Reviewerly/exHarmony huggingface dataset:
Constructing Data-Driven Personas through an Analysis of Mobile Application Store Data

As smartphone segments have become more complex in recent times, the importance of personas for designing and marketing has increased. Earlier, designers focused on traditional qualitative personas but have been criticised for the lack of evidence and outdated results. However, although several methods of quantitative persona creation have been developed over the last few years, the use of mobile application store data has not yet been studied. In this research, we propose a framework using work domain analysis to help designers and marketers to build personas easily from mobile phone application store data. We considered the top 100 applications, which were ranked based on the number of devices using each application, how often each application was used, and the usage time. After proposing a new framework, we analysed data from a mobile application store in January and August 2020. We then created quantitative personas based on the data and discussed with experts whether the created personas successfully reflected real changes in mobile application trends.
The Educational and Psychological Need for Learning the English Language and Understanding the Different Anxieties

This study examines the educational and psychological need for learning English and understands various anxieties. The first section, a literature review, examines the theoretical underpinnings of educational and psychological needs for English language learning and anxiety. An important subject running through this research is how anxiety plays a role in English language learning. On either hand, a student’s psychological state can affect the learning of the English language. A psychological factor focuses on the emotional or spiritual components of a student’s learning. In English as a foreign language-learning setting, the four essential psychological needs as stated in self-determination theory (i.e., autonomy, competence, relatedness, and novelty) are met. The major goal of this study was achieved using a quantitative technique. A mixed methodology was selected, which comprises both quantitative and qualitative methods for this research. A total of 70 enrolled students in the English language took part in this study. It has been identified that students in this study reportedly found it hard to talk in EFL classrooms, because of the anxiety. It shows that foreign language anxiety still has an important amount of power in explaining language achievement. It is found that education and psychological needs motivate students to learn the English language. The data also indicated that nearly all psychological elements are interconnected and have a significant impact on the students’ speaking performance in the context.
Iterative extrinsic calibration using virtual viewpoint for 3D reconstruction

We propose a camera calibration to generate a high-quality and photorealistic 3D (dimension) volumetric model using several low-cost RGB-D (depth) cameras located in a space. We show an efficient workflow to register a model and propose iterative calibration to construct it. Using multiple frames, calibration in the vertical direction between the upper and lower cameras is performed. After selecting any four pairs, the calibration is performed while rotating with the vertical calibration results from other adjacent viewpoints. After performing calibration between each camera pair, the calibration is repeated by generating a virtual viewpoint between each camera pair. The error function between 3D coordinates of feature points acquired from the RGB image is obtained, and an attempt is made to minimize this. When the error converges below a threshold value by optimizing the error function, calibration ends, and the final extrinsic parameter is obtained. After performing 3D reconstruction using the proposed calibration, a 3D point cloud is produced. Finally, a simple and efficient refinement is proposed to improve the 3D point cloud quality. We show the advantage of the proposed technique by quantitatively comparing the calibration results using two ground truth data and 3D reconstruction results in the experimental results.
Deep-learning-based quantum imaging using NOON states

Abstract The phase sensitivity of photonic NOON states scales O (1/ N ), which reaches the Heisenberg limit and indicates a great potential in high-quality optical phase sensing. However, the NOON states with large photon number N are experimentally difficult both to prepare and to operate. Such a fact severely limits their practical use. In this article, we soften the requirements for high-quality imaging based on NOON states with large N by introducing deep-learning methods. Specifically, we show that, with the help of deep-learning network, the fluctuation of the images obtained by the NOON states when N = 2 can be reduced to that of the currently infeasible imaging by the NOON states when N = 8. We numerically investigate our results obtained by two types of deep-learning models—deep neural network and convolutional denoising autoencoders, and characterize the imaging quality using the root mean square error. By comparison, we find that small- N NOON state imaging data is sufficient for training the deep-learning models of our schemes, which supports its direct application to the imaging processes.
Teaching in Secondary Education Teacher Training with a Hybrid Model: Students’ Perceptions

The exceptional situation experienced on the planet during the last two academic years has forced teachers to adapt their teaching. They had to pass their face-to-face teaching methodologies to online teaching. This study analyzed the perceptions of students in a master’s degree in secondary school teacher training, who have experienced the transition to bimodal teaching during the 2020–21 and 2021–22 academic years carried out by professors with high digital teaching competence. Data from two academic years are available, with a total of 207 students. The results indicate that the experience has been positive, and no significant differences were found in the follow-up of the contents when the class was followed online. However, the feeling of belonging to the group is lower in online classes than in face-to-face classes, and the perception of online teaching is more favorable in the academic year immediately developed after the COVID crisis. The bimodal modality has become a useful solution as long as the conditions of the equipment and networks are guaranteed as well as the training of teachers in digital teaching competence. In addition, the students’ perception of this modality is that it can continue once the exceptionality of the pandemic has been overcome.
Innovative methods applied to processing and interpreting geochemical data

The Editorial of this special issue focuses on Innovative methods applied to processing and interpreting geochemical data, summarizing the papers published therein, reviewing the advances made in analyzing and interpreting geochemical datasets, and highlighting some challenges facing present-day geochemical data analysis. Decades of research on mineral exploration and environmental pollution have led to three significant breakthroughs in analyzing geochemical datasets, namely the compositional nature of geochemical data, fractal/multifractal-based decomposition of anomalous patterns, and artificial intelligence (AI)-based geochemical pattern recognition. Some challenges still face the use of AI-based methods for the recognition of geochemical anomalies linked to ore-forming processes. The papers published in this issue employ state-of-the-art techniques to fully exploit the potential of geochemical datasets for exploration and environmental geochemistry. A total of 25 papers were submitted to this special issue of which 15 papers were published. The published papers can be categorized in three broad groups, namely (i) delineation of anomalous geochemical patterns, (ii) integrating geochemical datasets to other geospatial tools for mineral exploration, and (iii) geochemistry for hydrocarbon exploration.
Estrangement, immersion, and the future: Designing the speculative environments of the virtual reality “Museum of the Future”

This article examines the creation of three speculative environments within the Museum of the Future (MOTF), a virtual-reality environment (VRE) designed by Dstl to promote awareness of future defence and security technologies. In contrast to other areas of the MOTF, these speculative settings are experimental, and are intended to highlight the uncertainty of the future, using techniques of cognitive estrangement and other appropriate narrative and world-building techniques to encourage audiences to query their anticipatory assumptions and cognitive biases. The aim of this project is therefore to promote cognitive flexibility, enhance futures literacy (FL), and ameliorate against the effects of knowledge shields. After a brief overview of the three speculative environments, the article explores the contextual needs they were designed to address, and demonstrates how, through team composition and core design principles, as well as through the application of narrative techniques, the worlds described can offer audiences novel ways of considering possible futures.
Phase-to-Coordinates Calibration for Fringe Projection Profilometry Using Gaussian Process Regression

In the task of 3-D topography measurement by fringe projection profilometry (FPP), it is crucial to establish mapping from the phase map to the 3-D coordinates, known as 3-D calibration. The traditional methods are prone to select some specific functions to fit the phase-to-coordinates’ relationship, which needs to make a compromise between measurement accuracy and efficiency. This article proposes a novel calibration method based on the Gaussian process (GP) regression to solve this problem. In this work, according to the geometric and other systemic constraints, a pixel-dependent semiparameterized calibration model is derived to guarantee the efficiency of computation and data storage. Based on the spatial correlations of the calibration data, the GP regression method is applied to enhance the fitting ability and flexibility of the calibration model without any specific functions and parameters. The GP regression method is also applied to remove random noise from the phase map, which further improves the accuracy of 3-D coordinates. The experimental results of measuring a whiteboard and a double ball bar demonstrate the superiority of the proposed GP-based calibration model in terms of accuracy and robustness when compared with the traditional models.
Altitude Control of Quadcopters with Absolute Stability Analysis

This paper examines the vertical dynamics of a quadcopter, and presents an approach to achieve better altitude control. The control system includes a multi-sensor-based state observer for estimating vertical speed, a disturbance observer for improving system robustness, and a position controller for tracking the vehicle's altitude with respect to a reference value. This paper clarifies that it is essential to address the model of the propeller actuators to properly guarantee system stability. It was found that the altitude control system is a multi-input multi-output system. By showing the rank-1 interaction between the actuators, this paper derives the condition for the controller that ensures absolute stability of the control system. The condition can be checked conveniently using a graphical test with the Nyquist plot, thereby alleviating the complexity of system design and analysis. The effectiveness of the proposed approach is evaluated by both numerical simulations and real-time experiments. This approach can be easily extended to the motion control of other general multirotor vehicles.
Comparison And Validation Of Regression Model For Estimation Of Global Solar Radiation Using Python

The major purpose behind this paper is to compare linear, quadratic and cubic regression analysis for estimation of global solar radiation and validating them by comparing the calculated data with the standard measured data given by metrological department for providing a suitable model for estimation. This paper analyses the Angstrom-Prescott&#x0027;s model, Ogleman&#x0027;s model and Samuel&#x0027;s model are used for estimation of global solar radiation. After the experimental data being collected from the aforesaid models the data is being validated using Mean Absolute Percentage Error, Mean Bias Error, Root Mean Square Error, t-statistic. After the validation, a comparative study of the aforesaid models is done and finally a suitable model has been put forward.
Explaining Artificial Intelligence with Tailored Interactive Visualisations

Artificial intelligence (AI) is becoming ubiquitous in the lives of both researchers and non-researchers, but AI models often lack transparency. To make well-informed and trustworthy decisions based on these models, people require explanations that indicate how to interpret the model outcomes. This paper presents our ongoing research in explainable AI, which investigates how visual analytics interfaces and visual explanations, tailored to the target audience and application domain, can make AI models more transparent and allow interactive steering based on domain expertise. First, we present our research questions and methods, contextualised by related work at the intersection of AI, human-computer interaction, and information visualisation. Then, we discuss our work so far in healthcare, agriculture, and education. Finally, we share our research ideas for additional studies in these domains.
Recursive Optimal Finite Impulse Response Filter and Its Application to Adaptive Estimation

In this paper, the recursive form of an optimal finite impulse response filter is proposed for discrete time-varying state-space models. The recursive form of the finite impulse response filter is derived by employing finite horizon Kalman filtering with optimally estimated initial conditions. The horizon initial state and its error covariance on the horizon are optimally estimated by using recent finite measurements, in the sense of maximum likelihood estimation, then initiating the finite horizon Kalman filter. The optimality and unbiasedness of the proposed filter are proved by comparison with the conventional optimal finite impulse response filter in batch form. Moreover, an adaptive FIR filter is also proposed by applying the adaptive estimation scheme to the proposed recursive optimal FIR filter as its application. To evaluate the performance of the proposed algorithms, a computer simulation is performed to compare the conventional Kalman filter and adaptive Kalman filters for the gas turbine aircraft engine model.
vqSGD: Vector Quantized Stochastic Gradient Descent

In this work, we present a family of vector quantization schemes <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">vqSGD</i> (Vector-Quantized Stochastic Gradient Descent) that provide an asymptotic reduction in the communication cost with convergence guarantees in first-order distributed optimization. In the process we derive the following fundamental information theoretic fact: <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\Theta \left({\frac {d}{R^{2}}}\right)$ </tex-math></inline-formula> bits are necessary and sufficient (up to an additive <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$O(\log d)$ </tex-math></inline-formula> term) to describe an unbiased estimator <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\hat{\boldsymbol {g}}(\boldsymbol {g})$ </tex-math></inline-formula> for any <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\boldsymbol {g}$ </tex-math></inline-formula> in the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$d$ </tex-math></inline-formula> -dimensional unit sphere, under the constraint that <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\| \hat{\boldsymbol {g}}(\boldsymbol {g})\|_{2}\le R$ </tex-math></inline-formula> almost surely, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R &gt; 1$ </tex-math></inline-formula> . In particular, we consider a randomized scheme based on the convex hull of a point set, that returns an unbiased estimator of a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$d$ </tex-math></inline-formula> -dimensional gradient vector with almost surely bounded norm. We provide multiple efficient instances of our scheme, that are near optimal, and require <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$o(d)$ </tex-math></inline-formula> bits of communication at the expense of tolerable increase in error. The instances of our quantization scheme are obtained using well-known families of binary error-correcting codes and provide a smooth tradeoff between the communication and the estimation error of quantization. Furthermore, we show that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">vqSGD</i> also offers automatic privacy guarantees.
SDCN2: A Shallow Densely Connected CNN for Multi-Purpose Image Manipulation Detection

Digital image information can be easily tampered with to harm the integrity of someone. Thus, recognizing the truthfulness and processing history of an image is one of the essential concerns in multimedia forensics. Numerous forensic methods have been developed by researchers with the ability to detect targeted editing operations. However, creating a unified forensic approach capable of detecting multiple image manipulations remains a challenging problem. In this article, a new general-purpose forensic approach is designed based on a shallow densely connected convolutional neural network (SDCN2) that exploits local dense connections and global residual learning. The residual domain is considered in the proposed network rather than the spatial domain to analyze the image manipulation artifacts because the residual domain is less dependent on image content information. To attain this purpose, a residual convolutional layer is employed at the beginning of the proposed model to adaptively learn the image manipulation features by suppressing the image content information. Then, the obtained image residuals or prediction error features are further processed by the shallow densely connected convolutional neural network for high-level feature extraction. In addition, the hierarchical features produced by the densely connected blocks and prediction error features are fused globally for better information flow across the network. The extensive experiment results show that the proposed scheme outperforms the existing state-of-the-art general-purpose forensic schemes even under anti-forensic attacks, when tested on large-scale datasets. The proposed model offers overall detection accuracies of 98.34% and 99.22% for BOSSBase and Dresden datasets, respectively, for multiple image manipulation detection. Moreover, the proposed network is highly efficient in terms of computational complexity as compared to the existing approaches.
A Comparison of Architecture Paradigms for Dynamic Reconfıgurable Automotive Networks

The automotive industry is undergoing a transformation in vehicle architecture development as established signal-oriented networks seem to reach their limits. To support a higher degree of flexibility for adaptations during and after the development process, the service-oriented architecture (SOA) paradigm has been introduced and is gaining importance. In this paper, we analyze the pros and cons of the signal-and service-oriented paradigms based on different phases of a development process (V-model), as well as in operation. By using a modular vehicle concept, we evaluate specific criteria through an analysis of an exemplary SOA use case.
Adversarial attacks on YOLACT instance segmentation

Adversarial attacks have stimulated research interests in the field of deep learning security. In terms of autonomous driving technology, instance segmentation can help autonomous vehicles identify a drivable area in a driving environment and a traffic accident will happen once instance segmentation is attacked by adversarial examples. However, only few studies have been done on adversarial attacks about instance segmentation. It would be more meaningful and more practical to research adversarial attacks against instance segmentation. In this paper, we propose the improved Projected Gradient Descent (PGD) to produce adversarial examples on the total loss of You Only Look At CoefficienT (YOLACT) instance segmentation. Firstly, we design the loss function and calculate the adversarial gradient. Then we propose the improved PGD. Finally, we obtain adversarial examples of YOLACT instance segmentation. Our attack method is efficient and powerful in both white-box and black-box attack settings, and is applicable in a variety of neural network architectures. On COCO 2017, under white-box attacks, our method achieves 1.14% box mean average precision (mAP) and 1.50% mask mAP on YOLACT with ResNet101 backbone. We also compare the similarity between clean images and adversarial examples among three different backbones and compare the operation time among three different backbones. We also find that box regression loss, classification loss and mask loss are also effective separately for generating adversarial examples. Our research will provide inspiration for further efforts in efficient and effective defense methods on instance segmentation.
Video Anomaly Detection in Surveillance Cameras

Anomalies in videos are broadly defined as events or activities that are unusual and signify irregular behavior. The goal of anomaly detection is to identify cases that are unusual within the data. Anomaly detection in videos can lead to analysis of temporal and spatial outliers in data. The data derived from most models with access to more information, it is important to analyze and interpret the features correctly, when it comes to security. Once the outliers in the data are identified, we need to classify them and recognize the issues they pose to the society. Anomalies can include a variety of events like abuse, arson, robbery, accidents, movement of non-pedestrian entities on pathways. Surveillance videos can capture a variety of realistic anomalies.