Reviewerly/exHarmony huggingface dataset:
Scattering and three-dimensional imaging in surface topography measuring interference microscopy

Surface topography measuring interference microscopy is a three-dimensional (3D) imaging technique that provides quantitative analysis of industrial and biomedical specimens. Many different instrument modalities and configurations exist, but they all share the same theoretical foundation. In this paper, we discuss a unified theoretical framework for 3D image (interferogram) formation in interference microscopy. We show how the scattered amplitude is linearly related to the surface topography according to the Born and the Kirchhoff approximations and highlight the main differences and similarities of each. With reference to the Ewald and McCutchen spheres, the relationship between the spatial frequencies that characterize the illuminating and scattered waves, and those that characterize the object, are defined and formulated as a 3D linear filtering process. It is shown that for the case of near planar surfaces, the 3D filtering process can be reduced to two dimensions under the small height approximation. However, the unified 3D framework provides significant additional insight into the scanning methods used in interference microscopy, effects such as interferometric defocus and ways to mitigate errors introduced by aberrations of the optical system. Furthermore, it is possible to include the nonlinear effects of multiple scattering into the generalized framework. Finally, we consider the inherent nonlinearities introduced when estimating surface topography from the recorded interferogram.
A Framework for Coverage Path Planning Optimization Based on Point Cloud for Structural Inspection

Different practical applications have emerged in the last few years, requiring periodic and detailed inspections to verify possible structural changes. Inspections using Unmanned Aerial Vehicles (UAVs) should minimize flight time due to battery time restrictions and identify the terrain’s topographic features. In this sense, Coverage Path Planning (CPP) aims at finding the best path to coverage of a determined area respecting the operation’s restrictions. Photometric information from the terrain is used to create routes or even refine paths already created. Therefore, this research’s main contribution is developing a methodology that uses a metaheuristic algorithm based on point cloud data to inspect slope and dams structures. The technique was applied in a simulated and real scenario to verify its effectiveness. The results showed an increasing 3D reconstructions’ quality observing optimizing photometric and mission time criteria.
A novel satellite image encryption algorithm based on hyperchaotic systems and Josephus problem

With the rapid growth of the number of Earth observation satellite (EOS) supporting critical applications, it is required to improve the security techniques to protect the sensitive data and images during the transmission between the satellites and the ground stations. This paper introduces a new satellite image encryption algorithm based on the Linear Feedback Shift Register (LFSR) generator, SHA 512 hash function, hyperchaotic systems, and Josephus problem. LFSR generates a matrix that is used to construct the 512-bits value of the hash function. These bits are used to set the initial values and parameters of the proposed encryption algorithm. Firstly, the six dimensions (6-D) hyperchaotic system is divided into three parts, where every two equations are considered as one part. Secondly, the 1-D hyperchaotic logistic-tent system is considered as the controller to select one part. The selected part is used to generate a matrix that is XORed with the original image. Thirdly, the scrambling operation by Josephus sequences is applied to the output of the previous step by scrambling the rows and the columns according to the selected part to produce the pre-encrypted image. Finally, if the number of iterations is less than the required number which is considered as a parameter of the secret key, the previous operations will be repeated in the pre-encrypted image; otherwise, the pre-encrypted image is considered as the final cipher image. Experimental and analyses results show that the proposed algorithm has good performance in terms of high level of security, large enough key-space, tolerance to Single Event Upsets (SEU) as well as low time complexity.
A practical development of engineering simulation-assisted educational AR environments

Engineering simulations have opened several gates for today’s chemical engineers. They are powerful tools to provide technical content as physics-based numerical solvers. Augmented reality (AR) and virtual reality (VR), on the other hand, are already underway to digitize environments in many fields. The combination of AR/VR environments and simulations in engineering education has been attracting widespread interest. Literature has demonstrated a massive amount of digital educational environments in several contexts as being complementary to conventional educational methods. Nevertheless, hosting technical content produced by engineering simulations with educational AR/VR is still challenging and requires expertise from multiple disciplines throughout the technical development. Present work provides a facile and agile methodology for low-cost hardware but content-wise rich AR software development. Inspired by the Covid-19 pandemic, a case study is developed to teach chemical-engineering concepts using a liquid-soap synthesis process. Accordingly, we assess and conclude the digital development process to guide inexperienced developers for the digitalization of teaching content. The present contribution serves as an example of the power of integrating AR/VR with traditional engineering simulations for educational purposes. The digital tool developed in this work is shared in the online version.
Optical color image encryption based on fingerprint key and phase-shifting digital holography

In this paper, an optical color image encryption scheme based on fingerprint key and three-step phase-shifting digital holography is proposed. In the proposed encryption scheme, the fingerprint is served as secret key directly, and the random phase masks generated from the fingerprint using secure hash algorithm and chaotic map are just used as interim variables. With the help of the fingerprint-based random phase masks located in the linear canonical transform domain and the three-step phase-shifting digital holography, the primary color image that is hidden into a greyscale carrier image can be encrypted into three noise-like holograms. Since the fingerprint key shared by the sender and authorized receiver is strongly linked with the user and need not be transmitted via the networks, the security of the proposed encryption scheme will be enhanced greatly. In addition, the parameters of chaotic map and linear canonical transform can also provide additional security to the proposed encryption scheme. Numerical simulations and analysis have been carried out to verify the feasibility, security and robustness of the proposed encryption scheme.
Large-scale empirical validation of Bayesian Network structure learning algorithms with noisy data

Numerous Bayesian Network (BN) structure learning algorithms have been proposed in the literature over the past few decades. Each publication makes an empirical or theoretical case for the algorithm proposed in that publication and results across studies are often inconsistent in their claims about which algorithm is ‘best’. This is partly because there is no agreed evaluation approach to determine their effectiveness. Moreover, each algorithm is based on a set of assumptions, such as complete data and causal sufficiency, and tend to be evaluated with data that conforms to these assumptions, however unrealistic these assumptions may be in the real world. As a result, it is widely accepted that synthetic performance overestimates real performance, although to what degree this may happen remains unknown. This paper investigates the performance of 15 state-of-the-art, well-established, or recent promising structure learning algorithms. We propose a methodology that applies the algorithms to data that incorporates synthetic noise, in an effort to better understand the performance of structure learning algorithms when applied to real data. Each algorithm is tested over multiple case studies, sample sizes, types of noise, and assessed with multiple evaluation criteria. This work involved learning approximately 10,000 graphs with a total structure learning runtime of seven months. In investigating the impact of data noise, we provide the first large scale empirical comparison of BN structure learning algorithms under different assumptions of data noise. The results suggest that traditional synthetic performance may overestimate real-world performance by anywhere between 10% and more than 50%. They also show that while score-based learning is generally superior to constraint-based learning, a higher fitting score does not necessarily imply a more accurate causal graph. The comparisons extend to other outcomes of interest, such as runtime, reliability, and resilience to noise, assessed over both small and large networks, and with both limited and big data. To facilitate comparisons with future studies, we have made all data, raw results, graphs and BN models freely available online.
Adaptive weights learning in CNN feature fusion for crime scene investigation image classification

The combination of features from the convolutional layer and the fully connected layer of a convolutional neural network (CNN) provides an effective way to improve the performance of crime scene investigation (CSI) image classification. However, in existing work, as the weights in feature fusion do not change after the training phase, it may produce inaccurate image features which affect classification results. To solve this problem, this paper proposes an adaptive feature fusion method based on an auto-encoder to improve classification accuracy. The method includes the following steps: Firstly, the CNN model is trained by transfer learning. Next, the features of the convolution layer and the fully connected layer are extracted respectively. These extracted features are then passed into the auto-encoder for further learning with Softmax normalisation to obtain the adaptive weights for performing final classification. Experiments demonstrated that the proposed method achieves higher CSI image classification performance compared with fix weights feature fusion.
A Survey of Textual Emotion Recognition and Its Challenges

Textual language is the most natural carrier of human emotion. In natural language processing, textual emotion recognition (TER) has become an important topic due to its significant academic and commercial potential. With the advanced development of deep learning technologies, TER has attracted growing attention and has significantly been promoted in recent years. This article provides a systematic survey of the latest TER advances, focusing on approaches using deep neural networks. According to how deep learning works at each stage, TER approaches are reviewed on word embedding, architecture, and training levels, respectively. We discussed the remaining challenges and opportunities from four aspects: the shortage of large-scale and high-quality datasets, fuzzy emotional boundaries, incomplete extractable emotional information in texts, and TER in dialogue. This article creates a systematic and in-depth overview of deep TER technologies. It provides the necessary knowledge and new insights for relevant researchers to understand better the research state, remaining challenges, and future directions in this field.
Financial Applications of Gaussian Processes and Bayesian Optimization

In the last five years, the financial industry has been impacted by the emergence of digitalization and machine learning. In this article, we explore two methods that have undergone rapid development in recent years: Gaussian processes and Bayesian optimization. Gaussian processes can be seen as a generalization of Gaussian random vectors and are associated with the development of kernel methods. Bayesian optimization is an approach for performing derivative-free global optimization in a small dimension, and uses Gaussian processes to locate the global maximum of a black-box function. The first part of the article reviews these two tools and shows how they are connected. In particular, we focus on the Gaussian process regression, which is the core of Bayesian machine learning, and the issue of hyperparameter selection. The second part is dedicated to two financial applications. We first consider the modeling of the term structure of interest rates. More precisely, we test the fitting method and compare the GP prediction and the random walk model. The second application is the construction of trend-following strategies, in particular the online estimation of trend and covariance windows.
Preventing the digital scars of COVID-19

In this commentary, we consider the ambivalent role of ubiquitous computing during the COVID pandemic and we point to the risk that some negative, IT-related practices associated with the pandemic will endure after it. We call these lasting effects the digital scars of COVID-19. The same IT that has positive impacts for some people might have negative impacts for others – often vulnerable populations, minorities and socio-economically disadvantaged groups. Some issues stem from the longstanding digital divide that characterises modern societies. Yet, the pandemic is exacerbating these inequalities. We are worried that some debatable uses of technology will persist after the pandemic is over. We therefore point to the prominent role of the IS community in enabling positive aspects of IT use during and after the pandemic, while mitigating negative aspects, especially in the long run. Our sociotechnical background enables us to see these dynamics in a processual and holistic way. To shed light on these issues, we analyse three key technologies widely used to deal with COVID (social software, AI/ML and robotics) and identify critical topics and associated research questions where IS scholarship should focus its attention to generate novel theorising and impactful practical insights.
Enhancing computational performance of a spin-wave reservoir computer with input synchronization

A spin-wave delay-line active-ring oscillator has recently been proposed as a suitable substrate to implement the physical reservoir computing model. The concept displays the required properties of fading memory and nonlinearity characteristic to the model. In this paper, we improve the concept by increasing the signal delay time in the yttrium-iron garnet film by more than four times, and we examine further the improved system by evaluating experimentally the performance on two benchmark classification tasks. The short-term memory (STM) task evaluates the linear memory characteristics of the RC, while the parity-check (PC) task evaluates the nonlinear computing capability. Adequate performance on both is achieved, and the linear memory is shown to be strongly dependent on the synchronization between the reservoir computer (RC) inputs and the active-ring circulation time. The extended delay time and other major improvements result in STM and PC capacities reaching maximum values of 4.68 and 1.74, respectively.
Personal Fixations-Based Object Segmentation With Object Localization and Boundary Preservation

As a natural way for human-computer interaction, fixation provides a promising solution for interactive image segmentation. In this paper, we focus on Personal Fixations-based Object Segmentation (PFOS) to address issues in previous studies, such as the lack of appropriate dataset and the ambiguity in fixations-based interaction. In particular, we first construct a new PFOS dataset by carefully collecting pixel-level binary annotation data over an existing fixation prediction dataset, such dataset is expected to greatly facilitate the study along the line. Then, considering characteristics of personal fixations, we propose a novel network based on Object Localization and Boundary Preservation (OLBP) to segment the gazed objects. Specifically, the OLBP network utilizes an Object Localization Module (OLM) to analyze personal fixations and locates the gazed objects based on the interpretation. Then, a Boundary Preservation Module (BPM) is designed to introduce additional boundary information to guard the completeness of the gazed objects. Moreover, OLBP is organized in the mixed bottom-up and top-down manner with multiple types of deep supervision. Extensive experiments on the constructed PFOS dataset show the superiority of the proposed OLBP network over 17 state-of-the-art methods, and demonstrate the effectiveness of the proposed OLM and BPM components. The constructed PFOS dataset and the proposed OLBP network are available at https://github.com/MathLee/OLBPNet4PFOS.
Emotion detection in Roman Urdu text using machine learning

Emotion detection is playing a very important role in our life. People express their emotions in different ways i.e face expression, gestures, speech, and text. This research focuses on detecting emotions from the Roman Urdu text. Previously, A lot of work has been done on different languages for emotion detection but there is limited work done in Roman Urdu. Therefore, there is a need to explore Roman Urdu as it is the most widely used language on social media platforms for communication. One major issue for the Roman Urdu is the absence of benchmark corpora for emotion detection from text because language assets are essential for different natural language processing (NLP) tasks. There are many useful applications of the emotional analysis of a text such as improving the quality of products, dialog systems, investment trends, mental health. In this research, to focus on the emotional polarity of the Roman Urdu sentence we develop a comprehensive corpus of 18k sentences that are gathered from different domains and annotate it with six different classes. We applied different baseline algorithms like KNN, Decision tree, SVM, and Random Forest on our corpus. After experimentation and evaluation, the results showed that the SVM model achieves a better F-measure score.
Real-Time Sign Language Detection Using Human Pose Estimation

We propose a lightweight real-time sign language detection model, as we identify the need for such a case in videoconferencing. We extract optical flow features based on human pose estimation and, using a linear classifier, show these features are meaningful with an accuracy of 80%, evaluated on the Public DGS Corpus. Using a recurrent model directly on the input, we see improvements of up to 91% accuracy, while still working under 4 ms. We describe a demo application to sign language detection in the browser in order to demonstrate its usage possibility in videoconferencing applications.
Organizational science and cybersecurity: abundant opportunities for research at the interface

Cybersecurity is an ever-present problem for organizations, but organizational science has barely begun to enter the arena of cybersecurity research. As a result, the "human factor" in cybersecurity research is much less studied than its technological counterpart. The current manuscript serves as an introduction and invitation to cybersecurity research by organizational scientists. We define cybersecurity, provide definitions of key cybersecurity constructs relevant to employee behavior, illuminate the unique opportunities available to organizational scientists in the cybersecurity arena (e.g., publication venues that reach new audiences, novel sources of external funding), and provide overall conceptual frameworks of the antecedents of employees' cybersecurity behavior. In so doing, we emphasize both end-users of cybersecurity in organizations and employees focused specifically on cybersecurity work. We provide an expansive agenda for future organizational science research on cybersecurity-and we describe the benefits such research can provide not only to cybersecurity but also to basic research in organizational science itself. We end by providing a list of potential objections to the proposed research along with our responses to these objections. It is our hope that the current manuscript will catalyze research at the interface of organizational science and cybersecurity.
A privacy-preserving approach to streaming eye-tracking data

Eye-tracking technology is being increasingly integrated into mixed reality devices. Although critical applications are being enabled, there are significant possibilities for violating user privacy expectations. We show that there is an appreciable risk of unique user identification even under natural viewing conditions in virtual reality. This identification would allow an app to connect a user's personal ID with their work ID without needing their consent, for example. To mitigate such risks we propose a framework that incorporates gatekeeping via the design of the application programming interface and via software-implemented privacy mechanisms. Our results indicate that these mechanisms can reduce the rate of identification from as much as 85% to as low as 30%. The impact of introducing these mechanisms is less than 1.5$^\circ$ error in gaze position for gaze prediction. Gaze data streams can thus be made private while still allowing for gaze prediction, for example, during foveated rendering. Our approach is the first to support privacy-by-design in the flow of eye-tracking data within mixed reality use cases.
Adaptive Modality Distillation for Separable Multimodal Sentiment Analysis

Multimodal sentiment analysis has increasingly attracted attention since with the arrival of complementary data streams, it has great potential to improve and go beyond unimodal sentiment analysis. In this article, we present an efficient separable multimodal learning method to deal with the tasks with modality missing issue. In this method, the multimodal tensor is utilized to guide the evolution of each separated modality representation. To save the computational expense, Tucker decomposition is introduced, which leads to a general extension of the low-rank tensor fusion method with more modality interactions. The method, in turn, enhances our modality distillation processing. Comprehensive experiments on three popular multimodal sentiment analysis datasets, CMU-MOSI, POM, and IEMOCAP, show a superior performance especially when only partial modalities are available.