targets public-key cryptographic implementations where the recovery of each secret key bits is directly related to the model’s accuracy. Commonly used in the deep learning field, ensemble models are a well suited method that combine the predictions of multiple models to increase the ensemble accuracy by reducing the correlation between their errors. Linked to this correlation, the diversity is considered as an indicator of the ensemble model performance. In this paper, we propose a new loss, namely Ensembling Loss (EL), that generates an ensemble model which increases the diversity between the members. Based on the mutual information between the ensemble model and its related label, we theoretically demonstrate how the ensemble members interact during the training process. We also study how an attack’s accuracy gain translates to a drastic reduction of the remaining time complexity of a side-channel attacks through multiple scenarios on public-key implementations. Finally, we experimentally evaluate the benefits of our new learning metric on RSA and ECC secure implementations. The Ensembling Loss increases by up to 6.8% the performance of the ensemble model while the remaining brute-force is reduced by up to 222 operations depending on the attack scenario.
A Multiple Level-of-Detail 3D Data Transmission Approach for Low-Latency Remote Visualisation in Teleoperation Tasks

In robotic teleoperation, the knowledge of the state of the remote environment in real time is paramount. Advances in the development of highly accurate 3D cameras able to provide high-quality point clouds appear to be a feasible solution for generating live, up-to-date virtual environments. Unfortunately, the exceptional accuracy and high density of these data represent a burden for communications requiring a large bandwidth affecting setups where the local and remote systems are particularly geographically distant. This paper presents a multiple level-of-detail (LoD) compression strategy for 3D data based on tree-like codification structures capable of compressing a single data frame at multiple resolutions using dynamically configured parameters. The level of compression (resolution) of objects is prioritised based on: (i) placement on the scene; and (ii) the type of object. For the former, classical point cloud fitting and segmentation techniques are implemented; for the latter, user-defined prioritisation is considered. The results obtained are compared using a single LoD (whole-scene) compression technique previously proposed by the authors. Results showed a considerable improvement to the transmitted data size and updated frame rate while maintaining low distortion after decompression.